diff --git a/Microsoft.ML.sln b/Microsoft.ML.sln index 211a6f7ca3..012d56e705 100644 --- a/Microsoft.ML.sln +++ b/Microsoft.ML.sln @@ -115,6 +115,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Analyzer", "sr EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.StaticPipelineTesting", "test\Microsoft.ML.StaticPipelineTesting\Microsoft.ML.StaticPipelineTesting.csproj", "{8B38BF24-35F4-4787-A9C5-22D35987106E}" EndProject +Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Microsoft.ML.DnnAnalyzer", "src\Microsoft.ML.DnnAnalyzer\Microsoft.ML.DnnAnalyzer\Microsoft.ML.DnnAnalyzer.csproj", "{73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -419,6 +421,14 @@ Global {8B38BF24-35F4-4787-A9C5-22D35987106E}.Release|Any CPU.Build.0 = Release|Any CPU {8B38BF24-35F4-4787-A9C5-22D35987106E}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU {8B38BF24-35F4-4787-A9C5-22D35987106E}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Debug|Any CPU.Build.0 = Debug|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Debug-Intrinsics|Any CPU.Build.0 = Debug|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Release|Any CPU.ActiveCfg = Release|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Release|Any CPU.Build.0 = Release|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE @@ -466,6 +476,7 @@ Global {570A0B8A-5463-44D2-8521-54C0CA4CACA9} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {6DEF0F40-3853-47B3-8165-5F24BA5E14DF} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {8B38BF24-35F4-4787-A9C5-22D35987106E} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} + {73DAAC82-D308-48CC-8FFE-3B037F8BBCCA} = {09EADF06-BE25-4228-AB53-95AE3E15B530} EndGlobalSection GlobalSection(ExtensibilityGlobals) = postSolution SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D} diff --git a/build.proj b/build.proj index 0a44ea7de7..26a68f7b40 100644 --- a/build.proj +++ b/build.proj @@ -7,6 +7,7 @@ + @@ -78,11 +79,16 @@ - - + DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" /> + + + + + https://aka.ms/tlc-resources/benchmarks/%(Identity) + $(MSBuildThisFileDirectory)/test/data/external/%(Identity) + + + diff --git a/build/Dependencies.props b/build/Dependencies.props index 24f3153e4c..c1334615db 100644 --- a/build/Dependencies.props +++ b/build/Dependencies.props @@ -13,7 +13,7 @@ 0.11.1 1.10.0 1.5.0 - + 4.5.1 2.9.0 4.5.0 1.2.0 diff --git a/build/ExternalBenchmarkDataFiles.props b/build/ExternalBenchmarkDataFiles.props new file mode 100644 index 0000000000..ad3d350d60 --- /dev/null +++ b/build/ExternalBenchmarkDataFiles.props @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/pkg/Microsoft.ML/Microsoft.ML.nupkgproj b/pkg/Microsoft.ML/Microsoft.ML.nupkgproj index 7757e264b6..7aed922027 100644 --- a/pkg/Microsoft.ML/Microsoft.ML.nupkgproj +++ b/pkg/Microsoft.ML/Microsoft.ML.nupkgproj @@ -12,6 +12,7 @@ + diff --git a/src/Common/AssemblyLoadingUtils.cs b/src/Common/AssemblyLoadingUtils.cs new file mode 100644 index 0000000000..54f7580a5b --- /dev/null +++ b/src/Common/AssemblyLoadingUtils.cs @@ -0,0 +1,178 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Internal.Utilities; +using System; +using System.IO; +using System.IO.Compression; +using System.Reflection; + +namespace Microsoft.ML.Runtime +{ + internal static class AssemblyLoadingUtils + { + /// + /// Make sure the given assemblies are loaded and that their loadable classes have been catalogued. + /// + public static void LoadAndRegister(IHostEnvironment env, string[] assemblies) + { + Contracts.AssertValue(env); + + if (Utils.Size(assemblies) > 0) + { + foreach (string path in assemblies) + { + Exception ex = null; + try + { + // REVIEW: Will LoadFrom ever return null? + Contracts.CheckNonEmpty(path, nameof(path)); + var assem = LoadAssembly(env, path); + if (assem != null) + continue; + } + catch (Exception e) + { + ex = e; + } + + // If it is a zip file, load it that way. + ZipArchive zip; + try + { + zip = ZipFile.OpenRead(path); + } + catch (Exception e) + { + // Couldn't load as an assembly and not a zip, so warn the user. + ex = ex ?? e; + Console.Error.WriteLine("Warning: Could not load '{0}': {1}", path, ex.Message); + continue; + } + + string dir; + try + { + dir = CreateTempDirectory(); + } + catch (Exception e) + { + throw Contracts.ExceptIO(e, "Creating temp directory for extra assembly zip extraction failed: '{0}'", path); + } + + try + { + zip.ExtractToDirectory(dir); + } + catch (Exception e) + { + throw Contracts.ExceptIO(e, "Extracting extra assembly zip failed: '{0}'", path); + } + + LoadAssembliesInDir(env, dir); + } + } + } + + public static IDisposable CreateAssemblyRegistrar(IHostEnvironment env, string loadAssembliesPath = null) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValueOrNull(loadAssembliesPath); + + return new AssemblyRegistrar(env, loadAssembliesPath); + } + + public static void RegisterCurrentLoadedAssemblies(IHostEnvironment env) + { + Contracts.CheckValue(env, nameof(env)); + + foreach (Assembly a in AppDomain.CurrentDomain.GetAssemblies()) + { + // Ignore dynamic assemblies. + if (a.IsDynamic) + continue; + + env.ComponentCatalog.RegisterAssembly(a); + } + } + + private static string CreateTempDirectory() + { + string dir = GetTempPath(); + Directory.CreateDirectory(dir); + return dir; + } + + private static string GetTempPath() + { + Guid guid = Guid.NewGuid(); + return Path.GetFullPath(Path.Combine(Path.GetTempPath(), "MLNET_" + guid.ToString())); + } + + private static void LoadAssembliesInDir(IHostEnvironment env, string dir) + { + if (!Directory.Exists(dir)) + return; + + // Load all dlls in the given directory. + var paths = Directory.EnumerateFiles(dir, "*.dll"); + foreach (string path in paths) + { + LoadAssembly(env, path); + } + } + + /// + /// Given an assembly path, load the assembly and register it with the ComponentCatalog. + /// + private static Assembly LoadAssembly(IHostEnvironment env, string path) + { + try + { + var assembly = Assembly.LoadFrom(path); + env.ComponentCatalog.RegisterAssembly(assembly); + return assembly; + } + catch (Exception) + { + return null; + } + } + + private sealed class AssemblyRegistrar : IDisposable + { + private readonly IHostEnvironment _env; + + public AssemblyRegistrar(IHostEnvironment env, string path) + { + _env = env; + + RegisterCurrentLoadedAssemblies(_env); + + if (!string.IsNullOrEmpty(path)) + { + LoadAssembliesInDir(_env, path); + path = Path.Combine(path, "AutoLoad"); + LoadAssembliesInDir(_env, path); + } + + AppDomain.CurrentDomain.AssemblyLoad += CurrentDomainAssemblyLoad; + } + + public void Dispose() + { + AppDomain.CurrentDomain.AssemblyLoad -= CurrentDomainAssemblyLoad; + } + + private void CurrentDomainAssemblyLoad(object sender, AssemblyLoadEventArgs args) + { + // Don't try to index dynamic generated assembly + if (args.LoadedAssembly.IsDynamic) + return; + + _env.ComponentCatalog.RegisterAssembly(args.LoadedAssembly); + } + } + } +} diff --git a/src/Microsoft.ML.Api/ApiUtils.cs b/src/Microsoft.ML.Api/ApiUtils.cs index 96e821f16e..760ed1e768 100644 --- a/src/Microsoft.ML.Api/ApiUtils.cs +++ b/src/Microsoft.ML.Api/ApiUtils.cs @@ -19,11 +19,10 @@ private static OpCode GetAssignmentOpCode(Type t) { // REVIEW: This should be a Dictionary based solution. // DvTypes, strings, arrays, all nullable types, VBuffers and UInt128. - if (t == typeof(DvInt8) || t == typeof(DvInt4) || t == typeof(DvInt2) || t == typeof(DvInt1) || - t == typeof(DvBool) || t == typeof(DvText) || t == typeof(string) || t.IsArray || + if (t == typeof(ReadOnlyMemory) || t == typeof(string) || t.IsArray || (t.IsGenericType && t.GetGenericTypeDefinition() == typeof(VBuffer<>)) || (t.IsGenericType && t.GetGenericTypeDefinition() == typeof(Nullable<>)) || - t == typeof(DvDateTime) || t == typeof(DvDateTimeZone) || t == typeof(DvTimeSpan) || t == typeof(UInt128)) + t == typeof(DateTime) || t == typeof(DateTimeOffset) || t == typeof(TimeSpan) || t == typeof(UInt128)) { return OpCodes.Stobj; } diff --git a/src/Microsoft.ML.Api/ComponentCreation.cs b/src/Microsoft.ML.Api/ComponentCreation.cs index 1694a1cf4f..f34b25235c 100644 --- a/src/Microsoft.ML.Api/ComponentCreation.cs +++ b/src/Microsoft.ML.Api/ComponentCreation.cs @@ -439,7 +439,7 @@ private static TRes CreateCore(IHostEnvironment env, TArgs ar { env.CheckValue(args, nameof(args)); - var classes = ComponentCatalog.FindLoadableClasses(); + var classes = env.ComponentCatalog.FindLoadableClasses(); if (classes.Length == 0) throw env.Except("Couldn't find a {0} class that accepts {1} as arguments.", typeof(TRes).Name, typeof(TArgs).FullName); if (classes.Length > 1) diff --git a/src/Microsoft.ML.Api/DataViewConstructionUtils.cs b/src/Microsoft.ML.Api/DataViewConstructionUtils.cs index 6962080a7e..ca7ed9c07e 100644 --- a/src/Microsoft.ML.Api/DataViewConstructionUtils.cs +++ b/src/Microsoft.ML.Api/DataViewConstructionUtils.cs @@ -125,61 +125,11 @@ private Delegate CreateGetter(int index) if (outputType.IsArray) { Ch.Assert(colType.IsVector); - // String[] -> VBuffer + // String[] -> ReadOnlyMemory if (outputType.GetElementType() == typeof(string)) { Ch.Assert(colType.ItemType.IsText); - return CreateConvertingArrayGetterDelegate(index, x => x == null ? DvText.NA : new DvText(x)); - } - else if (outputType.GetElementType() == typeof(int)) - { - Ch.Assert(colType.ItemType == NumberType.I4); - return CreateConvertingArrayGetterDelegate(index, x => x); - } - else if (outputType.GetElementType() == typeof(int?)) - { - Ch.Assert(colType.ItemType == NumberType.I4); - return CreateConvertingArrayGetterDelegate(index, x => x ?? DvInt4.NA); - } - else if (outputType.GetElementType() == typeof(long)) - { - Ch.Assert(colType.ItemType == NumberType.I8); - return CreateConvertingArrayGetterDelegate(index, x => x); - } - else if (outputType.GetElementType() == typeof(long?)) - { - Ch.Assert(colType.ItemType == NumberType.I8); - return CreateConvertingArrayGetterDelegate(index, x => x ?? DvInt8.NA); - } - else if (outputType.GetElementType() == typeof(short)) - { - Ch.Assert(colType.ItemType == NumberType.I2); - return CreateConvertingArrayGetterDelegate(index, x => x); - } - else if (outputType.GetElementType() == typeof(short?)) - { - Ch.Assert(colType.ItemType == NumberType.I2); - return CreateConvertingArrayGetterDelegate(index, x => x ?? DvInt2.NA); - } - else if (outputType.GetElementType() == typeof(sbyte)) - { - Ch.Assert(colType.ItemType == NumberType.I1); - return CreateConvertingArrayGetterDelegate(index, x => x); - } - else if (outputType.GetElementType() == typeof(sbyte?)) - { - Ch.Assert(colType.ItemType == NumberType.I1); - return CreateConvertingArrayGetterDelegate(index, x => x ?? DvInt1.NA); - } - else if (outputType.GetElementType() == typeof(bool)) - { - Ch.Assert(colType.ItemType.IsBool); - return CreateConvertingArrayGetterDelegate(index, x => x); - } - else if (outputType.GetElementType() == typeof(bool?)) - { - Ch.Assert(colType.ItemType.IsBool); - return CreateConvertingArrayGetterDelegate(index, x => x ?? DvBool.NA); + return CreateConvertingArrayGetterDelegate>(index, x => x != null ? x.AsMemory() : ReadOnlyMemory.Empty); } // T[] -> VBuffer @@ -193,7 +143,7 @@ private Delegate CreateGetter(int index) else if (colType.IsVector) { // VBuffer -> VBuffer - // REVIEW: Do we care about accomodating VBuffer -> VBuffer? + // REVIEW: Do we care about accomodating VBuffer -> ReadOnlyMemory? Ch.Assert(outputType.IsGenericType); Ch.Assert(outputType.GetGenericTypeDefinition() == typeof(VBuffer<>)); Ch.Assert(outputType.GetGenericArguments()[0] == colType.ItemType.RawType); @@ -204,70 +154,11 @@ private Delegate CreateGetter(int index) { if (outputType == typeof(string)) { - // String -> DvText + // String -> ReadOnlyMemory Ch.Assert(colType.IsText); - return CreateConvertingGetterDelegate(index, x => x == null ? DvText.NA : new DvText(x)); - } - else if (outputType == typeof(bool)) - { - // Bool -> DvBool - Ch.Assert(colType.IsBool); - return CreateConvertingGetterDelegate(index, x => x); - } - else if (outputType == typeof(bool?)) - { - // Bool? -> DvBool - Ch.Assert(colType.IsBool); - return CreateConvertingGetterDelegate(index, x => x ?? DvBool.NA); - } - else if (outputType == typeof(int)) - { - // int -> DvInt4 - Ch.Assert(colType == NumberType.I4); - return CreateConvertingGetterDelegate(index, x => x); - } - else if (outputType == typeof(int?)) - { - // int? -> DvInt4 - Ch.Assert(colType == NumberType.I4); - return CreateConvertingGetterDelegate(index, x => x ?? DvInt4.NA); - } - else if (outputType == typeof(short)) - { - // short -> DvInt2 - Ch.Assert(colType == NumberType.I2); - return CreateConvertingGetterDelegate(index, x => x); - } - else if (outputType == typeof(short?)) - { - // short? -> DvInt2 - Ch.Assert(colType == NumberType.I2); - return CreateConvertingGetterDelegate(index, x => x ?? DvInt2.NA); - } - else if (outputType == typeof(long)) - { - // long -> DvInt8 - Ch.Assert(colType == NumberType.I8); - return CreateConvertingGetterDelegate(index, x => x); - } - else if (outputType == typeof(long?)) - { - // long? -> DvInt8 - Ch.Assert(colType == NumberType.I8); - return CreateConvertingGetterDelegate(index, x => x ?? DvInt8.NA); - } - else if (outputType == typeof(sbyte)) - { - // sbyte -> DvInt1 - Ch.Assert(colType == NumberType.I1); - return CreateConvertingGetterDelegate(index, x => x); - } - else if (outputType == typeof(sbyte?)) - { - // sbyte? -> DvInt1 - Ch.Assert(colType == NumberType.I1); - return CreateConvertingGetterDelegate(index, x => x ?? DvInt1.NA); + return CreateConvertingGetterDelegate>(index, x => x != null ? x.AsMemory() : ReadOnlyMemory.Empty); } + // T -> T if (outputType.IsGenericType && outputType.GetGenericTypeDefinition() == typeof(Nullable<>)) Ch.Assert(colType.RawType == Nullable.GetUnderlyingType(outputType)); @@ -805,12 +696,12 @@ public override ValueGetter GetGetter() var itemType = typeT.GetElementType(); var dstItemType = typeof(TDst).GetGenericArguments()[0]; - // String[] -> VBuffer + // String[] -> VBuffer> if (itemType == typeof(string)) { - Contracts.Check(dstItemType == typeof(DvText)); + Contracts.Check(dstItemType == typeof(ReadOnlyMemory)); - ValueGetter> method = GetStringArray; + ValueGetter>> method = GetStringArray; return method as ValueGetter; } @@ -825,7 +716,7 @@ public override ValueGetter GetGetter() if (MetadataType.IsVector) { // VBuffer -> VBuffer - // REVIEW: Do we care about accomodating VBuffer -> VBuffer? + // REVIEW: Do we care about accomodating VBuffer -> VBuffer>? Contracts.Assert(typeT.IsGenericType); Contracts.Check(typeof(TDst).IsGenericType); @@ -845,9 +736,9 @@ public override ValueGetter GetGetter() { if (typeT == typeof(string)) { - // String -> DvText + // String -> ReadOnlyMemory Contracts.Assert(MetadataType.IsText); - ValueGetter m = GetString; + ValueGetter> m = GetString; return m as ValueGetter; } // T -> T @@ -861,14 +752,14 @@ public class TElement { } - private void GetStringArray(ref VBuffer dst) + private void GetStringArray(ref VBuffer> dst) { var value = (string[])(object)Value; var n = Utils.Size(value); - dst = new VBuffer(n, Utils.Size(dst.Values) < n ? new DvText[n] : dst.Values, dst.Indices); + dst = new VBuffer>(n, Utils.Size(dst.Values) < n ? new ReadOnlyMemory[n] : dst.Values, dst.Indices); for (int i = 0; i < n; i++) - dst.Values[i] = new DvText(value[i]); + dst.Values[i] = value[i].AsMemory(); } @@ -890,9 +781,9 @@ private ValueGetter> GetVBufferGetter() return (ref VBuffer dst) => castValue.CopyTo(ref dst); } - private void GetString(ref DvText dst) + private void GetString(ref ReadOnlyMemory dst) { - dst = new DvText((string)(object)Value); + dst = ((string)(object)Value).AsMemory(); } private void GetDirectValue(ref TDst dst) diff --git a/src/Microsoft.ML.Api/SerializableLambdaTransform.cs b/src/Microsoft.ML.Api/SerializableLambdaTransform.cs index 7de6e522d8..38bd58e76d 100644 --- a/src/Microsoft.ML.Api/SerializableLambdaTransform.cs +++ b/src/Microsoft.ML.Api/SerializableLambdaTransform.cs @@ -29,7 +29,8 @@ public static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(SerializableLambdaTransform).Assembly.FullName); } public const string LoaderSignature = "UserLambdaMapTransform"; diff --git a/src/Microsoft.ML.Api/TypedCursor.cs b/src/Microsoft.ML.Api/TypedCursor.cs index 67fc91439e..50bb1cd575 100644 --- a/src/Microsoft.ML.Api/TypedCursor.cs +++ b/src/Microsoft.ML.Api/TypedCursor.cs @@ -276,61 +276,11 @@ private Action GenerateSetter(IRow input, int index, InternalSchemaDefinit if (fieldType.IsArray) { Ch.Assert(colType.IsVector); - // VBuffer -> String[] + // VBuffer> -> String[] if (fieldType.GetElementType() == typeof(string)) { Ch.Assert(colType.ItemType.IsText); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => x.ToString()); - } - else if (fieldType.GetElementType() == typeof(bool)) - { - Ch.Assert(colType.ItemType.IsBool); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (bool)x); - } - else if (fieldType.GetElementType() == typeof(bool?)) - { - Ch.Assert(colType.ItemType.IsBool); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (bool?)x); - } - else if (fieldType.GetElementType() == typeof(int)) - { - Ch.Assert(colType.ItemType == NumberType.I4); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (int)x); - } - else if (fieldType.GetElementType() == typeof(int?)) - { - Ch.Assert(colType.ItemType == NumberType.I4); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (int?)x); - } - else if (fieldType.GetElementType() == typeof(short)) - { - Ch.Assert(colType.ItemType == NumberType.I2); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (short)x); - } - else if (fieldType.GetElementType() == typeof(short?)) - { - Ch.Assert(colType.ItemType == NumberType.I2); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (short?)x); - } - else if (fieldType.GetElementType() == typeof(long)) - { - Ch.Assert(colType.ItemType == NumberType.I8); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (long)x); - } - else if (fieldType.GetElementType() == typeof(long?)) - { - Ch.Assert(colType.ItemType == NumberType.I8); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (long?)x); - } - else if (fieldType.GetElementType() == typeof(sbyte)) - { - Ch.Assert(colType.ItemType == NumberType.I1); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (sbyte)x); - } - else if (fieldType.GetElementType() == typeof(sbyte?)) - { - Ch.Assert(colType.ItemType == NumberType.I1); - return CreateConvertingVBufferSetter(input, index, poke, peek, x => (sbyte?)x); + return CreateConvertingVBufferSetter, string>(input, index, poke, peek, x => x.ToString()); } // VBuffer -> T[] @@ -344,7 +294,7 @@ private Action GenerateSetter(IRow input, int index, InternalSchemaDefinit else if (colType.IsVector) { // VBuffer -> VBuffer - // REVIEW: Do we care about accomodating VBuffer -> VBuffer? + // REVIEW: Do we care about accomodating VBuffer -> VBuffer>? Ch.Assert(fieldType.IsGenericType); Ch.Assert(fieldType.GetGenericTypeDefinition() == typeof(VBuffer<>)); Ch.Assert(fieldType.GetGenericArguments()[0] == colType.ItemType.RawType); @@ -355,71 +305,12 @@ private Action GenerateSetter(IRow input, int index, InternalSchemaDefinit { if (fieldType == typeof(string)) { - // DvText -> String + // ReadOnlyMemory -> String Ch.Assert(colType.IsText); Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => x.ToString()); - } - else if (fieldType == typeof(bool)) - { - Ch.Assert(colType.IsBool); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (bool)x); - } - else if (fieldType == typeof(bool?)) - { - Ch.Assert(colType.IsBool); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (bool?)x); - } - else if (fieldType == typeof(int)) - { - Ch.Assert(colType == NumberType.I4); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (int)x); - } - else if (fieldType == typeof(int?)) - { - Ch.Assert(colType == NumberType.I4); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (int?)x); - } - else if (fieldType == typeof(short)) - { - Ch.Assert(colType == NumberType.I2); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (short)x); - } - else if (fieldType == typeof(short?)) - { - Ch.Assert(colType == NumberType.I2); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (short?)x); - } - else if (fieldType == typeof(long)) - { - Ch.Assert(colType == NumberType.I8); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (long)x); - } - else if (fieldType == typeof(long?)) - { - Ch.Assert(colType == NumberType.I8); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (long?)x); - } - else if (fieldType == typeof(sbyte)) - { - Ch.Assert(colType == NumberType.I1); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (sbyte)x); - } - else if (fieldType == typeof(sbyte?)) - { - Ch.Assert(colType == NumberType.I1); - Ch.Assert(peek == null); - return CreateConvertingActionSetter(input, index, poke, x => (sbyte?)x); + return CreateConvertingActionSetter, string>(input, index, poke, x => x.ToString()); } + // T -> T if (fieldType.IsGenericType && fieldType.GetGenericTypeDefinition() == typeof(Nullable<>)) Ch.Assert(colType.RawType == Nullable.GetUnderlyingType(fieldType)); @@ -622,7 +513,7 @@ public ICursor GetRootCursor() /// public static class CursoringUtils { - private const string NeedEnvObsoleteMessage = "This method is obsolete. Please use the overload that takes an additional 'env' argument. An environment can be created via new TlcEnvironment()."; + private const string NeedEnvObsoleteMessage = "This method is obsolete. Please use the overload that takes an additional 'env' argument. An environment can be created via new LocalEnvironment()."; /// /// Generate a strongly-typed cursorable wrapper of the . diff --git a/src/Microsoft.ML.Core/CommandLine/CmdParser.cs b/src/Microsoft.ML.Core/CommandLine/CmdParser.cs index de89ddc602..6a24c8fdfe 100644 --- a/src/Microsoft.ML.Core/CommandLine/CmdParser.cs +++ b/src/Microsoft.ML.Core/CommandLine/CmdParser.cs @@ -1,4 +1,4 @@ -////////////////////////////////////////////////////////////////////////////// +////////////////////////////////////////////////////////////////////////////// // Command Line Argument Parser // ---------------------------- // Usage @@ -414,7 +414,7 @@ public static string CombineSettings(string[] settings) } // REVIEW: Add a method for cloning arguments, instead of going to text and back. - public static string GetSettings(IExceptionContext ectx, object values, object defaults, SettingsFlags flags = SettingsFlags.Default) + public static string GetSettings(IHostEnvironment env, object values, object defaults, SettingsFlags flags = SettingsFlags.Default) { Type t1 = values.GetType(); Type t2 = defaults.GetType(); @@ -430,7 +430,7 @@ public static string GetSettings(IExceptionContext ectx, object values, object d return null; var info = GetArgumentInfo(t, defaults); - return GetSettingsCore(ectx, info, values, flags); + return GetSettingsCore(env, info, values, flags); } public static IEnumerable> GetSettingPairs(IHostEnvironment env, object values, object defaults, SettingsFlags flags = SettingsFlags.None) @@ -862,20 +862,20 @@ private bool Parse(ArgumentInfo info, string[] strs, object destination) return !hadError; } - private static string GetSettingsCore(IExceptionContext ectx, ArgumentInfo info, object values, SettingsFlags flags) + private static string GetSettingsCore(IHostEnvironment env, ArgumentInfo info, object values, SettingsFlags flags) { StringBuilder sb = new StringBuilder(); if (info.ArgDef != null) { var val = info.ArgDef.GetValue(values); - info.ArgDef.AppendSetting(ectx, sb, val, flags); + info.ArgDef.AppendSetting(env, sb, val, flags); } foreach (Argument arg in info.Args) { var val = arg.GetValue(values); - arg.AppendSetting(ectx, sb, val, flags); + arg.AppendSetting(env, sb, val, flags); } return sb.ToString(); @@ -886,7 +886,7 @@ private static string GetSettingsCore(IExceptionContext ectx, ArgumentInfo info, /// It deals with custom "unparse" functionality, as well as quoting. It also appends to a StringBuilder /// instead of returning a string. /// - private static void AppendCustomItem(IExceptionContext ectx, ArgumentInfo info, object values, SettingsFlags flags, StringBuilder sb) + private static void AppendCustomItem(IHostEnvironment env, ArgumentInfo info, object values, SettingsFlags flags, StringBuilder sb) { int ich = sb.Length; // We always call unparse, even when NoUnparse is specified, since Unparse can "cleanse", which @@ -902,13 +902,13 @@ private static void AppendCustomItem(IExceptionContext ectx, ArgumentInfo info, if (info.ArgDef != null) { var val = info.ArgDef.GetValue(values); - info.ArgDef.AppendSetting(ectx, sb, val, flags); + info.ArgDef.AppendSetting(env, sb, val, flags); } foreach (Argument arg in info.Args) { var val = arg.GetValue(values); - arg.AppendSetting(ectx, sb, val, flags); + arg.AppendSetting(env, sb, val, flags); } string str = sb.ToString(ich, sb.Length - ich); @@ -916,14 +916,14 @@ private static void AppendCustomItem(IExceptionContext ectx, ArgumentInfo info, CmdQuoter.QuoteValue(str, sb, force: true); } - private IEnumerable> GetSettingPairsCore(IExceptionContext ectx, ArgumentInfo info, object values, SettingsFlags flags) + private IEnumerable> GetSettingPairsCore(IHostEnvironment env, ArgumentInfo info, object values, SettingsFlags flags) { StringBuilder buffer = new StringBuilder(); foreach (Argument arg in info.Args) { string key = arg.GetKey(flags); object value = arg.GetValue(values); - foreach (string val in arg.GetSettingStrings(ectx, value, buffer)) + foreach (string val in arg.GetSettingStrings(env, value, buffer)) yield return new KeyValuePair(key, val); } } @@ -943,13 +943,13 @@ public ArgumentHelpStrings(string syntax, string help) /// /// A user friendly usage string describing the command line argument syntax. /// - private string GetUsageString(IExceptionContext ectx, ArgumentInfo info, bool showRsp = true, int? columns = null) + private string GetUsageString(IHostEnvironment env, ArgumentInfo info, bool showRsp = true, int? columns = null) { int screenWidth = columns ?? Console.BufferWidth; if (screenWidth <= 0) screenWidth = 80; - ArgumentHelpStrings[] strings = GetAllHelpStrings(ectx, info, showRsp); + ArgumentHelpStrings[] strings = GetAllHelpStrings(env, info, showRsp); int maxParamLen = 0; foreach (ArgumentHelpStrings helpString in strings) @@ -1039,17 +1039,17 @@ private static void AddNewLine(string newLine, StringBuilder builder, ref int cu currentColumn = 0; } - private static ArgumentHelpStrings[] GetAllHelpStrings(IExceptionContext ectx, ArgumentInfo info, bool showRsp) + private ArgumentHelpStrings[] GetAllHelpStrings(IHostEnvironment env, ArgumentInfo info, bool showRsp) { List strings = new List(); if (info.ArgDef != null) - strings.Add(GetHelpStrings(ectx, info.ArgDef)); + strings.Add(GetHelpStrings(env, info.ArgDef)); foreach (Argument arg in info.Args) { if (!arg.IsHidden) - strings.Add(GetHelpStrings(ectx, arg)); + strings.Add(GetHelpStrings(env, arg)); } if (showRsp) @@ -1058,9 +1058,9 @@ private static ArgumentHelpStrings[] GetAllHelpStrings(IExceptionContext ectx, A return strings.ToArray(); } - private static ArgumentHelpStrings GetHelpStrings(IExceptionContext ectx, Argument arg) + private ArgumentHelpStrings GetHelpStrings(IHostEnvironment env, Argument arg) { - return new ArgumentHelpStrings(arg.GetSyntaxHelp(), arg.GetFullHelpText(ectx)); + return new ArgumentHelpStrings(arg.GetSyntaxHelp(), arg.GetFullHelpText(env, this)); } private bool LexFileArguments(string fileName, out string[] arguments) @@ -1994,7 +1994,7 @@ private bool ParseValue(CmdParser owner, string data, out object value) return false; } - private void AppendHelpValue(IExceptionContext ectx, StringBuilder builder, object value) + private void AppendHelpValue(IHostEnvironment env, CmdParser owner, StringBuilder builder, object value) { if (value == null) builder.Append("{}"); @@ -2006,19 +2006,19 @@ private void AppendHelpValue(IExceptionContext ectx, StringBuilder builder, obje foreach (object o in (System.Array)value) { builder.Append(pre); - AppendHelpValue(ectx, builder, o); + AppendHelpValue(env, owner, builder, o); pre = ", "; } } else if (value is IComponentFactory) { string name; - var catalog = ModuleCatalog.CreateInstance(ectx); + var catalog = owner._catalog.Value; var type = value.GetType(); bool success = catalog.TryGetComponentShortName(type, out name); Contracts.Assert(success); - var settings = GetSettings(ectx, value, Activator.CreateInstance(type)); + var settings = GetSettings(env, value, Activator.CreateInstance(type)); builder.Append(name); if (!string.IsNullOrWhiteSpace(settings)) { @@ -2035,7 +2035,7 @@ private void AppendHelpValue(IExceptionContext ectx, StringBuilder builder, obje } // If value differs from the default, appends the setting to sb. - public void AppendSetting(IExceptionContext ectx, StringBuilder sb, object value, SettingsFlags flags) + public void AppendSetting(IHostEnvironment env, StringBuilder sb, object value, SettingsFlags flags) { object def = DefaultValue; if (!IsCollection) @@ -2043,13 +2043,13 @@ public void AppendSetting(IExceptionContext ectx, StringBuilder sb, object value if (value == null) { if (def != null || IsRequired) - AppendSettingCore(ectx, sb, "", flags); + AppendSettingCore(env, sb, "", flags); } else if (def == null || !value.Equals(def)) { var buffer = new StringBuilder(); - if (!(value is IComponentFactory) || (GetString(ectx, value, buffer) != GetString(ectx, def, buffer))) - AppendSettingCore(ectx, sb, value, flags); + if (!(value is IComponentFactory) || (GetString(env, value, buffer) != GetString(env, def, buffer))) + AppendSettingCore(env, sb, value, flags); } return; } @@ -2076,10 +2076,10 @@ public void AppendSetting(IExceptionContext ectx, StringBuilder sb, object value } foreach (object x in vals) - AppendSettingCore(ectx, sb, x, flags); + AppendSettingCore(env, sb, x, flags); } - private void AppendSettingCore(IExceptionContext ectx, StringBuilder sb, object value, SettingsFlags flags) + private void AppendSettingCore(IHostEnvironment env, StringBuilder sb, object value, SettingsFlags flags) { if (sb.Length > 0) sb.Append(" "); @@ -2100,11 +2100,11 @@ private void AppendSettingCore(IExceptionContext ectx, StringBuilder sb, object else if (value is bool) sb.Append((bool)value ? "+" : "-"); else if (IsCustomItemType) - AppendCustomItem(ectx, _infoCustom, value, flags, sb); + AppendCustomItem(env, _infoCustom, value, flags, sb); else if (IsComponentFactory) { var buffer = new StringBuilder(); - sb.Append(GetString(ectx, value, buffer)); + sb.Append(GetString(env, value, buffer)); } else sb.Append(value.ToString()); @@ -2129,7 +2129,7 @@ private void ExtractTag(object value, out string tag, out object newValue) // If value differs from the default, return the string representation of 'value', // or an IEnumerable of string representations if 'value' is an array. - public IEnumerable GetSettingStrings(IExceptionContext ectx, object value, StringBuilder buffer) + public IEnumerable GetSettingStrings(IHostEnvironment env, object value, StringBuilder buffer) { object def = DefaultValue; @@ -2138,12 +2138,12 @@ public IEnumerable GetSettingStrings(IExceptionContext ectx, object valu if (value == null) { if (def != null || IsRequired) - yield return GetString(ectx, value, buffer); + yield return GetString(env, value, buffer); } else if (def == null || !value.Equals(def)) { - if (!(value is IComponentFactory) || (GetString(ectx, value, buffer) != GetString(ectx, def, buffer))) - yield return GetString(ectx, value, buffer); + if (!(value is IComponentFactory) || (GetString(env, value, buffer) != GetString(env, def, buffer))) + yield return GetString(env, value, buffer); } yield break; } @@ -2171,10 +2171,10 @@ public IEnumerable GetSettingStrings(IExceptionContext ectx, object valu } foreach (object x in vals) - yield return GetString(ectx, x, buffer); + yield return GetString(env, x, buffer); } - private string GetString(IExceptionContext ectx, object value, StringBuilder buffer) + private string GetString(IHostEnvironment env, object value, StringBuilder buffer) { if (value == null) return "{}"; @@ -2192,12 +2192,13 @@ private string GetString(IExceptionContext ectx, object value, StringBuilder buf if (value is IComponentFactory) { string name; - var catalog = ModuleCatalog.CreateInstance(ectx); + // TODO: ModuleCatalog should be on env + var catalog = ModuleCatalog.CreateInstance(env); var type = value.GetType(); bool isModuleComponent = catalog.TryGetComponentShortName(type, out name); if (isModuleComponent) { - var settings = GetSettings(ectx, value, Activator.CreateInstance(type)); + var settings = GetSettings(env, value, Activator.CreateInstance(type)); buffer.Clear(); buffer.Append(name); if (!string.IsNullOrWhiteSpace(settings)) @@ -2208,20 +2209,20 @@ private string GetString(IExceptionContext ectx, object value, StringBuilder buf } return buffer.ToString(); } - else if (value is ICommandLineComponentFactory) - { - return value.ToString(); - } - else - { - throw ectx.Except($"IComponentFactory instances either need to be EntryPointComponents or implement {nameof(ICommandLineComponentFactory)}."); - } + //else if (value is ICommandLineComponentFactory) + //{ + // return value.ToString(); + //} + //else + //{ + // //throw env.Except($"IComponentFactory instances either need to be EntryPointComponents or implement {nameof(ICommandLineComponentFactory)}."); + //} } return value.ToString(); } - public string GetFullHelpText(IExceptionContext ectx) + public string GetFullHelpText(IHostEnvironment env, CmdParser owner) { if (IsHidden) return null; @@ -2248,7 +2249,7 @@ public string GetFullHelpText(IExceptionContext ectx) if (builder.Length > 0) builder.Append(" "); builder.Append("Default value:'"); - AppendHelpValue(ectx, builder, DefaultValue); + AppendHelpValue(env, owner, builder, DefaultValue); builder.Append('\''); } if (Utils.Size(ShortNames) != 0) diff --git a/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs b/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs index 714995a56e..eb2dd75599 100644 --- a/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs +++ b/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs @@ -2,15 +2,13 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Internal.Utilities; using System; using System.Collections.Concurrent; using System.Collections.Generic; -using System.IO; -using System.IO.Compression; using System.Linq; using System.Reflection; -using Microsoft.ML.Runtime.Internal.Utilities; -using Microsoft.ML.Runtime.CommandLine; // REVIEW: Determine ideal namespace. namespace Microsoft.ML.Runtime @@ -22,8 +20,17 @@ namespace Microsoft.ML.Runtime /// types for component instantiation. Each component may also specify an "arguments object" that should /// be provided at instantiation time. /// - public static class ComponentCatalog + public sealed class ComponentCatalog { + internal ComponentCatalog() + { + _lock = new object(); + _cachedAssemblies = new HashSet(); + _classesByKey = new ConcurrentDictionary(); + _classes = new ConcurrentQueue(); + _signatures = new ConcurrentDictionary(); + } + /// /// Provides information on an instantiatable component, aka, loadable class. /// @@ -248,201 +255,18 @@ public object CreateArguments() } } - /// - /// Debug reporting level. - /// - public static int DebugLevel = 1; - - // Do not initialize this one - the initial null value is used as a "flag" to prime things. - private static ConcurrentQueue _assemblyQueue; - - // The assemblies that are loaded by Reflection.LoadAssembly or Assembly.Load* after we started tracking - // the load events. We will provide assembly resolving for these assemblies. This is created simultaneously - // with s_assemblyQueue. - private static ConcurrentDictionary _loadedAssemblies; - - // This lock protects s_cachedAssemblies and s_cachedPaths only. The collection of ClassInfos is concurrent + // This lock protects _cachedAssemblies only. The collection of ClassInfos is concurrent // so needs no protection. - private static object _lock = new object(); - private static HashSet _cachedAssemblies = new HashSet(); - private static HashSet _cachedPaths = new HashSet(); + private readonly object _lock; + private readonly HashSet _cachedAssemblies; // Map from key/name to loadable class. Note that the same ClassInfo may appear // multiple times. For the set of unique infos, use s_classes. - private static ConcurrentDictionary _classesByKey = new ConcurrentDictionary(); + private readonly ConcurrentDictionary _classesByKey; // The unique ClassInfos and Signatures. - private static ConcurrentQueue _classes = new ConcurrentQueue(); - private static ConcurrentDictionary _signatures = new ConcurrentDictionary(); - - public static string[] FilePrefixesToAvoid = new string[] { - "api-ms-win", - "clr", - "coreclr", - "dbgshim", - "ext-ms-win", - "microsoft.bond.", - "microsoft.cosmos.", - "microsoft.csharp", - "microsoft.data.", - "microsoft.hpc.", - "microsoft.live.", - "microsoft.platformbuilder.", - "microsoft.visualbasic", - "microsoft.visualstudio.", - "microsoft.win32", - "microsoft.windowsapicodepack.", - "microsoft.windowsazure.", - "mscor", - "msvc", - "petzold.", - "roslyn.", - "sho", - "sni", - "sqm", - "system.", - "zlib", - }; - - private static bool ShouldSkipPath(string path) - { - string name = Path.GetFileName(path).ToLowerInvariant(); - switch (name) - { - case "cqo.dll": - case "fasttreenative.dll": - case "libiomp5md.dll": - case "libvw.dll": - case "matrixinterf.dll": - case "microsoft.ml.neuralnetworks.gpucuda.dll": - case "mklimports.dll": - case "microsoft.research.controls.decisiontrees.dll": - case "microsoft.ml.neuralnetworks.sse.dll": - case "neuraltreeevaluator.dll": - case "optimizationbuilderdotnet.dll": - case "parallelcommunicator.dll": - case "microsoft.ml.runtime.runtests.dll": - case "scopecompiler.dll": - case "tbb.dll": - case "internallearnscope.dll": - case "unmanagedlib.dll": - case "vcclient.dll": - case "libxgboost.dll": - case "zedgraph.dll": - case "__scopecodegen__.dll": - case "cosmosClientApi.dll": - return true; - } - - foreach (var s in FilePrefixesToAvoid) - { - if (name.StartsWith(s)) - return true; - } - - return false; - } - - /// - /// This loads assemblies that are in our "root" directory (where this assembly is) and caches - /// information for the loadable classes in loaded assemblies. - /// - private static void CacheLoadedAssemblies() - { - // The target assembly is the one containing LoadableClassAttributeBase. If an assembly doesn't reference - // the target, then we don't want to scan its assembly attributes (there's no point in doing so). - var target = typeof(LoadableClassAttributeBase).Assembly; - - lock (_lock) - { - if (_assemblyQueue == null) - { - // Create the loaded assembly queue and dictionary, set up the AssemblyLoad / AssemblyResolve - // event handlers and populate the queue / dictionary with all assemblies that are currently loaded. - Contracts.Assert(_assemblyQueue == null); - Contracts.Assert(_loadedAssemblies == null); - - _assemblyQueue = new ConcurrentQueue(); - _loadedAssemblies = new ConcurrentDictionary(); - - AppDomain.CurrentDomain.AssemblyLoad += CurrentDomainAssemblyLoad; - AppDomain.CurrentDomain.AssemblyResolve += CurrentDomainAssemblyResolve; - - foreach (Assembly a in AppDomain.CurrentDomain.GetAssemblies()) - { - // Ignore dynamic assemblies. - if (a.IsDynamic) - continue; - - _assemblyQueue.Enqueue(a); - if (!_loadedAssemblies.TryAdd(a.FullName, a)) - { - // Duplicate loading. - Console.Error.WriteLine("Duplicate loaded assembly '{0}'", a.FullName); - } - } - - // Load all assemblies in our directory. - var moduleName = typeof(ComponentCatalog).Module.FullyQualifiedName; - - // If were are loaded in the context of SQL CLR then the FullyQualifiedName and Name properties are set to - // string "" and we skip scanning current directory. - if (moduleName != "") - { - string dir = Path.GetDirectoryName(moduleName); - LoadAssembliesInDir(dir, true); - dir = Path.Combine(dir, "AutoLoad"); - LoadAssembliesInDir(dir, true); - } - } - - Contracts.AssertValue(_assemblyQueue); - Contracts.AssertValue(_loadedAssemblies); - - Assembly assembly; - while (_assemblyQueue.TryDequeue(out assembly)) - { - if (!_cachedAssemblies.Add(assembly.FullName)) - continue; - - if (assembly != target) - { - bool found = false; - var targetName = target.GetName(); - foreach (var name in assembly.GetReferencedAssemblies()) - { - if (name.Name == targetName.Name) - { - found = true; - break; - } - } - if (!found) - continue; - } - - int added = 0; - foreach (LoadableClassAttributeBase attr in assembly.GetCustomAttributes(typeof(LoadableClassAttributeBase))) - { - MethodInfo getter = null; - ConstructorInfo ctor = null; - MethodInfo create = null; - bool requireEnvironment = false; - if (attr.InstanceType != typeof(void) && !TryGetIniters(attr.InstanceType, attr.LoaderType, attr.CtorTypes, out getter, out ctor, out create, out requireEnvironment)) - { - Console.Error.WriteLine( - "CacheClassesFromAssembly: can't instantiate loadable class {0} with name {1}", - attr.InstanceType.Name, attr.LoadNames[0]); - Contracts.Assert(getter == null && ctor == null && create == null); - } - var info = new LoadableClassInfo(attr, getter, ctor, create, requireEnvironment); - - AddClass(info, attr.LoadNames); - added++; - } - } - } - } + private readonly ConcurrentQueue _classes; + private readonly ConcurrentDictionary _signatures; private static bool TryGetIniters(Type instType, Type loaderType, Type[] parmTypes, out MethodInfo getter, out ConstructorInfo ctor, out MethodInfo create, out bool requireEnvironment) @@ -454,9 +278,9 @@ private static bool TryGetIniters(Type instType, Type loaderType, Type[] parmTyp var parmTypesWithEnv = Utils.Concat(new Type[1] { typeof(IHostEnvironment) }, parmTypes); if (Utils.Size(parmTypes) == 0 && (getter = FindInstanceGetter(instType, loaderType)) != null) return true; - if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(parmTypes ?? Type.EmptyTypes)) != null) + if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(BindingFlags.Instance | BindingFlags.Public | BindingFlags.NonPublic, null, parmTypes ?? Type.EmptyTypes, null)) != null) return true; - if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(parmTypesWithEnv ?? Type.EmptyTypes)) != null) + if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(BindingFlags.Instance | BindingFlags.Public | BindingFlags.NonPublic, null, parmTypesWithEnv ?? Type.EmptyTypes, null)) != null) { requireEnvironment = true; return true; @@ -472,7 +296,7 @@ private static bool TryGetIniters(Type instType, Type loaderType, Type[] parmTyp return false; } - private static void AddClass(LoadableClassInfo info, string[] loadNames) + private void AddClass(LoadableClassInfo info, string[] loadNames) { _classes.Enqueue(info); foreach (var sigType in info.SignatureTypes) @@ -528,157 +352,54 @@ private static MethodInfo FindCreateMethod(Type instType, Type loaderType, Type[ return meth; } - private static void LoadAssembliesInDir(string dir, bool filter) - { - if (!Directory.Exists(dir)) - return; - - // Load all dlls in the given directory. - var paths = Directory.EnumerateFiles(dir, "*.dll"); - foreach (string path in paths) - { - if (filter && ShouldSkipPath(path)) - continue; - // Loading the assembly is enough because of our event handler. - LoadAssembly(path); - } - } - - private static void CurrentDomainAssemblyLoad(object sender, AssemblyLoadEventArgs args) - { - // Don't try to index dynamic generated assembly - if (args.LoadedAssembly.IsDynamic) - return; - _assemblyQueue.Enqueue(args.LoadedAssembly); - if (!_loadedAssemblies.TryAdd(args.LoadedAssembly.FullName, args.LoadedAssembly)) - { - // Duplicate loading. - Console.Error.WriteLine("Duplicate loading of the assembly '{0}'", args.LoadedAssembly.FullName); - } - } - - private static Assembly CurrentDomainAssemblyResolve(object sender, ResolveEventArgs args) - { - // REVIEW: currently, the resolving happens on exact matches of the full name. - // This has proved to work with the C# transform. We might need to change the resolving logic when the need arises. - Assembly found; - if (_loadedAssemblies.TryGetValue(args.Name, out found)) - return found; - return null; - } - - /// - /// Given an assembly path, load the assembly. - /// - public static Assembly LoadAssembly(string path) - { - try - { - return LoadFrom(path); - } - catch (Exception e) - { - if (DebugLevel > 2) - Console.Error.WriteLine("Could not load assembly {0}:\n{1}", path, e.ToString()); - return null; - } - } - - private static Assembly LoadFrom(string path) - { - Contracts.CheckNonEmpty(path, nameof(path)); - return Assembly.LoadFrom(path); - } - /// - /// Make sure the given assemblies are loaded and that their loadable classes have been catalogued. + /// Registers all the components in the specified assembly by looking for loadable classes + /// and adding them to the catalog. /// - public static void CacheClassesExtra(string[] assemblies) + /// + /// The assembly to register. + /// + /// + /// true to throw an exception if there are errors with registering the components; + /// false to skip any errors. + /// + public void RegisterAssembly(Assembly assembly, bool throwOnError = true) { - if (Utils.Size(assemblies) > 0) + lock (_lock) { - lock (_lock) + if (_cachedAssemblies.Add(assembly.FullName)) { - foreach (string path in assemblies) + foreach (LoadableClassAttributeBase attr in assembly.GetCustomAttributes(typeof(LoadableClassAttributeBase))) { - if (!_cachedPaths.Add(path)) - continue; - - Exception ex = null; - try - { - // REVIEW: Will LoadFrom ever return null? - var assem = LoadFrom(path); - if (assem != null) - continue; - } - catch (Exception e) - { - ex = e; - } - - // If it is a zip file, load it that way. - ZipArchive zip; - try - { - zip = ZipFile.OpenRead(path); - } - catch (Exception e) - { - // Couldn't load as an assembly and not a zip, so warn the user. - ex = ex ?? e; - Console.Error.WriteLine("Warning: Could not load '{0}': {1}", path, ex.Message); - continue; - } - - string dir; - try - { - dir = CreateTempDirectory(); - } - catch (Exception e) - { - throw Contracts.ExceptIO(e, "Creating temp directory for extra assembly zip extraction failed: '{0}'", path); - } - - try - { - zip.ExtractToDirectory(dir); - } - catch (Exception e) + MethodInfo getter = null; + ConstructorInfo ctor = null; + MethodInfo create = null; + bool requireEnvironment = false; + if (attr.InstanceType != typeof(void) && !TryGetIniters(attr.InstanceType, attr.LoaderType, attr.CtorTypes, out getter, out ctor, out create, out requireEnvironment)) { - throw Contracts.ExceptIO(e, "Extracting extra assembly zip failed: '{0}'", path); + if (throwOnError) + { + throw new InvalidOperationException(string.Format( + "Can't instantiate loadable class {0} with name {1}", + attr.InstanceType.Name, attr.LoadNames[0])); + } + Contracts.Assert(getter == null && ctor == null && create == null); } + var info = new LoadableClassInfo(attr, getter, ctor, create, requireEnvironment); - LoadAssembliesInDir(dir, false); + AddClass(info, attr.LoadNames); } + } } - - CacheLoadedAssemblies(); - } - - private static string CreateTempDirectory() - { - string dir = GetTempPath(); - Directory.CreateDirectory(dir); - return dir; - } - - private static string GetTempPath() - { - Guid guid = Guid.NewGuid(); - return Path.GetFullPath(Path.Combine(Path.GetTempPath(), "TLC_" + guid.ToString())); } /// /// Return an array containing information for all instantiatable components. /// If provided, the given set of assemblies is loaded first. /// - public static LoadableClassInfo[] GetAllClasses(string[] assemblies = null) + public LoadableClassInfo[] GetAllClasses() { - CacheClassesExtra(assemblies); - return _classes.ToArray(); } @@ -686,13 +407,11 @@ public static LoadableClassInfo[] GetAllClasses(string[] assemblies = null) /// Return an array containing information for instantiatable components with the given /// signature and base type. If provided, the given set of assemblies is loaded first. /// - public static LoadableClassInfo[] GetAllDerivedClasses(Type typeBase, Type typeSig, string[] assemblies = null) + public LoadableClassInfo[] GetAllDerivedClasses(Type typeBase, Type typeSig) { Contracts.CheckValue(typeBase, nameof(typeBase)); Contracts.CheckValueOrNull(typeSig); - CacheClassesExtra(assemblies); - // Apply the default. if (typeSig == null) typeSig = typeof(SignatureDefault); @@ -706,10 +425,8 @@ public static LoadableClassInfo[] GetAllDerivedClasses(Type typeBase, Type typeS /// Return an array containing all the known signature types. If provided, the given set of assemblies /// is loaded first. /// - public static Type[] GetAllSignatureTypes(string[] assemblies = null) + public Type[] GetAllSignatureTypes() { - CacheClassesExtra(assemblies); - return _signatures.Select(kvp => kvp.Key).ToArray(); } @@ -726,58 +443,47 @@ public static string SignatureToString(Type sig) return kind; } - private static LoadableClassInfo FindClassCore(LoadableClassInfo.Key key) + private LoadableClassInfo FindClassCore(LoadableClassInfo.Key key) { LoadableClassInfo info; - if (_classesByKey.TryGetValue(key, out info)) - return info; - - CacheLoadedAssemblies(); - if (_classesByKey.TryGetValue(key, out info)) return info; return null; } - public static LoadableClassInfo[] FindLoadableClasses(string name) + public LoadableClassInfo[] FindLoadableClasses(string name) { name = name.ToLowerInvariant().Trim(); - CacheLoadedAssemblies(); - var res = _classes .Where(ci => ci.LoadNames.Select(n => n.ToLowerInvariant().Trim()).Contains(name)) .ToArray(); return res; } - public static LoadableClassInfo[] FindLoadableClasses() + public LoadableClassInfo[] FindLoadableClasses() { - CacheLoadedAssemblies(); - return _classes .Where(ci => ci.SignatureTypes.Contains(typeof(TSig))) .ToArray(); } - public static LoadableClassInfo[] FindLoadableClasses() + public LoadableClassInfo[] FindLoadableClasses() { // REVIEW: this and above methods perform a linear search over all the loadable classes. // On 6/15/2015, TLC release build contained 431 of them, so adding extra lookups looks unnecessary at this time. - CacheLoadedAssemblies(); - return _classes .Where(ci => ci.ArgType == typeof(TArgs) && ci.SignatureTypes.Contains(typeof(TSig))) .ToArray(); } - public static LoadableClassInfo GetLoadableClassInfo(string loadName) + public LoadableClassInfo GetLoadableClassInfo(string loadName) { return GetLoadableClassInfo(loadName, typeof(TSig)); } - public static LoadableClassInfo GetLoadableClassInfo(string loadName, Type signatureType) + public LoadableClassInfo GetLoadableClassInfo(string loadName, Type signatureType) { Contracts.CheckParam(signatureType.BaseType == typeof(MulticastDelegate), nameof(signatureType), "signatureType must be a delegate type"); Contracts.CheckValueOrNull(loadName); @@ -815,7 +521,7 @@ private static bool TryCreateInstance(IHostEnvironment env, Type signature env.CheckValueOrNull(name); string nameLower = (name ?? "").ToLowerInvariant().Trim(); - LoadableClassInfo info = FindClassCore(new LoadableClassInfo.Key(nameLower, signatureType)); + LoadableClassInfo info = env.ComponentCatalog.FindClassCore(new LoadableClassInfo.Key(nameLower, signatureType)); if (info == null) { result = null; diff --git a/src/Microsoft.ML.Core/Data/ColumnType.cs b/src/Microsoft.ML.Core/Data/ColumnType.cs index 96764d68f1..69a6505c51 100644 --- a/src/Microsoft.ML.Core/Data/ColumnType.cs +++ b/src/Microsoft.ML.Core/Data/ColumnType.cs @@ -120,47 +120,38 @@ public bool IsBool } /// - /// Whether this type is the standard timespan type. + /// Whether this type is the standard type. /// public bool IsTimeSpan { get { - if (!(this is TimeSpanType)) - return false; - // TimeSpanType is a singleton. - Contracts.Assert(this == TimeSpanType.Instance); - return true; + Contracts.Assert((this == TimeSpanType.Instance) == (this is TimeSpanType)); + return this is TimeSpanType; } } /// - /// Whether this type is a DvDateTime. + /// Whether this type is a . /// public bool IsDateTime { get { - if (!(this is DateTimeType)) - return false; - // DateTimeType is a singleton. - Contracts.Assert(this == DateTimeType.Instance); - return true; + Contracts.Assert((this == DateTimeType.Instance) == (this is DateTimeType)); + return this is DateTimeType; } } /// - /// Whether this type is a DvDateTimeZone. + /// Whether this type is a /// public bool IsDateTimeZone { get { - if (!(this is DateTimeZoneType)) - return false; - // DateTimeZoneType is a singleton. - Contracts.Assert(this == DateTimeZoneType.Instance); - return true; + Contracts.Assert((this == DateTimeOffsetType.Instance) == (this is DateTimeOffsetType)); + return this is DateTimeOffsetType; } } @@ -319,7 +310,7 @@ public static PrimitiveType FromKind(DataKind kind) if (kind == DataKind.DT) return DateTimeType.Instance; if (kind == DataKind.DZ) - return DateTimeZoneType.Instance; + return DateTimeOffsetType.Instance; return NumberType.FromKind(kind); } } @@ -341,7 +332,7 @@ public static TextType Instance } private TextType() - : base(typeof(DvText), DataKind.TX) + : base(typeof(ReadOnlyMemory), DataKind.TX) { } @@ -573,7 +564,7 @@ public static BoolType Instance } private BoolType() - : base(typeof(DvBool), DataKind.BL) + : base(typeof(bool), DataKind.BL) { } @@ -605,7 +596,7 @@ public static DateTimeType Instance } private DateTimeType() - : base(typeof(DvDateTime), DataKind.DT) + : base(typeof(DateTime), DataKind.DT) { } @@ -623,21 +614,21 @@ public override string ToString() } } - public sealed class DateTimeZoneType : PrimitiveType + public sealed class DateTimeOffsetType : PrimitiveType { - private static volatile DateTimeZoneType _instance; - public static DateTimeZoneType Instance + private static volatile DateTimeOffsetType _instance; + public static DateTimeOffsetType Instance { get { if (_instance == null) - Interlocked.CompareExchange(ref _instance, new DateTimeZoneType(), null); + Interlocked.CompareExchange(ref _instance, new DateTimeOffsetType(), null); return _instance; } } - private DateTimeZoneType() - : base(typeof(DvDateTimeZone), DataKind.DZ) + private DateTimeOffsetType() + : base(typeof(DateTimeOffset), DataKind.DZ) { } @@ -645,7 +636,7 @@ public override bool Equals(ColumnType other) { if (other == this) return true; - Contracts.Assert(!(other is DateTimeZoneType)); + Contracts.Assert(!(other is DateTimeOffsetType)); return false; } @@ -672,7 +663,7 @@ public static TimeSpanType Instance } private TimeSpanType() - : base(typeof(DvTimeSpan), DataKind.TS) + : base(typeof(TimeSpan), DataKind.TS) { } diff --git a/src/Microsoft.ML.Core/Data/DataKind.cs b/src/Microsoft.ML.Core/Data/DataKind.cs index 0249745691..ad8d8fbfe0 100644 --- a/src/Microsoft.ML.Core/Data/DataKind.cs +++ b/src/Microsoft.ML.Core/Data/DataKind.cs @@ -55,7 +55,7 @@ public enum DataKind : byte public static class DataKindExtensions { public const DataKind KindMin = DataKind.I1; - public const DataKind KindLim = DataKind.UG + 1; + public const DataKind KindLim = DataKind.U16 + 1; public const int KindCount = KindLim - KindMin; /// @@ -141,19 +141,19 @@ public static Type ToType(this DataKind kind) switch (kind) { case DataKind.I1: - return typeof(DvInt1); + return typeof(sbyte); case DataKind.U1: return typeof(byte); case DataKind.I2: - return typeof(DvInt2); + return typeof(short); case DataKind.U2: return typeof(ushort); case DataKind.I4: - return typeof(DvInt4); + return typeof(int); case DataKind.U4: return typeof(uint); case DataKind.I8: - return typeof(DvInt8); + return typeof(long); case DataKind.U8: return typeof(ulong); case DataKind.R4: @@ -161,15 +161,15 @@ public static Type ToType(this DataKind kind) case DataKind.R8: return typeof(Double); case DataKind.TX: - return typeof(DvText); + return typeof(ReadOnlyMemory); case DataKind.BL: - return typeof(DvBool); + return typeof(bool); case DataKind.TS: - return typeof(DvTimeSpan); + return typeof(TimeSpan); case DataKind.DT: - return typeof(DvDateTime); + return typeof(DateTime); case DataKind.DZ: - return typeof(DvDateTimeZone); + return typeof(DateTimeOffset); case DataKind.UG: return typeof(UInt128); } @@ -185,35 +185,35 @@ public static bool TryGetDataKind(this Type type, out DataKind kind) Contracts.CheckValueOrNull(type); // REVIEW: Make this more efficient. Should we have a global dictionary? - if (type == typeof(DvInt1) || type == typeof(sbyte) || type == typeof(sbyte?)) + if (type == typeof(sbyte)) kind = DataKind.I1; - else if (type == typeof(byte) || type == typeof(byte?)) + else if (type == typeof(byte)) kind = DataKind.U1; - else if (type == typeof(DvInt2)|| type== typeof(short) || type == typeof(short?)) + else if (type == typeof(short)) kind = DataKind.I2; - else if (type == typeof(ushort)|| type == typeof(ushort?)) + else if (type == typeof(ushort)) kind = DataKind.U2; - else if (type == typeof(DvInt4) || type == typeof(int)|| type == typeof(int?)) + else if (type == typeof(int)) kind = DataKind.I4; - else if (type == typeof(uint)|| type == typeof(uint?)) + else if (type == typeof(uint)) kind = DataKind.U4; - else if (type == typeof(DvInt8) || type==typeof(long)|| type == typeof(long?)) + else if (type == typeof(long)) kind = DataKind.I8; - else if (type == typeof(ulong)|| type == typeof(ulong?)) + else if (type == typeof(ulong)) kind = DataKind.U8; - else if (type == typeof(Single)|| type == typeof(Single?)) + else if (type == typeof(Single)) kind = DataKind.R4; - else if (type == typeof(Double)|| type == typeof(Double?)) + else if (type == typeof(Double)) kind = DataKind.R8; - else if (type == typeof(DvText)) + else if (type == typeof(ReadOnlyMemory) || type == typeof(string)) kind = DataKind.TX; - else if (type == typeof(DvBool) || type == typeof(bool) || type == typeof(bool?)) + else if (type == typeof(bool)) kind = DataKind.BL; - else if (type == typeof(DvTimeSpan)) + else if (type == typeof(TimeSpan)) kind = DataKind.TS; - else if (type == typeof(DvDateTime)) + else if (type == typeof(DateTime)) kind = DataKind.DT; - else if (type == typeof(DvDateTimeZone)) + else if (type == typeof(DateTimeOffset)) kind = DataKind.DZ; else if (type == typeof(UInt128)) kind = DataKind.UG; diff --git a/src/Microsoft.ML.Core/Data/DateTime.cs b/src/Microsoft.ML.Core/Data/DateTime.cs deleted file mode 100644 index d11be2a494..0000000000 --- a/src/Microsoft.ML.Core/Data/DateTime.cs +++ /dev/null @@ -1,550 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using Microsoft.ML.Runtime.Internal.Utilities; - -namespace Microsoft.ML.Runtime.Data -{ - using Conditional = System.Diagnostics.ConditionalAttribute; - using SysDateTime = System.DateTime; - using SysDateTimeOffset = System.DateTimeOffset; - using SysTimeSpan = System.TimeSpan; - - /// - /// A struct to represent a DateTime column type - /// - public struct DvDateTime : IEquatable, IComparable - { - public const long MaxTicks = 3155378975999999999; - private readonly DvInt8 _ticks; - - /// - /// This ctor initializes _ticks to the value of sdt.Ticks, and ignores its DateTimeKind value. - /// - public DvDateTime(SysDateTime sdt) - { - _ticks = sdt.Ticks; - AssertValid(); - } - - /// - /// This ctor accepts any value for ticks, but produces an NA if ticks is out of the legal range. - /// - public DvDateTime(DvInt8 ticks) - { - if ((ulong)ticks.RawValue > MaxTicks) - _ticks = DvInt8.NA; - else - _ticks = ticks; - AssertValid(); - } - - [Conditional("DEBUG")] - internal void AssertValid() - { - Contracts.Assert((ulong)_ticks.RawValue <= MaxTicks || _ticks.IsNA); - } - - public DvInt8 Ticks - { - get - { - AssertValid(); - return _ticks; - } - } - - // REVIEW: Add more System.DateTime members returning their corresponding 'Dv' types (task 4255). - /// - /// Gets the date component of this object. - /// - public DvDateTime Date - { - get - { - AssertValid(); - if (IsNA) - return NA; - return new DvDateTime(GetSysDateTime().Date); - } - } - - /// - /// Gets a DvDateTime object representing the current UTC date and time. - /// - public static DvDateTime UtcNow { get { return new DvDateTime(SysDateTime.UtcNow); } } - - public bool IsNA - { - get - { - AssertValid(); - return (ulong)_ticks.RawValue > MaxTicks; - } - } - - public static DvDateTime NA - { - get { return new DvDateTime(DvInt8.NA); } - } - - public static explicit operator SysDateTime?(DvDateTime dvDt) - { - if (dvDt.IsNA) - return null; - return dvDt.GetSysDateTime(); - } - - /// - /// Creates a new DvDateTime with the same number of ticks as in sdt, ignoring its DateTimeKind value. - /// - public static implicit operator DvDateTime(SysDateTime sdt) - { - return new DvDateTime(sdt); - } - - public static implicit operator DvDateTime(SysDateTime? sdt) - { - if (sdt == null) - return DvDateTime.NA; - return new DvDateTime(sdt.Value); - } - - public override string ToString() - { - AssertValid(); - if (IsNA) - return ""; - return GetSysDateTime().ToString("o"); - } - - internal SysDateTime GetSysDateTime() - { - AssertValid(); - Contracts.Assert(!IsNA); - return new SysDateTime(_ticks.RawValue); - } - - public bool Equals(DvDateTime other) - { - return _ticks.RawValue == other._ticks.RawValue; - } - - public override bool Equals(object obj) - { - return obj is DvDateTime && Equals((DvDateTime)obj); - } - - public int CompareTo(DvDateTime other) - { - if (_ticks.RawValue == other._ticks.RawValue) - return 0; - return _ticks.RawValue < other._ticks.RawValue ? -1 : 1; - } - - public override int GetHashCode() - { - return _ticks.GetHashCode(); - } - } - - /// - /// A struct to represent a DateTimeZone column type. - /// - public struct DvDateTimeZone : IEquatable, IComparable - { - public const long TicksPerMinute = 600000000; - public const long MaxMinutesOffset = 840; - public const long MinMinutesOffset = -840; - - // Stores the UTC date-time (convert to clock time by adding the offset). - private readonly DvDateTime _dateTime; - // Store the offset in minutes. - private readonly DvInt2 _offset; - - // This assumes (and asserts) that the dt/offset combination is valid. - // Callers should do the validation. - private DvDateTimeZone(DvDateTime dt, DvInt2 offset) - { - _dateTime = dt; - _offset = offset; - AssertValid(); - } - - /// - /// Given a number of ticks for the date time portion and a number of minutes for - /// the time zone offset, this constructs a new DvDateTimeZone. If anything is invalid, - /// it produces NA. - /// - /// The number of clock ticks in the date time portion - /// The time zone offset in minutes - public DvDateTimeZone(DvInt8 ticks, DvInt2 offset) - { - var dt = new DvDateTime(ticks); - if (dt.IsNA || offset.IsNA || MinMinutesOffset > offset.RawValue || offset.RawValue > MaxMinutesOffset) - { - _dateTime = DvDateTime.NA; - _offset = DvInt2.NA; - } - else - { - _offset = offset; - _dateTime = ValidateDate(dt, ref _offset); - } - AssertValid(); - } - - public DvDateTimeZone(SysDateTimeOffset dto) - { - // Since it is constructed from a SysDateTimeOffset, all the validations should work. - var success = TryValidateOffset(dto.Offset.Ticks, out _offset); - Contracts.Assert(success); - _dateTime = ValidateDate(new DvDateTime(dto.DateTime), ref _offset); - Contracts.Assert(!_dateTime.IsNA); - Contracts.Assert(!_offset.IsNA); - AssertValid(); - } - - /// - /// Constructs a DvDateTimeZone from a clock date-time and a time zone offset from UTC. - /// - /// The clock time - /// The offset - public DvDateTimeZone(DvDateTime dt, DvTimeSpan offset) - { - if (dt.IsNA || offset.IsNA || !TryValidateOffset(offset.Ticks, out _offset)) - { - _dateTime = DvDateTime.NA; - _offset = DvInt2.NA; - } - else - _dateTime = ValidateDate(dt, ref _offset); - AssertValid(); - } - - /// - /// This method takes a DvDateTime representing clock time, and a TimeSpan representing an offset, - /// validates that both the clock time and the UTC time (which is the clock time minus the offset) - /// are within the valid range, and returns a DvDateTime representing the UTC time (dateTime-offset). - /// - /// The clock time - /// The offset. This value is assumed to be validated as a legal offset: - /// a value in whole minutes, between -14 and 14 hours. - /// The UTC DvDateTime representing the input clock time minus the offset - private static DvDateTime ValidateDate(DvDateTime dateTime, ref DvInt2 offset) - { - Contracts.Assert(!dateTime.IsNA); - Contracts.Assert(!offset.IsNA); - - // Validate that both the UTC and clock times are legal. - Contracts.Assert(MinMinutesOffset <= offset.RawValue && offset.RawValue <= MaxMinutesOffset); - var offsetTicks = offset.RawValue * TicksPerMinute; - // This operation cannot overflow because offset should have already been validated to be within - // 14 hours and the DateTime instance is more than that distance from the boundaries of Int64. - long utcTicks = dateTime.Ticks.RawValue - offsetTicks; - var dvdt = new DvDateTime(utcTicks); - if (dvdt.IsNA) - offset = DvInt2.NA; - return dvdt; - } - - /// - /// This method takes a TimeSpan offset, validates that it is a legal offset for DvDateTimeZone (i.e. - /// in whole minutes, and between -14 and 14 hours), and returns the offset in number of minutes. - /// - /// - /// - /// - private static bool TryValidateOffset(DvInt8 offsetTicks, out DvInt2 offset) - { - if (offsetTicks.IsNA || offsetTicks.RawValue % TicksPerMinute != 0) - { - offset = DvInt2.NA; - return false; - } - - long mins = offsetTicks.RawValue / TicksPerMinute; - short res = (short)mins; - if (res != mins || res > MaxMinutesOffset || res < MinMinutesOffset) - { - offset = DvInt2.NA; - return false; - } - offset = res; - Contracts.Assert(!offset.IsNA); - return true; - } - - [Conditional("DEBUG")] - private void AssertValid() - { - _dateTime.AssertValid(); - if (_dateTime.IsNA) - Contracts.Assert(_offset.IsNA); - else - { - Contracts.Assert(MinMinutesOffset <= _offset.RawValue && _offset.RawValue <= MaxMinutesOffset); - Contracts.Assert((ulong)(_dateTime.Ticks.RawValue + _offset.RawValue * TicksPerMinute) - <= (ulong)DvDateTime.MaxTicks); - } - } - - public DvDateTime ClockDateTime - { - get - { - AssertValid(); - if (_dateTime.IsNA) - return DvDateTime.NA; - var res = new DvDateTime(_dateTime.Ticks.RawValue + _offset.RawValue * TicksPerMinute); - Contracts.Assert(!res.IsNA); - return res; - } - } - - /// - /// Gets the UTC date and time. - /// - public DvDateTime UtcDateTime - { - get - { - AssertValid(); - if (IsNA) - return DvDateTime.NA; - return _dateTime; - } - } - - /// - /// Gets the offset as a time span. - /// - public DvTimeSpan Offset - { - get - { - AssertValid(); - if (_offset.IsNA) - return DvTimeSpan.NA; - return new DvTimeSpan(_offset.RawValue * TicksPerMinute); - } - } - - /// - /// Gets the offset in minutes. - /// - public DvInt2 OffsetMinutes - { - get - { - AssertValid(); - return _offset; - } - } - - // REVIEW: Add more System.DateTimeOffset members returning their corresponding 'Dv' types (task 4255). - - /// - /// Gets the date component of the ClockDateTime. - /// - public DvDateTime ClockDate - { - get - { - AssertValid(); - if (IsNA) - return DvDateTime.NA; - return ClockDateTime.Date; - } - } - - /// - /// Gets the date component of the UtcDateTime. - /// - public DvDateTime UtcDate - { - get - { - AssertValid(); - if (IsNA) - return DvDateTime.NA; - return _dateTime.Date; - } - } - - /// - /// Gets a DvDateTimeZone object representing the current UTC date and time (with offset=0). - /// - public static DvDateTimeZone UtcNow { get { return new DvDateTimeZone(SysDateTimeOffset.UtcNow); } } - - public bool IsNA - { - get - { - AssertValid(); - return _dateTime.IsNA; - } - } - - // The missing value for DvDateTimeZone is represented by a DvDateTimeZone with _dateTime = DvDateTime.NA - // and _offset = 0. - public static DvDateTimeZone NA - { - get { return new DvDateTimeZone(DvDateTime.NA, DvInt2.NA); } - } - - public static explicit operator SysDateTimeOffset?(DvDateTimeZone dvDto) - { - if (dvDto.IsNA) - return null; - return dvDto.GetSysDateTimeOffset(); - } - - public static implicit operator DvDateTimeZone(SysDateTimeOffset sdto) - { - return new DvDateTimeZone(sdto); - } - - public static implicit operator DvDateTimeZone(SysDateTimeOffset? sdto) - { - if (sdto == null) - return DvDateTimeZone.NA; - return new DvDateTimeZone(sdto.Value); - } - - public override string ToString() - { - AssertValid(); - if (IsNA) - return ""; - - return GetSysDateTimeOffset().ToString("o"); - } - - private DateTimeOffset GetSysDateTimeOffset() - { - AssertValid(); - Contracts.Assert(!IsNA); - return new SysDateTimeOffset(ClockDateTime.GetSysDateTime(), new TimeSpan(0, _offset.RawValue, 0)); - } - - /// - /// Compare two values for equality. Note that this differs from System.DateTimeOffset's - /// definition of Equals, which only compares the UTC values, not the offsets. - /// - public bool Equals(DvDateTimeZone other) - { - return _offset.RawValue == other._offset.RawValue && _dateTime.Equals(other._dateTime); - } - - public override bool Equals(object obj) - { - return obj is DvDateTimeZone && Equals((DvDateTimeZone)obj); - } - - /// - /// Compare two values for ordering. Note that this differs from System.DateTimeOffset's - /// definition of CompareTo, which only compares the UTC values, not the offsets. - /// - public int CompareTo(DvDateTimeZone other) - { - AssertValid(); - other.AssertValid(); - - int res = _dateTime.CompareTo(other._dateTime); - if (res != 0) - return res; - if (_offset.RawValue == other._offset.RawValue) - return 0; - return _offset.RawValue < other._offset.RawValue ? -1 : 1; - } - - public override int GetHashCode() - { - return Hashing.CombineHash(_dateTime.GetHashCode(), _offset.GetHashCode()); - } - } - - /// - /// A struct to represent a DateTime column type - /// - public struct DvTimeSpan : IEquatable, IComparable - { - private readonly DvInt8 _ticks; - - public DvInt8 Ticks { get { return _ticks; } } - - public DvTimeSpan(DvInt8 ticks) - { - _ticks = ticks; - } - - public DvTimeSpan(SysTimeSpan sts) - { - _ticks = sts.Ticks; - } - - public DvTimeSpan(SysTimeSpan? sts) - { - _ticks = sts != null ? sts.GetValueOrDefault().Ticks : DvInt8.NA; - } - - public bool IsNA - { - get { return _ticks.IsNA; } - } - - public static DvTimeSpan NA - { - get { return new DvTimeSpan(DvInt8.NA); } - } - - public static explicit operator SysTimeSpan?(DvTimeSpan ts) - { - if (ts.IsNA) - return null; - return new SysTimeSpan(ts._ticks.RawValue); - } - - public static implicit operator DvTimeSpan(SysTimeSpan sts) - { - return new DvTimeSpan(sts); - } - - public static implicit operator DvTimeSpan(SysTimeSpan? sts) - { - return new DvTimeSpan(sts); - } - - public override string ToString() - { - if (IsNA) - return ""; - return new SysTimeSpan(_ticks.RawValue).ToString("c"); - } - - public bool Equals(DvTimeSpan other) - { - return _ticks.RawValue == other._ticks.RawValue; - } - - public override bool Equals(object obj) - { - return obj is DvTimeSpan && Equals((DvTimeSpan)obj); - } - - public int CompareTo(DvTimeSpan other) - { - if (_ticks.RawValue == other._ticks.RawValue) - return 0; - return _ticks.RawValue < other._ticks.RawValue ? -1 : 1; - } - - public override int GetHashCode() - { - return _ticks.GetHashCode(); - } - } -} diff --git a/src/Microsoft.ML.Core/Data/DvBool.cs b/src/Microsoft.ML.Core/Data/DvBool.cs deleted file mode 100644 index f17cb596d4..0000000000 --- a/src/Microsoft.ML.Core/Data/DvBool.cs +++ /dev/null @@ -1,226 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using System.Runtime.CompilerServices; - -namespace Microsoft.ML.Runtime.Data -{ - using BL = DvBool; - using R4 = Single; - using R8 = Double; - - public struct DvBool : IEquatable, IComparable - { - private const byte _false = 0; - private const byte _true = 1; - private const byte _na = 128; - public const byte RawNA = _na; - - private byte _value; - - public static BL False { get { BL res; res._value = _false; return res; } } - public static BL True { get { BL res; res._value = _true; return res; } } - public static BL NA { get { BL res; res._value = _na; return res; } } - - /// - /// Property to return the raw value. - /// - public byte RawValue - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value; } - } - - /// - /// Static method to return the raw value. This is more convenient than the - /// property in code-generation scenarios. - /// - public static byte GetRawBits(BL a) - { - return a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - private DvBool(int value) - { - Contracts.Assert(value == _true || value == _false || value == _na); - _value = (byte)value; - } - - /// - /// Returns whether this value is false. - /// - public bool IsFalse - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value == _false; } - } - - /// - /// Returns whether this value is true. - /// - public bool IsTrue - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value == _true; } - } - - /// - /// Returns whether this value is NA. - /// - public bool IsNA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value > _true; } - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator BL(bool value) - { - BL res; - res._value = value ? _true : _false; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator BL(bool? value) - { - BL res; - res._value = value == null ? _na : value.GetValueOrDefault() ? _true : _false; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator bool(BL value) - { - switch (value._value) - { - case _false: - return false; - case _true: - return true; - default: - throw Contracts.ExceptValue(nameof(value), "NA cast to bool"); - } - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator bool?(BL value) - { - switch (value._value) - { - case _false: - return false; - case _true: - return true; - default: - return null; - } - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R4(BL value) - { - if (value._value <= _true) - return value._value; - return Single.NaN; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R8(BL value) - { - if (value._value <= _true) - return value._value; - return Double.NaN; - } - - public override int GetHashCode() - { - return _value.GetHashCode(); - } - - public override bool Equals(object obj) - { - if (obj is BL) - return _value == ((BL)obj)._value; - return false; - } - - public bool Equals(BL other) - { - // Note that if one or both are "non-standard" NA values, this - // could return false. Theoretically, that should never happen, - // but unsafe code could cause it. - return _value == other._value; - } - - public int CompareTo(BL other) - { - // Note that if one or both are "non-standard" NA values, this could produce unexpected comparisons. - // Theoretically, that should never happen, but unsafe code could cause it. - Contracts.Assert(unchecked((sbyte)RawNA) < (sbyte)_false); - if (_value == other._value) - return 0; - return (sbyte)_value < (sbyte)other._value ? -1 : 1; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator ==(BL a, BL b) - { - if (a._value <= _true && b._value <= _true) - return a._value == b._value ? True : False; - return NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator !=(BL a, BL b) - { - if (a._value <= _true && b._value <= _true) - return a._value != b._value ? True : False; - return NA; - } - - public override string ToString() - { - if (_value == _false) - return "False"; - if (_value == _true) - return "True"; - return "NA"; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator !(BL a) - { - if (a._value <= _true) - a._value ^= 1; - return a; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator |(BL a, BL b) - { - if (a._value == _true) - return a; - if (b._value == _true) - return b; - if (a._value != _false) - return a; - return b; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator &(BL a, BL b) - { - if (a._value == _false) - return a; - if (b._value == _false) - return b; - if (a._value != _true) - return a; - return b; - } - } -} diff --git a/src/Microsoft.ML.Core/Data/DvInt1.cs b/src/Microsoft.ML.Core/Data/DvInt1.cs deleted file mode 100644 index ced2a4688d..0000000000 --- a/src/Microsoft.ML.Core/Data/DvInt1.cs +++ /dev/null @@ -1,264 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using System.Runtime.CompilerServices; - -namespace Microsoft.ML.Runtime.Data -{ - using BL = DvBool; - using I2 = DvInt2; - using I4 = DvInt4; - using I8 = DvInt8; - using IX = DvInt1; - using R4 = Single; - using R8 = Double; - using RawI8 = Int64; - using RawIX = SByte; - - public struct DvInt1 : IEquatable, IComparable - { - public const RawIX RawNA = RawIX.MinValue; - - // Ideally this would be readonly. However, note that this struct has no - // ctor, but instead only has conversion operators. The implicit conversion - // operator from RawIX to DvIX performs better than an equivalent ctor, - // and the conversion operator must assign the _value field. - private RawIX _value; - - /// - /// Property to return the raw value. - /// - public RawIX RawValue - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value; } - } - - /// - /// Static method to return the raw value. This is more convenient than the - /// property in code-generation scenarios. - /// - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static RawIX GetRawBits(IX a) - { - return a._value; - } - - public static IX NA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return RawNA; } - } - - public bool IsNA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value == RawNA; } - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX value) - { - IX res; - res._value = value; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX? value) - { - IX res; - res._value = value ?? RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX(IX value) - { - if (value._value == RawNA) - throw Contracts.ExceptValue(nameof(value), "NA cast to sbyte"); - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX?(IX value) - { - if (value._value == RawNA) - return null; - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(BL a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(I2 a) - { - RawIX res = (RawIX)a.RawValue; - if (res != a.RawValue) - return RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(I4 a) - { - RawIX res = (RawIX)a.RawValue; - if (res != a.RawValue) - return RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(I8 a) - { - RawIX res = (RawIX)a.RawValue; - if (res != a.RawValue) - return RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R4 a) - { - return (IX)(R8)a; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R4(IX a) - { - if (a._value == RawNA) - return R4.NaN; - return (R4)a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R8 a) - { - const R8 lim = -(R8)RawIX.MinValue; - if (-lim < a && a < lim) - { - RawIX n = (RawIX)a; -#if DEBUG - Contracts.Assert(!a.IsNA()); - Contracts.Assert(n != RawNA); - RawI8 nn = (RawI8)a; - Contracts.Assert(nn == n); - if (a >= 0) - Contracts.Assert(a - 1 < n & n <= a); - else - Contracts.Assert(a <= n & n < a + 1); -#endif - return n; - } - - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R8(IX a) - { - if (a._value == RawNA) - return R8.NaN; - return (R8)a._value; - } - - public override int GetHashCode() - { - return _value.GetHashCode(); - } - - public override bool Equals(object obj) - { - if (obj is IX) - return _value == ((IX)obj)._value; - return false; - } - - public bool Equals(IX other) - { - return _value == other._value; - } - - public int CompareTo(IX other) - { - if (_value == other._value) - return 0; - return _value < other._value ? -1 : 1; - } - - public override string ToString() - { - if (_value == RawNA) - return "NA"; - return _value.ToString(); - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator ==(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av == bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator !=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av != bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av < bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av <= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av >= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av > bv ? BL.True : BL.False; - return BL.NA; - } - } -} diff --git a/src/Microsoft.ML.Core/Data/DvInt2.cs b/src/Microsoft.ML.Core/Data/DvInt2.cs deleted file mode 100644 index 33599f6468..0000000000 --- a/src/Microsoft.ML.Core/Data/DvInt2.cs +++ /dev/null @@ -1,263 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using System.Runtime.CompilerServices; - -namespace Microsoft.ML.Runtime.Data -{ - using BL = DvBool; - using I1 = DvInt1; - using I4 = DvInt4; - using I8 = DvInt8; - using IX = DvInt2; - using R4 = Single; - using R8 = Double; - using RawI8 = Int64; - using RawIX = Int16; - - public struct DvInt2 : IEquatable, IComparable - { - public const RawIX RawNA = RawIX.MinValue; - - // Ideally this would be readonly. However, note that this struct has no - // ctor, but instead only has conversion operators. The implicit conversion - // operator from RawIX to DvIX performs better than an equivalent ctor, - // and the conversion operator must assign the _value field. - private RawIX _value; - - /// - /// Property to return the raw value. - /// - public RawIX RawValue - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value; } - } - - /// - /// Static method to return the raw value. This is more convenient than the - /// property in code-generation scenarios. - /// - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static RawIX GetRawBits(IX a) - { - return a._value; - } - - public static IX NA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return RawNA; } - } - - public bool IsNA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value == RawNA; } - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX value) - { - IX res; - res._value = value; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX? value) - { - IX res; - res._value = value ?? RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX(IX value) - { - if (value._value == RawNA) - throw Contracts.ExceptValue(nameof(value), "NA cast to short"); - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX?(IX value) - { - if (value._value == RawNA) - return null; - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(BL a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(I1 a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(I4 a) - { - RawIX res = (RawIX)a.RawValue; - if (res != a.RawValue) - return RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(I8 a) - { - RawIX res = (RawIX)a.RawValue; - if (res != a.RawValue) - return RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R4 a) - { - return (IX)(R8)a; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R4(IX a) - { - if (a._value == RawNA) - return R4.NaN; - return (R4)a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R8 a) - { - const R8 lim = -(R8)RawIX.MinValue; - if (-lim < a && a < lim) - { - RawIX n = (RawIX)a; -#if DEBUG - Contracts.Assert(!a.IsNA()); - Contracts.Assert(n != RawNA); - RawI8 nn = (RawI8)a; - Contracts.Assert(nn == n); - if (a >= 0) - Contracts.Assert(a - 1 < n & n <= a); - else - Contracts.Assert(a <= n & n < a + 1); -#endif - return n; - } - - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R8(IX a) - { - if (a._value == RawNA) - return R8.NaN; - return (R8)a._value; - } - - public override int GetHashCode() - { - return _value.GetHashCode(); - } - - public override bool Equals(object obj) - { - if (obj is IX) - return _value == ((IX)obj)._value; - return false; - } - - public bool Equals(IX other) - { - return _value == other._value; - } - - public int CompareTo(IX other) - { - if (_value == other._value) - return 0; - return _value < other._value ? -1 : 1; - } - - public override string ToString() - { - if (_value == RawNA) - return "NA"; - return _value.ToString(); - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator ==(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av == bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator !=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av != bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av < bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av <= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av >= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av > bv ? BL.True : BL.False; - return BL.NA; - } - } -} diff --git a/src/Microsoft.ML.Core/Data/DvInt4.cs b/src/Microsoft.ML.Core/Data/DvInt4.cs deleted file mode 100644 index 23c7e89242..0000000000 --- a/src/Microsoft.ML.Core/Data/DvInt4.cs +++ /dev/null @@ -1,456 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using System.Runtime.CompilerServices; - -namespace Microsoft.ML.Runtime.Data -{ - using BL = DvBool; - using I1 = DvInt1; - using I2 = DvInt2; - using I8 = DvInt8; - using IX = DvInt4; - using R4 = Single; - using R8 = Double; - using RawI8 = Int64; - using RawIX = Int32; - - public struct DvInt4 : IEquatable, IComparable - { - public const RawIX RawNA = RawIX.MinValue; - - // Ideally this would be readonly. However, note that this struct has no - // ctor, but instead only has conversion operators. The implicit conversion - // operator from RawIX to DvIX performs better than an equivalent ctor, - // and the conversion operator must assign the _value field. - private RawIX _value; - - /// - /// Property to return the raw value. - /// - public RawIX RawValue - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value; } - } - - /// - /// Static method to return the raw value. This is more convenient than the - /// property in code-generation scenarios. - /// - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static RawIX GetRawBits(IX a) - { - return a._value; - } - - public static IX NA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return RawNA; } - } - - public bool IsNA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value == RawNA; } - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX value) - { - IX res; - res._value = value; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX? value) - { - IX res; - res._value = value ?? RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX(IX value) - { - if (value._value == RawNA) - throw Contracts.ExceptValue(nameof(value), "NA cast to int"); - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX?(IX value) - { - if (value._value == RawNA) - return null; - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(BL a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(I1 a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(I2 a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(I8 a) - { - RawIX res = (RawIX)a.RawValue; - if (res != a.RawValue) - return RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R4 a) - { - return (IX)(R8)a; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R4(IX a) - { - if (a._value == RawNA) - return R4.NaN; - return (R4)a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R8 a) - { - const R8 lim = -(R8)RawIX.MinValue; - if (-lim < a && a < lim) - { - RawIX n = (RawIX)a; -#if DEBUG - Contracts.Assert(!a.IsNA()); - Contracts.Assert(n != RawNA); - RawI8 nn = (RawI8)a; - Contracts.Assert(nn == n); - if (a >= 0) - Contracts.Assert(a - 1 < n & n <= a); - else - Contracts.Assert(a <= n & n < a + 1); -#endif - return n; - } - - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R8(IX a) - { - if (a._value == RawNA) - return R8.NaN; - return (R8)a._value; - } - - public override int GetHashCode() - { - return _value.GetHashCode(); - } - - public override bool Equals(object obj) - { - if (obj is IX) - return _value == ((IX)obj)._value; - return false; - } - - public bool Equals(IX other) - { - return _value == other._value; - } - - public int CompareTo(IX other) - { - if (_value == other._value) - return 0; - return _value < other._value ? -1 : 1; - } - - public override string ToString() - { - if (_value == RawNA) - return "NA"; - return _value.ToString(); - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator ==(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av == bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator !=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av != bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av < bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av <= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av >= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av > bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator -(IX a) - { - return -a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator +(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - { - var res = av + bv; - // Overflow happens iff the sign of the result is different than both source values. - if ((av ^ res) >= 0) - return res; - if ((bv ^ res) >= 0) - return res; - } - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator -(IX a, IX b) - { - var av = a._value; - var bv = -b._value; - if (av != RawNA && bv != RawNA) - { - var res = av + bv; - // Overflow happens iff the sign of the result is different than both source values. - if ((av ^ res) >= 0) - return res; - if ((bv ^ res) >= 0) - return res; - } - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator *(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - { - RawI8 res = (RawI8)av * bv; - if (-RawIX.MaxValue <= res && res <= RawIX.MaxValue) - return (RawIX)res; - } - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator /(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA && bv != 0) - return av / bv; - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator %(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA && bv != 0) - return av % bv; - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Abs(IX a) - { - // Can't use Math.Abs since it throws on the RawNA value. - return a._value >= 0 ? a._value : -a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Sign(IX a) - { - var val = a._value; - var neg = -val; - // This works for NA since -RawNA == RawNA. - return val > neg ? +1 : val < neg ? -1 : val; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Min(IX a, IX b) - { - var v1 = a._value; - var v2 = b._value; - // This works for NA since RawNA == RawIX.MinValue. - return v1 <= v2 ? v1 : v2; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public IX Min(IX b) - { - var v1 = _value; - var v2 = b._value; - // This works for NA since RawNA == RawIX.MinValue. - return v1 <= v2 ? v1 : v2; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Max(IX a, IX b) - { - var v1 = a._value; - var v2 = b._value; - // This works for NA since RawNA - 1 == RawIX.MaxValue. - return v1 - 1 >= v2 - 1 ? v1 : v2; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public IX Max(IX b) - { - var v1 = _value; - var v2 = b._value; - // This works for NA since RawNA - 1 == RawIX.MaxValue. - return v1 - 1 >= v2 - 1 ? v1 : v2; - } - - /// - /// Raise a to the b power. Special cases: - /// * 1^NA => 1 - /// * NA^0 => 1 - /// - public static IX Pow(IX a, IX b) - { - var av = a.RawValue; - var bv = b.RawValue; - - if (av == 1) - return 1; - switch (bv) - { - case 0: - return 1; - case 1: - return av; - case 2: - return a * a; - case RawNA: - return RawNA; - } - if (av == -1) - return (bv & 1) == 0 ? 1 : -1; - if (bv < 0) - return RawNA; - if (av == RawNA) - return RawNA; - - // Since the abs of the base is at least two, the exponent must be less than 31. - if (bv >= 31) - return RawNA; - - bool neg = false; - if (av < 0) - { - av = -av; - neg = (bv & 1) != 0; - } - Contracts.Assert(av >= 2); - - // Since the exponent is at least three, the base must be <= 1290. - Contracts.Assert(bv >= 3); - if (av > 1290) - return RawNA; - - // REVIEW: Should we use a checked context and exception catching like I8 does? - ulong u = (ulong)(uint)av; - ulong result = 1; - for (; ; ) - { - if ((bv & 1) != 0 && (result *= u) > RawIX.MaxValue) - return RawNA; - bv >>= 1; - if (bv == 0) - break; - if ((u *= u) > RawIX.MaxValue) - return RawNA; - } - Contracts.Assert(result <= RawIX.MaxValue); - - var res = (RawIX)result; - if (neg) - res = -res; - return res; - } - } -} diff --git a/src/Microsoft.ML.Core/Data/DvInt8.cs b/src/Microsoft.ML.Core/Data/DvInt8.cs deleted file mode 100644 index 3212e21fa6..0000000000 --- a/src/Microsoft.ML.Core/Data/DvInt8.cs +++ /dev/null @@ -1,511 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using System.Runtime.CompilerServices; - -namespace Microsoft.ML.Runtime.Data -{ - using BL = DvBool; - using I1 = DvInt1; - using I2 = DvInt2; - using I4 = DvInt4; - using IX = DvInt8; - using R4 = Single; - using R8 = Double; - using RawIX = Int64; - - public struct DvInt8 : IEquatable, IComparable - { - public const RawIX RawNA = RawIX.MinValue; - - // Ideally this would be readonly. However, note that this struct has no - // ctor, but instead only has conversion operators. The implicit conversion - // operator from RawIX to DvIX performs better than an equivalent ctor, - // and the conversion operator must assign the _value field. - private RawIX _value; - - /// - /// Property to return the raw value. - /// - public RawIX RawValue - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value; } - } - - /// - /// Static method to return the raw value. This is more convenient than the - /// property in code-generation scenarios. - /// - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static RawIX GetRawBits(IX a) - { - return a._value; - } - - public static IX NA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return RawNA; } - } - - public bool IsNA - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - get { return _value == RawNA; } - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX value) - { - IX res; - res._value = value; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(RawIX? value) - { - IX res; - res._value = value ?? RawNA; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX(IX value) - { - if (value._value == RawNA) - throw Contracts.ExceptValue(nameof(value), "NA cast to long"); - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator RawIX?(IX value) - { - if (value._value == RawNA) - return null; - return value._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(BL a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(I1 a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(I2 a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static implicit operator IX(I4 a) - { - if (a.IsNA) - return RawNA; - return (RawIX)a.RawValue; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R4 a) - { - return (IX)(R8)a; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R4(IX a) - { - if (a._value == RawNA) - return R4.NaN; - return (R4)a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator IX(R8 a) - { - const R8 lim = -(R8)RawIX.MinValue; - if (-lim < a && a < lim) - { - RawIX n = (RawIX)a; -#if DEBUG - Contracts.Assert(!a.IsNA()); - Contracts.Assert(n != RawNA); - // Note that an R8 cannot represent long.MaxValue exactly so y + 1.0 below might be the same as y. - R8 x = a; - R8 y = n; - if (a < 0) - { - x = -x; - y = -y; - } - Contracts.Assert(y <= x); - Contracts.Assert(x < y + 1.0 | y + 1.0 == y & x == y); -#endif - return n; - } - - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static explicit operator R8(IX a) - { - if (a._value == RawNA) - return R8.NaN; - return (R8)a._value; - } - - public override int GetHashCode() - { - return _value.GetHashCode(); - } - - public override bool Equals(object obj) - { - if (obj is IX) - return _value == ((IX)obj)._value; - return false; - } - - public bool Equals(IX other) - { - return _value == other._value; - } - - public int CompareTo(IX other) - { - if (_value == other._value) - return 0; - return _value < other._value ? -1 : 1; - } - - public override string ToString() - { - if (_value == RawNA) - return "NA"; - return _value.ToString(); - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator ==(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av == bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator !=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av != bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av < bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator <=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av <= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >=(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av >= bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL operator >(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - return av > bv ? BL.True : BL.False; - return BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator -(IX a) - { - return -a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator +(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA) - { - var res = av + bv; - // Overflow happens iff the sign of the result is different than both source values. - if ((av ^ res) >= 0) - return res; - if ((bv ^ res) >= 0) - return res; - } - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator -(IX a, IX b) - { - var av = a._value; - var bv = -b._value; - if (av != RawNA && bv != RawNA) - { - var res = av + bv; - // Overflow happens iff the sign of the result is different than both source values. - if ((av ^ res) >= 0) - return res; - if ((bv ^ res) >= 0) - return res; - } - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator *(IX a, IX b) - { - var av = a._value; - var bv = b._value; - bool neg = (av ^ bv) < 0; - if (av < 0) - { - if (av == RawNA) - return RawNA; - av = -av; - } - if (bv < 0) - { - if (bv == RawNA) - return RawNA; - bv = -bv; - } - - // Deal with the low 32 bits. - ulong lo1 = (ulong)av & 0x00000000FFFFFFFF; - ulong lo2 = (ulong)bv & 0x00000000FFFFFFFF; - RawIX res = (RawIX)(lo1 * lo2); - if (res < 0) - return RawNA; - - // Get the high 32 bits, including cross terms. - ulong hi1 = (ulong)av >> 32; - ulong hi2 = (ulong)bv >> 32; - if (hi1 != 0) - { - // If both high words are non-zero, overflow is guaranteed. - if (hi2 != 0) - return RawNA; - // Compute the cross term. - ulong tmp = hi1 * lo2; - if ((tmp & 0xFFFFFFFF80000000) != 0) - return RawNA; - res += (long)(tmp << 32); - if (res < 0) - return RawNA; - } - else if (hi2 != 0) - { - // Compute the cross term. - ulong tmp = hi2 * lo1; - if ((tmp & 0xFFFFFFFF80000000) != 0) - return RawNA; - res += (long)(tmp << 32); - if (res < 0) - return RawNA; - } - - // Adjust the sign. - if (neg) - res = -res; - return res; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator /(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA && bv != 0) - return av / bv; - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX operator %(IX a, IX b) - { - var av = a._value; - var bv = b._value; - if (av != RawNA && bv != RawNA && bv != 0) - return av % bv; - return RawNA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Abs(IX a) - { - // Can't use Math.Abs since it throws on the RawNA value. - return a._value >= 0 ? a._value : -a._value; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Sign(IX a) - { - var val = a._value; - var neg = -val; - // This works for NA since -RawNA == RawNA. - return val > neg ? +1 : val < neg ? -1 : val; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Min(IX a, IX b) - { - var v1 = a._value; - var v2 = b._value; - // This works for NA since RawNA == RawIX.MinValue. - return v1 <= v2 ? v1 : v2; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public IX Min(IX b) - { - var v1 = _value; - var v2 = b._value; - // This works for NA since RawNA == RawIX.MinValue. - return v1 <= v2 ? v1 : v2; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static IX Max(IX a, IX b) - { - var v1 = a._value; - var v2 = b._value; - // This works for NA since RawNA - 1 == RawIX.MaxValue. - return v1 - 1 >= v2 - 1 ? v1 : v2; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public IX Max(IX b) - { - var v1 = _value; - var v2 = b._value; - // This works for NA since RawNA - 1 == RawIX.MaxValue. - return v1 - 1 >= v2 - 1 ? v1 : v2; - } - - /// - /// Raise a to the b power. Special cases: - /// * 1^NA => 1 - /// * NA^0 => 1 - /// - public static IX Pow(IX a, IX b) - { - var av = a.RawValue; - var bv = b.RawValue; - - if (av == 1) - return 1; - switch (bv) - { - case 0: - return 1; - case 1: - return av; - case 2: - return a * a; - case RawNA: - return RawNA; - } - if (av == -1) - return (bv & 1) == 0 ? 1 : -1; - if (bv < 0) - return RawNA; - if (av == RawNA) - return RawNA; - - // Since the abs of the base is at least two, the exponent must be less than 63. - if (bv >= 63) - return RawNA; - - bool neg = false; - if (av < 0) - { - av = -av; - neg = (bv & 1) != 0; - } - Contracts.Assert(av >= 2); - - // Since the exponent is at least three, the base must be < 2^21. - Contracts.Assert(bv >= 3); - if (av >= (1L << 21)) - return RawNA; - - long res = 1; - long x = av; - // REVIEW: Is the catch too slow in the overflow case? - try - { - checked - { - for (; ; ) - { - if ((bv & 1) != 0) - res *= x; - bv >>= 1; - if (bv == 0) - break; - x *= x; - } - } - } - catch (OverflowException) - { - return RawNA; - } - Contracts.Assert(res > 0); - - if (neg) - res = -res; - return res; - } - } -} diff --git a/src/Microsoft.ML.Core/Data/DvText.cs b/src/Microsoft.ML.Core/Data/DvText.cs deleted file mode 100644 index 04d3bd8918..0000000000 --- a/src/Microsoft.ML.Core/Data/DvText.cs +++ /dev/null @@ -1,680 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using System.Collections.Generic; -using System.Text; -using Microsoft.ML.Runtime.Internal.Utilities; - -namespace Microsoft.ML.Runtime.Data -{ - /// - /// A text value. This essentially wraps a portion of a string. This can distinguish between a length zero - /// span of characters and "NA", the latter having a Length of -1. - /// - public struct DvText : IEquatable, IComparable - { - /// - /// The fields/properties , , and are - /// private so client code can't easily "cheat" and look outside the characters. Client - /// code that absolutely needs access to this information can call . - /// - private readonly string _outerBuffer; - private readonly int _ichMin; - - /// - /// For the "NA" value, this is -1; otherwise, it is the number of characters in the text. - /// - public readonly int Length; - - private int IchLim => _ichMin + Length; - - /// - /// Gets a DvText that represents "NA", aka "Missing". - /// - public static DvText NA => new DvText(missing: true); - - /// - /// Gets an empty (zero character) DvText. - /// - public static DvText Empty => default(DvText); - - /// - /// Gets whether this DvText contains any characters. Equivalent to Length > 0. - /// - public bool HasChars => Length > 0; - - /// - /// Gets whether this DvText is empty (distinct from NA). Equivalent to Length == 0. - /// - public bool IsEmpty - { - get - { - Contracts.Assert(Length >= -1); - return Length == 0; - } - } - - /// - /// Gets whether this DvText represents "NA". Equivalent to Length == -1. - /// - public bool IsNA - { - get - { - Contracts.Assert(Length >= -1); - return Length < 0; - } - } - - /// - /// Gets the indicated character in the text. - /// - public char this[int ich] - { - get - { - Contracts.CheckParam(0 <= ich & ich < Length, nameof(ich)); - return _outerBuffer[ich + _ichMin]; - } - } - - private DvText(bool missing) - { - _outerBuffer = null; - _ichMin = 0; - Length = missing ? -1 : 0; - } - - /// - /// Constructor using the indicated range of characters in the given string. - /// - public DvText(string text, int ichMin, int ichLim) - { - Contracts.CheckValueOrNull(text); - Contracts.CheckParam(0 <= ichMin & ichMin <= Utils.Size(text), nameof(ichMin)); - Contracts.CheckParam(ichMin <= ichLim & ichLim <= Utils.Size(text), nameof(ichLim)); - Length = ichLim - ichMin; - if (Length == 0) - { - _outerBuffer = null; - _ichMin = 0; - } - else - { - _outerBuffer = text; - _ichMin = ichMin; - } - } - - /// - /// Constructor using the indicated string. - /// - public DvText(string text) - { - Contracts.CheckValueOrNull(text); - Length = Utils.Size(text); - if (Length == 0) - _outerBuffer = null; - else - _outerBuffer = text; - _ichMin = 0; - } - - /// - /// This method retrieves the raw buffer information. The only characters that should be - /// referenced in the returned string are those between the returned min and lim indices. - /// If this is an NA value, the min will be zero and the lim will be -1. For either an - /// empty or NA value, the returned string may be null. - /// - public string GetRawUnderlyingBufferInfo(out int ichMin, out int ichLim) - { - ichMin = _ichMin; - ichLim = ichMin + Length; - return _outerBuffer; - } - - /// - /// This compares the two text values with NA propagation semantics. - /// - public static DvBool operator ==(DvText a, DvText b) - { - if (a.IsNA || b.IsNA) - return DvBool.NA; - - if (a.Length != b.Length) - return DvBool.False; - for (int i = 0; i < a.Length; i++) - { - if (a._outerBuffer[a._ichMin + i] != b._outerBuffer[b._ichMin + i]) - return DvBool.False; - } - return DvBool.True; - } - - /// - /// This compares the two text values with NA propagation semantics. - /// - public static DvBool operator !=(DvText a, DvText b) - { - if (a.IsNA || b.IsNA) - return DvBool.NA; - - if (a.Length != b.Length) - return DvBool.True; - for (int i = 0; i < a.Length; i++) - { - if (a._outerBuffer[a._ichMin + i] != b._outerBuffer[b._ichMin + i]) - return DvBool.True; - } - return DvBool.False; - } - - public override int GetHashCode() - { - if (IsNA) - return 0; - return (int)Hash(42); - } - - public override bool Equals(object obj) - { - if (obj is DvText) - return Equals((DvText)obj); - return false; - } - - /// - /// This implements IEquatable's Equals method. Returns true if both are NA. - /// For NA propagating equality comparison, use the == operator. - /// - public bool Equals(DvText b) - { - if (Length != b.Length) - return false; - Contracts.Assert(HasChars == b.HasChars); - for (int i = 0; i < Length; i++) - { - if (_outerBuffer[_ichMin + i] != b._outerBuffer[b._ichMin + i]) - return false; - } - return true; - } - - /// - /// Does not propagate NA values. Returns true if both are NA (same as a.Equals(b)). - /// For NA propagating equality comparison, use the == operator. - /// - public static bool Identical(DvText a, DvText b) - { - if (a.Length != b.Length) - return false; - if (a.HasChars) - { - Contracts.Assert(b.HasChars); - for (int i = 0; i < a.Length; i++) - { - if (a._outerBuffer[a._ichMin + i] != b._outerBuffer[b._ichMin + i]) - return false; - } - } - return true; - } - - /// - /// Compare equality with the given system string value. Returns false if "this" is NA. - /// - public bool EqualsStr(string s) - { - Contracts.CheckValueOrNull(s); - - // Note that "NA" doesn't match any string. - if (s == null) - return Length == 0; - - if (s.Length != Length) - return false; - for (int i = 0; i < Length; i++) - { - if (s[i] != _outerBuffer[_ichMin + i]) - return false; - } - return true; - } - - /// - /// For implementation of . Uses code point comparison. - /// Generally, this is not appropriate for sorting for presentation to a user. - /// Sorts NA before everything else. - /// - public int CompareTo(DvText other) - { - if (IsNA) - return other.IsNA ? 0 : -1; - if (other.IsNA) - return +1; - - int len = Math.Min(Length, other.Length); - for (int ich = 0; ich < len; ich++) - { - char ch1 = _outerBuffer[_ichMin + ich]; - char ch2 = other._outerBuffer[other._ichMin + ich]; - if (ch1 != ch2) - return ch1 < ch2 ? -1 : +1; - } - if (len < other.Length) - return -1; - if (len < Length) - return +1; - return 0; - } - - /// - /// Return a DvText consisting of characters from ich to the end of this DvText. - /// - public DvText SubSpan(int ich) - { - Contracts.CheckParam(0 <= ich & ich <= Length, nameof(ich)); - return new DvText(_outerBuffer, ich + _ichMin, IchLim); - } - - /// - /// Return a DvText consisting of the indicated range of characters. - /// - public DvText SubSpan(int ichMin, int ichLim) - { - Contracts.CheckParam(0 <= ichMin & ichMin <= Length, nameof(ichMin)); - Contracts.CheckParam(ichMin <= ichLim & ichLim <= Length, nameof(ichLim)); - return new DvText(_outerBuffer, ichMin + _ichMin, ichLim + _ichMin); - } - - /// - /// Return a non-null string corresponding to the characters in this DvText. - /// Note that an empty string is returned for both Empty and NA. - /// - public override string ToString() - { - if (!HasChars) - return ""; - Contracts.AssertNonEmpty(_outerBuffer); - if (_ichMin == 0 && Length == _outerBuffer.Length) - return _outerBuffer; - return _outerBuffer.Substring(_ichMin, Length); - } - - public string ToString(int ichMin) - { - Contracts.CheckParam(0 <= ichMin & ichMin <= Length, nameof(ichMin)); - if (ichMin == Length) - return ""; - ichMin += _ichMin; - if (ichMin == 0 && Length == _outerBuffer.Length) - return _outerBuffer; - return _outerBuffer.Substring(ichMin, IchLim - ichMin); - } - - public IEnumerable Split(char[] separators) - { - Contracts.CheckValueOrNull(separators); - - if (!HasChars) - yield break; - - if (separators == null || separators.Length == 0) - { - yield return this; - yield break; - } - - string text = _outerBuffer; - int ichLim = IchLim; - if (separators.Length == 1) - { - char chSep = separators[0]; - for (int ichCur = _ichMin; ; ) - { - int ichMin = ichCur; - for (; ; ichCur++) - { - Contracts.Assert(ichCur <= ichLim); - if (ichCur >= ichLim) - { - yield return new DvText(text, ichMin, ichCur); - yield break; - } - if (text[ichCur] == chSep) - break; - } - - yield return new DvText(text, ichMin, ichCur); - - // Skip the separator. - ichCur++; - } - } - else - { - for (int ichCur = _ichMin; ; ) - { - int ichMin = ichCur; - for (; ; ichCur++) - { - Contracts.Assert(ichCur <= ichLim); - if (ichCur >= ichLim) - { - yield return new DvText(text, ichMin, ichCur); - yield break; - } - // REVIEW: Can this be faster? - if (ContainsChar(text[ichCur], separators)) - break; - } - - yield return new DvText(text, ichMin, ichCur); - - // Skip the separator. - ichCur++; - } - } - } - - /// - /// Splits this instance on the left-most occurrence of separator and produces the left - /// and right values. If this instance does not contain the separator character, - /// this returns false and sets to this instance and - /// to the default value. - /// - public bool SplitOne(char separator, out DvText left, out DvText right) - { - if (!HasChars) - { - left = this; - right = default(DvText); - return false; - } - - string text = _outerBuffer; - int ichMin = _ichMin; - int ichLim = IchLim; - - int ichCur = ichMin; - for (; ; ichCur++) - { - Contracts.Assert(ichMin <= ichCur && ichCur <= ichLim); - if (ichCur >= ichLim) - { - left = this; - right = default(DvText); - return false; - } - if (text[ichCur] == separator) - break; - } - - // Note that we don't use any fields of "this" here in case one - // of the out parameters is the same as "this". - left = new DvText(text, ichMin, ichCur); - right = new DvText(text, ichCur + 1, ichLim); - return true; - } - - /// - /// Splits this instance on the left-most occurrence of an element of separators character array and - /// produces the left and right values. If this instance does not contain any of the - /// characters in separators, thiss return false and initializes to this instance - /// and to the default value. - /// - public bool SplitOne(char[] separators, out DvText left, out DvText right) - { - Contracts.CheckValueOrNull(separators); - - if (!HasChars || separators == null || separators.Length == 0) - { - left = this; - right = default(DvText); - return false; - } - - string text = _outerBuffer; - int ichMin = _ichMin; - int ichLim = IchLim; - - int ichCur = ichMin; - if (separators.Length == 1) - { - // Note: This duplicates code of the other SplitOne, but doing so improves perf because this is - // used so heavily in instances parsing. - char chSep = separators[0]; - for (; ; ichCur++) - { - Contracts.Assert(ichMin <= ichCur && ichCur <= ichLim); - if (ichCur >= ichLim) - { - left = this; - right = default(DvText); - return false; - } - if (text[ichCur] == chSep) - break; - } - } - else - { - for (; ; ichCur++) - { - Contracts.Assert(ichMin <= ichCur && ichCur <= ichLim); - if (ichCur >= ichLim) - { - left = this; - right = default(DvText); - return false; - } - // REVIEW: Can this be faster? - if (ContainsChar(text[ichCur], separators)) - break; - } - } - - // Note that we don't use any fields of "this" here in case one - // of the out parameters is the same as "this". - left = new DvText(text, _ichMin, ichCur); - right = new DvText(text, ichCur + 1, ichLim); - return true; - } - - /// - /// Splits this instance on the right-most occurrence of separator and produces the left - /// and right values. If this instance does not contain the separator character, - /// this returns false and sets to this instance and - /// to the default value. - /// - public bool SplitOneRight(char separator, out DvText left, out DvText right) - { - if (!HasChars) - { - left = this; - right = default(DvText); - return false; - } - - string text = _outerBuffer; - int ichMin = _ichMin; - int ichLim = IchLim; - - int ichCur = ichLim; - for (; ; ) - { - Contracts.Assert(ichMin <= ichCur && ichCur <= ichLim); - if (--ichCur < ichMin) - { - left = this; - right = default(DvText); - return false; - } - if (text[ichCur] == separator) - break; - } - - // Note that we don't use any fields of "this" here in case one - // of the out parameters is the same as "this". - left = new DvText(text, ichMin, ichCur); - right = new DvText(text, ichCur + 1, ichLim); - return true; - } - - // REVIEW: Can this be faster? - private static bool ContainsChar(char ch, char[] rgch) - { - Contracts.CheckNonEmpty(rgch, nameof(rgch)); - - for (int i = 0; i < rgch.Length; i++) - { - if (rgch[i] == ch) - return true; - } - return false; - } - - /// - /// Returns a text span with leading and trailing spaces trimmed. Note that this - /// will remove only spaces, not any form of whitespace. - /// - public DvText Trim() - { - if (!HasChars) - return this; - int ichMin = _ichMin; - int ichLim = IchLim; - if (_outerBuffer[ichMin] != ' ' && _outerBuffer[ichLim - 1] != ' ') - return this; - - while (ichMin < ichLim && _outerBuffer[ichMin] == ' ') - ichMin++; - while (ichMin < ichLim && _outerBuffer[ichLim - 1] == ' ') - ichLim--; - return new DvText(_outerBuffer, ichMin, ichLim); - } - - /// - /// Returns a text span with leading and trailing whitespace trimmed. - /// - public DvText TrimWhiteSpace() - { - if (!HasChars) - return this; - int ichMin = _ichMin; - int ichLim = IchLim; - if (!char.IsWhiteSpace(_outerBuffer[ichMin]) && !char.IsWhiteSpace(_outerBuffer[ichLim - 1])) - return this; - - while (ichMin < ichLim && char.IsWhiteSpace(_outerBuffer[ichMin])) - ichMin++; - while (ichMin < ichLim && char.IsWhiteSpace(_outerBuffer[ichLim - 1])) - ichLim--; - return new DvText(_outerBuffer, ichMin, ichLim); - } - - /// - /// Returns a text span with trailing whitespace trimmed. - /// - public DvText TrimEndWhiteSpace() - { - if (!HasChars) - return this; - - int ichLim = IchLim; - if (!char.IsWhiteSpace(_outerBuffer[ichLim - 1])) - return this; - - int ichMin = _ichMin; - while (ichMin < ichLim && char.IsWhiteSpace(_outerBuffer[ichLim - 1])) - ichLim--; - - return new DvText(_outerBuffer, ichMin, ichLim); - } - - /// - /// This produces zero for an empty string. - /// - public bool TryParse(out Single value) - { - if (IsNA) - { - value = Single.NaN; - return true; - } - var res = DoubleParser.Parse(out value, _outerBuffer, _ichMin, IchLim); - Contracts.Assert(res != DoubleParser.Result.Empty || value == 0); - return res <= DoubleParser.Result.Empty; - } - - /// - /// This produces zero for an empty string. - /// - public bool TryParse(out Double value) - { - if (IsNA) - { - value = Double.NaN; - return true; - } - var res = DoubleParser.Parse(out value, _outerBuffer, _ichMin, IchLim); - Contracts.Assert(res != DoubleParser.Result.Empty || value == 0); - return res <= DoubleParser.Result.Empty; - } - - public uint Hash(uint seed) - { - Contracts.Check(!IsNA); - return Hashing.MurmurHash(seed, _outerBuffer, _ichMin, IchLim); - } - - // REVIEW: Add method to NormStr.Pool that deal with DvText instead of the other way around. - public NormStr AddToPool(NormStr.Pool pool) - { - Contracts.Check(!IsNA); - Contracts.CheckValue(pool, nameof(pool)); - return pool.Add(_outerBuffer, _ichMin, IchLim); - } - - public NormStr FindInPool(NormStr.Pool pool) - { - Contracts.CheckValue(pool, nameof(pool)); - if (IsNA) - return null; - return pool.Get(_outerBuffer, _ichMin, IchLim); - } - - public void AddToStringBuilder(StringBuilder sb) - { - Contracts.CheckValue(sb, nameof(sb)); - if (HasChars) - sb.Append(_outerBuffer, _ichMin, Length); - } - - public void AddLowerCaseToStringBuilder(StringBuilder sb) - { - Contracts.CheckValue(sb, nameof(sb)); - if (HasChars) - { - int min = _ichMin; - int j; - for (j = min; j < IchLim; j++) - { - char ch = CharUtils.ToLowerInvariant(_outerBuffer[j]); - if (ch != _outerBuffer[j]) - { - sb.Append(_outerBuffer, min, j - min).Append(ch); - min = j + 1; - } - } - - Contracts.Assert(j == IchLim); - if (min != j) - sb.Append(_outerBuffer, min, j - min); - } - } - } -} \ No newline at end of file diff --git a/src/Microsoft.ML.Core/Data/IEstimator.cs b/src/Microsoft.ML.Core/Data/IEstimator.cs index a410f02001..60e085d38c 100644 --- a/src/Microsoft.ML.Core/Data/IEstimator.cs +++ b/src/Microsoft.ML.Core/Data/IEstimator.cs @@ -229,14 +229,14 @@ public interface IDataReaderEstimator /// /// The transformer is a component that transforms data. - /// It also supports 'schema propagation' to answer the question of 'how the data with this schema look after you transform it?'. + /// It also supports 'schema propagation' to answer the question of 'how will the data with this schema look, after you transform it?'. /// public interface ITransformer { /// /// Schema propagation for transformers. /// Returns the output schema of the data, if the input schema is like the one provided. - /// Throws iff the input schema is not valid for the transformer. + /// Throws if the input schema is not valid for the transformer. /// ISchema GetOutputSchema(ISchema inputSchema); diff --git a/src/Microsoft.ML.Core/Data/IHostEnvironment.cs b/src/Microsoft.ML.Core/Data/IHostEnvironment.cs index b463e52a8e..bd08a75c9a 100644 --- a/src/Microsoft.ML.Core/Data/IHostEnvironment.cs +++ b/src/Microsoft.ML.Core/Data/IHostEnvironment.cs @@ -46,6 +46,11 @@ public interface IHostEnvironment : IChannelProvider, IProgressChannelProvider /// bool IsCancelled { get; } + /// + /// The catalog of loadable components () that are available in this host. + /// + ComponentCatalog ComponentCatalog { get; } + /// /// Return a file handle for an input "file". /// diff --git a/src/Microsoft.ML.Core/Data/MetadataUtils.cs b/src/Microsoft.ML.Core/Data/MetadataUtils.cs index f7b91c3715..d952f57782 100644 --- a/src/Microsoft.ML.Core/Data/MetadataUtils.cs +++ b/src/Microsoft.ML.Core/Data/MetadataUtils.cs @@ -42,12 +42,13 @@ public static class Kinds public const string ScoreColumnSetId = "ScoreColumnSetId"; /// - /// Metadata kind that indicates the prediction kind as a string. E.g. "BinaryClassification". The value is typically a DvText. + /// Metadata kind that indicates the prediction kind as a string. E.g. "BinaryClassification". + /// The value is typically a ReadOnlyMemory<char>. /// public const string ScoreColumnKind = "ScoreColumnKind"; /// - /// Metadata kind that indicates the value kind of the score column as a string. E.g. "Score", "PredictedLabel", "Probability". The value is typically a DvText. + /// Metadata kind that indicates the value kind of the score column as a string. E.g. "Score", "PredictedLabel", "Probability". The value is typically a ReadOnlyMemory. /// public const string ScoreValueKind = "ScoreValueKind"; @@ -283,9 +284,9 @@ public static IEnumerable GetColumnSet(this ISchema schema, string metadata var columnType = schema.GetMetadataTypeOrNull(metadataKind, col); if (columnType != null && columnType.IsText) { - DvText val = default(DvText); + ReadOnlyMemory val = default; schema.GetMetadata(metadataKind, col, ref val); - if (val.EqualsStr(value)) + if (ReadOnlyMemoryUtils.EqualsStr(value, val)) yield return col; } } @@ -295,7 +296,7 @@ public static IEnumerable GetColumnSet(this ISchema schema, string metadata /// Returns true if the specified column: /// * is a vector of length N (including 0) /// * has a SlotNames metadata - /// * metadata type is VBuffer<DvText> of length N + /// * metadata type is VBuffer<ReadOnlyMemory<char>> of length N /// public static bool HasSlotNames(this ISchema schema, int col, int vectorSize) { @@ -310,14 +311,14 @@ public static bool HasSlotNames(this ISchema schema, int col, int vectorSize) && type.ItemType.IsText; } - public static void GetSlotNames(RoleMappedSchema schema, RoleMappedSchema.ColumnRole role, int vectorSize, ref VBuffer slotNames) + public static void GetSlotNames(RoleMappedSchema schema, RoleMappedSchema.ColumnRole role, int vectorSize, ref VBuffer> slotNames) { Contracts.CheckValueOrNull(schema); Contracts.CheckParam(vectorSize >= 0, nameof(vectorSize)); IReadOnlyList list; if ((list = schema?.GetColumns(role)) == null || list.Count != 1 || !schema.Schema.HasSlotNames(list[0].Index, vectorSize)) - slotNames = new VBuffer(vectorSize, 0, slotNames.Values, slotNames.Indices); + slotNames = new VBuffer>(vectorSize, 0, slotNames.Values, slotNames.Indices); else schema.Schema.GetMetadata(Kinds.SlotNames, list[0].Index, ref slotNames); } @@ -343,12 +344,12 @@ public static bool HasKeyNames(this ISchema schema, int col, int keyCount) /// The schema to query /// Which column in the schema to query /// True if and only if the column has the metadata - /// set to the scalar value + /// set to the scalar value true public static bool IsNormalized(this ISchema schema, int col) { Contracts.CheckValue(schema, nameof(schema)); - var value = default(DvBool); - return schema.TryGetMetadata(BoolType.Instance, Kinds.IsNormalized, col, ref value) && value.IsTrue; + var value = default(bool); + return schema.TryGetMetadata(BoolType.Instance, Kinds.IsNormalized, col, ref value) && value; } /// @@ -436,9 +437,9 @@ public static bool TryGetCategoricalFeatureIndices(ISchema schema, int colIndex, return isValid; var type = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.CategoricalSlotRanges, colIndex); - if (type?.RawType == typeof(VBuffer)) + if (type?.RawType == typeof(VBuffer)) { - VBuffer catIndices = default(VBuffer); + VBuffer catIndices = default(VBuffer); schema.GetMetadata(MetadataUtils.Kinds.CategoricalSlotRanges, colIndex, ref catIndices); VBufferUtils.Densify(ref catIndices); int columnSlotsCount = schema.GetColumnType(colIndex).AsVector.VectorSizeCore; @@ -448,19 +449,19 @@ public static bool TryGetCategoricalFeatureIndices(ISchema schema, int colIndex, isValid = true; for (int i = 0; i < catIndices.Values.Length; i += 2) { - if (catIndices.Values[i].RawValue > catIndices.Values[i + 1].RawValue || - catIndices.Values[i].RawValue <= previousEndIndex || - catIndices.Values[i].RawValue >= columnSlotsCount || - catIndices.Values[i + 1].RawValue >= columnSlotsCount) + if (catIndices.Values[i] > catIndices.Values[i + 1] || + catIndices.Values[i] <= previousEndIndex || + catIndices.Values[i] >= columnSlotsCount || + catIndices.Values[i + 1] >= columnSlotsCount) { isValid = false; break; } - previousEndIndex = catIndices.Values[i + 1].RawValue; + previousEndIndex = catIndices.Values[i + 1]; } if (isValid) - categoricalFeatures = catIndices.Values.Select(val => val.RawValue).ToArray(); + categoricalFeatures = catIndices.Values.Select(val => val).ToArray(); } } diff --git a/src/Microsoft.ML.Core/Data/ReadOnlyMemoryUtils.cs b/src/Microsoft.ML.Core/Data/ReadOnlyMemoryUtils.cs new file mode 100644 index 0000000000..4b207ab507 --- /dev/null +++ b/src/Microsoft.ML.Core/Data/ReadOnlyMemoryUtils.cs @@ -0,0 +1,269 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Internal.Utilities; +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; +using System.Text; + +namespace Microsoft.ML.Runtime.Data +{ + public static class ReadOnlyMemoryUtils + { + + /// + /// Compare equality with the given system string value. + /// + public static bool EqualsStr(string s, ReadOnlyMemory memory) + { + Contracts.CheckValueOrNull(s); + + if (s == null) + return memory.Length == 0; + + if (s.Length != memory.Length) + return false; + + return memory.Span.SequenceEqual(s.AsSpan()); + } + + public static IEnumerable> Split(ReadOnlyMemory memory, char[] separators) + { + Contracts.CheckValueOrNull(separators); + + if (memory.IsEmpty) + yield break; + + if (separators == null || separators.Length == 0) + { + yield return memory; + yield break; + } + + var span = memory.Span; + if (separators.Length == 1) + { + char chSep = separators[0]; + for (int ichCur = 0; ;) + { + int nextSep = span.IndexOf(chSep); + if (nextSep == -1) + { + yield return memory.Slice(ichCur); + yield break; + } + + yield return memory.Slice(ichCur, nextSep); + + // Skip the separator. + ichCur += nextSep + 1; + span = memory.Slice(ichCur).Span; + } + } + else + { + for (int ichCur = 0; ;) + { + int nextSep = span.IndexOfAny(separators); + if (nextSep == -1) + { + yield return memory.Slice(ichCur); + yield break; + } + + yield return memory.Slice(ichCur, nextSep); + + // Skip the separator. + ichCur += nextSep + 1; + span = memory.Slice(ichCur).Span; + } + } + } + + /// + /// Splits on the left-most occurrence of separator and produces the left + /// and right of values. If does not contain the separator character, + /// this returns false and sets to this instance and + /// to the default of value. + /// + public static bool SplitOne(ReadOnlyMemory memory, char separator, out ReadOnlyMemory left, out ReadOnlyMemory right) + { + if (memory.IsEmpty) + { + left = memory; + right = default; + return false; + } + + int index = memory.Span.IndexOf(separator); + if (index == -1) + { + left = memory; + right = default; + return false; + } + + left = memory.Slice(0, index); + right = memory.Slice(index + 1, memory.Length - index - 1); + return true; + } + + /// + /// Splits on the left-most occurrence of an element of separators character array and + /// produces the left and right of values. If does not contain any of the + /// characters in separators, this return false and initializes to this instance + /// and to the default of value. + /// + public static bool SplitOne(ReadOnlyMemory memory, char[] separators, out ReadOnlyMemory left, out ReadOnlyMemory right) + { + Contracts.CheckValueOrNull(separators); + + if (memory.IsEmpty || separators == null || separators.Length == 0) + { + left = memory; + right = default; + return false; + } + + int index; + if (separators.Length == 1) + index = memory.Span.IndexOf(separators[0]); + else + index = memory.Span.IndexOfAny(separators); + + if (index == -1) + { + left = memory; + right = default; + return false; + } + + left = memory.Slice(0, index); + right = memory.Slice(index + 1, memory.Length - index - 1); + return true; + } + + /// + /// Returns a of with leading and trailing spaces trimmed. Note that this + /// will remove only spaces, not any form of whitespace. + /// + public static ReadOnlyMemory TrimSpaces(ReadOnlyMemory memory) + { + if (memory.IsEmpty) + return memory; + + int ichLim = memory.Length; + int ichMin = 0; + var span = memory.Span; + if (span[ichMin] != ' ' && span[ichLim - 1] != ' ') + return memory; + + while (ichMin < ichLim && span[ichMin] == ' ') + ichMin++; + while (ichMin < ichLim && span[ichLim - 1] == ' ') + ichLim--; + return memory.Slice(ichMin, ichLim - ichMin); + } + + /// + /// Returns a of with leading and trailing whitespace trimmed. + /// + public static ReadOnlyMemory TrimWhiteSpace(ReadOnlyMemory memory) + { + if (memory.IsEmpty) + return memory; + + int ichMin = 0; + int ichLim = memory.Length; + var span = memory.Span; + if (!char.IsWhiteSpace(span[ichMin]) && !char.IsWhiteSpace(span[ichLim - 1])) + return memory; + + while (ichMin < ichLim && char.IsWhiteSpace(span[ichMin])) + ichMin++; + while (ichMin < ichLim && char.IsWhiteSpace(span[ichLim - 1])) + ichLim--; + + return memory.Slice(ichMin, ichLim - ichMin); + } + + /// + /// Returns a of with trailing whitespace trimmed. + /// + public static ReadOnlyMemory TrimEndWhiteSpace(ReadOnlyMemory memory) + { + if (memory.IsEmpty) + return memory; + + int ichLim = memory.Length; + var span = memory.Span; + if (!char.IsWhiteSpace(span[ichLim - 1])) + return memory; + + while (0 < ichLim && char.IsWhiteSpace(span[ichLim - 1])) + ichLim--; + + return memory.Slice(0, ichLim); + } + + public static NormStr AddToPool(ReadOnlyMemory memory, NormStr.Pool pool) + { + Contracts.CheckValue(pool, nameof(pool)); + return pool.Add(memory); + } + + public static NormStr FindInPool(ReadOnlyMemory memory, NormStr.Pool pool) + { + Contracts.CheckValue(pool, nameof(pool)); + return pool.Get(memory); + } + + public static void AddLowerCaseToStringBuilder(ReadOnlySpan span, StringBuilder sb) + { + Contracts.CheckValue(sb, nameof(sb)); + + if (!span.IsEmpty) + { + int min = 0; + int j; + for (j = min; j < span.Length; j++) + { + char ch = CharUtils.ToLowerInvariant(span[j]); + if (ch != span[j]) + { + sb.AppendSpan(span.Slice(min, j - min)).Append(ch); + min = j + 1; + } + } + + Contracts.Assert(j == span.Length); + if (min != j) + sb.AppendSpan(span.Slice(min, j - min)); + } + } + + public static StringBuilder AppendMemory(this StringBuilder sb, ReadOnlyMemory memory) + { + Contracts.CheckValue(sb, nameof(sb)); + if (!memory.IsEmpty) + sb.AppendSpan(memory.Span); + + return sb; + } + + public static StringBuilder AppendSpan(this StringBuilder sb, ReadOnlySpan span) + { + unsafe + { + fixed (char* valueChars = &MemoryMarshal.GetReference(span)) + { + sb.Append(valueChars, span.Length); + } + } + + return sb; + } + } +} diff --git a/src/Microsoft.ML.Core/Data/ServerChannel.cs b/src/Microsoft.ML.Core/Data/ServerChannel.cs index fd0e1f2603..4b6db41541 100644 --- a/src/Microsoft.ML.Core/Data/ServerChannel.cs +++ b/src/Microsoft.ML.Core/Data/ServerChannel.cs @@ -171,16 +171,16 @@ public interface IServer : IDisposable /// for example, if a user opted to remove all implementations of and /// the associated for security reasons. /// - public static IServerFactory CreateDefaultServerFactoryOrNull(IExceptionContext ectx) + public static IServerFactory CreateDefaultServerFactoryOrNull(IHostEnvironment env) { - Contracts.CheckValue(ectx, nameof(ectx)); + Contracts.CheckValue(env, nameof(env)); // REVIEW: There should be a better way. There currently isn't, // but there should be. This is pretty horrifying, but it is preferable to // the alternative of having core components depend on an actual server // implementation, since we want those to be removable because of security // concerns in certain environments (since not everyone will be wild about // web servers popping up everywhere). - var cat = ModuleCatalog.CreateInstance(ectx); + var cat = ModuleCatalog.CreateInstance(env); ModuleCatalog.ComponentInfo component; if (!cat.TryFindComponent(typeof(IServerFactory), "mini", out component)) return null; diff --git a/src/Microsoft.ML.Core/Data/TypeUtils.cs b/src/Microsoft.ML.Core/Data/TypeUtils.cs index 30a9e4008b..c5d92f1ee7 100644 --- a/src/Microsoft.ML.Core/Data/TypeUtils.cs +++ b/src/Microsoft.ML.Core/Data/TypeUtils.cs @@ -2,102 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using System; -using System.Runtime.CompilerServices; - namespace Microsoft.ML.Runtime.Data { - using R4 = Single; - using R8 = Double; - using BL = DvBool; - using TX = DvText; - public delegate bool RefPredicate(ref T value); - - /// - /// Utilities for IDV standard types, including proper NA semantics. - /// - public static class TypeUtils - { - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static bool IsNA(this R4 src) { return R4.IsNaN(src); } - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static bool IsNA(this R8 src) { return R8.IsNaN(src); } - - #region R4 - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Eq(this R4 a, R4 b) - { - return a == b ? BL.True : a.IsNA() || b.IsNA() ? BL.NA : BL.False; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Ne(this R4 a, R4 b) - { - return a != b ? a.IsNA() || b.IsNA() ? BL.NA : BL.True : BL.False; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Lt(this R4 a, R4 b) - { - return a < b ? BL.True : a >= b ? BL.False : BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Le(this R4 a, R4 b) - { - return a <= b ? BL.True : a > b ? BL.False : BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Ge(this R4 a, R4 b) - { - return a >= b ? BL.True : a < b ? BL.False : BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Gt(this R4 a, R4 b) - { - return a > b ? BL.True : a <= b ? BL.False : BL.NA; - } - #endregion R4 - - #region R8 - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Eq(this R8 a, R8 b) - { - return a == b ? BL.True : a.IsNA() || b.IsNA() ? BL.NA : BL.False; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Ne(this R8 a, R8 b) - { - return a != b ? a.IsNA() || b.IsNA() ? BL.NA : BL.True : BL.False; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Lt(this R8 a, R8 b) - { - return a < b ? BL.True : a >= b ? BL.False : BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Le(this R8 a, R8 b) - { - return a <= b ? BL.True : a > b ? BL.False : BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Ge(this R8 a, R8 b) - { - return a >= b ? BL.True : a < b ? BL.False : BL.NA; - } - - [MethodImpl(MethodImplOptions.AggressiveInlining)] - public static BL Gt(this R8 a, R8 b) - { - return a > b ? BL.True : a <= b ? BL.False : BL.NA; - } - #endregion R8 - } } diff --git a/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs b/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs index 60511bfd39..a34242b6f4 100644 --- a/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs +++ b/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs @@ -152,7 +152,6 @@ internal ComponentInfo(IExceptionContext ectx, Type interfaceType, string kind, } } - private static volatile ModuleCatalog _instance; private readonly EntryPointInfo[] _entryPoints; private readonly Dictionary _entryPointMap; @@ -167,15 +166,15 @@ public IEnumerable AllEntryPoints() return _entryPoints.AsEnumerable(); } - private ModuleCatalog(IExceptionContext ectx) + private ModuleCatalog(IHostEnvironment env) { - Contracts.AssertValue(ectx); + Contracts.AssertValue(env); _entryPointMap = new Dictionary(); _componentMap = new Dictionary(); _components = new List(); - var moduleClasses = ComponentCatalog.FindLoadableClasses(); + var moduleClasses = env.ComponentCatalog.FindLoadableClasses(); var entryPoints = new List(); foreach (var lc in moduleClasses) @@ -189,7 +188,7 @@ private ModuleCatalog(IExceptionContext ectx) if (attr == null) continue; - var info = new EntryPointInfo(ectx, methodInfo, attr, + var info = new EntryPointInfo(env, methodInfo, attr, methodInfo.GetCustomAttributes(typeof(ObsoleteAttribute), false).FirstOrDefault() as ObsoleteAttribute); entryPoints.Add(info); @@ -205,9 +204,9 @@ private ModuleCatalog(IExceptionContext ectx) // Scan for components. // First scan ourself, and then all nested types, for component info. - ScanForComponents(ectx, type); + ScanForComponents(env, type); foreach (var nestedType in type.GetTypeInfo().GetNestedTypes()) - ScanForComponents(ectx, nestedType); + ScanForComponents(env, nestedType); } _entryPoints = entryPoints.ToArray(); } @@ -274,17 +273,13 @@ private static bool IsValidName(string name) } /// - /// Create a module catalog (or reuse the one created before). + /// Create a module catalog. /// - /// The exception context to use to report errors while scanning the assemblies. - public static ModuleCatalog CreateInstance(IExceptionContext ectx) + /// The host environment and exception context to use to report errors while scanning the assemblies. + public static ModuleCatalog CreateInstance(IHostEnvironment env) { - Contracts.CheckValueOrNull(ectx); -#pragma warning disable 420 // volatile with Interlocked.CompareExchange. - if (_instance == null) - Interlocked.CompareExchange(ref _instance, new ModuleCatalog(ectx), null); -#pragma warning restore 420 - return _instance; + Contracts.CheckValue(env, nameof(env)); + return new ModuleCatalog(env); } public bool TryFindEntryPoint(string name, out EntryPointInfo entryPoint) diff --git a/src/Microsoft.ML.Core/Environment/HostEnvironmentBase.cs b/src/Microsoft.ML.Core/Environment/HostEnvironmentBase.cs index d4ff5ccd96..b195de6108 100644 --- a/src/Microsoft.ML.Core/Environment/HostEnvironmentBase.cs +++ b/src/Microsoft.ML.Core/Environment/HostEnvironmentBase.cs @@ -6,7 +6,6 @@ using System.Collections.Concurrent; using System.Collections.Generic; using System.IO; -using System.Threading; namespace Microsoft.ML.Runtime.Data { @@ -382,6 +381,8 @@ public void RemoveListener(Action listenerFunc) public bool IsCancelled { get; protected set; } + public ComponentCatalog ComponentCatalog { get; } + public override int Depth => 0; protected bool IsDisposed => _tempFiles == null; @@ -402,6 +403,7 @@ protected HostEnvironmentBase(IRandom rand, bool verbose, int conc, _tempLock = new object(); _tempFiles = new List(); Root = this as TEnv; + ComponentCatalog = new ComponentCatalog(); } /// @@ -422,6 +424,7 @@ protected HostEnvironmentBase(HostEnvironmentBase source, IRandom rand, bo Root = source.Root; ListenerDict = source.ListenerDict; ProgressTracker = source.ProgressTracker; + ComponentCatalog = source.ComponentCatalog; } /// diff --git a/src/Microsoft.ML.Core/Microsoft.ML.Core.csproj b/src/Microsoft.ML.Core/Microsoft.ML.Core.csproj index c7bbd498d3..a326e4af34 100644 --- a/src/Microsoft.ML.Core/Microsoft.ML.Core.csproj +++ b/src/Microsoft.ML.Core/Microsoft.ML.Core.csproj @@ -11,4 +11,8 @@ + + + + diff --git a/src/Microsoft.ML.Core/Utilities/DoubleParser.cs b/src/Microsoft.ML.Core/Utilities/DoubleParser.cs index 9ee245443b..a1a82d5218 100644 --- a/src/Microsoft.ML.Core/Utilities/DoubleParser.cs +++ b/src/Microsoft.ML.Core/Utilities/DoubleParser.cs @@ -70,25 +70,44 @@ public enum Result Error = 3 } - public static Result Parse(out Single value, string s, int ichMin, int ichLim) + /// + /// This produces zero for an empty string. + /// + public static bool TryParse(ReadOnlySpan span, out Single value) + { + var res = Parse(span, out value); + Contracts.Assert(res != Result.Empty || value == 0); + return res <= Result.Empty; + } + + /// + /// This produces zero for an empty string. + /// + public static bool TryParse(ReadOnlySpan span, out Double value) { - Contracts.Assert(0 <= ichMin && ichMin <= ichLim && ichLim <= Utils.Size(s)); + var res = Parse(span, out value); + Contracts.Assert(res != Result.Empty || value == 0); + return res <= Result.Empty; + } - for (; ; ichMin++) + public static Result Parse(ReadOnlySpan span, out Single value) + { + int ich = 0; + for (; ; ich++) { - if (ichMin >= ichLim) + if (ich >= span.Length) { value = 0; return Result.Empty; } - if (!char.IsWhiteSpace(s[ichMin])) + if (!char.IsWhiteSpace(span[ich])) break; } // Handle the common case of a single digit or ? - if (ichLim - ichMin == 1) + if (span.Length - ich == 1) { - char ch = s[ichMin]; + char ch = span[ich]; if (ch >= '0' && ch <= '9') { value = ch - '0'; @@ -102,16 +121,16 @@ public static Result Parse(out Single value, string s, int ichMin, int ichLim) } int ichEnd; - if (!DoubleParser.TryParse(out value, s, ichMin, ichLim, out ichEnd)) + if (!DoubleParser.TryParse(span.Slice(ich, span.Length - ich), out value, out ichEnd)) { value = default(Single); return Result.Error; } // Make sure everything was consumed. - while (ichEnd < ichLim) + while (ichEnd < span.Length) { - if (!char.IsWhiteSpace(s[ichEnd])) + if (!char.IsWhiteSpace(span[ichEnd])) return Result.Extra; ichEnd++; } @@ -119,25 +138,24 @@ public static Result Parse(out Single value, string s, int ichMin, int ichLim) return Result.Good; } - public static Result Parse(out Double value, string s, int ichMin, int ichLim) + public static Result Parse(ReadOnlySpan span, out Double value) { - Contracts.Assert(0 <= ichMin && ichMin <= ichLim && ichLim <= Utils.Size(s)); - - for (; ; ichMin++) + int ich = 0; + for (; ; ich++) { - if (ichMin >= ichLim) + if (ich >= span.Length) { value = 0; return Result.Empty; } - if (!char.IsWhiteSpace(s[ichMin])) + if (!char.IsWhiteSpace(span[ich])) break; } // Handle the common case of a single digit or ? - if (ichLim - ichMin == 1) + if (span.Length - ich == 1) { - char ch = s[ichMin]; + char ch = span[ich]; if (ch >= '0' && ch <= '9') { value = ch - '0'; @@ -151,16 +169,16 @@ public static Result Parse(out Double value, string s, int ichMin, int ichLim) } int ichEnd; - if (!DoubleParser.TryParse(out value, s, ichMin, ichLim, out ichEnd)) + if (!DoubleParser.TryParse(span.Slice(ich, span.Length - ich), out value, out ichEnd)) { value = default(Double); return Result.Error; } // Make sure everything was consumed. - while (ichEnd < ichLim) + while (ichEnd < span.Length) { - if (!char.IsWhiteSpace(s[ichEnd])) + if (!char.IsWhiteSpace(span[ichEnd])) return Result.Extra; ichEnd++; } @@ -168,15 +186,15 @@ public static Result Parse(out Double value, string s, int ichMin, int ichLim) return Result.Good; } - public static bool TryParse(out Single value, string s, int ichMin, int ichLim, out int ichEnd) + public static bool TryParse(ReadOnlySpan span, out Single value, out int ichEnd) { bool neg = false; ulong num = 0; long exp = 0; - ichEnd = ichMin; - if (!TryParseCore(s, ref ichEnd, ichLim, ref neg, ref num, ref exp)) - return TryParseSpecial(out value, s, ref ichEnd, ichLim); + ichEnd = 0; + if (!TryParseCore(span, ref ichEnd, ref neg, ref num, ref exp)) + return TryParseSpecial(span, ref ichEnd, out value); if (num == 0) { @@ -231,7 +249,7 @@ public static bool TryParse(out Single value, string s, int ichMin, int ichLim, #if COMPARE_BCL if (!_failed) { - string str = s.Substring(ichMin, ichEnd - ichMin); + string str = span.ToString(); Single x; if (!Single.TryParse(str, out x)) { @@ -257,15 +275,15 @@ public static bool TryParse(out Single value, string s, int ichMin, int ichLim, return true; } - public static bool TryParse(out Double value, string s, int ichMin, int ichLim, out int ichEnd) + public static bool TryParse(ReadOnlySpan span, out Double value, out int ichEnd) { bool neg = false; ulong num = 0; long exp = 0; - ichEnd = ichMin; - if (!TryParseCore(s, ref ichEnd, ichLim, ref neg, ref num, ref exp)) - return TryParseSpecial(out value, s, ref ichEnd, ichLim); + ichEnd = 0; + if (!TryParseCore(span, ref ichEnd, ref neg, ref num, ref exp)) + return TryParseSpecial(span, ref ichEnd, out value); if (num == 0) { @@ -413,7 +431,7 @@ public static bool TryParse(out Double value, string s, int ichMin, int ichLim, value = -value; #if COMPARE_BCL - string str = s.Substring(ichMin, ichEnd - ichMin); + string str = span.ToString(); Double x; if (!Double.TryParse(str, out x)) { @@ -440,19 +458,19 @@ public static bool TryParse(out Double value, string s, int ichMin, int ichLim, return true; } - private static bool TryParseSpecial(out Double value, string s, ref int ich, int ichLim) + private static bool TryParseSpecial(ReadOnlySpan span, ref int ich, out Double value) { Single tmp; - bool res = TryParseSpecial(out tmp, s, ref ich, ichLim); + bool res = TryParseSpecial(span, ref ich, out tmp); value = tmp; return res; } - private static bool TryParseSpecial(out Single value, string s, ref int ich, int ichLim) + private static bool TryParseSpecial(ReadOnlySpan span, ref int ich, out Single value) { - if (ich < ichLim) + if (ich < span.Length) { - switch (s[ich]) + switch (span[ich]) { case '?': // We also interpret ? to mean NaN. @@ -461,7 +479,7 @@ private static bool TryParseSpecial(out Single value, string s, ref int ich, int return true; case 'N': - if (ich + 3 <= ichLim && s[ich + 1] == 'a' && s[ich + 2] == 'N') + if (ich + 3 <= span.Length && span[ich + 1] == 'a' && span[ich + 2] == 'N') { value = Single.NaN; ich += 3; @@ -470,7 +488,7 @@ private static bool TryParseSpecial(out Single value, string s, ref int ich, int break; case 'I': - if (ich + 8 <= ichLim && s[ich + 1] == 'n' && s[ich + 2] == 'f' && s[ich + 3] == 'i' && s[ich + 4] == 'n' && s[ich + 5] == 'i' && s[ich + 6] == 't' && s[ich + 7] == 'y') + if (ich + 8 <= span.Length && span[ich + 1] == 'n' && span[ich + 2] == 'f' && span[ich + 3] == 'i' && span[ich + 4] == 'n' && span[ich + 5] == 'i' && span[ich + 6] == 't' && span[ich + 7] == 'y') { value = Single.PositiveInfinity; ich += 8; @@ -479,14 +497,14 @@ private static bool TryParseSpecial(out Single value, string s, ref int ich, int break; case '-': - if (ich + 2 <= ichLim && s[ich + 1] == InfinitySymbol) + if (ich + 2 <= span.Length && span[ich + 1] == InfinitySymbol) { value = Single.NegativeInfinity; ich += 2; return true; } - if (ich + 9 <= ichLim && s[ich + 1] == 'I' && s[ich + 2] == 'n' && s[ich + 3] == 'f' && s[ich + 4] == 'i' && s[ich + 5] == 'n' && s[ich + 6] == 'i' && s[ich + 7] == 't' && s[ich + 8] == 'y') + if (ich + 9 <= span.Length && span[ich + 1] == 'I' && span[ich + 2] == 'n' && span[ich + 3] == 'f' && span[ich + 4] == 'i' && span[ich + 5] == 'n' && span[ich + 6] == 'i' && span[ich + 7] == 't' && span[ich + 8] == 'y') { value = Single.NegativeInfinity; ich += 9; @@ -505,15 +523,14 @@ private static bool TryParseSpecial(out Single value, string s, ref int ich, int return false; } - private static bool TryParseCore(string s, ref int ich, int ichLim, ref bool neg, ref ulong num, ref long exp) + private static bool TryParseCore(ReadOnlySpan span, ref int ich, ref bool neg, ref ulong num, ref long exp) { - Contracts.AssertValue(s); - Contracts.Assert(0 <= ich & ich <= ichLim & ichLim <= s.Length); + Contracts.Assert(0 <= ich & ich <= span.Length); Contracts.Assert(!neg); Contracts.Assert(num == 0); Contracts.Assert(exp == 0); - if (ich >= ichLim) + if (ich >= span.Length) return false; // If num gets bigger than this, we don't process additional digits. @@ -524,19 +541,19 @@ private static bool TryParseCore(string s, ref int ich, int ichLim, ref bool neg // Get started: handle sign int i = ich; - switch (s[i]) + switch (span[i]) { default: return false; case '-': - if (++i >= ichLim) + if (++i >= span.Length) return false; neg = true; break; case '+': - if (++i >= ichLim) + if (++i >= span.Length) return false; break; @@ -561,8 +578,8 @@ private static bool TryParseCore(string s, ref int ich, int ichLim, ref bool neg uint d; for (; ; ) { - Contracts.Assert(i < ichLim); - if ((d = (uint)s[i] - '0') > 9) + Contracts.Assert(i < span.Length); + if ((d = (uint)span[i] - '0') > 9) break; digits = true; @@ -571,33 +588,33 @@ private static bool TryParseCore(string s, ref int ich, int ichLim, ref bool neg else exp++; - if (++i >= ichLim) + if (++i >= span.Length) { ich = i; return true; } } - Contracts.Assert(i < ichLim); + Contracts.Assert(i < span.Length); - if (s[i] != '.') + if (span[i] != '.') goto LAfterDigits; LPoint: - Contracts.Assert(i < ichLim); - Contracts.Assert(s[i] == '.'); + Contracts.Assert(i < span.Length); + Contracts.Assert(span[i] == '.'); // Get the digits after '.' for (; ; ) { - if (++i >= ichLim) + if (++i >= span.Length) { if (digits) ich = i; return digits; } - Contracts.Assert(i < ichLim); - if ((d = (uint)s[i] - '0') > 9) + Contracts.Assert(i < span.Length); + if ((d = (uint)span[i] - '0') > 9) break; digits = true; @@ -609,7 +626,7 @@ private static bool TryParseCore(string s, ref int ich, int ichLim, ref bool neg } LAfterDigits: - Contracts.Assert(i < ichLim); + Contracts.Assert(i < span.Length); if (!digits) return false; @@ -617,30 +634,30 @@ private static bool TryParseCore(string s, ref int ich, int ichLim, ref bool neg ich = i; // Check for an exponent. - switch (s[i]) + switch (span[i]) { default: return true; case 'e': case 'E': - if (++i >= ichLim) + if (++i >= span.Length) return true; break; } // Handle the exponent sign. bool expNeg = false; - Contracts.Assert(i < ichLim); - switch (s[i]) + Contracts.Assert(i < span.Length); + switch (span[i]) { case '-': - if (++i >= ichLim) + if (++i >= span.Length) return true; expNeg = true; break; case '+': - if (++i >= ichLim) + if (++i >= span.Length) return true; break; } @@ -656,14 +673,14 @@ private static bool TryParseCore(string s, ref int ich, int ichLim, ref bool neg long e = 0; for (; ; ) { - Contracts.Assert(i < ichLim); - if ((d = (uint)s[i] - '0') > 9) + Contracts.Assert(i < span.Length); + if ((d = (uint)span[i] - '0') > 9) break; digits = true; if (e < eMax) e = 10 * e + (int)d; - if (++i >= ichLim) + if (++i >= span.Length) break; } diff --git a/src/Microsoft.ML.Core/Utilities/Hashing.cs b/src/Microsoft.ML.Core/Utilities/Hashing.cs index 5812937d72..a15677451b 100644 --- a/src/Microsoft.ML.Core/Utilities/Hashing.cs +++ b/src/Microsoft.ML.Core/Utilities/Hashing.cs @@ -11,6 +11,8 @@ namespace Microsoft.ML.Runtime.Internal.Utilities { public static class Hashing { + private const uint _defaultSeed = (5381 << 16) + 5381; + public static uint CombineHash(uint u1, uint u2) { return ((u1 << 7) | (u1 >> 25)) ^ u2; @@ -62,24 +64,10 @@ public static int HashInt(int n) } /// - /// Hash the characters in a string. This MUST produce the same result as the other - /// overloads (with equivalent characters). - /// - public static uint HashString(string str) - { - Contracts.AssertValue(str); - return MurmurHash((5381 << 16) + 5381, str, 0, str.Length); - } - - /// - /// Hash the characters in a sub-string. This MUST produce the same result - /// as HashString(str.SubString(ichMin, ichLim - ichMin)). + /// Hash the characters in a of . + /// This MUST produce the same result as the other overloads (with equivalent characters). /// - public static uint HashString(string str, int ichMin, int ichLim) - { - Contracts.Assert(0 <= ichMin & ichMin <= ichLim & ichLim <= Utils.Size(str)); - return MurmurHash((5381 << 16) + 5381, str, ichMin, ichLim); - } + public static uint HashString(ReadOnlySpan str) => MurmurHash(_defaultSeed, str); /// /// Hash the characters in a string builder. This MUST produce the same result @@ -88,12 +76,12 @@ public static uint HashString(string str, int ichMin, int ichLim) public static uint HashString(StringBuilder sb) { Contracts.AssertValue(sb); - return MurmurHash((5381 << 16) + 5381, sb, 0, sb.Length); + return MurmurHash(_defaultSeed, sb, 0, sb.Length); } public static uint HashSequence(uint[] sequence, int min, int lim) { - return MurmurHash((5381 << 16) + 5381, sequence, min, lim); + return MurmurHash(_defaultSeed, sequence, min, lim); } /// @@ -125,23 +113,21 @@ public static uint MurmurRound(uint hash, uint chunk) /// * 0x0800 to 0xFFFF : 1110xxxx 10xxxxxx 10xxxxxx /// NOTE: This MUST match the StringBuilder version below. /// - public static uint MurmurHash(uint hash, string data, int ichMin, int ichLim, bool toUpper = false) + public static uint MurmurHash(uint hash, ReadOnlySpan span, bool toUpper = false) { - Contracts.Assert(0 <= ichMin & ichMin <= ichLim & ichLim <= Utils.Size(data)); - // Byte length (in pseudo UTF-8 form). int len = 0; // Current bits, value and count. ulong cur = 0; int bits = 0; - for (int ich = ichMin; ich < ichLim; ich++) + for (int ich = 0; ich < span.Length; ich++) { Contracts.Assert((bits & 0x7) == 0); Contracts.Assert((uint)bits <= 24); Contracts.Assert(cur <= 0x00FFFFFF); - uint ch = toUpper ? char.ToUpperInvariant(data[ich]) : data[ich]; + uint ch = toUpper ? char.ToUpperInvariant(span[ich]) : span[ich]; if (ch <= 0x007F) { cur |= ch << bits; @@ -256,7 +242,7 @@ public static uint MurmurHash(uint hash, StringBuilder data, int ichMin, int ich // Final mixing ritual for the hash. hash = MixHash(hash); - Contracts.Assert(hash == MurmurHash(seed, data.ToString(), 0, data.Length)); + Contracts.Assert(hash == MurmurHash(seed, data.ToString().AsSpan())); return hash; } diff --git a/src/Microsoft.ML.Core/Utilities/NormStr.cs b/src/Microsoft.ML.Core/Utilities/NormStr.cs index 50b72196ed..fea018ac58 100644 --- a/src/Microsoft.ML.Core/Utilities/NormStr.cs +++ b/src/Microsoft.ML.Core/Utilities/NormStr.cs @@ -8,6 +8,7 @@ using System.Linq; using System.Threading; using System.Text; +using Microsoft.ML.Runtime.Data; namespace Microsoft.ML.Runtime.Internal.Utilities { @@ -18,37 +19,26 @@ namespace Microsoft.ML.Runtime.Internal.Utilities /// public sealed class NormStr { - public readonly string Value; + public readonly ReadOnlyMemory Value; public readonly int Id; private readonly uint _hash; /// /// NormStr's can only be created by the Pool. /// - private NormStr(string str, int id, uint hash) + private NormStr(ReadOnlyMemory str, int id, uint hash) { - Contracts.AssertValue(str); - Contracts.Assert(id >= 0 || id == -1 && str == ""); + Contracts.Assert(id >= 0 || id == -1 && str.IsEmpty); Value = str; Id = id; _hash = hash; } - public override string ToString() - { - return Value; - } - public override int GetHashCode() { return (int)_hash; } - public static implicit operator string(NormStr nstr) - { - return nstr.Value; - } - public sealed class Pool : IEnumerable { private int _mask; // Number of buckets minus 1. The number of buckets must be a power of two. @@ -107,7 +97,8 @@ public NormStr Get(string str, bool add = false) if (str == null) str = ""; - uint hash = Hashing.HashString(str); + var strSpan = str.AsSpan(); + uint hash = Hashing.HashString(strSpan); int ins = GetIns(hash); while (ins >= 0) { @@ -115,75 +106,50 @@ public NormStr Get(string str, bool add = false) if ((int)Utils.GetLo(meta) == str.Length) { var ns = GetNs(ins); - if (ns.Value == str) + if (strSpan.SequenceEqual(ns.Value.Span)) return ns; } ins = (int)Utils.GetHi(meta); } Contracts.Assert(ins == -1); - return add ? AddCore(str, hash) : null; + return add ? AddCore(str.AsMemory(), hash) : null; } - /// - /// Make sure the given string has an equivalent NormStr in the pool and return it. - /// - public NormStr Add(string str) - { - return Get(str, true); - } - - /// - /// Determine if the given sub-string has an equivalent NormStr in the pool. - /// - public NormStr Get(string str, int ichMin, int ichLim, bool add = false) + public NormStr Get(ReadOnlyMemory str, bool add = false) { AssertValid(); - Contracts.Assert(0 <= ichMin & ichMin <= ichLim & ichLim <= Utils.Size(str)); - if (str == null) - return Get("", add); - - if (ichMin == 0 && ichLim == str.Length) - return Get(str, add); - - uint hash = Hashing.HashString(str, ichMin, ichLim); + var span = str.Span; + uint hash = Hashing.HashString(span); int ins = GetIns(hash); - if (ins >= 0) + while (ins >= 0) { - int cch = ichLim - ichMin; - var rgmeta = _rgmeta; - for (; ; ) + ulong meta = _rgmeta[ins]; + if ((int)Utils.GetLo(meta) == str.Length) { - ulong meta = rgmeta[ins]; - if ((int)Utils.GetLo(meta) == cch) - { - var ns = GetNs(ins); - var value = ns.Value; - for (int ich = 0; ; ich++) - { - if (ich == cch) - return ns; - if (value[ich] != str[ich + ichMin]) - break; - } - } - ins = (int)Utils.GetHi(meta); - if (ins < 0) - break; + var ns = GetNs(ins); + if (ns.Value.Span.SequenceEqual(span)) + return ns; } + ins = (int)Utils.GetHi(meta); } Contracts.Assert(ins == -1); - return add ? AddCore(str.Substring(ichMin, ichLim - ichMin), hash) : null; + return add ? AddCore(str, hash) : null; } /// - /// Make sure the given sub-string has an equivalent NormStr in the pool and return it. + /// Make sure the given string has an equivalent NormStr in the pool and return it. /// - public NormStr Add(string str, int ichMin, int ichLim) + public NormStr Add(string str) { - return Get(str, ichMin, ichLim, true); + return Get(str, true); + } + + public NormStr Add(ReadOnlyMemory str) + { + return Get(str, true); } /// @@ -212,7 +178,7 @@ public NormStr Get(StringBuilder sb, bool add = false) { if (ich == cch) return ns; - if (value[ich] != sb[ich]) + if (value.Span[ich] != sb[ich]) break; } } @@ -220,7 +186,7 @@ public NormStr Get(StringBuilder sb, bool add = false) } Contracts.Assert(ins == -1); - return add ? AddCore(sb.ToString(), hash) : null; + return add ? AddCore(sb.ToString().AsMemory(), hash) : null; } /// @@ -234,11 +200,10 @@ public NormStr Add(StringBuilder sb) /// /// Adds the item. Does NOT check for whether the item is already present. /// - private NormStr AddCore(string str, uint hash) + private NormStr AddCore(ReadOnlyMemory str, uint hash) { - Contracts.AssertValue(str); Contracts.Assert(str.Length >= 0); - Contracts.Assert(Hashing.HashString(str) == hash); + Contracts.Assert(Hashing.HashString(str.Span) == hash); if (_rgns == null) { diff --git a/src/Microsoft.ML.Core/Utilities/Stream.cs b/src/Microsoft.ML.Core/Utilities/Stream.cs index 5e2974e2a5..7fbf0148b9 100644 --- a/src/Microsoft.ML.Core/Utilities/Stream.cs +++ b/src/Microsoft.ML.Core/Utilities/Stream.cs @@ -2,8 +2,6 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - using System; using System.Collections; using System.Collections.Generic; @@ -178,7 +176,7 @@ public static void WriteBytesNoCount(this BinaryWriter writer, byte[] values, in /// /// Writes a length prefixed array of Floats. /// - public static void WriteFloatArray(this BinaryWriter writer, Float[] values) + public static void WriteFloatArray(this BinaryWriter writer, float[] values) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -197,7 +195,7 @@ public static void WriteFloatArray(this BinaryWriter writer, Float[] values) /// /// Writes a length prefixed array of Floats. /// - public static void WriteFloatArray(this BinaryWriter writer, Float[] values, int count) + public static void WriteFloatArray(this BinaryWriter writer, float[] values, int count) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -211,7 +209,7 @@ public static void WriteFloatArray(this BinaryWriter writer, Float[] values, int /// /// Writes a specified number of floats starting at the specified index from an array. /// - public static void WriteFloatArray(this BinaryWriter writer, Float[] values, int start, int count) + public static void WriteFloatArray(this BinaryWriter writer, float[] values, int start, int count) { Contracts.AssertValue(writer); Contracts.AssertValue(values); @@ -225,7 +223,7 @@ public static void WriteFloatArray(this BinaryWriter writer, Float[] values, int /// /// Writes a length prefixed array of Floats. /// - public static void WriteFloatArray(this BinaryWriter writer, IEnumerable values, int count) + public static void WriteFloatArray(this BinaryWriter writer, IEnumerable values, int count) { Contracts.AssertValue(writer); Contracts.AssertValue(values); @@ -244,7 +242,7 @@ public static void WriteFloatArray(this BinaryWriter writer, IEnumerable /// /// Writes an array of Floats without the length prefix. /// - public static void WriteFloatsNoCount(this BinaryWriter writer, Float[] values, int count) + public static void WriteFloatsNoCount(this BinaryWriter writer, float[] values, int count) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -257,7 +255,7 @@ public static void WriteFloatsNoCount(this BinaryWriter writer, Float[] values, /// /// Writes a length prefixed array of singles. /// - public static void WriteSingleArray(this BinaryWriter writer, Single[] values) + public static void WriteSingleArray(this BinaryWriter writer, float[] values) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -276,7 +274,7 @@ public static void WriteSingleArray(this BinaryWriter writer, Single[] values) /// /// Writes a length prefixed array of singles. /// - public static void WriteSingleArray(this BinaryWriter writer, Single[] values, int count) + public static void WriteSingleArray(this BinaryWriter writer, float[] values, int count) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -290,7 +288,7 @@ public static void WriteSingleArray(this BinaryWriter writer, Single[] values, i /// /// Writes an array of singles without the length prefix. /// - public static void WriteSinglesNoCount(this BinaryWriter writer, Single[] values, int count) + public static void WriteSinglesNoCount(this BinaryWriter writer, float[] values, int count) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -303,7 +301,7 @@ public static void WriteSinglesNoCount(this BinaryWriter writer, Single[] values /// /// Writes a length prefixed array of doubles. /// - public static void WriteDoubleArray(this BinaryWriter writer, Double[] values) + public static void WriteDoubleArray(this BinaryWriter writer, double[] values) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -315,14 +313,14 @@ public static void WriteDoubleArray(this BinaryWriter writer, Double[] values) } writer.Write(values.Length); - foreach (Double val in values) + foreach (double val in values) writer.Write(val); } /// /// Writes a length prefixed array of doubles. /// - public static void WriteDoubleArray(this BinaryWriter writer, Double[] values, int count) + public static void WriteDoubleArray(this BinaryWriter writer, double[] values, int count) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -336,7 +334,7 @@ public static void WriteDoubleArray(this BinaryWriter writer, Double[] values, i /// /// Writes an array of doubles without the length prefix. /// - public static void WriteDoublesNoCount(this BinaryWriter writer, Double[] values, int count) + public static void WriteDoublesNoCount(this BinaryWriter writer, double[] values, int count) { Contracts.AssertValue(writer); Contracts.AssertValueOrNull(values); @@ -427,7 +425,7 @@ public static void WriteBitArray(this BinaryWriter writer, BitArray arr) } } - public static long WriteSByteStream(this BinaryWriter writer, IEnumerable e) + public static long WriteSByteStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -438,7 +436,7 @@ public static long WriteSByteStream(this BinaryWriter writer, IEnumerable return c; } - public static long WriteByteStream(this BinaryWriter writer, IEnumerable e) + public static long WriteByteStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -449,7 +447,7 @@ public static long WriteByteStream(this BinaryWriter writer, IEnumerable e return c; } - public static long WriteIntStream(this BinaryWriter writer, IEnumerable e) + public static long WriteIntStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -460,7 +458,7 @@ public static long WriteIntStream(this BinaryWriter writer, IEnumerable e return c; } - public static long WriteUIntStream(this BinaryWriter writer, IEnumerable e) + public static long WriteUIntStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -471,7 +469,7 @@ public static long WriteUIntStream(this BinaryWriter writer, IEnumerable return c; } - public static long WriteShortStream(this BinaryWriter writer, IEnumerable e) + public static long WriteShortStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -482,7 +480,7 @@ public static long WriteShortStream(this BinaryWriter writer, IEnumerable return c; } - public static long WriteUShortStream(this BinaryWriter writer, IEnumerable e) + public static long WriteUShortStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -493,7 +491,7 @@ public static long WriteUShortStream(this BinaryWriter writer, IEnumerable e) + public static long WriteLongStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -504,7 +502,7 @@ public static long WriteLongStream(this BinaryWriter writer, IEnumerable return c; } - public static long WriteULongStream(this BinaryWriter writer, IEnumerable e) + public static long WriteULongStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -515,7 +513,7 @@ public static long WriteULongStream(this BinaryWriter writer, IEnumerable e) + public static long WriteSingleStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -526,7 +524,7 @@ public static long WriteSingleStream(this BinaryWriter writer, IEnumerable e) + public static long WriteDoubleStream(this BinaryWriter writer, IEnumerable e) { long c = 0; foreach (var v in e) @@ -606,12 +604,12 @@ public static bool ReadBoolByte(this BinaryReader reader) return b != 0; } - public static Float ReadFloat(this BinaryReader reader) + public static float ReadFloat(this BinaryReader reader) { return reader.ReadSingle(); } - public static Float[] ReadFloatArray(this BinaryReader reader) + public static float[] ReadFloatArray(this BinaryReader reader) { Contracts.AssertValue(reader); @@ -620,16 +618,16 @@ public static Float[] ReadFloatArray(this BinaryReader reader) return ReadFloatArray(reader, size); } - public static Float[] ReadFloatArray(this BinaryReader reader, int size) + public static float[] ReadFloatArray(this BinaryReader reader, int size) { Contracts.AssertValue(reader); Contracts.Assert(size >= 0); if (size == 0) return null; - var values = new Float[size]; + var values = new float[size]; - long bufferSizeInBytes = (long)size * sizeof(Float); + long bufferSizeInBytes = (long)size * sizeof(float); if (bufferSizeInBytes < _bulkReadThresholdInBytes) { for (int i = 0; i < size; i++) @@ -649,14 +647,14 @@ public static Float[] ReadFloatArray(this BinaryReader reader, int size) return values; } - public static void ReadFloatArray(this BinaryReader reader, Float[] array, int start, int count) + public static void ReadFloatArray(this BinaryReader reader, float[] array, int start, int count) { Contracts.AssertValue(reader); Contracts.AssertValue(array); Contracts.Assert(0 <= start && start < array.Length); Contracts.Assert(0 < count && count <= array.Length - start); - long bufferReadLengthInBytes = (long)count * sizeof(Float); + long bufferReadLengthInBytes = (long)count * sizeof(float); if (bufferReadLengthInBytes < _bulkReadThresholdInBytes) { for (int i = 0; i < count; i++) @@ -668,15 +666,15 @@ public static void ReadFloatArray(this BinaryReader reader, Float[] array, int s { fixed (void* dst = array) { - long bufferBeginOffsetInBytes = (long)start * sizeof(Float); - long bufferSizeInBytes = ((long)array.Length - start) * sizeof(Float); + long bufferBeginOffsetInBytes = (long)start * sizeof(float); + long bufferSizeInBytes = ((long)array.Length - start) * sizeof(float); ReadBytes(reader, (byte*)dst + bufferBeginOffsetInBytes, bufferSizeInBytes, bufferReadLengthInBytes); } } } } - public static Single[] ReadSingleArray(this BinaryReader reader) + public static float[] ReadSingleArray(this BinaryReader reader) { Contracts.AssertValue(reader); int size = reader.ReadInt32(); @@ -684,15 +682,15 @@ public static Single[] ReadSingleArray(this BinaryReader reader) return ReadSingleArray(reader, size); } - public static Single[] ReadSingleArray(this BinaryReader reader, int size) + public static float[] ReadSingleArray(this BinaryReader reader, int size) { Contracts.AssertValue(reader); Contracts.Assert(size >= 0); if (size == 0) return null; - var values = new Single[size]; + var values = new float[size]; - long bufferSizeInBytes = (long)size * sizeof(Single); + long bufferSizeInBytes = (long)size * sizeof(float); if (bufferSizeInBytes < _bulkReadThresholdInBytes) { for (int i = 0; i < size; i++) @@ -712,7 +710,7 @@ public static Single[] ReadSingleArray(this BinaryReader reader, int size) return values; } - public static Double[] ReadDoubleArray(this BinaryReader reader) + public static double[] ReadDoubleArray(this BinaryReader reader) { Contracts.AssertValue(reader); @@ -721,15 +719,15 @@ public static Double[] ReadDoubleArray(this BinaryReader reader) return ReadDoubleArray(reader, size); } - public static Double[] ReadDoubleArray(this BinaryReader reader, int size) + public static double[] ReadDoubleArray(this BinaryReader reader, int size) { Contracts.AssertValue(reader); Contracts.Assert(size >= 0); if (size == 0) return null; - var values = new Double[size]; + var values = new double[size]; - long bufferSizeInBytes = (long)size * sizeof(Double); + long bufferSizeInBytes = (long)size * sizeof(double); if (bufferSizeInBytes < _bulkReadThresholdInBytes) { for (int i = 0; i < size; i++) diff --git a/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs b/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs index b0f2e12ef1..f8d7783fe4 100644 --- a/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs +++ b/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs @@ -118,7 +118,7 @@ public override void Run() using (var ch = Host.Start(LoadName)) using (var server = InitServer(ch)) { - var settings = CmdParser.GetSettings(ch, Args, new Arguments()); + var settings = CmdParser.GetSettings(Host, Args, new Arguments()); string cmd = string.Format("maml.exe {0} {1}", LoadName, settings); ch.Info(cmd); @@ -559,7 +559,7 @@ private FoldResult RunFold(int fold) var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor, scorerFactorySettings: _scorer as ICommandLineComponentFactory); ch.AssertValue(bindable); var mapper = bindable.Bind(host, testData.Schema); - var scorerComp = _scorer ?? ScoreUtils.GetScorerComponent(mapper); + var scorerComp = _scorer ?? ScoreUtils.GetScorerComponent(host, mapper); IDataScorerTransform scorePipe = scorerComp.CreateComponent(host, testData.Data, mapper, trainData.Schema); // Save per-fold model. diff --git a/src/Microsoft.ML.Data/Commands/ScoreCommand.cs b/src/Microsoft.ML.Data/Commands/ScoreCommand.cs index 610c4ee25f..014fdf87f8 100644 --- a/src/Microsoft.ML.Data/Commands/ScoreCommand.cs +++ b/src/Microsoft.ML.Data/Commands/ScoreCommand.cs @@ -120,7 +120,7 @@ private void RunCore(IChannel ch) var mapper = bindable.Bind(Host, schema); if (scorer == null) - scorer = ScoreUtils.GetScorerComponent(mapper); + scorer = ScoreUtils.GetScorerComponent(Host, mapper); loader = CompositeDataLoader.ApplyTransform(Host, loader, "Scorer", scorer.ToString(), (env, view) => scorer.CreateComponent(env, view, mapper, trainSchema)); @@ -284,7 +284,7 @@ private static TScorerFactory GetScorerComponentAndMapper( mapper = bindable.Bind(env, schema); if (scorerFactory != null) return scorerFactory; - return GetScorerComponent(mapper); + return GetScorerComponent(env, mapper); } /// @@ -292,22 +292,25 @@ private static TScorerFactory GetScorerComponentAndMapper( /// metadata on the first column of the mapper. If that text is found and maps to a scorer loadable class, /// that component is used. Otherwise, the GenericScorer is used. /// + /// The host environment.. /// The schema bound mapper to get the default scorer.. /// An optional suffix to append to the default column names. public static TScorerFactory GetScorerComponent( + IHostEnvironment environment, ISchemaBoundMapper mapper, string suffix = null) { + Contracts.CheckValue(environment, nameof(environment)); Contracts.AssertValue(mapper); ComponentCatalog.LoadableClassInfo info = null; - DvText scoreKind = default; + ReadOnlyMemory scoreKind = default; if (mapper.OutputSchema.ColumnCount > 0 && mapper.OutputSchema.TryGetMetadata(TextType.Instance, MetadataUtils.Kinds.ScoreColumnKind, 0, ref scoreKind) && - scoreKind.HasChars) + !scoreKind.IsEmpty) { var loadName = scoreKind.ToString(); - info = ComponentCatalog.GetLoadableClassInfo(loadName); + info = environment.ComponentCatalog.GetLoadableClassInfo(loadName); if (info == null || !typeof(IDataScorerTransform).IsAssignableFrom(info.Type)) info = null; } diff --git a/src/Microsoft.ML.Data/Commands/ShowSchemaCommand.cs b/src/Microsoft.ML.Data/Commands/ShowSchemaCommand.cs index 305dadd4f2..46bb704b6f 100644 --- a/src/Microsoft.ML.Data/Commands/ShowSchemaCommand.cs +++ b/src/Microsoft.ML.Data/Commands/ShowSchemaCommand.cs @@ -132,7 +132,7 @@ private static void PrintSchema(TextWriter writer, Arguments args, ISchema schem var itw = IndentingTextWriter.Wrap(writer); using (itw.Nest()) { - var names = default(VBuffer); + var names = default(VBuffer>); for (int col = 0; col < colLim; col++) { var name = schema.GetColumnName(col); @@ -171,7 +171,7 @@ private static void PrintSchema(TextWriter writer, Arguments args, ISchema schem bool verbose = args.Verbose ?? false; foreach (var kvp in names.Items(all: verbose)) { - if (verbose || kvp.Value.HasChars) + if (verbose || !kvp.Value.IsEmpty) itw.WriteLine("{0}:{1}", kvp.Key, kvp.Value); } } diff --git a/src/Microsoft.ML.Data/Commands/TestCommand.cs b/src/Microsoft.ML.Data/Commands/TestCommand.cs index ca32da0ddd..1630d9af57 100644 --- a/src/Microsoft.ML.Data/Commands/TestCommand.cs +++ b/src/Microsoft.ML.Data/Commands/TestCommand.cs @@ -67,7 +67,7 @@ public override void Run() using (var ch = Host.Start(command)) using (var server = InitServer(ch)) { - var settings = CmdParser.GetSettings(ch, Args, new Arguments()); + var settings = CmdParser.GetSettings(Host, Args, new Arguments()); ch.Info("maml.exe {0} {1}", command, settings); SendTelemetry(Host); diff --git a/src/Microsoft.ML.Data/Commands/TrainCommand.cs b/src/Microsoft.ML.Data/Commands/TrainCommand.cs index 431681ec2a..1a90881464 100644 --- a/src/Microsoft.ML.Data/Commands/TrainCommand.cs +++ b/src/Microsoft.ML.Data/Commands/TrainCommand.cs @@ -108,7 +108,7 @@ public override void Run() using (var ch = Host.Start(command)) using (var server = InitServer(ch)) { - var settings = CmdParser.GetSettings(ch, Args, new Arguments()); + var settings = CmdParser.GetSettings(Host, Args, new Arguments()); string cmd = string.Format("maml.exe {0} {1}", command, settings); ch.Info(cmd); @@ -443,7 +443,6 @@ public static bool AddNormalizerIfNeeded(IHostEnvironment env, IChannel ch, ITra { if (autoNorm != NormalizeOption.Yes) { - DvBool isNormalized = DvBool.False; if (!trainer.Info.NeedNormalization || schema.IsNormalized(featCol)) { ch.Info("Not adding a normalizer."); diff --git a/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs b/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs index 270f7c3b03..63c8691747 100644 --- a/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs +++ b/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs @@ -94,7 +94,7 @@ public override void Run() using (var ch = Host.Start(LoadName)) using (var server = InitServer(ch)) { - var settings = CmdParser.GetSettings(ch, Args, new Arguments()); + var settings = CmdParser.GetSettings(Host, Args, new Arguments()); string cmd = string.Format("maml.exe {0} {1}", LoadName, settings); ch.Info(cmd); diff --git a/src/Microsoft.ML.Data/Data/BufferBuilder.cs b/src/Microsoft.ML.Data/Data/BufferBuilder.cs index 1c0e7cde08..b5f20eac5a 100644 --- a/src/Microsoft.ML.Data/Data/BufferBuilder.cs +++ b/src/Microsoft.ML.Data/Data/BufferBuilder.cs @@ -89,8 +89,8 @@ private void AssertValid() public static BufferBuilder CreateDefault() { - if (typeof(T) == typeof(DvText)) - return (BufferBuilder)(object)new BufferBuilder(TextCombiner.Instance); + if (typeof(T) == typeof(ReadOnlyMemory)) + return (BufferBuilder)(object)new BufferBuilder>(TextCombiner.Instance); if (typeof(T) == typeof(float)) return (BufferBuilder)(object)new BufferBuilder(FloatAdder.Instance); throw Contracts.Except($"Unrecognized type '{typeof(T)}' for default {nameof(BufferBuilder)}"); diff --git a/src/Microsoft.ML.Data/Data/Combiner.cs b/src/Microsoft.ML.Data/Data/Combiner.cs index ee45aee3e3..6335620b8b 100644 --- a/src/Microsoft.ML.Data/Data/Combiner.cs +++ b/src/Microsoft.ML.Data/Data/Combiner.cs @@ -19,7 +19,7 @@ public abstract class Combiner public abstract void Combine(ref T dst, T src); } - public sealed class TextCombiner : Combiner + public sealed class TextCombiner : Combiner> { private static volatile TextCombiner _instance; public static TextCombiner Instance @@ -36,8 +36,8 @@ private TextCombiner() { } - public override bool IsDefault(DvText value) { return value.Length == 0; } - public override void Combine(ref DvText dst, DvText src) + public override bool IsDefault(ReadOnlyMemory value) { return value.Length == 0; } + public override void Combine(ref ReadOnlyMemory dst, ReadOnlyMemory src) { Contracts.Check(IsDefault(dst)); dst = src; diff --git a/src/Microsoft.ML.Data/Data/Conversion.cs b/src/Microsoft.ML.Data/Data/Conversion.cs index 0a9833064a..1f08d63fc3 100644 --- a/src/Microsoft.ML.Data/Data/Conversion.cs +++ b/src/Microsoft.ML.Data/Data/Conversion.cs @@ -14,22 +14,18 @@ namespace Microsoft.ML.Runtime.Data.Conversion { - using BL = DvBool; - using DT = DvDateTime; - using DZ = DvDateTimeZone; - using I1 = DvInt1; - using I2 = DvInt2; - using I4 = DvInt4; - using I8 = DvInt8; + using BL = Boolean; + using DT = DateTime; + using DZ = DateTimeOffset; using R4 = Single; using R8 = Double; - using RawI1 = SByte; - using RawI2 = Int16; - using RawI4 = Int32; - using RawI8 = Int64; + using I1 = SByte; + using I2 = Int16; + using I4 = Int32; + using I8 = Int64; using SB = StringBuilder; - using TS = DvTimeSpan; - using TX = DvText; + using TX = ReadOnlyMemory; + using TS = TimeSpan; using U1 = Byte; using U2 = UInt16; using U4 = UInt32; @@ -244,41 +240,14 @@ private Conversions() AddStd(Convert); AddAux(Convert); - AddIsNA(IsNA); - AddIsNA(IsNA); - AddIsNA(IsNA); - AddIsNA(IsNA); AddIsNA(IsNA); AddIsNA(IsNA); - AddIsNA(IsNA); - AddIsNA(IsNA); - AddIsNA(IsNA); - AddIsNA
(IsNA); - AddIsNA(IsNA); - - AddGetNA(GetNA); - AddGetNA(GetNA); - AddGetNA(GetNA); - AddGetNA(GetNA); + AddGetNA(GetNA); AddGetNA(GetNA); - AddGetNA(GetNA); - AddGetNA(GetNA); - AddGetNA(GetNA); - AddGetNA
(GetNA); - AddGetNA(GetNA); - - AddHasNA(HasNA); - AddHasNA(HasNA); - AddHasNA(HasNA); - AddHasNA(HasNA); + AddHasNA(HasNA); AddHasNA(HasNA); - AddHasNA(HasNA); - AddHasNA(HasNA); - AddHasNA(HasNA); - AddHasNA
(HasNA); - AddHasNA(HasNA); AddIsDef(IsDefault); AddIsDef(IsDefault); @@ -533,7 +502,7 @@ public bool TryGetStringConversion(ColumnType type, out ValueMapper(out ValueMapper conv) { DataKind kindSrc; - if (!_kinds.TryGetValue(typeof (TSrc), out kindSrc)) + if (!_kinds.TryGetValue(typeof(TSrc), out kindSrc)) { conv = null; return false; @@ -846,42 +815,24 @@ public ValueGetter GetNAOrDefaultGetter(ColumnType type) // The IsNA methods are for efficient delegates (instance instead of static). #region IsNA - private bool IsNA(ref I1 src) => src.IsNA; - private bool IsNA(ref I2 src) => src.IsNA; - private bool IsNA(ref I4 src) => src.IsNA; - private bool IsNA(ref I8 src) => src.IsNA; - private bool IsNA(ref R4 src) => src.IsNA(); - private bool IsNA(ref R8 src) => src.IsNA(); - private bool IsNA(ref BL src) => src.IsNA; - private bool IsNA(ref TS src) => src.IsNA; - private bool IsNA(ref DT src) => src.IsNA; - private bool IsNA(ref DZ src) => src.IsNA; - private bool IsNA(ref TX src) => src.IsNA; + private bool IsNA(ref R4 src) => R4.IsNaN(src); + private bool IsNA(ref R8 src) => R8.IsNaN(src); #endregion IsNA #region HasNA - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA()) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA()) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer
src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } - private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (src.Values[i].IsNA) return true; } return false; } + private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (R4.IsNaN(src.Values[i])) return true; } return false; } + private bool HasNA(ref VBuffer src) { for (int i = 0; i < src.Count; i++) { if (R8.IsNaN(src.Values[i])) return true; } return false; } #endregion HasNA #region IsDefault - private bool IsDefault(ref I1 src) => src.RawValue == 0; - private bool IsDefault(ref I2 src) => src.RawValue == 0; - private bool IsDefault(ref I4 src) => src.RawValue == 0; - private bool IsDefault(ref I8 src) => src.RawValue == 0; + private bool IsDefault(ref I1 src) => src == default(I1); + private bool IsDefault(ref I2 src) => src == default(I2); + private bool IsDefault(ref I4 src) => src == default(I4); + private bool IsDefault(ref I8 src) => src == default(I8); private bool IsDefault(ref R4 src) => src == 0; private bool IsDefault(ref R8 src) => src == 0; private bool IsDefault(ref TX src) => src.IsEmpty; - private bool IsDefault(ref BL src) => src.IsFalse; + private bool IsDefault(ref BL src) => src == default; private bool IsDefault(ref U1 src) => src == 0; private bool IsDefault(ref U2 src) => src == 0; private bool IsDefault(ref U4 src) => src == 0; @@ -900,17 +851,8 @@ public ValueGetter GetNAOrDefaultGetter(ColumnType type) #endregion HasZero #region GetNA - private void GetNA(ref I1 value) => value = I1.NA; - private void GetNA(ref I2 value) => value = I2.NA; - private void GetNA(ref I4 value) => value = I4.NA; - private void GetNA(ref I8 value) => value = I8.NA; private void GetNA(ref R4 value) => value = R4.NaN; private void GetNA(ref R8 value) => value = R8.NaN; - private void GetNA(ref BL value) => value = BL.NA; - private void GetNA(ref TS value) => value = TS.NA; - private void GetNA(ref DT value) => value = DT.NA; - private void GetNA(ref DZ value) => value = DZ.NA; - private void GetNA(ref TX value) => value = TX.NA; #endregion GetNA #region ToI1 @@ -1022,28 +964,28 @@ public ValueGetter GetNAOrDefaultGetter(ColumnType type) #endregion ToR8 #region ToStringBuilder - public void Convert(ref I1 src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA) dst.Append(src.RawValue); } - public void Convert(ref I2 src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA) dst.Append(src.RawValue); } - public void Convert(ref I4 src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA) dst.Append(src.RawValue); } - public void Convert(ref I8 src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA) dst.Append(src.RawValue); } + public void Convert(ref I1 src, ref SB dst) { ClearDst(ref dst); dst.Append(src); } + public void Convert(ref I2 src, ref SB dst) { ClearDst(ref dst); dst.Append(src); } + public void Convert(ref I4 src, ref SB dst) { ClearDst(ref dst); dst.Append(src); } + public void Convert(ref I8 src, ref SB dst) { ClearDst(ref dst); dst.Append(src); } public void Convert(ref U1 src, ref SB dst) => ClearDst(ref dst).Append(src); public void Convert(ref U2 src, ref SB dst) => ClearDst(ref dst).Append(src); public void Convert(ref U4 src, ref SB dst) => ClearDst(ref dst).Append(src); public void Convert(ref U8 src, ref SB dst) => ClearDst(ref dst).Append(src); public void Convert(ref UG src, ref SB dst) { ClearDst(ref dst); dst.AppendFormat("0x{0:x16}{1:x16}", src.Hi, src.Lo); } - public void Convert(ref R4 src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA()) dst.AppendFormat(CultureInfo.InvariantCulture, "{0:R}", src); } - public void Convert(ref R8 src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA()) dst.AppendFormat(CultureInfo.InvariantCulture, "{0:G17}", src); } + public void Convert(ref R4 src, ref SB dst) { ClearDst(ref dst); if (R4.IsNaN(src)) dst.AppendFormat(CultureInfo.InvariantCulture, "{0}", "?"); else dst.AppendFormat(CultureInfo.InvariantCulture, "{0:R}", src); } + public void Convert(ref R8 src, ref SB dst) { ClearDst(ref dst); if (R8.IsNaN(src)) dst.AppendFormat(CultureInfo.InvariantCulture, "{0}", "?"); else dst.AppendFormat(CultureInfo.InvariantCulture, "{0:G17}", src); } public void Convert(ref BL src, ref SB dst) { ClearDst(ref dst); - if (src.IsFalse) + if (!src) dst.Append("0"); - else if (src.IsTrue) + else dst.Append("1"); } - public void Convert(ref TS src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA) dst.AppendFormat("{0:c}", (TimeSpan)src); } - public void Convert(ref DT src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA) dst.AppendFormat("{0:o}", (DateTime)src); } - public void Convert(ref DZ src, ref SB dst) { ClearDst(ref dst); if (!src.IsNA) dst.AppendFormat("{0:o}", (DateTimeOffset)src); } + public void Convert(ref TS src, ref SB dst) { ClearDst(ref dst); dst.AppendFormat("{0:c}", src); } + public void Convert(ref DT src, ref SB dst) { ClearDst(ref dst); dst.AppendFormat("{0:o}", src); } + public void Convert(ref DZ src, ref SB dst) { ClearDst(ref dst); dst.AppendFormat("{0:o}", src); } #endregion ToStringBuilder #region FromR4 @@ -1108,16 +1050,13 @@ public bool TryParse(ref TX src, out U4 dst) ///
public bool TryParse(ref TX src, out U8 dst) { - if (src.IsNA) + if (src.IsEmpty) { dst = 0; return false; } - int ichMin; - int ichLim; - string text = src.GetRawUnderlyingBufferInfo(out ichMin, out ichLim); - return TryParseCore(text, ichMin, ichLim, out dst); + return TryParseCore(src.Span, out dst); } /// @@ -1130,16 +1069,15 @@ public bool TryParse(ref TX src, out U8 dst) /// and had only digits and the letters 'a' through 'f' or 'A' through 'F' as characters public bool TryParse(ref TX src, out UG dst) { + var span = src.Span; // REVIEW: Accomodate numeric inputs? - if (src.Length != 34 || src[0] != '0' || (src[1] != 'x' && src[1] != 'X')) + if (src.Length != 34 || span[0] != '0' || (span[1] != 'x' && span[1] != 'X')) { dst = default(UG); return false; } - int ichMin; - int ichLim; - string tx = src.GetRawUnderlyingBufferInfo(out ichMin, out ichLim); - int offset = ichMin + 2; + + int offset = 2; ulong hi = 0; ulong num = 0; for (int i = 0; i < 2; ++i) @@ -1147,7 +1085,7 @@ public bool TryParse(ref TX src, out UG dst) for (int d = 0; d < 16; ++d) { num <<= 4; - char c = tx[offset++]; + char c = span[offset++]; // REVIEW: An exhaustive switch statement *might* be faster, maybe, at the // cost of being significantly longer. if ('0' <= c && c <= '9') @@ -1168,7 +1106,7 @@ public bool TryParse(ref TX src, out UG dst) num = 0; } } - Contracts.Assert(offset == ichLim); + Contracts.Assert(offset == src.Length); // The first read bits are the higher order bits, so they are listed second here. dst = new UG(num, hi); return true; @@ -1181,44 +1119,44 @@ public bool TryParse(ref TX src, out UG dst) /// The standard representations are any casing of: /// ? NaN NA N/A /// - private bool IsStdMissing(ref TX src) + private bool IsStdMissing(ref ReadOnlySpan span) { - Contracts.Assert(src.HasChars); + Contracts.Assert(!span.IsEmpty); char ch; - switch (src.Length) + switch (span.Length) { - default: - return false; - - case 1: - if (src[0] == '?') - return true; - return false; - case 2: - if ((ch = src[0]) != 'N' && ch != 'n') - return false; - if ((ch = src[1]) != 'A' && ch != 'a') + default: return false; - return true; - case 3: - if ((ch = src[0]) != 'N' && ch != 'n') + + case 1: + if (span[0] == '?') + return true; return false; - if ((ch = src[1]) == '/') - { - // Check for N/A. - if ((ch = src[2]) != 'A' && ch != 'a') + case 2: + if ((ch = span[0]) != 'N' && ch != 'n') return false; - } - else - { - // Check for NaN. - if (ch != 'a' && ch != 'A') + if ((ch = span[1]) != 'A' && ch != 'a') return false; - if ((ch = src[2]) != 'N' && ch != 'n') + return true; + case 3: + if ((ch = span[0]) != 'N' && ch != 'n') return false; - } - return true; + if ((ch = span[1]) == '/') + { + // Check for N/A. + if ((ch = span[2]) != 'A' && ch != 'a') + return false; + } + else + { + // Check for NaN. + if (ch != 'a' && ch != 'A') + return false; + if ((ch = span[2]) != 'N' && ch != 'n') + return false; + } + return true; } } @@ -1226,11 +1164,13 @@ private bool IsStdMissing(ref TX src) /// Utility to assist in parsing key-type values. The min and max values define /// the legal input value bounds. The output dst value is "normalized" so min is /// mapped to 1, max is mapped to 1 + (max - min). - /// Missing values are mapped to zero with a true return. + /// Exception is thrown for missing values. /// Unparsable or out of range values are mapped to zero with a false return. ///
public bool TryParseKey(ref TX src, U8 min, U8 max, out U8 dst) { + var span = src.Span; + Contracts.Check(!IsStdMissing(ref span), "Missing text value cannot be converted to unsigned integer type."); Contracts.Assert(min <= max); // This simply ensures we don't have min == 0 and max == U8.MaxValue. This is illegal since @@ -1240,22 +1180,19 @@ public bool TryParseKey(ref TX src, U8 min, U8 max, out U8 dst) // Both empty and missing map to zero (NA for key values) and that mapping is valid, // hence the true return. - if (!src.HasChars) + if (src.IsEmpty) { dst = 0; return true; } // Parse a ulong. - int ichMin; - int ichLim; - string text = src.GetRawUnderlyingBufferInfo(out ichMin, out ichLim); ulong uu; - if (!TryParseCore(text, ichMin, ichLim, out uu)) + if (!TryParseCore(span, out uu)) { dst = 0; // Return true only for standard forms for NA. - return IsStdMissing(ref src); + return false; } if (min > uu || uu > max) @@ -1268,14 +1205,13 @@ public bool TryParseKey(ref TX src, U8 min, U8 max, out U8 dst) return true; } - private bool TryParseCore(string text, int ich, int lim, out ulong dst) + private bool TryParseCore(ReadOnlySpan span, out ulong dst) { - Contracts.Assert(0 <= ich && ich <= lim && lim <= Utils.Size(text)); - ulong res = 0; - while (ich < lim) + int ich = 0; + while (ich < span.Length) { - uint d = (uint)text[ich++] - (uint)'0'; + uint d = (uint)span[ich++] - (uint)'0'; if (d >= 10) goto LFail; @@ -1301,71 +1237,70 @@ private bool TryParseCore(string text, int ich, int lim, out ulong dst) /// /// This produces zero for empty. It returns false if the text is not parsable or overflows. - /// On failure, it sets dst to the NA value. + /// On failure, it sets dst to the default value. /// public bool TryParse(ref TX src, out I1 dst) { - long res; - bool f = TryParseSigned(RawI1.MaxValue, ref src, out res); - Contracts.Assert(f || res == I1.RawNA); - Contracts.Assert((RawI1)res == res); - dst = (RawI1)res; - return f; + dst = default; + TryParseSigned(I1.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to sbyte."); + Contracts.Check((I1)res == res, "Overflow or underflow occured while converting value in text to sbyte."); + dst = (I1)res; + return true; } /// /// This produces zero for empty. It returns false if the text is not parsable or overflows. - /// On failure, it sets dst to the NA value. + /// On failure, it sets dst to the default value. /// public bool TryParse(ref TX src, out I2 dst) { - long res; - bool f = TryParseSigned(RawI2.MaxValue, ref src, out res); - Contracts.Assert(f || res == I2.RawNA); - Contracts.Assert((RawI2)res == res); - dst = (RawI2)res; - return f; + dst = default; + TryParseSigned(I2.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to short."); + Contracts.Check((I2)res == res, "Overflow or underflow occured while converting value in text to short."); + dst = (I2)res; + return true; } /// /// This produces zero for empty. It returns false if the text is not parsable or overflows. - /// On failure, it sets dst to the NA value. + /// On failure, it sets dst to the defualt value. /// public bool TryParse(ref TX src, out I4 dst) { - long res; - bool f = TryParseSigned(RawI4.MaxValue, ref src, out res); - Contracts.Assert(f || res == I4.RawNA); - Contracts.Assert((RawI4)res == res); - dst = (RawI4)res; - return f; + dst = default; + TryParseSigned(I4.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to int32."); + Contracts.Check((I4)res == res, "Overflow or underflow occured while converting value in text to int."); + dst = (I4)res; + return true; } /// /// This produces zero for empty. It returns false if the text is not parsable or overflows. - /// On failure, it sets dst to the NA value. + /// On failure, it sets dst to the default value. /// public bool TryParse(ref TX src, out I8 dst) { - long res; - bool f = TryParseSigned(RawI8.MaxValue, ref src, out res); - Contracts.Assert(f || res == I8.RawNA); - dst = res; - return f; + dst = default; + TryParseSigned(I8.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to long."); + dst = (I8)res; + return true; } /// /// Returns false if the text is not parsable as an non-negative long or overflows. /// - private bool TryParseNonNegative(string text, int ich, int lim, out long result) + private bool TryParseNonNegative(ReadOnlySpan span, out long result) { - Contracts.Assert(0 <= ich && ich <= lim && lim <= Utils.Size(text)); - long res = 0; - while (ich < lim) + int ich = 0; + while (ich < span.Length) { Contracts.Assert(res >= 0); - uint d = (uint)text[ich++] - (uint)'0'; + uint d = (uint)span[ich++] - (uint)'0'; if (d >= 10) goto LFail; @@ -1389,61 +1324,53 @@ private bool TryParseNonNegative(string text, int ich, int lim, out long result) /// /// This produces zero for empty. It returns false if the text is not parsable as a signed integer - /// or the result overflows. The min legal value is -max. The NA value is -max - 1. + /// or the result overflows. The min legal value is -max. The NA value null. /// When it returns false, result is set to the NA value. The result can be NA on true return, /// since some representations of NA are not considered parse failure. /// - private bool TryParseSigned(long max, ref TX span, out long result) + private void TryParseSigned(long max, ref TX text, out long? result) { Contracts.Assert(max > 0); Contracts.Assert((max & (max + 1)) == 0); - if (!span.HasChars) + if (text.IsEmpty) { - if (span.IsNA) - result = -max - 1; - else - result = 0; - return true; + result = default(long); + return; } - int ichMin; - int ichLim; - string text = span.GetRawUnderlyingBufferInfo(out ichMin, out ichLim); - - long val; + ulong val; + var span = text.Span; if (span[0] == '-') { - if (span.Length == 1 || - !TryParseNonNegative(text, ichMin + 1, ichLim, out val) || - val > max) + if (span.Length == 1 || !TryParseCore(span.Slice(1), out val) || (val > ((ulong)max + 1))) { - result = -max - 1; - return false; + result = null; + return; } Contracts.Assert(val >= 0); result = -(long)val; - Contracts.Assert(long.MinValue < result && result <= 0); - return true; + Contracts.Assert(long.MinValue <= result && result <= 0); + return; } - if (!TryParseNonNegative(text, ichMin, ichLim, out val)) + long sVal; + if (!TryParseNonNegative(span, out sVal)) { - // Check for acceptable NA forms: ? NaN NA and N/A. - result = -max - 1; - return IsStdMissing(ref span); + result = null; + return; } - Contracts.Assert(val >= 0); - if (val > max) + Contracts.Assert(sVal >= 0); + if (sVal > max) { - result = -max - 1; - return false; + result = null; + return; } - result = (long)val; + result = (long)sVal; Contracts.Assert(0 <= result && result <= long.MaxValue); - return true; + return; } /// @@ -1452,10 +1379,11 @@ private bool TryParseSigned(long max, ref TX span, out long result) /// public bool TryParse(ref TX src, out R4 dst) { - if (src.TryParse(out dst)) + var span = src.Span; + if (DoubleParser.TryParse(span, out dst)) return true; dst = R4.NaN; - return IsStdMissing(ref src); + return IsStdMissing(ref span); } /// @@ -1464,108 +1392,90 @@ public bool TryParse(ref TX src, out R4 dst) /// public bool TryParse(ref TX src, out R8 dst) { - if (src.TryParse(out dst)) + var span = src.Span; + if (DoubleParser.TryParse(span, out dst)) return true; dst = R8.NaN; - return IsStdMissing(ref src); + return IsStdMissing(ref span); } public bool TryParse(ref TX src, out TS dst) { - if (!src.HasChars) + if (src.IsEmpty) { - if (src.IsNA) - dst = TS.NA; - else - dst = default(TS); + dst = default; return true; } - TimeSpan res; - if (TimeSpan.TryParse(src.ToString(), CultureInfo.InvariantCulture, out res)) - { - dst = new TS(res); + + if (TimeSpan.TryParse(src.ToString(), CultureInfo.InvariantCulture, out dst)) return true; - } - dst = TS.NA; - return IsStdMissing(ref src); + var span = src.Span; + Contracts.Check(!IsStdMissing(ref span), "Missing values cannot be converted to boolean value."); + return true; } public bool TryParse(ref TX src, out DT dst) { - if (!src.HasChars) + if (src.IsEmpty) { - if (src.IsNA) - dst = DvDateTime.NA; - else - dst = default(DvDateTime); + dst = default; return true; } - DateTime res; - if (DateTime.TryParse(src.ToString(), CultureInfo.InvariantCulture, DateTimeStyles.AssumeUniversal | DateTimeStyles.AdjustToUniversal, out res)) - { - dst = new DT(res); + + if (DateTime.TryParse(src.ToString(), CultureInfo.InvariantCulture, DateTimeStyles.AssumeUniversal | DateTimeStyles.AdjustToUniversal, out dst)) return true; - } - dst = DvDateTime.NA; - return IsStdMissing(ref src); + + var span = src.Span; + Contracts.Check(!IsStdMissing(ref span), "Missing values cannot be converted to boolean value."); + return true; } public bool TryParse(ref TX src, out DZ dst) { - if (!src.HasChars) + if (src.IsEmpty) { - if (src.IsNA) - dst = DvDateTimeZone.NA; - else - dst = default(DvDateTimeZone); + dst = default; return true; } - DateTimeOffset res; - if (DateTimeOffset.TryParse(src.ToString(), CultureInfo.InvariantCulture, DateTimeStyles.AssumeUniversal, out res)) - { - dst = new DZ(res); + + if (DateTimeOffset.TryParse(src.ToString(), CultureInfo.InvariantCulture, DateTimeStyles.AssumeUniversal, out dst)) return true; - } - dst = DvDateTimeZone.NA; - return IsStdMissing(ref src); + + var span = src.Span; + Contracts.Check(!IsStdMissing(ref span), "Missing values cannot be converted to boolean value."); + return true; } - // These map unparsable and overflow values to "NA", which is the value Ix.MinValue. Note that this NA - // value is the "evil" value - the non-zero value, x, such that x == -x. Note also, that for I4, this - // matches R's representation of NA. + // These throw an exception for unparsable and overflow values. private I1 ParseI1(ref TX src) { - long res; - bool f = TryParseSigned(RawI1.MaxValue, ref src, out res); - Contracts.Assert(f || res == I1.RawNA); - Contracts.Assert((RawI1)res == res); - return (RawI1)res; + TryParseSigned(I1.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to sbyte."); + Contracts.Check((I1)res == res, "Overflow or underflow occured while converting value in text to sbyte."); + return (I1)res; } private I2 ParseI2(ref TX src) { - long res; - bool f = TryParseSigned(RawI2.MaxValue, ref src, out res); - Contracts.Assert(f || res == I2.RawNA); - Contracts.Assert((RawI2)res == res); - return (RawI2)res; + TryParseSigned(I2.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to short."); + Contracts.Check((I2)res == res, "Overflow or underflow occured while converting value in text to short."); + return (I2)res; } private I4 ParseI4(ref TX src) { - long res; - bool f = TryParseSigned(RawI4.MaxValue, ref src, out res); - Contracts.Assert(f || res == I4.RawNA); - Contracts.Assert((RawI4)res == res); - return (RawI4)res; + TryParseSigned(I4.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to int."); + Contracts.Check((I4)res == res, "Overflow or underflow occured while converting value in text to int."); + return (I4)res; } private I8 ParseI8(ref TX src) { - long res; - bool f = TryParseSigned(RawI8.MaxValue, ref src, out res); - Contracts.Assert(f || res == I8.RawNA); - return res; + TryParseSigned(I8.MaxValue, ref src, out long? res); + Contracts.Check(res.HasValue, "Value could not be parsed from text to long."); + return res.Value; } // These map unparsable and overflow values to zero. The unsigned integer types do not have an NA value. @@ -1618,116 +1528,113 @@ private U8 ParseU8(ref TX span) ///
public bool TryParse(ref TX src, out BL dst) { - // NA text fails. - if (src.IsNA) - { - dst = BL.NA; - return true; - } + var span = src.Span; + + Contracts.Check(!IsStdMissing(ref span), "Missing text values cannot be converted to bool value."); char ch; switch (src.Length) { - case 0: - // Empty succeeds and maps to false. - dst = BL.False; - return true; - - case 1: - switch (src[0]) - { - case 'T': - case 't': - case 'Y': - case 'y': - case '1': - case '+': - dst = BL.True; - return true; - case 'F': - case 'f': - case 'N': - case 'n': - case '0': - case '-': - dst = BL.False; + case 0: + // Empty succeeds and maps to false. + dst = false; return true; - } - break; - case 2: - switch (src[0]) - { - case 'N': - case 'n': - if ((ch = src[1]) != 'O' && ch != 'o') - break; - dst = BL.False; - return true; - case '+': - if ((ch = src[1]) != '1') - break; - dst = BL.True; - return true; - case '-': - if ((ch = src[1]) != '1') - break; - dst = BL.False; - return true; - } - break; + case 1: + switch (span[0]) + { + case 'T': + case 't': + case 'Y': + case 'y': + case '1': + case '+': + dst = true; + return true; + case 'F': + case 'f': + case 'N': + case 'n': + case '0': + case '-': + dst = false; + return true; + } + break; - case 3: - switch (src[0]) - { - case 'Y': - case 'y': - if ((ch = src[1]) != 'E' && ch != 'e') - break; - if ((ch = src[2]) != 'S' && ch != 's') - break; - dst = BL.True; - return true; - } - break; + case 2: + switch (span[0]) + { + case 'N': + case 'n': + if ((ch = span[1]) != 'O' && ch != 'o') + break; + dst = false; + return true; + case '+': + if ((ch = span[1]) != '1') + break; + dst = true; + return true; + case '-': + if ((ch = span[1]) != '1') + break; + dst = false; + return true; + } + break; - case 4: - switch (src[0]) - { - case 'T': - case 't': - if ((ch = src[1]) != 'R' && ch != 'r') - break; - if ((ch = src[2]) != 'U' && ch != 'u') - break; - if ((ch = src[3]) != 'E' && ch != 'e') - break; - dst = BL.True; - return true; - } - break; + case 3: + switch (span[0]) + { + case 'Y': + case 'y': + if ((ch = span[1]) != 'E' && ch != 'e') + break; + if ((ch = span[2]) != 'S' && ch != 's') + break; + dst = true; + return true; + } + break; - case 5: - switch (src[0]) - { - case 'F': - case 'f': - if ((ch = src[1]) != 'A' && ch != 'a') - break; - if ((ch = src[2]) != 'L' && ch != 'l') - break; - if ((ch = src[3]) != 'S' && ch != 's') - break; - if ((ch = src[4]) != 'E' && ch != 'e') - break; - dst = BL.False; - return true; - } - break; + case 4: + switch (span[0]) + { + case 'T': + case 't': + if ((ch = span[1]) != 'R' && ch != 'r') + break; + if ((ch = span[2]) != 'U' && ch != 'u') + break; + if ((ch = span[3]) != 'E' && ch != 'e') + break; + dst = true; + return true; + } + break; + + case 5: + switch (span[0]) + { + case 'F': + case 'f': + if ((ch = span[1]) != 'A' && ch != 'a') + break; + if ((ch = span[2]) != 'L' && ch != 'l') + break; + if ((ch = span[3]) != 'S' && ch != 's') + break; + if ((ch = span[4]) != 'E' && ch != 'e') + break; + dst = false; + return true; + } + break; } - dst = BL.NA; - return IsStdMissing(ref src); + dst = false; + return false; } private bool TryParse(ref TX src, out TX dst) @@ -1773,16 +1680,18 @@ public void Convert(ref TX span, ref UG value) if (!TryParse(ref span, out value)) Contracts.Assert(value.Equals(default(UG))); } - public void Convert(ref TX span, ref R4 value) + public void Convert(ref TX src, ref R4 value) { - if (span.TryParse(out value)) + var span = src.Span; + if (DoubleParser.TryParse(span, out value)) return; // Unparsable is mapped to NA. value = R4.NaN; } - public void Convert(ref TX span, ref R8 value) + public void Convert(ref TX src, ref R8 value) { - if (span.TryParse(out value)) + var span = src.Span; + if (DoubleParser.TryParse(span, out value)) return; // Unparsable is mapped to NA. value = R8.NaN; @@ -1791,43 +1700,32 @@ public void Convert(ref TX span, ref TX value) { value = span; } - public void Convert(ref TX span, ref BL value) + public void Convert(ref TX src, ref BL value) { - // When TryParseBL returns false, it should have set value to NA. - if (!TryParse(ref span, out value)) - Contracts.Assert(value.IsNA); + // When TryParseBL returns false, it should have set value to false. + if (!TryParse(ref src, out value)) + Contracts.Assert(!value); } public void Convert(ref TX src, ref SB dst) { ClearDst(ref dst); - if (src.HasChars) - src.AddToStringBuilder(dst); + if (!src.IsEmpty) + dst.AppendMemory(src); } - public void Convert(ref TX span, ref TS value) - { - if (!TryParse(ref span, out value)) - Contracts.Assert(value.IsNA); - } - public void Convert(ref TX span, ref DT value) - { - if (!TryParse(ref span, out value)) - Contracts.Assert(value.IsNA); - } - public void Convert(ref TX span, ref DZ value) - { - if (!TryParse(ref span, out value)) - Contracts.Assert(value.IsNA); - } + public void Convert(ref TX span, ref TS value) => TryParse(ref span, out value); + public void Convert(ref TX span, ref DT value) => TryParse(ref span, out value); + public void Convert(ref TX span, ref DZ value) => TryParse(ref span, out value); + #endregion FromTX #region FromBL - public void Convert(ref BL src, ref I1 dst) => dst = (I1)src; - public void Convert(ref BL src, ref I2 dst) => dst = (I2)src; - public void Convert(ref BL src, ref I4 dst) => dst = (I4)src; - public void Convert(ref BL src, ref I8 dst) => dst = (I8)src; - public void Convert(ref BL src, ref R4 dst) => dst = (R4)src; - public void Convert(ref BL src, ref R8 dst) => dst = (R8)src; + public void Convert(ref BL src, ref I1 dst) => dst = (I1)(object)src; + public void Convert(ref BL src, ref I2 dst) => dst = (I2)(object)src; + public void Convert(ref BL src, ref I4 dst) => dst = (I4)(object)src; + public void Convert(ref BL src, ref I8 dst) => dst = (I8)(object)src; + public void Convert(ref BL src, ref R4 dst) => dst = System.Convert.ToSingle(src); + public void Convert(ref BL src, ref R8 dst) => dst = System.Convert.ToDouble(src); public void Convert(ref BL src, ref BL dst) => dst = src; #endregion FromBL } diff --git a/src/Microsoft.ML.Data/Data/DataViewUtils.cs b/src/Microsoft.ML.Data/Data/DataViewUtils.cs index 1db4d5ad0a..17307186fd 100644 --- a/src/Microsoft.ML.Data/Data/DataViewUtils.cs +++ b/src/Microsoft.ML.Data/Data/DataViewUtils.cs @@ -1312,14 +1312,14 @@ public ValueGetter GetGetter(int col) } } - public static ValueGetter[] PopulateGetterArray(IRowCursor cursor, List colIndices) + public static ValueGetter>[] PopulateGetterArray(IRowCursor cursor, List colIndices) { var n = colIndices.Count; - var getters = new ValueGetter[n]; + var getters = new ValueGetter>[n]; for (int i = 0; i < n; i++) { - ValueGetter getter; + ValueGetter> getter; var srcColIndex = colIndices[i]; var colType = cursor.Schema.GetColumnType(srcColIndex); @@ -1340,7 +1340,7 @@ public static ValueGetter[] PopulateGetterArray(IRowCursor cursor, List< return getters; } - public static ValueGetter GetSingleValueGetter(IRow cursor, int i, ColumnType colType) + public static ValueGetter> GetSingleValueGetter(IRow cursor, int i, ColumnType colType) { var floatGetter = cursor.GetGetter(i); T v = default(T); @@ -1359,18 +1359,18 @@ public static ValueGetter GetSingleValueGetter(IRow cursor, int i, Co } StringBuilder dst = null; - ValueGetter getter = - (ref DvText value) => + ValueGetter> getter = + (ref ReadOnlyMemory value) => { floatGetter(ref v); conversion(ref v, ref dst); string text = dst.ToString(); - value = new DvText(text); + value = text.AsMemory(); }; return getter; } - public static ValueGetter GetVectorFlatteningGetter(IRow cursor, int colIndex, ColumnType colType) + public static ValueGetter> GetVectorFlatteningGetter(IRow cursor, int colIndex, ColumnType colType) { var vecGetter = cursor.GetGetter>(colIndex); var vbuf = default(VBuffer); @@ -1378,8 +1378,8 @@ public static ValueGetter GetVectorFlatteningGetter(IRow cursor, int ValueMapper conversion; Conversions.Instance.TryGetStringConversion(colType, out conversion); StringBuilder dst = null; - ValueGetter getter = - (ref DvText value) => + ValueGetter> getter = + (ref ReadOnlyMemory value) => { vecGetter(ref vbuf); @@ -1393,7 +1393,7 @@ public static ValueGetter GetVectorFlatteningGetter(IRow cursor, int conversion(ref v, ref dst); return dst.ToString(); })); - value = new DvText(string.Format("<{0}{1}>", stringRep, suffix)); + value = string.Format("<{0}{1}>", stringRep, suffix).AsMemory(); }; return getter; } diff --git a/src/Microsoft.ML.Data/Data/RowCursorUtils.cs b/src/Microsoft.ML.Data/Data/RowCursorUtils.cs index 091fe26cb2..9f09e0da51 100644 --- a/src/Microsoft.ML.Data/Data/RowCursorUtils.cs +++ b/src/Microsoft.ML.Data/Data/RowCursorUtils.cs @@ -394,16 +394,16 @@ private static ValueGetter GetLabelGetterNotFloat(IRow cursor, int label Contracts.Assert(type != NumberType.R4 && type != NumberType.R8); - // DvBool type label mapping: True -> 1, False -> 0, NA -> NaN. + // boolean type label mapping: True -> 1, False -> 0. if (type.IsBool) { - var getBoolSrc = cursor.GetGetter(labelIndex); + var getBoolSrc = cursor.GetGetter(labelIndex); return (ref Single dst) => { - DvBool src = DvBool.NA; + bool src = default; getBoolSrc(ref src); - dst = (Single)src; + dst = Convert.ToSingle(src); }; } diff --git a/src/Microsoft.ML.Data/DataLoadSave/Binary/BinaryLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/Binary/BinaryLoader.cs index 7bc0a8d2ad..9991956fcb 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Binary/BinaryLoader.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Binary/BinaryLoader.cs @@ -729,7 +729,13 @@ public void GetMetadata(string kind, int col, ref TValue value) /// /// Upper inclusive bound of versions this reader can read. /// - private const ulong ReaderVersion = MissingTextVersion; + private const ulong ReaderVersion = StandardDataTypesVersion; + + /// + /// The first version that removes DvTypes and uses .NET standard + /// data types. + /// + private const ulong StandardDataTypesVersion = 0x0001000100010006; /// /// The first version of the format that accomodated DvText.NA. @@ -772,7 +778,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Number of blocks to put in the shuffle pool verReadableCur: 0x00010003, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(BinaryLoader).Assembly.FullName); } private BinaryLoader(Arguments args, IHost host, Stream stream, bool leaveOpen) diff --git a/src/Microsoft.ML.Data/DataLoadSave/Binary/CodecFactory.cs b/src/Microsoft.ML.Data/DataLoadSave/Binary/CodecFactory.cs index d04adaf099..544a8c60f5 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Binary/CodecFactory.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Binary/CodecFactory.cs @@ -44,26 +44,28 @@ public CodecFactory(IHostEnvironment env, MemoryStreamPool memPool = null) _loadNameToCodecCreator = new Dictionary(); _simpleCodecTypeMap = new Dictionary(); // Register the current codecs. - RegisterSimpleCodec(new UnsafeTypeCodec(this)); + RegisterSimpleCodec(new UnsafeTypeCodec(this)); RegisterSimpleCodec(new UnsafeTypeCodec(this)); - RegisterSimpleCodec(new UnsafeTypeCodec(this)); + RegisterSimpleCodec(new UnsafeTypeCodec(this)); RegisterSimpleCodec(new UnsafeTypeCodec(this)); - RegisterSimpleCodec(new UnsafeTypeCodec(this)); + RegisterSimpleCodec(new UnsafeTypeCodec(this)); RegisterSimpleCodec(new UnsafeTypeCodec(this)); - RegisterSimpleCodec(new UnsafeTypeCodec(this)); + RegisterSimpleCodec(new UnsafeTypeCodec(this)); RegisterSimpleCodec(new UnsafeTypeCodec(this)); - RegisterSimpleCodec(new UnsafeTypeCodec(this)); - RegisterSimpleCodec(new UnsafeTypeCodec(this)); - RegisterSimpleCodec(new UnsafeTypeCodec(this)); - RegisterSimpleCodec(new DvTextCodec(this)); + RegisterSimpleCodec(new UnsafeTypeCodec(this)); + RegisterSimpleCodec(new UnsafeTypeCodec(this)); + RegisterSimpleCodec(new UnsafeTypeCodec(this)); + RegisterSimpleCodec(new TextCodec(this)); RegisterSimpleCodec(new BoolCodec(this)); RegisterSimpleCodec(new DateTimeCodec(this)); - RegisterSimpleCodec(new DateTimeZoneCodec(this)); + RegisterSimpleCodec(new DateTimeOffsetCodec(this)); RegisterSimpleCodec(new UnsafeTypeCodec(this)); - // Register the old boolean reading codec. - var oldBool = new OldBoolCodec(this); - RegisterOtherCodec(oldBool.LoadName, oldBool.GetCodec); + // Register the old type system reading codec. + RegisterOtherCodec("DvBool", new OldBoolCodec(this).GetCodec); + RegisterOtherCodec("DvDateTimeZone", new DateTimeOffsetCodec(this).GetCodec); + RegisterOtherCodec("DvDateTime", new DateTimeCodec(this).GetCodec); + RegisterOtherCodec("DvTimeSpan", new UnsafeTypeCodec(this).GetCodec); RegisterOtherCodec("VBuffer", GetVBufferCodec); RegisterOtherCodec("Key", GetKeyCodec); diff --git a/src/Microsoft.ML.Data/DataLoadSave/Binary/Codecs.cs b/src/Microsoft.ML.Data/DataLoadSave/Binary/Codecs.cs index f840773872..3e4f997431 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Binary/Codecs.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Binary/Codecs.cs @@ -179,10 +179,10 @@ public override string LoadName } // Gatekeeper to ensure T is a type that is supported by UnsafeTypeCodec. - // Throws an exception if T is neither a DvTimeSpan nor a NumberType. + // Throws an exception if T is neither a TimeSpan nor a NumberType. private static ColumnType UnsafeColumnType(Type type) { - return type == typeof(DvTimeSpan) ? (ColumnType)TimeSpanType.Instance : NumberType.FromType(type); + return type == typeof(TimeSpan) ? (ColumnType)TimeSpanType.Instance : NumberType.FromType(type); } public UnsafeTypeCodec(CodecFactory factory) @@ -305,9 +305,8 @@ public override void Read(T[] values, int index, int count) } } - private sealed class DvTextCodec : SimpleCodec + private sealed class TextCodec : SimpleCodec> { - private const int MissingBit = unchecked((int)0x80000000); private const int LengthMask = unchecked((int)0x7FFFFFFF); public override string LoadName @@ -320,43 +319,38 @@ public override string LoadName // int[entries]: The non-decreasing end-boundary character index array, with high bit set for "missing" values. // string: The UTF-8 encoded string, with the standard LEB128 byte-length preceeding it. - public DvTextCodec(CodecFactory factory) + public TextCodec(CodecFactory factory) : base(factory, TextType.Instance) { } - public override IValueWriter OpenWriter(Stream stream) + public override IValueWriter> OpenWriter(Stream stream) { return new Writer(this, stream); } - public override IValueReader OpenReader(Stream stream, int items) + public override IValueReader> OpenReader(Stream stream, int items) { return new Reader(this, stream, items); } - private sealed class Writer : ValueWriterBase + private sealed class Writer : ValueWriterBase> { private StringBuilder _builder; private List _boundaries; - public Writer(DvTextCodec codec, Stream stream) + public Writer(TextCodec codec, Stream stream) : base(codec.Factory, stream) { _builder = new StringBuilder(); _boundaries = new List(); } - public override void Write(ref DvText value) + public override void Write(ref ReadOnlyMemory value) { Contracts.Check(_builder != null, "writer was already committed"); - if (value.IsNA) - _boundaries.Add(_builder.Length | MissingBit); - else - { - value.AddToStringBuilder(_builder); - _boundaries.Add(_builder.Length); - } + _builder.AppendMemory(value); + _boundaries.Add(_builder.Length); } public override void Commit() @@ -378,14 +372,14 @@ public override long GetCommitLengthEstimate() } } - private sealed class Reader : ValueReaderBase + private sealed class Reader : ValueReaderBase> { private readonly int _entries; private readonly int[] _boundaries; private int _index; private string _text; - public Reader(DvTextCodec codec, Stream stream, int items) + public Reader(TextCodec codec, Stream stream, int items) : base(codec.Factory, stream) { _entries = Reader.ReadInt32(); @@ -408,29 +402,34 @@ public override void MoveNext() Contracts.Check(++_index < _entries, "reader already read all values"); } - public override void Get(ref DvText value) + public override void Get(ref ReadOnlyMemory value) { Contracts.Assert(_index < _entries); int b = _boundaries[_index + 1]; - if (b < 0) - value = DvText.NA; + int start = _boundaries[_index] & LengthMask; + if (b >= 0) + value = _text.AsMemory().Slice(start, (b & LengthMask) - start); else - value = new DvText(_text, _boundaries[_index] & LengthMask, b & LengthMask); + { + //For backward compatiblity when NA values existed, treat them + //as empty string. + value = ReadOnlyMemory.Empty; + } } } } /// - /// This is an older boolean code that reads from a form that serialized - /// 1 bit per value. The new encoding (implemented by a different codec) + /// This is a boolean code that reads from a form that serialized + /// 1 bit per value. The old encoding (implemented by a different codec) /// uses 2 bits per value so NA values can be supported. /// - private sealed class OldBoolCodec : SimpleCodec + private sealed class BoolCodec : SimpleCodec { // *** Binary block format *** // Packed bits. - public OldBoolCodec(CodecFactory factory) + public BoolCodec(CodecFactory factory) : base(factory, BoolType.Instance) { } @@ -440,24 +439,70 @@ public override string LoadName get { return typeof(bool).Name; } } - public override IValueWriter OpenWriter(Stream stream) + public override IValueWriter OpenWriter(Stream stream) { - Contracts.Assert(false, "This older form only supports reading"); - throw Contracts.ExceptNotSupp("Writing single bit booleans no longer supported"); + return new Writer(this, stream); + } + + private sealed class Writer : ValueWriterBase + { + // Pack 8 values into 8 bits. + private byte _currentBits; + private long _numWritten; + private byte _currentIndex; + + public Writer(BoolCodec codec, Stream stream) + : base(codec.Factory, stream) + { + } + + public override void Write(ref bool value) + { + Contracts.Assert(0 <= _currentIndex && _currentIndex < 8); + + _numWritten++; + if (value) + _currentBits |= (byte)(1 << _currentIndex); + + _currentIndex++; + if (_currentIndex == 8) + { + Writer.Write(_currentBits); + _currentBits = 0; + _currentIndex = 0; + } + } + + // REVIEW: More efficient array writers are certainly possible. + + public override long GetCommitLengthEstimate() + { + return 4 * (((_numWritten - 1) >> 4) + 1); + } + + public override void Commit() + { + if (_currentIndex > 0) + { + Writer.Write(_currentBits); + _currentBits = 0; + _currentIndex = 0; + } + } } - public override IValueReader OpenReader(Stream stream, int items) + public override IValueReader OpenReader(Stream stream, int items) { return new Reader(this, stream, items); } - private sealed class Reader : ValueReaderBase + private sealed class Reader : ValueReaderBase { private byte _currentBits; private int _currentIndex; private int _remaining; - public Reader(OldBoolCodec codec, Stream stream, int items) + public Reader(BoolCodec codec, Stream stream, int items) : base(codec.Factory, stream) { _remaining = items; @@ -474,7 +519,7 @@ public override void MoveNext() _currentBits >>= 1; } - public override void Get(ref DvBool value) + public override void Get(ref bool value) { Contracts.Assert(0 <= _currentIndex, "have not moved in"); Contracts.Assert(_currentIndex < 8); @@ -483,83 +528,34 @@ public override void Get(ref DvBool value) } } - private sealed class BoolCodec : SimpleCodec + private sealed class OldBoolCodec : SimpleCodec { // *** Binary block format *** // Pack 16 values into 32 bits, with 00 for false, 01 for true and 10 for NA. - public BoolCodec(CodecFactory factory) + public OldBoolCodec(CodecFactory factory) : base(factory, BoolType.Instance) { } - public override IValueWriter OpenWriter(Stream stream) + public override IValueWriter OpenWriter(Stream stream) { - return new Writer(this, stream); + Contracts.Assert(false, "This older form only supports reading"); + throw Contracts.ExceptNotSupp("Writing single bit booleans no longer supported"); } - public override IValueReader OpenReader(Stream stream, int items) + public override IValueReader OpenReader(Stream stream, int items) { return new Reader(this, stream, items); } - private sealed class Writer : ValueWriterBase - { - // Pack 16 values into 32 bits. - private int _currentBits; - private long _numWritten; - private int _currentIndex; - - public Writer(BoolCodec codec, Stream stream) - : base(codec.Factory, stream) - { - } - - public override void Write(ref DvBool value) - { - Contracts.Assert(0 <= _currentIndex && _currentIndex < 32); - Contracts.Assert((_currentIndex & 1) == 0); - - _numWritten++; - if (value.IsTrue) - _currentBits |= 1 << _currentIndex; - else if (!value.IsFalse) - _currentBits |= 2 << _currentIndex; - - _currentIndex += 2; - if (_currentIndex == 32) - { - Writer.Write(_currentBits); - _currentBits = 0; - _currentIndex = 0; - } - } - - // REVIEW: More efficient array writers are certainly possible. - - public override long GetCommitLengthEstimate() - { - return 4 * (((_numWritten - 1) >> 4) + 1); - } - - public override void Commit() - { - if (_currentIndex > 0) - { - Writer.Write(_currentBits); - _currentBits = 0; - _currentIndex = 0; - } - } - } - - private sealed class Reader : ValueReaderBase + private sealed class Reader : ValueReaderBase { private int _currentBits; private int _currentSlot; private int _remaining; - public Reader(BoolCodec codec, Stream stream, int items) + public Reader(OldBoolCodec codec, Stream stream, int items) : base(codec.Factory, stream) { _remaining = items; @@ -576,20 +572,20 @@ public override void MoveNext() _currentBits = (int)((uint)_currentBits >> 2); } - public override void Get(ref DvBool value) + public override void Get(ref bool value) { Contracts.Assert(0 <= _currentSlot, "have not moved in"); Contracts.Assert(_currentSlot < 16); switch (_currentBits & 0x3) { case 0x0: - value = DvBool.False; + value = false; break; case 0x1: - value = DvBool.True; + value = true; break; case 0x2: - value = DvBool.NA; + value = false; break; default: throw Contracts.ExceptDecode("Invalid bit pattern in BoolCodec"); @@ -598,24 +594,24 @@ public override void Get(ref DvBool value) } } - private sealed class DateTimeCodec : SimpleCodec + private sealed class DateTimeCodec : SimpleCodec { public DateTimeCodec(CodecFactory factory) : base(factory, DateTimeType.Instance) { } - public override IValueWriter OpenWriter(Stream stream) + public override IValueWriter OpenWriter(Stream stream) { return new Writer(this, stream); } - public override IValueReader OpenReader(Stream stream, int items) + public override IValueReader OpenReader(Stream stream, int items) { return new Reader(this, stream, items); } - private sealed class Writer : ValueWriterBase + private sealed class Writer : ValueWriterBase { private long _numWritten; @@ -624,11 +620,9 @@ public Writer(DateTimeCodec codec, Stream stream) { } - public override void Write(ref DvDateTime value) + public override void Write(ref DateTime value) { - var ticks = value.Ticks.RawValue; - Contracts.Assert(ticks == DvInt8.RawNA || (ulong)ticks <= DvDateTime.MaxTicks); - Writer.Write(ticks); + Writer.Write(value.Ticks); _numWritten++; } @@ -639,14 +633,14 @@ public override void Commit() public override long GetCommitLengthEstimate() { - return _numWritten * sizeof(Int64); + return _numWritten * sizeof(long); } } - private sealed class Reader : ValueReaderBase + private sealed class Reader : ValueReaderBase { private int _remaining; - private DvDateTime _value; + private DateTime _value; public Reader(DateTimeCodec codec, Stream stream, int items) : base(codec.Factory, stream) @@ -657,74 +651,64 @@ public Reader(DateTimeCodec codec, Stream stream, int items) public override void MoveNext() { Contracts.Assert(_remaining > 0, "already consumed all values"); - var value = Reader.ReadInt64(); - Contracts.CheckDecode(value == DvInt8.RawNA || (ulong)value <= DvDateTime.MaxTicks); - _value = new DvDateTime(value); + + var ticks = Reader.ReadInt64(); + _value = new DateTime(ticks == long.MinValue ? default : ticks); _remaining--; } - public override void Get(ref DvDateTime value) + public override void Get(ref DateTime value) { value = _value; } } } - private sealed class DateTimeZoneCodec : SimpleCodec + private sealed class DateTimeOffsetCodec : SimpleCodec { - private readonly MadeObjectPool _shortBufferPool; private readonly MadeObjectPool _longBufferPool; + private readonly MadeObjectPool _shortBufferPool; - public DateTimeZoneCodec(CodecFactory factory) - : base(factory, DateTimeZoneType.Instance) + public DateTimeOffsetCodec(CodecFactory factory) + : base(factory, DateTimeOffsetType.Instance) { - _shortBufferPool = new MadeObjectPool(() => null); _longBufferPool = new MadeObjectPool(() => null); + _shortBufferPool = new MadeObjectPool(() => null); } - public override IValueWriter OpenWriter(Stream stream) + public override IValueWriter OpenWriter(Stream stream) { return new Writer(this, stream); } - public override IValueReader OpenReader(Stream stream, int items) + public override IValueReader OpenReader(Stream stream, int items) { return new Reader(this, stream, items); } - private sealed class Writer : ValueWriterBase + private sealed class Writer : ValueWriterBase { private List _offsets; private List _ticks; - public Writer(DateTimeZoneCodec codec, Stream stream) + public Writer(DateTimeOffsetCodec codec, Stream stream) : base(codec.Factory, stream) { _offsets = new List(); _ticks = new List(); } - public override void Write(ref DvDateTimeZone value) + public override void Write(ref DateTimeOffset value) { Contracts.Assert(_offsets != null, "writer was already committed"); - var ticks = value.ClockDateTime.Ticks; - var offset = value.OffsetMinutes; + _ticks.Add(value.DateTime.Ticks); - _ticks.Add(ticks.RawValue); - if (ticks.IsNA) - { - Contracts.Assert(offset.IsNA); - _offsets.Add(0); - } - else - { - Contracts.Assert( - offset.RawValue >= DvDateTimeZone.MinMinutesOffset && - offset.RawValue <= DvDateTimeZone.MaxMinutesOffset); - Contracts.Assert(0 <= ticks.RawValue && ticks.RawValue <= DvDateTime.MaxTicks); - _offsets.Add(offset.RawValue); - } + //DateTimeOffset exposes its offset as a TimeSpan, but internally it uses short and in minutes. + //https://github.com/dotnet/coreclr/blob/9499b08eefd895158c3f3c7834e185a73619128d/src/System.Private.CoreLib/shared/System/DateTimeOffset.cs#L51-L53 + //https://github.com/dotnet/coreclr/blob/9499b08eefd895158c3f3c7834e185a73619128d/src/System.Private.CoreLib/shared/System/DateTimeOffset.cs#L286-L292 + //From everything online(ISO8601, RFC3339, SQL Server doc, the offset supports the range -14 to 14 hours, and only supports minute precision. + _offsets.Add((short)(value.Offset.TotalMinutes)); } public override void Commit() @@ -740,13 +724,13 @@ public override void Commit() public override long GetCommitLengthEstimate() { - return (long)_offsets.Count * (sizeof(Int64) + sizeof(Int16)); + return (long)_offsets.Count * (sizeof(long) + sizeof(short)); } } - private sealed class Reader : ValueReaderBase + private sealed class Reader : ValueReaderBase { - private readonly DateTimeZoneCodec _codec; + private readonly DateTimeOffsetCodec _codec; private readonly int _entries; private short[] _offsets; @@ -754,7 +738,7 @@ private sealed class Reader : ValueReaderBase private int _index; private bool _disposed; - public Reader(DateTimeZoneCodec codec, Stream stream, int items) + public Reader(DateTimeOffsetCodec codec, Stream stream, int items) : base(codec.Factory, stream) { _codec = codec; @@ -764,17 +748,12 @@ public Reader(DateTimeZoneCodec codec, Stream stream, int items) _offsets = _codec._shortBufferPool.Get(); Utils.EnsureSize(ref _offsets, _entries, false); for (int i = 0; i < _entries; i++) - { _offsets[i] = Reader.ReadInt16(); - Contracts.CheckDecode(DvDateTimeZone.MinMinutesOffset <= _offsets[i] && _offsets[i] <= DvDateTimeZone.MaxMinutesOffset); - } + _ticks = _codec._longBufferPool.Get(); Utils.EnsureSize(ref _ticks, _entries, false); for (int i = 0; i < _entries; i++) - { _ticks[i] = Reader.ReadInt64(); - Contracts.CheckDecode(_ticks[i] == DvInt8.RawNA || (ulong)_ticks[i] <= DvDateTime.MaxTicks); - } } public override void MoveNext() @@ -783,10 +762,12 @@ public override void MoveNext() Contracts.Check(++_index < _entries, "reader already read all values"); } - public override void Get(ref DvDateTimeZone value) + public override void Get(ref DateTimeOffset value) { Contracts.Assert(!_disposed); - value = new DvDateTimeZone(_ticks[_index], _offsets[_index]); + var ticks = _ticks[_index]; + var offset = _offsets[_index]; + value = new DateTimeOffset(new DateTime(ticks == long.MinValue ? default : ticks), new TimeSpan(0, offset == short.MinValue ? default : offset, 0)); } public override void Dispose() diff --git a/src/Microsoft.ML.Data/DataLoadSave/Binary/Header.cs b/src/Microsoft.ML.Data/DataLoadSave/Binary/Header.cs index 36186cf7af..b552ab6523 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Binary/Header.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Binary/Header.cs @@ -34,8 +34,9 @@ public struct Header //public const ulong WriterVersion = 0x0001000100010002; // Codec changes. //public const ulong WriterVersion = 0x0001000100010003; // Slot names. //public const ulong WriterVersion = 0x0001000100010004; // Column metadata. - public const ulong WriterVersion = 0x0001000100010005; // "NA" DvText support. - public const ulong CanBeReadByVersion = 0x0001000100010005; + //public const ulong WriterVersion = 0x0001000100010005; // "NA" DvText support. + public const ulong WriterVersion = 0x0001000100010006; // Replace DvTypes with .NET Standard data types. + public const ulong CanBeReadByVersion = 0x0001000100010006; internal static string VersionToString(ulong v) { diff --git a/src/Microsoft.ML.Data/DataLoadSave/Binary/UnsafeTypeOps.cs b/src/Microsoft.ML.Data/DataLoadSave/Binary/UnsafeTypeOps.cs index 026228d6be..b63f361fe2 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Binary/UnsafeTypeOps.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Binary/UnsafeTypeOps.cs @@ -32,21 +32,17 @@ internal static class UnsafeTypeOpsFactory static UnsafeTypeOpsFactory() { _type2ops = new Dictionary(); - _type2ops[typeof(SByte)] = new SByteUnsafeTypeOps(); - _type2ops[typeof(DvInt1)] = new DvI1UnsafeTypeOps(); + _type2ops[typeof(sbyte)] = new SByteUnsafeTypeOps(); _type2ops[typeof(Byte)] = new ByteUnsafeTypeOps(); - _type2ops[typeof(Int16)] = new Int16UnsafeTypeOps(); - _type2ops[typeof(DvInt2)] = new DvI2UnsafeTypeOps(); + _type2ops[typeof(short)] = new Int16UnsafeTypeOps(); _type2ops[typeof(UInt16)] = new UInt16UnsafeTypeOps(); - _type2ops[typeof(Int32)] = new Int32UnsafeTypeOps(); - _type2ops[typeof(DvInt4)] = new DvI4UnsafeTypeOps(); + _type2ops[typeof(int)] = new Int32UnsafeTypeOps(); _type2ops[typeof(UInt32)] = new UInt32UnsafeTypeOps(); - _type2ops[typeof(Int64)] = new Int64UnsafeTypeOps(); - _type2ops[typeof(DvInt8)] = new DvI8UnsafeTypeOps(); + _type2ops[typeof(long)] = new Int64UnsafeTypeOps(); _type2ops[typeof(UInt64)] = new UInt64UnsafeTypeOps(); _type2ops[typeof(Single)] = new SingleUnsafeTypeOps(); _type2ops[typeof(Double)] = new DoubleUnsafeTypeOps(); - _type2ops[typeof(DvTimeSpan)] = new DvTimeSpanUnsafeTypeOps(); + _type2ops[typeof(TimeSpan)] = new TimeSpanUnsafeTypeOps(); _type2ops[typeof(UInt128)] = new UgUnsafeTypeOps(); } @@ -55,29 +51,16 @@ public static UnsafeTypeOps Get() return (UnsafeTypeOps)_type2ops[typeof(T)]; } - private sealed class SByteUnsafeTypeOps : UnsafeTypeOps + private sealed class SByteUnsafeTypeOps : UnsafeTypeOps { - public override int Size { get { return sizeof(SByte); } } - public override unsafe void Apply(SByte[] array, Action func) + public override int Size { get { return sizeof(sbyte); } } + public override unsafe void Apply(sbyte[] array, Action func) { - fixed (SByte* pArray = array) + fixed (sbyte* pArray = array) func(new IntPtr(pArray)); } - public override void Write(SByte a, BinaryWriter writer) { writer.Write(a); } - public override SByte Read(BinaryReader reader) { return reader.ReadSByte(); } - } - - private sealed class DvI1UnsafeTypeOps : UnsafeTypeOps - { - public override int Size { get { return sizeof(SByte); } } - public override unsafe void Apply(DvInt1[] array, Action func) - { - fixed (DvInt1* pArray = array) - func(new IntPtr(pArray)); - } - - public override void Write(DvInt1 a, BinaryWriter writer) { writer.Write(a.RawValue); } - public override DvInt1 Read(BinaryReader reader) { return reader.ReadSByte(); } + public override void Write(sbyte a, BinaryWriter writer) { writer.Write(a); } + public override sbyte Read(BinaryReader reader) { return reader.ReadSByte(); } } private sealed class ByteUnsafeTypeOps : UnsafeTypeOps @@ -92,29 +75,16 @@ public override unsafe void Apply(Byte[] array, Action func) public override Byte Read(BinaryReader reader) { return reader.ReadByte(); } } - private sealed class Int16UnsafeTypeOps : UnsafeTypeOps + private sealed class Int16UnsafeTypeOps : UnsafeTypeOps { - public override int Size { get { return sizeof(Int16); } } - public override unsafe void Apply(Int16[] array, Action func) + public override int Size { get { return sizeof(short); } } + public override unsafe void Apply(short[] array, Action func) { - fixed (Int16* pArray = array) + fixed (short* pArray = array) func(new IntPtr(pArray)); } - public override void Write(Int16 a, BinaryWriter writer) { writer.Write(a); } - public override Int16 Read(BinaryReader reader) { return reader.ReadInt16(); } - } - - private sealed class DvI2UnsafeTypeOps : UnsafeTypeOps - { - public override int Size { get { return sizeof(Int16); } } - public override unsafe void Apply(DvInt2[] array, Action func) - { - fixed (DvInt2* pArray = array) - func(new IntPtr(pArray)); - } - - public override void Write(DvInt2 a, BinaryWriter writer) { writer.Write(a.RawValue); } - public override DvInt2 Read(BinaryReader reader) { return reader.ReadInt16(); } + public override void Write(short a, BinaryWriter writer) { writer.Write(a); } + public override short Read(BinaryReader reader) { return reader.ReadInt16(); } } private sealed class UInt16UnsafeTypeOps : UnsafeTypeOps @@ -129,29 +99,16 @@ public override unsafe void Apply(UInt16[] array, Action func) public override UInt16 Read(BinaryReader reader) { return reader.ReadUInt16(); } } - private sealed class Int32UnsafeTypeOps : UnsafeTypeOps - { - public override int Size { get { return sizeof(Int32); } } - public override unsafe void Apply(Int32[] array, Action func) - { - fixed (Int32* pArray = array) - func(new IntPtr(pArray)); - } - public override void Write(Int32 a, BinaryWriter writer) { writer.Write(a); } - public override Int32 Read(BinaryReader reader) { return reader.ReadInt32(); } - } - - private sealed class DvI4UnsafeTypeOps : UnsafeTypeOps + private sealed class Int32UnsafeTypeOps : UnsafeTypeOps { - public override int Size { get { return sizeof(Int32); } } - public override unsafe void Apply(DvInt4[] array, Action func) + public override int Size { get { return sizeof(int); } } + public override unsafe void Apply(int[] array, Action func) { - fixed (DvInt4* pArray = array) + fixed (int* pArray = array) func(new IntPtr(pArray)); } - - public override void Write(DvInt4 a, BinaryWriter writer) { writer.Write(a.RawValue); } - public override DvInt4 Read(BinaryReader reader) { return reader.ReadInt32(); } + public override void Write(int a, BinaryWriter writer) { writer.Write(a); } + public override int Read(BinaryReader reader) { return reader.ReadInt32(); } } private sealed class UInt32UnsafeTypeOps : UnsafeTypeOps @@ -166,29 +123,16 @@ public override unsafe void Apply(UInt32[] array, Action func) public override UInt32 Read(BinaryReader reader) { return reader.ReadUInt32(); } } - private sealed class Int64UnsafeTypeOps : UnsafeTypeOps + private sealed class Int64UnsafeTypeOps : UnsafeTypeOps { - public override int Size { get { return sizeof(Int64); } } - public override unsafe void Apply(Int64[] array, Action func) + public override int Size { get { return sizeof(long); } } + public override unsafe void Apply(long[] array, Action func) { - fixed (Int64* pArray = array) + fixed (long* pArray = array) func(new IntPtr(pArray)); } - public override void Write(Int64 a, BinaryWriter writer) { writer.Write(a); } - public override Int64 Read(BinaryReader reader) { return reader.ReadInt64(); } - } - - private sealed class DvI8UnsafeTypeOps : UnsafeTypeOps - { - public override int Size { get { return sizeof(Int64); } } - public override unsafe void Apply(DvInt8[] array, Action func) - { - fixed (DvInt8* pArray = array) - func(new IntPtr(pArray)); - } - - public override void Write(DvInt8 a, BinaryWriter writer) { writer.Write(a.RawValue); } - public override DvInt8 Read(BinaryReader reader) { return reader.ReadInt64(); } + public override void Write(long a, BinaryWriter writer) { writer.Write(a); } + public override long Read(BinaryReader reader) { return reader.ReadInt64(); } } private sealed class UInt64UnsafeTypeOps : UnsafeTypeOps @@ -227,17 +171,21 @@ public override unsafe void Apply(Double[] array, Action func) public override Double Read(BinaryReader reader) { return reader.ReadDouble(); } } - private sealed class DvTimeSpanUnsafeTypeOps : UnsafeTypeOps + private sealed class TimeSpanUnsafeTypeOps : UnsafeTypeOps { - public override int Size { get { return sizeof(Int64); } } - public override unsafe void Apply(DvTimeSpan[] array, Action func) + public override int Size { get { return sizeof(long); } } + public override unsafe void Apply(TimeSpan[] array, Action func) { - fixed (DvTimeSpan* pArray = array) + fixed (TimeSpan* pArray = array) func(new IntPtr(pArray)); } - public override void Write(DvTimeSpan a, BinaryWriter writer) { writer.Write(a.Ticks.RawValue); } - public override DvTimeSpan Read(BinaryReader reader) { return new DvTimeSpan(reader.ReadInt64()); } + public override void Write(TimeSpan a, BinaryWriter writer) { writer.Write(a.Ticks); } + public override TimeSpan Read(BinaryReader reader) + { + var ticks = reader.ReadInt64(); + return new TimeSpan(ticks == long.MinValue ? default : ticks); + } } private sealed class UgUnsafeTypeOps : UnsafeTypeOps diff --git a/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs index cb18bdce1d..d71f6b9ac5 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs @@ -71,7 +71,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Added transform tags and args strings verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CompositeDataLoader).Assembly.FullName); } // The composition of loader plus transforms in order. diff --git a/src/Microsoft.ML.Data/DataLoadSave/FakeSchema.cs b/src/Microsoft.ML.Data/DataLoadSave/FakeSchema.cs new file mode 100644 index 0000000000..d94219a453 --- /dev/null +++ b/src/Microsoft.ML.Data/DataLoadSave/FakeSchema.cs @@ -0,0 +1,110 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Core.Data; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; +using System.Collections.Generic; +using System.Linq; + +namespace Microsoft.ML.Data.DataLoadSave +{ + + /// + /// A fake schema that is manufactured out of a SchemaShape. + /// It will pretend that all vector sizes are equal to 10, all key value counts are equal to 10, + /// and all values are defaults (for metadata). + /// + internal sealed class FakeSchema : ISchema + { + private const int AllVectorSizes = 10; + private const int AllKeySizes = 10; + + private readonly IHostEnvironment _env; + private readonly SchemaShape _shape; + private readonly Dictionary _colMap; + + public FakeSchema(IHostEnvironment env, SchemaShape inputShape) + { + _env = env; + _shape = inputShape; + _colMap = Enumerable.Range(0, _shape.Columns.Length) + .ToDictionary(idx => _shape.Columns[idx].Name, idx => idx); + } + + public int ColumnCount => _shape.Columns.Length; + + public string GetColumnName(int col) + { + _env.Check(0 <= col && col < ColumnCount); + return _shape.Columns[col].Name; + } + + public ColumnType GetColumnType(int col) + { + _env.Check(0 <= col && col < ColumnCount); + var inputCol = _shape.Columns[col]; + return MakeColumnType(inputCol); + } + + public bool TryGetColumnIndex(string name, out int col) => _colMap.TryGetValue(name, out col); + + private static ColumnType MakeColumnType(SchemaShape.Column inputCol) + { + ColumnType curType = inputCol.ItemType; + if (inputCol.IsKey) + curType = new KeyType(curType.AsPrimitive.RawKind, 0, AllKeySizes); + if (inputCol.Kind == SchemaShape.Column.VectorKind.VariableVector) + curType = new VectorType(curType.AsPrimitive, 0); + else if (inputCol.Kind == SchemaShape.Column.VectorKind.Vector) + curType = new VectorType(curType.AsPrimitive, AllVectorSizes); + return curType; + } + + public void GetMetadata(string kind, int col, ref TValue value) + { + _env.Check(0 <= col && col < ColumnCount); + var inputCol = _shape.Columns[col]; + var metaShape = inputCol.Metadata; + if (metaShape == null || !metaShape.TryFindColumn(kind, out var metaColumn)) + throw _env.ExceptGetMetadata(); + + var colType = MakeColumnType(metaColumn); + _env.Check(colType.RawType.Equals(typeof(TValue))); + + if (colType.IsVector) + { + // This as an atypical use of VBuffer: we create it in GetMetadataVec, and then pass through + // via boxing to be returned out of this method. This is intentional. + value = (TValue)Utils.MarshalInvoke(GetMetadataVec, colType.ItemType.RawType); + } + else + value = default; + } + + private object GetMetadataVec() => new VBuffer(AllVectorSizes, 0, null, null); + + public ColumnType GetMetadataTypeOrNull(string kind, int col) + { + _env.Check(0 <= col && col < ColumnCount); + var inputCol = _shape.Columns[col]; + var metaShape = inputCol.Metadata; + if (metaShape == null || !metaShape.TryFindColumn(kind, out var metaColumn)) + return null; + return MakeColumnType(metaColumn); + } + + public IEnumerable> GetMetadataTypes(int col) + { + _env.Check(0 <= col && col < ColumnCount); + var inputCol = _shape.Columns[col]; + var metaShape = inputCol.Metadata; + if (metaShape == null) + return Enumerable.Empty>(); + + return metaShape.Columns.Select(c => new KeyValuePair(c.Name, MakeColumnType(c))); + } + } +} diff --git a/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs index d1b43dcf65..6c52cefa56 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs @@ -60,7 +60,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoadName); + loaderSignature: LoadName, + loaderAssemblyName: typeof(PartitionedFileLoader).Assembly.FullName); } public class Arguments @@ -368,7 +369,7 @@ private sealed class Cursor : RootCursorBase, IRowCursor private Delegate[] _getters; private Delegate[] _subGetters; // Cached getters of the sub-cursor. - private DvText[] _colValues; // Column values cached from the file path. + private ReadOnlyMemory[] _colValues; // Column values cached from the file path. private IRowCursor _subCursor; // Sub cursor of the current file. private IEnumerator _fileOrder; @@ -384,7 +385,7 @@ public Cursor(IChannelProvider provider, PartitionedFileLoader parent, IMultiStr _active = Utils.BuildArray(Schema.ColumnCount, predicate); _subActive = _active.Take(SubColumnCount).ToArray(); - _colValues = new DvText[Schema.ColumnCount - SubColumnCount]; + _colValues = new ReadOnlyMemory[Schema.ColumnCount - SubColumnCount]; _subGetters = new Delegate[SubColumnCount]; _getters = CreateGetters(); @@ -537,13 +538,13 @@ private void UpdateColumnValues(string path, List values) var source = _parent._srcDirIndex[i]; if (source >= 0 && source < values.Count) { - _colValues[i] = new DvText(values[source]); + _colValues[i] = values[source].AsMemory(); } else if (source == FilePathColIndex) { // Force Unix path for consistency. var cleanPath = path.Replace(@"\", @"/"); - _colValues[i] = new DvText(cleanPath); + _colValues[i] = cleanPath.AsMemory(); } } } @@ -602,7 +603,7 @@ private ValueGetter GetterDelegateCore(int col, ColumnType type) Ch.Check(col >= 0 && col < _colValues.Length); Ch.AssertValue(type); - var conv = Conversions.Instance.GetStandardConversion(TextType.Instance, type) as ValueMapper; + var conv = Conversions.Instance.GetStandardConversion(TextType.Instance, type) as ValueMapper, TValue>; if (conv == null) { throw Ch.Except("Invalid TValue: '{0}' of the conversion.", typeof(TValue)); diff --git a/src/Microsoft.ML.Data/DataLoadSave/PartitionedPathParser.cs b/src/Microsoft.ML.Data/DataLoadSave/PartitionedPathParser.cs index 70d8f898ab..c10fe09116 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/PartitionedPathParser.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/PartitionedPathParser.cs @@ -91,7 +91,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoadName); + loaderSignature: LoadName, + loaderAssemblyName: typeof(SimplePartitionedPathParser).Assembly.FullName); } private IHost _host; @@ -214,7 +215,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoadName); + loaderSignature: LoadName, + loaderAssemblyName: typeof(ParquetPartitionedPathParser).Assembly.FullName); } public ParquetPartitionedPathParser() diff --git a/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoader.cs index 391fb65739..195befe518 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoader.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoader.cs @@ -511,12 +511,12 @@ private sealed class Bindings : ISchema { public readonly ColInfo[] Infos; public readonly Dictionary NameToInfoIndex; - private readonly VBuffer[] _slotNames; + private readonly VBuffer>[] _slotNames; // Empty iff either header+ not set in args, or if no header present, or upon load // there was no header stored in the model. - private readonly DvText _header; + private readonly ReadOnlyMemory _header; - private readonly MetadataUtils.MetadataGetter> _getSlotNames; + private readonly MetadataUtils.MetadataGetter>> _getSlotNames; private Bindings() { @@ -546,7 +546,7 @@ public Bindings(TextLoader parent, Column[] cols, IMultiStreamSource headerFile, int inputSize = parent._inputSize; ch.Assert(0 <= inputSize & inputSize < SrcLim); - List lines = null; + List> lines = null; if (headerFile != null) Cursor.GetSomeLines(headerFile, 1, ref lines); if (needInputSize && inputSize == 0) @@ -712,11 +712,11 @@ public Bindings(TextLoader parent, Column[] cols, IMultiStreamSource headerFile, Infos[iinfoOther] = ColInfo.Create(cols[iinfoOther].Name.Trim(), typeOther, segsNew.ToArray(), true); } - _slotNames = new VBuffer[Infos.Length]; + _slotNames = new VBuffer>[Infos.Length]; if ((parent.HasHeader || headerFile != null) && Utils.Size(lines) > 0) _header = lines[0]; - if (_header.HasChars) + if (!_header.IsEmpty) Parser.ParseSlotNames(parent, _header, Infos, _slotNames); ch.Done(); @@ -797,12 +797,12 @@ public Bindings(ModelLoadContext ctx, TextLoader parent) NameToInfoIndex[name] = iinfo; } - _slotNames = new VBuffer[Infos.Length]; + _slotNames = new VBuffer>[Infos.Length]; string result = null; ctx.TryLoadTextStream("Header.txt", reader => result = reader.ReadLine()); if (!string.IsNullOrEmpty(result)) - Parser.ParseSlotNames(parent, _header = new DvText(result), Infos, _slotNames); + Parser.ParseSlotNames(parent, _header = result.AsMemory(), Infos, _slotNames); } public void Save(ModelSaveContext ctx) @@ -850,7 +850,7 @@ public void Save(ModelSaveContext ctx) } // Save header in an easily human inspectable separate entry. - if (_header.HasChars) + if (!_header.IsEmpty) ctx.SaveTextStream("Header.txt", writer => writer.WriteLine(_header.ToString())); } @@ -924,7 +924,7 @@ public void GetMetadata(string kind, int col, ref TValue value) } } - private void GetSlotNames(int col, ref VBuffer dst) + private void GetSlotNames(int col, ref VBuffer> dst) { Contracts.Assert(0 <= col && col < ColumnCount); @@ -960,7 +960,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x0001000B, // Header now retained if used and present verReadableCur: 0x0001000A, verWeCanReadBack: 0x00010009, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TextLoader).Assembly.FullName); } /// @@ -1180,13 +1181,13 @@ private static bool TryParseSchema(IHost host, IMultiStreamSource files, goto LDone; // Make sure the loader binds to us. - var info = ComponentCatalog.GetLoadableClassInfo(loader.Name); + var info = host.ComponentCatalog.GetLoadableClassInfo(loader.Name); if (info.Type != typeof(IDataLoader) || info.ArgType != typeof(Arguments)) goto LDone; var argsNew = new Arguments(); // Copy the non-core arguments to the new args (we already know that all the core arguments are default). - var parsed = CmdParser.ParseArguments(host, CmdParser.GetSettings(ch, args, new Arguments()), argsNew); + var parsed = CmdParser.ParseArguments(host, CmdParser.GetSettings(host, args, new Arguments()), argsNew); ch.Assert(parsed); // Copy the core arguments to the new args. if (!CmdParser.ParseArguments(host, loader.GetSettingsString(), argsNew, typeof(ArgumentsCore), msg => ch.Error(msg))) diff --git a/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderCursor.cs b/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderCursor.cs index 19b6d640cc..b23637b1a9 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderCursor.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderCursor.cs @@ -212,7 +212,7 @@ public override ValueGetter GetIdGetter() }; } - public static void GetSomeLines(IMultiStreamSource source, int count, ref List lines) + public static void GetSomeLines(IMultiStreamSource source, int count, ref List> lines) { Contracts.AssertValue(source); Contracts.Assert(count > 0); @@ -236,7 +236,7 @@ public static void GetSomeLines(IMultiStreamSource source, int count, ref List @@ -495,7 +495,7 @@ private void ThreadProc() for (; ; ) { // REVIEW: Avoid allocating a string for every line. This would probably require - // introducing a CharSpan type (similar to DvText but based on char[] or StringBuilder) + // introducing a CharSpan type (similar to ReadOnlyMemory but based on char[] or StringBuilder) // and implementing all the necessary conversion functionality on it. See task 3871. text = rdr.ReadLine(); if (text == null) diff --git a/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderParser.cs b/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderParser.cs index c0d0f25b17..0d4331c59b 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderParser.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderParser.cs @@ -228,7 +228,7 @@ protected ColumnPipe(RowSet rows) public abstract void Reset(int irow, int size); // Passed by-ref for effeciency, not so it can be modified. - public abstract bool Consume(int irow, int index, ref DvText text); + public abstract bool Consume(int irow, int index, ref ReadOnlyMemory text); public abstract Delegate GetGetter(); } @@ -255,7 +255,7 @@ public override void Reset(int irow, int size) _values[irow] = default(TResult); } - public override bool Consume(int irow, int index, ref DvText text) + public override bool Consume(int irow, int index, ref ReadOnlyMemory text) { Contracts.Assert(0 <= irow && irow < _values.Length); Contracts.Assert(index == 0); @@ -332,7 +332,7 @@ public void Reset(int size) AssertValid(); } - public bool Consume(int index, ref DvText text) + public bool Consume(int index, ref ReadOnlyMemory text) { AssertValid(); Contracts.Assert(_indexPrev < index & index < _size); @@ -439,7 +439,7 @@ public override void Reset(int irow, int size) _values[irow].Reset(size); } - public override bool Consume(int irow, int index, ref DvText text) + public override bool Consume(int irow, int index, ref ReadOnlyMemory text) { Contracts.Assert(0 <= irow && irow < _values.Length); return _values[irow].Consume(index, ref text); @@ -510,7 +510,7 @@ private struct ScanInfo /// /// The current text for the entire line (all fields), and possibly more. /// - public readonly string TextBuf; + public ReadOnlyMemory TextBuf; /// /// The min position in to consider (all fields). @@ -531,7 +531,7 @@ private struct ScanInfo /// /// The (unquoted) text of the field. /// - public DvText Span; + public ReadOnlyMemory Span; /// /// Whether there was a quoting error in the field. @@ -558,16 +558,17 @@ private struct ScanInfo /// /// Initializes the ScanInfo. /// - public ScanInfo(ref DvText text, string path, long line) + public ScanInfo(ref ReadOnlyMemory text, string path, long line) : this() { - Contracts.Assert(!text.IsNA); Contracts.AssertValueOrNull(path); Contracts.Assert(line >= 0); Path = path; Line = line; - TextBuf = text.GetRawUnderlyingBufferInfo(out IchMinBuf, out IchLimBuf); + TextBuf = text; + IchMinBuf = 0; + IchLimBuf = text.Length; IchMinNext = IchMinBuf; } } @@ -584,13 +585,13 @@ private sealed class FieldSet // Source indices and associated text (parallel arrays). public int[] Indices; - public DvText[] Spans; + public ReadOnlyMemory[] Spans; public FieldSet() { // Always allocate/size Columns after Spans so even if exceptions are thrown we // are guaranteed that Spans.Length >= Columns.Length. - Spans = new DvText[8]; + Spans = new ReadOnlyMemory[8]; Indices = new int[8]; } @@ -687,7 +688,7 @@ public Parser(TextLoader parent) Contracts.Assert(_inputSize >= 0); } - public static void GetInputSize(TextLoader parent, List lines, out int minSize, out int maxSize) + public static void GetInputSize(TextLoader parent, List> lines, out int minSize, out int maxSize) { Contracts.AssertNonEmpty(lines); Contracts.Assert(parent._inputSize == 0, "Why is this being called when inputSize is known?"); @@ -700,12 +701,12 @@ public static void GetInputSize(TextLoader parent, List lines, out int m { foreach (var line in lines) { - var text = (parent._flags & Options.TrimWhitespace) != 0 ? line.TrimEndWhiteSpace() : line; - if (!text.HasChars) + var text = (parent._flags & Options.TrimWhitespace) != 0 ? ReadOnlyMemoryUtils.TrimEndWhiteSpace(line) : line; + if (text.IsEmpty) continue; // REVIEW: This is doing more work than we need, but makes sure we're consistent.... - int srcLim = impl.GatherFields(text); + int srcLim = impl.GatherFields(text, text.Span); // Don't need the fields, just srcLim. impl.Fields.Clear(); @@ -724,9 +725,9 @@ public static void GetInputSize(TextLoader parent, List lines, out int m } } - public static void ParseSlotNames(TextLoader parent, DvText textHeader, ColInfo[] infos, VBuffer[] slotNames) + public static void ParseSlotNames(TextLoader parent, ReadOnlyMemory textHeader, ColInfo[] infos, VBuffer>[] slotNames) { - Contracts.Assert(textHeader.HasChars); + Contracts.Assert(!textHeader.IsEmpty); Contracts.Assert(infos.Length == slotNames.Length); var sb = new StringBuilder(); @@ -734,7 +735,7 @@ public static void ParseSlotNames(TextLoader parent, DvText textHeader, ColInfo[ var impl = new HelperImpl(stats, parent._flags, parent._separators, parent._inputSize, int.MaxValue); try { - impl.GatherFields(textHeader); + impl.GatherFields(textHeader, textHeader.Span); } finally { @@ -742,7 +743,7 @@ public static void ParseSlotNames(TextLoader parent, DvText textHeader, ColInfo[ } var header = impl.Fields; - var bldr = BufferBuilder.CreateDefault(); + var bldr = BufferBuilder>.CreateDefault(); for (int iinfo = 0; iinfo < infos.Length; iinfo++) { var info = infos[iinfo]; @@ -771,7 +772,7 @@ public static void ParseSlotNames(TextLoader parent, DvText textHeader, ColInfo[ { var srcCur = header.Indices[isrc]; Contracts.Assert(min <= srcCur & srcCur < lim); - bldr.AddFeature(indexBase + srcCur, header.Spans[isrc].TrimWhiteSpace()); + bldr.AddFeature(indexBase + srcCur, ReadOnlyMemoryUtils.TrimWhiteSpace(header.Spans[isrc])); } } ivDst += sizeSeg; @@ -795,6 +796,24 @@ public RowSet CreateRowSet(ParseStats stats, int count, bool[] active) return rows; } + /// + /// Returns a of with trailing whitespace trimmed. + /// + private ReadOnlyMemory TrimEndWhiteSpace(ReadOnlyMemory memory, ReadOnlySpan span) + { + if (memory.IsEmpty) + return memory; + + int ichLim = memory.Length; + if (!char.IsWhiteSpace(span[ichLim - 1])) + return memory; + + while (0 < ichLim && char.IsWhiteSpace(span[ichLim - 1])) + ichLim--; + + return memory.Slice(0, ichLim); + } + public void ParseRow(RowSet rows, int irow, Helper helper, bool[] active, string path, long line, string text) { Contracts.AssertValue(rows); @@ -803,13 +822,14 @@ public void ParseRow(RowSet rows, int irow, Helper helper, bool[] active, string Contracts.Assert(active == null | Utils.Size(active) == _infos.Length); var impl = (HelperImpl)helper; - DvText lineSpan = new DvText(text); + var lineSpan = text.AsMemory(); + var span = lineSpan.Span; if ((_flags & Options.TrimWhitespace) != 0) - lineSpan = lineSpan.TrimEndWhiteSpace(); + lineSpan = TrimEndWhiteSpace(lineSpan, span); try { // Parse the spans into items, ensuring that sparse don't precede non-sparse. - int srcLim = impl.GatherFields(lineSpan, path, line); + int srcLim = impl.GatherFields(lineSpan, span, path, line); impl.Fields.AssertValid(); // REVIEW: When should we report inconsistency? @@ -855,7 +875,7 @@ private sealed class HelperImpl : Helper private readonly StringBuilder _sb; // Result of a blank field - either Missing or Empty, depending on _quoting. - private readonly DvText _blank; + private readonly ReadOnlyMemory _blank; public readonly FieldSet Fields; @@ -878,7 +898,7 @@ public HelperImpl(ParseStats stats, Options flags, char[] seps, int inputSize, i _quoting = (flags & Options.AllowQuoting) != 0; _sparse = (flags & Options.AllowSparse) != 0; _sb = new StringBuilder(); - _blank = _quoting ? DvText.NA : DvText.Empty; + _blank = ReadOnlyMemory.Empty; Fields = new FieldSet(); } @@ -902,7 +922,7 @@ private bool IsSep(char ch) /// Process the line of text into fields, stored in the Fields field. Ensures that sparse /// don't precede non-sparse. Returns the lim of the src columns. /// - public int GatherFields(DvText lineSpan, string path = null, long line = 0) + public int GatherFields(ReadOnlyMemory lineSpan, ReadOnlySpan span, string path = null, long line = 0) { Fields.AssertEmpty(); @@ -915,7 +935,7 @@ public int GatherFields(DvText lineSpan, string path = null, long line = 0) for (; ; ) { Contracts.Assert(scan.IchMinBuf <= scan.IchMinNext && scan.IchMinNext <= scan.IchLimBuf); - bool more = FetchNextField(ref scan); + bool more = FetchNextField(ref scan, span); Contracts.Assert(scan.IchMinBuf <= scan.IchMinNext && scan.IchMinNext <= scan.IchLimBuf); Contracts.Assert(scan.Index == -1); @@ -946,7 +966,7 @@ public int GatherFields(DvText lineSpan, string path = null, long line = 0) for (; ; ) { Contracts.Assert(scan.IchMinBuf <= scan.IchMinNext && scan.IchMinNext <= scan.IchLimBuf); - bool more = FetchNextField(ref scan); + bool more = FetchNextField(ref scan, span); Contracts.Assert(scan.IchMinBuf <= scan.IchMinNext && scan.IchMinNext <= scan.IchLimBuf); Contracts.Assert(scan.Index >= -1); @@ -992,16 +1012,24 @@ public int GatherFields(DvText lineSpan, string path = null, long line = 0) } var spanT = Fields.Spans[Fields.Count - 1]; - // Note that Convert produces NA if the text is unparsable. - DvInt4 csrc = default(DvInt4); - Conversion.Conversions.Instance.Convert(ref spanT, ref csrc); - csrcSparse = csrc.RawValue; - if (csrcSparse <= 0) + // Note that Convert throws exception the text is unparsable. + int csrc = default; + try + { + Conversions.Instance.Convert(ref spanT, ref csrc); + } + catch + { + Contracts.Assert(csrc == default); + } + + if (csrc <= 0) { _stats.LogBadFmt(ref scan, "Bad dimensionality or ambiguous sparse item. Use sparse=- for non-sparse file, and/or quote the value."); break; } + csrcSparse = csrc; srcLimFixed = Fields.Indices[--Fields.Count]; if (csrcSparse >= SrcLim - srcLimFixed) csrcSparse = SrcLim - srcLimFixed - 1; @@ -1065,18 +1093,17 @@ public int GatherFields(DvText lineSpan, string path = null, long line = 0) return inputSize; } - private bool FetchNextField(ref ScanInfo scan) + private bool FetchNextField(ref ScanInfo scan, ReadOnlySpan span) { Contracts.Assert(scan.IchMinBuf <= scan.IchMinNext && scan.IchMinNext <= scan.IchLimBuf); var text = scan.TextBuf; int ichLim = scan.IchLimBuf; int ichCur = scan.IchMinNext; - if (!_sepContainsSpace) { // Ignore leading spaces - while (ichCur < ichLim && text[ichCur] == ' ') + while (ichCur < ichLim && span[ichCur] == ' ') ichCur++; } @@ -1093,29 +1120,29 @@ private bool FetchNextField(ref ScanInfo scan) } int ichMinRaw = ichCur; - if (_sparse && (uint)(text[ichCur] - '0') <= 9) + if (_sparse && (uint)(span[ichCur] - '0') <= 9) { // See if it is sparse. Avoid overflow by limiting the index to 9 digits. // REVIEW: This limits the src index to a billion. Is this acceptable? int ichEnd = Math.Min(ichLim, ichCur + 9); int ichCol = ichCur + 1; Contracts.Assert(ichCol <= ichEnd); - while (ichCol < ichEnd && (uint)(text[ichCol] - '0') <= 9) + while (ichCol < ichEnd && (uint)(span[ichCol] - '0') <= 9) ichCol++; - if (ichCol < ichLim && text[ichCol] == ':') + if (ichCol < ichLim && span[ichCol] == ':') { // It is sparse. Compute the index. int ind = 0; for (int ich = ichCur; ich < ichCol; ich++) - ind = ind * 10 + (text[ich] - '0'); + ind = ind * 10 + (span[ich] - '0'); ichCur = ichCol + 1; scan.Index = ind; // Skip spaces again. if (!_sepContainsSpace) { - while (ichCur < ichLim && text[ichCur] == ' ') + while (ichCur < ichLim && span[ichCur] == ' ') ichCur++; } @@ -1129,7 +1156,7 @@ private bool FetchNextField(ref ScanInfo scan) } Contracts.Assert(ichCur < ichLim); - if (text[ichCur] == '"' && _quoting) + if (span[ichCur] == '"' && _quoting) { // Quoted case. ichCur++; @@ -1144,13 +1171,13 @@ private bool FetchNextField(ref ScanInfo scan) scan.QuotingError = true; break; } - if (text[ichCur] == '"') + if (span[ichCur] == '"') { if (ichCur > ichRun) - _sb.Append(text, ichRun, ichCur - ichRun); + _sb.AppendSpan(span.Slice(ichRun, ichCur - ichRun)); if (++ichCur >= ichLim) break; - if (text[ichCur] != '"') + if (span[ichCur] != '"') break; ichRun = ichCur; } @@ -1159,7 +1186,7 @@ private bool FetchNextField(ref ScanInfo scan) // Ignore any spaces between here and the next separator. Anything else is a formatting "error". for (; ichCur < ichLim; ichCur++) { - if (text[ichCur] == ' ') + if (span[ichCur] == ' ') { // End the loop if space is a sep, otherwise ignore this space. if (_sepContainsSpace) @@ -1168,18 +1195,16 @@ private bool FetchNextField(ref ScanInfo scan) else { // End the loop if this nonspace char is a sep, otherwise it is an error. - if (IsSep(text[ichCur])) + if (IsSep(span[ichCur])) break; scan.QuotingError = true; } } - if (scan.QuotingError) - scan.Span = DvText.NA; - else if (_sb.Length == 0) - scan.Span = DvText.Empty; + if (scan.QuotingError || _sb.Length == 0) + scan.Span = String.Empty.AsMemory(); else - scan.Span = new DvText(_sb.ToString()); + scan.Span = _sb.ToString().AsMemory(); } else { @@ -1193,7 +1218,7 @@ private bool FetchNextField(ref ScanInfo scan) Contracts.Assert(ichCur <= ichLim); if (ichCur >= ichLim) break; - if (_sep0 == text[ichCur]) + if (_sep0 == span[ichCur]) break; } } @@ -1204,7 +1229,7 @@ private bool FetchNextField(ref ScanInfo scan) Contracts.Assert(ichCur <= ichLim); if (ichCur >= ichLim) break; - if (_sep0 == text[ichCur] || _sep1 == text[ichCur]) + if (_sep0 == span[ichCur] || _sep1 == span[ichCur]) break; } } @@ -1215,7 +1240,7 @@ private bool FetchNextField(ref ScanInfo scan) Contracts.Assert(ichCur <= ichLim); if (ichCur >= ichLim) break; - if (IsSep(text[ichCur])) + if (IsSep(span[ichCur])) break; } } @@ -1223,7 +1248,7 @@ private bool FetchNextField(ref ScanInfo scan) if (ichMin >= ichCur) scan.Span = _blank; else - scan.Span = new DvText(text, ichMin, ichCur); + scan.Span = text.Slice(ichMin, ichCur - ichMin); } scan.IchLim = ichCur; @@ -1233,7 +1258,7 @@ private bool FetchNextField(ref ScanInfo scan) return false; } - Contracts.Assert(_seps.Contains(text[ichCur])); + Contracts.Assert(_seps.Contains(span[ichCur])); scan.IchMinNext = ichCur + 1; return true; } diff --git a/src/Microsoft.ML.Data/DataLoadSave/Text/TextSaver.cs b/src/Microsoft.ML.Data/DataLoadSave/Text/TextSaver.cs index 48f3f9ddc3..73f512b478 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Text/TextSaver.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Text/TextSaver.cs @@ -94,22 +94,22 @@ protected ValueWriterBase(PrimitiveType type, int source, char sep) if (type.IsText) { // For text we need to deal with escaping. - ValueMapper c = MapText; + ValueMapper, StringBuilder> c = MapText; Conv = (ValueMapper)(Delegate)c; } else if (type.IsTimeSpan) { - ValueMapper c = MapTimeSpan; + ValueMapper c = MapTimeSpan; Conv = (ValueMapper)(Delegate)c; } else if (type.IsDateTime) { - ValueMapper c = MapDateTime; + ValueMapper c = MapDateTime; Conv = (ValueMapper)(Delegate)c; } else if (type.IsDateTimeZone) { - ValueMapper c = MapDateTimeZone; + ValueMapper c = MapDateTimeZone; Conv = (ValueMapper)(Delegate)c; } else @@ -120,22 +120,22 @@ protected ValueWriterBase(PrimitiveType type, int source, char sep) Default = Sb.ToString(); } - protected void MapText(ref DvText src, ref StringBuilder sb) + protected void MapText(ref ReadOnlyMemory src, ref StringBuilder sb) { - TextSaverUtils.MapText(ref src, ref sb, Sep); + TextSaverUtils.MapText(src.Span, ref sb, Sep); } - protected void MapTimeSpan(ref DvTimeSpan src, ref StringBuilder sb) + protected void MapTimeSpan(ref TimeSpan src, ref StringBuilder sb) { TextSaverUtils.MapTimeSpan(ref src, ref sb); } - protected void MapDateTime(ref DvDateTime src, ref StringBuilder sb) + protected void MapDateTime(ref DateTime src, ref StringBuilder sb) { TextSaverUtils.MapDateTime(ref src, ref sb); } - protected void MapDateTimeZone(ref DvDateTimeZone src, ref StringBuilder sb) + protected void MapDateTimeZone(ref DateTimeOffset src, ref StringBuilder sb) { TextSaverUtils.MapDateTimeZone(ref src, ref sb); } @@ -145,7 +145,7 @@ private sealed class VecValueWriter : ValueWriterBase { private readonly ValueGetter> _getSrc; private VBuffer _src; - private readonly VBuffer _slotNames; + private readonly VBuffer> _slotNames; private readonly int _slotCount; public VecValueWriter(IRowCursor cursor, VectorType type, int source, char sep) @@ -225,7 +225,7 @@ public override void WriteData(Action appendItem, out int le public override void WriteHeader(Action appendItem, out int length) { - var span = new DvText(_columnName); + var span = _columnName.AsMemory(); MapText(ref span, ref Sb); appendItem(Sb, 0); length = 1; @@ -468,7 +468,7 @@ private string CreateLoaderArguments(ISchema schema, ValueWriter[] pipes, bool h sb.Append(" col="); if (!column.TryUnparse(sb)) { - var settings = CmdParser.GetSettings(ch, column, new TextLoader.Column()); + var settings = CmdParser.GetSettings(_host, column, new TextLoader.Column()); CmdQuoter.QuoteValue(settings, sb, true); } if (type.IsVector && !type.IsKnownSizeVector && i != pipes.Length - 1) @@ -796,29 +796,28 @@ private void WriteDenseTo(int dstLim, string defaultStr = null) internal static class TextSaverUtils { /// - /// Converts a DvText to a StringBuilder using TextSaver escaping and string quoting rules. + /// Converts a ReadOnlySpan to a StringBuilder using TextSaver escaping and string quoting rules. /// - internal static void MapText(ref DvText src, ref StringBuilder sb, char sep) + internal static void MapText(ReadOnlySpan span, ref StringBuilder sb, char sep) { if (sb == null) sb = new StringBuilder(); else sb.Clear(); - if (src.IsEmpty) + if (span.IsEmpty) sb.Append("\"\""); - else if (!src.IsNA) + else { - int ichMin; - int ichLim; - string text = src.GetRawUnderlyingBufferInfo(out ichMin, out ichLim); + int ichMin = 0; + int ichLim = span.Length; int ichCur = ichMin; int ichRun = ichCur; bool quoted = false; // Strings that start with space need to be quoted. Contracts.Assert(ichCur < ichLim); - if (text[ichCur] == ' ') + if (span[ichCur] == ' ') { quoted = true; sb.Append('"'); @@ -826,7 +825,7 @@ internal static void MapText(ref DvText src, ref StringBuilder sb, char sep) for (; ichCur < ichLim; ichCur++) { - char ch = text[ichCur]; + char ch = span[ichCur]; if (ch != '"' && ch != sep && ch != ':') continue; if (!quoted) @@ -838,47 +837,47 @@ internal static void MapText(ref DvText src, ref StringBuilder sb, char sep) if (ch == '"') { if (ichRun < ichCur) - sb.Append(text, ichRun, ichCur - ichRun); + sb.AppendSpan(span.Slice(ichRun, ichCur - ichRun)); sb.Append("\"\""); ichRun = ichCur + 1; } } Contracts.Assert(ichCur == ichLim); if (ichRun < ichCur) - sb.Append(text, ichRun, ichCur - ichRun); + sb.AppendSpan(span.Slice(ichRun, ichCur - ichRun)); if (quoted) sb.Append('"'); } } - internal static void MapTimeSpan(ref DvTimeSpan src, ref StringBuilder sb) + internal static void MapTimeSpan(ref TimeSpan src, ref StringBuilder sb) { if (sb == null) sb = new StringBuilder(); else sb.Clear(); - if (!src.IsNA) - sb.AppendFormat("\"{0:c}\"", (TimeSpan)src); + + sb.AppendFormat("\"{0:c}\"", src); } - internal static void MapDateTime(ref DvDateTime src, ref StringBuilder sb) + internal static void MapDateTime(ref DateTime src, ref StringBuilder sb) { if (sb == null) sb = new StringBuilder(); else sb.Clear(); - if (!src.IsNA) - sb.AppendFormat("\"{0:o}\"", (DateTime)src); + + sb.AppendFormat("\"{0:o}\"", src); } - internal static void MapDateTimeZone(ref DvDateTimeZone src, ref StringBuilder sb) + internal static void MapDateTimeZone(ref DateTimeOffset src, ref StringBuilder sb) { if (sb == null) sb = new StringBuilder(); else sb.Clear(); - if (!src.IsNA) - sb.AppendFormat("\"{0:o}\"", (DateTimeOffset)src); + + sb.AppendFormat("\"{0:o}\"", src); } } } diff --git a/src/Microsoft.ML.Data/DataLoadSave/TransformWrapper.cs b/src/Microsoft.ML.Data/DataLoadSave/TransformWrapper.cs new file mode 100644 index 0000000000..cd4450615d --- /dev/null +++ b/src/Microsoft.ML.Data/DataLoadSave/TransformWrapper.cs @@ -0,0 +1,153 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Core.Data; +using Microsoft.ML.Data.DataLoadSave; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.Model; +using System.Collections.Generic; + +[assembly: LoadableClass(typeof(TransformWrapper), null, typeof(SignatureLoadModel), + "Transform wrapper", TransformWrapper.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Data +{ + // REVIEW: this class is public, as long as the Wrappers.cs in tests still rely on it. + // It needs to become internal. + public sealed class TransformWrapper : ITransformer, ICanSaveModel + { + public const string LoaderSignature = "TransformWrapper"; + private const string TransformDirTemplate = "Step_{0:000}"; + + private readonly IHost _host; + private readonly IDataView _xf; + + public TransformWrapper(IHostEnvironment env, IDataView xf) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(nameof(TransformWrapper)); + _host.CheckValue(xf, nameof(xf)); + _xf = xf; + } + + public ISchema GetOutputSchema(ISchema inputSchema) + { + _host.CheckValue(inputSchema, nameof(inputSchema)); + + var dv = new EmptyDataView(_host, inputSchema); + var output = ApplyTransformUtils.ApplyAllTransformsToData(_host, _xf, dv); + return output.Schema; + } + + public void Save(ModelSaveContext ctx) + { + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + var dataPipe = _xf; + var transforms = new List(); + while (dataPipe is IDataTransform xf) + { + // REVIEW: a malicious user could construct a loop in the Source chain, that would + // cause this method to iterate forever (and throw something when the list overflows). There's + // no way to insulate from ALL malicious behavior. + transforms.Add(xf); + dataPipe = xf.Source; + Contracts.AssertValue(dataPipe); + } + transforms.Reverse(); + + ctx.SaveSubModel("Loader", c => BinaryLoader.SaveInstance(_host, c, dataPipe.Schema)); + + ctx.Writer.Write(transforms.Count); + for (int i = 0; i < transforms.Count; i++) + { + var dirName = string.Format(TransformDirTemplate, i); + ctx.SaveModel(transforms[i], dirName); + } + } + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "XF WRPR", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TransformWrapper).Assembly.FullName); + } + + // Factory for SignatureLoadModel. + public TransformWrapper(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(nameof(TransformWrapper)); + _host.CheckValue(ctx, nameof(ctx)); + + ctx.CheckAtModel(GetVersionInfo()); + int n = ctx.Reader.ReadInt32(); + _host.CheckDecode(n >= 0); + + ctx.LoadModel(env, out var loader, "Loader", new MultiFileSource(null)); + + IDataView data = loader; + for (int i = 0; i < n; i++) + { + var dirName = string.Format(TransformDirTemplate, i); + ctx.LoadModel(env, out var xf, dirName, data); + data = xf; + } + + _xf = data; + } + + public IDataView Transform(IDataView input) => ApplyTransformUtils.ApplyAllTransformsToData(_host, _xf, input); + } + + /// + /// Estimator for trained wrapped transformers. + /// + internal abstract class TrainedWrapperEstimatorBase : IEstimator + { + private readonly IHost _host; + + protected TrainedWrapperEstimatorBase(IHost host) + { + Contracts.CheckValue(host, nameof(host)); + _host = host; + } + + public abstract TransformWrapper Fit(IDataView input); + + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + _host.CheckValue(inputSchema, nameof(inputSchema)); + + var fakeSchema = new FakeSchema(_host, inputSchema); + var transformer = Fit(new EmptyDataView(_host, fakeSchema)); + return SchemaShape.Create(transformer.GetOutputSchema(fakeSchema)); + } + } + + /// + /// Estimator for untrained wrapped transformers. + /// + public abstract class TrivialWrapperEstimator : TrivialEstimator + { + protected TrivialWrapperEstimator(IHost host, TransformWrapper transformer) + : base(host, transformer) + { + } + + public override SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + Host.CheckValue(inputSchema, nameof(inputSchema)); + var fakeSchema = new FakeSchema(Host, inputSchema); + return SchemaShape.Create(Transformer.GetOutputSchema(fakeSchema)); + } + } +} diff --git a/src/Microsoft.ML.Data/DataLoadSave/TransformerChain.cs b/src/Microsoft.ML.Data/DataLoadSave/TransformerChain.cs index d5f246689a..31ab59a100 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/TransformerChain.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/TransformerChain.cs @@ -55,7 +55,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: TransformerChain.LoaderSignature); + loaderSignature: TransformerChain.LoaderSignature, + loaderAssemblyName: typeof(TransformerChain<>).Assembly.FullName); } /// diff --git a/src/Microsoft.ML.Data/DataLoadSave/Transpose/TransposeLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/Transpose/TransposeLoader.cs index 37cbe23b92..a6c12b48ed 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/Transpose/TransposeLoader.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/Transpose/TransposeLoader.cs @@ -354,7 +354,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoadName); + loaderSignature: LoadName, + loaderAssemblyName: typeof(TransposeLoader).Assembly.FullName); } // We return the schema view's schema, because we don't necessarily want diff --git a/src/Microsoft.ML.Data/DataView/ArrayDataViewBuilder.cs b/src/Microsoft.ML.Data/DataView/ArrayDataViewBuilder.cs index 098c652203..98b0dba355 100644 --- a/src/Microsoft.ML.Data/DataView/ArrayDataViewBuilder.cs +++ b/src/Microsoft.ML.Data/DataView/ArrayDataViewBuilder.cs @@ -21,8 +21,8 @@ public sealed class ArrayDataViewBuilder private readonly IHost _host; private readonly List _columns; private readonly List _names; - private readonly Dictionary>> _getSlotNames; - private readonly Dictionary>> _getKeyValues; + private readonly Dictionary>>> _getSlotNames; + private readonly Dictionary>>> _getKeyValues; private int? RowCount { @@ -41,8 +41,8 @@ public ArrayDataViewBuilder(IHostEnvironment env) _columns = new List(); _names = new List(); - _getSlotNames = new Dictionary>>(); - _getKeyValues = new Dictionary>>(); + _getSlotNames = new Dictionary>>>(); + _getKeyValues = new Dictionary>>>(); } /// @@ -62,7 +62,7 @@ private void CheckLength(string name, T[] values) /// by being assigned. Output values are returned simply by being assigned, so the /// type should be a type where assigning to a different /// value does not compromise the immutability of the source object (so, for example, - /// a scalar, string, or DvText would be perfectly acceptable, but a + /// a scalar, string, or ReadOnlyMemory would be perfectly acceptable, but a /// HashSet or VBuffer would not be). /// public void AddColumn(string name, PrimitiveType type, params T[] values) @@ -77,7 +77,7 @@ public void AddColumn(string name, PrimitiveType type, params T[] values) /// Constructs a new key column from an array where values are copied to output simply /// by being assigned. /// - public void AddColumn(string name, ValueGetter> getKeyValues, ulong keyMin, int keyCount, params uint[] values) + public void AddColumn(string name, ValueGetter>> getKeyValues, ulong keyMin, int keyCount, params uint[] values) { _host.CheckValue(getKeyValues, nameof(getKeyValues)); _host.CheckParam(keyCount > 0, nameof(keyCount)); @@ -90,7 +90,7 @@ public void AddColumn(string name, ValueGetter> getKeyValues, ul /// /// Creates a column with slot names from arrays. The added column will be re-interpreted as a buffer. /// - public void AddColumn(string name, ValueGetter> getNames, PrimitiveType itemType, params T[][] values) + public void AddColumn(string name, ValueGetter>> getNames, PrimitiveType itemType, params T[][] values) { _host.CheckValue(getNames, nameof(getNames)); _host.CheckParam(itemType != null && itemType.RawType == typeof(T), nameof(itemType)); @@ -115,7 +115,7 @@ public void AddColumn(string name, PrimitiveType itemType, params T[][] value /// /// Creates a column with slot names from arrays. The added column will be re-interpreted as a buffer and possibly sparsified. /// - public void AddColumn(string name, ValueGetter> getNames, PrimitiveType itemType, Combiner combiner, params T[][] values) + public void AddColumn(string name, ValueGetter>> getNames, PrimitiveType itemType, Combiner combiner, params T[][] values) { _host.CheckValue(getNames, nameof(getNames)); _host.CheckParam(itemType != null && itemType.RawType == typeof(T), nameof(itemType)); @@ -151,7 +151,7 @@ public void AddColumn(string name, PrimitiveType itemType, params VBuffer[ /// /// Adds a VBuffer{T} valued column. /// - public void AddColumn(string name, ValueGetter> getNames, PrimitiveType itemType, params VBuffer[] values) + public void AddColumn(string name, ValueGetter>> getNames, PrimitiveType itemType, params VBuffer[] values) { _host.CheckValue(getNames, nameof(getNames)); _host.CheckParam(itemType != null && itemType.RawType == typeof(T), nameof(itemType)); @@ -162,7 +162,7 @@ public void AddColumn(string name, ValueGetter> getNames, Pri } /// - /// Adds a DvText valued column from an array of strings. + /// Adds a ReadOnlyMemory valued column from an array of strings. /// public void AddColumn(string name, params string[] values) { @@ -196,8 +196,8 @@ private class SchemaImpl : ISchema private readonly ColumnType[] _columnTypes; private readonly string[] _names; private readonly Dictionary _name2col; - private readonly Dictionary>> _getSlotNamesDict; - private readonly Dictionary>> _getKeyValuesDict; + private readonly Dictionary>>> _getSlotNamesDict; + private readonly Dictionary>>> _getKeyValuesDict; public SchemaImpl(IExceptionContext ectx, ColumnType[] columnTypes, string[] names, ArrayDataViewBuilder builder) { @@ -268,25 +268,25 @@ public void GetMetadata(string kind, int col, ref TValue value) _ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); if (kind == MetadataUtils.Kinds.SlotNames && _getSlotNamesDict.ContainsKey(_names[col])) - MetadataUtils.Marshal, TValue>(GetSlotNames, col, ref value); + MetadataUtils.Marshal>, TValue>(GetSlotNames, col, ref value); else if (kind == MetadataUtils.Kinds.KeyValues && _getKeyValuesDict.ContainsKey(_names[col])) - MetadataUtils.Marshal, TValue>(GetKeyValues, col, ref value); + MetadataUtils.Marshal>, TValue>(GetKeyValues, col, ref value); else throw MetadataUtils.ExceptGetMetadata(); } - private void GetSlotNames(int col, ref VBuffer dst) + private void GetSlotNames(int col, ref VBuffer> dst) { Contracts.Assert(_getSlotNamesDict.ContainsKey(_names[col])); - ValueGetter> get; + ValueGetter>> get; _getSlotNamesDict.TryGetValue(_names[col], out get); get(ref dst); } - private void GetKeyValues(int col, ref VBuffer dst) + private void GetKeyValues(int col, ref VBuffer> dst) { Contracts.Assert(_getKeyValuesDict.ContainsKey(_names[col])); - ValueGetter> get; + ValueGetter>> get; _getKeyValuesDict.TryGetValue(_names[col], out get); get(ref dst); } @@ -514,16 +514,16 @@ protected override void CopyOut(ref T src, ref T dst) /// /// A convenience column for converting strings into textspans. /// - private sealed class StringToTextColumn : Column + private sealed class StringToTextColumn : Column> { public StringToTextColumn(string[] values) : base(TextType.Instance, values) { } - protected override void CopyOut(ref string src, ref DvText dst) + protected override void CopyOut(ref string src, ref ReadOnlyMemory dst) { - dst = new DvText(src); + dst = src.AsMemory(); } } diff --git a/src/Microsoft.ML.Data/DataView/LambdaColumnMapper.cs b/src/Microsoft.ML.Data/DataView/LambdaColumnMapper.cs index 50602ca96b..26f9c2ade7 100644 --- a/src/Microsoft.ML.Data/DataView/LambdaColumnMapper.cs +++ b/src/Microsoft.ML.Data/DataView/LambdaColumnMapper.cs @@ -18,7 +18,7 @@ public static class LambdaColumnMapper // REVIEW: It would be nice to support propagation of select metadata. public static IDataView Create(IHostEnvironment env, string name, IDataView input, string src, string dst, ColumnType typeSrc, ColumnType typeDst, ValueMapper mapper, - ValueGetter> keyValueGetter = null, ValueGetter> slotNamesGetter = null) + ValueGetter>> keyValueGetter = null, ValueGetter>> slotNamesGetter = null) { Contracts.CheckValue(env, nameof(env)); env.CheckNonEmpty(name, nameof(name)); @@ -69,7 +69,7 @@ public static IDataView Create(IHostEnvironment env, string name, ID else { Func, - ValueMapper, ValueGetter>, ValueGetter>, + ValueMapper, ValueGetter>>, ValueGetter>>, Impl> del = CreateImpl; var meth = del.GetMethodInfo().GetGenericMethodDefinition() .MakeGenericMethod(typeOrig.RawType, typeof(TSrc), typeof(TDst)); @@ -82,7 +82,7 @@ public static IDataView Create(IHostEnvironment env, string name, ID private static Impl CreateImpl( IHostEnvironment env, string name, IDataView input, Column col, ColumnType typeDst, ValueMapper map1, ValueMapper map2, - ValueGetter> keyValueGetter, ValueGetter> slotNamesGetter) + ValueGetter>> keyValueGetter, ValueGetter>> slotNamesGetter) { return new Impl(env, name, input, col, typeDst, map1, map2, keyValueGetter); } @@ -104,7 +104,7 @@ private sealed class Impl : OneToOneTransformBase public Impl(IHostEnvironment env, string name, IDataView input, OneToOneColumn col, ColumnType typeDst, ValueMapper map1, ValueMapper map2 = null, - ValueGetter> keyValueGetter = null, ValueGetter> slotNamesGetter = null) + ValueGetter>> keyValueGetter = null, ValueGetter>> slotNamesGetter = null) : base(env, name, new[] { col }, input, x => null) { Host.Assert(typeDst.RawType == typeof(T3)); @@ -122,15 +122,15 @@ public Impl(IHostEnvironment env, string name, IDataView input, OneToOneColumn c if (keyValueGetter != null) { Host.Assert(_typeDst.ItemType.KeyCount > 0); - MetadataUtils.MetadataGetter> mdGetter = - (int c, ref VBuffer dst) => keyValueGetter(ref dst); + MetadataUtils.MetadataGetter>> mdGetter = + (int c, ref VBuffer> dst) => keyValueGetter(ref dst); bldr.AddGetter(MetadataUtils.Kinds.KeyValues, new VectorType(TextType.Instance, _typeDst.ItemType.KeyCount), mdGetter); } if (slotNamesGetter != null) { Host.Assert(_typeDst.VectorSize > 0); - MetadataUtils.MetadataGetter> mdGetter = - (int c, ref VBuffer dst) => slotNamesGetter(ref dst); + MetadataUtils.MetadataGetter>> mdGetter = + (int c, ref VBuffer> dst) => slotNamesGetter(ref dst); bldr.AddGetter(MetadataUtils.Kinds.SlotNames, new VectorType(TextType.Instance, _typeDst.VectorSize), mdGetter); } } diff --git a/src/Microsoft.ML.Data/DataView/RowToRowMapperTransform.cs b/src/Microsoft.ML.Data/DataView/RowToRowMapperTransform.cs index ef19012bf6..799741fa1a 100644 --- a/src/Microsoft.ML.Data/DataView/RowToRowMapperTransform.cs +++ b/src/Microsoft.ML.Data/DataView/RowToRowMapperTransform.cs @@ -241,7 +241,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RowToRowMapperTransform).Assembly.FullName); } public override ISchema Schema { get { return _bindings; } } diff --git a/src/Microsoft.ML.Data/DataView/SimpleRow.cs b/src/Microsoft.ML.Data/DataView/SimpleRow.cs index 28700c59f9..b0ba12b5ab 100644 --- a/src/Microsoft.ML.Data/DataView/SimpleRow.cs +++ b/src/Microsoft.ML.Data/DataView/SimpleRow.cs @@ -64,97 +64,135 @@ public bool IsColumnActive(int col) /// An that takes all column names and types as constructor parameters. /// The columns do not have metadata. /// - public sealed class SimpleSchema : ISchema + public abstract class SimpleSchemaBase : ISchema { - private readonly IExceptionContext _ectx; + protected readonly IExceptionContext Ectx; private readonly string[] _names; - private readonly ColumnType[] _types; - private readonly Dictionary _columnNameMap; - private readonly MetadataUtils.MetadataGetter>[] _keyValueGetters; + protected readonly ColumnType[] Types; + protected readonly Dictionary ColumnNameMap; - public int ColumnCount => _types.Length; + public int ColumnCount => Types.Length; - public SimpleSchema(IExceptionContext ectx, params KeyValuePair[] columns) + protected SimpleSchemaBase(IExceptionContext ectx, params KeyValuePair[] columns) { Contracts.CheckValueOrNull(ectx); - _ectx = ectx; - _ectx.CheckValue(columns, nameof(columns)); + Ectx = ectx; + Ectx.CheckValue(columns, nameof(columns)); _names = new string[columns.Length]; - _types = new ColumnType[columns.Length]; - _columnNameMap = new Dictionary(); + Types = new ColumnType[columns.Length]; + ColumnNameMap = new Dictionary(); for (int i = 0; i < columns.Length; i++) { _names[i] = columns[i].Key; - _types[i] = columns[i].Value; - if (_columnNameMap.ContainsKey(columns[i].Key)) + Types[i] = columns[i].Value; + if (ColumnNameMap.ContainsKey(columns[i].Key)) throw ectx.ExceptParam(nameof(columns), $"Duplicate column name: '{columns[i].Key}'"); - _columnNameMap[columns[i].Key] = i; - } - _keyValueGetters = new MetadataUtils.MetadataGetter>[ColumnCount]; - } - - public SimpleSchema(IExceptionContext ectx, KeyValuePair[] columns, Dictionary>> keyValues) - : this(ectx, columns) - { - foreach (var kvp in keyValues) - { - var name = kvp.Key; - var getter = kvp.Value; - if (!_columnNameMap.TryGetValue(name, out int col)) - throw _ectx.ExceptParam(nameof(keyValues), $"Output schema does not contain column '{name}'"); - if (!_types[col].ItemType.IsKey) - throw _ectx.ExceptParam(nameof(keyValues), $"Column '{name}' is not a key column, so it cannot have key value metadata"); - _keyValueGetters[col] = getter; + ColumnNameMap[columns[i].Key] = i; } } public bool TryGetColumnIndex(string name, out int col) { - return _columnNameMap.TryGetValue(name, out col); + return ColumnNameMap.TryGetValue(name, out col); } public string GetColumnName(int col) { - _ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); + Ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); return _names[col]; } public ColumnType GetColumnType(int col) { - _ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); - return _types[col]; + Ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); + return Types[col]; } public IEnumerable> GetMetadataTypes(int col) { - _ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); + Ectx.Assert(0 <= col && col < ColumnCount); + return GetMetadataTypesCore(col); + } + + protected abstract IEnumerable> GetMetadataTypesCore(int col); + + public ColumnType GetMetadataTypeOrNull(string kind, int col) + { + Ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); + return GetMetadataTypeOrNullCore(kind, col); + } + + protected abstract ColumnType GetMetadataTypeOrNullCore(string kind, int col); + + public void GetMetadata(string kind, int col, ref TValue value) + { + Ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); + GetMetadataCore(kind, col, ref value); + } + + protected abstract void GetMetadataCore(string kind, int col, ref TValue value); + } + + /// + /// An that takes all column names and types as constructor parameters. + /// The columns can optionally have text metadata. + /// + public sealed class SimpleSchema : SimpleSchemaBase + { + private readonly MetadataUtils.MetadataGetter>>[] _keyValueGetters; + + public SimpleSchema(IExceptionContext ectx, params KeyValuePair[] columns) + : base(ectx, columns) + { + _keyValueGetters = new MetadataUtils.MetadataGetter>>[ColumnCount]; + } + + public SimpleSchema(IExceptionContext ectx, KeyValuePair[] columns, + Dictionary>>> keyValues) + : this(ectx, columns) + { + foreach (var kvp in keyValues) + { + var name = kvp.Key; + var getter = kvp.Value; + if (!ColumnNameMap.TryGetValue(name, out int col)) + throw Ectx.ExceptParam(nameof(keyValues), $"Output schema does not contain column '{name}'"); + if (!Types[col].ItemType.IsKey) + throw Ectx.ExceptParam(nameof(keyValues), $"Column '{name}' is not a key column, so it cannot have key value metadata"); + _keyValueGetters[col] = getter; + } + } + + protected override IEnumerable> GetMetadataTypesCore(int col) + { + Ectx.Assert(0 <= col && col < ColumnCount); if (_keyValueGetters[col] != null) { - _ectx.Assert(_types[col].ItemType.IsKey); + Ectx.Assert(Types[col].ItemType.IsKey); yield return new KeyValuePair(MetadataUtils.Kinds.KeyValues, - new VectorType(TextType.Instance, _types[col].ItemType.KeyCount)); + new VectorType(TextType.Instance, Types[col].ItemType.KeyCount)); } } - public ColumnType GetMetadataTypeOrNull(string kind, int col) + protected override ColumnType GetMetadataTypeOrNullCore(string kind, int col) { - _ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); + Ectx.Assert(0 <= col && col < ColumnCount); if (kind == MetadataUtils.Kinds.KeyValues && _keyValueGetters[col] != null) { - _ectx.Assert(_types[col].ItemType.IsKey); - return new VectorType(TextType.Instance, _types[col].ItemType.KeyCount); + Ectx.Assert(Types[col].ItemType.IsKey); + return new VectorType(TextType.Instance, Types[col].ItemType.KeyCount); } return null; } - public void GetMetadata(string kind, int col, ref TValue value) + protected override void GetMetadataCore(string kind, int col, ref TValue value) { - _ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); + Ectx.Assert(0 <= col && col < ColumnCount); if (kind == MetadataUtils.Kinds.KeyValues && _keyValueGetters[col] != null) _keyValueGetters[col].Marshal(col, ref value); else - throw _ectx.ExceptGetMetadata(); + throw Ectx.ExceptGetMetadata(); } } } \ No newline at end of file diff --git a/src/Microsoft.ML.Data/Depricated/Instances/HeaderSchema.cs b/src/Microsoft.ML.Data/Depricated/Instances/HeaderSchema.cs index f08d52fe85..dfa90bfec6 100644 --- a/src/Microsoft.ML.Data/Depricated/Instances/HeaderSchema.cs +++ b/src/Microsoft.ML.Data/Depricated/Instances/HeaderSchema.cs @@ -27,7 +27,7 @@ private sealed class FeatureNameCollectionSchema : ISchema private readonly FeatureNameCollection _collection; - private readonly MetadataUtils.MetadataGetter> _getSlotNames; + private readonly MetadataUtils.MetadataGetter>> _getSlotNames; public int ColumnCount => 1; @@ -86,21 +86,21 @@ public bool TryGetColumnIndex(string name, out int col) return name == RoleMappedSchema.ColumnRole.Feature.Value; } - private void GetSlotNames(int col, ref VBuffer dst) + private void GetSlotNames(int col, ref VBuffer> dst) { Contracts.Assert(col == 0); - var nameList = new List(); + var nameList = new List>(); var indexList = new List(); foreach (var kvp in _collection.GetNonDefaultFeatureNames()) { - nameList.Add(new DvText(kvp.Value)); + nameList.Add(kvp.Value.AsMemory()); indexList.Add(kvp.Key); } var vals = dst.Values; if (Utils.Size(vals) < nameList.Count) - vals = new DvText[nameList.Count]; + vals = new ReadOnlyMemory[nameList.Count]; Array.Copy(nameList.ToArray(), vals, nameList.Count); if (nameList.Count < _collection.Count) { @@ -108,10 +108,10 @@ private void GetSlotNames(int col, ref VBuffer dst) if (Utils.Size(indices) < indexList.Count) indices = new int[indexList.Count]; Array.Copy(indexList.ToArray(), indices, indexList.Count); - dst = new VBuffer(_collection.Count, nameList.Count, vals, indices); + dst = new VBuffer>(_collection.Count, nameList.Count, vals, indices); } else - dst = new VBuffer(_collection.Count, vals, dst.Indices); + dst = new VBuffer>(_collection.Count, vals, dst.Indices); } } @@ -193,15 +193,15 @@ public static FeatureNameCollection Create(RoleMappedSchema schema) Contracts.CheckParam(schema.Feature != null, nameof(schema), "Cannot create feature name collection if we have no features"); Contracts.CheckParam(schema.Feature.Type.ValueCount > 0, nameof(schema), "Cannot create feature name collection if our features are not of known size"); - VBuffer slotNames = default(VBuffer); + VBuffer> slotNames = default; int len = schema.Feature.Type.ValueCount; if (schema.Schema.HasSlotNames(schema.Feature.Index, len)) schema.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, schema.Feature.Index, ref slotNames); else - slotNames = VBufferUtils.CreateEmpty(len); + slotNames = VBufferUtils.CreateEmpty>(len); string[] names = new string[slotNames.Count]; for (int i = 0; i < slotNames.Count; ++i) - names[i] = slotNames.Values[i].HasChars ? slotNames.Values[i].ToString() : null; + names[i] = !slotNames.Values[i].IsEmpty ? slotNames.Values[i].ToString() : null; if (slotNames.IsDense) return new Dense(names.Length, names); @@ -222,10 +222,11 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FeatureNameCollection).Assembly.FullName); } - public static void Save(ModelSaveContext ctx, ref VBuffer names) + public static void Save(ModelSaveContext ctx, ref VBuffer> names) { Contracts.AssertValue(ctx); ctx.CheckAtModel(); diff --git a/src/Microsoft.ML.Data/Dirty/ChooseColumnsByIndexTransform.cs b/src/Microsoft.ML.Data/Dirty/ChooseColumnsByIndexTransform.cs index da237611b9..df0e0ff049 100644 --- a/src/Microsoft.ML.Data/Dirty/ChooseColumnsByIndexTransform.cs +++ b/src/Microsoft.ML.Data/Dirty/ChooseColumnsByIndexTransform.cs @@ -189,7 +189,8 @@ private static VersionInfo GetVersionInfo() verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, loaderSignature: LoaderSignature, - loaderSignatureAlt: LoaderSignatureOld); + loaderSignatureAlt: LoaderSignatureOld, + loaderAssemblyName: typeof(ChooseColumnsByIndexTransform).Assembly.FullName); } private readonly Bindings _bindings; diff --git a/src/Microsoft.ML.Data/EntryPoints/PredictorModel.cs b/src/Microsoft.ML.Data/EntryPoints/PredictorModel.cs index 055b2fa299..604cb71977 100644 --- a/src/Microsoft.ML.Data/EntryPoints/PredictorModel.cs +++ b/src/Microsoft.ML.Data/EntryPoints/PredictorModel.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Collections.Generic; using System.IO; using System.Linq; @@ -128,7 +129,7 @@ public string[] GetLabelInfo(IHostEnvironment env, out ColumnType labelType) if (labelType.IsKey && trainRms.Schema.HasKeyNames(trainRms.Label.Index, labelType.KeyCount)) { - VBuffer keyValues = default(VBuffer); + VBuffer> keyValues = default; trainRms.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues, trainRms.Label.Index, ref keyValues); return keyValues.DenseValues().Select(v => v.ToString()).ToArray(); diff --git a/src/Microsoft.ML.Data/EntryPoints/ScoreColumnSelector.cs b/src/Microsoft.ML.Data/EntryPoints/ScoreColumnSelector.cs index c3ebc54678..65bc1daf7b 100644 --- a/src/Microsoft.ML.Data/EntryPoints/ScoreColumnSelector.cs +++ b/src/Microsoft.ML.Data/EntryPoints/ScoreColumnSelector.cs @@ -89,10 +89,10 @@ public static CommonOutputs.TransformOutput RenameBinaryPredictionScoreColumns(I if (!ShouldAddColumn(input.Data.Schema, i, null, maxScoreId)) continue; // Do not rename the PredictedLabel column. - DvText tmp = default(DvText); + ReadOnlyMemory tmp = default; if (input.Data.Schema.TryGetMetadata(TextType.Instance, MetadataUtils.Kinds.ScoreValueKind, i, ref tmp) - && tmp.EqualsStr(MetadataUtils.Const.ScoreValueKind.PredictedLabel)) + && ReadOnlyMemoryUtils.EqualsStr(MetadataUtils.Const.ScoreValueKind.PredictedLabel, tmp)) { continue; } diff --git a/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs b/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs index 260ff37f26..195b2ec5f6 100644 --- a/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs +++ b/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs @@ -83,7 +83,7 @@ public static Output Score(IHostEnvironment env, Input input) ch.AssertValue(bindable); var mapper = bindable.Bind(host, data.Schema); - var scorer = ScoreUtils.GetScorerComponent(mapper, input.Suffix); + var scorer = ScoreUtils.GetScorerComponent(host, mapper, input.Suffix); scoredPipe = scorer.CreateComponent(host, data.Data, mapper, input.PredictorModel.GetTrainingSchema(host)); ch.Done(); } @@ -134,7 +134,7 @@ public static Output MakeScoringTransform(IHostEnvironment env, ModelInput input ch.AssertValue(bindable); var mapper = bindable.Bind(host, data.Schema); - var scorer = ScoreUtils.GetScorerComponent(mapper); + var scorer = ScoreUtils.GetScorerComponent(host, mapper); scoredPipe = scorer.CreateComponent(host, data.Data, mapper, input.PredictorModel.GetTrainingSchema(host)); ch.Done(); } diff --git a/src/Microsoft.ML.Data/Evaluators/AnomalyDetectionEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/AnomalyDetectionEvaluator.cs index 8e4f3be56c..d9f8066ecc 100644 --- a/src/Microsoft.ML.Data/Evaluators/AnomalyDetectionEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/AnomalyDetectionEvaluator.cs @@ -125,10 +125,10 @@ public override IEnumerable GetOverallMetricColumns() } protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate) + out Action, Aggregator> addAgg, out Func> consolidate) { var stratCol = new List(); - var stratVal = new List(); + var stratVal = new List>(); var auc = new List(); var drAtK = new List(); var drAtP = new List(); @@ -136,13 +136,13 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A var thresholdAtK = new List(); var thresholdAtP = new List(); var thresholdAtNumAnomalies = new List(); - var numAnoms = new List(); + var numAnoms = new List(); var scores = new List(); var labels = new List(); - var names = new List(); + var names = new List>(); var topKStratCol = new List(); - var topKStratVal = new List(); + var topKStratVal = new List>(); bool hasStrats = Utils.Size(dictionaries) > 0; @@ -438,9 +438,9 @@ private struct TopExamplesInfo private ValueGetter _labelGetter; private ValueGetter _scoreGetter; - private ValueGetter _nameGetter; + private ValueGetter> _nameGetter; - public readonly DvText[] Names; + public readonly ReadOnlyMemory[] Names; public readonly Single[] Scores; public readonly Single[] Labels; public int NumTopExamples; @@ -464,7 +464,7 @@ public Aggregator(IHostEnvironment env, int reservoirSize, int topK, int k, Doub AggCounters = new TwoPassCounters(_k, _p); _aucAggregator = new UnweightedAucAggregator(Host.Rand, reservoirSize); - Names = new DvText[_topK]; + Names = new ReadOnlyMemory[_topK]; Scores = new Single[_topK]; Labels = new Single[_topK]; } @@ -491,7 +491,7 @@ private void FinishOtherMetrics() NumTopExamples = _topExamples.Count; while (_topExamples.Count > 0) { - Names[_topExamples.Count - 1] = new DvText(_topExamples.Top.Name); + Names[_topExamples.Count - 1] = _topExamples.Top.Name.AsMemory(); Scores[_topExamples.Count - 1] = _topExamples.Top.Score; Labels[_topExamples.Count - 1] = _topExamples.Top.Label; _topExamples.Pop(); @@ -516,10 +516,10 @@ public override void InitializeNextPass(IRow row, RoleMappedSchema schema) if (_nameIndex < 0) { int rowCounter = 0; - _nameGetter = (ref DvText dst) => dst = new DvText((rowCounter++).ToString()); + _nameGetter = (ref ReadOnlyMemory dst) => dst = (rowCounter++).ToString().AsMemory(); } else - _nameGetter = row.GetGetter(_nameIndex); + _nameGetter = row.GetGetter>(_nameIndex); } } @@ -552,7 +552,7 @@ public override void ProcessRow() _aucAggregator.ProcessRow(label, score); AggCounters.Update(label, score); - var name = default(DvText); + var name = default(ReadOnlyMemory); _nameGetter(ref name); if (_topExamples.Count >= _topK) { @@ -632,7 +632,7 @@ protected override void PrintFoldResultsCore(IChannel ch, Dictionary(index); + var instanceGetter = cursor.GetGetter>(index); if (!top.Schema.TryGetColumnIndex(AnomalyDetectionEvaluator.TopKResultsColumns.AnomalyScore, out index)) throw Host.Except("Data view does not contain the 'Anomaly Score' column"); var scoreGetter = cursor.GetGetter(index); @@ -651,7 +651,7 @@ protected override void PrintFoldResultsCore(IChannel ch, Dictionary); Single score = 0; Single label = 0; instanceGetter(ref name); @@ -678,11 +678,11 @@ protected override void PrintFoldResultsCore(IChannel ch, Dictionary col == numAnomIndex || (hasStrat && col == stratCol))) { - var numAnomGetter = cursor.GetGetter(numAnomIndex); + var numAnomGetter = cursor.GetGetter(numAnomIndex); ValueGetter stratGetter = null; if (hasStrat) { diff --git a/src/Microsoft.ML.Data/Evaluators/BinaryClassifierEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/BinaryClassifierEvaluator.cs index a567abef39..2247c32637 100644 --- a/src/Microsoft.ML.Data/Evaluators/BinaryClassifierEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/BinaryClassifierEvaluator.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -170,11 +170,11 @@ protected override Aggregator GetAggregatorCore(RoleMappedSchema schema, string return new Aggregator(Host, classNames, schema.Weight != null, _aucCount, _auPrcCount, _threshold, _useRaw, _prCount, stratName); } - private DvText[] GetClassNames(RoleMappedSchema schema) + private ReadOnlyMemory[] GetClassNames(RoleMappedSchema schema) { // Get the label names if they exist, or use the default names. ColumnType type; - var labelNames = default(VBuffer); + var labelNames = default(VBuffer>); if (schema.Label.Type.IsKey && (type = schema.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.KeyValues, schema.Label.Index)) != null && type.ItemType.IsKnownSizeVector && type.ItemType.IsText) @@ -182,8 +182,9 @@ private DvText[] GetClassNames(RoleMappedSchema schema) schema.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues, schema.Label.Index, ref labelNames); } else - labelNames = new VBuffer(2, new[] { new DvText("positive"), new DvText("negative") }); - DvText[] names = new DvText[2]; + labelNames = new VBuffer>(2, new[] { "positive".AsMemory(), "negative".AsMemory() }); + + ReadOnlyMemory[] names = new ReadOnlyMemory[2]; labelNames.CopyTo(names); return names; } @@ -216,11 +217,11 @@ public override IEnumerable GetOverallMetricColumns() } protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate) + out Action, Aggregator> addAgg, out Func> consolidate) { var stratCol = new List(); - var stratVal = new List(); - var isWeighted = new List(); + var stratVal = new List>(); + var isWeighted = new List(); var auc = new List(); var accuracy = new List(); var posPrec = new List(); @@ -236,7 +237,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A var counts = new List(); var weights = new List(); var confStratCol = new List(); - var confStratVal = new List(); + var confStratVal = new List>(); var scores = new List(); var precision = new List(); @@ -246,7 +247,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A var weightedRecall = new List(); var weightedFpr = new List(); var prStratCol = new List(); - var prStratVal = new List(); + var prStratVal = new List>(); bool hasStrats = Utils.Size(dictionaries) > 0; bool hasWeight = aggregator.Weighted; @@ -261,7 +262,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A agg.Finish(); stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.False); + isWeighted.Add(false); auc.Add(agg.UnweightedAuc); accuracy.Add(agg.UnweightedCounters.Acc); posPrec.Add(agg.UnweightedCounters.PrecisionPos); @@ -300,7 +301,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A { stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.True); + isWeighted.Add(true); auc.Add(agg.WeightedAuc); accuracy.Add(agg.WeightedCounters.Acc); posPrec.Add(agg.WeightedCounters.PrecisionPos); @@ -359,9 +360,9 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A confDvBldr.AddColumn(MetricKinds.ColumnNames.StratCol, GetKeyValueGetter(dictionaries), 0, dictionaries.Length, confStratCol.ToArray()); confDvBldr.AddColumn(MetricKinds.ColumnNames.StratVal, TextType.Instance, confStratVal.ToArray()); } - ValueGetter> getSlotNames = - (ref VBuffer dst) => - dst = new VBuffer(aggregator.ClassNames.Length, aggregator.ClassNames); + ValueGetter>> getSlotNames = + (ref VBuffer> dst) => + dst = new VBuffer>(aggregator.ClassNames.Length, aggregator.ClassNames); confDvBldr.AddColumn(MetricKinds.ColumnNames.Count, getSlotNames, NumberType.R8, counts.ToArray()); if (hasWeight) @@ -549,9 +550,9 @@ private struct RocInfo private Single _label; private Single _weight; - public readonly DvText[] ClassNames; + public readonly ReadOnlyMemory[] ClassNames; - public Aggregator(IHostEnvironment env, DvText[] classNames, bool weighted, int aucReservoirSize, + public Aggregator(IHostEnvironment env, ReadOnlyMemory[] classNames, bool weighted, int aucReservoirSize, int auPrcReservoirSize, Single threshold, bool useRaw, int prCount, string stratName) : base(env, stratName) { @@ -1033,7 +1034,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(BinaryPerInstanceEvaluator).Assembly.FullName); } private const int AssignedCol = 0; @@ -1165,7 +1167,7 @@ public override Delegate[] CreateGetters(IRow input, Func activeCols, scoreGetter = nanGetter; Action updateCacheIfNeeded; - Func getPredictedLabel; + Func getPredictedLabel; if (_useRaw) { updateCacheIfNeeded = @@ -1199,8 +1201,8 @@ public override Delegate[] CreateGetters(IRow input, Func activeCols, var getters = _probIndex >= 0 ? new Delegate[2] : new Delegate[1]; if (activeCols(AssignedCol)) { - ValueGetter predFn = - (ref DvBool dst) => + ValueGetter predFn = + (ref bool dst) => { updateCacheIfNeeded(); dst = getPredictedLabel(); @@ -1229,9 +1231,10 @@ private Double GetLogLoss(Single prob, Single label) return -Math.Log(1.0 - prob, 2); } - private DvBool GetPredictedLabel(Single val) + private bool GetPredictedLabel(Single val) { - return val.IsNA() ? DvBool.NA : val > _threshold ? DvBool.True : DvBool.False; + //Behavior for NA values is undefined. + return Single.IsNaN(val) ? false : val > _threshold; } public override RowMapperColumnInfo[] GetOutputColumns() diff --git a/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs index bec1ac144a..2c476eb468 100644 --- a/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -115,11 +115,11 @@ public override IEnumerable GetOverallMetricColumns() } protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate) + out Action, Aggregator> addAgg, out Func> consolidate) { var stratCol = new List(); - var stratVal = new List(); - var isWeighted = new List(); + var stratVal = new List>(); + var isWeighted = new List(); var nmi = new List(); var avgMinScores = new List(); var dbi = new List(); @@ -136,7 +136,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.False); + isWeighted.Add(false); nmi.Add(agg.UnweightedCounters.Nmi); avgMinScores.Add(agg.UnweightedCounters.AvgMinScores); if (agg.UnweightedCounters.CalculateDbi) @@ -145,7 +145,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A { stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.True); + isWeighted.Add(true); nmi.Add(agg.WeightedCounters.Nmi); avgMinScores.Add(agg.WeightedCounters.AvgMinScores); if (agg.WeightedCounters.CalculateDbi) @@ -529,7 +529,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ClusteringPerInstanceEvaluator).Assembly.FullName); } private const int ClusterIdCol = 0; @@ -685,10 +686,10 @@ public override RowMapperColumnInfo[] GetOutputColumns() var slotNamesType = new VectorType(TextType.Instance, _numClusters); var sortedClusters = new ColumnMetadataInfo(SortedClusters); - sortedClusters.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, + sortedClusters.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(_numClusters, "Cluster"))); var sortedClusterScores = new ColumnMetadataInfo(SortedClusterScores); - sortedClusterScores.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, + sortedClusterScores.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(_numClusters, "Score"))); infos[SortedClusterCol] = new RowMapperColumnInfo(SortedClusters, _types[SortedClusterCol], sortedClusters); @@ -698,17 +699,17 @@ public override RowMapperColumnInfo[] GetOutputColumns() } // REVIEW: Figure out how to avoid having the column name in each slot name. - private MetadataUtils.MetadataGetter> CreateSlotNamesGetter(int numTopClusters, string suffix) + private MetadataUtils.MetadataGetter>> CreateSlotNamesGetter(int numTopClusters, string suffix) { return - (int col, ref VBuffer dst) => + (int col, ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < numTopClusters) - values = new DvText[numTopClusters]; + values = new ReadOnlyMemory[numTopClusters]; for (int i = 1; i <= numTopClusters; i++) - values[i - 1] = new DvText(string.Format("#{0} {1}", i, suffix)); - dst = new VBuffer(numTopClusters, values); + values[i - 1] = string.Format("#{0} {1}", i, suffix).AsMemory(); + dst = new VBuffer>(numTopClusters, values); }; } diff --git a/src/Microsoft.ML.Data/Evaluators/EvaluatorBase.cs b/src/Microsoft.ML.Data/Evaluators/EvaluatorBase.cs index c628cff1e4..91e5f77d0e 100644 --- a/src/Microsoft.ML.Data/Evaluators/EvaluatorBase.cs +++ b/src/Microsoft.ML.Data/Evaluators/EvaluatorBase.cs @@ -167,18 +167,18 @@ private Dictionary ProcessData(IDataView data, RoleMappedSche needMorePasses = finishPass(); } - Action addAgg; + Action, TAgg> addAgg; Func> consolidate; GetAggregatorConsolidationFuncs(aggregator, dictionaries, out addAgg, out consolidate); uint stratColKey = 0; - addAgg(stratColKey, DvText.NA, aggregator); + addAgg(stratColKey, default, aggregator); for (int i = 0; i < Utils.Size(dictionaries); i++) { var dict = dictionaries[i]; stratColKey++; foreach (var agg in dict.GetAll()) - addAgg(stratColKey, new DvText(agg.StratName), agg); + addAgg(stratColKey, agg.StratName.AsMemory(), agg); } return consolidate(); } @@ -192,21 +192,21 @@ private Dictionary ProcessData(IDataView data, RoleMappedSche /// the dictionary of metric data views. /// protected abstract void GetAggregatorConsolidationFuncs(TAgg aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate); + out Action, TAgg> addAgg, out Func> consolidate); - protected ValueGetter> GetKeyValueGetter(AggregatorDictionaryBase[] dictionaries) + protected ValueGetter>> GetKeyValueGetter(AggregatorDictionaryBase[] dictionaries) { if (Utils.Size(dictionaries) == 0) return null; return - (ref VBuffer dst) => + (ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < dictionaries.Length) - values = new DvText[dictionaries.Length]; + values = new ReadOnlyMemory[dictionaries.Length]; for (int i = 0; i < dictionaries.Length; i++) - values[i] = new DvText(dictionaries[i].ColName); - dst = new VBuffer(dictionaries.Length, values, dst.Indices); + values[i] = dictionaries[i].ColName.AsMemory(); + dst = new VBuffer>(dictionaries.Length, values, dst.Indices); }; } @@ -296,7 +296,7 @@ public void GetWarnings(Dictionary dict, IHostEnvironment env { var dvBldr = new ArrayDataViewBuilder(env); dvBldr.AddColumn(MetricKinds.ColumnNames.WarningText, TextType.Instance, - warnings.Select(s => new DvText(s)).ToArray()); + warnings.Select(s => s.AsMemory()).ToArray()); dict.Add(MetricKinds.Warnings, dvBldr.GetDataView()); } } diff --git a/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs b/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs index 5244762d90..ffc8820e1a 100644 --- a/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs +++ b/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs @@ -55,7 +55,7 @@ public static Dictionary> Instanc public static IMamlEvaluator GetEvaluator(IHostEnvironment env, ISchema schema) { Contracts.CheckValueOrNull(env); - DvText tmp = default; + ReadOnlyMemory tmp = default; schema.GetMaxMetadataKind(out int col, MetadataUtils.Kinds.ScoreColumnSetId, CheckScoreColumnKindIsKnown); if (col >= 0) { @@ -83,7 +83,7 @@ private static bool CheckScoreColumnKindIsKnown(ISchema schema, int col) var columnType = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, col); if (columnType == null || !columnType.IsText) return false; - DvText tmp = default(DvText); + ReadOnlyMemory tmp = default; schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, col, ref tmp); var map = DefaultEvaluatorTable.Instance; return map.ContainsKey(tmp.ToString()); @@ -125,18 +125,18 @@ public static ColumnInfo GetScoreColumnInfo(IExceptionContext ectx, ISchema sche var maxSetNum = schema.GetMaxMetadataKind(out colTmp, MetadataUtils.Kinds.ScoreColumnSetId, (s, c) => IsScoreColumnKind(ectx, s, c, kind)); - DvText tmp = default(DvText); + ReadOnlyMemory tmp = default; foreach (var col in schema.GetColumnSet(MetadataUtils.Kinds.ScoreColumnSetId, maxSetNum)) { #if DEBUG schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, col, ref tmp); - ectx.Assert(tmp.EqualsStr(kind)); + ectx.Assert(ReadOnlyMemoryUtils.EqualsStr(kind, tmp)); #endif // REVIEW: What should this do about hidden columns? Currently we ignore them. if (schema.IsHidden(col)) continue; if (schema.TryGetMetadata(TextType.Instance, MetadataUtils.Kinds.ScoreValueKind, col, ref tmp) && - tmp.EqualsStr(valueKind)) + ReadOnlyMemoryUtils.EqualsStr(valueKind, tmp)) { return ColumnInfo.CreateFromIndex(schema, col); } @@ -187,14 +187,14 @@ public static ColumnInfo GetOptAuxScoreColumnInfo(IExceptionContext ectx, ISchem uint setId = 0; schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnSetId, colScore, ref setId); - DvText tmp = default(DvText); + ReadOnlyMemory tmp = default; foreach (var col in schema.GetColumnSet(MetadataUtils.Kinds.ScoreColumnSetId, setId)) { // REVIEW: What should this do about hidden columns? Currently we ignore them. if (schema.IsHidden(col)) continue; if (schema.TryGetMetadata(TextType.Instance, MetadataUtils.Kinds.ScoreValueKind, col, ref tmp) && - tmp.EqualsStr(valueKind)) + ReadOnlyMemoryUtils.EqualsStr(valueKind, tmp)) { var res = ColumnInfo.CreateFromIndex(schema, col); if (testType(res.Type)) @@ -216,9 +216,9 @@ private static bool IsScoreColumnKind(IExceptionContext ectx, ISchema schema, in var type = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, col); if (type == null || !type.IsText) return false; - var tmp = default(DvText); + var tmp = default(ReadOnlyMemory); schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, col, ref tmp); - return tmp.EqualsStr(kind); + return ReadOnlyMemoryUtils.EqualsStr(kind, tmp); } /// @@ -272,12 +272,12 @@ public static IEnumerable> GetMetrics(IDataView met using (var cursor = metricsView.GetRowCursor(col => true)) { - DvBool isWeighted = DvBool.False; - ValueGetter isWeightedGetter; + bool isWeighted = false; + ValueGetter isWeightedGetter; if (hasWeighted) - isWeightedGetter = cursor.GetGetter(isWeightedCol); + isWeightedGetter = cursor.GetGetter(isWeightedCol); else - isWeightedGetter = (ref DvBool dst) => dst = DvBool.False; + isWeightedGetter = (ref bool dst) => dst = false; ValueGetter stratColGetter; if (hasStrats) @@ -313,7 +313,7 @@ public static IEnumerable> GetMetrics(IDataView met while (cursor.MoveNext()) { isWeightedGetter(ref isWeighted); - if (isWeighted.IsTrue) + if (isWeighted) continue; stratColGetter(ref strat); @@ -341,17 +341,17 @@ public static IEnumerable> GetMetrics(IDataView met // For R8 vector valued columns the names of the metrics are the column name, // followed by the slot name if it exists, or "Label_i" if it doesn't. - VBuffer names = default(VBuffer); + VBuffer> names = default; var size = schema.GetColumnType(i).VectorSize; var slotNamesType = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, i); if (slotNamesType != null && slotNamesType.VectorSize == size && slotNamesType.ItemType.IsText) schema.GetMetadata(MetadataUtils.Kinds.SlotNames, i, ref names); else { - var namesArray = new DvText[size]; + var namesArray = new ReadOnlyMemory[size]; for (int j = 0; j < size; j++) - namesArray[j] = new DvText(string.Format("({0})", j)); - names = new VBuffer(size, namesArray); + namesArray[j] = string.Format("({0})", j).AsMemory(); + names = new VBuffer>(size, namesArray); } var colName = schema.GetColumnName(i); foreach (var metric in metricVals.Items(all: true)) @@ -370,7 +370,7 @@ private static IDataView AddTextColumn(IHostEnvironment env, IDataView inp { Contracts.Check(typeSrc.RawType == typeof(TSrc)); return LambdaColumnMapper.Create(env, registrationName, input, inputColName, outputColName, typeSrc, TextType.Instance, - (ref TSrc src, ref DvText dst) => dst = new DvText(value)); + (ref TSrc src, ref ReadOnlyMemory dst) => dst = value.AsMemory()); } /// @@ -400,7 +400,7 @@ public static IDataView AddFoldIndex(IHostEnvironment env, IDataView input, int } private static IDataView AddKeyColumn(IHostEnvironment env, IDataView input, string inputColName, string outputColName, - ColumnType typeSrc, int keyCount, int value, string registrationName, ValueGetter> keyValueGetter) + ColumnType typeSrc, int keyCount, int value, string registrationName, ValueGetter>> keyValueGetter) { Contracts.Check(typeSrc.RawType == typeof(TSrc)); return LambdaColumnMapper.Create(env, registrationName, input, inputColName, outputColName, typeSrc, @@ -439,7 +439,7 @@ public static IDataView AddFoldIndex(IHostEnvironment env, IDataView input, int var inputColType = input.Schema.GetColumnType(inputCol); return Utils.MarshalInvoke(AddKeyColumn, inputColType.RawType, env, input, inputColName, MetricKinds.ColumnNames.FoldIndex, - inputColType, numFolds, curFold + 1, "FoldIndex", default(ValueGetter>)); + inputColType, numFolds, curFold + 1, "FoldIndex", default(ValueGetter>>)); } /// @@ -456,9 +456,9 @@ public static IDataView AddFoldIndex(IHostEnvironment env, IDataView input, int Contracts.CheckParam(typeof(T) == itemType.RawType, nameof(itemType), "Generic type does not match the item type"); var numIdvs = views.Length; - var slotNames = new Dictionary(); + var slotNames = new Dictionary(); var maps = new int[numIdvs][]; - var slotNamesCur = default(VBuffer); + var slotNamesCur = default(VBuffer>); var typeSrc = new ColumnType[numIdvs]; // Create mappings from the original slots to the reconciled slots. for (int i = 0; i < numIdvs; i++) @@ -477,23 +477,23 @@ public static IDataView AddFoldIndex(IHostEnvironment env, IDataView input, int foreach (var kvp in slotNamesCur.Items(true)) { var index = kvp.Key; - var name = kvp.Value; + var name = kvp.Value.ToString(); if (!slotNames.ContainsKey(name)) slotNames[name] = slotNames.Count; map[index] = slotNames[name]; } } - var reconciledSlotNames = new VBuffer(slotNames.Count, slotNames.Keys.ToArray()); - ValueGetter> slotNamesGetter = - (ref VBuffer dst) => + var reconciledSlotNames = new VBuffer>(slotNames.Count, slotNames.Keys.Select(k => k.AsMemory()).ToArray()); + ValueGetter>> slotNamesGetter = + (ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < reconciledSlotNames.Length) - values = new DvText[reconciledSlotNames.Length]; + values = new ReadOnlyMemory[reconciledSlotNames.Length]; Array.Copy(reconciledSlotNames.Values, values, reconciledSlotNames.Length); - dst = new VBuffer(reconciledSlotNames.Length, values, dst.Indices); + dst = new VBuffer>(reconciledSlotNames.Length, values, dst.Indices); }; // For each input data view, create the reconciled key column by wrapping it in a LambdaColumnMapper. @@ -553,7 +553,7 @@ public static IDataView AddFoldIndex(IHostEnvironment env, IDataView input, int } private static int[][] MapKeys(ISchema[] schemas, string columnName, bool isVec, - int[] indices, Dictionary reconciledKeyNames) + int[] indices, Dictionary, int> reconciledKeyNames) { Contracts.AssertValue(indices); Contracts.AssertValue(reconciledKeyNames); @@ -582,7 +582,7 @@ private static int[][] MapKeys(ISchema[] schemas, string columnName, bool isV foreach (var kvp in keyNamesCur.Items(true)) { var key = kvp.Key; - var name = new DvText(kvp.Value.ToString()); + var name = kvp.Value.ToString().AsMemory(); if (!reconciledKeyNames.ContainsKey(name)) reconciledKeyNames[name] = reconciledKeyNames.Count; keyValueMappers[i][key] = reconciledKeyNames[name]; @@ -606,14 +606,14 @@ public static void ReconcileKeyValues(IHostEnvironment env, IDataView[] views, s // Create mappings from the original key types to the reconciled key type. var indices = new int[dvCount]; - var keyNames = new Dictionary(); + var keyNames = new Dictionary, int>(); // We use MarshalInvoke so that we can call MapKeys with the correct generic: keyValueType.RawType. var keyValueMappers = Utils.MarshalInvoke(MapKeys, keyValueType.RawType, views.Select(view => view.Schema).ToArray(), columnName, false, indices, keyNames); var keyType = new KeyType(DataKind.U4, 0, keyNames.Count); - var keyNamesVBuffer = new VBuffer(keyNames.Count, keyNames.Keys.ToArray()); - ValueGetter> keyValueGetter = - (ref VBuffer dst) => - dst = new VBuffer(keyNamesVBuffer.Length, keyNamesVBuffer.Count, keyNamesVBuffer.Values, keyNamesVBuffer.Indices); + var keyNamesVBuffer = new VBuffer>(keyNames.Count, keyNames.Keys.ToArray()); + ValueGetter>> keyValueGetter = + (ref VBuffer> dst) => + dst = new VBuffer>(keyNamesVBuffer.Length, keyNamesVBuffer.Count, keyNamesVBuffer.Values, keyNamesVBuffer.Indices); // For each input data view, create the reconciled key column by wrapping it in a LambdaColumnMapper. for (int i = 0; i < dvCount; i++) @@ -674,14 +674,14 @@ public static void ReconcileVectorKeyValues(IHostEnvironment env, IDataView[] vi var dvCount = views.Length; - var keyNames = new Dictionary(); + var keyNames = new Dictionary, int>(); var columnIndices = new int[dvCount]; var keyValueMappers = Utils.MarshalInvoke(MapKeys, keyValueType.RawType, views.Select(view => view.Schema).ToArray(), columnName, true, columnIndices, keyNames); var keyType = new KeyType(DataKind.U4, 0, keyNames.Count); - var keyNamesVBuffer = new VBuffer(keyNames.Count, keyNames.Keys.ToArray()); - ValueGetter> keyValueGetter = - (ref VBuffer dst) => - dst = new VBuffer(keyNamesVBuffer.Length, keyNamesVBuffer.Count, keyNamesVBuffer.Values, keyNamesVBuffer.Indices); + var keyNamesVBuffer = new VBuffer>(keyNames.Count, keyNames.Keys.ToArray()); + ValueGetter>> keyValueGetter = + (ref VBuffer> dst) => + dst = new VBuffer>(keyNamesVBuffer.Length, keyNamesVBuffer.Count, keyNamesVBuffer.Values, keyNamesVBuffer.Indices); for (int i = 0; i < dvCount; i++) { @@ -720,14 +720,14 @@ public static void ReconcileVectorKeyValues(IHostEnvironment env, IDataView[] vi } }; - ValueGetter> slotNamesGetter = null; + ValueGetter>> slotNamesGetter = null; var type = views[i].Schema.GetColumnType(columnIndices[i]); if (views[i].Schema.HasSlotNames(columnIndices[i], type.VectorSize)) { var schema = views[i].Schema; int index = columnIndices[i]; slotNamesGetter = - (ref VBuffer dst) => schema.GetMetadata(MetadataUtils.Kinds.SlotNames, index, ref dst); + (ref VBuffer> dst) => schema.GetMetadata(MetadataUtils.Kinds.SlotNames, index, ref dst); } views[i] = LambdaColumnMapper.Create(env, "ReconcileKeyValues", views[i], columnName, columnName, type, new VectorType(keyType, type.AsVector), mapper, keyValueGetter, slotNamesGetter); @@ -810,7 +810,7 @@ private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, string // Make sure there are no variable size vector columns. // This is a dictionary from the column name to its vector size. var vectorSizes = new Dictionary(); - var firstDvSlotNames = new Dictionary>(); + var firstDvSlotNames = new Dictionary>>(); ColumnType labelColKeyValuesType = null; var firstDvKeyWithNamesColumns = new List(); var firstDvKeyNoNamesColumns = new Dictionary(); @@ -840,7 +840,7 @@ private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, string // Store the slot names of the 1st idv and use them as baseline. if (dv.Schema.HasSlotNames(i, type.VectorSize)) { - VBuffer slotNames = default(VBuffer); + VBuffer> slotNames = default; dv.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, i, ref slotNames); firstDvSlotNames.Add(name, slotNames); } @@ -849,7 +849,7 @@ private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, string int cachedSize; if (vectorSizes.TryGetValue(name, out cachedSize)) { - VBuffer slotNames; + VBuffer> slotNames; // In the event that no slot names were recorded here, then slotNames will be // the default, length 0 vector. firstDvSlotNames.TryGetValue(name, out slotNames); @@ -949,7 +949,7 @@ private static IEnumerable FindHiddenColumns(ISchema schema, string colName } private static bool VerifyVectorColumnsMatch(int cachedSize, int col, IDataView dv, - ColumnType type, ref VBuffer firstDvSlotNames) + ColumnType type, ref VBuffer> firstDvSlotNames) { if (cachedSize != type.VectorSize) return false; @@ -958,7 +958,7 @@ private static bool VerifyVectorColumnsMatch(int cachedSize, int col, IDataView if (dv.Schema.HasSlotNames(col, type.VectorSize)) { // Verify that slots match with slots from 1st idv. - VBuffer currSlotNames = default(VBuffer); + VBuffer> currSlotNames = default; dv.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, col, ref currSlotNames); if (currSlotNames.Length != firstDvSlotNames.Length) @@ -967,7 +967,7 @@ private static bool VerifyVectorColumnsMatch(int cachedSize, int col, IDataView { var result = true; VBufferUtils.ForEachEitherDefined(ref currSlotNames, ref firstDvSlotNames, - (slot, val1, val2) => result = result && DvText.Identical(val1, val2)); + (slot, val1, val2) => result = result && val1.Span.SequenceEqual(val2.Span)); return result; } } @@ -995,7 +995,7 @@ private static List GetMetricNames(IChannel ch, ISchema schema, IRow row // Get the names of the metrics. For R8 valued columns the metric name is the column name. For R8 vector valued columns // the names of the metrics are the column name, followed by the slot name if it exists, or "Label_i" if it doesn't. - VBuffer names = default(VBuffer); + VBuffer> names = default; int metricCount = 0; var metricNames = new List(); for (int i = 0; i < schema.ColumnCount; i++) @@ -1028,10 +1028,10 @@ private static List GetMetricNames(IChannel ch, ISchema schema, IRow row { var namesArray = names.Values; if (Utils.Size(namesArray) < type.VectorSize) - namesArray = new DvText[type.VectorSize]; + namesArray = new ReadOnlyMemory[type.VectorSize]; for (int j = 0; j < type.VectorSize; j++) - namesArray[j] = new DvText(string.Format("Label_{0}", j)); - names = new VBuffer(type.VectorSize, namesArray); + namesArray[j] = string.Format("Label_{0}", j).AsMemory(); + names = new VBuffer>(type.VectorSize, namesArray); } foreach (var name in names.Items(all: true)) metricNames.Add(string.Format("{0}{1}", metricName, name.Value)); @@ -1095,12 +1095,12 @@ internal static AggregatedMetric[] ComputeMetricsSum(IHostEnvironment env, IData AggregatedMetric[] agg; using (var cursor = data.GetRowCursor(col => true)) { - DvBool isWeighted = DvBool.False; - ValueGetter isWeightedGetter; + bool isWeighted = false; + ValueGetter isWeightedGetter; if (hasWeighted) - isWeightedGetter = cursor.GetGetter(isWeightedCol); + isWeightedGetter = cursor.GetGetter(isWeightedCol); else - isWeightedGetter = (ref DvBool dst) => dst = DvBool.False; + isWeightedGetter = (ref bool dst) => dst = false; ValueGetter stratColGetter; if (hasStrats) @@ -1138,7 +1138,7 @@ internal static AggregatedMetric[] ComputeMetricsSum(IHostEnvironment env, IData continue; isWeightedGetter(ref isWeighted); - if (isWeighted.IsTrue) + if (isWeighted) { // If !average, we should have only one relevant row. if (numWeightedResults > numFolds) @@ -1233,8 +1233,8 @@ internal static IDataView GetAverageToDataView(IHostEnvironment env, ISchema sch MetricKinds.ColumnNames.StratCol); } - ValueGetter> getKeyValues = - (ref VBuffer dst) => + ValueGetter>> getKeyValues = + (ref VBuffer> dst) => { schema.GetMetadata(MetadataUtils.Kinds.KeyValues, stratCol, ref dst); Contracts.Assert(dst.IsDense); @@ -1246,19 +1246,20 @@ internal static IDataView GetAverageToDataView(IHostEnvironment env, ISchema sch } else if (i == stratVal) { - var stratVals = foldCol >= 0 ? new[] { DvText.NA, DvText.NA } : new[] { DvText.NA }; + //REVIEW: Not sure if empty string makes sense here. + var stratVals = foldCol >= 0 ? new[] { "".AsMemory(), "".AsMemory() } : new[] { "".AsMemory() }; dvBldr.AddColumn(MetricKinds.ColumnNames.StratVal, TextType.Instance, stratVals); weightedDvBldr?.AddColumn(MetricKinds.ColumnNames.StratVal, TextType.Instance, stratVals); } else if (i == isWeightedCol) { env.AssertValue(weightedDvBldr); - dvBldr.AddColumn(MetricKinds.ColumnNames.IsWeighted, BoolType.Instance, foldCol >= 0 ? new[] { DvBool.False, DvBool.False } : new[] { DvBool.False }); - weightedDvBldr.AddColumn(MetricKinds.ColumnNames.IsWeighted, BoolType.Instance, foldCol >= 0 ? new[] { DvBool.True, DvBool.True } : new[] { DvBool.True }); + dvBldr.AddColumn(MetricKinds.ColumnNames.IsWeighted, BoolType.Instance, foldCol >= 0 ? new[] { false, false} : new[] { false }); + weightedDvBldr.AddColumn(MetricKinds.ColumnNames.IsWeighted, BoolType.Instance, foldCol >= 0 ? new[] { true, true } : new[] { true }); } else if (i == foldCol) { - var foldVals = new[] { new DvText("Average"), new DvText("Standard Deviation") }; + var foldVals = new[] { "Average".AsMemory(), "Standard Deviation".AsMemory() }; dvBldr.AddColumn(MetricKinds.ColumnNames.FoldIndex, TextType.Instance, foldVals); weightedDvBldr?.AddColumn(MetricKinds.ColumnNames.FoldIndex, TextType.Instance, foldVals); } @@ -1297,11 +1298,11 @@ private static void AddVectorColumn(this ArrayDataViewBuilder dvBldr, IHostEnvir for (int j = 0; j < vectorStdevMetrics.Length; j++) vectorStdevMetrics[j] = Math.Sqrt(agg[iMetric + j].SumSq / numFolds - vectorMetrics[j] * vectorMetrics[j]); } - var names = new DvText[type.VectorSize]; + var names = new ReadOnlyMemory[type.VectorSize]; for (int j = 0; j < names.Length; j++) - names[j] = new DvText(agg[iMetric + j].Name); - var slotNames = new VBuffer(type.VectorSize, names); - ValueGetter> getSlotNames = (ref VBuffer dst) => dst = slotNames; + names[j] = agg[iMetric + j].Name.AsMemory(); + var slotNames = new VBuffer>(type.VectorSize, names); + ValueGetter>> getSlotNames = (ref VBuffer> dst) => dst = slotNames; if (vectorStdevMetrics != null) { env.AssertValue(vectorStdevMetrics); @@ -1356,7 +1357,7 @@ public static string GetConfusionTable(IHost host, IDataView confusionDataView, var type = confusionDataView.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, countCol); host.Check(type != null && type.IsKnownSizeVector && type.ItemType.IsText, "The Count column does not have a text vector metadata of kind SlotNames."); - var labelNames = default(VBuffer); + var labelNames = default(VBuffer>); confusionDataView.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, countCol, ref labelNames); host.Check(labelNames.IsDense, "Slot names vector must be dense"); @@ -1477,7 +1478,7 @@ private static double[][] GetConfusionTableAsArray(IDataView confusionDataView, /// stratified metrics, it must contain two text columns named "StratCol" and "StratVal", containing the stratification column /// name, and a text description of the value. In this case, the value of column StratVal in the row corresponding to the entire /// dataset should contain the text "overall", and the value of column StratCol should be DvText.NA. If weighted metrics are present - /// then the data view should also contain a DvBool column named "IsWeighted". + /// then the data view should also contain a bool column named "IsWeighted". /// If the IsWeighted column exists, this is assigned the string representation of the weighted /// metrics. Otherwise it is assigned null. public static string GetPerFoldResults(IHostEnvironment env, IDataView fold, out string weightedMetrics) @@ -1507,7 +1508,7 @@ private static string GetOverallMetricsAsString(double[] sumMetrics, double[] su // This method returns a string representation of a set of metrics. If there are stratification columns, it looks for columns named // StratCol and StratVal, and outputs the metrics in the rows with NA in the StratCol column. If weighted is true, it looks - // for a DvBool column named "IsWeighted" and outputs the metrics in the rows with a value of true in that column. + // for a bool column named "IsWeighted" and outputs the metrics in the rows with a value of true in that column. // If nonAveragedCols is non-null, it computes the average and standard deviation over all the relevant rows and populates // nonAveragedCols with columns that are either hidden, or are not of a type that we can display (i.e., either a numeric column, // or a known length vector of doubles). @@ -1536,7 +1537,7 @@ private static string GetFoldMetricsAsString(IHostEnvironment env, IDataView dat // Get a string representation of a confusion table. private static string GetConfusionTableAsString(double[][] confusionTable, double[] rowSums, double[] columnSums, - DvText[] predictedLabelNames, string prefix = "", bool sampled = false, bool binary = true) + ReadOnlyMemory[] predictedLabelNames, string prefix = "", bool sampled = false, bool binary = true) { int numLabels = Utils.Size(confusionTable); @@ -1687,8 +1688,8 @@ public static void PrintWarnings(IChannel ch, Dictionary metr { using (var cursor = warnings.GetRowCursor(c => c == col)) { - var warning = default(DvText); - var getter = cursor.GetGetter(col); + var warning = default(ReadOnlyMemory); + var getter = cursor.GetGetter>(col); while (cursor.MoveNext()) { getter(ref warning); diff --git a/src/Microsoft.ML.Data/Evaluators/MultiOutputRegressionEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/MultiOutputRegressionEvaluator.cs index d57835b168..c264d438b0 100644 --- a/src/Microsoft.ML.Data/Evaluators/MultiOutputRegressionEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/MultiOutputRegressionEvaluator.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -94,11 +94,11 @@ public override IEnumerable GetOverallMetricColumns() } protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate) + out Action, Aggregator> addAgg, out Func> consolidate) { var stratCol = new List(); - var stratVal = new List(); - var isWeighted = new List(); + var stratVal = new List>(); + var isWeighted = new List(); var l1 = new List(); var l2 = new List(); var dist = new List(); @@ -117,7 +117,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.False); + isWeighted.Add(false); l1.Add(agg.UnweightedCounters.L1); l2.Add(agg.UnweightedCounters.L2); dist.Add(agg.UnweightedCounters.Dist); @@ -129,7 +129,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A { stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.True); + isWeighted.Add(true); l1.Add(agg.WeightedCounters.L1); l2.Add(agg.WeightedCounters.L2); dist.Add(agg.WeightedCounters.Dist); @@ -361,15 +361,15 @@ public override void ProcessRow() WeightedCounters.Update(score, label, _size, weight); } - public void GetSlotNames(ref VBuffer slotNames) + public void GetSlotNames(ref VBuffer> slotNames) { var values = slotNames.Values; if (Utils.Size(values) < _size) - values = new DvText[_size]; + values = new ReadOnlyMemory[_size]; for (int i = 0; i < _size; i++) - values[i] = new DvText(string.Format("(Label_{0})", i)); - slotNames = new VBuffer(_size, values); + values[i] = string.Format("(Label_{0})", i).AsMemory(); + slotNames = new VBuffer>(_size, values); } } } @@ -385,7 +385,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiOutputRegressionPerInstanceEvaluator).Assembly.FullName); } private const int LabelOutput = 0; @@ -555,7 +556,7 @@ private void CheckInputColumnTypes(ISchema schema, out ColumnType labelType, out labelType = new VectorType(t.ItemType.AsPrimitive, t.VectorSize); var slotNamesType = new VectorType(TextType.Instance, t.VectorSize); labelMetadata = new ColumnMetadataInfo(LabelCol); - labelMetadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, + labelMetadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(schema, LabelIndex, labelType.VectorSize, "True"))); t = schema.GetColumnType(ScoreIndex); @@ -563,10 +564,10 @@ private void CheckInputColumnTypes(ISchema schema, out ColumnType labelType, out throw Host.Except("Score column '{0}' has type '{1}' but must be a known length vector of type R4", ScoreCol, t); scoreType = new VectorType(t.ItemType.AsPrimitive, t.VectorSize); scoreMetadata = new ColumnMetadataInfo(ScoreCol); - scoreMetadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, + scoreMetadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(schema, ScoreIndex, scoreType.VectorSize, "Predicted"))); - scoreMetadata.Add(MetadataUtils.Kinds.ScoreColumnKind, new MetadataInfo(TextType.Instance, GetScoreColumnKind)); - scoreMetadata.Add(MetadataUtils.Kinds.ScoreValueKind, new MetadataInfo(TextType.Instance, GetScoreValueKind)); + scoreMetadata.Add(MetadataUtils.Kinds.ScoreColumnKind, new MetadataInfo>(TextType.Instance, GetScoreColumnKind)); + scoreMetadata.Add(MetadataUtils.Kinds.ScoreValueKind, new MetadataInfo>(TextType.Instance, GetScoreValueKind)); scoreMetadata.Add(MetadataUtils.Kinds.ScoreColumnSetId, new MetadataInfo(MetadataUtils.ScoreColumnSetIdType, GetScoreColumnSetId(schema))); } @@ -580,33 +581,33 @@ private MetadataUtils.MetadataGetter GetScoreColumnSetId(ISchema schema) (int col, ref uint dst) => dst = id; } - private void GetScoreColumnKind(int col, ref DvText dst) + private void GetScoreColumnKind(int col, ref ReadOnlyMemory dst) { - dst = new DvText(MetadataUtils.Const.ScoreColumnKind.MultiOutputRegression); + dst = MetadataUtils.Const.ScoreColumnKind.MultiOutputRegression.AsMemory(); } - private void GetScoreValueKind(int col, ref DvText dst) + private void GetScoreValueKind(int col, ref ReadOnlyMemory dst) { - dst = new DvText(MetadataUtils.Const.ScoreValueKind.Score); + dst = MetadataUtils.Const.ScoreValueKind.Score.AsMemory(); } - private MetadataUtils.MetadataGetter> CreateSlotNamesGetter(ISchema schema, int column, int length, string prefix) + private MetadataUtils.MetadataGetter>> CreateSlotNamesGetter(ISchema schema, int column, int length, string prefix) { var type = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, column); if (type != null && type.IsText) { return - (int col, ref VBuffer dst) => schema.GetMetadata(MetadataUtils.Kinds.SlotNames, column, ref dst); + (int col, ref VBuffer> dst) => schema.GetMetadata(MetadataUtils.Kinds.SlotNames, column, ref dst); } return - (int col, ref VBuffer dst) => + (int col, ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < length) - values = new DvText[length]; + values = new ReadOnlyMemory[length]; for (int i = 0; i < length; i++) - values[i] = new DvText(string.Format("{0}_{1}", prefix, i)); - dst = new VBuffer(length, values); + values[i] = string.Format("{0}_{1}", prefix, i).AsMemory(); + dst = new VBuffer>(length, values); }; } } @@ -680,12 +681,12 @@ protected override void PrintFoldResultsCore(IChannel ch, Dictionary true)) { - DvBool isWeighted = DvBool.False; - ValueGetter isWeightedGetter; + bool isWeighted = false; + ValueGetter isWeightedGetter; if (needWeighted) - isWeightedGetter = cursor.GetGetter(isWeightedCol); + isWeightedGetter = cursor.GetGetter(isWeightedCol); else - isWeightedGetter = (ref DvBool dst) => dst = DvBool.False; + isWeightedGetter = (ref bool dst) => dst = false; ValueGetter stratGetter; if (hasStrats) @@ -715,9 +716,9 @@ protected override void PrintFoldResultsCore(IChannel ch, Dictionary[labelCount]; for (int j = 0; j < labelCount; j++) - labelNames[j] = new DvText(string.Format("Label_{0}", j)); + labelNames[j] = string.Format("Label_{0}", j).AsMemory(); var sb = new StringBuilder(); sb.AppendLine("Per-label metrics:"); @@ -733,12 +734,12 @@ protected override void PrintFoldResultsCore(IChannel ch, Dictionary[] GetClassNames(RoleMappedSchema schema) { - DvText[] names; + ReadOnlyMemory[] names; // Get the label names from the score column if they exist, or use the default names. var scoreInfo = schema.GetUniqueColumn(MetadataUtils.Const.ScoreValueKind.Score); var mdType = schema.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, scoreInfo.Index); - var labelNames = default(VBuffer); + var labelNames = default(VBuffer>); if (mdType != null && mdType.IsKnownSizeVector && mdType.ItemType.IsText) { schema.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, scoreInfo.Index, ref labelNames); - names = new DvText[labelNames.Length]; + names = new ReadOnlyMemory[labelNames.Length]; labelNames.CopyTo(names); } else @@ -111,7 +111,7 @@ private DvText[] GetClassNames(RoleMappedSchema schema) Host.Assert(Utils.Size(score) == 1); Host.Assert(score[0].Type.VectorSize > 0); int numClasses = score[0].Type.VectorSize; - names = Enumerable.Range(0, numClasses).Select(i => new DvText(i.ToString())).ToArray(); + names = Enumerable.Range(0, numClasses).Select(i => i.ToString().AsMemory()).ToArray(); } return names; } @@ -137,23 +137,21 @@ public override IEnumerable GetOverallMetricColumns() } protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate) + out Action, Aggregator> addAgg, out Func> consolidate) { var stratCol = new List(); - var stratVal = new List(); - var isWeighted = new List(); - + var stratVal = new List>(); + var isWeighted = new List(); var microAcc = new List(); var macroAcc = new List(); var logLoss = new List(); var logLossRed = new List(); var topKAcc = new List(); var perClassLogLoss = new List(); - var counts = new List(); var weights = new List(); var confStratCol = new List(); - var confStratVal = new List(); + var confStratVal = new List>(); bool hasStrats = Utils.Size(dictionaries) > 0; bool hasWeight = aggregator.Weighted; @@ -167,7 +165,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.False); + isWeighted.Add(false); microAcc.Add(agg.UnweightedCounters.MicroAvgAccuracy); macroAcc.Add(agg.UnweightedCounters.MacroAvgAccuracy); logLoss.Add(agg.UnweightedCounters.LogLoss); @@ -184,7 +182,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A { stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.True); + isWeighted.Add(true); microAcc.Add(agg.WeightedCounters.MicroAvgAccuracy); macroAcc.Add(agg.WeightedCounters.MacroAvgAccuracy); logLoss.Add(agg.WeightedCounters.LogLoss); @@ -221,9 +219,9 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A confDvBldr.AddColumn(MetricKinds.ColumnNames.StratCol, GetKeyValueGetter(dictionaries), 0, dictionaries.Length, confStratCol.ToArray()); confDvBldr.AddColumn(MetricKinds.ColumnNames.StratVal, TextType.Instance, confStratVal.ToArray()); } - ValueGetter> getSlotNames = - (ref VBuffer dst) => - dst = new VBuffer(aggregator.ClassNames.Length, aggregator.ClassNames); + ValueGetter>> getSlotNames = + (ref VBuffer> dst) => + dst = new VBuffer>(aggregator.ClassNames.Length, aggregator.ClassNames); confDvBldr.AddColumn(MetricKinds.ColumnNames.Count, getSlotNames, NumberType.R8, counts.ToArray()); if (hasWeight) @@ -372,9 +370,9 @@ public void Update(int[] indices, double loglossCurr, int label, float weight) private long _numUnknownClassInstances; private long _numNegOrNonIntegerLabels; - public readonly DvText[] ClassNames; + public readonly ReadOnlyMemory[] ClassNames; - public Aggregator(IHostEnvironment env, DvText[] classNames, int scoreVectorSize, bool weighted, int? outputTopKAcc, string stratName) + public Aggregator(IHostEnvironment env, ReadOnlyMemory[] classNames, int scoreVectorSize, bool weighted, int? outputTopKAcc, string stratName) : base(env, stratName) { Host.Assert(outputTopKAcc == null || outputTopKAcc > 0); @@ -488,15 +486,15 @@ protected override List GetWarningsCore() return warnings; } - public void GetSlotNames(ref VBuffer slotNames) + public void GetSlotNames(ref VBuffer> slotNames) { var values = slotNames.Values; if (Utils.Size(values) < ClassNames.Length) - values = new DvText[ClassNames.Length]; + values = new ReadOnlyMemory[ClassNames.Length]; for (int i = 0; i < ClassNames.Length; i++) - values[i] = new DvText(string.Format("(class {0})", ClassNames[i])); - slotNames = new VBuffer(ClassNames.Length, values); + values[i] = string.Format("(class {0})", ClassNames[i]).AsMemory(); + slotNames = new VBuffer>(ClassNames.Length, values); } } @@ -673,7 +671,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Serialize the class names verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiClassPerInstanceEvaluator).Assembly.FullName); } private const int AssignedCol = 0; @@ -691,7 +690,7 @@ private static VersionInfo GetVersionInfo() private const float Epsilon = (float)1e-15; private readonly int _numClasses; - private readonly DvText[] _classNames; + private readonly ReadOnlyMemory[] _classNames; private readonly ColumnType[] _types; public MultiClassPerInstanceEvaluator(IHostEnvironment env, ISchema schema, ColumnInfo scoreInfo, string labelCol) @@ -704,13 +703,13 @@ public MultiClassPerInstanceEvaluator(IHostEnvironment env, ISchema schema, Colu if (schema.HasSlotNames(ScoreIndex, _numClasses)) { - var classNames = default(VBuffer); + var classNames = default(VBuffer>); schema.GetMetadata(MetadataUtils.Kinds.SlotNames, ScoreIndex, ref classNames); - _classNames = new DvText[_numClasses]; + _classNames = new ReadOnlyMemory[_numClasses]; classNames.CopyTo(_classNames); } else - _classNames = Utils.BuildArray(_numClasses, i => new DvText(i.ToString())); + _classNames = Utils.BuildArray(_numClasses, i => i.ToString().AsMemory()); var key = new KeyType(DataKind.U4, 0, _numClasses); _types[AssignedCol] = key; @@ -733,12 +732,12 @@ private MultiClassPerInstanceEvaluator(IHostEnvironment env, ModelLoadContext ct Host.CheckDecode(_numClasses > 0); if (ctx.Header.ModelVerWritten > VerInitial) { - _classNames = new DvText[_numClasses]; + _classNames = new ReadOnlyMemory[_numClasses]; for (int i = 0; i < _numClasses; i++) - _classNames[i] = new DvText(ctx.LoadNonEmptyString()); + _classNames[i] = ctx.LoadNonEmptyString().AsMemory(); } else - _classNames = Utils.BuildArray(_numClasses, i => new DvText(i.ToString())); + _classNames = Utils.BuildArray(_numClasses, i => i.ToString().AsMemory()); _types = new ColumnType[4]; var key = new KeyType(DataKind.U4, 0, _numClasses); @@ -898,19 +897,19 @@ public override RowMapperColumnInfo[] GetOutputColumns() var assignedColKeyValues = new ColumnMetadataInfo(Assigned); var keyValueType = new VectorType(TextType.Instance, _numClasses); - assignedColKeyValues.Add(MetadataUtils.Kinds.KeyValues, new MetadataInfo>(keyValueType, CreateKeyValueGetter())); + assignedColKeyValues.Add(MetadataUtils.Kinds.KeyValues, new MetadataInfo>>(keyValueType, CreateKeyValueGetter())); infos[AssignedCol] = new RowMapperColumnInfo(Assigned, _types[AssignedCol], assignedColKeyValues); infos[LogLossCol] = new RowMapperColumnInfo(LogLoss, _types[LogLossCol], null); var slotNamesType = new VectorType(TextType.Instance, _numClasses); var sortedScores = new ColumnMetadataInfo(SortedScores); - sortedScores.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, + sortedScores.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(_numClasses, "Score"))); var sortedClasses = new ColumnMetadataInfo(SortedClasses); - sortedClasses.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, + sortedClasses.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(_numClasses, "Class"))); - sortedClasses.Add(MetadataUtils.Kinds.KeyValues, new MetadataInfo>(keyValueType, CreateKeyValueGetter())); + sortedClasses.Add(MetadataUtils.Kinds.KeyValues, new MetadataInfo>>(keyValueType, CreateKeyValueGetter())); infos[SortedScoresCol] = new RowMapperColumnInfo(SortedScores, _types[SortedScoresCol], sortedScores); infos[SortedClassesCol] = new RowMapperColumnInfo(SortedClasses, _types[SortedClassesCol], sortedClasses); @@ -918,31 +917,31 @@ public override RowMapperColumnInfo[] GetOutputColumns() } // REVIEW: Figure out how to avoid having the column name in each slot name. - private MetadataUtils.MetadataGetter> CreateSlotNamesGetter(int numTopClasses, string suffix) + private MetadataUtils.MetadataGetter>> CreateSlotNamesGetter(int numTopClasses, string suffix) { return - (int col, ref VBuffer dst) => + (int col, ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < numTopClasses) - values = new DvText[numTopClasses]; + values = new ReadOnlyMemory[numTopClasses]; for (int i = 1; i <= numTopClasses; i++) - values[i - 1] = new DvText(string.Format("#{0} {1}", i, suffix)); - dst = new VBuffer(numTopClasses, values); + values[i - 1] = string.Format("#{0} {1}", i, suffix).AsMemory(); + dst = new VBuffer>(numTopClasses, values); }; } - private MetadataUtils.MetadataGetter> CreateKeyValueGetter() + private MetadataUtils.MetadataGetter>> CreateKeyValueGetter() { return - (int col, ref VBuffer dst) => + (int col, ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < _numClasses) - values = new DvText[_numClasses]; + values = new ReadOnlyMemory[_numClasses]; for (int i = 0; i < _numClasses; i++) values[i] = _classNames[i]; - dst = new VBuffer(_numClasses, values); + dst = new VBuffer>(_numClasses, values); }; } @@ -1149,9 +1148,9 @@ protected override IDataView GetPerInstanceMetricsCore(IDataView perInst, RoleMa var labelType = perInst.Schema.GetColumnType(labelCol); if (labelType.IsKey && (!perInst.Schema.HasKeyNames(labelCol, labelType.KeyCount) || labelType.RawKind != DataKind.U4)) { - perInst = LambdaColumnMapper.Create(Host, "ConvertToLong", perInst, schema.Label.Name, - schema.Label.Name, perInst.Schema.GetColumnType(labelCol), NumberType.I8, - (ref uint src, ref DvInt8 dst) => dst = src == 0 ? DvInt8.NA : src - 1 + (long)labelType.AsKey.Min); + perInst = LambdaColumnMapper.Create(Host, "ConvertToDouble", perInst, schema.Label.Name, + schema.Label.Name, perInst.Schema.GetColumnType(labelCol), NumberType.R8, + (ref uint src, ref double dst) => dst = src == 0 ? double.NaN : src - 1 + (double)labelType.AsKey.Min); } var perInstSchema = perInst.Schema; diff --git a/src/Microsoft.ML.Data/Evaluators/QuantileRegressionEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/QuantileRegressionEvaluator.cs index fb8d9c1249..9a2f22de5d 100644 --- a/src/Microsoft.ML.Data/Evaluators/QuantileRegressionEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/QuantileRegressionEvaluator.cs @@ -46,7 +46,7 @@ protected override IRowMapper CreatePerInstanceRowMapper(RoleMappedSchema schema int scoreSize = scoreInfo.Type.VectorSize; var type = schema.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, scoreInfo.Index); Host.Check(type != null && type.IsKnownSizeVector && type.ItemType.IsText, "Quantile regression score column must have slot names"); - var quantiles = default(VBuffer); + var quantiles = default(VBuffer>); schema.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, scoreInfo.Index, ref quantiles); Host.Assert(quantiles.IsDense && quantiles.Length == scoreSize); @@ -73,7 +73,7 @@ protected override Aggregator GetAggregatorCore(RoleMappedSchema schema, string var scoreInfo = schema.GetUniqueColumn(MetadataUtils.Const.ScoreValueKind.Score); var t = scoreInfo.Type; Host.Assert(t.VectorSize > 0 && (t.ItemType == NumberType.R4 || t.ItemType == NumberType.R8)); - var slotNames = default(VBuffer); + var slotNames = default(VBuffer>); t = schema.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, scoreInfo.Index); if (t != null && t.VectorSize == scoreInfo.Type.VectorSize && t.ItemType.IsText) schema.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, scoreInfo.Index, ref slotNames); @@ -205,14 +205,14 @@ protected override VBuffer Zero() private readonly Counters _counters; private readonly Counters _weightedCounters; - private VBuffer _slotNames; + private VBuffer> _slotNames; public override CountersBase UnweightedCounters { get { return _counters; } } public override CountersBase WeightedCounters { get { return _weightedCounters; } } public Aggregator(IHostEnvironment env, IRegressionLoss lossFunction, bool weighted, int size, - ref VBuffer slotNames, string stratName) + ref VBuffer> slotNames, string stratName) : base(env, lossFunction, weighted, stratName) { Host.Assert(size > 0); @@ -242,8 +242,8 @@ public override void AddColumn(ArrayDataViewBuilder dvBldr, string metricName, p Host.AssertValue(dvBldr); if (_slotNames.Length > 0) { - ValueGetter> getSlotNames = - (ref VBuffer dst) => dst = _slotNames; + ValueGetter>> getSlotNames = + (ref VBuffer> dst) => dst = _slotNames; dvBldr.AddColumn(metricName, getSlotNames, NumberType.R8, metric); } else @@ -262,7 +262,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(QuantileRegressionPerInstanceEvaluator).Assembly.FullName); } private const int L1Col = 0; @@ -272,10 +273,10 @@ private static VersionInfo GetVersionInfo() public const string L2 = "L2-loss"; private readonly int _scoreSize; - private readonly DvText[] _quantiles; + private readonly ReadOnlyMemory[] _quantiles; private readonly ColumnType _outputType; - public QuantileRegressionPerInstanceEvaluator(IHostEnvironment env, ISchema schema, string scoreCol, string labelCol, int scoreSize, DvText[] quantiles) + public QuantileRegressionPerInstanceEvaluator(IHostEnvironment env, ISchema schema, string scoreCol, string labelCol, int scoreSize, ReadOnlyMemory[] quantiles) : base(env, schema, scoreCol, labelCol) { Host.CheckParam(scoreSize > 0, nameof(scoreSize), "must be greater than 0"); @@ -299,9 +300,9 @@ private QuantileRegressionPerInstanceEvaluator(IHostEnvironment env, ModelLoadCo _scoreSize = ctx.Reader.ReadInt32(); Host.CheckDecode(_scoreSize > 0); - _quantiles = new DvText[_scoreSize]; + _quantiles = new ReadOnlyMemory[_scoreSize]; for (int i = 0; i < _scoreSize; i++) - _quantiles[i] = new DvText(ctx.LoadNonEmptyString()); + _quantiles[i] = ctx.LoadNonEmptyString().AsMemory(); _outputType = new VectorType(NumberType.R8, _scoreSize); } @@ -344,26 +345,26 @@ public override RowMapperColumnInfo[] GetOutputColumns() var slotNamesType = new VectorType(TextType.Instance, _scoreSize); var l1Metadata = new ColumnMetadataInfo(L1); - l1Metadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, CreateSlotNamesGetter(L1))); + l1Metadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(L1))); var l2Metadata = new ColumnMetadataInfo(L2); - l2Metadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(slotNamesType, CreateSlotNamesGetter(L2))); + l2Metadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(slotNamesType, CreateSlotNamesGetter(L2))); infos[L1Col] = new RowMapperColumnInfo(L1, _outputType, l1Metadata); infos[L2Col] = new RowMapperColumnInfo(L2, _outputType, l2Metadata); return infos; } - private MetadataUtils.MetadataGetter> CreateSlotNamesGetter(string prefix) + private MetadataUtils.MetadataGetter>> CreateSlotNamesGetter(string prefix) { return - (int col, ref VBuffer dst) => + (int col, ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < _scoreSize) - values = new DvText[_scoreSize]; + values = new ReadOnlyMemory[_scoreSize]; for (int i = 0; i < _scoreSize; i++) - values[i] = new DvText(string.Format("{0} ({1})", prefix, _quantiles[i])); - dst = new VBuffer(_scoreSize, values); + values[i] = string.Format("{0} ({1})", prefix, _quantiles[i]).AsMemory(); + dst = new VBuffer>(_scoreSize, values); }; } diff --git a/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs index 616cff8394..d071a906e3 100644 --- a/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -147,20 +147,20 @@ public override IEnumerable GetOverallMetricColumns() } protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate) + out Action, Aggregator> addAgg, out Func> consolidate) { var stratCol = new List(); - var stratVal = new List(); - var isWeighted = new List(); + var stratVal = new List>(); + var isWeighted = new List(); var ndcg = new List(); var dcg = new List(); - var groupName = new List(); + var groupName = new List>(); var groupNdcg = new List(); var groupDcg = new List(); var groupMaxDcg = new List(); var groupStratCol = new List(); - var groupStratVal = new List(); + var groupStratVal = new List>(); bool hasStrats = Utils.Size(dictionaries) > 0; bool hasWeight = aggregator.Weighted; @@ -175,14 +175,14 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.False); + isWeighted.Add(false); ndcg.Add(agg.UnweightedCounters.Ndcg); dcg.Add(agg.UnweightedCounters.Dcg); if (agg.UnweightedCounters.GroupSummary) { groupStratCol.AddRange(agg.UnweightedCounters.GroupDcg.Select(x => stratColKey)); groupStratVal.AddRange(agg.UnweightedCounters.GroupDcg.Select(x => stratColVal)); - groupName.AddRange(agg.GroupId.Select(sb => new DvText(sb.ToString()))); + groupName.AddRange(agg.GroupId.Select(sb => sb.ToString().AsMemory())); groupNdcg.AddRange(agg.UnweightedCounters.GroupNdcg); groupDcg.AddRange(agg.UnweightedCounters.GroupDcg); groupMaxDcg.AddRange(agg.UnweightedCounters.GroupMaxDcg); @@ -192,7 +192,7 @@ protected override void GetAggregatorConsolidationFuncs(Aggregator aggregator, A { stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.True); + isWeighted.Add(true); ndcg.Add(agg.WeightedCounters.Ndcg); dcg.Add(agg.WeightedCounters.Dcg); } @@ -386,7 +386,7 @@ public void UpdateGroup(Single weight) public readonly Counters UnweightedCounters; public readonly Counters WeightedCounters; public readonly bool Weighted; - public readonly List GroupId; + public readonly List> GroupId; private int _groupSize; public Aggregator(IHostEnvironment env, Double[] labelGains, int truncationLevel, bool groupSummary, bool weighted, string stratName) @@ -402,7 +402,7 @@ public Aggregator(IHostEnvironment env, Double[] labelGains, int truncationLevel _currentQueryWeight = Single.NaN; if (groupSummary) - GroupId = new List(); + GroupId = new List>(); } public override void InitializeNextPass(IRow row, RoleMappedSchema schema) @@ -472,7 +472,7 @@ private void ProcessGroup() if (WeightedCounters != null) WeightedCounters.UpdateGroup(_currentQueryWeight); if (GroupId != null) - GroupId.Add(new DvText(_groupSb.ToString())); + GroupId.Add(_groupSb.ToString().AsMemory()); _currentQueryWeight = Single.NaN; } @@ -483,30 +483,30 @@ protected override void FinishPassCore() ProcessGroup(); } - public ValueGetter> GetGroupSummarySlotNames(string prefix) + public ValueGetter>> GetGroupSummarySlotNames(string prefix) { return - (ref VBuffer dst) => + (ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < UnweightedCounters.TruncationLevel) - values = new DvText[UnweightedCounters.TruncationLevel]; + values = new ReadOnlyMemory[UnweightedCounters.TruncationLevel]; for (int i = 0; i < UnweightedCounters.TruncationLevel; i++) - values[i] = new DvText(string.Format("{0}@{1}", prefix, i + 1)); - dst = new VBuffer(UnweightedCounters.TruncationLevel, values); + values[i] = string.Format("{0}@{1}", prefix, i + 1).AsMemory(); + dst = new VBuffer>(UnweightedCounters.TruncationLevel, values); }; } - public void GetSlotNames(ref VBuffer slotNames) + public void GetSlotNames(ref VBuffer> slotNames) { var values = slotNames.Values; if (Utils.Size(values) < UnweightedCounters.TruncationLevel) - values = new DvText[UnweightedCounters.TruncationLevel]; + values = new ReadOnlyMemory[UnweightedCounters.TruncationLevel]; for (int i = 0; i < UnweightedCounters.TruncationLevel; i++) - values[i] = new DvText(string.Format("@{0}", i + 1)); - slotNames = new VBuffer(UnweightedCounters.TruncationLevel, values); + values[i] = string.Format("@{0}", i + 1).AsMemory(); + slotNames = new VBuffer>(UnweightedCounters.TruncationLevel, values); } } } @@ -523,7 +523,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RankerPerInstanceTransform).Assembly.FullName); } public const string Ndcg = "NDCG"; @@ -588,7 +589,7 @@ private sealed class Bindings : BindingsBase private readonly ColumnType _outputType; private readonly ColumnType _slotNamesType; private readonly int _truncationLevel; - private readonly MetadataUtils.MetadataGetter> _slotNamesGetter; + private readonly MetadataUtils.MetadataGetter>> _slotNamesGetter; public Bindings(IExceptionContext ectx, ISchema input, bool user, string labelCol, string scoreCol, string groupCol, int truncationLevel) @@ -633,17 +634,17 @@ protected override void GetMetadataCore(string kind, int iinfo, ref TVal base.GetMetadataCore(kind, iinfo, ref value); } - private void SlotNamesGetter(int iinfo, ref VBuffer dst) + private void SlotNamesGetter(int iinfo, ref VBuffer> dst) { Contracts.Assert(0 <= iinfo && iinfo < InfoCount); var values = dst.Values; if (Utils.Size(values) < _truncationLevel) - values = new DvText[_truncationLevel]; + values = new ReadOnlyMemory[_truncationLevel]; for (int i = 0; i < _truncationLevel; i++) values[i] = - new DvText(string.Format("{0}@{1}", iinfo == NdcgCol ? Ndcg : iinfo == DcgCol ? Dcg : MaxDcg, - i + 1)); - dst = new VBuffer(_truncationLevel, values); + string.Format("{0}@{1}", iinfo == NdcgCol ? Ndcg : iinfo == DcgCol ? Dcg : MaxDcg, + i + 1).AsMemory(); + dst = new VBuffer>(_truncationLevel, values); } } diff --git a/src/Microsoft.ML.Data/Evaluators/RegressionEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/RegressionEvaluator.cs index b20492d6dc..203050d774 100644 --- a/src/Microsoft.ML.Data/Evaluators/RegressionEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/RegressionEvaluator.cs @@ -293,7 +293,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RegressionPerInstanceEvaluator).Assembly.FullName); } private const int L1Col = 0; diff --git a/src/Microsoft.ML.Data/Evaluators/RegressionEvaluatorBase.cs b/src/Microsoft.ML.Data/Evaluators/RegressionEvaluatorBase.cs index 9962897373..c4f55ba2ba 100644 --- a/src/Microsoft.ML.Data/Evaluators/RegressionEvaluatorBase.cs +++ b/src/Microsoft.ML.Data/Evaluators/RegressionEvaluatorBase.cs @@ -43,11 +43,11 @@ protected RegressionEvaluatorBase(ArgumentsBase args, IHostEnvironment env, stri } protected override void GetAggregatorConsolidationFuncs(TAgg aggregator, AggregatorDictionaryBase[] dictionaries, - out Action addAgg, out Func> consolidate) + out Action, TAgg> addAgg, out Func> consolidate) { var stratCol = new List(); - var stratVal = new List(); - var isWeighted = new List(); + var stratVal = new List>(); + var isWeighted = new List(); var l1 = new List(); var l2 = new List(); var rms = new List(); @@ -64,7 +64,7 @@ protected override void GetAggregatorConsolidationFuncs(TAgg aggregator, Aggrega stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.False); + isWeighted.Add(false); l1.Add(agg.UnweightedCounters.L1); l2.Add(agg.UnweightedCounters.L2); rms.Add(agg.UnweightedCounters.Rms); @@ -74,7 +74,7 @@ protected override void GetAggregatorConsolidationFuncs(TAgg aggregator, Aggrega { stratCol.Add(stratColKey); stratVal.Add(stratColVal); - isWeighted.Add(DvBool.True); + isWeighted.Add(true); l1.Add(agg.WeightedCounters.L1); l2.Add(agg.WeightedCounters.L2); rms.Add(agg.WeightedCounters.Rms); diff --git a/src/Microsoft.ML.Data/Microsoft.ML.Data.csproj b/src/Microsoft.ML.Data/Microsoft.ML.Data.csproj index 125ee8f479..5f147c496c 100644 --- a/src/Microsoft.ML.Data/Microsoft.ML.Data.csproj +++ b/src/Microsoft.ML.Data/Microsoft.ML.Data.csproj @@ -1,4 +1,4 @@ - + netstandard2.0 @@ -18,7 +18,6 @@ - diff --git a/src/Microsoft.ML.Data/Model/ModelHeader.cs b/src/Microsoft.ML.Data/Model/ModelHeader.cs index 8f060cf86f..4d61bc34d3 100644 --- a/src/Microsoft.ML.Data/Model/ModelHeader.cs +++ b/src/Microsoft.ML.Data/Model/ModelHeader.cs @@ -20,10 +20,13 @@ public struct ModelHeader public const ulong SignatureValue = 0x4C45444F4D004C4DUL; public const ulong TailSignatureValue = 0x4D4C004D4F44454CUL; + private const uint VerAssemblyNameSupported = 0x00010002; + // These are private since they change over time. If we make them public we risk // another assembly containing a "copy" of their value when the other assembly // was compiled, which might not match the code that can load this. - private const uint VerWrittenCur = 0x00010001; + //private const uint VerWrittenCur = 0x00010001; // Initial + private const uint VerWrittenCur = 0x00010002; // Added AssemblyName private const uint VerReadableCur = 0x00010001; private const uint VerWeCanReadBack = 0x00010001; @@ -85,7 +88,13 @@ public struct ModelHeader [FieldOffset(0x88)] public long FpLim; - // Lots of padding.... + // Location of the fully qualified assembly name string (in UTF-16). + // Note that it is legal for both to be zero. + [FieldOffset(0x90)] + public long FpAssemblyName; + [FieldOffset(0x98)] + public uint CbAssemblyName; + public const int Size = 0x0100; // Utilities for writing. @@ -116,7 +125,7 @@ public static void BeginWrite(BinaryWriter writer, out long fpMin, out ModelHead /// The current writer position should be the end of the model blob. Records the model size, writes the string table, /// completes and writes the header, and writes the tail. /// - public static void EndWrite(BinaryWriter writer, long fpMin, ref ModelHeader header, NormStr.Pool pool = null) + public static void EndWrite(BinaryWriter writer, long fpMin, ref ModelHeader header, NormStr.Pool pool = null, string loaderAssemblyName = null) { Contracts.CheckValue(writer, nameof(writer)); Contracts.CheckParam(fpMin >= 0, nameof(fpMin)); @@ -150,16 +159,35 @@ public static void EndWrite(BinaryWriter writer, long fpMin, ref ModelHeader hea Contracts.Assert(header.FpStringChars == header.FpStringTable + header.CbStringTable); foreach (var ns in pool) { - foreach (var ch in ns.Value) + foreach (var ch in ns.Value.Span) writer.Write((short)ch); } header.CbStringChars = writer.FpCur() - header.FpStringChars - fpMin; Contracts.Assert(offset == header.CbStringChars); } + WriteLoaderAssemblyName(writer, fpMin, ref header, loaderAssemblyName); + WriteHeaderAndTailCore(writer, fpMin, ref header); } + private static void WriteLoaderAssemblyName(BinaryWriter writer, long fpMin, ref ModelHeader header, string loaderAssemblyName) + { + if (!string.IsNullOrEmpty(loaderAssemblyName)) + { + header.FpAssemblyName = writer.FpCur() - fpMin; + header.CbAssemblyName = (uint)loaderAssemblyName.Length * sizeof(char); + + foreach (var ch in loaderAssemblyName) + writer.Write((short)ch); + } + else + { + header.FpAssemblyName = 0; + header.CbAssemblyName = 0; + } + } + /// /// The current writer position should be where the tail belongs. Writes the header and tail. /// Typically this isn't called directly unless you are doing custom string table serialization. @@ -289,7 +317,7 @@ public static void MarshalToBytes(ref ModelHeader header, byte[] bytes) /// Read the model header, strings, etc from reader. Also validates the header (throws if bad). /// Leaves the reader position at the beginning of the model blob. /// - public static void BeginRead(out long fpMin, out ModelHeader header, out string[] strings, BinaryReader reader) + public static void BeginRead(out long fpMin, out ModelHeader header, out string[] strings, out string loaderAssemblyName, BinaryReader reader) { fpMin = reader.FpCur(); @@ -298,7 +326,7 @@ public static void BeginRead(out long fpMin, out ModelHeader header, out string[ ModelHeader.MarshalFromBytes(out header, headerBytes); Exception ex; - if (!ModelHeader.TryValidate(ref header, reader, fpMin, out strings, out ex)) + if (!ModelHeader.TryValidate(ref header, reader, fpMin, out strings, out loaderAssemblyName, out ex)) throw ex; reader.Seek(header.FpModel + fpMin); @@ -375,7 +403,10 @@ public static bool TryValidate(ref ModelHeader header, long size, out Exception Contracts.CheckDecode(header.CbStringTable == 0); Contracts.CheckDecode(header.FpStringChars == 0); Contracts.CheckDecode(header.CbStringChars == 0); - Contracts.CheckDecode(header.FpTail == header.FpModel + header.CbModel); + if (header.VerWritten < VerAssemblyNameSupported || header.FpAssemblyName == 0) + { + Contracts.CheckDecode(header.FpTail == header.FpModel + header.CbModel); + } } else { @@ -387,8 +418,34 @@ public static bool TryValidate(ref ModelHeader header, long size, out Exception Contracts.CheckDecode(header.FpStringChars == header.FpStringTable + header.CbStringTable); Contracts.CheckDecode(header.CbStringChars % sizeof(char) == 0); Contracts.CheckDecode(header.FpStringChars + header.CbStringChars >= header.FpStringChars); - Contracts.CheckDecode(header.FpTail == header.FpStringChars + header.CbStringChars); + if (header.VerWritten < VerAssemblyNameSupported || header.FpAssemblyName == 0) + { + Contracts.CheckDecode(header.FpTail == header.FpStringChars + header.CbStringChars); + } } + + if (header.VerWritten >= VerAssemblyNameSupported) + { + if (header.FpAssemblyName == 0) + { + Contracts.CheckDecode(header.CbAssemblyName == 0); + } + else + { + // the assembly name always immediately after the string table, if there is one + if (header.FpStringTable == 0) + { + Contracts.CheckDecode(header.FpAssemblyName == header.FpModel + header.CbModel); + } + else + { + Contracts.CheckDecode(header.FpAssemblyName == header.FpStringChars + header.CbStringChars); + } + Contracts.CheckDecode(header.CbAssemblyName % sizeof(char) == 0); + Contracts.CheckDecode(header.FpTail == header.FpAssemblyName + header.CbAssemblyName); + } + } + Contracts.CheckDecode(header.FpLim == header.FpTail + sizeof(ulong)); Contracts.CheckDecode(size == 0 || size >= header.FpLim); @@ -405,7 +462,7 @@ public static bool TryValidate(ref ModelHeader header, long size, out Exception /// /// Checks the validity of the header, reads the string table, etc. /// - public static bool TryValidate(ref ModelHeader header, BinaryReader reader, long fpMin, out string[] strings, out Exception ex) + public static bool TryValidate(ref ModelHeader header, BinaryReader reader, long fpMin, out string[] strings, out string loaderAssemblyName, out Exception ex) { Contracts.CheckValue(reader, nameof(reader)); Contracts.Check(fpMin >= 0); @@ -413,62 +470,85 @@ public static bool TryValidate(ref ModelHeader header, BinaryReader reader, long if (!TryValidate(ref header, reader.BaseStream.Length - fpMin, out ex)) { strings = null; + loaderAssemblyName = null; return false; } - if (header.FpStringTable == 0) - { - // No strings. - strings = null; - ex = null; - return true; - } - try { long fpOrig = reader.FpCur(); - reader.Seek(header.FpStringTable + fpMin); - Contracts.Assert(reader.FpCur() == header.FpStringTable + fpMin); - long cstr = header.CbStringTable / sizeof(long); - Contracts.Assert(cstr < int.MaxValue); - long[] offsets = reader.ReadLongArray((int)cstr); - Contracts.Assert(header.FpStringChars == reader.FpCur() - fpMin); - Contracts.CheckDecode(offsets[cstr - 1] == header.CbStringChars); + StringBuilder sb = null; + if (header.FpStringTable == 0) + { + // No strings. + strings = null; + } + else + { + reader.Seek(header.FpStringTable + fpMin); + Contracts.Assert(reader.FpCur() == header.FpStringTable + fpMin); + + long cstr = header.CbStringTable / sizeof(long); + Contracts.Assert(cstr < int.MaxValue); + long[] offsets = reader.ReadLongArray((int)cstr); + Contracts.Assert(header.FpStringChars == reader.FpCur() - fpMin); + Contracts.CheckDecode(offsets[cstr - 1] == header.CbStringChars); + + strings = new string[cstr]; + long offset = 0; + sb = new StringBuilder(); + for (int i = 0; i < offsets.Length; i++) + { + Contracts.CheckDecode(header.FpStringChars + offset == reader.FpCur() - fpMin); + + long offsetPrev = offset; + offset = offsets[i]; + Contracts.CheckDecode(offsetPrev <= offset & offset <= header.CbStringChars); + Contracts.CheckDecode(offset % sizeof(char) == 0); + long cch = (offset - offsetPrev) / sizeof(char); + Contracts.CheckDecode(cch < int.MaxValue); + + sb.Clear(); + for (long ich = 0; ich < cch; ich++) + sb.Append((char)reader.ReadUInt16()); + strings[i] = sb.ToString(); + } + Contracts.CheckDecode(offset == header.CbStringChars); + Contracts.CheckDecode(header.FpStringChars + header.CbStringChars == reader.FpCur() - fpMin); + } - strings = new string[cstr]; - long offset = 0; - var sb = new StringBuilder(); - for (int i = 0; i < offsets.Length; i++) + if (header.VerWritten >= VerAssemblyNameSupported && header.FpAssemblyName != 0) { - Contracts.CheckDecode(header.FpStringChars + offset == reader.FpCur() - fpMin); + reader.Seek(header.FpAssemblyName + fpMin); + int assemblyNameLength = (int)header.CbAssemblyName / sizeof(char); - long offsetPrev = offset; - offset = offsets[i]; - Contracts.CheckDecode(offsetPrev <= offset & offset <= header.CbStringChars); - Contracts.CheckDecode(offset % sizeof(char) == 0); - long cch = (offset - offsetPrev) / sizeof(char); - Contracts.CheckDecode(cch < int.MaxValue); + sb = sb != null ? sb.Clear() : new StringBuilder(assemblyNameLength); - sb.Clear(); - for (long ich = 0; ich < cch; ich++) + for (long ich = 0; ich < assemblyNameLength; ich++) sb.Append((char)reader.ReadUInt16()); - strings[i] = sb.ToString(); + + loaderAssemblyName = sb.ToString(); } - Contracts.CheckDecode(offset == header.CbStringChars); - Contracts.CheckDecode(header.FpStringChars + header.CbStringChars == reader.FpCur() - fpMin); + else + { + loaderAssemblyName = null; + } + Contracts.CheckDecode(header.FpTail == reader.FpCur() - fpMin); ulong tail = reader.ReadUInt64(); Contracts.CheckDecode(tail == TailSignatureValue, "Corrupt model file tail"); - reader.Seek(fpOrig); ex = null; + + reader.Seek(fpOrig); return true; } catch (Exception e) { strings = null; + loaderAssemblyName = null; ex = e; return false; } @@ -529,12 +609,13 @@ public static string GetLoaderSigAlt(ref ModelHeader header) /// This is used to simplify version checking boiler-plate code. It is an optional /// utility type. /// - public struct VersionInfo + public readonly struct VersionInfo { public readonly ulong ModelSignature; public readonly uint VerWrittenCur; public readonly uint VerReadableCur; public readonly uint VerWeCanReadBack; + public readonly string LoaderAssemblyName; public readonly string LoaderSignature; public readonly string LoaderSignatureAlt; @@ -543,7 +624,7 @@ public struct VersionInfo /// all less than 0x100. Spaces are mapped to zero. This assumes little-endian. /// public VersionInfo(string modelSignature, uint verWrittenCur, uint verReadableCur, uint verWeCanReadBack, - string loaderSignature = null, string loaderSignatureAlt = null) + string loaderAssemblyName, string loaderSignature = null, string loaderSignatureAlt = null) { Contracts.Check(Utils.Size(modelSignature) == 8, "Model signature must be eight characters"); ModelSignature = 0; @@ -559,17 +640,7 @@ public VersionInfo(string modelSignature, uint verWrittenCur, uint verReadableCu VerWrittenCur = verWrittenCur; VerReadableCur = verReadableCur; VerWeCanReadBack = verWeCanReadBack; - LoaderSignature = loaderSignature; - LoaderSignatureAlt = loaderSignatureAlt; - } - - public VersionInfo(ulong modelSignature, uint verWrittenCur, uint verReadableCur, uint verWeCanReadBack, - string loaderSignature = null, string loaderSignatureAlt = null) - { - ModelSignature = modelSignature; - VerWrittenCur = verWrittenCur; - VerReadableCur = verReadableCur; - VerWeCanReadBack = verWeCanReadBack; + LoaderAssemblyName = loaderAssemblyName; LoaderSignature = loaderSignature; LoaderSignatureAlt = loaderSignatureAlt; } diff --git a/src/Microsoft.ML.Data/Model/ModelLoadContext.cs b/src/Microsoft.ML.Data/Model/ModelLoadContext.cs index c09b7b09a2..7efd9f4b47 100644 --- a/src/Microsoft.ML.Data/Model/ModelLoadContext.cs +++ b/src/Microsoft.ML.Data/Model/ModelLoadContext.cs @@ -39,6 +39,14 @@ public sealed partial class ModelLoadContext : IDisposable /// public readonly string[] Strings; + /// + /// The name of the assembly that the loader lives in. + /// + /// + /// This may be null or empty if one was never written to the model, or is an older model version. + /// + public readonly string LoaderAssemblyName; + /// /// The main stream's model header. /// @@ -76,7 +84,7 @@ public ModelLoadContext(RepositoryReader rep, Repository.Entry ent, string dir) Reader = new BinaryReader(ent.Stream, Encoding.UTF8, leaveOpen: true); try { - ModelHeader.BeginRead(out FpMin, out Header, out Strings, Reader); + ModelHeader.BeginRead(out FpMin, out Header, out Strings, out LoaderAssemblyName, Reader); } catch { @@ -97,7 +105,7 @@ public ModelLoadContext(BinaryReader reader, IExceptionContext ectx = null) Repository = null; Directory = null; Reader = reader; - ModelHeader.BeginRead(out FpMin, out Header, out Strings, Reader); + ModelHeader.BeginRead(out FpMin, out Header, out Strings, out LoaderAssemblyName, Reader); } public void CheckAtModel() diff --git a/src/Microsoft.ML.Data/Model/ModelLoading.cs b/src/Microsoft.ML.Data/Model/ModelLoading.cs index c69461ea79..981da9e797 100644 --- a/src/Microsoft.ML.Data/Model/ModelLoading.cs +++ b/src/Microsoft.ML.Data/Model/ModelLoading.cs @@ -4,6 +4,7 @@ using System; using System.IO; +using System.Reflection; using System.Text; using Microsoft.ML.Runtime.Internal.Utilities; @@ -212,6 +213,8 @@ private bool TryLoadModelCore(IHostEnvironment env, out TRes result, var args = ConcatArgsRev(extra, this); + EnsureLoaderAssemblyIsRegistered(env.ComponentCatalog); + object tmp; string sig = ModelHeader.GetLoaderSig(ref Header); if (!string.IsNullOrWhiteSpace(sig) && @@ -246,6 +249,15 @@ private bool TryLoadModelCore(IHostEnvironment env, out TRes result, return false; } + private void EnsureLoaderAssemblyIsRegistered(ComponentCatalog catalog) + { + if (!string.IsNullOrEmpty(LoaderAssemblyName)) + { + var assembly = Assembly.Load(LoaderAssemblyName); + catalog.RegisterAssembly(assembly); + } + } + private static object[] ConcatArgsRev(object[] args2, params object[] args1) { Contracts.AssertNonEmpty(args1); diff --git a/src/Microsoft.ML.Data/Model/ModelSaveContext.cs b/src/Microsoft.ML.Data/Model/ModelSaveContext.cs index 82c9c7c3cc..c5a9199758 100644 --- a/src/Microsoft.ML.Data/Model/ModelSaveContext.cs +++ b/src/Microsoft.ML.Data/Model/ModelSaveContext.cs @@ -24,7 +24,7 @@ public sealed partial class ModelSaveContext : IDisposable public readonly RepositoryWriter Repository; /// - /// When in repository mode, this is the direcory we're reading from. Null means the root + /// When in repository mode, this is the directory we're reading from. Null means the root /// of the repository. It is always null in single-stream mode. /// public readonly string Directory; @@ -59,6 +59,11 @@ public sealed partial class ModelSaveContext : IDisposable ///
private readonly IExceptionContext _ectx; + /// + /// The assembly name where the loader resides. + /// + private string _loaderAssemblyName; + /// /// Returns whether this context is in repository mode (true) or single-stream mode (false). /// @@ -131,6 +136,7 @@ public void CheckAtModel() public void SetVersionInfo(VersionInfo ver) { ModelHeader.SetVersionInfo(ref Header, ver); + _loaderAssemblyName = ver.LoaderAssemblyName; } public void SaveTextStream(string name, Action action) @@ -185,6 +191,11 @@ public void SaveString(string str) Writer.Write(Strings.Add(str).Id); } + public void SaveString(ReadOnlyMemory str) + { + Writer.Write(Strings.Add(str).Id); + } + /// /// Puts a string into the context pool, and writes the integer code of the string ID /// to the write stream. @@ -195,6 +206,11 @@ public void SaveNonEmptyString(string str) Writer.Write(Strings.Add(str).Id); } + public void SaveNonEmptyString(ReadOnlyMemory str) + { + Writer.Write(Strings.Add(str).Id); + } + /// /// Commit the save operation. This completes writing of the main stream. When in repository /// mode, it disposes the Writer (but not the repository). @@ -202,7 +218,7 @@ public void SaveNonEmptyString(string str) public void Done() { _ectx.Check(Header.ModelSignature != 0, "ModelSignature not specified!"); - ModelHeader.EndWrite(Writer, FpMin, ref Header, Strings); + ModelHeader.EndWrite(Writer, FpMin, ref Header, Strings, _loaderAssemblyName); Dispose(); } diff --git a/src/Microsoft.ML.Data/Model/Onnx/OnnxNode.cs b/src/Microsoft.ML.Data/Model/Onnx/OnnxNode.cs index 259a6d27d4..79df068b9b 100644 --- a/src/Microsoft.ML.Data/Model/Onnx/OnnxNode.cs +++ b/src/Microsoft.ML.Data/Model/Onnx/OnnxNode.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Collections.Generic; using Microsoft.ML.Runtime.Data; @@ -17,14 +18,14 @@ public abstract class OnnxNode { public abstract void AddAttribute(string argName, double value); public abstract void AddAttribute(string argName, long value); - public abstract void AddAttribute(string argName, DvText value); + public abstract void AddAttribute(string argName, ReadOnlyMemory value); public abstract void AddAttribute(string argName, string value); public abstract void AddAttribute(string argName, bool value); public abstract void AddAttribute(string argName, IEnumerable value); public abstract void AddAttribute(string argName, IEnumerable value); public abstract void AddAttribute(string argName, IEnumerable value); - public abstract void AddAttribute(string argName, IEnumerable value); + public abstract void AddAttribute(string argName, IEnumerable> value); public abstract void AddAttribute(string argName, string[] value); public abstract void AddAttribute(string argName, IEnumerable value); public abstract void AddAttribute(string argName, IEnumerable value); diff --git a/src/Microsoft.ML.Data/Prediction/Calibrator.cs b/src/Microsoft.ML.Data/Prediction/Calibrator.cs index 6cee31b9d2..310774a8a5 100644 --- a/src/Microsoft.ML.Data/Prediction/Calibrator.cs +++ b/src/Microsoft.ML.Data/Prediction/Calibrator.cs @@ -332,7 +332,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CalibratedPredictor).Assembly.FullName); } private static VersionInfo GetVersionInfoBulk() { @@ -341,7 +342,8 @@ private static VersionInfo GetVersionInfoBulk() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CalibratedPredictor).Assembly.FullName); } private CalibratedPredictor(IHostEnvironment env, ModelLoadContext ctx) @@ -393,7 +395,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FeatureWeightsCalibratedPredictor).Assembly.FullName); } private FeatureWeightsCalibratedPredictor(IHostEnvironment env, ModelLoadContext ctx) @@ -455,7 +458,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ParameterMixingCalibratedPredictor).Assembly.FullName); } private ParameterMixingCalibratedPredictor(IHostEnvironment env, ModelLoadContext ctx) @@ -608,7 +612,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(SchemaBindableCalibratedPredictor).Assembly.FullName); } /// @@ -967,7 +972,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(NaiveCalibrator).Assembly.FullName); } private readonly IHost _host; @@ -1334,7 +1340,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(PlattCalibrator).Assembly.FullName); } private readonly IHost _host; @@ -1569,7 +1576,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(PavCalibrator).Assembly.FullName); } // Epsilon for 0-comparisons diff --git a/src/Microsoft.ML.Data/Prediction/IPredictionTransformer.cs b/src/Microsoft.ML.Data/Prediction/IPredictionTransformer.cs index 899c7622dc..06c1894f0a 100644 --- a/src/Microsoft.ML.Data/Prediction/IPredictionTransformer.cs +++ b/src/Microsoft.ML.Data/Prediction/IPredictionTransformer.cs @@ -2,22 +2,37 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System.Collections.Generic; using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime.Data; -using Microsoft.ML.Runtime.Internal.Calibration; -using System; -using System.Collections.Generic; -using System.Text; namespace Microsoft.ML.Runtime { + /// + /// An interface for all the transformer that can transform data based on the field. + /// The implemendations of this interface either have no feature column, or have more than one feature column, and cannot implement the + /// , which most of the ML.Net tranformer implement. + /// + /// The used for the data transformation. public interface IPredictionTransformer : ITransformer where TModel : IPredictor { + TModel Model { get; } + } + + /// + /// An ISingleFeaturePredictionTransformer contains the name of the + /// and its type, . Implementations of this interface, have the ability + /// to score the data of an input through the + /// + /// The used for the data transformation. + public interface ISingleFeaturePredictionTransformer : IPredictionTransformer + where TModel : IPredictor + { + /// The name of the feature column. string FeatureColumn { get; } + /// Holds information about the type of the feature column. ColumnType FeatureColumnType { get; } - - TModel Model { get; } } } diff --git a/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs b/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs index 6da402431d..dc9a5bbdf9 100644 --- a/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs +++ b/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs @@ -40,7 +40,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010004, // ISchemaBindableMapper update verReadableCur: 0x00010004, verWeCanReadBack: 0x00010004, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(BinaryClassifierScorer).Assembly.FullName); } private const string RegistrationName = "BinaryClassifierScore"; @@ -250,8 +251,8 @@ protected override Delegate GetPredictedLabelGetter(IRow output, out Delegate sc return predFnAsKey; } - ValueGetter predFn = - (ref DvBool dst) => + ValueGetter predFn = + (ref bool dst) => { EnsureCachedPosition(ref cachedPosition, ref score, output, mapperScoreGetter); GetPredictedLabelCore(score, ref dst); @@ -259,9 +260,10 @@ protected override Delegate GetPredictedLabelGetter(IRow output, out Delegate sc return predFn; } - private void GetPredictedLabelCore(Float score, ref DvBool value) + private void GetPredictedLabelCore(Float score, ref bool value) { - value = score > _threshold ? DvBool.True : score <= _threshold ? DvBool.False : DvBool.NA; + //Behavior for NA values is undefined. + value = score > _threshold; } private void GetPredictedLabelCoreAsKey(Float score, ref uint value) diff --git a/src/Microsoft.ML.Data/Scorers/ClusteringScorer.cs b/src/Microsoft.ML.Data/Scorers/ClusteringScorer.cs index 7a065fe4d4..26eae0154d 100644 --- a/src/Microsoft.ML.Data/Scorers/ClusteringScorer.cs +++ b/src/Microsoft.ML.Data/Scorers/ClusteringScorer.cs @@ -37,7 +37,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // ISchemaBindableMapper update verReadableCur: 0x00010003, verWeCanReadBack: 0x00010003, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ClusteringScorer).Assembly.FullName); } private const string RegistrationName = "ClusteringScore"; diff --git a/src/Microsoft.ML.Data/Scorers/GenericScorer.cs b/src/Microsoft.ML.Data/Scorers/GenericScorer.cs index 41c12e94ed..0b261502ed 100644 --- a/src/Microsoft.ML.Data/Scorers/GenericScorer.cs +++ b/src/Microsoft.ML.Data/Scorers/GenericScorer.cs @@ -130,7 +130,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(GenericScorer).Assembly.FullName); } private const string RegistrationName = "GenericScore"; diff --git a/src/Microsoft.ML.Data/Scorers/MultiClassClassifierScorer.cs b/src/Microsoft.ML.Data/Scorers/MultiClassClassifierScorer.cs index c12fd9b4d1..79387c2727 100644 --- a/src/Microsoft.ML.Data/Scorers/MultiClassClassifierScorer.cs +++ b/src/Microsoft.ML.Data/Scorers/MultiClassClassifierScorer.cs @@ -48,7 +48,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // ISchemaBindableMapper update verReadableCur: 0x00010003, verWeCanReadBack: 0x00010003, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiClassClassifierScorer).Assembly.FullName); } private const string RegistrationName = "MultiClassClassifierScore"; @@ -88,7 +89,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Added metadataKind verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LabelNameBindableMapper).Assembly.FullName); } private const int VersionAddedMetadataKind = 0x00010002; diff --git a/src/Microsoft.ML.Data/Scorers/PredictedLabelScorerBase.cs b/src/Microsoft.ML.Data/Scorers/PredictedLabelScorerBase.cs index 2fd039897a..643c7cb3b2 100644 --- a/src/Microsoft.ML.Data/Scorers/PredictedLabelScorerBase.cs +++ b/src/Microsoft.ML.Data/Scorers/PredictedLabelScorerBase.cs @@ -39,8 +39,8 @@ protected sealed class BindingsImpl : BindingsBase // The ScoreColumnKind metadata value for all score columns. public readonly string ScoreColumnKind; - private readonly MetadataUtils.MetadataGetter _getScoreColumnKind; - private readonly MetadataUtils.MetadataGetter _getScoreValueKind; + private readonly MetadataUtils.MetadataGetter> _getScoreColumnKind; + private readonly MetadataUtils.MetadataGetter> _getScoreValueKind; private readonly IRow _predColMetadata; private BindingsImpl(ISchema input, ISchemaBoundRowMapper mapper, string suffix, string scoreColumnKind, @@ -251,17 +251,17 @@ protected override void GetMetadataCore(string kind, int iinfo, ref TVal base.GetMetadataCore(kind, iinfo, ref value); } - private void GetScoreColumnKind(int iinfo, ref DvText dst) + private void GetScoreColumnKind(int iinfo, ref ReadOnlyMemory dst) { Contracts.Assert(0 <= iinfo && iinfo < InfoCount); - dst = new DvText(ScoreColumnKind); + dst = ScoreColumnKind.AsMemory(); } - private void GetScoreValueKind(int iinfo, ref DvText dst) + private void GetScoreValueKind(int iinfo, ref ReadOnlyMemory dst) { // This should only get called for the derived column. Contracts.Assert(0 <= iinfo && iinfo < DerivedColumnCount); - dst = new DvText(MetadataUtils.Const.ScoreValueKind.PredictedLabel); + dst = MetadataUtils.Const.ScoreValueKind.PredictedLabel.AsMemory(); } public override Func GetActiveMapperColumns(bool[] active) diff --git a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs index f04febd055..904b591dd4 100644 --- a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs +++ b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs @@ -2,7 +2,6 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using System; using System.IO; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; @@ -18,52 +17,54 @@ [assembly: LoadableClass(typeof(RegressionPredictionTransformer>), typeof(RegressionPredictionTransformer), null, typeof(SignatureLoadModel), "", RegressionPredictionTransformer.LoaderSignature)] +[assembly: LoadableClass(typeof(RankingPredictionTransformer>), typeof(RankingPredictionTransformer), null, typeof(SignatureLoadModel), + "", RankingPredictionTransformer.LoaderSignature)] + namespace Microsoft.ML.Runtime.Data { - public abstract class PredictionTransformerBase : IPredictionTransformer, ICanSaveModel + + /// + /// Base class for transformers with no feature column, or more than one feature columns. + /// + /// + public abstract class PredictionTransformerBase : IPredictionTransformer where TModel : class, IPredictor { - private const string DirModel = "Model"; - private const string DirTransSchema = "TrainSchema"; + /// + /// The model. + /// + public TModel Model { get; } + protected const string DirModel = "Model"; + protected const string DirTransSchema = "TrainSchema"; protected readonly IHost Host; - protected readonly ISchemaBindableMapper BindableMapper; - protected readonly ISchema TrainSchema; - - public string FeatureColumn { get; } - - public ColumnType FeatureColumnType { get; } + protected ISchemaBindableMapper BindableMapper; + protected ISchema TrainSchema; - public TModel Model { get; } - - public PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, string featureColumn) + protected PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema) { Contracts.CheckValue(host, nameof(host)); + Host = host; Host.CheckValue(trainSchema, nameof(trainSchema)); Model = model; - FeatureColumn = featureColumn; - if (!trainSchema.TryGetColumnIndex(featureColumn, out int col)) - throw Host.ExceptSchemaMismatch(nameof(featureColumn), RoleMappedSchema.ColumnRole.Feature.Value, featureColumn); - FeatureColumnType = trainSchema.GetColumnType(col); - TrainSchema = trainSchema; - BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model); } - internal PredictionTransformerBase(IHost host, ModelLoadContext ctx) + protected PredictionTransformerBase(IHost host, ModelLoadContext ctx) + { Host = host; - ctx.LoadModel(host, out TModel model, DirModel); - Model = model; - // *** Binary format *** // model: prediction model. // stream: empty data view that contains train schema. // id of string: feature column. + ctx.LoadModel(host, out TModel model, DirModel); + Model = model; + // Clone the stream with the schema into memory. var ms = new MemoryStream(); ctx.TryLoadBinaryStream(DirTransSchema, reader => @@ -74,29 +75,104 @@ internal PredictionTransformerBase(IHost host, ModelLoadContext ctx) ms.Position = 0; var loader = new BinaryLoader(host, new BinaryLoader.Arguments(), ms); TrainSchema = loader.Schema; + } - FeatureColumn = ctx.LoadString(); - if (!TrainSchema.TryGetColumnIndex(FeatureColumn, out int col)) - throw Host.ExceptSchemaMismatch(nameof(FeatureColumn), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn); - FeatureColumnType = TrainSchema.GetColumnType(col); + /// + /// Gets the output schema resulting from the + /// + /// The of the input data. + /// The resulting . + public abstract ISchema GetOutputSchema(ISchema inputSchema); + + /// + /// Transforms the input data. + /// + /// The input data. + /// The transformed + public abstract IDataView Transform(IDataView input); + + protected void SaveModel(ModelSaveContext ctx) + { + // *** Binary format *** + // + // stream: empty data view that contains train schema. + + ctx.SaveModel(Model, DirModel); + ctx.SaveBinaryStream(DirTransSchema, writer => + { + using (var ch = Host.Start("Saving train schema")) + { + var saver = new BinarySaver(Host, new BinarySaver.Arguments { Silent = true }); + DataSaverUtils.SaveDataView(ch, saver, new EmptyDataView(Host, TrainSchema), writer.BaseStream); + } + }); + } + } + + /// + /// The base class for all the transformers implementing the . + /// Those are all the transformers that work with one feature column. + /// + /// The model used to transform the data. + public abstract class SingleFeaturePredictionTransformerBase : PredictionTransformerBase, ISingleFeaturePredictionTransformer, ICanSaveModel + where TModel : class, IPredictor + { + /// + /// The name of the feature column used by the prediction transformer. + /// + public string FeatureColumn { get; } + + /// + /// The type of the prediction transformer + /// + public ColumnType FeatureColumnType { get; } + + public SingleFeaturePredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, string featureColumn) + :base(host, model, trainSchema) + { + FeatureColumn = featureColumn; + + FeatureColumn = featureColumn; + if (featureColumn == null) + FeatureColumnType = null; + else if (!trainSchema.TryGetColumnIndex(featureColumn, out int col)) + throw Host.ExceptSchemaMismatch(nameof(featureColumn), RoleMappedSchema.ColumnRole.Feature.Value, featureColumn); + else + FeatureColumnType = trainSchema.GetColumnType(col); BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model); } - public ISchema GetOutputSchema(ISchema inputSchema) + internal SingleFeaturePredictionTransformerBase(IHost host, ModelLoadContext ctx) + :base(host, ctx) + { + FeatureColumn = ctx.LoadStringOrNull(); + + if (FeatureColumn == null) + FeatureColumnType = null; + else if (!TrainSchema.TryGetColumnIndex(FeatureColumn, out int col)) + throw Host.ExceptSchemaMismatch(nameof(FeatureColumn), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn); + else + FeatureColumnType = TrainSchema.GetColumnType(col); + + BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, Model); + } + + public override ISchema GetOutputSchema(ISchema inputSchema) { Host.CheckValue(inputSchema, nameof(inputSchema)); - if (!inputSchema.TryGetColumnIndex(FeatureColumn, out int col)) - throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), null); - if (!inputSchema.GetColumnType(col).Equals(FeatureColumnType)) - throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), inputSchema.GetColumnType(col).ToString()); + if(FeatureColumn != null) + { + if (!inputSchema.TryGetColumnIndex(FeatureColumn, out int col)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), null); + if (!inputSchema.GetColumnType(col).Equals(FeatureColumnType)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), inputSchema.GetColumnType(col).ToString()); + } return Transform(new EmptyDataView(Host, inputSchema)).Schema; } - public abstract IDataView Transform(IDataView input); - public void Save(ModelSaveContext ctx) { Host.CheckValue(ctx, nameof(ctx)); @@ -106,26 +182,16 @@ public void Save(ModelSaveContext ctx) protected virtual void SaveCore(ModelSaveContext ctx) { - // *** Binary format *** - // model: prediction model. - // stream: empty data view that contains train schema. - // id of string: feature column. - - ctx.SaveModel(Model, DirModel); - ctx.SaveBinaryStream(DirTransSchema, writer => - { - using (var ch = Host.Start("Saving train schema")) - { - var saver = new BinarySaver(Host, new BinarySaver.Arguments { Silent = true }); - DataSaverUtils.SaveDataView(ch, saver, new EmptyDataView(Host, TrainSchema), writer.BaseStream); - } - }); - - ctx.SaveString(FeatureColumn); + SaveModel(ctx); + ctx.SaveStringOrNull(FeatureColumn); } } - public sealed class BinaryPredictionTransformer : PredictionTransformerBase + /// + /// Base class for the working on binary classification tasks. + /// + /// An implementation of the + public sealed class BinaryPredictionTransformer : SingleFeaturePredictionTransformerBase where TModel : class, IPredictorProducing { private readonly BinaryClassifierScorer _scorer; @@ -190,11 +256,16 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: BinaryPredictionTransformer.LoaderSignature); + loaderSignature: BinaryPredictionTransformer.LoaderSignature, + loaderAssemblyName: typeof(BinaryPredictionTransformer<>).Assembly.FullName); } } - public sealed class MulticlassPredictionTransformer : PredictionTransformerBase + /// + /// Base class for the working on multi-class classification tasks. + /// + /// An implementation of the + public sealed class MulticlassPredictionTransformer : SingleFeaturePredictionTransformerBase where TModel : class, IPredictorProducing> { private readonly MultiClassClassifierScorer _scorer; @@ -251,11 +322,16 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: MulticlassPredictionTransformer.LoaderSignature); + loaderSignature: MulticlassPredictionTransformer.LoaderSignature, + loaderAssemblyName: typeof(MulticlassPredictionTransformer<>).Assembly.FullName); } } - public sealed class RegressionPredictionTransformer : PredictionTransformerBase + /// + /// Base class for the working on regression tasks. + /// + /// An implementation of the + public sealed class RegressionPredictionTransformer : SingleFeaturePredictionTransformerBase where TModel : class, IPredictorProducing { private readonly GenericScorer _scorer; @@ -297,7 +373,55 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: RegressionPredictionTransformer.LoaderSignature); + loaderSignature: RegressionPredictionTransformer.LoaderSignature, + loaderAssemblyName: typeof(RegressionPredictionTransformer<>).Assembly.FullName); + } + } + + public sealed class RankingPredictionTransformer : SingleFeaturePredictionTransformerBase + where TModel : class, IPredictorProducing + { + private readonly GenericScorer _scorer; + + public RankingPredictionTransformer(IHostEnvironment env, TModel model, ISchema inputSchema, string featureColumn) + : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(RankingPredictionTransformer)), model, inputSchema, featureColumn) + { + var schema = new RoleMappedSchema(inputSchema, null, featureColumn); + _scorer = new GenericScorer(Host, new GenericScorer.Arguments(), new EmptyDataView(Host, inputSchema), BindableMapper.Bind(Host, schema), schema); + } + + internal RankingPredictionTransformer(IHostEnvironment env, ModelLoadContext ctx) + : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(RankingPredictionTransformer)), ctx) + { + var schema = new RoleMappedSchema(TrainSchema, null, FeatureColumn); + _scorer = new GenericScorer(Host, new GenericScorer.Arguments(), new EmptyDataView(Host, TrainSchema), BindableMapper.Bind(Host, schema), schema); + } + + public override IDataView Transform(IDataView input) + { + Host.CheckValue(input, nameof(input)); + return _scorer.ApplyToData(Host, input); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + Contracts.AssertValue(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // + base.SaveCore(ctx); + } + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "MC RANK", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: RankingPredictionTransformer.LoaderSignature, + loaderAssemblyName: typeof(RankingPredictionTransformer<>).Assembly.FullName); } } @@ -324,4 +448,12 @@ internal static class RegressionPredictionTransformer public static RegressionPredictionTransformer> Create(IHostEnvironment env, ModelLoadContext ctx) => new RegressionPredictionTransformer>(env, ctx); } + + internal static class RankingPredictionTransformer + { + public const string LoaderSignature = "RankingPredXfer"; + + public static RankingPredictionTransformer> Create(IHostEnvironment env, ModelLoadContext ctx) + => new RankingPredictionTransformer>(env, ctx); + } } diff --git a/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs b/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs index 1e07f587f7..8422d5042c 100644 --- a/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs +++ b/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs @@ -108,21 +108,22 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema) using (var ch = env.Register("SchemaBindableWrapper").Start("Bind")) { ch.CheckValue(schema, nameof(schema)); - ch.CheckParam(schema.Feature != null, nameof(schema), "Need a features column"); - // Ensure that the feature column type is compatible with the needed input type. - var type = schema.Feature.Type; - var typeIn = ValueMapper != null ? ValueMapper.InputType : new VectorType(NumberType.Float); - if (type != typeIn) + if (schema.Feature != null) { - if (!type.ItemType.Equals(typeIn.ItemType)) - throw ch.Except("Incompatible features column type item type: '{0}' vs '{1}'", type.ItemType, typeIn.ItemType); - if (type.IsVector != typeIn.IsVector) - throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); - // typeIn can legally have unknown size. - if (type.VectorSize != typeIn.VectorSize && typeIn.VectorSize > 0) - throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); + // Ensure that the feature column type is compatible with the needed input type. + var type = schema.Feature.Type; + var typeIn = ValueMapper != null ? ValueMapper.InputType : new VectorType(NumberType.Float); + if (type != typeIn) + { + if (!type.ItemType.Equals(typeIn.ItemType)) + throw ch.Except("Incompatible features column type item type: '{0}' vs '{1}'", type.ItemType, typeIn.ItemType); + if (type.IsVector != typeIn.IsVector) + throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); + // typeIn can legally have unknown size. + if (type.VectorSize != typeIn.VectorSize && typeIn.VectorSize > 0) + throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); + } } - var mapper = BindCore(ch, schema); ch.Done(); return mapper; @@ -241,7 +242,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // ISchemaBindableWrapper update verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(SchemaBindablePredictorWrapper).Assembly.FullName); } private readonly string _scoreColumnKind; @@ -352,7 +354,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // ISchemaBindableWrapper update verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(SchemaBindableBinaryPredictorWrapper).Assembly.FullName); } private readonly IValueMapperDist _distMapper; @@ -463,15 +466,18 @@ public CalibratedRowMapper(RoleMappedSchema schema, SchemaBindableBinaryPredicto Contracts.AssertValue(parent); Contracts.Assert(parent._distMapper != null); Contracts.AssertValue(schema); - Contracts.AssertValue(schema.Feature); + Contracts.AssertValueOrNull(schema.Feature); _parent = parent; _inputSchema = schema; _outputSchema = new BinaryClassifierSchema(); - var typeSrc = _inputSchema.Feature.Type; - Contracts.Check(typeSrc.IsKnownSizeVector && typeSrc.ItemType == NumberType.Float, - "Invalid feature column type"); + if (schema.Feature != null) + { + var typeSrc = _inputSchema.Feature.Type; + Contracts.Check(typeSrc.IsKnownSizeVector && typeSrc.ItemType == NumberType.Float, + "Invalid feature column type"); + } } public RoleMappedSchema InputSchema { get { return _inputSchema; } } @@ -484,7 +490,7 @@ public Func GetDependencies(Func predicate) { for (int i = 0; i < OutputSchema.ColumnCount; i++) { - if (predicate(i)) + if (predicate(i) && _inputSchema.Feature != null) return col => col == _inputSchema.Feature.Index; } return col => false; @@ -492,7 +498,7 @@ public Func GetDependencies(Func predicate) public IEnumerable> GetInputColumnRoles() { - yield return RoleMappedSchema.ColumnRole.Feature.Bind(_inputSchema.Feature.Name); + yield return RoleMappedSchema.ColumnRole.Feature.Bind(_inputSchema.Feature != null ? _inputSchema.Feature.Name : null); } private Delegate[] CreateGetters(IRow input, bool[] active) @@ -504,7 +510,7 @@ private Delegate[] CreateGetters(IRow input, bool[] active) if (active[0] || active[1]) { // Put all captured locals at this scope. - var featureGetter = input.GetGetter>(_inputSchema.Feature.Index); + var featureGetter = _inputSchema.Feature!= null ? input.GetGetter>(_inputSchema.Feature.Index) : null; Float prob = 0; Float score = 0; long cachedPosition = -1; @@ -543,7 +549,9 @@ private static void EnsureCachedResultValueMapper(ValueMapper, Fl Contracts.AssertValue(mapper); if (cachedPosition != input.Position) { - featureGetter(ref features); + if (featureGetter != null) + featureGetter(ref features); + mapper(ref features, ref score, ref prob); cachedPosition = input.Position; } @@ -575,7 +583,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // ISchemaBindableWrapper update verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(SchemaBindableQuantileRegressionPredictor).Assembly.FullName); } private readonly IQuantileValueMapper _qpred; @@ -672,7 +681,7 @@ protected override Delegate GetPredictionGetter(IRow input, int colSrc) private sealed class Schema : ScoreMapperSchemaBase { private readonly string[] _slotNames; - private readonly MetadataUtils.MetadataGetter> _getSlotNames; + private readonly MetadataUtils.MetadataGetter>> _getSlotNames; public Schema(ColumnType scoreType, Double[] quantiles) : base(scoreType, MetadataUtils.Const.ScoreColumnKind.QuantileRegression) @@ -731,7 +740,7 @@ public override ColumnType GetColumnType(int col) return new VectorType(NumberType.Float, _slotNames.Length); } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Contracts.Assert(iinfo == 0); Contracts.Assert(Utils.Size(_slotNames) > 0); @@ -739,10 +748,10 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) int size = Utils.Size(_slotNames); var values = dst.Values; if (Utils.Size(values) < size) - values = new DvText[size]; + values = new ReadOnlyMemory[size]; for (int i = 0; i < _slotNames.Length; i++) - values[i] = new DvText(_slotNames[i]); - dst = new VBuffer(size, values, dst.Indices); + values[i] = _slotNames[i].AsMemory(); + dst = new VBuffer>(size, values, dst.Indices); } } } diff --git a/src/Microsoft.ML.Data/Scorers/ScoreMapperSchema.cs b/src/Microsoft.ML.Data/Scorers/ScoreMapperSchema.cs index 0f115bb2f0..023a2f46f0 100644 --- a/src/Microsoft.ML.Data/Scorers/ScoreMapperSchema.cs +++ b/src/Microsoft.ML.Data/Scorers/ScoreMapperSchema.cs @@ -4,6 +4,7 @@ using Float = System.Single; using System.Collections.Generic; +using System; namespace Microsoft.ML.Runtime.Data { @@ -17,8 +18,8 @@ public abstract class ScoreMapperSchemaBase : ISchema { protected readonly ColumnType ScoreType; protected readonly string ScoreColumnKind; - protected readonly MetadataUtils.MetadataGetter ScoreValueKindGetter; - protected readonly MetadataUtils.MetadataGetter ScoreColumnKindGetter; + protected readonly MetadataUtils.MetadataGetter> ScoreValueKindGetter; + protected readonly MetadataUtils.MetadataGetter> ScoreColumnKindGetter; public ScoreMapperSchemaBase(ColumnType scoreType, string scoreColumnKind) { @@ -117,16 +118,16 @@ public virtual void GetMetadata(string kind, int col, ref TValue value) } } - protected virtual void GetScoreValueKind(int col, ref DvText dst) + protected virtual void GetScoreValueKind(int col, ref ReadOnlyMemory dst) { Contracts.Assert(0 <= col && col < ColumnCount); CheckColZero(col, "GetScoreValueKind"); - dst = new DvText(MetadataUtils.Const.ScoreValueKind.Score); + dst = MetadataUtils.Const.ScoreValueKind.Score.AsMemory(); } - private void GetScoreColumnKind(int col, ref DvText dst) + private void GetScoreColumnKind(int col, ref ReadOnlyMemory dst) { - dst = new DvText(ScoreColumnKind); + dst = ScoreColumnKind.AsMemory(); } } @@ -205,21 +206,21 @@ public override void GetMetadata(string kind, int col, ref TValue value) Contracts.CheckParam(0 <= col && col < ColumnCount, nameof(col)); if (col == base.ColumnCount && kind == MetadataUtils.Kinds.IsNormalized) - MetadataUtils.Marshal(IsNormalized, col, ref value); + MetadataUtils.Marshal(IsNormalized, col, ref value); else base.GetMetadata(kind, col, ref value); } - private void IsNormalized(int col, ref DvBool dst) + private void IsNormalized(int col, ref bool dst) { - dst = DvBool.True; + dst = true; } - protected override void GetScoreValueKind(int col, ref DvText dst) + protected override void GetScoreValueKind(int col, ref ReadOnlyMemory dst) { Contracts.Assert(0 <= col && col < ColumnCount); if (col == base.ColumnCount) - dst = new DvText(MetadataUtils.Const.ScoreValueKind.Probability); + dst = MetadataUtils.Const.ScoreValueKind.Probability.AsMemory(); else base.GetScoreValueKind(col, ref dst); } @@ -228,8 +229,8 @@ protected override void GetScoreValueKind(int col, ref DvText dst) public sealed class SequencePredictorSchema : ScoreMapperSchemaBase { private readonly VectorType _keyNamesType; - private readonly VBuffer _keyNames; - private readonly MetadataUtils.MetadataGetter> _getKeyNames; + private readonly VBuffer> _keyNames; + private readonly MetadataUtils.MetadataGetter>> _getKeyNames; private bool HasKeyNames { get { return _keyNamesType != null; } } @@ -241,7 +242,7 @@ public sealed class SequencePredictorSchema : ScoreMapperSchemaBase /// metadata. Note that we do not copy /// the input key names, but instead take a reference to it. /// - public SequencePredictorSchema(ColumnType type, ref VBuffer keyNames, string scoreColumnKind) + public SequencePredictorSchema(ColumnType type, ref VBuffer> keyNames, string scoreColumnKind) : base(type, scoreColumnKind) { if (keyNames.Length > 0) @@ -273,7 +274,7 @@ public override string GetColumnName(int col) return MetadataUtils.Const.ScoreValueKind.PredictedLabel; } - private void GetKeyNames(int col, ref VBuffer dst) + private void GetKeyNames(int col, ref VBuffer> dst) { Contracts.Assert(col == 0); Contracts.AssertValue(_keyNamesType); @@ -321,10 +322,10 @@ public override ColumnType GetMetadataTypeOrNull(string kind, int col) } } - protected override void GetScoreValueKind(int col, ref DvText dst) + protected override void GetScoreValueKind(int col, ref ReadOnlyMemory dst) { Contracts.Assert(col == 0); - dst = new DvText(MetadataUtils.Const.ScoreValueKind.PredictedLabel); + dst = MetadataUtils.Const.ScoreValueKind.PredictedLabel.AsMemory(); } } } diff --git a/src/Microsoft.ML.Data/StaticPipe/StaticSchemaShape.cs b/src/Microsoft.ML.Data/StaticPipe/StaticSchemaShape.cs index 39cc6cd316..161ea8f879 100644 --- a/src/Microsoft.ML.Data/StaticPipe/StaticSchemaShape.cs +++ b/src/Microsoft.ML.Data/StaticPipe/StaticSchemaShape.cs @@ -168,7 +168,7 @@ private static Type GetTypeOrNull(SchemaShape.Column col) pt == NumberType.I1 || pt == NumberType.I2 || pt == NumberType.I4 || pt == NumberType.I4 || pt == NumberType.U1 || pt == NumberType.U2 || pt == NumberType.U4 || pt == NumberType.U4 || pt == NumberType.R4 || pt == NumberType.R8 || pt == NumberType.UG || pt == BoolType.Instance || - pt == DateTimeType.Instance || pt == DateTimeZoneType.Instance || pt == TimeSpanType.Instance || + pt == DateTimeType.Instance || pt == DateTimeOffsetType.Instance || pt == TimeSpanType.Instance || pt == TextType.Instance)) { return (vecType ?? typeof(Scalar<>)).MakeGenericType(physType); @@ -254,9 +254,9 @@ private static Type GetTypeOrNull(IColumn col) var normtype = meta.Schema.GetColumnType(normcol); if (normtype == BoolType.Instance) { - DvBool val = default; - meta.GetGetter(normcol)(ref val); - if (val.IsTrue) + bool val = default; + meta.GetGetter(normcol)(ref val); + if (val) vecType = typeof(NormVector<>); } } @@ -312,7 +312,7 @@ private static Type GetTypeOrNull(IColumn col) pt == NumberType.I1 || pt == NumberType.I2 || pt == NumberType.I4 || pt == NumberType.I8 || pt == NumberType.U1 || pt == NumberType.U2 || pt == NumberType.U4 || pt == NumberType.U8 || pt == NumberType.R4 || pt == NumberType.R8 || pt == NumberType.UG || pt == BoolType.Instance || - pt == DateTimeType.Instance || pt == DateTimeZoneType.Instance || pt == TimeSpanType.Instance || + pt == DateTimeType.Instance || pt == DateTimeOffsetType.Instance || pt == TimeSpanType.Instance || pt == TextType.Instance)) { return (vecType ?? typeof(Scalar<>)).MakeGenericType(physType); diff --git a/src/Microsoft.ML.Data/Training/ITrainerEstimator.cs b/src/Microsoft.ML.Data/Training/ITrainerEstimator.cs index 4eb6ea1482..2c9942e8d0 100644 --- a/src/Microsoft.ML.Data/Training/ITrainerEstimator.cs +++ b/src/Microsoft.ML.Data/Training/ITrainerEstimator.cs @@ -7,7 +7,7 @@ namespace Microsoft.ML.Runtime.Training { public interface ITrainerEstimator: IEstimator - where TTransformer: IPredictionTransformer + where TTransformer: ISingleFeaturePredictionTransformer where TPredictor: IPredictor { TrainerInfo Info { get; } diff --git a/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs b/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs index 02c8c60667..7ae6475c35 100644 --- a/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs +++ b/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs @@ -15,7 +15,7 @@ namespace Microsoft.ML.Runtime.Training /// It produces a 'prediction transformer'. /// public abstract class TrainerEstimatorBase : ITrainerEstimator, ITrainer - where TTransformer : IPredictionTransformer + where TTransformer : ISingleFeaturePredictionTransformer where TModel : IPredictor { /// diff --git a/src/Microsoft.ML.Data/Training/TrainerEstimatorContext.cs b/src/Microsoft.ML.Data/Training/TrainerEstimatorContext.cs new file mode 100644 index 0000000000..f1388dffd2 --- /dev/null +++ b/src/Microsoft.ML.Data/Training/TrainerEstimatorContext.cs @@ -0,0 +1,50 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Training; + +namespace Microsoft.ML.Core.Prediction +{ + /// + /// Holds information relevant to trainers. It is passed to the constructor of the + /// holding additional data needed to fit the estimator. The additional data can be a validation set or an initial model. + /// This holds at least a training set, as well as optioonally a predictor. + /// + public class TrainerEstimatorContext + { + /// + /// The validation set. Can be null. Note that passing a non-null validation set into + /// a trainer that does not support validation sets should not be considered an error condition. It + /// should simply be ignored in that case. + /// + public IDataView ValidationSet { get; } + + /// + /// The initial predictor, for incremental training. Note that if a implementor + /// does not support incremental training, then it can ignore it similarly to how one would ignore + /// . However, if the trainer does support incremental training and there + /// is something wrong with a non-null value of this, then the trainer ought to throw an exception. + /// + public IPredictor InitialPredictor { get; } + + /// + /// Initializes a new instance of , given a training set and optional other arguments. + /// + /// Will set to this value if specified + /// Will set to this value if specified + public TrainerEstimatorContext(IDataView validationSet = null, IPredictor initialPredictor = null) + { + Contracts.CheckValueOrNull(validationSet); + Contracts.CheckValueOrNull(initialPredictor); + + ValidationSet = validationSet; + InitialPredictor = initialPredictor; + } + } +} diff --git a/src/Microsoft.ML.Data/Training/TrainerUtils.cs b/src/Microsoft.ML.Data/Training/TrainerUtils.cs index 33d3d1490d..b86daaace4 100644 --- a/src/Microsoft.ML.Data/Training/TrainerUtils.cs +++ b/src/Microsoft.ML.Data/Training/TrainerUtils.cs @@ -593,7 +593,7 @@ public void Signal(CursOpt opt) } /// - /// This supports Weight (Float), Group (ulong), and Id (DvInt8) columns. + /// This supports Weight (Float), Group (ulong), and Id (UInt128) columns. /// public class StandardScalarCursor : TrainingCursorBase { diff --git a/src/Microsoft.ML.Data/Transforms/ChooseColumnsTransform.cs b/src/Microsoft.ML.Data/Transforms/ChooseColumnsTransform.cs index 1459f55cab..e1300e38cd 100644 --- a/src/Microsoft.ML.Data/Transforms/ChooseColumnsTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/ChooseColumnsTransform.cs @@ -449,7 +449,8 @@ private static VersionInfo GetVersionInfo() verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, loaderSignature: LoaderSignature, - loaderSignatureAlt: LoaderSignatureOld); + loaderSignatureAlt: LoaderSignatureOld, + loaderAssemblyName: typeof(ChooseColumnsTransform).Assembly.FullName); } private readonly Bindings _bindings; diff --git a/src/Microsoft.ML.Data/Transforms/ConcatTransform.cs b/src/Microsoft.ML.Data/Transforms/ConcatTransform.cs index e0f6c1dfdf..1ec6c13d61 100644 --- a/src/Microsoft.ML.Data/Transforms/ConcatTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/ConcatTransform.cs @@ -126,7 +126,6 @@ public sealed class TaggedArguments public sealed class ColumnInfo { public readonly string Output; - private readonly (string name, string alias)[] _inputs; public IReadOnlyList<(string name, string alias)> Inputs => _inputs.AsReadOnly(); @@ -231,7 +230,6 @@ public ConcatTransform(IHostEnvironment env, params ColumnInfo[] columns) Contracts.CheckValue(env, nameof(env)); _host = env.Register(nameof(ConcatTransform)); Contracts.CheckValue(columns, nameof(columns)); - _columns = columns.ToArray(); } @@ -245,7 +243,8 @@ private static VersionInfo GetVersionInfo() verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, loaderSignature: LoaderSignature, - loaderSignatureAlt: LoaderSignatureOld); + loaderSignatureAlt: LoaderSignatureOld, + loaderAssemblyName: typeof(ConcatTransform).Assembly.FullName); } private const int VersionAddedAliases = 0x00010002; @@ -568,20 +567,20 @@ public RowMapperColumnInfo MakeColumnInfo() var metadata = new ColumnMetadataInfo(_columnInfo.Output); if (_isNormalized) - metadata.Add(MetadataUtils.Kinds.IsNormalized, new MetadataInfo(BoolType.Instance, GetIsNormalized)); + metadata.Add(MetadataUtils.Kinds.IsNormalized, new MetadataInfo(BoolType.Instance, GetIsNormalized)); if (_hasSlotNames) - metadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(_slotNamesType, GetSlotNames)); + metadata.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(_slotNamesType, GetSlotNames)); if (_hasCategoricals) - metadata.Add(MetadataUtils.Kinds.CategoricalSlotRanges, new MetadataInfo>(_categoricalRangeType, GetCategoricalSlotRanges)); + metadata.Add(MetadataUtils.Kinds.CategoricalSlotRanges, new MetadataInfo>(_categoricalRangeType, GetCategoricalSlotRanges)); return new RowMapperColumnInfo(_columnInfo.Output, OutputType, metadata); } - private void GetIsNormalized(int col, ref DvBool value) => value = _isNormalized; + private void GetIsNormalized(int col, ref bool value) => value = _isNormalized; - private void GetCategoricalSlotRanges(int iiinfo, ref VBuffer dst) + private void GetCategoricalSlotRanges(int iiinfo, ref VBuffer dst) { - List allValues = new List(); + List allValues = new List(); int slotCount = 0; for (int i = 0; i < SrcIndices.Length; i++) { @@ -602,10 +601,10 @@ private void GetCategoricalSlotRanges(int iiinfo, ref VBuffer dst) Contracts.Assert(allValues.Count > 0); - dst = new VBuffer(allValues.Count, allValues.ToArray()); + dst = new VBuffer(allValues.Count, allValues.ToArray()); } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Contracts.Assert(!_isIdentity); Contracts.Assert(OutputType.VectorSize > 0); @@ -613,11 +612,11 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) Contracts.AssertValue(_slotNamesType); Contracts.Assert(_slotNamesType.VectorSize == OutputType.VectorSize); - var bldr = BufferBuilder.CreateDefault(); + var bldr = BufferBuilder>.CreateDefault(); bldr.Reset(_slotNamesType.VectorSize, dense: false); var sb = new StringBuilder(); - var names = default(VBuffer); + var names = default(VBuffer>); int slot = 0; for (int i = 0; i < _srcTypes.Length; i++) { @@ -628,7 +627,7 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) var nameSrc = _columnInfo.Inputs[i].alias ?? colName; if (!typeSrc.IsVector) { - bldr.AddFeature(slot++, new DvText(nameSrc)); + bldr.AddFeature(slot++, nameSrc.AsMemory()); continue; } @@ -643,11 +642,11 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) int len = sb.Length; foreach (var kvp in names.Items()) { - if (!kvp.Value.HasChars) + if (kvp.Value.IsEmpty) continue; sb.Length = len; - kvp.Value.AddToStringBuilder(sb); - bldr.AddFeature(slot + kvp.Key, new DvText(sb.ToString())); + sb.AppendMemory(kvp.Value); + bldr.AddFeature(slot + kvp.Key, sb.ToString().AsMemory()); } } slot += _srcTypes[i].VectorSize; diff --git a/src/Microsoft.ML.Data/Transforms/ConvertTransform.cs b/src/Microsoft.ML.Data/Transforms/ConvertTransform.cs index 52005c7558..62906ffa55 100644 --- a/src/Microsoft.ML.Data/Transforms/ConvertTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/ConvertTransform.cs @@ -161,7 +161,8 @@ private static VersionInfo GetVersionInfo() verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, loaderSignature: LoaderSignature, - loaderSignatureAlt: LoaderSignatureOld); + loaderSignatureAlt: LoaderSignatureOld, + loaderAssemblyName: typeof(ConvertTransform).Assembly.FullName); } private const string RegistrationName = "Convert"; @@ -246,7 +247,7 @@ private void SetMetadata() using (var bldr = md.BuildMetadata(iinfo, Source.Schema, info.Source, PassThrough)) { if (info.TypeSrc.IsBool && _exes[iinfo].TypeDst.ItemType.IsNumber) - bldr.AddPrimitive(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, DvBool.True); + bldr.AddPrimitive(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, true); } } md.Seal(); diff --git a/src/Microsoft.ML.Data/Transforms/CopyColumnsTransform.cs b/src/Microsoft.ML.Data/Transforms/CopyColumnsTransform.cs index 478d509c24..8ca1260ebf 100644 --- a/src/Microsoft.ML.Data/Transforms/CopyColumnsTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/CopyColumnsTransform.cs @@ -93,7 +93,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CopyColumnsTransform).Assembly.FullName); } public CopyColumnsTransform(IHostEnvironment env, params (string source, string name)[] columns) @@ -224,7 +225,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CopyColumnsRowMapper).Assembly.FullName); } // Factory method for SignatureLoadRowMapper. diff --git a/src/Microsoft.ML.Data/Transforms/DropColumnsTransform.cs b/src/Microsoft.ML.Data/Transforms/DropColumnsTransform.cs index 3e15199ff7..aa5026a914 100644 --- a/src/Microsoft.ML.Data/Transforms/DropColumnsTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/DropColumnsTransform.cs @@ -229,7 +229,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Added KeepColumns verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(DropColumnsTransform).Assembly.FullName); } private readonly Bindings _bindings; diff --git a/src/Microsoft.ML.Data/Transforms/DropSlotsTransform.cs b/src/Microsoft.ML.Data/Transforms/DropSlotsTransform.cs index 230cfbe680..6f8185b75a 100644 --- a/src/Microsoft.ML.Data/Transforms/DropSlotsTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/DropSlotsTransform.cs @@ -188,7 +188,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(DropSlotsTransform).Assembly.FullName); } private const string RegistrationName = "DropSlots"; @@ -384,7 +385,7 @@ private void ComputeType(ISchema input, int[] slotsMin, int[] slotsMax, int iinf if (hasSlotNames && dstLength > 0) { // Add slot name metadata. - bldr.AddGetter>(MetadataUtils.Kinds.SlotNames, + bldr.AddGetter>>(MetadataUtils.Kinds.SlotNames, new VectorType(TextType.Instance, dstLength), GetSlotNames); } } @@ -393,14 +394,14 @@ private void ComputeType(ISchema input, int[] slotsMin, int[] slotsMax, int iinf { if (MetadataUtils.TryGetCategoricalFeatureIndices(Source.Schema, Infos[iinfo].Source, out categoricalRanges)) { - VBuffer dst = default(VBuffer); + VBuffer dst = default(VBuffer); GetCategoricalSlotRangesCore(iinfo, slotDropper.SlotsMin, slotDropper.SlotsMax, categoricalRanges, ref dst); // REVIEW: cache dst as opposed to caculating it again. if (dst.Length > 0) { Contracts.Assert(dst.Length % 2 == 0); - bldr.AddGetter>(MetadataUtils.Kinds.CategoricalSlotRanges, + bldr.AddGetter>(MetadataUtils.Kinds.CategoricalSlotRanges, MetadataUtils.GetCategoricalType(dst.Length / 2), GetCategoricalSlotRanges); } } @@ -433,17 +434,17 @@ protected override ColumnType GetColumnTypeCore(int iinfo) return _exes[iinfo].TypeDst; } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); - var names = default(VBuffer); + var names = default(VBuffer>); Source.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, Infos[iinfo].Source, ref names); var infoEx = _exes[iinfo]; infoEx.SlotDropper.DropSlots(ref names, ref dst); } - private void GetCategoricalSlotRanges(int iinfo, ref VBuffer dst) + private void GetCategoricalSlotRanges(int iinfo, ref VBuffer dst) { if (_exes[iinfo].CategoricalRanges != null) { @@ -452,7 +453,7 @@ private void GetCategoricalSlotRanges(int iinfo, ref VBuffer dst) } } - private void GetCategoricalSlotRangesCore(int iinfo, int[] slotsMin, int[] slotsMax, int[] catRanges, ref VBuffer dst) + private void GetCategoricalSlotRangesCore(int iinfo, int[] slotsMin, int[] slotsMax, int[] catRanges, ref VBuffer dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); Host.Assert(slotsMax != null && slotsMin != null); @@ -467,9 +468,9 @@ private void GetCategoricalSlotRangesCore(int iinfo, int[] slotsMin, int[] slots int previousDropSlotsIndex = 0; int droppedSlotsCount = 0; bool combine = false; - DvInt4 min = -1; - DvInt4 max = -1; - List newCategoricalSlotRanges = new List(); + int min = -1; + int max = -1; + List newCategoricalSlotRanges = new List(); // Six possible ways a drop slot range interacts with categorical slots range. // @@ -498,7 +499,7 @@ private void GetCategoricalSlotRangesCore(int iinfo, int[] slotsMin, int[] slots } else { - Contracts.Assert(min.RawValue == -1 && max.RawValue == -1); + Contracts.Assert(min == -1 && max == -1); min = ranges[rangesIndex] - droppedSlotsCount; max = ranges[rangesIndex + 1] - droppedSlotsCount; } @@ -515,14 +516,14 @@ private void GetCategoricalSlotRangesCore(int iinfo, int[] slotsMin, int[] slots rangesIndex += 2; if (combine) { - Contracts.Assert(min.RawValue >= 0 && min.RawValue <= max.RawValue); + Contracts.Assert(min >= 0 && min <= max); newCategoricalSlotRanges.Add(min); newCategoricalSlotRanges.Add(max); min = max = -1; combine = false; } - Contracts.Assert(min.RawValue == -1 && max.RawValue == -1); + Contracts.Assert(min == -1 && max == -1); } else if (slotsMin[dropSlotsIndex] > ranges[rangesIndex] && @@ -535,7 +536,7 @@ private void GetCategoricalSlotRangesCore(int iinfo, int[] slotsMin, int[] slots } else { - Contracts.Assert(min.RawValue == -1 && max.RawValue == -1); + Contracts.Assert(min == -1 && max == -1); min = ranges[rangesIndex] - droppedSlotsCount; max = slotsMin[dropSlotsIndex] - 1 - droppedSlotsCount; @@ -576,28 +577,28 @@ private void GetCategoricalSlotRangesCore(int iinfo, int[] slotsMin, int[] slots min = max = -1; } - Contracts.Assert(min.RawValue == -1 && max.RawValue == -1); + Contracts.Assert(min == -1 && max == -1); for (int i = rangesIndex; i < ranges.Length; i++) newCategoricalSlotRanges.Add(ranges[i] - droppedSlotsCount); Contracts.Assert(newCategoricalSlotRanges.Count % 2 == 0); - Contracts.Assert(newCategoricalSlotRanges.TrueForAll(x => x.RawValue >= 0)); + Contracts.Assert(newCategoricalSlotRanges.TrueForAll(x => x >= 0)); Contracts.Assert(0 <= droppedSlotsCount && droppedSlotsCount <= slotsMax[slotsMax.Length - 1] + 1); if (newCategoricalSlotRanges.Count > 0) - dst = new VBuffer(newCategoricalSlotRanges.Count, newCategoricalSlotRanges.ToArray()); + dst = new VBuffer(newCategoricalSlotRanges.Count, newCategoricalSlotRanges.ToArray()); } private void CombineRanges( - DvInt4 minRange1, DvInt4 maxRange1, DvInt4 minRange2, DvInt4 maxRange2, - out DvInt4 newRangeMin, out DvInt4 newRangeMax) + int minRange1, int maxRange1, int minRange2, int maxRange2, + out int newRangeMin, out int newRangeMax) { - Contracts.Assert(minRange2.RawValue >= 0 && maxRange2.RawValue >= 0); - Contracts.Assert(minRange2.RawValue <= maxRange2.RawValue); - Contracts.Assert(minRange1.RawValue >= 0 && maxRange1.RawValue >= 0); - Contracts.Assert(minRange1.RawValue <= maxRange1.RawValue); - Contracts.Assert(maxRange1.RawValue + 1 == minRange2.RawValue); + Contracts.Assert(minRange2 >= 0 && maxRange2 >= 0); + Contracts.Assert(minRange2 <= maxRange2); + Contracts.Assert(minRange1 >= 0 && maxRange1 >= 0); + Contracts.Assert(minRange1 <= maxRange1); + Contracts.Assert(maxRange1 + 1 == minRange2); newRangeMin = minRange1; newRangeMax = maxRange2; diff --git a/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs b/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs index cacd681141..11adab9ab6 100644 --- a/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs @@ -213,17 +213,17 @@ protected override void GetMetadataCore(string kind, int iinfo, ref TVal Contracts.Assert(0 <= iinfo & iinfo < InfoCount); if (kind == MetadataUtils.Kinds.IsNormalized && !UseCounter[iinfo]) { - MetadataUtils.Marshal(IsNormalized, iinfo, ref value); + MetadataUtils.Marshal(IsNormalized, iinfo, ref value); return; } base.GetMetadataCore(kind, iinfo, ref value); } - private void IsNormalized(int iinfo, ref DvBool dst) + private void IsNormalized(int iinfo, ref bool dst) { Contracts.Assert(0 <= iinfo & iinfo < InfoCount); - dst = DvBool.True; + dst = true; } public Func GetDependencies(Func predicate) @@ -249,7 +249,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(GenerateNumberTransform).Assembly.FullName); } private readonly Bindings _bindings; @@ -430,9 +431,9 @@ public ValueGetter GetGetter(int col) return fn; } - private ValueGetter MakeGetter() + private ValueGetter MakeGetter() { - return (ref DvInt8 value) => + return (ref long value) => { Ch.Check(IsGood); value = Input.Position; diff --git a/src/Microsoft.ML.Data/Transforms/HashTransform.cs b/src/Microsoft.ML.Data/Transforms/HashTransform.cs index e0aa599e49..ed31d5af95 100644 --- a/src/Microsoft.ML.Data/Transforms/HashTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/HashTransform.cs @@ -185,13 +185,14 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Invert hash key values, hash fix verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(HashTransform).Assembly.FullName); } private readonly ColInfoEx[] _exes; private readonly ColumnType[] _types; - private readonly VBuffer[] _keyValues; + private readonly VBuffer>[] _keyValues; private readonly ColumnType[] _kvTypes; public static HashTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) @@ -311,7 +312,7 @@ public HashTransform(IHostEnvironment env, Arguments args, IDataView input) for (int i = 0; i < helpers.Length; ++i) helpers[i].Process(); } - _keyValues = new VBuffer[_exes.Length]; + _keyValues = new VBuffer>[_exes.Length]; _kvTypes = new ColumnType[_exes.Length]; for (int i = 0; i < helpers.Length; ++i) { @@ -390,13 +391,13 @@ private void SetMetadata() MetadataUtils.Kinds.SlotNames)) { if (_kvTypes != null && _kvTypes[iinfo] != null) - bldr.AddGetter>(MetadataUtils.Kinds.KeyValues, _kvTypes[iinfo], GetTerms); + bldr.AddGetter>>(MetadataUtils.Kinds.KeyValues, _kvTypes[iinfo], GetTerms); } } md.Seal(); } - private void GetTerms(int iinfo, ref VBuffer dst) + private void GetTerms(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); Host.Assert(Utils.Size(_keyValues) == Infos.Length); @@ -433,7 +434,7 @@ private ValueGetter ComposeGetterOne(IRow input, int iinfo) switch (colType.RawKind) { case DataKind.Text: - return ComposeGetterOneCore(GetSrcGetter(input, iinfo), seed, mask); + return ComposeGetterOneCore(GetSrcGetter>(input, iinfo), seed, mask); case DataKind.U1: return ComposeGetterOneCore(GetSrcGetter(input, iinfo), seed, mask); case DataKind.U2: @@ -450,9 +451,9 @@ private ValueGetter ComposeGetterOne(IRow input, int iinfo) } } - private ValueGetter ComposeGetterOneCore(ValueGetter getSrc, uint seed, uint mask) + private ValueGetter ComposeGetterOneCore(ValueGetter> getSrc, uint seed, uint mask) { - DvText src = default(DvText); + ReadOnlyMemory src = default; return (ref uint dst) => { @@ -546,7 +547,7 @@ private ValueGetter> ComposeGetterVec(IRow input, int iinfo) switch (colType.ItemType.RawKind) { case DataKind.Text: - return ComposeGetterVecCore(input, iinfo, HashUnord, HashDense, HashSparse); + return ComposeGetterVecCore>(input, iinfo, HashUnord, HashDense, HashSparse); case DataKind.U1: return ComposeGetterVecCore(input, iinfo, HashUnord, HashDense, HashSparse); case DataKind.U2: @@ -670,28 +671,28 @@ private ValueGetter> ComposeGetterVecCoreFloat(IRow input, int #region Core Hash functions, with and without index [MethodImpl(MethodImplOptions.AggressiveInlining)] - private static uint HashCore(uint seed, ref DvText value, uint mask) + private static uint HashCore(uint seed, ref ReadOnlyMemory value, uint mask) { Contracts.Assert(Utils.IsPowerOfTwo(mask + 1)); - if (!value.HasChars) + if (value.IsEmpty) return 0; - return (value.Trim().Hash(seed) & mask) + 1; + return (Hashing.MurmurHash(seed, value.Span.Trim(' ')) & mask) + 1; } [MethodImpl(MethodImplOptions.AggressiveInlining)] - private static uint HashCore(uint seed, ref DvText value, int i, uint mask) + private static uint HashCore(uint seed, ref ReadOnlyMemory value, int i, uint mask) { Contracts.Assert(Utils.IsPowerOfTwo(mask + 1)); - if (!value.HasChars) + if (value.IsEmpty) return 0; - return (value.Trim().Hash(Hashing.MurmurRound(seed, (uint)i)) & mask) + 1; + return (Hashing.MurmurHash(Hashing.MurmurRound(seed, (uint)i), value.Span.Trim(' ')) & mask) + 1; } [MethodImpl(MethodImplOptions.AggressiveInlining)] private static uint HashCore(uint seed, ref float value, uint mask) { Contracts.Assert(Utils.IsPowerOfTwo(mask + 1)); - if (value.IsNA()) + if (float.IsNaN(value)) return 0; // (value == 0 ? 0 : value) takes care of negative 0, its equal to positive 0 according to the IEEE 754 standard return (Hashing.MixHash(Hashing.MurmurRound(seed, FloatUtils.GetBits(value == 0 ? 0 : value))) & mask) + 1; @@ -701,7 +702,7 @@ private static uint HashCore(uint seed, ref float value, uint mask) private static uint HashCore(uint seed, ref float value, int i, uint mask) { Contracts.Assert(Utils.IsPowerOfTwo(mask + 1)); - if (value.IsNA()) + if (float.IsNaN(value)) return 0; return (Hashing.MixHash(Hashing.MurmurRound(Hashing.MurmurRound(seed, (uint)i), FloatUtils.GetBits(value == 0 ? 0: value))) & mask) + 1; @@ -711,7 +712,7 @@ private static uint HashCore(uint seed, ref float value, int i, uint mask) private static uint HashCore(uint seed, ref double value, uint mask) { Contracts.Assert(Utils.IsPowerOfTwo(mask + 1)); - if (value.IsNA()) + if (double.IsNaN(value)) return 0; ulong v = FloatUtils.GetBits(value == 0 ? 0 : value); @@ -727,7 +728,7 @@ private static uint HashCore(uint seed, ref double value, int i, uint mask) { // If the high word is zero, this should produce the same value as the uint version. Contracts.Assert(Utils.IsPowerOfTwo(mask + 1)); - if (value.IsNA()) + if (double.IsNaN(value)) return 0; ulong v = FloatUtils.GetBits(value == 0 ? 0 : value); @@ -790,7 +791,7 @@ private static uint HashCore(uint seed, ulong value, int i, uint mask) #endregion Core Hash functions, with and without index #region Unordered Loop: ignore indices - private static void HashUnord(int count, int[] indices, DvText[] src, uint[] dst, uint seed, uint mask) + private static void HashUnord(int count, int[] indices, ReadOnlyMemory[] src, uint[] dst, uint seed, uint mask) { AssertValid(count, src, dst); @@ -848,7 +849,7 @@ private static void HashUnord(int count, int[] indices, double[] src, uint[] dst #endregion Unordered Loop: ignore indices #region Dense Loop: ignore indices - private static void HashDense(int count, int[] indices, DvText[] src, uint[] dst, uint seed, uint mask) + private static void HashDense(int count, int[] indices, ReadOnlyMemory[] src, uint[] dst, uint seed, uint mask) { AssertValid(count, src, dst); @@ -905,7 +906,7 @@ private static void HashDense(int count, int[] indices, double[] src, uint[] dst #endregion Dense Loop: ignore indices #region Sparse Loop: use indices - private static void HashSparse(int count, int[] indices, DvText[] src, uint[] dst, uint seed, uint mask) + private static void HashSparse(int count, int[] indices, ReadOnlyMemory[] src, uint[] dst, uint seed, uint mask) { AssertValid(count, src, dst); Contracts.Assert(count <= Utils.Size(indices)); @@ -1051,9 +1052,9 @@ public static InvertHashHelper Create(IRow row, ColInfo info, ColInfoEx ex, int /// public abstract void Process(); - public abstract VBuffer GetKeyValuesMetadata(); + public abstract VBuffer> GetKeyValuesMetadata(); - private sealed class TextEqualityComparer : IEqualityComparer + private sealed class TextEqualityComparer : IEqualityComparer> { // REVIEW: Is this sufficiently useful? Should we be using term map, instead? private readonly uint _seed; @@ -1063,16 +1064,13 @@ public TextEqualityComparer(uint seed) _seed = seed; } - public bool Equals(DvText x, DvText y) - { - return x.Equals(y); - } + public bool Equals(ReadOnlyMemory x, ReadOnlyMemory y) => x.Span.SequenceEqual(y.Span); - public int GetHashCode(DvText obj) + public int GetHashCode(ReadOnlyMemory obj) { - if (!obj.HasChars) + if (obj.IsEmpty) return 0; - return (int)obj.Trim().Hash(_seed) + 1; + return (int)Hashing.MurmurHash(_seed, obj.Span.Trim(' ')) + 1; } } @@ -1099,7 +1097,7 @@ public int GetHashCode(KeyValuePair obj) private IEqualityComparer GetSimpleComparer() { Contracts.Assert(_info.TypeSrc.ItemType.RawType == typeof(T)); - if (typeof(T) == typeof(DvText)) + if (typeof(T) == typeof(ReadOnlyMemory)) { // We are hashing twice, once to assign to the slot, and then again, // to build a set of encountered elements. Obviously we cannot use the @@ -1140,7 +1138,7 @@ protected virtual IEqualityComparer GetComparer() return GetSimpleComparer(); } - public override VBuffer GetKeyValuesMetadata() + public override VBuffer> GetKeyValuesMetadata() { return Collector.GetMetadata(); } diff --git a/src/Microsoft.ML.Data/Transforms/InvertHashUtils.cs b/src/Microsoft.ML.Data/Transforms/InvertHashUtils.cs index d615b96894..4c90bc1d9d 100644 --- a/src/Microsoft.ML.Data/Transforms/InvertHashUtils.cs +++ b/src/Microsoft.ML.Data/Transforms/InvertHashUtils.cs @@ -49,9 +49,9 @@ public static ValueMapper GetSimpleMapper(ISchema schema, i { // REVIEW: Non-textual KeyValues are certainly possible. Should we handle them? // Get the key names. - VBuffer keyValues = default(VBuffer); + VBuffer> keyValues = default; schema.GetMetadata(MetadataUtils.Kinds.KeyValues, col, ref keyValues); - DvText value = default(DvText); + ReadOnlyMemory value = default; // REVIEW: We could optimize for identity, but it's probably not worthwhile. var keyMapper = conv.GetStandardConversion(type, NumberType.U4, out identity); @@ -64,7 +64,7 @@ public static ValueMapper GetSimpleMapper(ISchema schema, i if (intermediate == 0) return; keyValues.GetItemOrDefault((int)(intermediate - 1), ref value); - value.AddToStringBuilder(dst); + dst.AppendMemory(value); }; } @@ -181,7 +181,7 @@ public InvertHashCollector(int slots, int maxCount, ValueMapper dst = src); } - private DvText Textify(ref StringBuilder sb, ref StringBuilder temp, ref char[] cbuffer, ref Pair[] buffer, HashSet pairs) + private ReadOnlyMemory Textify(ref StringBuilder sb, ref StringBuilder temp, ref char[] cbuffer, ref Pair[] buffer, HashSet pairs) { Contracts.AssertValueOrNull(sb); Contracts.AssertValueOrNull(temp); @@ -200,7 +200,7 @@ private DvText Textify(ref StringBuilder sb, ref StringBuilder temp, ref char[] { var value = buffer[0].Value; _stringifyMapper(ref value, ref temp); - return Utils.Size(temp) > 0 ? new DvText(temp.ToString()) : DvText.Empty; + return Utils.Size(temp) > 0 ? temp.ToString().AsMemory() : String.Empty.AsMemory(); } Array.Sort(buffer, 0, count, Comparer.Create((x, y) => x.Order - y.Order)); @@ -219,12 +219,12 @@ private DvText Textify(ref StringBuilder sb, ref StringBuilder temp, ref char[] InvertHashUtils.AppendToEnd(temp, sb, ref cbuffer); } sb.Append('}'); - var retval = new DvText(sb.ToString()); + var retval = sb.ToString().AsMemory(); sb.Clear(); return retval; } - public VBuffer GetMetadata() + public VBuffer> GetMetadata() { int count = _slotToValueSet.Count; Contracts.Assert(count <= _slots); @@ -238,7 +238,7 @@ public VBuffer GetMetadata() { // Sparse var indices = new int[count]; - var values = new DvText[count]; + var values = new ReadOnlyMemory[count]; int i = 0; foreach (var p in _slotToValueSet) { @@ -248,18 +248,18 @@ public VBuffer GetMetadata() } Contracts.Assert(i == count); Array.Sort(indices, values); - return new VBuffer((int)_slots, count, values, indices); + return new VBuffer>((int)_slots, count, values, indices); } else { // Dense - var values = new DvText[_slots]; + var values = new ReadOnlyMemory[_slots]; foreach (var p in _slotToValueSet) { Contracts.Assert(0 <= p.Key && p.Key < _slots); values[p.Key] = Textify(ref sb, ref temp, ref cbuffer, ref pairs, p.Value); } - return new VBuffer(values.Length, values); + return new VBuffer>(values.Length, values); } } @@ -315,7 +315,7 @@ public void Add(uint hash, T key) } /// - /// Simple utility class for saving a of + /// Simple utility class for saving a of ReadOnlyMemory /// as a model, both in a binary and more easily human readable form. /// public static class TextModelHelper @@ -329,17 +329,18 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TextModelHelper).Assembly.FullName); } - private static void Load(IChannel ch, ModelLoadContext ctx, CodecFactory factory, ref VBuffer values) + private static void Load(IChannel ch, ModelLoadContext ctx, CodecFactory factory, ref VBuffer> values) { Contracts.AssertValue(ch); ch.CheckValue(ctx, nameof(ctx)); ctx.CheckAtModel(GetVersionInfo()); // *** Binary format *** - // Codec parameterization: A codec parameterization that should be a VBuffer codec + // Codec parameterization: A codec parameterization that should be a ReadOnlyMemory codec // int: n, the number of bytes used to write the values // byte[n]: As encoded using the codec @@ -355,7 +356,7 @@ private static void Load(IChannel ch, ModelLoadContext ctx, CodecFactory factory ch.AssertValue(codec); ch.CheckDecode(codec.Type.IsVector); ch.CheckDecode(codec.Type.ItemType.IsText); - var textCodec = (IValueCodec>)codec; + var textCodec = (IValueCodec>>)codec; var bufferLen = ctx.Reader.ReadInt32(); ch.CheckDecode(bufferLen >= 0); @@ -364,14 +365,14 @@ private static void Load(IChannel ch, ModelLoadContext ctx, CodecFactory factory using (var reader = textCodec.OpenReader(stream, 1)) { reader.MoveNext(); - values = default(VBuffer); + values = default(VBuffer>); reader.Get(ref values); } ch.CheckDecode(stream.ReadByte() == -1); } } - private static void Save(IChannel ch, ModelSaveContext ctx, CodecFactory factory, ref VBuffer values) + private static void Save(IChannel ch, ModelSaveContext ctx, CodecFactory factory, ref VBuffer> values) { Contracts.AssertValue(ch); ch.CheckValue(ctx, nameof(ctx)); @@ -379,7 +380,7 @@ private static void Save(IChannel ch, ModelSaveContext ctx, CodecFactory factory ctx.SetVersionInfo(GetVersionInfo()); // *** Binary format *** - // Codec parameterization: A codec parameterization that should be a VBuffer codec + // Codec parameterization: A codec parameterization that should be a ReadOnlyMemory codec // int: n, the number of bytes used to write the values // byte[n]: As encoded using the codec @@ -389,8 +390,8 @@ private static void Save(IChannel ch, ModelSaveContext ctx, CodecFactory factory ch.Assert(result); ch.Assert(codec.Type.IsVector); ch.Assert(codec.Type.VectorSize == 0); - ch.Assert(codec.Type.ItemType.RawType == typeof(DvText)); - IValueCodec> textCodec = (IValueCodec>)codec; + ch.Assert(codec.Type.ItemType.RawType == typeof(ReadOnlyMemory)); + IValueCodec>> textCodec = (IValueCodec>>)codec; factory.WriteCodec(ctx.Writer.BaseStream, codec); using (var mem = new MemoryStream()) @@ -420,22 +421,23 @@ private static void Save(IChannel ch, ModelSaveContext ctx, CodecFactory factory writer.Write("{0}\t", pair.Key); // REVIEW: What about escaping this, *especially* for linebreaks? // Do C# and .NET really have no equivalent to Python's "repr"? :( - if (!text.HasChars) + if (text.IsEmpty) { writer.WriteLine(); continue; } Utils.EnsureSize(ref buffer, text.Length); - int ichMin; - int ichLim; - string str = text.GetRawUnderlyingBufferInfo(out ichMin, out ichLim); - str.CopyTo(ichMin, buffer, 0, text.Length); + + var span = text.Span; + for (int i = 0; i < text.Length; i++) + buffer[i] = span[i]; + writer.WriteLine(buffer, 0, text.Length); } }); } - public static void LoadAll(IHost host, ModelLoadContext ctx, int infoLim, out VBuffer[] keyValues, out ColumnType[] kvTypes) + public static void LoadAll(IHost host, ModelLoadContext ctx, int infoLim, out VBuffer>[] keyValues, out ColumnType[] kvTypes) { Contracts.AssertValue(host); host.AssertValue(ctx); @@ -443,7 +445,7 @@ public static void LoadAll(IHost host, ModelLoadContext ctx, int infoLim, out VB using (var ch = host.Start("LoadTextValues")) { // Try to find the key names. - VBuffer[] keyValuesLocal = null; + VBuffer>[] keyValuesLocal = null; ColumnType[] kvTypesLocal = null; CodecFactory factory = null; const string dirFormat = "Vocabulary_{0:000}"; @@ -455,7 +457,7 @@ public static void LoadAll(IHost host, ModelLoadContext ctx, int infoLim, out VB // Load the lazily initialized structures, if needed. if (keyValuesLocal == null) { - keyValuesLocal = new VBuffer[infoLim]; + keyValuesLocal = new VBuffer>[infoLim]; kvTypesLocal = new ColumnType[infoLim]; factory = new CodecFactory(host); } @@ -470,7 +472,7 @@ public static void LoadAll(IHost host, ModelLoadContext ctx, int infoLim, out VB } } - public static void SaveAll(IHost host, ModelSaveContext ctx, int infoLim, VBuffer[] keyValues) + public static void SaveAll(IHost host, ModelSaveContext ctx, int infoLim, VBuffer>[] keyValues) { Contracts.AssertValue(host); host.AssertValue(ctx); diff --git a/src/Microsoft.ML.Data/Transforms/KeyToValueTransform.cs b/src/Microsoft.ML.Data/Transforms/KeyToValueTransform.cs index de8463a804..d9e75fb178 100644 --- a/src/Microsoft.ML.Data/Transforms/KeyToValueTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/KeyToValueTransform.cs @@ -76,7 +76,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(KeyToValueTransform).Assembly.FullName); } /// diff --git a/src/Microsoft.ML.Data/Transforms/KeyToVectorTransform.cs b/src/Microsoft.ML.Data/Transforms/KeyToVectorTransform.cs index 5352040958..fd177d6a14 100644 --- a/src/Microsoft.ML.Data/Transforms/KeyToVectorTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/KeyToVectorTransform.cs @@ -143,7 +143,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Get rid of writing float size in model context verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(KeyToVectorTransform).Assembly.FullName); } public override void Save(ModelSaveContext ctx) @@ -280,6 +281,7 @@ private ColInfo[] CreateInfos(ISchema inputSchema) if (!inputSchema.TryGetColumnIndex(_parent.ColumnPairs[i].input, out int colSrc)) throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.ColumnPairs[i].input); var type = inputSchema.GetColumnType(colSrc); + _parent.CheckInputColumn(inputSchema, i, colSrc); infos[i] = new ColInfo(_parent.ColumnPairs[i].output, _parent.ColumnPairs[i].input, type); } return infos; @@ -313,11 +315,11 @@ private void AddMetadata(int i, ColumnMetadataInfo colMetaInfo) { if (typeNames != null) { - MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer dst) => + MetadataUtils.MetadataGetter>> getter = (int col, ref VBuffer> dst) => { InputSchema.GetMetadata(MetadataUtils.Kinds.KeyValues, srcCol, ref dst); }; - var info = new MetadataInfo>(typeNames, getter); + var info = new MetadataInfo>>(typeNames, getter); colMetaInfo.Add(MetadataUtils.Kinds.SlotNames, info); } } @@ -325,38 +327,38 @@ private void AddMetadata(int i, ColumnMetadataInfo colMetaInfo) { if (typeNames != null && _types[i].IsKnownSizeVector) { - MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer dst) => + MetadataUtils.MetadataGetter>> getter = (int col, ref VBuffer> dst) => { GetSlotNames(i, ref dst); }; - var info = new MetadataInfo>(new VectorType(TextType.Instance, _types[i]), getter); + var info = new MetadataInfo>>(new VectorType(TextType.Instance, _types[i]), getter); colMetaInfo.Add(MetadataUtils.Kinds.SlotNames, info); } } if (!_parent._columns[i].Bag && srcType.ValueCount > 0) { - MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer dst) => + MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer dst) => { GetCategoricalSlotRanges(i, ref dst); }; - var info = new MetadataInfo>(MetadataUtils.GetCategoricalType(_infos[i].TypeSrc.ValueCount), getter); + var info = new MetadataInfo>(MetadataUtils.GetCategoricalType(_infos[i].TypeSrc.ValueCount), getter); colMetaInfo.Add(MetadataUtils.Kinds.CategoricalSlotRanges, info); } if (!_parent._columns[i].Bag || srcType.ValueCount == 1) { - MetadataUtils.MetadataGetter getter = (int col, ref DvBool dst) => + MetadataUtils.MetadataGetter getter = (int col, ref bool dst) => { dst = true; }; - var info = new MetadataInfo(BoolType.Instance, getter); + var info = new MetadataInfo(BoolType.Instance, getter); colMetaInfo.Add(MetadataUtils.Kinds.IsNormalized, info); } } // Combines source key names and slot names to produce final slot names. - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < _infos.Length); Host.Assert(_types[iinfo].IsKnownSizeVector); @@ -367,7 +369,7 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) Host.Assert(typeSrc.VectorSize > 1); // Get the source slot names, defaulting to empty text. - var namesSlotSrc = default(VBuffer); + var namesSlotSrc = default(VBuffer>); InputSchema.TryGetColumnIndex(_infos[iinfo].Source, out int srcCol); Host.Assert(srcCol >= 0); var typeSlotSrc = InputSchema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, srcCol); @@ -377,22 +379,22 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) Host.Check(namesSlotSrc.Length == typeSrc.VectorSize); } else - namesSlotSrc = VBufferUtils.CreateEmpty(typeSrc.VectorSize); + namesSlotSrc = VBufferUtils.CreateEmpty>(typeSrc.VectorSize); int keyCount = typeSrc.ItemType.ItemType.KeyCount; int slotLim = _types[iinfo].VectorSize; Host.Assert(slotLim == (long)typeSrc.VectorSize * keyCount); // Get the source key names, in an array (since we will use them multiple times). - var namesKeySrc = default(VBuffer); + var namesKeySrc = default(VBuffer>); InputSchema.GetMetadata(MetadataUtils.Kinds.KeyValues, srcCol, ref namesKeySrc); Host.Check(namesKeySrc.Length == keyCount); - var keys = new DvText[keyCount]; + var keys = new ReadOnlyMemory[keyCount]; namesKeySrc.CopyTo(keys); var values = dst.Values; if (Utils.Size(values) < slotLim) - values = new DvText[slotLim]; + values = new ReadOnlyMemory[slotLim]; var sb = new StringBuilder(); int slot = 0; @@ -400,8 +402,8 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) { Contracts.Assert(slot == (long)kvpSlot.Key * keyCount); sb.Clear(); - if (kvpSlot.Value.HasChars) - kvpSlot.Value.AddToStringBuilder(sb); + if (!kvpSlot.Value.IsEmpty) + sb.AppendMemory(kvpSlot.Value); else sb.Append('[').Append(kvpSlot.Key).Append(']'); sb.Append('.'); @@ -410,16 +412,16 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) foreach (var key in keys) { sb.Length = len; - key.AddToStringBuilder(sb); - values[slot++] = new DvText(sb.ToString()); + sb.AppendMemory(key); + values[slot++] = sb.ToString().AsMemory(); } } Host.Assert(slot == slotLim); - dst = new VBuffer(slotLim, values, dst.Indices); + dst = new VBuffer>(slotLim, values, dst.Indices); } - private void GetCategoricalSlotRanges(int iinfo, ref VBuffer dst) + private void GetCategoricalSlotRanges(int iinfo, ref VBuffer dst) { Host.Assert(0 <= iinfo && iinfo < _infos.Length); @@ -427,7 +429,7 @@ private void GetCategoricalSlotRanges(int iinfo, ref VBuffer dst) Host.Assert(info.TypeSrc.ValueCount > 0); - DvInt4[] ranges = new DvInt4[info.TypeSrc.ValueCount * 2]; + int[] ranges = new int[info.TypeSrc.ValueCount * 2]; int size = info.TypeSrc.ItemType.KeyCount; ranges[0] = 0; @@ -438,7 +440,7 @@ private void GetCategoricalSlotRanges(int iinfo, ref VBuffer dst) ranges[i + 1] = ranges[i] + size - 1; } - dst = new VBuffer(ranges.Length, ranges); + dst = new VBuffer(ranges.Length, ranges); } protected override Delegate MakeGetter(IRow input, int iinfo, out Action disposer) diff --git a/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs b/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs index 8817833f40..b5943b14fa 100644 --- a/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs @@ -58,7 +58,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial. verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LabelConvertTransform).Assembly.FullName); } private const string RegistrationName = "LabelConvert"; diff --git a/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs b/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs index a7672b5a1c..5844d61d31 100644 --- a/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs @@ -39,7 +39,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LabelIndicatorTransform).Assembly.FullName); } public sealed class Column : OneToOneColumn @@ -174,7 +175,7 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, return GetGetter(ch, input, iinfo); } - private ValueGetter GetGetter(IChannel ch, IRow input, int iinfo) + private ValueGetter GetGetter(IChannel ch, IRow input, int iinfo) { Host.AssertValue(ch); ch.AssertValue(input); @@ -190,7 +191,7 @@ private ValueGetter GetGetter(IChannel ch, IRow input, int iinfo) uint cls = (uint)(_classIndex[iinfo] + 1); return - (ref DvBool dst) => + (ref bool dst) => { srcGetter(ref src); dst = src == cls; @@ -202,7 +203,7 @@ private ValueGetter GetGetter(IChannel ch, IRow input, int iinfo) var src = default(float); return - (ref DvBool dst) => + (ref bool dst) => { srcGetter(ref src); dst = src == _classIndex[iinfo]; @@ -214,7 +215,7 @@ private ValueGetter GetGetter(IChannel ch, IRow input, int iinfo) var src = default(double); return - (ref DvBool dst) => + (ref bool dst) => { srcGetter(ref src); dst = src == _classIndex[iinfo]; diff --git a/src/Microsoft.ML.Data/Transforms/NAFilter.cs b/src/Microsoft.ML.Data/Transforms/NAFilter.cs index c8515291f3..ed738f3e63 100644 --- a/src/Microsoft.ML.Data/Transforms/NAFilter.cs +++ b/src/Microsoft.ML.Data/Transforms/NAFilter.cs @@ -70,7 +70,8 @@ private static VersionInfo GetVersionInfo() loaderSignature: LoaderSignature, // This is an older name and can be removed once we don't care about old code // being able to load this. - loaderSignatureAlt: "MissingFeatureFilter"); + loaderSignatureAlt: "MissingFeatureFilter", + loaderAssemblyName: typeof(NAFilter).Assembly.FullName); } private readonly ColInfo[] _infos; diff --git a/src/Microsoft.ML.Data/Transforms/NopTransform.cs b/src/Microsoft.ML.Data/Transforms/NopTransform.cs index 0a71a3ec07..a367073f2f 100644 --- a/src/Microsoft.ML.Data/Transforms/NopTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/NopTransform.cs @@ -55,7 +55,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(NopTransform).Assembly.FullName); } internal static string RegistrationName = "NopTransform"; diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs index 69021d30a3..a91c4645eb 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs @@ -557,7 +557,7 @@ public override void AttachMetadata(MetadataDispatcher.Builder bldr, ColumnType Host.Check(typeSrc.RawType == typeof(TFloat)); bldr.AddPrimitive("CdfMean", typeSrc, Mean); bldr.AddPrimitive("CdfStdDev", typeSrc, Stddev); - bldr.AddPrimitive("CdfUseLog", BoolType.Instance, (DvBool)UseLog); + bldr.AddPrimitive("CdfUseLog", BoolType.Instance, UseLog); } } @@ -590,7 +590,7 @@ public override void AttachMetadata(MetadataDispatcher.Builder bldr, ColumnType Host.Check(typeSrc.ItemType.RawType == typeof(TFloat)); bldr.AddGetter>("CdfMean", typeSrc, MeanMetadataGetter); bldr.AddGetter>("CdfStdDev", typeSrc, StddevMetadataGetter); - bldr.AddPrimitive("CdfUseLog", BoolType.Instance, (DvBool)UseLog); + bldr.AddPrimitive("CdfUseLog", BoolType.Instance, UseLog); } private void MeanMetadataGetter(int col, ref VBuffer dst) @@ -615,7 +615,8 @@ public static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CdfColumnFunction).Assembly.FullName); } } @@ -677,7 +678,8 @@ public static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(BinColumnFunction).Assembly.FullName); } } @@ -1139,7 +1141,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Scales multiply instead of divide verReadableCur: 0x00010003, verWeCanReadBack: 0x00010003, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(AffineNormSerializationUtils).Assembly.FullName); } } @@ -1154,7 +1157,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(BinNormSerializationUtils).Assembly.FullName); } } diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs index ab79ab4e11..4cb4a2892f 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs @@ -146,7 +146,6 @@ public static CommonOutputs.TransformOutput Bin(IHostEnvironment env, NormalizeT EntryPointNode node) { var schema = input.Data.Schema; - DvBool isNormalized = DvBool.False; var columnsToNormalize = new List(); foreach (var column in input.Column) { diff --git a/src/Microsoft.ML.Data/Transforms/Normalizer.cs b/src/Microsoft.ML.Data/Transforms/Normalizer.cs index 8f7c25166b..4c3d9fd51d 100644 --- a/src/Microsoft.ML.Data/Transforms/Normalizer.cs +++ b/src/Microsoft.ML.Data/Transforms/Normalizer.cs @@ -216,7 +216,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(NormalizerTransformer).Assembly.FullName); } private class ColumnInfo @@ -473,19 +474,19 @@ private ColumnMetadataInfo MakeMetadata(int iinfo) { var colInfo = _parent._columns[iinfo]; var result = new ColumnMetadataInfo(colInfo.Output); - result.Add(MetadataUtils.Kinds.IsNormalized, new MetadataInfo(BoolType.Instance, IsNormalizedGetter)); + result.Add(MetadataUtils.Kinds.IsNormalized, new MetadataInfo(BoolType.Instance, IsNormalizedGetter)); if (InputSchema.HasSlotNames(ColMapNewToOld[iinfo], colInfo.InputType.VectorSize)) { - MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer slotNames) => + MetadataUtils.MetadataGetter>> getter = (int col, ref VBuffer> slotNames) => InputSchema.GetMetadata(MetadataUtils.Kinds.SlotNames, ColMapNewToOld[iinfo], ref slotNames); var metaType = InputSchema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, ColMapNewToOld[iinfo]); Contracts.AssertValue(metaType); - result.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(metaType, getter)); + result.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>>(metaType, getter)); } return result; } - private void IsNormalizedGetter(int col, ref DvBool dst) + private void IsNormalizedGetter(int col, ref bool dst) { dst = true; } diff --git a/src/Microsoft.ML.Data/Transforms/RangeFilter.cs b/src/Microsoft.ML.Data/Transforms/RangeFilter.cs index 142779dee2..d20446f7f5 100644 --- a/src/Microsoft.ML.Data/Transforms/RangeFilter.cs +++ b/src/Microsoft.ML.Data/Transforms/RangeFilter.cs @@ -64,7 +64,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RangeFilter).Assembly.FullName); } private const string RegistrationName = "RangeFilter"; diff --git a/src/Microsoft.ML.Data/Transforms/ShuffleTransform.cs b/src/Microsoft.ML.Data/Transforms/ShuffleTransform.cs index 3940bbe979..4043e3c42b 100644 --- a/src/Microsoft.ML.Data/Transforms/ShuffleTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/ShuffleTransform.cs @@ -71,7 +71,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Force shuffle source saving verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ShuffleTransform).Assembly.FullName); } private const string RegistrationName = "Shuffle"; diff --git a/src/Microsoft.ML.Data/Transforms/SkipTakeFilter.cs b/src/Microsoft.ML.Data/Transforms/SkipTakeFilter.cs index 2adb17258e..440d68ae84 100644 --- a/src/Microsoft.ML.Data/Transforms/SkipTakeFilter.cs +++ b/src/Microsoft.ML.Data/Transforms/SkipTakeFilter.cs @@ -76,7 +76,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(SkipTakeFilter).Assembly.FullName); } private readonly long _skip; diff --git a/src/Microsoft.ML.Data/Transforms/TermTransform.cs b/src/Microsoft.ML.Data/Transforms/TermTransform.cs index 70b8002be6..c3084a9fa7 100644 --- a/src/Microsoft.ML.Data/Transforms/TermTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/TermTransform.cs @@ -193,7 +193,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Generalize to multiple types beyond text verReadableCur: 0x00010003, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TermTransform).Assembly.FullName); } private const uint VerNonTextTypesSupported = 0x00010003; @@ -224,7 +225,8 @@ private static VersionInfo GetTermManagerVersionInfo() verWrittenCur: 0x00010002, // Generalize to multiple types beyond text verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: TermManagerLoaderSignature); + loaderSignature: TermManagerLoaderSignature, + loaderAssemblyName: typeof(TermTransform).Assembly.FullName); } private readonly TermMap[] _unboundMaps; @@ -546,16 +548,16 @@ private static TermMap[] Train(IHostEnvironment env, IChannel ch, ColInfo[] info for (int iinfo = 0; iinfo < infos.Length; iinfo++) { // First check whether we have a terms argument, and handle it appropriately. - var terms = new DvText(columns[iinfo].Terms); + var terms = columns[iinfo].Terms.AsMemory(); var termsArray = columns[iinfo].Term; - terms = terms.Trim(); - if (terms.HasChars || (termsArray != null && termsArray.Length > 0)) + terms = ReadOnlyMemoryUtils.TrimSpaces(terms); + if (!terms.IsEmpty || (termsArray != null && termsArray.Length > 0)) { // We have terms! Pass it in. var sortOrder = columns[iinfo].Sort; var bldr = Builder.Create(infos[iinfo].TypeSrc, sortOrder); - if (terms.HasChars) + if (!terms.IsEmpty) bldr.ParseAddTermArg(ref terms, ch); else bldr.ParseAddTermArg(termsArray, ch); @@ -795,8 +797,8 @@ private bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string src if (!info.TypeSrc.ItemType.IsText) return false; - var terms = default(VBuffer); - TermMap map = (TermMap)_termMap[iinfo].Map; + var terms = default(VBuffer>); + TermMap> map = (TermMap>)_termMap[iinfo].Map; map.GetTerms(ref terms); string opType = "LabelEncoder"; var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType)); @@ -870,8 +872,8 @@ private JToken SaveAsPfaCore(BoundPfaContext ctx, int iinfo, ColInfo info, JToke if (!info.TypeSrc.ItemType.IsText) return null; - var terms = default(VBuffer); - TermMap map = (TermMap)_termMap[iinfo].Map; + var terms = default(VBuffer>); + TermMap> map = (TermMap>)_termMap[iinfo].Map; map.GetTerms(ref terms); var jsonMap = new JObject(); foreach (var kv in terms.Items()) diff --git a/src/Microsoft.ML.Data/Transforms/TermTransformImpl.cs b/src/Microsoft.ML.Data/Transforms/TermTransformImpl.cs index fcc0155617..e94c457658 100644 --- a/src/Microsoft.ML.Data/Transforms/TermTransformImpl.cs +++ b/src/Microsoft.ML.Data/Transforms/TermTransformImpl.cs @@ -79,7 +79,7 @@ private static Builder CreateCore(PrimitiveType type, bool sorted) /// /// The input terms argument /// The channel against which to report errors and warnings - public abstract void ParseAddTermArg(ref DvText terms, IChannel ch); + public abstract void ParseAddTermArg(ref ReadOnlyMemory terms, IChannel ch); /// /// Handling for the "term" arg. @@ -88,7 +88,7 @@ private static Builder CreateCore(PrimitiveType type, bool sorted) /// The channel against which to report errors and warnings public abstract void ParseAddTermArg(string[] terms, IChannel ch); - private sealed class TextImpl : Builder + private sealed class TextImpl : Builder> { private readonly NormStr.Pool _pool; private readonly bool _sorted; @@ -105,12 +105,12 @@ public TextImpl(bool sorted) _sorted = sorted; } - public override bool TryAdd(ref DvText val) + public override bool TryAdd(ref ReadOnlyMemory val) { - if (!val.HasChars) + if (val.IsEmpty) return false; int count = _pool.Count; - return val.AddToPool(_pool).Id == count; + return ReadOnlyMemoryUtils.AddToPool(val, _pool).Id == count; } public override TermMap Finish() @@ -119,7 +119,7 @@ public override TermMap Finish() return new TermMap.TextImpl(_pool); // REVIEW: Should write a Sort method in NormStr.Pool to make sorting more memory efficient. var perm = Utils.GetIdentityPermutation(_pool.Count); - Comparison comp = (i, j) => _pool.GetNormStrById(i).Value.CompareTo(_pool.GetNormStrById(j).Value); + Comparison comp = (i, j) => _pool.GetNormStrById(i).Value.Span.CompareTo(_pool.GetNormStrById(j).Value.Span, StringComparison.Ordinal); Array.Sort(perm, comp); var sortedPool = new NormStr.Pool(); @@ -127,7 +127,7 @@ public override TermMap Finish() { var nstr = sortedPool.Add(_pool.GetNormStrById(perm[i]).Value); Contracts.Assert(nstr.Id == i); - Contracts.Assert(i == 0 || sortedPool.GetNormStrById(i - 1).Value.CompareTo(sortedPool.GetNormStrById(i).Value) < 0); + Contracts.Assert(i == 0 || sortedPool.GetNormStrById(i - 1).Value.Span.CompareTo(sortedPool.GetNormStrById(i).Value.Span, StringComparison.Ordinal) < 0); } Contracts.Assert(sortedPool.Count == _pool.Count); return new TermMap.TextImpl(sortedPool); @@ -201,16 +201,16 @@ protected Builder(PrimitiveType type) /// /// The input terms argument /// The channel against which to report errors and warnings - public override void ParseAddTermArg(ref DvText terms, IChannel ch) + public override void ParseAddTermArg(ref ReadOnlyMemory terms, IChannel ch) { T val; var tryParse = Conversion.Conversions.Instance.GetParseConversion(ItemType); for (bool more = true; more;) { - DvText term; - more = terms.SplitOne(',', out term, out terms); - term = term.Trim(); - if (!term.HasChars) + ReadOnlyMemory term; + more = ReadOnlyMemoryUtils.SplitOne(terms, ',', out term, out terms); + term = ReadOnlyMemoryUtils.TrimSpaces(term); + if (term.IsEmpty) ch.Warning("Empty strings ignored in 'terms' specification"); else if (!tryParse(ref term, out val)) ch.Warning("Item '{0}' ignored in 'terms' specification since it could not be parsed as '{1}'", term, ItemType); @@ -233,9 +233,9 @@ public override void ParseAddTermArg(string[] terms, IChannel ch) var tryParse = Conversion.Conversions.Instance.GetParseConversion(ItemType); foreach (var sterm in terms) { - DvText term = new DvText(sterm); - term = term.Trim(); - if (!term.HasChars) + ReadOnlyMemory term = sterm.AsMemory(); + term = ReadOnlyMemoryUtils.TrimSpaces(term); + if (term.IsEmpty) ch.Warning("Empty strings ignored in 'term' specification"); else if (!tryParse(ref term, out val)) ch.Warning("Item '{0}' ignored in 'term' specification since it could not be parsed as '{1}'", term, ItemType); @@ -569,7 +569,7 @@ private static TermMap LoadCodecCore(ModelLoadContext ctx, IExceptionContext public abstract void WriteTextTerms(TextWriter writer); - public sealed class TextImpl : TermMap + public sealed class TextImpl : TermMap> { private readonly NormStr.Pool _pool; @@ -631,35 +631,35 @@ internal override void Save(ModelSaveContext ctx, IHostEnvironment host, CodecFa } } - private void KeyMapper(ref DvText src, ref uint dst) + private void KeyMapper(ref ReadOnlyMemory src, ref uint dst) { - var nstr = src.FindInPool(_pool); + var nstr = ReadOnlyMemoryUtils.FindInPool(src, _pool); if (nstr == null) dst = 0; else dst = (uint)nstr.Id + 1; } - public override ValueMapper GetKeyMapper() + public override ValueMapper, uint> GetKeyMapper() { return KeyMapper; } - public override void GetTerms(ref VBuffer dst) + public override void GetTerms(ref VBuffer> dst) { - DvText[] values = dst.Values; + ReadOnlyMemory[] values = dst.Values; if (Utils.Size(values) < _pool.Count) - values = new DvText[_pool.Count]; + values = new ReadOnlyMemory[_pool.Count]; int slot = 0; foreach (var nstr in _pool) { Contracts.Assert(0 <= nstr.Id & nstr.Id < values.Length); Contracts.Assert(nstr.Id == slot); - values[nstr.Id] = new DvText(nstr.Value); + values[nstr.Id] = nstr.Value; slot++; } - dst = new VBuffer(_pool.Count, values, dst.Indices); + dst = new VBuffer>(_pool.Count, values, dst.Indices); } public override void WriteTextTerms(TextWriter writer) @@ -770,7 +770,7 @@ protected TermMap(PrimitiveType type, int count) public abstract void GetTerms(ref VBuffer dst); } - private static void GetTextTerms(ref VBuffer src, ValueMapper stringMapper, ref VBuffer dst) + private static void GetTextTerms(ref VBuffer src, ValueMapper stringMapper, ref VBuffer> dst) { // REVIEW: This convenience function is not optimized. For non-string // types, creating a whole bunch of string objects on the heap is one that is @@ -778,23 +778,23 @@ private static void GetTextTerms(ref VBuffer src, ValueMapper)); StringBuilder sb = null; - DvText[] values = dst.Values; + ReadOnlyMemory[] values = dst.Values; // We'd obviously have to adjust this a bit, if we ever had sparse metadata vectors. // The way the term map metadata getters are structured right now, this is impossible. Contracts.Assert(src.IsDense); if (Utils.Size(values) < src.Length) - values = new DvText[src.Length]; + values = new ReadOnlyMemory[src.Length]; for (int i = 0; i < src.Length; ++i) { stringMapper(ref src.Values[i], ref sb); - values[i] = new DvText(sb.ToString()); + values[i] = sb.ToString().AsMemory(); } - dst = new VBuffer(src.Length, values, dst.Indices); + dst = new VBuffer>(src.Length, values, dst.Indices); } /// @@ -1048,8 +1048,8 @@ public override void AddMetadata(ColumnMetadataInfo colMetaInfo) var conv = Conversion.Conversions.Instance; var stringMapper = conv.GetStringConversion(TypedMap.ItemType); - MetadataUtils.MetadataGetter> getter = - (int iinfo, ref VBuffer dst) => + MetadataUtils.MetadataGetter>> getter = + (int iinfo, ref VBuffer> dst) => { // No buffer sharing convenient here. VBuffer dstT = default(VBuffer); @@ -1057,7 +1057,7 @@ public override void AddMetadata(ColumnMetadataInfo colMetaInfo) GetTextTerms(ref dstT, stringMapper, ref dst); }; var columnType = new VectorType(TextType.Instance, TypedMap.OutputType.KeyCount); - var info = new MetadataInfo>(columnType, getter); + var info = new MetadataInfo>>(columnType, getter); colMetaInfo.Add(MetadataUtils.Kinds.KeyValues, info); } else @@ -1144,8 +1144,8 @@ private bool AddMetadataCore(ColumnType srcMetaType, ColumnMetadataInfo c if (IsTextMetadata && !srcMetaType.IsText) { var stringMapper = convInst.GetStringConversion(srcMetaType); - MetadataUtils.MetadataGetter> mgetter = - (int iinfo, ref VBuffer dst) => + MetadataUtils.MetadataGetter>> mgetter = + (int iinfo, ref VBuffer> dst) => { _host.Assert(iinfo == _iinfo); var tempMeta = default(VBuffer); @@ -1155,7 +1155,7 @@ private bool AddMetadataCore(ColumnType srcMetaType, ColumnMetadataInfo c _host.Assert(dst.Length == TypedMap.OutputType.KeyCount); }; var columnType = new VectorType(TextType.Instance, TypedMap.OutputType.KeyCount); - var info = new MetadataInfo>(columnType, mgetter); + var info = new MetadataInfo>>(columnType, mgetter); colMetaInfo.Add(MetadataUtils.Kinds.KeyValues, info); } else diff --git a/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs b/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs index b53c721f61..080bad4c51 100644 --- a/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs +++ b/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Collections.Generic; using System.IO; using System.Linq; @@ -188,7 +189,7 @@ public static IDataLoader LoadLoader(IHostEnvironment env, RepositoryReader rep, } /// - /// REVIEW: consider adding an overload that returns + /// REVIEW: consider adding an overload that returns of /// Loads optionally feature names from the repository directory. /// Returns false iff no stream was found for feature names, iff result is set to null. /// @@ -287,10 +288,10 @@ public static IEnumerable> LoadRoleMappingsOrNu using (var cursor = loader.GetRowCursor(c => true)) { - var roleGetter = cursor.GetGetter(0); - var colGetter = cursor.GetGetter(1); - var role = default(DvText); - var col = default(DvText); + var roleGetter = cursor.GetGetter>(0); + var colGetter = cursor.GetGetter>(1); + var role = default(ReadOnlyMemory); + var col = default(ReadOnlyMemory); while (cursor.MoveNext()) { roleGetter(ref role); diff --git a/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/DnnAnalyzer.cs b/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/DnnAnalyzer.cs new file mode 100644 index 0000000000..48fd32fc31 --- /dev/null +++ b/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/DnnAnalyzer.cs @@ -0,0 +1,31 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Transforms.TensorFlow; +using System; +using System.Linq; + +namespace Microsoft.ML.DnnAnalyzer +{ + public static class DnnAnalyzer + { + public static void Main(string[] args) + { + if (Utils.Size(args) != 1) + { + Console.Error.WriteLine("Usage: dotnet DnnAnalyzer.dll "); + return; + } + + foreach (var (name, opType, type, inputs) in TensorFlowUtils.GetModelNodes(args[0])) + { + var inputsString = inputs.Length == 0 ? "" : $", input nodes: {string.Join(", ", inputs)}"; + Console.WriteLine($"Graph node: '{name}', operation type: '{opType}', output type: '{type}'{inputsString}"); + } + } + } +} diff --git a/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer.csproj b/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer.csproj new file mode 100644 index 0000000000..7c77ff2ffa --- /dev/null +++ b/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer.csproj @@ -0,0 +1,19 @@ + + + + Exe + netcoreapp2.1 + DnnAnalyzer + Microsoft.ML.TensorFlow + + + + + + + + + + + + diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Average.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Average.cs index 45cd764d13..de1e5ef505 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/Average.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Average.cs @@ -25,7 +25,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(Average).Assembly.FullName); } public Average(IHostEnvironment env) diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Median.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Median.cs index de8d950de4..95bc0cc991 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/Median.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Median.cs @@ -30,7 +30,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(Median).Assembly.FullName); } public Median(IHostEnvironment env) diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiAverage.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiAverage.cs index c147f932f3..fef6fa087e 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiAverage.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiAverage.cs @@ -28,7 +28,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiAverage).Assembly.FullName); } [TlcModule.Component(Name = LoadName, FriendlyName = Average.UserName)] diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiMedian.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiMedian.cs index c3e6869d69..86312393de 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiMedian.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiMedian.cs @@ -31,7 +31,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiMedian).Assembly.FullName); } [TlcModule.Component(Name = LoadName, FriendlyName = Median.UserName)] diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs index b6740fe5f6..fd6af4dc53 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs @@ -35,7 +35,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiStacking).Assembly.FullName); } [TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)] @@ -57,7 +58,7 @@ public Arguments() env => new Ova(env, new Ova.Arguments() { PredictorType = ComponentFactoryUtils.CreateFromFunction( - e => new AveragedPerceptronTrainer(e, new AveragedPerceptronTrainer.Arguments())) + e => new FastTreeBinaryClassificationTrainer(e, DefaultColumnNames.Label, DefaultColumnNames.Features)) })); } } diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiVoting.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiVoting.cs index ee55b94c77..0c68f70287 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiVoting.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiVoting.cs @@ -29,7 +29,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiVoting).Assembly.FullName); } private sealed class Arguments : ArgumentsBase diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiWeightedAverage.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiWeightedAverage.cs index 9bda1d151a..a2f52b1451 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiWeightedAverage.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiWeightedAverage.cs @@ -35,7 +35,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiWeightedAverage).Assembly.FullName); } [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs index 436e365b79..f1503ed23d 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs @@ -33,7 +33,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RegressionStacking).Assembly.FullName); } [TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)] @@ -49,7 +50,7 @@ public sealed class Arguments : ArgumentsBase, ISupportRegressionOutputCombinerF public Arguments() { BasePredictorType = ComponentFactoryUtils.CreateFromFunction( - env => new FastTreeRegressionTrainer(env, new FastTreeRegressionTrainer.Arguments())); + env => new FastTreeRegressionTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features)); } public IRegressionOutputCombiner CreateComponent(IHostEnvironment env) => new RegressionStacking(env, this); diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs index f0ced9d947..9a569cb2af 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs @@ -5,6 +5,7 @@ using System; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Ensemble.OutputCombiners; using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.FastTree; @@ -30,7 +31,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(Stacking).Assembly.FullName); } [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] @@ -46,7 +48,7 @@ public sealed class Arguments : ArgumentsBase, ISupportBinaryOutputCombinerFacto public Arguments() { BasePredictorType = ComponentFactoryUtils.CreateFromFunction( - env => new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments())); + env => new FastTreeBinaryClassificationTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features)); } public IBinaryOutputCombiner CreateComponent(IHostEnvironment env) => new Stacking(env, this); diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Voting.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Voting.cs index 932f99d93a..d352439d55 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/Voting.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Voting.cs @@ -27,7 +27,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(Voting).Assembly.FullName); } public Voting(IHostEnvironment env) diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/WeightedAverage.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/WeightedAverage.cs index 8b16ffd0a2..e11f0c9bb9 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/WeightedAverage.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/WeightedAverage.cs @@ -32,7 +32,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(WeightedAverage).Assembly.FullName); } [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] diff --git a/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs b/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs index e4d6cb0d9e..a13903f1a3 100644 --- a/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs +++ b/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs @@ -377,7 +377,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Save predictor models in a subdirectory verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(SchemaBindablePipelineEnsembleBase).Assembly.FullName); } public const string UserName = "Pipeline Ensemble"; public const string LoaderSignature = "PipelineEnsemble"; diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsembleDistributionPredictor.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsembleDistributionPredictor.cs index 547800c152..a15563a289 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/EnsembleDistributionPredictor.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsembleDistributionPredictor.cs @@ -37,7 +37,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Don't serialize the "IsAveraged" property of the metrics verReadableCur: 0x00010003, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(EnsembleDistributionPredictor).Assembly.FullName); } private readonly Single[] _averagedWeights; diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictor.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictor.cs index 08c8f0dd8d..1f2ed87bf6 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictor.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictor.cs @@ -35,7 +35,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Don't serialize the "IsAveraged" property of the metrics verReadableCur: 0x00010003, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(EnsemblePredictor).Assembly.FullName); } private readonly IValueMapper[] _mappers; diff --git a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/EnsembleMultiClassPredictor.cs b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/EnsembleMultiClassPredictor.cs index 558d0afd6e..4dfaf3983a 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/EnsembleMultiClassPredictor.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/EnsembleMultiClassPredictor.cs @@ -32,7 +32,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Don't serialize the "IsAveraged" property of the metrics verReadableCur: 0x00010003, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(EnsembleMultiClassPredictor).Assembly.FullName); } private readonly ColumnType _inputType; diff --git a/src/Microsoft.ML.FastTree/BinFile/BinFinder.cs b/src/Microsoft.ML.FastTree/BinFile/BinFinder.cs index f72ba30977..4014afceba 100644 --- a/src/Microsoft.ML.FastTree/BinFile/BinFinder.cs +++ b/src/Microsoft.ML.FastTree/BinFile/BinFinder.cs @@ -66,7 +66,7 @@ private int FindDistinctCounts(ref VBuffer values, double[] distinctValu // list (that is, NaN is considered to be ordered "below" any other value for the purpose of // a sort, including negative infinity). So when checking if values contains no NaN values, it // suffices to check only the first item. - if (valArray[0].IsNA()) + if (double.IsNaN(valArray[0])) return -1; int idist = 0; // Index into the "distinct" arrays. if (!values.IsDense && valArray[0] > 0) diff --git a/src/Microsoft.ML.FastTree/BoostingFastTree.cs b/src/Microsoft.ML.FastTree/BoostingFastTree.cs index 6373e78215..1b2fb7e07c 100644 --- a/src/Microsoft.ML.FastTree/BoostingFastTree.cs +++ b/src/Microsoft.ML.FastTree/BoostingFastTree.cs @@ -6,17 +6,25 @@ using System; using System.Linq; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.FastTree.Internal; using Microsoft.ML.Runtime.Internal.Internallearn; namespace Microsoft.ML.Runtime.FastTree { - public abstract class BoostingFastTreeTrainerBase : FastTreeTrainerBase + public abstract class BoostingFastTreeTrainerBase : FastTreeTrainerBase + where TTransformer : ISingleFeaturePredictionTransformer where TArgs : BoostedTreeArgs, new() - where TPredictor : IPredictorProducing + where TModel : IPredictorProducing { - public BoostingFastTreeTrainerBase(IHostEnvironment env, TArgs args) : base(env, args) + protected BoostingFastTreeTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label) : base(env, args, label) + { + } + + protected BoostingFastTreeTrainerBase(IHostEnvironment env, SchemaShape.Column label, string featureColumn, + string weightColumn = null, string groupIdColumn = null, Action advancedSettings = null) + : base(env, label, featureColumn, weightColumn, groupIdColumn, advancedSettings) { } diff --git a/src/Microsoft.ML.FastTree/Dataset/Dataset.cs b/src/Microsoft.ML.FastTree/Dataset/Dataset.cs index f31ce73a94..b1b24bd4a1 100644 --- a/src/Microsoft.ML.FastTree/Dataset/Dataset.cs +++ b/src/Microsoft.ML.FastTree/Dataset/Dataset.cs @@ -609,7 +609,7 @@ public int[][] GetAssignments(double[] fraction, int randomSeed, out int[][] ass for (int i = 0; i < numParts; ++i) { cumulative += fraction[i]; - thresh[i] = (int)(cumulative * Int32.MaxValue); + thresh[i] = (int)(cumulative * int.MaxValue); if (fraction[i] == 0.0) thresh[i]--; } diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs index 8e5d48f260..ca103f3c25 100644 --- a/src/Microsoft.ML.FastTree/FastTree.cs +++ b/src/Microsoft.ML.FastTree/FastTree.cs @@ -12,6 +12,7 @@ using System.IO; using System.Linq; using System.Text; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.Conversion; @@ -43,10 +44,11 @@ internal static class FastTreeShared public static readonly object TrainLock = new object(); } - public abstract class FastTreeTrainerBase : - TrainerBase + public abstract class FastTreeTrainerBase : + TrainerEstimatorBase + where TTransformer: ISingleFeaturePredictionTransformer where TArgs : TreeArgs, new() - where TPredictor : IPredictorProducing + where TModel : IPredictorProducing { protected readonly TArgs Args; protected readonly bool AllowGC; @@ -87,8 +89,41 @@ public abstract class FastTreeTrainerBase : private protected virtual bool NeedCalibration => false; - private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args) - : base(env, RegisterName) + /// + /// Constructor to use when instantiating the classing deriving from here through the API. + /// + private protected FastTreeTrainerBase(IHostEnvironment env, SchemaShape.Column label, string featureColumn, + string weightColumn = null, string groupIdColumn = null, Action advancedSettings = null) + : base(Contracts.CheckRef(env, nameof(env)).Register(RegisterName), MakeFeatureColumn(featureColumn), label, MakeWeightColumn(weightColumn)) + { + Args = new TArgs(); + + //apply the advanced args, if the user supplied any + advancedSettings?.Invoke(Args); + Args.LabelColumn = label.Name; + + if (weightColumn != null) + Args.WeightColumn = weightColumn; + + if (groupIdColumn != null) + Args.GroupIdColumn = groupIdColumn; + + // The discretization step renders this trainer non-parametric, and therefore it does not need normalization. + // Also since it builds its own internal discretized columnar structures, it cannot benefit from caching. + // Finally, even the binary classifiers, being logitboost, tend to not benefit from external calibration. + Info = new TrainerInfo(normalization: false, caching: false, calibration: NeedCalibration, supportValid: true); + // REVIEW: CLR 4.6 has a bug that is only exposed in Scope, and if we trigger GC.Collect in scope environment + // with memory consumption more than 5GB, GC get stuck in infinite loop. So for now let's call GC only if we call things from LocalEnvironment. + AllowGC = (env is HostEnvironmentBase); + + Initialize(env); + } + + /// + /// Legacy constructor that is used when invoking the classsing deriving from this, through maml. + /// + private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label) + : base(Contracts.CheckRef(env, nameof(env)).Register(RegisterName), MakeFeatureColumn(args.FeatureColumn), label, MakeWeightColumn(args.WeightColumn)) { Host.CheckValue(args, nameof(args)); Args = args; @@ -96,25 +131,11 @@ private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args) // Also since it builds its own internal discretized columnar structures, it cannot benefit from caching. // Finally, even the binary classifiers, being logitboost, tend to not benefit from external calibration. Info = new TrainerInfo(normalization: false, caching: false, calibration: NeedCalibration, supportValid: true); - int numThreads = Args.NumThreads ?? Environment.ProcessorCount; - if (Host.ConcurrencyFactor > 0 && numThreads > Host.ConcurrencyFactor) - { - using (var ch = Host.Start("FastTreeTrainerBase")) - { - numThreads = Host.ConcurrencyFactor; - ch.Warning("The number of threads specified in trainer arguments is larger than the concurrency factor " - + "setting of the environment. Using {0} training threads instead.", numThreads); - ch.Done(); - } - } - ParallelTraining = Args.ParallelTrainer != null ? Args.ParallelTrainer.CreateComponent(env) : new SingleTrainer(); - ParallelTraining.InitEnvironment(); // REVIEW: CLR 4.6 has a bug that is only exposed in Scope, and if we trigger GC.Collect in scope environment - // with memory consumption more than 5GB, GC get stuck in infinite loop. So for now let's call GC only if we call things from ConsoleEnvironment. - AllowGC = (env is HostEnvironmentBase); - Tests = new List(); + // with memory consumption more than 5GB, GC get stuck in infinite loop. So for now let's call GC only if we call things from LocalEnvironment. + AllowGC = (env is HostEnvironmentBase); - InitializeThreads(numThreads); + Initialize(env); } protected abstract void PrepareLabels(IChannel ch); @@ -133,6 +154,39 @@ protected virtual Float GetMaxLabel() return Float.PositiveInfinity; } + private static SchemaShape.Column MakeWeightColumn(string weightColumn) + { + if (weightColumn == null) + return null; + return new SchemaShape.Column(weightColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + } + + private static SchemaShape.Column MakeFeatureColumn(string featureColumn) + { + return new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); + } + + private void Initialize(IHostEnvironment env) + { + int numThreads = Args.NumThreads ?? Environment.ProcessorCount; + if (Host.ConcurrencyFactor > 0 && numThreads > Host.ConcurrencyFactor) + { + using (var ch = Host.Start("FastTreeTrainerBase")) + { + numThreads = Host.ConcurrencyFactor; + ch.Warning("The number of threads specified in trainer arguments is larger than the concurrency factor " + + "setting of the environment. Using {0} training threads instead.", numThreads); + ch.Done(); + } + } + ParallelTraining = Args.ParallelTrainer != null ? Args.ParallelTrainer.CreateComponent(env) : new SingleTrainer(); + ParallelTraining.InitEnvironment(); + + Tests = new List(); + + InitializeThreads(numThreads); + } + protected void ConvertData(RoleMappedData trainData) { trainData.Schema.Schema.TryGetColumnIndex(DefaultColumnNames.Features, out int featureIndex); @@ -765,7 +819,7 @@ protected virtual void PrintPrologInfo(IChannel ch) { Contracts.AssertValue(ch); ch.Trace("Host = {0}", Environment.MachineName); - ch.Trace("CommandLine = {0}", CmdParser.GetSettings(ch, Args, new TArgs())); + ch.Trace("CommandLine = {0}", CmdParser.GetSettings(Host, Args, new TArgs())); ch.Trace("GCSettings.IsServerGC = {0}", System.Runtime.GCSettings.IsServerGC); ch.Trace("{0}", Args); } @@ -939,7 +993,7 @@ public static DataConverter Create(RoleMappedData data, IHost host, Double[][] b return conv; } - protected void GetFeatureNames(RoleMappedData data, ref VBuffer names) + protected void GetFeatureNames(RoleMappedData data, ref VBuffer> names) { // The existing implementations will have verified this by the time this utility // function is called. @@ -952,11 +1006,11 @@ protected void GetFeatureNames(RoleMappedData data, ref VBuffer names) if (sch.HasSlotNames(feat.Index, feat.Type.ValueCount)) sch.GetMetadata(MetadataUtils.Kinds.SlotNames, feat.Index, ref names); else - names = new VBuffer(feat.Type.ValueCount, 0, names.Values, names.Indices); + names = new VBuffer>(feat.Type.ValueCount, 0, names.Values, names.Indices); } #if !CORECLR - protected void GetFeatureIniContent(RoleMappedData data, ref VBuffer content) + protected void GetFeatureIniContent(RoleMappedData data, ref VBuffer> content) { // The existing implementations will have verified this by the time this utility // function is called. @@ -968,7 +1022,7 @@ protected void GetFeatureIniContent(RoleMappedData data, ref VBuffer con var sch = data.Schema.Schema; var type = sch.GetMetadataTypeOrNull(BingBinLoader.IniContentMetadataKind, feat.Index); if (type == null || type.VectorSize != feat.Type.ValueCount || !type.IsVector || !type.ItemType.IsText) - content = new VBuffer(feat.Type.ValueCount, 0, content.Values, content.Indices); + content = new VBuffer>(feat.Type.ValueCount, 0, content.Values, content.Indices); else sch.GetMetadata(BingBinLoader.IniContentMetadataKind, feat.Index, ref content); } @@ -3138,7 +3192,7 @@ private IEnumerable> GetSortedFeatureGains(RoleMapp { var gainMap = new FeatureToGainMap(TrainedEnsemble.Trees.ToList(), normalize: true); - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, NumFeatures, ref names); var ordered = gainMap.OrderByDescending(pair => pair.Value); Double max = ordered.FirstOrDefault().Value; @@ -3170,7 +3224,7 @@ private void SaveEnsembleAsCode(TextWriter writer, RoleMappedSchema schema) { Host.AssertValueOrNull(schema); - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, NumFeatures, ref names); int i = 0; @@ -3190,13 +3244,13 @@ private void SaveEnsembleAsCode(TextWriter writer, RoleMappedSchema schema) /// /// Convert a single tree to code, called recursively /// - private void SaveTreeAsCode(RegressionTree tree, TextWriter writer, ref VBuffer names) + private void SaveTreeAsCode(RegressionTree tree, TextWriter writer, ref VBuffer> names) { ToCSharp(tree, writer, 0, ref names); } // converts a subtree into a C# expression - private void ToCSharp(RegressionTree tree, TextWriter writer, int node, ref VBuffer names) + private void ToCSharp(RegressionTree tree, TextWriter writer, int node, ref VBuffer> names) { if (node < 0) { @@ -3277,7 +3331,7 @@ public int GetLeaf(int treeId, ref VBuffer features, ref List path) public IRow GetSummaryIRowOrNull(RoleMappedSchema schema) { - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, NumFeatures, ref names); var slotNamesCol = RowColumnUtils.GetColumn(MetadataUtils.Kinds.SlotNames, new VectorType(TextType.Instance, NumFeatures), ref names); diff --git a/src/Microsoft.ML.FastTree/FastTreeClassification.cs b/src/Microsoft.ML.FastTree/FastTreeClassification.cs index 4afec1787b..8537555ec9 100644 --- a/src/Microsoft.ML.FastTree/FastTreeClassification.cs +++ b/src/Microsoft.ML.FastTree/FastTreeClassification.cs @@ -2,11 +2,10 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; @@ -36,7 +35,7 @@ "fastrank", "fastrankwrapper")] -[assembly: LoadableClass(typeof(IPredictorProducing), typeof(FastTreeBinaryPredictor), null, typeof(SignatureLoadModel), +[assembly: LoadableClass(typeof(IPredictorProducing), typeof(FastTreeBinaryPredictor), null, typeof(SignatureLoadModel), "FastTree Binary Executor", FastTreeBinaryPredictor.LoaderSignature)] @@ -59,7 +58,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010005, //Categorical splits. verReadableCur: 0x00010005, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FastTreeBinaryPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010002; @@ -84,7 +84,7 @@ protected override void SaveCore(ModelSaveContext ctx) ctx.SetVersionInfo(GetVersionInfo()); } - public static IPredictorProducing Create(IHostEnvironment env, ModelLoadContext ctx) + public static IPredictorProducing Create(IHostEnvironment env, ModelLoadContext ctx) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(ctx, nameof(ctx)); @@ -102,26 +102,63 @@ public static IPredictorProducing Create(IHostEnvironment env, ModelLoadC /// public sealed partial class FastTreeBinaryClassificationTrainer : - BoostingFastTreeTrainerBase> + BoostingFastTreeTrainerBase>, IPredictorWithFeatureWeights> { + /// + /// The LoadName for the assembly containing the trainer. + /// public const string LoadNameValue = "FastTreeBinaryClassification"; internal const string UserNameValue = "FastTree (Boosted Trees) Classification"; internal const string Summary = "Uses a logit-boost boosted tree learner to perform binary classification."; internal const string ShortName = "ftc"; private bool[] _trainSetLabels; + private readonly SchemaShape.Column[] _outputColumns; + + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// The name of the label column. + /// The name of the feature column. + /// The name for the column containing the group ID. + /// The name for the column containing the initial weight. + /// A delegate to apply all the advanced arguments to the algorithm. + public FastTreeBinaryClassificationTrainer(IHostEnvironment env, string labelColumn, string featureColumn, + string groupIdColumn = null, string weightColumn = null, Action advancedSettings = null) + : base(env, MakeLabelColumn(labelColumn), featureColumn, weightColumn, groupIdColumn, advancedSettings) + { + // Set the sigmoid parameter to the 2 * learning rate, for traditional FastTreeClassification loss + _sigmoidParameter = 2.0 * Args.LearningRates; + + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; + } private double _sigmoidParameter; - public FastTreeBinaryClassificationTrainer(IHostEnvironment env, Arguments args) - : base(env, args) + /// + /// Initializes a new instance of by using the legacy class. + /// + internal FastTreeBinaryClassificationTrainer(IHostEnvironment env, Arguments args) + : base(env, args, MakeLabelColumn(args.LabelColumn)) { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; // Set the sigmoid parameter to the 2 * learning rate, for traditional FastTreeClassification loss _sigmoidParameter = 2.0 * Args.LearningRates; } public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; - public override IPredictorWithFeatureWeights Train(TrainContext context) + protected override IPredictorWithFeatureWeights TrainModelCore(TrainContext context) { Host.CheckValue(context, nameof(context)); var trainData = context.TrainingSet; @@ -194,6 +231,11 @@ protected override void PrepareLabels(IChannel ch) //Here we set regression labels to what is in bin file if the values were not overriden with floats } + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + { + return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); + } + protected override Test ConstructTestForTrainingData() { return new BinaryClassificationTest(ConstructScoreTracker(TrainSet), _trainSetLabels, _sigmoidParameter); @@ -237,6 +279,12 @@ protected override void InitializeTests() } } } + + protected override BinaryPredictionTransformer> MakeTransformer(IPredictorWithFeatureWeights model, ISchema trainSchema) + => new BinaryPredictionTransformer>(Host, model, trainSchema, FeatureColumn.Name); + + protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) => _outputColumns; + internal sealed class ObjectiveImpl : ObjectiveFunctionBase, IStepSearch { private readonly bool[] _labels; diff --git a/src/Microsoft.ML.FastTree/FastTreeRanking.cs b/src/Microsoft.ML.FastTree/FastTreeRanking.cs index 7173a9f6a3..0c8419846a 100644 --- a/src/Microsoft.ML.FastTree/FastTreeRanking.cs +++ b/src/Microsoft.ML.FastTree/FastTreeRanking.cs @@ -7,6 +7,7 @@ using System.Linq; using System.Runtime.InteropServices; using System.Text; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; @@ -39,10 +40,11 @@ namespace Microsoft.ML.Runtime.FastTree { /// - public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase, - IHasLabelGains + public sealed partial class FastTreeRankingTrainer + : BoostingFastTreeTrainerBase, FastTreeRankingPredictor>, + IHasLabelGains { - public const string LoadNameValue = "FastTreeRanking"; + internal const string LoadNameValue = "FastTreeRanking"; internal const string UserNameValue = "FastTree (Boosted Trees) Ranking"; internal const string Summary = "Trains gradient boosted decision trees to the LambdaRank quasi-gradient."; internal const string ShortName = "ftrank"; @@ -51,11 +53,42 @@ public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase private Test _specialTrainSetTest; private TestHistory _firstTestSetHistory; + /// + /// The prediction kind for this trainer. + /// public override PredictionKind PredictionKind => PredictionKind.Ranking; - public FastTreeRankingTrainer(IHostEnvironment env, Arguments args) - : base(env, args) + private readonly SchemaShape.Column[] _outputColumns; + + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// The name of the label column. + /// The name of the feature column. + /// The name for the column containing the group ID. + /// The name for the column containing the initial weight. + /// A delegate to apply all the advanced arguments to the algorithm. + public FastTreeRankingTrainer(IHostEnvironment env, string labelColumn, string featureColumn, string groupIdColumn, + string weightColumn = null, Action advancedSettings = null) + : base(env, MakeLabelColumn(labelColumn), featureColumn, weightColumn, groupIdColumn, advancedSettings: advancedSettings) { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; + } + + /// + /// Initializes a new instance of by using the legacy class. + /// + internal FastTreeRankingTrainer(IHostEnvironment env, Arguments args) + : base(env, args, MakeLabelColumn(args.LabelColumn)) + { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; } protected override float GetMaxLabel() @@ -63,7 +96,7 @@ protected override float GetMaxLabel() return GetLabelGains().Length - 1; } - public override FastTreeRankingPredictor Train(TrainContext context) + protected override FastTreeRankingPredictor TrainModelCore(TrainContext context) { Host.CheckValue(context, nameof(context)); var trainData = context.TrainingSet; @@ -125,6 +158,11 @@ protected override void CheckArgs(IChannel ch) base.CheckArgs(ch); } + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + { + return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.U4, true); + } + protected override void Initialize(IChannel ch) { base.Initialize(ch); @@ -405,6 +443,11 @@ protected override string GetTestGraphHeader() return headerBuilder.ToString(); } + protected override RankingPredictionTransformer MakeTransformer(FastTreeRankingPredictor model, ISchema trainSchema) + => new RankingPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); + + protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) => _outputColumns; + public sealed class LambdaRankObjectiveFunction : ObjectiveFunctionBase, IStepSearch { private readonly short[] _labels; @@ -1057,7 +1100,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010005, // Categorical splits. verReadableCur: 0x00010004, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FastTreeRankingPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010002; diff --git a/src/Microsoft.ML.FastTree/FastTreeRegression.cs b/src/Microsoft.ML.FastTree/FastTreeRegression.cs index 287cfe9e1c..fde54781d9 100644 --- a/src/Microsoft.ML.FastTree/FastTreeRegression.cs +++ b/src/Microsoft.ML.FastTree/FastTreeRegression.cs @@ -2,8 +2,10 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Linq; using System.Text; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; @@ -32,7 +34,8 @@ namespace Microsoft.ML.Runtime.FastTree { /// - public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase + public sealed partial class FastTreeRegressionTrainer + : BoostingFastTreeTrainerBase, FastTreeRegressionPredictor> { public const string LoadNameValue = "FastTreeRegression"; internal const string UserNameValue = "FastTree (Boosted Trees) Regression"; @@ -43,14 +46,45 @@ public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerB private Test _trainRegressionTest; private Test _testRegressionTest; + /// + /// The type of prediction for the trainer. + /// public override PredictionKind PredictionKind => PredictionKind.Regression; - public FastTreeRegressionTrainer(IHostEnvironment env, Arguments args) - : base(env, args) + private readonly SchemaShape.Column[] _outputColumns; + + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// The name of the label column. + /// The name of the feature column. + /// The name for the column containing the group ID. + /// The name for the column containing the initial weight. + /// A delegate to apply all the advanced arguments to the algorithm. + public FastTreeRegressionTrainer(IHostEnvironment env, string labelColumn, string featureColumn, + string weightColumn = null, string groupIdColumn = null, Action advancedSettings = null) + : base(env, MakeLabelColumn(labelColumn), featureColumn, weightColumn, groupIdColumn, advancedSettings) { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; } - public override FastTreeRegressionPredictor Train(TrainContext context) + /// + /// Initializes a new instance of by using the legacy class. + /// + internal FastTreeRegressionTrainer(IHostEnvironment env, Arguments args) + : base(env, args, MakeLabelColumn(args.LabelColumn)) + { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; + } + + protected override FastTreeRegressionPredictor TrainModelCore(TrainContext context) { Host.CheckValue(context, nameof(context)); var trainData = context.TrainingSet; @@ -79,6 +113,11 @@ protected override void CheckArgs(IChannel ch) "earlyStoppingMetrics should be 1 or 2. (1: L1, 2: L2)"); } + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + { + return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + } + protected override ObjectiveFunctionBase ConstructObjFunc(IChannel ch) { return new ObjectiveImpl(TrainSet, Args); @@ -124,6 +163,11 @@ protected override Test ConstructTestForTrainingData() return new RegressionTest(ConstructScoreTracker(TrainSet)); } + protected override RegressionPredictionTransformer MakeTransformer(FastTreeRegressionPredictor model, ISchema trainSchema) + => new RegressionPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); + + protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) => _outputColumns; + private void AddFullRegressionTests() { // Always compute training L1/L2 errors. @@ -403,7 +447,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010005, // Categorical splits. verReadableCur: 0x00010004, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FastTreeRegressionPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010002; diff --git a/src/Microsoft.ML.FastTree/FastTreeTweedie.cs b/src/Microsoft.ML.FastTree/FastTreeTweedie.cs index d02928884f..c028ef963a 100644 --- a/src/Microsoft.ML.FastTree/FastTreeTweedie.cs +++ b/src/Microsoft.ML.FastTree/FastTreeTweedie.cs @@ -5,6 +5,7 @@ using System; using System.Linq; using System.Text; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; @@ -31,7 +32,8 @@ namespace Microsoft.ML.Runtime.FastTree // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models." // https://arxiv.org/pdf/1508.06378.pdf /// - public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase + public sealed partial class FastTreeTweedieTrainer + : BoostingFastTreeTrainerBase, FastTreeTweediePredictor> { public const string LoadNameValue = "FastTreeTweedieRegression"; public const string UserNameValue = "FastTree (Boosted Trees) Tweedie Regression"; @@ -44,13 +46,34 @@ public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase public override PredictionKind PredictionKind => PredictionKind.Regression; - public FastTreeTweedieTrainer(IHostEnvironment env, Arguments args) - : base(env, args) + private SchemaShape.Column[] _outputColumns; + + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// The name of the label column. + /// The name of the feature column. + /// The name for the column containing the group ID. + /// The name for the column containing the initial weight. + /// A delegate to apply all the advanced arguments to the algorithm. + public FastTreeTweedieTrainer(IHostEnvironment env, string labelColumn, string featureColumn, + string groupIdColumn, string weightColumn = null, Action advancedSettings = null) + : base(env, MakeLabelColumn(labelColumn), featureColumn, weightColumn, groupIdColumn, advancedSettings) { - Host.CheckUserArg(1 <= Args.Index && Args.Index <= 2, nameof(Args.Index), "Must be in the range [1, 2]"); + Initialize(); + } + + /// + /// Initializes a new instance of by using the legacy class. + /// + internal FastTreeTweedieTrainer(IHostEnvironment env, Arguments args) + : base(env, args, MakeLabelColumn(args.LabelColumn)) + { + Initialize(); } - public override FastTreeTweediePredictor Train(TrainContext context) + protected override FastTreeTweediePredictor TrainModelCore(TrainContext context) { Host.CheckValue(context, nameof(context)); var trainData = context.TrainingSet; @@ -134,6 +157,16 @@ protected override Test ConstructTestForTrainingData() return new RegressionTest(ConstructScoreTracker(TrainSet)); } + private void Initialize() + { + Host.CheckUserArg(1 <= Args.Index && Args.Index <= 2, nameof(Args.Index), "Must be in the range [1, 2]"); + + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false) + }; + } + private void AddFullRegressionTests() { // Always compute training L1/L2 errors. @@ -269,6 +302,16 @@ protected override void Train(IChannel ch) PrintTestGraph(ch); } + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + { + return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + } + + protected override RegressionPredictionTransformer MakeTransformer(FastTreeTweediePredictor model, ISchema trainSchema) + => new RegressionPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); + + protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) => _outputColumns; + private sealed class ObjectiveImpl : ObjectiveFunctionBase, IStepSearch { private readonly float[] _labels; @@ -399,7 +442,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Categorical splits. verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FastTreeTweediePredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010001; diff --git a/src/Microsoft.ML.FastTree/GamClassification.cs b/src/Microsoft.ML.FastTree/GamClassification.cs index e8aad81d13..184cbdc565 100644 --- a/src/Microsoft.ML.FastTree/GamClassification.cs +++ b/src/Microsoft.ML.FastTree/GamClassification.cs @@ -46,7 +46,7 @@ public sealed class Arguments : ArgumentsBase public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; private protected override bool NeedCalibration => true; - public BinaryClassificationGamTrainer(IHostEnvironment env, Arguments args) + internal BinaryClassificationGamTrainer(IHostEnvironment env, Arguments args) : base(env, args) { _sigmoidParameter = 1; @@ -132,7 +132,8 @@ public static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(BinaryClassGamPredictor).Assembly.FullName); } public static IPredictorProducing Create(IHostEnvironment env, ModelLoadContext ctx) diff --git a/src/Microsoft.ML.FastTree/GamRegression.cs b/src/Microsoft.ML.FastTree/GamRegression.cs index 9fe4610c8c..e55a3ee008 100644 --- a/src/Microsoft.ML.FastTree/GamRegression.cs +++ b/src/Microsoft.ML.FastTree/GamRegression.cs @@ -40,7 +40,7 @@ public partial class Arguments : ArgumentsBase public override PredictionKind PredictionKind => PredictionKind.Regression; - public RegressionGamTrainer(IHostEnvironment env, Arguments args) + internal RegressionGamTrainer(IHostEnvironment env, Arguments args) : base(env, args) { } internal override void CheckLabel(RoleMappedData data) @@ -87,7 +87,8 @@ public static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RegressionGamPredictor).Assembly.FullName); } public static RegressionGamPredictor Create(IHostEnvironment env, ModelLoadContext ctx) diff --git a/src/Microsoft.ML.FastTree/GamTrainer.cs b/src/Microsoft.ML.FastTree/GamTrainer.cs index 6f1b387ce9..43bbb60e15 100644 --- a/src/Microsoft.ML.FastTree/GamTrainer.cs +++ b/src/Microsoft.ML.FastTree/GamTrainer.cs @@ -908,18 +908,17 @@ public void SaveAsText(TextWriter writer, RoleMappedSchema schema) // A useful test in this case would be a model trained with: // maml.exe train data=Samples\breast-cancer-withheader.txt loader=text{header+ col=Label:0 col=F1:1-4 col=F2:4 col=F3:5-*} // xf =expr{col=F2 expr=x:0.0} xf=concat{col=Features:F1,F2,F3} tr=gam out=bubba2.zip - // Write out the intercept writer.WriteLine("-1\tIntercept"); - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, _inputLength, ref names); for (int internalIndex = 0; internalIndex < _numFeatures; internalIndex++) { int featureIndex = _featureMap[internalIndex]; var name = names.GetItemOrDefault(featureIndex); - writer.WriteLine(name.HasChars ? "{0}\t{1}" : "{0}\tFeature {0}", featureIndex, name); + writer.WriteLine(!name.IsEmpty ? "{0}\t{1}" : "{0}\tFeature {0}", featureIndex, name); } writer.WriteLine(); @@ -957,11 +956,11 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to open the GAM visualization page URL", ShortName = "o", SortOrder = 3)] public bool Open = true; - internal Arguments SetServerIfNeeded(IExceptionContext ectx) + internal Arguments SetServerIfNeeded(IHostEnvironment env) { // We assume that if someone invoked this, they really did mean to start the web server. - if (ectx != null && Server == null) - Server = ServerChannel.CreateDefaultServerFactoryOrNull(ectx); + if (env != null && Server == null) + Server = ServerChannel.CreateDefaultServerFactoryOrNull(env); return this; } } @@ -994,7 +993,7 @@ private sealed class Context private readonly GamPredictorBase _pred; private readonly RoleMappedData _data; - private readonly VBuffer _featNames; + private readonly VBuffer> _featNames; // The scores. private readonly float[] _scores; // The labels. @@ -1038,7 +1037,7 @@ public Context(IChannel ch, GamPredictorBase pred, RoleMappedData data, IEvaluat if (schema.Schema.HasSlotNames(schema.Feature.Index, len)) schema.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, schema.Feature.Index, ref _featNames); else - _featNames = VBufferUtils.CreateEmpty(len); + _featNames = VBufferUtils.CreateEmpty>(len); var numFeatures = _pred._binEffects.Length; _binDocsList = new List[numFeatures][]; diff --git a/src/Microsoft.ML.FastTree/RandomForest.cs b/src/Microsoft.ML.FastTree/RandomForest.cs index 88676754d5..057841d78c 100644 --- a/src/Microsoft.ML.FastTree/RandomForest.cs +++ b/src/Microsoft.ML.FastTree/RandomForest.cs @@ -2,20 +2,34 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - +using System; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime.FastTree.Internal; namespace Microsoft.ML.Runtime.FastTree { - public abstract class RandomForestTrainerBase : FastTreeTrainerBase + public abstract class RandomForestTrainerBase : FastTreeTrainerBase where TArgs : FastForestArgumentsBase, new() - where TPredictor : IPredictorProducing + where TModel : IPredictorProducing + where TTransformer: ISingleFeaturePredictionTransformer { private readonly bool _quantileEnabled; - protected RandomForestTrainerBase(IHostEnvironment env, TArgs args, bool quantileEnabled = false) - : base(env, args) + /// + /// Constructor invoked by the maml code-path. + /// + protected RandomForestTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label, bool quantileEnabled = false) + : base(env, args, label) + { + _quantileEnabled = quantileEnabled; + } + + /// + /// Constructor invoked by the API code-path. + /// + protected RandomForestTrainerBase(IHostEnvironment env, SchemaShape.Column label, string featureColumn, + string weightColumn = null, string groupIdColumn = null, bool quantileEnabled = false, Action advancedSettings = null) + : base(env, label, featureColumn, weightColumn, groupIdColumn, advancedSettings) { _quantileEnabled = quantileEnabled; } diff --git a/src/Microsoft.ML.FastTree/RandomForestClassification.cs b/src/Microsoft.ML.FastTree/RandomForestClassification.cs index ae79c991d3..fe79ab5d46 100644 --- a/src/Microsoft.ML.FastTree/RandomForestClassification.cs +++ b/src/Microsoft.ML.FastTree/RandomForestClassification.cs @@ -2,10 +2,9 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - using System; using System.Linq; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; @@ -25,7 +24,7 @@ FastForestClassification.ShortName, "ffc")] -[assembly: LoadableClass(typeof(IPredictorProducing), typeof(FastForestClassificationPredictor), null, typeof(SignatureLoadModel), +[assembly: LoadableClass(typeof(IPredictorProducing), typeof(FastForestClassificationPredictor), null, typeof(SignatureLoadModel), "FastForest Binary Executor", FastForestClassificationPredictor.LoaderSignature)] @@ -64,7 +63,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010006, // Categorical splits. verReadableCur: 0x00010005, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FastForestClassificationPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010003; @@ -73,13 +73,15 @@ private static VersionInfo GetVersionInfo() protected override uint VerCategoricalSplitSerialized => 0x00010006; + /// + /// The type of prediction for this trainer. + /// public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; internal FastForestClassificationPredictor(IHostEnvironment env, Ensemble trainedEnsemble, int featureCount, string innerArgs) : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs) - { - } + { } private FastForestClassificationPredictor(IHostEnvironment env, ModelLoadContext ctx) : base(env, RegistrationName, ctx, GetVersionInfo()) @@ -92,7 +94,7 @@ protected override void SaveCore(ModelSaveContext ctx) ctx.SetVersionInfo(GetVersionInfo()); } - public static IPredictorProducing Create(IHostEnvironment env, ModelLoadContext ctx) + public static IPredictorProducing Create(IHostEnvironment env, ModelLoadContext ctx) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(ctx, nameof(ctx)); @@ -108,7 +110,7 @@ public static IPredictorProducing Create(IHostEnvironment env, ModelLoadC /// public sealed partial class FastForestClassification : - RandomForestTrainerBase> + RandomForestTrainerBase>, IPredictorWithFeatureWeights> { public sealed class Arguments : FastForestArgumentsBase { @@ -123,21 +125,52 @@ public sealed class Arguments : FastForestArgumentsBase } internal const string LoadNameValue = "FastForestClassification"; - public const string UserNameValue = "Fast Forest Classification"; - public const string Summary = "Uses a random forest learner to perform binary classification."; - public const string ShortName = "ff"; + internal const string UserNameValue = "Fast Forest Classification"; + internal const string Summary = "Uses a random forest learner to perform binary classification."; + internal const string ShortName = "ff"; private bool[] _trainSetLabels; public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; private protected override bool NeedCalibration => true; + private readonly SchemaShape.Column[] _outputColumns; + + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// The name of the label column. + /// The name of the feature column. + /// The name for the column containing the group ID. + /// The name for the column containing the initial weight. + /// A delegate to apply all the advanced arguments to the algorithm. + public FastForestClassification(IHostEnvironment env, string labelColumn, string featureColumn, + string groupIdColumn = null, string weightColumn = null, Action advancedSettings = null) + : base(env, MakeLabelColumn(labelColumn), featureColumn, weightColumn, groupIdColumn, advancedSettings: advancedSettings) + { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false) + }; + } - public FastForestClassification(IHostEnvironment env, Arguments args) - : base(env, args) + /// + /// Initializes a new instance of by using the legacy class. + /// + internal FastForestClassification(IHostEnvironment env, Arguments args) + : base(env, args, MakeLabelColumn(args.LabelColumn)) { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false) + }; } - public override IPredictorWithFeatureWeights Train(TrainContext context) + protected override IPredictorWithFeatureWeights TrainModelCore(TrainContext context) { Host.CheckValue(context, nameof(context)); var trainData = context.TrainingSet; @@ -175,11 +208,21 @@ protected override void PrepareLabels(IChannel ch) _trainSetLabels = TrainSet.Ratings.Select(x => x >= 1).ToArray(TrainSet.NumDocs); } + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + { + return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); + } + protected override Test ConstructTestForTrainingData() { return new BinaryClassificationTest(ConstructScoreTracker(TrainSet), _trainSetLabels, 1); } + protected override BinaryPredictionTransformer> MakeTransformer(IPredictorWithFeatureWeights model, ISchema trainSchema) + => new BinaryPredictionTransformer>(Host, model, trainSchema, FeatureColumn.Name); + + protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) => _outputColumns; + private sealed class ObjectiveFunctionImpl : RandomForestObjectiveFunction { private readonly bool[] _labels; diff --git a/src/Microsoft.ML.FastTree/RandomForestRegression.cs b/src/Microsoft.ML.FastTree/RandomForestRegression.cs index c580851534..68294596e4 100644 --- a/src/Microsoft.ML.FastTree/RandomForestRegression.cs +++ b/src/Microsoft.ML.FastTree/RandomForestRegression.cs @@ -2,8 +2,6 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - using System; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; @@ -15,6 +13,7 @@ using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Training; using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Core.Data; [assembly: LoadableClass(FastForestRegression.Summary, typeof(FastForestRegression), typeof(FastForestRegression.Arguments), new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer), typeof(SignatureTreeEnsembleTrainer), typeof(SignatureFeatureScorerTrainer) }, @@ -50,7 +49,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010006, // Categorical splits. verReadableCur: 0x00010005, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FastForestRegressionPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010003; @@ -59,7 +59,7 @@ private static VersionInfo GetVersionInfo() protected override uint VerCategoricalSplitSerialized => 0x00010006; - internal FastForestRegressionPredictor(IHostEnvironment env, Ensemble trainedEnsemble, int featureCount, + public FastForestRegressionPredictor(IHostEnvironment env, Ensemble trainedEnsemble, int featureCount, string innerArgs, int samplesCount) : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs) { @@ -101,32 +101,32 @@ public static FastForestRegressionPredictor Create(IHostEnvironment env, ModelLo public override PredictionKind PredictionKind => PredictionKind.Regression; - protected override void Map(ref VBuffer src, ref Float dst) + protected override void Map(ref VBuffer src, ref float dst) { if (InputType.VectorSize > 0) Host.Check(src.Length == InputType.VectorSize); else Host.Check(src.Length > MaxSplitFeatIdx); - dst = (Float)TrainedEnsemble.GetOutput(ref src) / TrainedEnsemble.NumTrees; + dst = (float)TrainedEnsemble.GetOutput(ref src) / TrainedEnsemble.NumTrees; } - public ValueMapper, VBuffer> GetMapper(Float[] quantiles) + public ValueMapper, VBuffer> GetMapper(float[] quantiles) { return - (ref VBuffer src, ref VBuffer dst) => + (ref VBuffer src, ref VBuffer dst) => { // REVIEW: Should make this more efficient - it repeatedly allocates too much stuff. - Float[] weights = null; + float[] weights = null; var distribution = TrainedEnsemble.GetDistribution(ref src, _quantileSampleCount, out weights); var qdist = new QuantileStatistics(distribution, weights); var values = dst.Values; if (Utils.Size(values) < quantiles.Length) - values = new Float[quantiles.Length]; + values = new float[quantiles.Length]; for (int i = 0; i < quantiles.Length; i++) - values[i] = qdist.GetQuantile((Float)quantiles[i]); - dst = new VBuffer(quantiles.Length, values, dst.Indices); + values[i] = qdist.GetQuantile((float)quantiles[i]); + dst = new VBuffer(quantiles.Length, values, dst.Indices); }; } @@ -138,7 +138,8 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles) } /// - public sealed partial class FastForestRegression : RandomForestTrainerBase + public sealed partial class FastForestRegression + : RandomForestTrainerBase, FastForestRegressionPredictor> { public sealed class Arguments : FastForestArgumentsBase { @@ -147,20 +148,47 @@ public sealed class Arguments : FastForestArgumentsBase public bool ShuffleLabels; } - internal const string Summary = "Trains a random forest to fit target values using least-squares."; + public override PredictionKind PredictionKind => PredictionKind.Regression; + internal const string Summary = "Trains a random forest to fit target values using least-squares."; internal const string LoadNameValue = "FastForestRegression"; internal const string UserNameValue = "Fast Forest Regression"; internal const string ShortName = "ffr"; - public FastForestRegression(IHostEnvironment env, Arguments args) - : base(env, args, true) + private readonly SchemaShape.Column[] _outputColumns; + + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// The name of the label column. + /// The name of the feature column. + /// The name for the column containing the group ID. + /// The name for the column containing the initial weight. + /// A delegate to apply all the advanced arguments to the algorithm. + public FastForestRegression(IHostEnvironment env, string labelColumn, string featureColumn, + string groupIdColumn = null, string weightColumn = null, Action advancedSettings = null) + : base(env, MakeLabelColumn(labelColumn), featureColumn, weightColumn, groupIdColumn, true, advancedSettings) { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false) + }; } - public override PredictionKind PredictionKind => PredictionKind.Regression; + /// + /// Initializes a new instance of by using the legacy class. + /// + internal FastForestRegression(IHostEnvironment env, Arguments args) + : base(env, args, MakeLabelColumn(args.LabelColumn), true) + { + _outputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false) + }; + } - public override FastForestRegressionPredictor Train(TrainContext context) + protected override FastForestRegressionPredictor TrainModelCore(TrainContext context) { Host.CheckValue(context, nameof(context)); var trainData = context.TrainingSet; @@ -194,6 +222,16 @@ protected override Test ConstructTestForTrainingData() return new RegressionTest(ConstructScoreTracker(TrainSet)); } + protected override RegressionPredictionTransformer MakeTransformer(FastForestRegressionPredictor model, ISchema trainSchema) + => new RegressionPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); + + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + { + return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + } + + protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) => _outputColumns; + private abstract class ObjectiveFunctionImplBase : RandomForestObjectiveFunction { private readonly float[] _labels; diff --git a/src/Microsoft.ML.FastTree/TreeEnsemble/Ensemble.cs b/src/Microsoft.ML.FastTree/TreeEnsemble/Ensemble.cs index 12b4a3cc36..fedde0f2e8 100644 --- a/src/Microsoft.ML.FastTree/TreeEnsemble/Ensemble.cs +++ b/src/Microsoft.ML.FastTree/TreeEnsemble/Ensemble.cs @@ -397,8 +397,8 @@ public FeatureToGainMap(IList trees, bool normalize) /// public sealed class FeaturesToContentMap { - private readonly VBuffer _content; - private readonly VBuffer _names; + private readonly VBuffer> _content; + private readonly VBuffer> _names; public int Count => _names.Length; @@ -419,15 +419,15 @@ public FeaturesToContentMap(RoleMappedSchema schema) if (sch.HasSlotNames(feat.Index, feat.Type.ValueCount)) sch.GetMetadata(MetadataUtils.Kinds.SlotNames, feat.Index, ref _names); else - _names = VBufferUtils.CreateEmpty(feat.Type.ValueCount); + _names = VBufferUtils.CreateEmpty>(feat.Type.ValueCount); #if !CORECLR var type = sch.GetMetadataTypeOrNull(BingBinLoader.IniContentMetadataKind, feat.Index); if (type != null && type.IsVector && type.VectorSize == feat.Type.ValueCount && type.ItemType.IsText) sch.GetMetadata(BingBinLoader.IniContentMetadataKind, feat.Index, ref _content); else - _content = VBufferUtils.CreateEmpty(feat.Type.ValueCount); + _content = VBufferUtils.CreateEmpty>(feat.Type.ValueCount); #else - _content = VBufferUtils.CreateEmpty(feat.Type.ValueCount); + _content = VBufferUtils.CreateEmpty>(feat.Type.ValueCount); #endif Contracts.Assert(_names.Length == _content.Length); } @@ -435,15 +435,15 @@ public FeaturesToContentMap(RoleMappedSchema schema) public string GetName(int ifeat) { Contracts.Assert(0 <= ifeat && ifeat < Count); - DvText name = _names.GetItemOrDefault(ifeat); - return name.HasChars ? name.ToString() : string.Format("f{0}", ifeat); + ReadOnlyMemory name = _names.GetItemOrDefault(ifeat); + return !name.IsEmpty ? name.ToString() : string.Format("f{0}", ifeat); } public string GetContent(int ifeat) { Contracts.Assert(0 <= ifeat && ifeat < Count); - DvText content = _content.GetItemOrDefault(ifeat); - return content.HasChars ? content.ToString() : DatasetUtils.GetDefaultTransform(GetName(ifeat)); + ReadOnlyMemory content = _content.GetItemOrDefault(ifeat); + return !content.IsEmpty ? content.ToString() : DatasetUtils.GetDefaultTransform(GetName(ifeat)); } } } diff --git a/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs b/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs index 37b52ff806..6faeba3cd8 100644 --- a/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs +++ b/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs @@ -135,20 +135,20 @@ public void GetMetadata(string kind, int col, ref TValue value) _ectx.CheckParam(0 <= col && col < ColumnCount, nameof(col)); if ((col == PathIdx || col == LeafIdx) && kind == MetadataUtils.Kinds.IsNormalized) - MetadataUtils.Marshal(IsNormalized, col, ref value); + MetadataUtils.Marshal(IsNormalized, col, ref value); else if (kind == MetadataUtils.Kinds.SlotNames) { switch (col) { case TreeIdx: - MetadataUtils.Marshal, TValue>(_parent.GetTreeSlotNames, col, ref value); + MetadataUtils.Marshal>, TValue>(_parent.GetTreeSlotNames, col, ref value); break; case LeafIdx: - MetadataUtils.Marshal, TValue>(_parent.GetLeafSlotNames, col, ref value); + MetadataUtils.Marshal>, TValue>(_parent.GetLeafSlotNames, col, ref value); break; default: Contracts.Assert(col == PathIdx); - MetadataUtils.Marshal, TValue>(_parent.GetPathSlotNames, col, ref value); + MetadataUtils.Marshal>, TValue>(_parent.GetPathSlotNames, col, ref value); break; } } @@ -156,9 +156,9 @@ public void GetMetadata(string kind, int col, ref TValue value) throw _ectx.ExceptGetMetadata(); } - private void IsNormalized(int iinfo, ref DvBool dst) + private void IsNormalized(int iinfo, ref bool dst) { - dst = DvBool.True; + dst = true; } } @@ -418,7 +418,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Add _defaultValueForMissing verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TreeEnsembleFeaturizerBindableMapper).Assembly.FullName); } private readonly IHost _host; @@ -478,48 +479,48 @@ private static int CountLeaves(FastTreePredictionWrapper ensemble) return totalLeafCount; } - private void GetTreeSlotNames(int col, ref VBuffer dst) + private void GetTreeSlotNames(int col, ref VBuffer> dst) { var numTrees = _ensemble.NumTrees; var names = dst.Values; if (Utils.Size(names) < numTrees) - names = new DvText[numTrees]; + names = new ReadOnlyMemory[numTrees]; for (int t = 0; t < numTrees; t++) - names[t] = new DvText(string.Format("Tree{0:000}", t)); + names[t] = string.Format("Tree{0:000}", t).AsMemory(); - dst = new VBuffer(numTrees, names, dst.Indices); + dst = new VBuffer>(numTrees, names, dst.Indices); } - private void GetLeafSlotNames(int col, ref VBuffer dst) + private void GetLeafSlotNames(int col, ref VBuffer> dst) { var numTrees = _ensemble.NumTrees; var names = dst.Values; if (Utils.Size(names) < _totalLeafCount) - names = new DvText[_totalLeafCount]; + names = new ReadOnlyMemory[_totalLeafCount]; int i = 0; int t = 0; foreach (var tree in _ensemble.GetTrees()) { for (int l = 0; l < tree.NumLeaves; l++) - names[i++] = new DvText(string.Format("Tree{0:000}Leaf{1:000}", t, l)); + names[i++] = string.Format("Tree{0:000}Leaf{1:000}", t, l).AsMemory(); t++; } _host.Assert(i == _totalLeafCount); - dst = new VBuffer(_totalLeafCount, names, dst.Indices); + dst = new VBuffer>(_totalLeafCount, names, dst.Indices); } - private void GetPathSlotNames(int col, ref VBuffer dst) + private void GetPathSlotNames(int col, ref VBuffer> dst) { var numTrees = _ensemble.NumTrees; var totalNodeCount = _totalLeafCount - numTrees; var names = dst.Values; if (Utils.Size(names) < totalNodeCount) - names = new DvText[totalNodeCount]; + names = new ReadOnlyMemory[totalNodeCount]; int i = 0; int t = 0; @@ -527,11 +528,11 @@ private void GetPathSlotNames(int col, ref VBuffer dst) { var numLeaves = tree.NumLeaves; for (int l = 0; l < tree.NumLeaves - 1; l++) - names[i++] = new DvText(string.Format("Tree{0:000}Node{1:000}", t, l)); + names[i++] = string.Format("Tree{0:000}Node{1:000}", t, l).AsMemory(); t++; } _host.Assert(i == totalNodeCount); - dst = new VBuffer(totalNodeCount, names, dst.Indices); + dst = new VBuffer>(totalNodeCount, names, dst.Indices); } public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema) diff --git a/src/Microsoft.ML.FastTree/Utils/PseudorandomFunction.cs b/src/Microsoft.ML.FastTree/Utils/PseudorandomFunction.cs index 43304b22f7..f94701856a 100644 --- a/src/Microsoft.ML.FastTree/Utils/PseudorandomFunction.cs +++ b/src/Microsoft.ML.FastTree/Utils/PseudorandomFunction.cs @@ -19,7 +19,7 @@ public sealed class PseudorandomFunction public PseudorandomFunction(Random rand) { - _data = _periodics.Select(x => Enumerable.Range(0, x).Select(y => rand.Next(-1, Int32.MaxValue) + 1).ToArray()).ToArray(); + _data = _periodics.Select(x => Enumerable.Range(0, x).Select(y => rand.Next(-1, int.MaxValue) + 1).ToArray()).ToArray(); } public int Apply(ulong seed) diff --git a/src/Microsoft.ML.HalLearners/OlsLinearRegression.cs b/src/Microsoft.ML.HalLearners/OlsLinearRegression.cs index dbf2999657..879a32b95f 100644 --- a/src/Microsoft.ML.HalLearners/OlsLinearRegression.cs +++ b/src/Microsoft.ML.HalLearners/OlsLinearRegression.cs @@ -500,7 +500,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(OlsLinearRegressionPredictor).Assembly.FullName); } // The following will be null iff RSquaredAdjusted is NaN. @@ -688,7 +689,7 @@ public static OlsLinearRegressionPredictor Create(IHostEnvironment env, ModelLoa public override void SaveSummary(TextWriter writer, RoleMappedSchema schema) { - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, Weight.Length, ref names); writer.WriteLine("Ordinary Least Squares Model Summary"); @@ -706,7 +707,7 @@ public override void SaveSummary(TextWriter writer, RoleMappedSchema schema) for (int i = 0; i < coeffs.Length; i++) { var name = names.GetItemOrDefault(i); - writer.WriteLine(format, i, DvText.Identical(name, DvText.Empty) ? $"f{i}" : name.ToString(), + writer.WriteLine(format, i, name.IsEmpty ? $"f{i}" : name.ToString(), coeffs[i], _standardErrors[i + 1], _tValues[i + 1], _pValues[i + 1]); } } @@ -721,7 +722,7 @@ public override void SaveSummary(TextWriter writer, RoleMappedSchema schema) for (int i = 0; i < coeffs.Length; i++) { var name = names.GetItemOrDefault(i); - writer.WriteLine(format, i, DvText.Identical(name, DvText.Empty) ? $"f{i}" : name.ToString(), coeffs[i]); + writer.WriteLine(format, i, name.IsEmpty ? $"f{i}" : name.ToString(), coeffs[i]); } } } diff --git a/src/Microsoft.ML.ImageAnalytics/ImageGrayscaleTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImageGrayscaleTransform.cs index 5744fe384d..a137dcfd6a 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImageGrayscaleTransform.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImageGrayscaleTransform.cs @@ -74,7 +74,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ImageGrayscaleTransform).Assembly.FullName); } private const string RegistrationName = "ImageGrayscale"; diff --git a/src/Microsoft.ML.ImageAnalytics/ImageLoaderTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImageLoaderTransform.cs index b012d59d34..90e3fd6b3e 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImageLoaderTransform.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImageLoaderTransform.cs @@ -31,7 +31,7 @@ namespace Microsoft.ML.Runtime.ImageAnalytics { /// - /// Transform which takes one or many columns of type and loads them as + /// Transform which takes one or many columns of type ReadOnlyMemory and loads them as /// public sealed class ImageLoaderTransform : OneToOneTransformerBase { @@ -141,7 +141,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Swith from OpenCV to Bitmap verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ImageLoaderTransform).Assembly.FullName); } protected override IRowMapper MakeRowMapper(ISchema schema) @@ -165,8 +166,8 @@ protected override Delegate MakeGetter(IRow input, int iinfo, out Action dispose Contracts.Assert(0 <= iinfo && iinfo < _parent.ColumnPairs.Length); disposer = null; - var getSrc = input.GetGetter(ColMapNewToOld[iinfo]); - DvText src = default; + var getSrc = input.GetGetter>(ColMapNewToOld[iinfo]); + ReadOnlyMemory src = default; ValueGetter del = (ref Bitmap dst) => { diff --git a/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractorTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractorTransform.cs index 1e563fbc2c..c2cd0f1a83 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractorTransform.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractorTransform.cs @@ -297,7 +297,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Swith from OpenCV to Bitmap verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ImagePixelExtractorTransform).Assembly.FullName); } private const string RegistrationName = "ImagePixelExtractor"; diff --git a/src/Microsoft.ML.ImageAnalytics/ImageResizerTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImageResizerTransform.cs index 1755430cf3..2de1354205 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImageResizerTransform.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImageResizerTransform.cs @@ -154,7 +154,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // No more sizeof(float) verReadableCur: 0x00010003, verWeCanReadBack: 0x00010003, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ImageResizerTransform).Assembly.FullName); } private const string RegistrationName = "ImageScaler"; diff --git a/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs b/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs index 2446b7a7b6..a6f69204c4 100644 --- a/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs +++ b/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs @@ -236,7 +236,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Swith from OpenCV to Bitmap verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(VectorToImageTransform).Assembly.FullName); } private const string RegistrationName = "VectorToImageConverter"; diff --git a/src/Microsoft.ML.KMeansClustering/KMeansPredictor.cs b/src/Microsoft.ML.KMeansClustering/KMeansPredictor.cs index e3249da34d..e1b54f472a 100644 --- a/src/Microsoft.ML.KMeansClustering/KMeansPredictor.cs +++ b/src/Microsoft.ML.KMeansClustering/KMeansPredictor.cs @@ -38,7 +38,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Allow sparse centroids verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(KMeansPredictor).Assembly.FullName); } public override PredictionKind PredictionKind => PredictionKind.Clustering; diff --git a/src/Microsoft.ML.Legacy/CSharpApi.cs b/src/Microsoft.ML.Legacy/CSharpApi.cs index 7f86b36bd7..ba6f0f866e 100644 --- a/src/Microsoft.ML.Legacy/CSharpApi.cs +++ b/src/Microsoft.ML.Legacy/CSharpApi.cs @@ -13983,7 +13983,7 @@ public MinMaxNormalizerPipelineStep(Output output) namespace Legacy.Transforms { - public enum NAHandleTransformReplacementKind + public enum NAHandleTransformReplacementKind : byte { DefaultValue = 0, Mean = 1, @@ -14444,7 +14444,7 @@ public MissingValuesRowDropperPipelineStep(Output output) namespace Legacy.Transforms { - public enum NAReplaceTransformReplacementKind + public enum NAReplaceTransformReplacementKind : byte { DefaultValue = 0, Mean = 1, diff --git a/src/Microsoft.ML.Legacy/Data/TextLoader.cs b/src/Microsoft.ML.Legacy/Data/TextLoader.cs index 7d215c0a00..84ab83807c 100644 --- a/src/Microsoft.ML.Legacy/Data/TextLoader.cs +++ b/src/Microsoft.ML.Legacy/Data/TextLoader.cs @@ -160,19 +160,19 @@ private static bool TryGetDataKind(Type type, out DataKind kind) Contracts.AssertValue(type); // REVIEW: Make this more efficient. Should we have a global dictionary? - if (type == typeof(DvInt1) || type == typeof(sbyte)) + if (type == typeof(sbyte)) kind = DataKind.I1; else if (type == typeof(byte) || type == typeof(char)) kind = DataKind.U1; - else if (type == typeof(DvInt2) || type == typeof(short)) + else if (type == typeof(short)) kind = DataKind.I2; else if (type == typeof(ushort)) kind = DataKind.U2; - else if (type == typeof(DvInt4) || type == typeof(int)) + else if ( type == typeof(int)) kind = DataKind.I4; else if (type == typeof(uint)) kind = DataKind.U4; - else if (type == typeof(DvInt8) || type == typeof(long)) + else if (type == typeof(long)) kind = DataKind.I8; else if (type == typeof(ulong)) kind = DataKind.U8; @@ -180,15 +180,15 @@ private static bool TryGetDataKind(Type type, out DataKind kind) kind = DataKind.R4; else if (type == typeof(Double)) kind = DataKind.R8; - else if (type == typeof(DvText) || type == typeof(string)) + else if (type == typeof(ReadOnlyMemory) || type == typeof(string)) kind = DataKind.TX; - else if (type == typeof(DvBool) || type == typeof(bool)) + else if (type == typeof(bool)) kind = DataKind.BL; - else if (type == typeof(DvTimeSpan) || type == typeof(TimeSpan)) + else if (type == typeof(TimeSpan)) kind = DataKind.TS; - else if (type == typeof(DvDateTime) || type == typeof(DateTime)) + else if (type == typeof(DateTime)) kind = DataKind.DT; - else if (type == typeof(DvDateTimeZone) || type == typeof(TimeZoneInfo)) + else if (type == typeof(DateTimeOffset)) kind = DataKind.DZ; else if (type == typeof(UInt128)) kind = DataKind.UG; diff --git a/src/Microsoft.ML.Legacy/LearningPipelineDebugProxy.cs b/src/Microsoft.ML.Legacy/LearningPipelineDebugProxy.cs index 5df87ac098..c9b3dd20f8 100644 --- a/src/Microsoft.ML.Legacy/LearningPipelineDebugProxy.cs +++ b/src/Microsoft.ML.Legacy/LearningPipelineDebugProxy.cs @@ -92,11 +92,11 @@ private PipelineItemDebugColumn[] BuildColumns() var n = dataView.Schema.GetColumnType(colIndex).VectorSize; if (dataView.Schema.HasSlotNames(colIndex, n)) { - var slots = default(VBuffer); + var slots = default(VBuffer>); dataView.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref slots); bool appendEllipse = false; - IEnumerable slotNames = slots.Items(true).Select(x => x.Value); + IEnumerable> slotNames = slots.Items(true).Select(x => x.Value); if (slots.Length > MaxSlotNamesToDisplay) { appendEllipse = true; @@ -175,7 +175,7 @@ private PipelineItemDebugRow[] BuildRows() var getters = DataViewUtils.PopulateGetterArray(cursor, colIndices); - var row = new DvText[colCount]; + var row = new ReadOnlyMemory[colCount]; while (cursor.MoveNext() && i < MaxDisplayRows) { for (int column = 0; column < colCount; column++) diff --git a/src/Microsoft.ML.Legacy/Microsoft.ML.Legacy.csproj b/src/Microsoft.ML.Legacy/Microsoft.ML.Legacy.csproj index bd3d72ab68..a2eeeb046c 100644 --- a/src/Microsoft.ML.Legacy/Microsoft.ML.Legacy.csproj +++ b/src/Microsoft.ML.Legacy/Microsoft.ML.Legacy.csproj @@ -6,6 +6,10 @@ CORECLR + + + + diff --git a/src/Microsoft.ML.Legacy/Models/ConfusionMatrix.cs b/src/Microsoft.ML.Legacy/Models/ConfusionMatrix.cs index 2c2fd64215..643d122f14 100644 --- a/src/Microsoft.ML.Legacy/Models/ConfusionMatrix.cs +++ b/src/Microsoft.ML.Legacy/Models/ConfusionMatrix.cs @@ -52,7 +52,7 @@ internal static List Create(IHostEnvironment env, IDataView con } IRowCursor cursor = confusionMatrix.GetRowCursor(col => col == countColumn); - var slots = default(VBuffer); + var slots = default(VBuffer>); confusionMatrix.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, countColumn, ref slots); string[] classNames = new string[slots.Count]; for (int i = 0; i < slots.Count; i++) diff --git a/src/Microsoft.ML.Legacy/PredictionModel.cs b/src/Microsoft.ML.Legacy/PredictionModel.cs index ae8c58995f..fc39a61c1e 100644 --- a/src/Microsoft.ML.Legacy/PredictionModel.cs +++ b/src/Microsoft.ML.Legacy/PredictionModel.cs @@ -49,7 +49,7 @@ public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = D if (!schema.HasSlotNames(colIndex, expectedLabelCount)) return false; - VBuffer labels = default; + VBuffer> labels = default; schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels); if (labels.Length != expectedLabelCount) @@ -126,6 +126,7 @@ public static Task> ReadAsync( throw new ArgumentNullException(nameof(stream)); using (var environment = new ConsoleEnvironment()) + using (AssemblyLoadingUtils.CreateAssemblyRegistrar(environment)) { BatchPredictionEngine predictor = environment.CreateBatchPredictionEngine(stream); diff --git a/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CodeGen/TransformGenerators.cs b/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CodeGen/TransformGenerators.cs index 26d4c94dda..b17efbbbe8 100644 --- a/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CodeGen/TransformGenerators.cs +++ b/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CodeGen/TransformGenerators.cs @@ -151,7 +151,7 @@ protected override void GenerateImplCall(IndentingTextWriter w, string prefix, C var argumentInfo = CmdParser.GetArgInfo(component.ArgType, component.CreateArguments()); foreach (var arg in argumentInfo.Args.Where(a => !a.IsHidden)) GenerateImplCall(w, arg, ""); - w.WriteLine("var env = new TlcEnvironment(1, verbose: true);"); + w.WriteLine("var env = new LocalEnvironment(1, verbose: true);"); w.WriteLine("var view = builder.Create{0}{1}Impl(env, data);", prefix, component.LoadNames[0]); w.WriteLine("return new Tuple(view, new DataTransform(view));"); } diff --git a/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CrossValidationMacro.cs b/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CrossValidationMacro.cs index d018e0ce7f..dfe8a0887b 100644 --- a/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CrossValidationMacro.cs +++ b/src/Microsoft.ML.Legacy/Runtime/EntryPoints/CrossValidationMacro.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Collections.Generic; using System.Linq; using Microsoft.ML.Runtime; @@ -446,7 +447,7 @@ public static CombinedOutput CombineMetrics(IHostEnvironment env, CombineMetrics var dvBldr = new ArrayDataViewBuilder(env); var warn = $"Detected columns of variable length: {string.Join(", ", variableSizeVectorColumnNames)}." + $" Consider setting collateMetrics- for meaningful per-Folds results."; - dvBldr.AddColumn(MetricKinds.ColumnNames.WarningText, TextType.Instance, new DvText(warn)); + dvBldr.AddColumn(MetricKinds.ColumnNames.WarningText, TextType.Instance, warn.AsMemory()); warnings.Add(dvBldr.GetDataView()); } diff --git a/src/Microsoft.ML.Legacy/Runtime/EntryPoints/FeatureCombiner.cs b/src/Microsoft.ML.Legacy/Runtime/EntryPoints/FeatureCombiner.cs index 95548a0e8a..09dee2037b 100644 --- a/src/Microsoft.ML.Legacy/Runtime/EntryPoints/FeatureCombiner.cs +++ b/src/Microsoft.ML.Legacy/Runtime/EntryPoints/FeatureCombiner.cs @@ -121,7 +121,7 @@ private static string GetTerms(IDataView data, string colName) var type = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.KeyValues, col); if (type == null || !type.IsKnownSizeVector || !type.ItemType.IsText) return null; - var metadata = default(VBuffer); + var metadata = default(VBuffer>); schema.GetMetadata(MetadataUtils.Kinds.KeyValues, col, ref metadata); if (!metadata.IsDense) return null; @@ -130,7 +130,7 @@ private static string GetTerms(IDataView data, string colName) for (int i = 0; i < metadata.Length; i++) { sb.Append(pre); - metadata.Values[i].AddToStringBuilder(sb); + sb.AppendMemory(metadata.Values[i]); pre = ","; } return sb.ToString(); diff --git a/src/Microsoft.ML.Legacy/Runtime/Experiment/Experiment.cs b/src/Microsoft.ML.Legacy/Runtime/Experiment/Experiment.cs index 108befb74b..efb9cb33d0 100644 --- a/src/Microsoft.ML.Legacy/Runtime/Experiment/Experiment.cs +++ b/src/Microsoft.ML.Legacy/Runtime/Experiment/Experiment.cs @@ -37,6 +37,8 @@ private sealed class SerializationHelper public Experiment(Runtime.IHostEnvironment env) { _env = env; + AssemblyLoadingUtils.RegisterCurrentLoadedAssemblies(_env); + _catalog = ModuleCatalog.CreateInstance(_env); _jsonNodes = new List(); _serializer = new JsonSerializer(); diff --git a/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs index f788b4feab..6fe77564b7 100644 --- a/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs +++ b/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs @@ -40,7 +40,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010005, // Categorical splits. verReadableCur: 0x00010004, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LightGbmBinaryPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010002; diff --git a/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs index 3fe4628182..7373fbbac9 100644 --- a/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs +++ b/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs @@ -38,7 +38,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010005, // Categorical splits. verReadableCur: 0x00010004, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LightGbmRankingPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010002; diff --git a/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs index 0011a8d8e6..08331cb45f 100644 --- a/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs +++ b/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs @@ -38,7 +38,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010005, // Categorical splits. verReadableCur: 0x00010004, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LightGbmRegressionPredictor).Assembly.FullName); } protected override uint VerNumFeaturesSerialized => 0x00010002; diff --git a/src/Microsoft.ML.Maml/HelpCommand.cs b/src/Microsoft.ML.Maml/HelpCommand.cs index 401cb7dd10..6c6d800ac7 100644 --- a/src/Microsoft.ML.Maml/HelpCommand.cs +++ b/src/Microsoft.ML.Maml/HelpCommand.cs @@ -100,7 +100,7 @@ public void Run() public void Run(int? columns) { - ComponentCatalog.CacheClassesExtra(_extraAssemblies); + AssemblyLoadingUtils.LoadAndRegister(_env, _extraAssemblies); using (var ch = _env.Start("Help")) using (var sw = new StringWriter(CultureInfo.InvariantCulture)) @@ -137,7 +137,7 @@ private void ShowHelp(IndentingTextWriter writer, int? columns = null) // Note that we don't check IsHidden here. The current policy is when IsHidden is true, we don't // show the item in "list all" functionality, but will still show help when explicitly requested. - var infos = ComponentCatalog.FindLoadableClasses(name) + var infos = _env.ComponentCatalog.FindLoadableClasses(name) .OrderBy(x => ComponentCatalog.SignatureToString(x.SignatureTypes[0]).ToLowerInvariant()); var kinds = new StringBuilder(); var components = new List(); @@ -188,7 +188,7 @@ private void ShowAllHelp(IndentingTextWriter writer, int? columns = null) { string sig = _kind?.ToLowerInvariant(); - var infos = ComponentCatalog.GetAllClasses() + var infos = _env.ComponentCatalog.GetAllClasses() .OrderBy(info => info.LoadNames[0].ToLowerInvariant()) .ThenBy(info => ComponentCatalog.SignatureToString(info.SignatureTypes[0]).ToLowerInvariant()); var components = new List(); @@ -256,7 +256,7 @@ private void ShowComponents(IndentingTextWriter writer) else { kind = _kind.ToLowerInvariant(); - var sigs = ComponentCatalog.GetAllSignatureTypes(); + var sigs = _env.ComponentCatalog.GetAllSignatureTypes(); typeSig = sigs.FirstOrDefault(t => ComponentCatalog.SignatureToString(t).ToLowerInvariant() == kind); if (typeSig == null) { @@ -272,7 +272,7 @@ private void ShowComponents(IndentingTextWriter writer) writer.WriteLine("Available components for kind '{0}':", ComponentCatalog.SignatureToString(typeSig)); } - var infos = ComponentCatalog.GetAllDerivedClasses(typeRes, typeSig) + var infos = _env.ComponentCatalog.GetAllDerivedClasses(typeRes, typeSig) .Where(x => !x.IsHidden) .OrderBy(x => x.LoadNames[0].ToLowerInvariant()); using (writer.Nest()) @@ -322,7 +322,7 @@ private void ShowAliases(IndentingTextWriter writer, IReadOnlyList names private void ListKinds(IndentingTextWriter writer) { - var sigs = ComponentCatalog.GetAllSignatureTypes() + var sigs = _env.ComponentCatalog.GetAllSignatureTypes() .Select(ComponentCatalog.SignatureToString) .OrderBy(x => x); diff --git a/src/Microsoft.ML.Maml/MAML.cs b/src/Microsoft.ML.Maml/MAML.cs index cac407c21a..c88ee6112c 100644 --- a/src/Microsoft.ML.Maml/MAML.cs +++ b/src/Microsoft.ML.Maml/MAML.cs @@ -55,7 +55,10 @@ public static int Main() private static int MainWithProgress(string args) { + string currentDirectory = Path.GetDirectoryName(typeof(Maml).Module.FullyQualifiedName); + using (var env = CreateEnvironment()) + using (AssemblyLoadingUtils.CreateAssemblyRegistrar(env, currentDirectory)) using (var progressCancel = new CancellationTokenSource()) { var progressTrackerTask = Task.Run(() => TrackProgress(env, progressCancel.Token)); diff --git a/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj b/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj index 88b1e9e55b..219b6bf0b8 100644 --- a/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj +++ b/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj @@ -3,10 +3,14 @@ true CORECLR - Microsoft.ML - netstandard2.0 + Microsoft.ML + netstandard2.0 + + + + diff --git a/src/Microsoft.ML.Onnx/OnnxNodeImpl.cs b/src/Microsoft.ML.Onnx/OnnxNodeImpl.cs index 9b30fd1d87..369de019fc 100644 --- a/src/Microsoft.ML.Onnx/OnnxNodeImpl.cs +++ b/src/Microsoft.ML.Onnx/OnnxNodeImpl.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Collections.Generic; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.UniversalModelFormat.Onnx; @@ -30,11 +31,11 @@ public override void AddAttribute(string argName, long value) => OnnxUtils.NodeAddAttributes(_node, argName, value); public override void AddAttribute(string argName, IEnumerable value) => OnnxUtils.NodeAddAttributes(_node, argName, value); - public override void AddAttribute(string argName, DvText value) + public override void AddAttribute(string argName, ReadOnlyMemory value) => OnnxUtils.NodeAddAttributes(_node, argName, value); public override void AddAttribute(string argName, string[] value) => OnnxUtils.NodeAddAttributes(_node, argName, value); - public override void AddAttribute(string argName, IEnumerable value) + public override void AddAttribute(string argName, IEnumerable> value) => OnnxUtils.NodeAddAttributes(_node, argName, value); public override void AddAttribute(string argName, IEnumerable value) => OnnxUtils.NodeAddAttributes(_node, argName, value); diff --git a/src/Microsoft.ML.Onnx/OnnxUtils.cs b/src/Microsoft.ML.Onnx/OnnxUtils.cs index 9605226846..9fe761f42e 100644 --- a/src/Microsoft.ML.Onnx/OnnxUtils.cs +++ b/src/Microsoft.ML.Onnx/OnnxUtils.cs @@ -8,6 +8,7 @@ using Google.Protobuf; using Microsoft.ML.Runtime.UniversalModelFormat.Onnx; using Microsoft.ML.Runtime.Data; +using System; namespace Microsoft.ML.Runtime.Model.Onnx { @@ -186,13 +187,13 @@ public static void NodeAddAttributes(NodeProto node, string argName, long value) public static void NodeAddAttributes(NodeProto node, string argName, IEnumerable value) => node.Attribute.Add(MakeAttribute(argName, value)); - public static void NodeAddAttributes(NodeProto node, string argName, DvText value) + public static void NodeAddAttributes(NodeProto node, string argName, ReadOnlyMemory value) => node.Attribute.Add(MakeAttribute(argName, StringToByteString(value))); public static void NodeAddAttributes(NodeProto node, string argName, string[] value) => node.Attribute.Add(MakeAttribute(argName, StringToByteString(value))); - public static void NodeAddAttributes(NodeProto node, string argName, IEnumerable value) + public static void NodeAddAttributes(NodeProto node, string argName, IEnumerable> value) => node.Attribute.Add(MakeAttribute(argName, StringToByteString(value))); public static void NodeAddAttributes(NodeProto node, string argName, IEnumerable value) @@ -210,8 +211,8 @@ public static void NodeAddAttributes(NodeProto node, string argName, IEnumerable public static void NodeAddAttributes(NodeProto node, string argName, bool value) => node.Attribute.Add(MakeAttribute(argName, value)); - private static ByteString StringToByteString(DvText str) => ByteString.CopyFrom(Encoding.UTF8.GetBytes(str.ToString())); - private static IEnumerable StringToByteString(IEnumerable str) + private static ByteString StringToByteString(ReadOnlyMemory str) => ByteString.CopyFrom(Encoding.UTF8.GetBytes(str.ToString())); + private static IEnumerable StringToByteString(IEnumerable> str) => str.Select(s => ByteString.CopyFrom(Encoding.UTF8.GetBytes(s.ToString()))); private static IEnumerable StringToByteString(IEnumerable str) @@ -252,7 +253,7 @@ public static ModelProto MakeModel(List nodes, string producerName, s model.Domain = domain; model.ProducerName = producerName; model.ProducerVersion = producerVersion; - model.IrVersion = (long)Version.IrVersion; + model.IrVersion = (long)UniversalModelFormat.Onnx.Version.IrVersion; model.ModelVersion = modelVersion; model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "ai.onnx.ml", Version = 1 }); model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "", Version = 7 }); diff --git a/src/Microsoft.ML.PCA/PcaTrainer.cs b/src/Microsoft.ML.PCA/PcaTrainer.cs index 2654af5d13..b784f1f6ee 100644 --- a/src/Microsoft.ML.PCA/PcaTrainer.cs +++ b/src/Microsoft.ML.PCA/PcaTrainer.cs @@ -307,7 +307,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(PcaPredictor).Assembly.FullName); } private readonly int _dimension; diff --git a/src/Microsoft.ML.PCA/PcaTransform.cs b/src/Microsoft.ML.PCA/PcaTransform.cs index abb4b9b821..dd1596f330 100644 --- a/src/Microsoft.ML.PCA/PcaTransform.cs +++ b/src/Microsoft.ML.PCA/PcaTransform.cs @@ -184,7 +184,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(PcaTransform).Assembly.FullName); } // These are parallel to Infos. diff --git a/src/Microsoft.ML.Parquet/ParquetLoader.cs b/src/Microsoft.ML.Parquet/ParquetLoader.cs index 503debae65..667600c44e 100644 --- a/src/Microsoft.ML.Parquet/ParquetLoader.cs +++ b/src/Microsoft.ML.Parquet/ParquetLoader.cs @@ -109,7 +109,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Add Schema to Model Context verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ParquetLoader).Assembly.FullName); } public ParquetLoader(IHostEnvironment env, Arguments args, IMultiStreamSource files) @@ -358,7 +359,7 @@ private ColumnType ConvertFieldType(DataType parquetType) case DataType.Decimal: return NumberType.R8; case DataType.DateTimeOffset: - return DateTimeZoneType.Instance; + return DateTimeOffsetType.Instance; case DataType.Interval: return TimeSpanType.Instance; default: @@ -495,31 +496,31 @@ private Delegate CreateGetterDelegate(int col) switch (parquetType) { case DataType.Boolean: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Byte: return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.SignedByte: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.UnsignedByte: return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Short: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.UnsignedShort: return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Int16: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.UnsignedInt16: return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Int32: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Int64: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Int96: return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.ByteArray: return CreateGetterDelegateCore>(col, _parquetConversions.Conv); case DataType.String: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore>(col, _parquetConversions.Conv); case DataType.Float: return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Double: @@ -527,11 +528,11 @@ private Delegate CreateGetterDelegate(int col) case DataType.Decimal: return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.DateTimeOffset: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); case DataType.Interval: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore(col, _parquetConversions.Conv); default: - return CreateGetterDelegateCore(col, _parquetConversions.Conv); + return CreateGetterDelegateCore>(col, _parquetConversions.Conv); } } @@ -678,17 +679,17 @@ public ParquetConversions(IChannel channel) public void Conv(ref byte[] src, ref VBuffer dst) => dst = src != null ? new VBuffer(src.Length, src) : new VBuffer(0, new byte[0]); - public void Conv(ref sbyte? src, ref DvInt1 dst) => dst = src ?? DvInt1.NA; + public void Conv(ref sbyte? src, ref sbyte dst) => dst = (sbyte)src; public void Conv(ref byte src, ref byte dst) => dst = src; - public void Conv(ref short? src, ref DvInt2 dst) => dst = src ?? DvInt2.NA; + public void Conv(ref short? src, ref short dst) => dst = (short)src; public void Conv(ref ushort src, ref ushort dst) => dst = src; - public void Conv(ref int? src, ref DvInt4 dst) => dst = src ?? DvInt4.NA; + public void Conv(ref int? src, ref int dst) => dst = (int)src; - public void Conv(ref long? src, ref DvInt8 dst) => dst = src ?? DvInt8.NA; + public void Conv(ref long? src, ref long dst) => dst = (long)src; public void Conv(ref float? src, ref Single dst) => dst = src ?? Single.NaN; @@ -696,13 +697,14 @@ public ParquetConversions(IChannel channel) public void Conv(ref decimal? src, ref Double dst) => dst = src != null ? Decimal.ToDouble((decimal)src) : Double.NaN; - public void Conv(ref string src, ref DvText dst) => dst = new DvText(src); + public void Conv(ref string src, ref ReadOnlyMemory dst) => dst = src.AsMemory(); - public void Conv(ref bool? src, ref DvBool dst) => dst = src ?? DvBool.NA; + //Behavior for NA values is undefined. + public void Conv(ref bool src, ref bool dst) => dst = src; - public void Conv(ref DateTimeOffset src, ref DvDateTimeZone dst) => dst = src; + public void Conv(ref DateTimeOffset src, ref DateTimeOffset dst) => dst = src; - public void Conv(ref IList src, ref DvText dst) => dst = new DvText(ConvertListToString(src)); + public void Conv(ref IList src, ref ReadOnlyMemory dst) => dst = ConvertListToString(src).AsMemory(); /// /// Converts a System.Numerics.BigInteger value to a UInt128 data type value. @@ -727,22 +729,13 @@ public void Conv(ref BigInteger src, ref UInt128 dst) } /// - /// Converts a Parquet Interval data type value to a DvTimeSpan data type value. + /// Converts a Parquet Interval data type value to a TimeSpan data type value. /// /// Parquet Interval value (int : months, int : days, int : milliseconds). - /// DvTimeSpan object. - public void Conv(ref Interval src, ref DvTimeSpan dst) + /// TimeSpan object. + public void Conv(ref Interval src, ref TimeSpan dst) { - try - { - dst = new DvTimeSpan(TimeSpan.FromDays(src.Months * 30 + src.Days) + TimeSpan.FromMilliseconds(src.Millis)); - } - catch (Exception ex) - { - // Handle TimeSpan OverflowException - _ch.Error("Cannot convert Inteval to DvTimeSpan. Exception : '{0}'", ex.Message); - dst = DvTimeSpan.NA; - } + dst = TimeSpan.FromDays(src.Months * 30 + src.Days) + TimeSpan.FromMilliseconds(src.Millis); } private string ConvertListToString(IList list) diff --git a/src/Microsoft.ML.PipelineInference/ColumnTypeInference.cs b/src/Microsoft.ML.PipelineInference/ColumnTypeInference.cs index ca51218c94..5c589c50bf 100644 --- a/src/Microsoft.ML.PipelineInference/ColumnTypeInference.cs +++ b/src/Microsoft.ML.PipelineInference/ColumnTypeInference.cs @@ -8,6 +8,7 @@ using System.Text.RegularExpressions; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.Conversion; +using Microsoft.ML.Runtime.Internal.Utilities; namespace Microsoft.ML.Runtime.PipelineInference { @@ -38,7 +39,7 @@ public Arguments() private class IntermediateColumn { - private readonly DvText[] _data; + private readonly ReadOnlyMemory[] _data; private readonly int _columnId; private PrimitiveType _suggestedType; private bool? _hasHeader; @@ -60,13 +61,13 @@ public bool? HasHeader set { _hasHeader = value; } } - public IntermediateColumn(DvText[] data, int columnId) + public IntermediateColumn(ReadOnlyMemory[] data, int columnId) { _data = data; _columnId = columnId; } - public DvText[] RawData { get { return _data; } } + public ReadOnlyMemory[] RawData { get { return _data; } } } public struct Column @@ -88,9 +89,9 @@ public struct InferenceResult public readonly Column[] Columns; public readonly bool HasHeader; public readonly bool IsSuccess; - public readonly DvText[][] Data; + public readonly ReadOnlyMemory[][] Data; - private InferenceResult(bool isSuccess, Column[] columns, bool hasHeader, DvText[][] data) + private InferenceResult(bool isSuccess, Column[] columns, bool hasHeader, ReadOnlyMemory[][] data) { IsSuccess = isSuccess; Columns = columns; @@ -98,7 +99,7 @@ private InferenceResult(bool isSuccess, Column[] columns, bool hasHeader, DvText Data = data; } - public static InferenceResult Success(Column[] columns, bool hasHeader, DvText[][] data) + public static InferenceResult Success(Column[] columns, bool hasHeader, ReadOnlyMemory[][] data) { return new InferenceResult(true, columns, hasHeader, data); } @@ -132,7 +133,7 @@ public void Apply(IntermediateColumn[] columns) if (!col.RawData.Skip(1) .All(x => { - DvBool value; + bool value; return Conversions.Instance.TryParse(ref x, out value); }) ) @@ -141,7 +142,7 @@ public void Apply(IntermediateColumn[] columns) } col.SuggestedType = BoolType.Instance; - DvBool first; + bool first; col.HasHeader = !Conversions.Instance.TryParse(ref col.RawData[0], out first); } @@ -168,7 +169,7 @@ public void Apply(IntermediateColumn[] columns) col.SuggestedType = NumberType.R4; Single first; - col.HasHeader = !col.RawData[0].TryParse(out first); + col.HasHeader = !DoubleParser.TryParse(col.RawData[0].Span, out first); } } } @@ -187,7 +188,7 @@ public void Apply(IntermediateColumn[] columns) } } - private bool? IsLookLikeHeader(DvText value) + private bool? IsLookLikeHeader(ReadOnlyMemory value) { var v = value.ToString(); if (v.Length > 100) @@ -264,7 +265,7 @@ private static InferenceResult InferTextFileColumnTypesCore(IHostEnvironment env // Read all the data into memory. // List items are rows of the dataset. - var data = new List(); + var data = new List[]>(); using (var cursor = idv.GetRowCursor(col => true)) { int columnIndex; @@ -272,26 +273,26 @@ private static InferenceResult InferTextFileColumnTypesCore(IHostEnvironment env Contracts.Assert(found); var colType = cursor.Schema.GetColumnType(columnIndex); Contracts.Assert(colType.ItemType.IsText); - ValueGetter> vecGetter = null; - ValueGetter oneGetter = null; + ValueGetter>> vecGetter = null; + ValueGetter> oneGetter = null; bool isVector = colType.IsVector; if (isVector) - vecGetter = cursor.GetGetter>(columnIndex); + vecGetter = cursor.GetGetter>>(columnIndex); else { Contracts.Assert(args.ColumnCount == 1); - oneGetter = cursor.GetGetter(columnIndex); + oneGetter = cursor.GetGetter>(columnIndex); } - VBuffer line = default(VBuffer); - DvText tsValue = default(DvText); + VBuffer> line = default; + ReadOnlyMemory tsValue = default; while (cursor.MoveNext()) { if (isVector) { vecGetter(ref line); Contracts.Assert(line.Length == args.ColumnCount); - var values = new DvText[args.ColumnCount]; + var values = new ReadOnlyMemory[args.ColumnCount]; line.CopyTo(values); data.Add(values); } diff --git a/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs b/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs index 45decbb88b..ae4cb42bfc 100644 --- a/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs +++ b/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs @@ -110,14 +110,14 @@ public Column(string name, ColumnPurpose purpose, DataKind? dataKind, string ran public sealed class Arguments { - public readonly DvText[][] Data; + public readonly ReadOnlyMemory[][] Data; public readonly Column[] Columns; public readonly long? ApproximateRowCount; public readonly long? FullFileSize; public readonly bool InferencedSchema; public readonly Guid Id; public readonly bool PrettyPrint; - public Arguments(DvText[][] data, Column[] columns, long? fullFileSize, + public Arguments(ReadOnlyMemory[][] data, Column[] columns, long? fullFileSize, long? approximateRowCount, bool inferencedSchema, Guid id, bool prettyPrint = false) { Data = data; @@ -132,7 +132,7 @@ public Arguments(DvText[][] data, Column[] columns, long? fullFileSize, private interface ITypeInferenceExpert { - void Apply(DvText[][] data, Column[] columns); + void Apply(ReadOnlyMemory[][] data, Column[] columns); bool AddMe(); string FeatureName(); } @@ -175,7 +175,7 @@ public ColumnSchema() public string FeatureName() => nameof(ColumnSchema); - public void Apply(DvText[][] data, Column[] columns) + public void Apply(ReadOnlyMemory[][] data, Column[] columns) { Columns = columns; foreach (var column in columns) @@ -245,7 +245,7 @@ public LabelFeatures() LabelFeature = new List(); } - private void ApplyCore(DvText[][] data, Column column) + private void ApplyCore(ReadOnlyMemory[][] data, Column column) { _containsLabelColumns = true; Dictionary histogram = new Dictionary(); @@ -261,12 +261,6 @@ private void ApplyCore(DvText[][] data, Column column) Contracts.Check(data[index].Length > i); - if (data[index][i].IsNA) - { - missingValues++; - continue; - } - label += data[index][i].ToString(); } @@ -288,7 +282,7 @@ private void ApplyCore(DvText[][] data, Column column) }); } - public void Apply(DvText[][] data, Column[] columns) + public void Apply(ReadOnlyMemory[][] data, Column[] columns) { foreach (var column in columns.Where(col => col.ColumnPurpose == ColumnPurpose.Label)) ApplyCore(data, column); @@ -311,7 +305,7 @@ public sealed class MissingValues : ITypeInferenceExpert public int NumberOfFeaturesWithMissingValues; public double PercentageOfFeaturesWithMissingValues; - public void Apply(DvText[][] data, Column[] columns) + public void Apply(ReadOnlyMemory[][] data, Column[] columns) { if (data.GetLength(0) == 0) return; @@ -331,16 +325,6 @@ public void Apply(DvText[][] data, Column[] columns) break; Contracts.Check(data[index].Length > i); - - if (data[index][i].IsNA) - { - NumberOfMissingValues++; - instanceWithMissingValue = true; - if (column.ColumnPurpose == ColumnPurpose.TextFeature || - column.ColumnPurpose == ColumnPurpose.NumericFeature || - column.ColumnPurpose == ColumnPurpose.CategoricalFeature) - featuresWithMissingValues.Set(index, true); - } } } @@ -388,7 +372,7 @@ public ColumnFeatures() StatsPerColumnPurposeWithSpaces = new Dictionary(); } - private void ApplyCore(DvText[][] data, Column column) + private void ApplyCore(ReadOnlyMemory[][] data, Column column) { bool numericColumn = CmdParser.IsNumericType(column.Kind?.ToType()); //Statistics for numeric column or length of the text in the case of non-numeric column. @@ -401,11 +385,8 @@ private void ApplyCore(DvText[][] data, Column column) if (index >= data.GetLength(0)) break; - foreach (DvText value in data[index]) + foreach (ReadOnlyMemory value in data[index]) { - if (value.IsNA) - continue; - string columnPurposeString = column.Purpose; Stats statsPerPurpose; Stats statsPerPurposeSpaces; @@ -452,7 +433,7 @@ private void ApplyCore(DvText[][] data, Column column) } } - public void Apply(DvText[][] data, Column[] columns) + public void Apply(ReadOnlyMemory[][] data, Column[] columns) { foreach (var column in columns) ApplyCore(data, column); diff --git a/src/Microsoft.ML.PipelineInference/ExperimentsGenerator.cs b/src/Microsoft.ML.PipelineInference/ExperimentsGenerator.cs index 053b86a40b..ee0fa11bde 100644 --- a/src/Microsoft.ML.PipelineInference/ExperimentsGenerator.cs +++ b/src/Microsoft.ML.PipelineInference/ExperimentsGenerator.cs @@ -110,7 +110,7 @@ public static List GenerateCandidates(IHostEnvironment env, string dataFi //get all the trainers for this task, and generate the initial set of candidates. // Exclude the hidden learners, and the metalinear learners. - var trainers = ComponentCatalog.GetAllDerivedClasses(typeof(ITrainer), predictorType).Where(cls => !cls.IsHidden); + var trainers = env.ComponentCatalog.GetAllDerivedClasses(typeof(ITrainer), predictorType).Where(cls => !cls.IsHidden); if (!string.IsNullOrEmpty(loaderSettings)) { diff --git a/src/Microsoft.ML.PipelineInference/InferenceUtils.cs b/src/Microsoft.ML.PipelineInference/InferenceUtils.cs index a9527b9504..478e732b9f 100644 --- a/src/Microsoft.ML.PipelineInference/InferenceUtils.cs +++ b/src/Microsoft.ML.PipelineInference/InferenceUtils.cs @@ -52,14 +52,21 @@ public static Type InferPredictorCategoryType(IDataView data, PurposeInference.C data = data.Take(1000); using (var cursor = data.GetRowCursor(index => index == label.ColumnIndex)) { - ValueGetter getter = DataViewUtils.PopulateGetterArray(cursor, new List { label.ColumnIndex })[0]; + ValueGetter> getter = DataViewUtils.PopulateGetterArray(cursor, new List { label.ColumnIndex })[0]; while (cursor.MoveNext()) { - var currentLabel = new DvText(); + var currentLabel = default(ReadOnlyMemory); getter(ref currentLabel); string currentLabelString = currentLabel.ToString(); if (!String.IsNullOrEmpty(currentLabelString) && !uniqueLabelValues.Contains(currentLabelString)) + { + //Missing values in float and doubles are converted to "NaN" in text and they should not + //be treated as label values. + if ((label.ItemKind == DataKind.R4 || label.ItemKind == DataKind.R8) && currentLabelString == "?") + continue; + uniqueLabelValues.Add(currentLabelString); + } } } diff --git a/src/Microsoft.ML.PipelineInference/Macros/PipelineSweeperMacro.cs b/src/Microsoft.ML.PipelineInference/Macros/PipelineSweeperMacro.cs index 60137b5b4b..bfd9f30414 100644 --- a/src/Microsoft.ML.PipelineInference/Macros/PipelineSweeperMacro.cs +++ b/src/Microsoft.ML.PipelineInference/Macros/PipelineSweeperMacro.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Collections.Generic; using System.Linq; using Microsoft.ML.Runtime; @@ -101,12 +102,12 @@ public static Output ExtractSweepResult(IHostEnvironment env, ResultInput input) else { var builder = new ArrayDataViewBuilder(env); - builder.AddColumn(col1.Key, (PrimitiveType)col1.Value, rows.Select(r => new DvText(r.GraphJson)).ToArray()); + builder.AddColumn(col1.Key, (PrimitiveType)col1.Value, rows.Select(r => r.GraphJson.AsMemory()).ToArray()); builder.AddColumn(col2.Key, (PrimitiveType)col2.Value, rows.Select(r => r.MetricValue).ToArray()); - builder.AddColumn(col3.Key, (PrimitiveType)col3.Value, rows.Select(r => new DvText(r.PipelineId)).ToArray()); + builder.AddColumn(col3.Key, (PrimitiveType)col3.Value, rows.Select(r => r.PipelineId.AsMemory()).ToArray()); builder.AddColumn(col4.Key, (PrimitiveType)col4.Value, rows.Select(r => r.TrainingMetricValue).ToArray()); - builder.AddColumn(col5.Key, (PrimitiveType)col5.Value, rows.Select(r => new DvText(r.FirstInput)).ToArray()); - builder.AddColumn(col6.Key, (PrimitiveType)col6.Value, rows.Select(r => new DvText(r.PredictorModel)).ToArray()); + builder.AddColumn(col5.Key, (PrimitiveType)col5.Value, rows.Select(r => r.FirstInput.AsMemory()).ToArray()); + builder.AddColumn(col6.Key, (PrimitiveType)col6.Value, rows.Select(r => r.PredictorModel.AsMemory()).ToArray()); outputView = builder.GetDataView(); } return new Output { Results = outputView, State = autoMlState }; diff --git a/src/Microsoft.ML.PipelineInference/PipelinePattern.cs b/src/Microsoft.ML.PipelineInference/PipelinePattern.cs index 8a279bb579..0984d3fef5 100644 --- a/src/Microsoft.ML.PipelineInference/PipelinePattern.cs +++ b/src/Microsoft.ML.PipelineInference/PipelinePattern.cs @@ -239,17 +239,17 @@ public static PipelineResultRow[] ExtractResults(IHostEnvironment env, IDataView using (var cursor = data.GetRowCursor(col => true)) { var getter1 = cursor.GetGetter(metricCol); - var getter2 = cursor.GetGetter(graphCol); - var getter3 = cursor.GetGetter(pipelineIdCol); + var getter2 = cursor.GetGetter>(graphCol); + var getter3 = cursor.GetGetter>(pipelineIdCol); var getter4 = cursor.GetGetter(trainingMetricCol); - var getter5 = cursor.GetGetter(firstInputCol); - var getter6 = cursor.GetGetter(predictorModelCol); + var getter5 = cursor.GetGetter>(firstInputCol); + var getter6 = cursor.GetGetter>(predictorModelCol); double metricValue = 0; double trainingMetricValue = 0; - DvText graphJson = new DvText(); - DvText pipelineId = new DvText(); - DvText firstInput = new DvText(); - DvText predictorModel = new DvText(); + ReadOnlyMemory graphJson = default; + ReadOnlyMemory pipelineId = default; + ReadOnlyMemory firstInput = default; + ReadOnlyMemory predictorModel = default; while (cursor.MoveNext()) { diff --git a/src/Microsoft.ML.PipelineInference/PurposeInference.cs b/src/Microsoft.ML.PipelineInference/PurposeInference.cs index 8e7c32084e..b2f57328a8 100644 --- a/src/Microsoft.ML.PipelineInference/PurposeInference.cs +++ b/src/Microsoft.ML.PipelineInference/PurposeInference.cs @@ -7,6 +7,7 @@ using System.Linq; using System.Text.RegularExpressions; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; namespace Microsoft.ML.Runtime.PipelineInference { @@ -172,7 +173,7 @@ public void Apply(IChannel ch, IntermediateColumn[] columns) { if (column.IsPurposeSuggested || !column.Type.IsText) continue; - var data = column.GetData(); + var data = column.GetData>(); long sumLength = 0; int sumSpaces = 0; @@ -181,7 +182,7 @@ public void Apply(IChannel ch, IntermediateColumn[] columns) foreach (var span in data) { sumLength += span.Length; - seen.Add(span.IsNA ? 0 : span.Hash(0)); + seen.Add(Hashing.MurmurHash(0, span.Span)); string spanStr = span.ToString(); sumSpaces += spanStr.Count(x => x == ' '); diff --git a/src/Microsoft.ML.PipelineInference/RecipeInference.cs b/src/Microsoft.ML.PipelineInference/RecipeInference.cs index e4e6c083a0..4363c8a4d7 100644 --- a/src/Microsoft.ML.PipelineInference/RecipeInference.cs +++ b/src/Microsoft.ML.PipelineInference/RecipeInference.cs @@ -128,13 +128,13 @@ public InferenceResult(SuggestedRecipe[] suggestedRecipes) } } - private static IEnumerable GetRecipes() + private static IEnumerable GetRecipes(IHostEnvironment env) { yield return new DefaultRecipe(); - yield return new BalancedTextClassificationRecipe(); - yield return new AccuracyFocusedRecipe(); - yield return new ExplorationComboRecipe(); - yield return new TreeLeafRecipe(); + yield return new BalancedTextClassificationRecipe(env); + yield return new AccuracyFocusedRecipe(env); + yield return new ExplorationComboRecipe(env); + yield return new TreeLeafRecipe(env); } public abstract class Recipe @@ -202,7 +202,7 @@ protected override IEnumerable ApplyCore(Type predictorType, TransformInference.SuggestedTransform[] transforms) { yield return - new SuggestedRecipe(ToString(), transforms, new SuggestedRecipe.SuggestedLearner[0], Int32.MinValue + 1); + new SuggestedRecipe(ToString(), transforms, new SuggestedRecipe.SuggestedLearner[0], int.MinValue + 1); } public override string ToString() => "Default transforms"; @@ -210,6 +210,14 @@ protected override IEnumerable ApplyCore(Type predictorType, public abstract class MultiClassRecipies : Recipe { + protected IHostEnvironment Host { get; } + + protected MultiClassRecipies(IHostEnvironment host) + { + Contracts.CheckValue(host, nameof(host)); + Host = host; + } + public override List AllowedTransforms() => base.AllowedTransforms().Where( expert => expert != typeof(TransformInference.Experts.Text) && @@ -227,6 +235,11 @@ public override List AllowedTransforms() => base.AllowedTransforms().Where public sealed class BalancedTextClassificationRecipe : MultiClassRecipies { + public BalancedTextClassificationRecipe(IHostEnvironment host) + : base(host) + { + } + public override List QualifierTransforms() => new List { typeof(TransformInference.Experts.TextBiGramTriGram) }; @@ -236,12 +249,12 @@ protected override IEnumerable ApplyCore(Type predictorType, SuggestedRecipe.SuggestedLearner learner = new SuggestedRecipe.SuggestedLearner(); if (predictorType == typeof(SignatureMultiClassClassifierTrainer)) { - learner.LoadableClassInfo = ComponentCatalog.GetLoadableClassInfo("OVA"); + learner.LoadableClassInfo = Host.ComponentCatalog.GetLoadableClassInfo("OVA"); learner.Settings = "p=AveragedPerceptron{iter=10}"; } else { - learner.LoadableClassInfo = ComponentCatalog.GetLoadableClassInfo(Learners.AveragedPerceptronTrainer.LoadNameValue); + learner.LoadableClassInfo = Host.ComponentCatalog.GetLoadableClassInfo(Learners.AveragedPerceptronTrainer.LoadNameValue); learner.Settings = "iter=10"; var epInput = new Legacy.Trainers.AveragedPerceptronBinaryClassifier { @@ -251,7 +264,7 @@ protected override IEnumerable ApplyCore(Type predictorType, } yield return - new SuggestedRecipe(ToString(), transforms, new[] { learner }, Int32.MaxValue); + new SuggestedRecipe(ToString(), transforms, new[] { learner }, int.MaxValue); } public override string ToString() => "Text classification optimized for speed and accuracy"; @@ -259,6 +272,11 @@ protected override IEnumerable ApplyCore(Type predictorType, public sealed class AccuracyFocusedRecipe : MultiClassRecipies { + public AccuracyFocusedRecipe(IHostEnvironment host) + : base(host) + { + } + public override List QualifierTransforms() => new List { typeof(TransformInference.Experts.TextUniGramTriGram) }; @@ -268,13 +286,13 @@ protected override IEnumerable ApplyCore(Type predictorType, SuggestedRecipe.SuggestedLearner learner = new SuggestedRecipe.SuggestedLearner(); if (predictorType == typeof(SignatureMultiClassClassifierTrainer)) { - learner.LoadableClassInfo = ComponentCatalog.GetLoadableClassInfo("OVA"); + learner.LoadableClassInfo = Host.ComponentCatalog.GetLoadableClassInfo("OVA"); learner.Settings = "p=FastTreeBinaryClassification"; } else { learner.LoadableClassInfo = - ComponentCatalog.GetLoadableClassInfo(FastTreeBinaryClassificationTrainer.LoadNameValue); + Host.ComponentCatalog.GetLoadableClassInfo(FastTreeBinaryClassificationTrainer.LoadNameValue); learner.Settings = ""; var epInput = new Legacy.Trainers.FastTreeBinaryClassifier(); learner.PipelineNode = new TrainerPipelineNode(epInput); @@ -288,6 +306,11 @@ protected override IEnumerable ApplyCore(Type predictorType, public sealed class ExplorationComboRecipe : MultiClassRecipies { + public ExplorationComboRecipe(IHostEnvironment host) + : base(host) + { + } + public override List QualifierTransforms() => new List { typeof(TransformInference.Experts.SdcaTransform) }; @@ -298,12 +321,12 @@ protected override IEnumerable ApplyCore(Type predictorType, if (predictorType == typeof(SignatureMultiClassClassifierTrainer)) { learner.LoadableClassInfo = - ComponentCatalog.GetLoadableClassInfo(Learners.SdcaMultiClassTrainer.LoadNameValue); + Host.ComponentCatalog.GetLoadableClassInfo(Learners.SdcaMultiClassTrainer.LoadNameValue); } else { learner.LoadableClassInfo = - ComponentCatalog.GetLoadableClassInfo(Learners.LinearClassificationTrainer.LoadNameValue); + Host.ComponentCatalog.GetLoadableClassInfo(Learners.LinearClassificationTrainer.LoadNameValue); var epInput = new Legacy.Trainers.StochasticDualCoordinateAscentBinaryClassifier(); learner.PipelineNode = new TrainerPipelineNode(epInput); } @@ -317,6 +340,11 @@ protected override IEnumerable ApplyCore(Type predictorType, public sealed class TreeLeafRecipe : MultiClassRecipies { + public TreeLeafRecipe(IHostEnvironment host) + : base(host) + { + } + public override List QualifierTransforms() => new List { typeof(TransformInference.Experts.NaiveBayesTransform) }; @@ -325,7 +353,7 @@ protected override IEnumerable ApplyCore(Type predictorType, { SuggestedRecipe.SuggestedLearner learner = new SuggestedRecipe.SuggestedLearner(); learner.LoadableClassInfo = - ComponentCatalog.GetLoadableClassInfo(Learners.MultiClassNaiveBayesTrainer.LoadName); + Host.ComponentCatalog.GetLoadableClassInfo(Learners.MultiClassNaiveBayesTrainer.LoadName); learner.Settings = ""; var epInput = new Legacy.Trainers.NaiveBayesClassifier(); learner.PipelineNode = new TrainerPipelineNode(epInput); @@ -403,7 +431,7 @@ public static SuggestedRecipe[] InferRecipesFromData(IHostEnvironment env, strin AllowQuoting = splitResult.AllowQuote }; - settingsString = CommandLine.CmdParser.GetSettings(ch, finalLoaderArgs, new TextLoader.Arguments()); + settingsString = CommandLine.CmdParser.GetSettings(h, finalLoaderArgs, new TextLoader.Arguments()); ch.Info($"Loader options: {settingsString}"); ch.Info("Inferring recipes"); @@ -440,7 +468,7 @@ public static InferenceResult InferRecipes(IHostEnvironment env, TransformInfere using (var ch = h.Start("InferRecipes")) { var list = new List(); - foreach (var recipe in GetRecipes()) + foreach (var recipe in GetRecipes(h)) list.AddRange(recipe.Apply(transformInferenceResult, predictorType, ch)); if (list.Count == 0) diff --git a/src/Microsoft.ML.PipelineInference/TextFileContents.cs b/src/Microsoft.ML.PipelineInference/TextFileContents.cs index aeb72ee0c9..2ba5e23d0b 100644 --- a/src/Microsoft.ML.PipelineInference/TextFileContents.cs +++ b/src/Microsoft.ML.PipelineInference/TextFileContents.cs @@ -131,9 +131,9 @@ private static bool TryParseFile(IChannel ch, TextLoader.Arguments args, IMultiS using (var cursor = idv.GetRowCursor(x => x == columnIndex)) { - var getter = cursor.GetGetter>(columnIndex); + var getter = cursor.GetGetter>>(columnIndex); - VBuffer line = default(VBuffer); + VBuffer> line = default; while (cursor.MoveNext()) { getter(ref line); diff --git a/src/Microsoft.ML.PipelineInference/TransformInference.cs b/src/Microsoft.ML.PipelineInference/TransformInference.cs index 6cfdc58b1b..477e566060 100644 --- a/src/Microsoft.ML.PipelineInference/TransformInference.cs +++ b/src/Microsoft.ML.PipelineInference/TransformInference.cs @@ -364,7 +364,7 @@ public override IEnumerable Apply(IntermediateColumn[] colum if (col.Type.IsText) { - col.GetUniqueValueCounts(out var unique, out var _, out var _); + col.GetUniqueValueCounts>(out var unique, out var _, out var _); ch.Info("Label column '{0}' is text. Suggested auto-labeling.", col.ColumnName); var args = new TransformString("AutoLabel", columnArgument.ToString()); @@ -695,7 +695,7 @@ private bool IsDictionaryOk(IntermediateColumn column, Double dataSampleFraction // Sparse Data for the Language Model Component of a Speech Recognizer" (1987), taking into account that // the singleton count was estimated from a fraction of the data (and assuming the estimate is // roughly the same for the entire sample). - column.GetUniqueValueCounts(out unique, out singletons, out total); + column.GetUniqueValueCounts>(out unique, out singletons, out total); var expectedUnseenValues = singletons / dataSampleFraction; return expectedUnseenValues < 1000 && unique < 10000; } diff --git a/src/Microsoft.ML.ResultProcessor/Microsoft.ML.ResultProcessor.csproj b/src/Microsoft.ML.ResultProcessor/Microsoft.ML.ResultProcessor.csproj index b420da0eb0..e5610126df 100644 --- a/src/Microsoft.ML.ResultProcessor/Microsoft.ML.ResultProcessor.csproj +++ b/src/Microsoft.ML.ResultProcessor/Microsoft.ML.ResultProcessor.csproj @@ -1,4 +1,4 @@ - + netstandard2.0 @@ -7,6 +7,10 @@ true + + + + diff --git a/src/Microsoft.ML.ResultProcessor/ResultProcessor.cs b/src/Microsoft.ML.ResultProcessor/ResultProcessor.cs index eb42e452bd..b142cea0ba 100644 --- a/src/Microsoft.ML.ResultProcessor/ResultProcessor.cs +++ b/src/Microsoft.ML.ResultProcessor/ResultProcessor.cs @@ -12,7 +12,6 @@ using Microsoft.ML.Runtime.Command; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; -using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Tools; @@ -152,9 +151,9 @@ public void GetDefaultSettingValues(IHostEnvironment env, string predictorName, /// private Dictionary GetDefaultSettings(IHostEnvironment env, string predictorName, string[] extraAssemblies = null) { - ComponentCatalog.CacheClassesExtra(extraAssemblies); + AssemblyLoadingUtils.LoadAndRegister(env, extraAssemblies); - var cls = ComponentCatalog.GetLoadableClassInfo(predictorName); + var cls = env.ComponentCatalog.GetLoadableClassInfo(predictorName); if (cls == null) { Console.Error.WriteLine("Can't load trainer '{0}'", predictorName); @@ -521,7 +520,7 @@ private static bool ValidateMamlOutput(string filename, string[] rawLines, out L ICommandLineComponentFactory commandLineTrainer = trainer as ICommandLineComponentFactory; Contracts.AssertValue(commandLineTrainer, "ResultProcessor can only work with ICommandLineComponentFactory."); - trainerClass = ComponentCatalog.GetLoadableClassInfo(commandLineTrainer.Name); + trainerClass = env.ComponentCatalog.GetLoadableClassInfo(commandLineTrainer.Name); trainerArgs = trainerClass.CreateArguments(); Dictionary predictorSettings; if (trainerArgs == null) @@ -678,7 +677,12 @@ public static bool ParseCommandArguments(IHostEnvironment env, string commandlin return false; } - commandClass = ComponentCatalog.GetLoadableClassInfo(kind); + commandClass = env.ComponentCatalog.GetLoadableClassInfo(kind); + if (commandClass == null) + { + commandArgs = null; + return false; + } commandArgs = commandClass.CreateArguments(); CmdParser.ParseArguments(env, settings, commandArgs); return true; @@ -1147,10 +1151,15 @@ private static object Load(Stream stream) } public static int Main(string[] args) + { + return Main(new ConsoleEnvironment(42), args); + } + + public static int Main(IHostEnvironment env, string[] args) { try { - Run(args); + Run(env, args); return 0; } catch (Exception e) @@ -1170,10 +1179,9 @@ public static int Main(string[] args) } } - protected static void Run(string[] args) + protected static void Run(IHostEnvironment env, string[] args) { ResultProcessorArguments cmd = new ResultProcessorArguments(); - ConsoleEnvironment env = new ConsoleEnvironment(42); List predictorResultsList = new List(); PredictionUtil.ParseArguments(env, cmd, PredictionUtil.CombineSettings(args)); @@ -1185,8 +1193,8 @@ protected static void Run(string[] args) if (cmd.IncludePerFoldResults) cmd.PerFoldResultSeparator = "" + PredictionUtil.SepCharFromString(cmd.PerFoldResultSeparator); - foreach (var dll in cmd.ExtraAssemblies) - ComponentCatalog.LoadAssembly(dll); + + AssemblyLoadingUtils.LoadAndRegister(env, cmd.ExtraAssemblies); if (cmd.Metrics.Length == 0) cmd.Metrics = null; diff --git a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs index 83b1335c7a..2f2161fa12 100644 --- a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs +++ b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs @@ -5,6 +5,8 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML.Core.Data; +using Microsoft.ML.Core.Prediction; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; @@ -14,8 +16,9 @@ using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Training; -[assembly: LoadableClass(FieldAwareFactorizationMachineTrainer.Summary, typeof(FieldAwareFactorizationMachineTrainer), typeof(FieldAwareFactorizationMachineTrainer.Arguments), - new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, FieldAwareFactorizationMachineTrainer.UserName, FieldAwareFactorizationMachineTrainer.LoadName, +[assembly: LoadableClass(FieldAwareFactorizationMachineTrainer.Summary, typeof(FieldAwareFactorizationMachineTrainer), + typeof(FieldAwareFactorizationMachineTrainer.Arguments), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) } + , FieldAwareFactorizationMachineTrainer.UserName, FieldAwareFactorizationMachineTrainer.LoadName, FieldAwareFactorizationMachineTrainer.ShortName, DocName = "trainer/FactorizationMachine.md")] [assembly: LoadableClass(typeof(void), typeof(FieldAwareFactorizationMachineTrainer), null, typeof(SignatureEntryPointModule), FieldAwareFactorizationMachineTrainer.LoadName)] @@ -24,18 +27,19 @@ namespace Microsoft.ML.Runtime.FactorizationMachine { /* Train a field-aware factorization machine using ADAGRAD (an advanced stochastic gradient method). See references below - for details. This trainer is essentially faster the one introduced in [2] because of some implemtation tricks[3]. + for details. This trainer is essentially faster the one introduced in [2] because of some implementation tricks[3]. [1] http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf [2] https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf [3] https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf */ /// - public sealed class FieldAwareFactorizationMachineTrainer : TrainerBase + public sealed class FieldAwareFactorizationMachineTrainer : TrainerBase, + IEstimator { - public const string Summary = "Train a field-aware factorization machine for binary classification"; - public const string UserName = "Field-aware Factorization Machine"; - public const string LoadName = "FieldAwareFactorizationMachine"; - public const string ShortName = "ffm"; + internal const string Summary = "Train a field-aware factorization machine for binary classification"; + internal const string UserName = "Field-aware Factorization Machine"; + internal const string LoadName = "FieldAwareFactorizationMachine"; + internal const string ShortName = "ffm"; public sealed class Arguments : LearnerInputBaseWithLabel { @@ -74,19 +78,95 @@ public sealed class Arguments : LearnerInputBaseWithLabel } public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + + /// + /// The feature column that the trainer expects. + /// + public readonly SchemaShape.Column[] FeatureColumns; + + /// + /// The label column that the trainer expects. Can be null, which indicates that label + /// is not used for training. + /// + public readonly SchemaShape.Column LabelColumn; + + /// + /// The weight column that the trainer expects. Can be null, which indicates that weight is + /// not used for training. + /// + public readonly SchemaShape.Column WeightColumn; + + /// + /// The containing at least the training data for this trainer. + /// public override TrainerInfo Info { get; } - private readonly int _latentDim; - private readonly int _latentDimAligned; - private readonly float _lambdaLinear; - private readonly float _lambdaLatent; - private readonly float _learningRate; - private readonly int _numIterations; - private readonly bool _norm; - private readonly bool _shuffle; - private readonly bool _verbose; - private readonly float _radius; - - public FieldAwareFactorizationMachineTrainer(IHostEnvironment env, Arguments args) : base(env, LoadName) + + /// + /// Additional data for training, through + /// + public readonly TrainerEstimatorContext Context; + + private int _latentDim; + private int _latentDimAligned; + private float _lambdaLinear; + private float _lambdaLatent; + private float _learningRate; + private int _numIterations; + private bool _norm; + private bool _shuffle; + private bool _verbose; + private float _radius; + + /// + /// Legacy constructor initializing a new instance of through the legacy + /// class. + /// + /// The private instance of . + /// An instance of the legacy to apply advanced parameters to the algorithm. + public FieldAwareFactorizationMachineTrainer(IHostEnvironment env, Arguments args) + :base(env, LoadName) + { + Initialize(env, args); + Info = new TrainerInfo(supportValid: true, supportIncrementalTrain: true); + } + + /// + /// Initializing a new instance of . + /// + /// The private instance of . + /// The name of the label column. + /// The name of column hosting the features. + /// A delegate to apply all the advanced arguments to the algorithm. + /// The name of the weight column. + /// The for additional input data to training. + public FieldAwareFactorizationMachineTrainer(IHostEnvironment env, string labelColumn, string[] featureColumns, + string weightColumn = null, TrainerEstimatorContext context = null, Action advancedSettings= null) + : base(env, LoadName) + { + var args = new Arguments(); + advancedSettings?.Invoke(args); + + Initialize(env, args); + Info = new TrainerInfo(supportValid: true, supportIncrementalTrain: true); + + Context = context; + + FeatureColumns = new SchemaShape.Column[featureColumns.Length]; + + for(int i=0; i< featureColumns.Length; i++) + FeatureColumns[i] = new SchemaShape.Column(featureColumns[i], SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); + + LabelColumn = new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); + WeightColumn = weightColumn != null? new SchemaShape.Column(weightColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false): null; + } + + /// + /// Initializes the instance. Shared between the two constructors. + /// REVIEW: Once the legacy constructor goes away, this can move to the only constructor and most of the fields can be back to readonly. + /// + /// + /// + private void Initialize(IHostEnvironment env, Arguments args) { Host.CheckUserArg(args.LatentDim > 0, nameof(args.LatentDim), "Must be positive"); Host.CheckUserArg(args.LambdaLinear >= 0, nameof(args.LambdaLinear), "Must be non-negative"); @@ -103,7 +183,6 @@ public FieldAwareFactorizationMachineTrainer(IHostEnvironment env, Arguments arg _shuffle = args.Shuffle; _verbose = args.Verbose; _radius = args.Radius; - Info = new TrainerInfo(supportValid: true, supportIncrementalTrain: true); } private void InitializeTrainingState(int fieldCount, int featureCount, FieldAwareFactorizationMachinePredictor predictor, out float[] linearWeights, @@ -342,6 +421,7 @@ private FieldAwareFactorizationMachinePredictor TrainCore(IChannel ch, IProgress ch.Warning($"Skipped {badExampleCount} examples with bad label/weight/features in training set"); if (validBadExampleCount != 0) ch.Warning($"Skipped {validBadExampleCount} examples with bad label/weight/features in validation set"); + return new FieldAwareFactorizationMachinePredictor(Host, _norm, fieldCount, totalFeatureCount, _latentDim, linearWeights, latentWeightsAligned); } @@ -376,5 +456,80 @@ public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironm return LearnerEntryPointsUtils.Train(host, input, () => new FieldAwareFactorizationMachineTrainer(host, input), () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn)); } + + public FieldAwareFactorizationMachinePredictionTransformer Fit(IDataView input) + { + FieldAwareFactorizationMachinePredictor model = null; + + var roles = new List>(); + foreach (var feat in FeatureColumns) + roles.Add(new KeyValuePair(RoleMappedSchema.ColumnRole.Feature, feat.Name)); + + roles.Add(new KeyValuePair(RoleMappedSchema.ColumnRole.Label, LabelColumn.Name)); + + if(WeightColumn != null) + roles.Add(new KeyValuePair(RoleMappedSchema.ColumnRole.Feature, WeightColumn.Name)); + + var trainingData = new RoleMappedData(input, roles); + + RoleMappedData validData = null; + if (Context != null) + validData = new RoleMappedData(Context.ValidationSet, roles); + + using (var ch = Host.Start("Training")) + using (var pch = Host.StartProgressChannel("Training")) + { + var pred = TrainCore(ch, pch, trainingData, validData, Context?.InitialPredictor as FieldAwareFactorizationMachinePredictor); + ch.Done(); + model = pred; + } + + return new FieldAwareFactorizationMachinePredictionTransformer(Host, model, input.Schema, FeatureColumns.Select(x => x.Name).ToArray() ); + } + + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + + Host.CheckValue(inputSchema, nameof(inputSchema)); + + void CheckColumnsCompatible(SchemaShape.Column column, string defaultName){ + + if (!inputSchema.TryFindColumn(column.Name, out var col)) + throw Host.ExceptSchemaMismatch(nameof(col), defaultName, defaultName); + + if (!column.IsCompatibleWith(col)) + throw Host.Except($"{defaultName} column '{column.Name}' is not compatible"); + } + + if (LabelColumn != null) + CheckColumnsCompatible(LabelColumn, DefaultColumnNames.Label); + + foreach (var feat in FeatureColumns) + { + CheckColumnsCompatible(feat, DefaultColumnNames.Features); + } + + if (WeightColumn != null) + CheckColumnsCompatible(WeightColumn, DefaultColumnNames.Weight); + + var outColumns = inputSchema.Columns.ToDictionary(x => x.Name); + foreach (var col in GetOutputColumnsCore(inputSchema)) + outColumns[col.Name] = col; + + return new SchemaShape(outColumns.Values); + } + + private SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) + { + bool success = inputSchema.TryFindColumn(LabelColumn.Name, out var labelCol); + Contracts.Assert(success); + + return new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; + } } } diff --git a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachinePredictor.cs b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachinePredictor.cs index 37261cb55b..8e95682b51 100644 --- a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachinePredictor.cs +++ b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachinePredictor.cs @@ -3,8 +3,11 @@ // See the LICENSE file in the project root for more information. using System; +using System.Collections.Generic; +using System.IO; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.FactorizationMachine; using Microsoft.ML.Runtime.Internal.CpuMath; using Microsoft.ML.Runtime.Internal.Internallearn; @@ -13,6 +16,9 @@ [assembly: LoadableClass(typeof(FieldAwareFactorizationMachinePredictor), null, typeof(SignatureLoadModel), "Field Aware Factorization Machine", FieldAwareFactorizationMachinePredictor.LoaderSignature)] +[assembly: LoadableClass(typeof(FieldAwareFactorizationMachinePredictionTransformer), typeof(FieldAwareFactorizationMachinePredictionTransformer), null, typeof(SignatureLoadModel), + "", FieldAwareFactorizationMachinePredictionTransformer.LoaderSignature)] + namespace Microsoft.ML.Runtime.FactorizationMachine { public sealed class FieldAwareFactorizationMachinePredictor : PredictorBase, ISchemaBindableMapper, ICanSaveModel @@ -34,7 +40,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FieldAwareFactorizationMachinePredictor).Assembly.FullName); } internal FieldAwareFactorizationMachinePredictor(IHostEnvironment env, bool norm, int fieldCount, int featureCount, int latentDim, @@ -125,6 +132,9 @@ protected override void SaveCore(ModelSaveContext ctx) // float[]: linear coefficients // float[]: latent representation of features + // REVIEW:FAFM needs to store the names of the features, so that they prediction data does not have the + // restriciton of the columns needing to be ordered the same as the training data. + Host.Assert(FieldCount > 0); Host.Assert(FeatureCount > 0); Host.Assert(LatentDim > 0); @@ -163,9 +173,7 @@ internal float CalculateResponse(ValueGetter>[] getters, VBuffer< } public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema) - { - return new FieldAwareFactorizationMachineScalarRowMapper(env, schema, new BinaryClassifierSchema(), this); - } + => new FieldAwareFactorizationMachineScalarRowMapper(env, schema, new BinaryClassifierSchema(), this); internal void CopyLinearWeightsTo(float[] linearWeights) { @@ -181,4 +189,176 @@ internal void CopyLatentWeightsTo(AlignedArray latentWeights) latentWeights.CopyFrom(_latentWeightsAligned); } } + + public sealed class FieldAwareFactorizationMachinePredictionTransformer : PredictionTransformerBase, ICanSaveModel + { + public const string LoaderSignature = "FAFMPredXfer"; + + /// + /// The name of the feature column used by the prediction transformer. + /// + public string[] FeatureColumns { get; } + + /// + /// The type of the feature columns. + /// + public ColumnType[] FeatureColumnTypes { get; } + + private readonly BinaryClassifierScorer _scorer; + + private readonly string _thresholdColumn; + private readonly float _threshold; + + public FieldAwareFactorizationMachinePredictionTransformer(IHostEnvironment host, FieldAwareFactorizationMachinePredictor model, ISchema trainSchema, + string[] featureColumns, float threshold = 0f, string thresholdColumn = DefaultColumnNames.Score) + :base(Contracts.CheckRef(host, nameof(host)).Register(nameof(FieldAwareFactorizationMachinePredictionTransformer)), model, trainSchema) + { + Host.CheckNonEmpty(thresholdColumn, nameof(thresholdColumn)); + _threshold = threshold; + _thresholdColumn = thresholdColumn; + + Host.CheckValue(featureColumns, nameof(featureColumns)); + int featCount = featureColumns.Length; + Host.Check(featCount >= 0, "Empty features column."); + + FeatureColumns = featureColumns; + FeatureColumnTypes = new ColumnType[featCount]; + + int i = 0; + foreach (var feat in featureColumns) + { + if (!trainSchema.TryGetColumnIndex(feat, out int col)) + throw Host.ExceptSchemaMismatch(nameof(featureColumns), RoleMappedSchema.ColumnRole.Feature.Value, feat); + FeatureColumnTypes[i++] = trainSchema.GetColumnType(col); + } + + BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model); + + var schema = GetSchema(); + var args = new BinaryClassifierScorer.Arguments { Threshold = _threshold, ThresholdColumn = _thresholdColumn }; + _scorer = new BinaryClassifierScorer(Host, args, new EmptyDataView(Host, trainSchema), BindableMapper.Bind(Host, schema), schema); + } + + public FieldAwareFactorizationMachinePredictionTransformer(IHostEnvironment host, ModelLoadContext ctx) + :base(Contracts.CheckRef(host, nameof(host)).Register(nameof(FieldAwareFactorizationMachinePredictionTransformer)), ctx) + { + // *** Binary format *** + // + // ids of strings: feature columns. + // float: scorer threshold + // id of string: scorer threshold column + + // count of feature columns. FAFM uses more than one. + int featCount = Model.FieldCount; + + FeatureColumns = new string[featCount]; + FeatureColumnTypes = new ColumnType[featCount]; + + for (int i = 0; i < featCount; i++) + { + FeatureColumns[i] = ctx.LoadString(); + if (!TrainSchema.TryGetColumnIndex(FeatureColumns[i], out int col)) + throw Host.ExceptSchemaMismatch(nameof(FeatureColumns), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumns[i]); + FeatureColumnTypes[i] = TrainSchema.GetColumnType(col); + } + + _threshold = ctx.Reader.ReadSingle(); + _thresholdColumn = ctx.LoadString(); + + BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, Model); + + var schema = GetSchema(); + var args = new BinaryClassifierScorer.Arguments { Threshold = _threshold, ThresholdColumn = _thresholdColumn }; + _scorer = new BinaryClassifierScorer(Host, args, new EmptyDataView(Host, TrainSchema), BindableMapper.Bind(Host, schema), schema); + } + + /// + /// Gets the result after applying . + /// + /// The of the input data. + /// The post transformation . + public override ISchema GetOutputSchema(ISchema inputSchema) + { + for (int i = 0; i < FeatureColumns.Length; i++) + { + var feat = FeatureColumns[i]; + if (!inputSchema.TryGetColumnIndex(feat, out int col)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, feat, FeatureColumnTypes[i].ToString(), null); + + if (!inputSchema.GetColumnType(col).Equals(FeatureColumnTypes[i])) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, feat, FeatureColumnTypes[i].ToString(), inputSchema.GetColumnType(col).ToString()); + } + + return Transform(new EmptyDataView(Host, inputSchema)).Schema; + } + + /// + /// Applies the transformer to the , scoring it through the . + /// + /// The data to be scored with the . + /// The scored . + public override IDataView Transform(IDataView input) + { + Host.CheckValue(input, nameof(input)); + return _scorer.ApplyToData(Host, input); + } + + /// + /// Saves the transformer to file. + /// + /// The that facilitates saving to the . + public void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // model: prediction model. + // stream: empty data view that contains train schema. + // ids of strings: feature columns. + // float: scorer threshold + // id of string: scorer threshold column + + ctx.SaveModel(Model, DirModel); + ctx.SaveBinaryStream(DirTransSchema, writer => + { + using (var ch = Host.Start("Saving train schema")) + { + var saver = new BinarySaver(Host, new BinarySaver.Arguments { Silent = true }); + DataSaverUtils.SaveDataView(ch, saver, new EmptyDataView(Host, TrainSchema), writer.BaseStream); + } + }); + + for (int i = 0; i < Model.FieldCount; i++) + ctx.SaveString(FeatureColumns[i]); + + ctx.Writer.Write(_threshold); + ctx.SaveString(_thresholdColumn); + } + + private RoleMappedSchema GetSchema() + { + var roles = new List>(); + foreach (var feat in FeatureColumns) + roles.Add(new KeyValuePair(RoleMappedSchema.ColumnRole.Feature, feat)); + + var schema = new RoleMappedSchema(TrainSchema, roles); + return schema; + } + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "FAFMPRED", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(FieldAwareFactorizationMachinePredictionTransformer).Assembly.FullName); + } + + private static FieldAwareFactorizationMachinePredictionTransformer Create(IHostEnvironment env, ModelLoadContext ctx) + => new FieldAwareFactorizationMachinePredictionTransformer(env, ctx); + } } diff --git a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs index 67c53223d7..11881b1925 100644 --- a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs +++ b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs @@ -101,8 +101,12 @@ public IRow GetOutputRow(IRow input, Func predicate, out Action actio var featureIndexBuffer = new int[_pred.FeatureCount]; var featureValueBuffer = new float[_pred.FeatureCount]; var inputGetters = new ValueGetter>[_pred.FieldCount]; - for (int f = 0; f < _pred.FieldCount; f++) - inputGetters[f] = input.GetGetter>(_inputColumnIndexes[f]); + + if (predicate(0) || predicate(1)) + { + for (int f = 0; f < _pred.FieldCount; f++) + inputGetters[f] = input.GetGetter>(_inputColumnIndexes[f]); + } action = null; var getters = new Delegate[2]; diff --git a/src/Microsoft.ML.StandardLearners/Optimizer/Optimizer.cs b/src/Microsoft.ML.StandardLearners/Optimizer/Optimizer.cs index 88fcd47531..31a9199093 100644 --- a/src/Microsoft.ML.StandardLearners/Optimizer/Optimizer.cs +++ b/src/Microsoft.ML.StandardLearners/Optimizer/Optimizer.cs @@ -645,7 +645,7 @@ public void Minimize(DifferentiableFunction function, ref VBuffer initial double? improvement = null; double x; int end; - if (message != null && DoubleParser.TryParse(out x, message, 0, message.Length, out end)) + if (message != null && DoubleParser.TryParse(message.AsMemory().Span, out x, out end)) improvement = x; pch.Checkpoint(state.Value, improvement, state.Iter); diff --git a/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs b/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs index 7a862af977..8722b79ea6 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs @@ -147,7 +147,7 @@ protected virtual int ComputeNumThreads(FloatLabelCursor.Factory cursorFactory) } public abstract class SdcaTrainerBase : StochasticTrainerBase - where TTransformer : IPredictionTransformer + where TTransformer : ISingleFeaturePredictionTransformer where TModel : IPredictor { // REVIEW: Making it even faster and more accurate: diff --git a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs index 2a5d73705f..ed41a14174 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs @@ -347,7 +347,7 @@ public virtual IRow GetSummaryIRowOrNull(RoleMappedSchema schema) { var cols = new List(); - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, Weight.Length, ref names); var slotNamesCol = RowColumnUtils.GetColumn(MetadataUtils.Kinds.SlotNames, new VectorType(TextType.Instance, Weight.Length), ref names); @@ -409,7 +409,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00020002, // Added model statistics verReadableCur: 0x00020001, verWeCanReadBack: 0x00020001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LinearBinaryPredictor).Assembly.FullName); } /// @@ -529,7 +530,7 @@ public override IRow GetStatsIRowOrNull(RoleMappedSchema schema) if (_stats == null) return null; var cols = new List(); - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, Weight.Length, ref names); // Add the stat columns. @@ -597,7 +598,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00020001, // Fixed sparse serialization verReadableCur: 0x00020001, verWeCanReadBack: 0x00020001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LinearRegressionPredictor).Assembly.FullName); } /// @@ -679,7 +681,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00020001, // Fixed sparse serialization verReadableCur: 0x00020001, verWeCanReadBack: 0x00020001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(PoissonRegressionPredictor).Assembly.FullName); } internal PoissonRegressionPredictor(IHostEnvironment env, ref VBuffer weights, Float bias) diff --git a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictorUtils.cs b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictorUtils.cs index c337abccfc..505d3363e3 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictorUtils.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictorUtils.cs @@ -35,7 +35,7 @@ public static void SaveAsCode(TextWriter writer, ref VBuffer weights, Flo Contracts.CheckValue(writer, nameof(writer)); Contracts.CheckValueOrNull(schema); - var featureNames = default(VBuffer); + var featureNames = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, weights.Length, ref featureNames); int numNonZeroWeights = 0; @@ -103,7 +103,7 @@ public static string LinearModelAsIni(ref VBuffer weights, Float bias, IP StringBuilder aggregatedNodesBuilder = new StringBuilder("Nodes="); StringBuilder weightsBuilder = new StringBuilder("Weights="); - var featureNames = default(VBuffer); + var featureNames = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, weights.Length, ref featureNames); int numNonZeroWeights = 0; @@ -118,7 +118,7 @@ public static string LinearModelAsIni(ref VBuffer weights, Float bias, IP var name = featureNames.GetItemOrDefault(idx); inputBuilder.AppendLine("[Input:" + numNonZeroWeights + "]"); - inputBuilder.AppendLine("Name=" + (featureNames.Count == 0 ? "Feature_" + idx : DvText.Identical(name, DvText.Empty) ? $"f{idx}" : name.ToString())); + inputBuilder.AppendLine("Name=" + (featureNames.Count == 0 ? "Feature_" + idx : name.IsEmpty ? $"f{idx}" : name.ToString())); inputBuilder.AppendLine("Transform=linear"); inputBuilder.AppendLine("Slope=1"); inputBuilder.AppendLine("Intercept=0"); @@ -206,7 +206,7 @@ public static string LinearModelAsText( } public static IEnumerable> GetSortedLinearModelFeatureNamesAndWeights(Single bias, - ref VBuffer weights, ref VBuffer names) + ref VBuffer weights, ref VBuffer> names) { var orderedWeights = weights.Items() .Where(weight => Math.Abs(weight.Value) >= Epsilon) @@ -217,8 +217,7 @@ public static IEnumerable> GetSortedLinearModelFeat { int index = weight.Key; var name = names.GetItemOrDefault(index); - list.Add(new KeyValuePair( - DvText.Identical(name, DvText.Empty) ? $"f{index}" : name.ToString(), weight.Value)); + list.Add(new KeyValuePair(name.IsEmpty ? $"f{index}" : name.ToString(), weight.Value)); } return list; @@ -230,7 +229,7 @@ public static IEnumerable> GetSortedLinearModelFeat public static void SaveLinearModelWeightsInKeyValuePairs( ref VBuffer weights, Float bias, RoleMappedSchema schema, List> results) { - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, weights.Length, ref names); var pairs = GetSortedLinearModelFeatureNamesAndWeights(bias, ref weights, ref names); diff --git a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs index 47b08c586a..d4c265962e 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs @@ -141,7 +141,7 @@ protected override void ComputeTrainingStatistics(IChannel ch, FloatLabelCursor. var featureColIdx = cursorFactory.Data.Schema.Feature.Index; var schema = cursorFactory.Data.Data.Schema; var featureLength = CurrentWeights.Length - BiasCount; - var namesSpans = VBufferUtils.CreateEmpty(featureLength); + var namesSpans = VBufferUtils.CreateEmpty>(featureLength); if (schema.HasSlotNames(featureColIdx, featureLength)) schema.GetMetadata(MetadataUtils.Kinds.SlotNames, featureColIdx, ref namesSpans); Host.Assert(namesSpans.Length == featureLength); diff --git a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/MulticlassLogisticRegression.cs b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/MulticlassLogisticRegression.cs index ca850fe46e..2a90e6cb2a 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/MulticlassLogisticRegression.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/MulticlassLogisticRegression.cs @@ -98,7 +98,7 @@ protected override void CheckLabel(RoleMappedData data) return; } - VBuffer labelNames = default(VBuffer); + VBuffer> labelNames = default; schema.GetMetadata(MetadataUtils.Kinds.KeyValues, labelIdx, ref labelNames); // If label names is not dense or contain NA or default value, then it follows that @@ -113,14 +113,14 @@ protected override void CheckLabel(RoleMappedData data) } _labelNames = new string[_numClasses]; - DvText[] values = labelNames.Values; + ReadOnlyMemory[] values = labelNames.Values; // This hashset is used to verify the uniqueness of label names. HashSet labelNamesSet = new HashSet(); for (int i = 0; i < _numClasses; i++) { - DvText value = values[i]; - if (value.IsEmpty || value.IsNA) + ReadOnlyMemory value = values[i]; + if (value.IsEmpty) { _labelNames = null; break; @@ -304,7 +304,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010003, // Added model stats verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MulticlassLogisticRegressionPredictor).Assembly.FullName); } private const string ModelStatsSubModelFilename = "ModelStats"; @@ -754,7 +755,7 @@ public IList> GetSummaryInKeyValuePairs(RoleMappedS List> results = new List>(); - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, _numFeatures, ref names); for (int classNumber = 0; classNumber < _biases.Length; classNumber++) { @@ -776,7 +777,7 @@ public IList> GetSummaryInKeyValuePairs(RoleMappedS var name = names.GetItemOrDefault(index); results.Add(new KeyValuePair( - string.Format("{0}+{1}", GetLabelName(classNumber), DvText.Identical(name, DvText.Empty) ? $"f{index}" : name.ToString()), + string.Format("{0}+{1}", GetLabelName(classNumber), name.IsEmpty ? $"f{index}" : name.ToString()), value )); } @@ -927,8 +928,8 @@ public IDataView GetSummaryDataView(RoleMappedSchema schema) { var bldr = new ArrayDataViewBuilder(Host); - ValueGetter> getSlotNames = - (ref VBuffer dst) => + ValueGetter>> getSlotNames = + (ref VBuffer> dst) => MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, _numFeatures, ref dst); // Add the bias and the weight columns. @@ -949,7 +950,7 @@ public IRow GetStatsIRowOrNull(RoleMappedSchema schema) return null; var cols = new List(); - var names = default(VBuffer); + var names = default(VBuffer>); _stats.AddStatsColumns(cols, null, schema, ref names); return RowColumnUtils.GetRow(null, cols.ToArray()); } diff --git a/src/Microsoft.ML.StandardLearners/Standard/ModelStatistics.cs b/src/Microsoft.ML.StandardLearners/Standard/ModelStatistics.cs index 91874291b0..cc28c1caf1 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/ModelStatistics.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/ModelStatistics.cs @@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LinearModelStatistics).Assembly.FullName); } private readonly IHostEnvironment _env; @@ -225,8 +226,8 @@ public static bool TryGetBiasStatistics(LinearModelStatistics stats, Single bias return true; } - private static void GetUnorderedCoefficientStatistics(LinearModelStatistics stats, ref VBuffer weights, ref VBuffer names, - ref VBuffer estimate, ref VBuffer stdErr, ref VBuffer zScore, ref VBuffer pValue, out ValueGetter> getSlotNames) + private static void GetUnorderedCoefficientStatistics(LinearModelStatistics stats, ref VBuffer weights, ref VBuffer> names, + ref VBuffer estimate, ref VBuffer stdErr, ref VBuffer zScore, ref VBuffer pValue, out ValueGetter>> getSlotNames) { if (!stats._coeffStdError.HasValue) { @@ -270,17 +271,17 @@ private static void GetUnorderedCoefficientStatistics(LinearModelStatistics stat var slotNames = names; getSlotNames = - (ref VBuffer dst) => + (ref VBuffer> dst) => { var values = dst.Values; if (Utils.Size(values) < stats.ParametersCount - 1) - values = new DvText[stats.ParametersCount - 1]; + values = new ReadOnlyMemory[stats.ParametersCount - 1]; for (int i = 1; i < stats.ParametersCount; i++) { int wi = denseStdError ? i - 1 : stdErrorIndices[i] - 1; values[i - 1] = slotNames.GetItemOrDefault(wi); } - dst = new VBuffer(stats.ParametersCount - 1, values, dst.Indices); + dst = new VBuffer>(stats.ParametersCount - 1, values, dst.Indices); }; } @@ -296,7 +297,7 @@ private IEnumerable GetUnorderedCoefficientStatistics(Lin _env.Assert(_paramCount == 1 || weights != null); _env.Assert(_coeffStdError.Value.Length == weights.Count + 1); - var names = default(VBuffer); + var names = default(VBuffer>); MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, weights.Count, ref names); Single[] stdErrorValues = _coeffStdError.Value.Values; @@ -408,13 +409,13 @@ public void SaveSummaryInKeyValuePairs(LinearBinaryPredictor parent, } } - public void AddStatsColumns(List list, LinearBinaryPredictor parent, RoleMappedSchema schema, ref VBuffer names) + public void AddStatsColumns(List list, LinearBinaryPredictor parent, RoleMappedSchema schema, ref VBuffer> names) { _env.AssertValue(list); _env.AssertValueOrNull(parent); _env.AssertValue(schema); - DvInt8 count = _trainingExampleCount; + long count = _trainingExampleCount; list.Add(RowColumnUtils.GetColumn("Count of training examples", NumberType.I8, ref count)); var dev = _deviance; list.Add(RowColumnUtils.GetColumn("Residual Deviance", NumberType.R4, ref dev)); @@ -444,7 +445,7 @@ public void AddStatsColumns(List list, LinearBinaryPredictor parent, Ro var stdErr = default(VBuffer); var zScore = default(VBuffer); var pValue = default(VBuffer); - ValueGetter> getSlotNames; + ValueGetter>> getSlotNames; GetUnorderedCoefficientStatistics(parent.Statistics, ref weights, ref names, ref estimate, ref stdErr, ref zScore, ref pValue, out getSlotNames); var slotNamesCol = RowColumnUtils.GetColumn(MetadataUtils.Kinds.SlotNames, diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs index 00563b7fc6..ee100e9e59 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs @@ -15,10 +15,10 @@ namespace Microsoft.ML.Runtime.Learners { - using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; + using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; public abstract class MetaMulticlassTrainer : ITrainerEstimator, ITrainer - where TTransformer : IPredictionTransformer + where TTransformer : ISingleFeaturePredictionTransformer where TModel : IPredictor { public abstract class ArgumentsBase diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MultiClassNaiveBayesTrainer.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MultiClassNaiveBayesTrainer.cs index 8c96ee1e0b..0817db47a5 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MultiClassNaiveBayesTrainer.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MultiClassNaiveBayesTrainer.cs @@ -151,7 +151,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(MultiClassNaiveBayesPredictor).Assembly.FullName); } private readonly int[] _labelHistogram; diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs index 4f9416ecef..6229e9f70f 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs @@ -34,7 +34,7 @@ namespace Microsoft.ML.Runtime.Learners { using TScalarPredictor = IPredictorProducing; - using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; + using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; using TDistPredictor = IDistPredictorProducing; using CR = RoleMappedSchema.ColumnRole; @@ -111,7 +111,7 @@ protected override OvaPredictor TrainCore(IChannel ch, RoleMappedData data, int return OvaPredictor.Create(Host, _args.UseProbabilities, predictors); } - private IPredictionTransformer TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls) + private ISingleFeaturePredictionTransformer TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls) { var view = MapLabels(data, cls); @@ -214,7 +214,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(OvaPredictor).Assembly.FullName); } private const string SubPredictorFmt = "SubPredictor_{0:000}"; diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs index 9e7063cd70..da9bfc99b6 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs @@ -26,7 +26,7 @@ namespace Microsoft.ML.Runtime.Learners { using TDistPredictor = IDistPredictorProducing; - using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; + using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; using CR = RoleMappedSchema.ColumnRole; using TTransformer = MulticlassPredictionTransformer; @@ -73,7 +73,7 @@ public sealed class Arguments : ArgumentsBase /// Developers should instantiate by supplying the trainer argument directly to the constructor /// using the other public constructor. /// - public Pkpd(IHostEnvironment env, Arguments args) + internal Pkpd(IHostEnvironment env, Arguments args) : base(env, args, LoadNameValue) { } @@ -119,7 +119,7 @@ protected override PkpdPredictor TrainCore(IChannel ch, RoleMappedData data, int return new PkpdPredictor(Host, predModels); } - private IPredictionTransformer TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls1, int cls2) + private ISingleFeaturePredictionTransformer TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls1, int cls2) { // this should not be necessary when the legacy constructor doesn't exist, and the label column is not an optional parameter on the // MetaMulticlassTrainer constructor. @@ -225,7 +225,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(PkpdPredictor).Assembly.FullName); } private const string SubPredictorFmt = "SubPredictor_{0:000}"; diff --git a/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedLinear.cs b/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedLinear.cs index 402d227fad..70ee279a1c 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedLinear.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedLinear.cs @@ -54,7 +54,7 @@ public abstract class AveragedLinearArguments : OnlineLinearArguments } public abstract class AveragedLinearTrainer : OnlineLinearTrainer - where TTransformer : IPredictionTransformer + where TTransformer : ISingleFeaturePredictionTransformer where TModel : IPredictor { protected readonly new AveragedLinearArguments Args; diff --git a/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedPerceptron.cs b/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedPerceptron.cs index f371322dca..5101e009eb 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedPerceptron.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Online/AveragedPerceptron.cs @@ -95,9 +95,7 @@ protected override void CheckLabelCompatible(SchemaShape.Column labelCol) } private static SchemaShape.Column MakeLabelColumn(string labelColumn) - { - return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); - } + => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); protected override LinearBinaryPredictor CreatePredictor() { diff --git a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs index d7b7d4cf2f..15bd5da290 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs @@ -44,7 +44,7 @@ public abstract class OnlineLinearArguments : LearnerInputBaseWithLabel } public abstract class OnlineLinearTrainer : TrainerEstimatorBase - where TTransformer : IPredictionTransformer + where TTransformer : ISingleFeaturePredictionTransformer where TModel : IPredictor { protected readonly OnlineLinearArguments Args; diff --git a/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs b/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs index 4941764c35..59199b3549 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs @@ -70,7 +70,7 @@ protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSc }; } - public SdcaMultiClassTrainer(IHostEnvironment env, Arguments args) + internal SdcaMultiClassTrainer(IHostEnvironment env, Arguments args) : this(env, args, args.FeatureColumn, args.LabelColumn) { } diff --git a/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs b/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs index 4f6755e528..5148ca11ab 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs @@ -67,7 +67,7 @@ public SdcaRegressionTrainer(IHostEnvironment env, Arguments args, string featur }; } - public SdcaRegressionTrainer(IHostEnvironment env, Arguments args) + internal SdcaRegressionTrainer(IHostEnvironment env, Arguments args) : this(env, args, args.FeatureColumn, args.LabelColumn) { } diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index abfff554c9..804943ac0d 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -2,17 +2,16 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - -using System; +using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime; -using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Learners; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Training; -using Microsoft.ML.Runtime.Internal.Internallearn; +using System; +using System.Linq; [assembly: LoadableClass(RandomTrainer.Summary, typeof(RandomTrainer), typeof(RandomTrainer.Arguments), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, @@ -38,20 +37,16 @@ namespace Microsoft.ML.Runtime.Learners /// /// A trainer that trains a predictor that returns random values /// - public sealed class RandomTrainer : TrainerBase + + public sealed class RandomTrainer : TrainerBase, + ITrainerEstimator, RandomPredictor> { internal const string LoadNameValue = "RandomPredictor"; internal const string UserNameValue = "Random Predictor"; internal const string Summary = "A toy predictor that returns a random value."; - public class Arguments + public sealed class Arguments { - // Some sample arguments - [Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate", ShortName = "lr")] - public Float LearningRate = (Float)1.0; - - [Argument(ArgumentType.AtMostOnce, HelpText = "Some bool arg", ShortName = "boolarg")] - public bool BooleanArg = false; } public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; @@ -59,17 +54,52 @@ public class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; + /// + /// Initializes RandomTrainer object. + /// + public RandomTrainer(IHostEnvironment env) + : base(env, LoadNameValue) + { + } + public RandomTrainer(IHostEnvironment env, Arguments args) : base(env, LoadNameValue) { Host.CheckValue(args, nameof(args)); } + public BinaryPredictionTransformer Fit(IDataView input) + { + var cachedTrain = Info.WantCaching ? new CacheDataView(Host, input, prefetch: null) : input; + + RoleMappedData trainRoles = new RoleMappedData(cachedTrain); + var pred = Train(new TrainContext(trainRoles)); + return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, featureColumn: null); + } + public override RandomPredictor Train(TrainContext context) { Host.CheckValue(context, nameof(context)); return new RandomPredictor(Host, Host.Rand.Next()); } + + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + Host.CheckValue(inputSchema, nameof(inputSchema)); + + var outColumns = inputSchema.Columns.ToDictionary(x => x.Name); + + var newColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; + foreach (SchemaShape.Column column in newColumns) + outColumns[column.Name] = column; + + return new SchemaShape(outColumns.Values); + } } /// @@ -77,8 +107,8 @@ public override RandomPredictor Train(TrainContext context) /// uniform random probability and classification assignment. /// public sealed class RandomPredictor : - PredictorBase, - IDistPredictorProducing, + PredictorBase, + IDistPredictorProducing, IValueMapperDist, ICanSaveModel { @@ -90,13 +120,14 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RandomPredictor).Assembly.FullName); } // Keep all the serializable state here. private readonly int _seed; private readonly object _instanceLock; - private readonly Random _random; + private readonly IRandom _random; public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; public ColumnType InputType { get; } @@ -109,7 +140,7 @@ public RandomPredictor(IHostEnvironment env, int seed) _seed = seed; _instanceLock = new object(); - _random = new Random(_seed); + _random = RandomUtils.Create(_seed); InputType = new VectorType(NumberType.Float); } @@ -126,7 +157,9 @@ private RandomPredictor(IHostEnvironment env, ModelLoadContext ctx) _seed = ctx.Reader.ReadInt32(); _instanceLock = new object(); - _random = new Random(_seed); + _random = RandomUtils.Create(_seed); + + InputType = new VectorType(NumberType.Float); } public static RandomPredictor Create(IHostEnvironment env, ModelLoadContext ctx) @@ -154,24 +187,24 @@ protected override void SaveCore(ModelSaveContext ctx) public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); - ValueMapper, Float> del = Map; + ValueMapper, float> del = Map; return (ValueMapper)(Delegate)del; } public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); - Contracts.Check(typeof(TDist) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); + Contracts.Check(typeof(TDist) == typeof(float)); - ValueMapper, Float, Float> del = MapDist; + ValueMapper, float, float> del = MapDist; return (ValueMapper)(Delegate)del; } - private Float PredictCore() + private float PredictCore() { // Predict can be called from different threads. // Ensure your implementation is thread-safe @@ -183,20 +216,23 @@ private Float PredictCore() } } - private void Map(ref VBuffer src, ref Float dst) + private void Map(ref VBuffer src, ref float dst) { dst = PredictCore(); } - private void MapDist(ref VBuffer src, ref Float score, ref Float prob) + private void MapDist(ref VBuffer src, ref float score, ref float prob) { score = PredictCore(); prob = (score + 1) / 2; } } - // Learns the prior distribution for 0/1 class labels and just outputs that. - public sealed class PriorTrainer : TrainerBase + /// + /// Learns the prior distribution for 0/1 class labels and just outputs that. + /// + public sealed class PriorTrainer : TrainerBase, + ITrainerEstimator, PriorPredictor> { internal const string LoadNameValue = "PriorPredictor"; internal const string UserNameValue = "Prior Predictor"; @@ -205,6 +241,9 @@ public sealed class Arguments { } + private readonly String _labelColumnName; + private readonly String _weightColumnName; + public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); @@ -216,6 +255,28 @@ public PriorTrainer(IHostEnvironment env, Arguments args) Host.CheckValue(args, nameof(args)); } + /// + /// Initializes PriorTrainer object. + /// + public PriorTrainer(IHost host, String labelColumn, String weightColunn = null) + : base(host, LoadNameValue) + { + Contracts.CheckValue(labelColumn, nameof(labelColumn)); + Contracts.CheckValueOrNull(weightColunn); + + _labelColumnName = labelColumn; + _weightColumnName = weightColunn != null ? weightColunn : null; + } + + public BinaryPredictionTransformer Fit(IDataView input) + { + var cachedTrain = Info.WantCaching ? new CacheDataView(Host, input, prefetch: null) : input; + + RoleMappedData trainRoles = new RoleMappedData(cachedTrain, feature: null, label: _labelColumnName, weight: _weightColumnName); + var pred = Train(new TrainContext(trainRoles)); + return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, featureColumn: null); + } + public override PriorPredictor Train(TrainContext context) { Contracts.CheckValue(context, nameof(context)); @@ -234,16 +295,16 @@ public override PriorPredictor Train(TrainContext context) using (var cursor = data.Data.GetRowCursor(c => c == col || c == colWeight)) { var getLab = cursor.GetLabelFloatGetter(data); - var getWeight = colWeight >= 0 ? cursor.GetGetter(colWeight) : null; - Float lab = default(Float); - Float weight = 1; + var getWeight = colWeight >= 0 ? cursor.GetGetter(colWeight) : null; + float lab = default; + float weight = 1; while (cursor.MoveNext()) { getLab(ref lab); if (getWeight != null) { getWeight(ref weight); - if (!(0 < weight && weight < Float.PositiveInfinity)) + if (!(0 < weight && weight < float.PositiveInfinity)) continue; } @@ -255,14 +316,38 @@ public override PriorPredictor Train(TrainContext context) } } - Float prob = prob = pos + neg > 0 ? (Float)(pos / (pos + neg)) : Float.NaN; + float prob = prob = pos + neg > 0 ? (float)(pos / (pos + neg)) : float.NaN; return new PriorPredictor(Host, prob); } + + private static SchemaShape.Column MakeFeatureColumn(string featureColumn) + => new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); + + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + Host.CheckValue(inputSchema, nameof(inputSchema)); + + var outColumns = inputSchema.Columns.ToDictionary(x => x.Name); + + var newColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) + }; + foreach (SchemaShape.Column column in newColumns) + outColumns[column.Name] = column; + + return new SchemaShape(outColumns.Values); + } } public sealed class PriorPredictor : - PredictorBase, - IDistPredictorProducing, + PredictorBase, + IDistPredictorProducing, IValueMapperDist, ICanSaveModel { @@ -274,16 +359,17 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(PriorPredictor).Assembly.FullName); } - private readonly Float _prob; - private readonly Float _raw; + private readonly float _prob; + private readonly float _raw; - public PriorPredictor(IHostEnvironment env, Float prob) + public PriorPredictor(IHostEnvironment env, float prob) : base(env, LoaderSignature) { - Host.Check(!Float.IsNaN(prob)); + Host.Check(!float.IsNaN(prob)); _prob = prob; _raw = 2 * _prob - 1; // This could be other functions -- logodds for instance @@ -298,7 +384,7 @@ private PriorPredictor(IHostEnvironment env, ModelLoadContext ctx) // Float: _prob _prob = ctx.Reader.ReadFloat(); - Host.CheckDecode(!Float.IsNaN(_prob)); + Host.CheckDecode(!float.IsNaN(_prob)); _raw = 2 * _prob - 1; @@ -321,7 +407,7 @@ protected override void SaveCore(ModelSaveContext ctx) // *** Binary format *** // Float: _prob - Contracts.Assert(!Float.IsNaN(_prob)); + Contracts.Assert(!float.IsNaN(_prob)); ctx.Writer.Write(_prob); } @@ -333,29 +419,29 @@ public override PredictionKind PredictionKind public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); - ValueMapper, Float> del = Map; + ValueMapper, float> del = Map; return (ValueMapper)(Delegate)del; } public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); - Contracts.Check(typeof(TDist) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); + Contracts.Check(typeof(TDist) == typeof(float)); - ValueMapper, Float, Float> del = MapDist; + ValueMapper, float, float> del = MapDist; return (ValueMapper)(Delegate)del; } - private void Map(ref VBuffer src, ref Float dst) + private void Map(ref VBuffer src, ref float dst) { dst = _raw; } - private void MapDist(ref VBuffer src, ref Float score, ref Float prob) + private void MapDist(ref VBuffer src, ref float score, ref float prob) { score = _raw; prob = _prob; diff --git a/src/Microsoft.ML.StandardLearners/Standard/StochasticTrainerBase.cs b/src/Microsoft.ML.StandardLearners/Standard/StochasticTrainerBase.cs index 7fc70ce621..82e2223e46 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/StochasticTrainerBase.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/StochasticTrainerBase.cs @@ -11,7 +11,7 @@ namespace Microsoft.ML.Runtime.Learners { public abstract class StochasticTrainerBase : TrainerEstimatorBase - where TTransformer : IPredictionTransformer + where TTransformer : ISingleFeaturePredictionTransformer where TModel : IPredictor { public StochasticTrainerBase(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight = null) diff --git a/src/Microsoft.ML.Sweeper/Algorithms/SmacSweeper.cs b/src/Microsoft.ML.Sweeper/Algorithms/SmacSweeper.cs index b5614e3cbf..2aaa3e0d62 100644 --- a/src/Microsoft.ML.Sweeper/Algorithms/SmacSweeper.cs +++ b/src/Microsoft.ML.Sweeper/Algorithms/SmacSweeper.cs @@ -137,13 +137,13 @@ private FastForestRegressionPredictor FitModel(IEnumerable previousR using (IChannel ch = _host.Start("Single training")) { // Set relevant random forest arguments. - FastForestRegression.Arguments args = new FastForestRegression.Arguments(); - args.FeatureFraction = _args.SplitRatio; - args.NumTrees = _args.NumOfTrees; - args.MinDocumentsInLeafs = _args.NMinForSplit; - // Train random forest. - var trainer = new FastForestRegression(_host, args); + var trainer = new FastForestRegression(_host, DefaultColumnNames.Label, DefaultColumnNames.Features, advancedSettings: s => + { + s.FeatureFraction = _args.SplitRatio; + s.NumTrees = _args.NumOfTrees; + s.MinDocumentsInLeafs = _args.NMinForSplit; + }); var predictor = trainer.Train(data); // Return random forest predictor. diff --git a/src/Microsoft.ML.Sweeper/Parameters.cs b/src/Microsoft.ML.Sweeper/Parameters.cs index dd46374732..6f78bcf521 100644 --- a/src/Microsoft.ML.Sweeper/Parameters.cs +++ b/src/Microsoft.ML.Sweeper/Parameters.cs @@ -588,7 +588,7 @@ public bool TryParseParameter(string paramValue, Type paramType, string paramNam } if (option.StartsWith("steps")) { - numSteps = Int32.Parse(option.Substring(option.IndexOf(':') + 1)); + numSteps = int.Parse(option.Substring(option.IndexOf(':') + 1)); optionsSpecified[1] = true; } if (option.StartsWith("inc")) @@ -613,9 +613,9 @@ public bool TryParseParameter(string paramValue, Type paramType, string paramNam if (paramType == typeof(UInt16) || paramType == typeof(UInt32) || paramType == typeof(UInt64) - || paramType == typeof(Int16) - || paramType == typeof(Int32) - || paramType == typeof(Int64)) + || paramType == typeof(short) + || paramType == typeof(int) + || paramType == typeof(long)) { long min; long max; diff --git a/src/Microsoft.ML.Sweeper/SweepResultEvaluator.cs b/src/Microsoft.ML.Sweeper/SweepResultEvaluator.cs index ec80ce89b1..fded15ddaf 100644 --- a/src/Microsoft.ML.Sweeper/SweepResultEvaluator.cs +++ b/src/Microsoft.ML.Sweeper/SweepResultEvaluator.cs @@ -42,7 +42,7 @@ private string FindMetric(string userMetric, out bool maximizing) { StringBuilder sb = new StringBuilder(); - var evaluators = ComponentCatalog.GetAllDerivedClasses(typeof(IMamlEvaluator), typeof(SignatureMamlEvaluator)); + var evaluators = _host.ComponentCatalog.GetAllDerivedClasses(typeof(IMamlEvaluator), typeof(SignatureMamlEvaluator)); foreach (var evalInfo in evaluators) { var args = evalInfo.CreateArguments(); diff --git a/src/Microsoft.ML.TensorFlow/TensorFlow/Tensorflow.cs b/src/Microsoft.ML.TensorFlow/TensorFlow/Tensorflow.cs index 4fd4258794..e63e4f56c2 100644 --- a/src/Microsoft.ML.TensorFlow/TensorFlow/Tensorflow.cs +++ b/src/Microsoft.ML.TensorFlow/TensorFlow/Tensorflow.cs @@ -23,6 +23,7 @@ using size_t = System.UIntPtr; using System.Collections.Generic; +using System.Collections; #pragma warning disable MSML_GeneralName #pragma warning disable MSML_PrivateFieldName @@ -492,7 +493,7 @@ public void SetConfig(IntPtr protoData, int length, TFStatus status = null) /// "hot", and add a "sub" operation there the result will be "demo/hot/sub". /// /// - internal partial class TFGraph : TFDisposableThreadSafe + internal partial class TFGraph : TFDisposableThreadSafe, IEnumerable { // extern TF_Graph * TF_NewGraph (); [DllImport(NativeBinding.TensorFlowLibrary)] @@ -696,6 +697,33 @@ public override string ToString() IntPtr len; return TF_GraphDebugString(Handle, out len); } + + [DllImport(NativeBinding.TensorFlowLibrary)] + private static unsafe extern TF_Operation TF_GraphNextOperation(TF_Graph graph, ref IntPtr pos); + + /// + /// Returns the enumerator that returns all the TFOperations in a graph. + /// + /// The enumerator. + private IEnumerable GetEnumerable() + { + if (handle == IntPtr.Zero) + ObjectDisposedException(); + IntPtr token = IntPtr.Zero; + IntPtr operll; + while ((operll = TF_GraphNextOperation(handle, ref token)) != IntPtr.Zero) + yield return new TFOperation(this, operll); + } + + public IEnumerator GetEnumerator() + { + return GetEnumerable().GetEnumerator(); + } + + IEnumerator IEnumerable.GetEnumerator() + { + return GetEnumerator(); + } } /// @@ -736,6 +764,48 @@ public TFOutput this[int idx] return new TFOutput(this, idx); } } + + // extern TF_Output TF_OperationInput (TF_Input oper_in); + [DllImport(NativeBinding.TensorFlowLibrary)] + private static extern TFOutput TF_OperationInput(TFInput oper_in); + + public TFOutput GetInput(int idx) + { + return TF_OperationInput(new TFInput() { Operation = handle, Index = idx }); + } + + [DllImport(NativeBinding.TensorFlowLibrary)] + private static extern IntPtr TF_OperationName(TF_Operation oper); + + /// + /// The name for this operation/ + /// + /// The name. + public string Name => handle == IntPtr.Zero ? "" : TF_OperationName(handle).GetStr(); + + [DllImport(NativeBinding.TensorFlowLibrary)] + private static extern IntPtr TF_OperationOpType(TF_Operation oper); + + public string OpType => handle == IntPtr.Zero ? "" : TF_OperationOpType(handle).GetStr(); + + [DllImport(NativeBinding.TensorFlowLibrary)] + private static extern int TF_OperationNumOutputs(TF_Operation oper); + + /// + /// Gets the number of outputs on this operation. + /// + /// The number outputs. + public int NumOutputs => handle == IntPtr.Zero ? -1 : TF_OperationNumOutputs(handle); + + [DllImport(NativeBinding.TensorFlowLibrary)] + private static extern int TF_OperationNumInputs(TF_Operation oper); + + /// + /// Gets the number of inputs for this operation. + /// Import a serialized graph into this graph, using the specified importing options. + /// + /// The number inputs. + public int NumInputs => TF_OperationNumInputs(handle); } /// @@ -1768,15 +1838,6 @@ internal struct TFInput /// public int Index; - // extern TF_Output TF_OperationInput (TF_Input oper_in); - [DllImport(NativeBinding.TensorFlowLibrary)] - private static extern TFOutput TF_OperationInput(TFInput oper_in); - - public TFOutput GetOutput(TFInput operIn) - { - return TF_OperationInput(operIn); - } - // extern TF_DataType TF_OperationInputType (TF_Input oper_in); [DllImport(NativeBinding.TensorFlowLibrary)] private static extern TFDataType TF_OperationInputType(TFInput oper_in); diff --git a/src/Microsoft.ML.TensorFlow/TensorFlow/TensorflowUtils.cs b/src/Microsoft.ML.TensorFlow/TensorFlow/TensorflowUtils.cs index e77309c8b0..54030aec91 100644 --- a/src/Microsoft.ML.TensorFlow/TensorFlow/TensorflowUtils.cs +++ b/src/Microsoft.ML.TensorFlow/TensorFlow/TensorflowUtils.cs @@ -4,16 +4,21 @@ using System; using System.Collections.Generic; +using System.IO; using System.Linq; using System.Runtime.InteropServices; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.ImageAnalytics.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; namespace Microsoft.ML.Transforms.TensorFlow { public static class TensorFlowUtils { + public const string OpType = "OpType"; + public const string InputOps = "InputOps"; + // This method is needed for the Pipeline API, since ModuleCatalog does not load entry points that are located // in assemblies that aren't directly used in the code. Users who want to use TensorFlow components will have to call // TensorFlowUtils.Initialize() before creating the pipeline. @@ -25,7 +30,95 @@ public static void Initialize() ImageAnalytics.Initialize(); } + private static ISchema GetModelSchema(IExceptionContext ectx, TFGraph graph) + { + var res = new List>(); + var opTypeGetters = new List>>(); + var inputOpsGetters = new List>>>(); + var inputOpsLengths = new List(); + foreach (var op in graph) + { + var tfType = op[0].OutputType; + var mlType = Tf2MlNetTypeOrNull(tfType); + + // If the type is not supported in ML.NET then we cannot represent it as a column in an ISchema. + // We also cannot output it with a TensorFlowTransform, so we skip it. + if (mlType == null) + continue; + + var shape = graph.GetTensorShape(op[0]); + var shapeArray = shape.ToIntArray(); + + inputOpsLengths.Add(op.NumInputs); + MetadataUtils.MetadataGetter>> inputOpsGetter = null; + if (op.NumInputs > 0) + { + var inputOps = new ReadOnlyMemory[op.NumInputs]; + for (int i = 0; i < op.NumInputs; i++) + { + var input = op.GetInput(i); + inputOps[i] = new ReadOnlyMemory(input.Operation.Name.ToArray()); + } + inputOpsGetter = (int col, ref VBuffer> dst) => + dst = new VBuffer>(op.NumInputs, inputOps); + } + inputOpsGetters.Add(inputOpsGetter); + + var opType = op.OpType; + MetadataUtils.MetadataGetter> opTypeGetter = + (int col, ref ReadOnlyMemory dst) => dst = new ReadOnlyMemory(opType.ToArray()); + opTypeGetters.Add(opTypeGetter); + + var columnType = Utils.Size(shapeArray) == 1 && shapeArray[0] == -1 ? new VectorType(mlType) : + Utils.Size(shapeArray) > 0 && shapeArray.Skip(1).All(x => x > 0) ? + new VectorType(mlType, shapeArray[0] > 0 ? shapeArray : shapeArray.Skip(1).ToArray()) + : new VectorType(mlType); + res.Add(new KeyValuePair(op.Name, columnType)); + } + return new TensorFlowSchema(ectx, res.ToArray(), opTypeGetters.ToArray(), inputOpsGetters.ToArray(), inputOpsLengths.ToArray()); + } + + public static ISchema GetModelSchema(IExceptionContext ectx, string modelFile) + { + var bytes = File.ReadAllBytes(modelFile); + var session = LoadTFSession(ectx, bytes, modelFile); + return GetModelSchema(ectx, session.Graph); + } + + public static IEnumerable<(string, string, ColumnType, string[])> GetModelNodes(string modelFile) + { + var schema = GetModelSchema(null, modelFile); + + for (int i = 0; i < schema.ColumnCount; i++) + { + var name = schema.GetColumnName(i); + var type = schema.GetColumnType(i); + + var metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, i); + Contracts.Assert(metadataType != null && metadataType.IsText); + ReadOnlyMemory opType = default; + schema.GetMetadata(TensorFlowUtils.OpType, i, ref opType); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, i); + VBuffer> inputOps = default; + if (metadataType != null) + { + Contracts.Assert(metadataType.IsKnownSizeVector && metadataType.ItemType.IsText); + schema.GetMetadata(TensorFlowUtils.InputOps, i, ref inputOps); + } + yield return (name, opType.ToString(), type, + Utils.Size(inputOps.Values) > 0 ? inputOps.Values.Select(input => input.ToString()).ToArray() : new string[0]); + } + } + internal static PrimitiveType Tf2MlNetType(TFDataType type) + { + var mlNetType = Tf2MlNetTypeOrNull(type); + if (mlNetType == null) + throw new NotSupportedException("TensorFlow type not supported."); + return mlNetType; + } + + private static PrimitiveType Tf2MlNetTypeOrNull(TFDataType type) { switch (type) { @@ -42,10 +135,29 @@ internal static PrimitiveType Tf2MlNetType(TFDataType type) case TFDataType.UInt64: return NumberType.U8; default: - throw new NotSupportedException("TensorFlow type not supported."); + return null; } } + internal static TFSession LoadTFSession(IExceptionContext ectx, byte[] modelBytes, string modelFile = null) + { + var graph = new TFGraph(); + try + { + graph.Import(modelBytes, ""); + } + catch (Exception ex) + { + if (!string.IsNullOrEmpty(modelFile)) + throw ectx.Except($"TensorFlow exception triggered while loading model from '{modelFile}'"); +#pragma warning disable MSML_NoMessagesForLoadContext + throw ectx.ExceptDecode(ex, "Tensorflow exception triggered while loading model."); +#pragma warning restore MSML_NoMessagesForLoadContext + + } + return new TFSession(graph); + } + internal static unsafe void FetchData(IntPtr data, T[] result) { var size = result.Length; @@ -73,5 +185,55 @@ internal static bool IsTypeSupported(TFDataType tfoutput) return false; } } + + private sealed class TensorFlowSchema : SimpleSchemaBase + { + private readonly MetadataUtils.MetadataGetter>[] _opTypeGetters; + private readonly MetadataUtils.MetadataGetter>>[] _inputOpsGetters; + private readonly int[] _inputOpsLengths; + + public TensorFlowSchema(IExceptionContext ectx, KeyValuePair[] columns, + MetadataUtils.MetadataGetter>[] opTypeGetters, + MetadataUtils.MetadataGetter>>[] inputOpsGetters, int[] inputOpsLengths) + : base(ectx, columns) + { + ectx.CheckParam(Utils.Size(opTypeGetters) == ColumnCount, nameof(opTypeGetters)); + ectx.CheckParam(Utils.Size(inputOpsGetters) == ColumnCount, nameof(inputOpsGetters)); + ectx.CheckParam(Utils.Size(inputOpsLengths) == ColumnCount, nameof(inputOpsLengths)); + + _opTypeGetters = opTypeGetters; + _inputOpsGetters = inputOpsGetters; + _inputOpsLengths = inputOpsLengths; + } + + protected override void GetMetadataCore(string kind, int col, ref TValue value) + { + Ectx.Assert(0 <= col && col < ColumnCount); + if (kind == OpType) + _opTypeGetters[col].Marshal(col, ref value); + else if (kind == InputOps && _inputOpsGetters[col] != null) + _inputOpsGetters[col].Marshal(col, ref value); + else + throw Ectx.ExceptGetMetadata(); + } + + protected override ColumnType GetMetadataTypeOrNullCore(string kind, int col) + { + Ectx.Assert(0 <= col && col < ColumnCount); + if (kind == OpType) + return TextType.Instance; + if (kind == InputOps && _inputOpsGetters[col] != null) + return new VectorType(TextType.Instance, _inputOpsLengths[col]); + return null; + } + + protected override IEnumerable> GetMetadataTypesCore(int col) + { + Ectx.Assert(0 <= col && col < ColumnCount); + yield return new KeyValuePair(OpType, TextType.Instance); + if (_inputOpsGetters[col] != null) + yield return new KeyValuePair(InputOps, new VectorType(TextType.Instance, _inputOpsLengths[col])); + } + } } } diff --git a/src/Microsoft.ML.TensorFlow/TensorflowTransform.cs b/src/Microsoft.ML.TensorFlow/TensorflowTransform.cs index 578a9d5778..0afb03b7a2 100644 --- a/src/Microsoft.ML.TensorFlow/TensorflowTransform.cs +++ b/src/Microsoft.ML.TensorFlow/TensorflowTransform.cs @@ -76,7 +76,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Upgraded when change for multiple outputs was implemented. verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TensorFlowTransform).Assembly.FullName); } /// @@ -122,6 +123,7 @@ private static TensorFlowTransform Create(IHostEnvironment env, ModelLoadContext byte[] modelBytes = null; if (!ctx.TryLoadBinaryStream("TFModel", r => modelBytes = r.ReadByteArray())) throw env.ExceptDecode(); + var session = TensorFlowUtils.LoadTFSession(env, modelBytes); var numInputs = ctx.Reader.ReadInt32(); env.CheckDecode(numInputs > 0); string[] inputs = new string[numInputs]; @@ -138,7 +140,7 @@ private static TensorFlowTransform Create(IHostEnvironment env, ModelLoadContext for (int j = 0; j < outputs.Length; j++) outputs[j] = ctx.LoadNonEmptyString(); - return new TensorFlowTransform(env, modelBytes, inputs, outputs); + return new TensorFlowTransform(env, session, inputs, outputs); } // Factory method for SignatureDataTransform. @@ -160,27 +162,12 @@ private static IDataTransform Create(IHostEnvironment env, ModelLoadContext ctx, private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, ISchema inputSchema) => Create(env, ctx).MakeRowMapper(inputSchema); - private TFSession LoadTFSession(byte[] modelBytes) - { - var graph = new TFGraph(); - try - { - graph.Import(modelBytes, ""); - } - catch (Exception ex) - { -#pragma warning disable MSML_NoMessagesForLoadContext - throw _host.ExceptDecode(ex, "Tensorflow exception triggered while loading model."); -#pragma warning restore MSML_NoMessagesForLoadContext - } - return new TFSession(graph); - } - - private static byte[] CheckFileAndRead(IHostEnvironment env, string modelFile) + private static TFSession CheckFileAndRead(IHostEnvironment env, string modelFile) { env.CheckNonWhiteSpace(modelFile, nameof(modelFile)); env.CheckUserArg(File.Exists(modelFile), nameof(modelFile)); - return File.ReadAllBytes(modelFile); + var bytes = File.ReadAllBytes(modelFile); + return TensorFlowUtils.LoadTFSession(env, bytes, modelFile); } public TensorFlowTransform(IHostEnvironment env, string modelFile, string[] inputs, string[] outputs) : @@ -188,15 +175,14 @@ public TensorFlowTransform(IHostEnvironment env, string modelFile, string[] inpu { } - private TensorFlowTransform(IHostEnvironment env, byte[] modelBytes, string[] inputs, string[] outputs) + private TensorFlowTransform(IHostEnvironment env, TFSession session, string[] inputs, string[] outputs) { Contracts.CheckValue(env, nameof(env)); _host = env.Register(nameof(RegistrationName)); - _host.CheckValue(modelBytes, nameof(modelBytes)); + _host.CheckValue(session, nameof(session)); _host.CheckNonEmpty(inputs, nameof(inputs)); _host.CheckNonEmpty(outputs, nameof(outputs)); - - Session = LoadTFSession(modelBytes); + Session = session; foreach (var input in inputs) { _host.CheckNonWhiteSpace(input, nameof(inputs)); @@ -204,7 +190,7 @@ private TensorFlowTransform(IHostEnvironment env, byte[] modelBytes, string[] in throw _host.ExceptParam(nameof(inputs), $"Input column '{input}' does not exist in the model"); var tfInput = new TFOutput(Session.Graph[input]); if (!TensorFlowUtils.IsTypeSupported(tfInput.OutputType)) - throw _host.ExceptParam(nameof(modelBytes), $"Input type '{tfInput.OutputType}' of input column '{input}' is not supported in TensorFlow"); + throw _host.ExceptParam(nameof(session), $"Input type '{tfInput.OutputType}' of input column '{input}' is not supported in TensorFlow"); } var newNames = new HashSet(); diff --git a/src/Microsoft.ML.Transforms/BootstrapSampleTransform.cs b/src/Microsoft.ML.Transforms/BootstrapSampleTransform.cs index 91106bd445..dc4bde762c 100644 --- a/src/Microsoft.ML.Transforms/BootstrapSampleTransform.cs +++ b/src/Microsoft.ML.Transforms/BootstrapSampleTransform.cs @@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(BootstrapSampleTransform).Assembly.FullName); } internal const string RegistrationName = "BootstrapSample"; diff --git a/src/Microsoft.ML.Transforms/CompositeTransform.cs b/src/Microsoft.ML.Transforms/CompositeTransform.cs index 4dfeb3b312..48cdfe345f 100644 --- a/src/Microsoft.ML.Transforms/CompositeTransform.cs +++ b/src/Microsoft.ML.Transforms/CompositeTransform.cs @@ -30,7 +30,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CompositeTransform).Assembly.FullName); } public static IDataTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) diff --git a/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs b/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs index 7bf05df50c..a2485df673 100644 --- a/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs +++ b/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs @@ -150,7 +150,7 @@ public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, env.CheckValue(input, nameof(input)); var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input); - var view = new WordEmbeddingsTransform(h, input, input.Data); + var view = WordEmbeddingsTransform.Create(h, input, input.Data); return new CommonOutputs.TransformOutput() { Model = new TransformModel(h, view, input.Data), diff --git a/src/Microsoft.ML.Transforms/FourierDistributionSampler.cs b/src/Microsoft.ML.Transforms/FourierDistributionSampler.cs index fbb544da50..ae9a721bd9 100644 --- a/src/Microsoft.ML.Transforms/FourierDistributionSampler.cs +++ b/src/Microsoft.ML.Transforms/FourierDistributionSampler.cs @@ -55,7 +55,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(GaussianFourierSampler).Assembly.FullName); } public const string LoadName = "GaussianRandom"; @@ -130,7 +131,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LaplacianFourierSampler).Assembly.FullName); } public const string LoaderSignature = "RandLaplacianFourierExec"; diff --git a/src/Microsoft.ML.Transforms/GcnTransform.cs b/src/Microsoft.ML.Transforms/GcnTransform.cs index 617752b2c0..d8d1618a41 100644 --- a/src/Microsoft.ML.Transforms/GcnTransform.cs +++ b/src/Microsoft.ML.Transforms/GcnTransform.cs @@ -236,7 +236,8 @@ private static VersionInfo GetVersionInfo() verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, loaderSignature: LoaderSignature, - loaderSignatureAlt: LoaderSignatureOld); + loaderSignatureAlt: LoaderSignatureOld, + loaderAssemblyName: typeof(LpNormNormalizerTransform).Assembly.FullName); } private const string RegistrationName = "LpNormNormalizer"; @@ -413,7 +414,7 @@ private void SetMetadata() for (int iinfo = 0; iinfo < Infos.Length; iinfo++) { using (var bldr = md.BuildMetadata(iinfo, Source.Schema, Infos[iinfo].Source, MetadataUtils.Kinds.SlotNames)) - bldr.AddPrimitive(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, DvBool.True); + bldr.AddPrimitive(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, true); } md.Seal(); } diff --git a/src/Microsoft.ML.Transforms/GroupTransform.cs b/src/Microsoft.ML.Transforms/GroupTransform.cs index 8e07b11e06..a40a9ea827 100644 --- a/src/Microsoft.ML.Transforms/GroupTransform.cs +++ b/src/Microsoft.ML.Transforms/GroupTransform.cs @@ -65,7 +65,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(GroupTransform).Assembly.FullName); } // REVIEW: maybe we want to have an option to keep all non-group scalar columns, as opposed to @@ -426,7 +427,6 @@ private sealed class GroupKeyColumnChecker public readonly Func IsSameKey; private static Func MakeSameChecker(IRow row, int col) - where T : IEquatable { T oldValue = default(T); T newValue = default(T); @@ -436,7 +436,16 @@ private static Func MakeSameChecker(IRow row, int col) () => { getter(ref newValue); - bool result = first || oldValue.Equals(newValue); + bool result; + + if ((typeof(IEquatable).IsAssignableFrom(typeof(T)))) + result = oldValue.Equals(newValue); + else if ((typeof(ReadOnlyMemory).IsAssignableFrom(typeof(T)))) + result = ((ReadOnlyMemory)(object)oldValue).Span.SequenceEqual(((ReadOnlyMemory)(object)newValue).Span); + else + Contracts.Check(result = false, "Invalid type."); + + result = result || first; oldValue = newValue; first = false; return result; diff --git a/src/Microsoft.ML.Transforms/HashJoinTransform.cs b/src/Microsoft.ML.Transforms/HashJoinTransform.cs index 8120ffc078..bd3d007fa8 100644 --- a/src/Microsoft.ML.Transforms/HashJoinTransform.cs +++ b/src/Microsoft.ML.Transforms/HashJoinTransform.cs @@ -168,7 +168,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010005, // Hash fix verReadableCur: 0x00010005, verWeCanReadBack: 0x00010005, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(HashJoinTransform).Assembly.FullName); } private readonly ColumnInfoEx[] _exes; @@ -343,11 +344,11 @@ private ColumnInfoEx CreateColumnInfoEx(bool join, string customSlotMap, int has private int[][] CompileSlotMap(string slotMapString, int srcSlotCount) { - var parts = new DvText(slotMapString).Split(new[] { ';' }).ToArray(); + var parts = ReadOnlyMemoryUtils.Split(slotMapString.AsMemory(), new[] { ';' }).ToArray(); var slotMap = new int[parts.Length][]; for (int i = 0; i < slotMap.Length; i++) { - var slotIndices = parts[i].Split(new[] { ',' }).ToArray(); + var slotIndices = ReadOnlyMemoryUtils.Split(parts[i], new[] { ',' }).ToArray(); var slots = new int[slotIndices.Length]; slotMap[i] = slots; for (int j = 0; j < slots.Length; j++) @@ -397,14 +398,14 @@ private void SetMetadata() continue; using (var bldr = md.BuildMetadata(i)) { - bldr.AddGetter>(MetadataUtils.Kinds.SlotNames, + bldr.AddGetter>>(MetadataUtils.Kinds.SlotNames, new VectorType(TextType.Instance, ex.SlotMap.Length), GetSlotNames); } } md.Seal(); } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); @@ -413,11 +414,11 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) int n = _exes[iinfo].OutputValueCount; var output = dst.Values; if (Utils.Size(output) < n) - output = new DvText[n]; + output = new ReadOnlyMemory[n]; var srcColumnName = Source.Schema.GetColumnName(Infos[iinfo].Source); bool useDefaultSlotNames = !Source.Schema.HasSlotNames(Infos[iinfo].Source, Infos[iinfo].TypeSrc.VectorSize); - VBuffer srcSlotNames = default(VBuffer); + VBuffer> srcSlotNames = default; if (!useDefaultSlotNames) { Source.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, Infos[iinfo].Source, ref srcSlotNames); @@ -444,10 +445,10 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) outputSlotName.Append(srcSlotNames.Values[inputSlotIndex]); } - output[slot] = new DvText(outputSlotName.ToString()); + output[slot] = outputSlotName.ToString().AsMemory(); } - dst = new VBuffer(n, output, dst.Indices); + dst = new VBuffer>(n, output, dst.Indices); } private delegate uint HashDelegate(ref TSrc value, uint seed); diff --git a/src/Microsoft.ML.Transforms/KeyToBinaryVectorTransform.cs b/src/Microsoft.ML.Transforms/KeyToBinaryVectorTransform.cs index cde6ae9aa1..4f947415af 100644 --- a/src/Microsoft.ML.Transforms/KeyToBinaryVectorTransform.cs +++ b/src/Microsoft.ML.Transforms/KeyToBinaryVectorTransform.cs @@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00000001, // Initial verReadableCur: 0x00000001, verWeCanReadBack: 0x00000001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(KeyToBinaryVectorTransform).Assembly.FullName); } private const string RegistrationName = "KeyToBinary"; @@ -69,6 +70,7 @@ private static (string input, string output)[] GetColumnPairs(ColumnInfo[] colum Contracts.CheckValue(columns, nameof(columns)); return columns.Select(x => (x.Input, x.Output)).ToArray(); } + public IReadOnlyCollection Columns => _columns.AsReadOnly(); private readonly ColumnInfo[] _columns; @@ -209,7 +211,7 @@ private ColInfo[] CreateInfos(ISchema inputSchema) if (!inputSchema.TryGetColumnIndex(_parent.ColumnPairs[i].input, out int colSrc)) throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.ColumnPairs[i].input); var type = inputSchema.GetColumnType(colSrc); - + _parent.CheckInputColumn(inputSchema, i, colSrc); infos[i] = new ColInfo(_parent.ColumnPairs[i].output, _parent.ColumnPairs[i].input, type); } return infos; @@ -246,46 +248,46 @@ private void AddMetadata(int i, ColumnMetadataInfo colMetaInfo) { if (typeNames != null) { - MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer dst) => + MetadataUtils.MetadataGetter>> getter = (int col, ref VBuffer> dst) => { GenerateBitSlotName(i, ref dst); }; - var info = new MetadataInfo>(new VectorType(TextType.Instance, _types[i]), getter); + var info = new MetadataInfo>>(new VectorType(TextType.Instance, _types[i]), getter); colMetaInfo.Add(MetadataUtils.Kinds.SlotNames, info); } - MetadataUtils.MetadataGetter normalizeGetter = (int col, ref DvBool dst) => + MetadataUtils.MetadataGetter normalizeGetter = (int col, ref bool dst) => { dst = true; }; - var normalizeInfo = new MetadataInfo(BoolType.Instance, normalizeGetter); + var normalizeInfo = new MetadataInfo(BoolType.Instance, normalizeGetter); colMetaInfo.Add(MetadataUtils.Kinds.IsNormalized, normalizeInfo); } else { if (typeNames != null && _types[i].IsKnownSizeVector) { - MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer dst) => + MetadataUtils.MetadataGetter>> getter = (int col, ref VBuffer> dst) => { GetSlotNames(i, ref dst); }; - var info = new MetadataInfo>(new VectorType(TextType.Instance, _types[i]), getter); + var info = new MetadataInfo>>(new VectorType(TextType.Instance, _types[i]), getter); colMetaInfo.Add(MetadataUtils.Kinds.SlotNames, info); } } } - private void GenerateBitSlotName(int iinfo, ref VBuffer dst) + private void GenerateBitSlotName(int iinfo, ref VBuffer> dst) { const string slotNamePrefix = "Bit"; - var bldr = new BufferBuilder(TextCombiner.Instance); + var bldr = new BufferBuilder>(TextCombiner.Instance); bldr.Reset(_bitsPerKey[iinfo], true); for (int i = 0; i < _bitsPerKey[iinfo]; i++) - bldr.AddFeature(i, new DvText(slotNamePrefix + (_bitsPerKey[iinfo] - i - 1))); + bldr.AddFeature(i, (slotNamePrefix + (_bitsPerKey[iinfo] - i - 1)).AsMemory()); bldr.GetResult(ref dst); } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < _infos.Length); Host.Assert(_types[iinfo].IsKnownSizeVector); @@ -295,7 +297,7 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) Host.Assert(typeSrc.VectorSize > 1); // Get the source slot names, defaulting to empty text. - var namesSlotSrc = default(VBuffer); + var namesSlotSrc = default(VBuffer>); InputSchema.TryGetColumnIndex(_infos[iinfo].Source, out int srcCol); Host.Assert(srcCol >= 0); var typeSlotSrc = InputSchema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, srcCol); @@ -305,25 +307,25 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) Host.Check(namesSlotSrc.Length == typeSrc.VectorSize); } else - namesSlotSrc = VBufferUtils.CreateEmpty(typeSrc.VectorSize); + namesSlotSrc = VBufferUtils.CreateEmpty>(typeSrc.VectorSize); int slotLim = _types[iinfo].VectorSize; Host.Assert(slotLim == (long)typeSrc.VectorSize * _bitsPerKey[iinfo]); var values = dst.Values; if (Utils.Size(values) < slotLim) - values = new DvText[slotLim]; + values = new ReadOnlyMemory[slotLim]; var sb = new StringBuilder(); int slot = 0; - VBuffer bits = default; + VBuffer> bits = default; GenerateBitSlotName(iinfo, ref bits); foreach (var kvpSlot in namesSlotSrc.Items(all: true)) { Contracts.Assert(slot == (long)kvpSlot.Key * _bitsPerKey[iinfo]); sb.Clear(); - if (kvpSlot.Value.HasChars) - kvpSlot.Value.AddToStringBuilder(sb); + if (!kvpSlot.Value.IsEmpty) + sb.AppendMemory(kvpSlot.Value); else sb.Append('[').Append(kvpSlot.Key).Append(']'); sb.Append('.'); @@ -332,13 +334,13 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) foreach (var key in bits.Values) { sb.Length = len; - key.AddToStringBuilder(sb); - values[slot++] = new DvText(sb.ToString()); + sb.AppendMemory(key); + values[slot++] = sb.ToString().AsMemory(); } } Host.Assert(slot == slotLim); - dst = new VBuffer(slotLim, values, dst.Indices); + dst = new VBuffer>(slotLim, values, dst.Indices); } protected override Delegate MakeGetter(IRow input, int iinfo, out Action disposer) diff --git a/src/Microsoft.ML.Transforms/MissingValueIndicatorTransform.cs b/src/Microsoft.ML.Transforms/MissingValueIndicatorTransform.cs index 7020bc0830..41412e6e26 100644 --- a/src/Microsoft.ML.Transforms/MissingValueIndicatorTransform.cs +++ b/src/Microsoft.ML.Transforms/MissingValueIndicatorTransform.cs @@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo() loaderSignature: LoaderSignature, // This is an older name and can be removed once we don't care about old code // being able to load this. - loaderSignatureAlt: "MissingFeatureFunction"); + loaderSignatureAlt: "MissingFeatureFunction", + loaderAssemblyName: typeof(MissingValueIndicatorTransform).Assembly.FullName); } private const string RegistrationName = "MissingIndicator"; @@ -159,7 +160,7 @@ private VectorType[] GetTypesAndMetadata() // Add slot names metadata. using (var bldr = md.BuildMetadata(iinfo)) { - bldr.AddGetter>(MetadataUtils.Kinds.SlotNames, + bldr.AddGetter>>(MetadataUtils.Kinds.SlotNames, MetadataUtils.GetNamesType(types[iinfo].VectorSize), GetSlotNames); } } @@ -173,7 +174,7 @@ protected override ColumnType GetColumnTypeCore(int iinfo) return _types[iinfo]; } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); @@ -183,15 +184,15 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) var values = dst.Values; if (Utils.Size(values) < size) - values = new DvText[size]; + values = new ReadOnlyMemory[size]; var type = Infos[iinfo].TypeSrc; if (!type.IsVector) { Host.Assert(_types[iinfo].VectorSize == 2); var columnName = Source.Schema.GetColumnName(Infos[iinfo].Source); - values[0] = new DvText(columnName); - values[1] = new DvText(columnName + IndicatorSuffix); + values[0] = columnName.AsMemory(); + values[1] = (columnName + IndicatorSuffix).AsMemory(); } else { @@ -203,7 +204,7 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) if (typeNames == null || typeNames.VectorSize != type.VectorSize || !typeNames.ItemType.IsText) throw MetadataUtils.ExceptGetMetadata(); - var names = default(VBuffer); + var names = default(VBuffer>); Source.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, Infos[iinfo].Source, ref names); // We both assert and check. If this fails, there is a bug somewhere (possibly in this code @@ -219,22 +220,22 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) Host.Assert(slot % 2 == 0); sb.Clear(); - if (!kvp.Value.HasChars) + if (kvp.Value.IsEmpty) sb.Append('[').Append(slot / 2).Append(']'); else - kvp.Value.AddToStringBuilder(sb); + sb.AppendMemory(kvp.Value); int len = sb.Length; sb.Append(IndicatorSuffix); var str = sb.ToString(); - values[slot++] = new DvText(str, 0, len); - values[slot++] = new DvText(str); + values[slot++] = str.AsMemory().Slice(0, len); + values[slot++] = str.AsMemory(); } Host.Assert(slot == size); } - dst = new VBuffer(size, values, dst.Indices); + dst = new VBuffer>(size, values, dst.Indices); } protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) diff --git a/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs b/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs index 0af833a046..28bfc8d511 100644 --- a/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs +++ b/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs @@ -300,7 +300,7 @@ private sealed class Impl private int[] _featureSums; private readonly List _singles; private readonly List _doubles; - private ValueMapper, VBuffer> _boolMapper; + private ValueMapper, VBuffer> _boolMapper; public Impl(IHost host) { @@ -407,7 +407,7 @@ private void GetLabels(Transposer trans, ColumnType labelType, int labelCol) // Note: NAs have their own separate bin. if (labelType == NumberType.I4) { - var tmp = default(VBuffer); + var tmp = default(VBuffer); trans.GetSingleSlotValue(labelCol, ref tmp); BinInts(ref tmp, ref labels, _numBins, out min, out lim); _numLabels = lim - min; @@ -428,7 +428,7 @@ private void GetLabels(Transposer trans, ColumnType labelType, int labelCol) } else if (labelType.IsBool) { - var tmp = default(VBuffer); + var tmp = default(VBuffer); trans.GetSingleSlotValue(labelCol, ref tmp); BinBools(ref tmp, ref labels); _numLabels = 3; @@ -486,7 +486,7 @@ private Single[] ComputeMutualInformation(Transposer trans, int col) if (type.ItemType == NumberType.I4) { return ComputeMutualInformation(trans, col, - (ref VBuffer src, ref VBuffer dst, out int min, out int lim) => + (ref VBuffer src, ref VBuffer dst, out int min, out int lim) => { BinInts(ref src, ref dst, _numBins, out min, out lim); }); @@ -510,7 +510,7 @@ private Single[] ComputeMutualInformation(Transposer trans, int col) if (type.ItemType.IsBool) { return ComputeMutualInformation(trans, col, - (ref VBuffer src, ref VBuffer dst, out int min, out int lim) => + (ref VBuffer src, ref VBuffer dst, out int min, out int lim) => { min = -1; lim = 2; @@ -674,29 +674,20 @@ private static ValueMapper, VBuffer> BinKeys(ColumnType colTy } /// - /// Maps from DvInt4 to ints. NaNs (and only NaNs) are mapped to the first bin. + /// Maps Ints. /// - private void BinInts(ref VBuffer input, ref VBuffer output, + private void BinInts(ref VBuffer input, ref VBuffer output, int numBins, out int min, out int lim) { Contracts.Assert(_singles.Count == 0); - if (input.Values != null) - { - for (int i = 0; i < input.Count; i++) - { - var val = input.Values[i]; - if (!val.IsNA) - _singles.Add((Single)val); - } - } var bounds = _binFinder.FindBins(numBins, _singles, input.Length - input.Count); min = -1 - bounds.FindIndexSorted(0); lim = min + bounds.Length + 1; int offset = min; - ValueMapper mapper = - (ref DvInt4 src, ref int dst) => - dst = src.IsNA ? offset : offset + 1 + bounds.FindIndexSorted((Single)src); + ValueMapper mapper = + (ref int src, ref int dst) => + dst = offset + 1 + bounds.FindIndexSorted((Single)src); mapper.MapVector(ref input, ref output); _singles.Clear(); } @@ -756,16 +747,16 @@ private void BinDoubles(ref VBuffer input, ref VBuffer output, _doubles.Clear(); } - private void BinBools(ref VBuffer input, ref VBuffer output) + private void BinBools(ref VBuffer input, ref VBuffer output) { if (_boolMapper == null) - _boolMapper = CreateVectorMapper(BinOneBool); + _boolMapper = CreateVectorMapper(BinOneBool); _boolMapper(ref input, ref output); } - private void BinOneBool(ref DvBool src, ref int dst) + private void BinOneBool(ref bool src, ref int dst) { - dst = src.IsNA ? -1 : src.IsFalse ? 0 : 1; + dst = Convert.ToInt32(src); } } diff --git a/src/Microsoft.ML.Transforms/NADropTransform.cs b/src/Microsoft.ML.Transforms/NADropTransform.cs index 80e88f3ae3..6d67fa5efe 100644 --- a/src/Microsoft.ML.Transforms/NADropTransform.cs +++ b/src/Microsoft.ML.Transforms/NADropTransform.cs @@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(NADropTransform).Assembly.FullName); } private const string RegistrationName = "DropNAs"; @@ -100,7 +101,7 @@ private Delegate[] InitIsNAAndMetadata() MetadataUtils.Kinds.IsNormalized, MetadataUtils.Kinds.KeyValues)) { // Output does not have missings. - bldr.AddPrimitive(MetadataUtils.Kinds.HasMissingValues, BoolType.Instance, DvBool.False); + bldr.AddPrimitive(MetadataUtils.Kinds.HasMissingValues, BoolType.Instance, false); } } md.Seal(); diff --git a/src/Microsoft.ML.Transforms/NAHandleTransform.cs b/src/Microsoft.ML.Transforms/NAHandleTransform.cs index bb31510252..bf3ab5f41a 100644 --- a/src/Microsoft.ML.Transforms/NAHandleTransform.cs +++ b/src/Microsoft.ML.Transforms/NAHandleTransform.cs @@ -20,28 +20,28 @@ namespace Microsoft.ML.Runtime.Data /// public static class NAHandleTransform { - public enum ReplacementKind + public enum ReplacementKind : byte { /// /// Replace with the default value of the column based on it's type. For example, 'zero' for numeric and 'empty' for string/text columns. /// [EnumValueDisplay("Zero/empty")] - DefaultValue, + DefaultValue = 0, /// /// Replace with the mean value of the column. Supports only numeric/time span/ DateTime columns. /// - Mean, + Mean = 1, /// /// Replace with the minimum value of the column. Supports only numeric/time span/ DateTime columns. /// - Minimum, + Minimum = 2, /// /// Replace with the maximum value of the column. Supports only numeric/time span/ DateTime columns. /// - Maximum, + Maximum = 3, [HideEnumValue] Def = DefaultValue, @@ -135,7 +135,7 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV h.CheckValue(input, nameof(input)); h.CheckUserArg(Utils.Size(args.Column) > 0, nameof(args.Column)); - var replaceCols = new List(); + var replaceCols = new List(); var naIndicatorCols = new List(); var naConvCols = new List(); var concatCols = new List(); @@ -149,26 +149,16 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV var addInd = column.ConcatIndicator ?? args.Concat; if (!addInd) { - replaceCols.Add( - new NAReplaceTransform.Column() - { - Kind = (NAReplaceTransform.ReplacementKind?)column.Kind, - Name = column.Name, - Source = column.Source, - Slot = column.ImputeBySlot - }); + replaceCols.Add(new NAReplaceTransform.ColumnInfo(column.Source, column.Name, (NAReplaceTransform.ColumnInfo.ReplacementMode)(column.Kind ?? args.ReplaceWith), column.ImputeBySlot ?? args.ImputeBySlot)); continue; } // Check that the indicator column has a type that can be converted to the NAReplaceTransform output type, // so that they can be concatenated. - int inputCol; - if (!input.Schema.TryGetColumnIndex(column.Source, out inputCol)) + if (!input.Schema.TryGetColumnIndex(column.Source, out int inputCol)) throw h.Except("Column '{0}' does not exist", column.Source); var replaceType = input.Schema.GetColumnType(inputCol); - Delegate conv; - bool identity; - if (!Conversions.Instance.TryGetStandardConversion(BoolType.Instance, replaceType.ItemType, out conv, out identity)) + if (!Conversions.Instance.TryGetStandardConversion(BoolType.Instance, replaceType.ItemType, out Delegate conv, out bool identity)) { throw h.Except("Cannot concatenate indicator column of type '{0}' to input column of type '{1}'", BoolType.Instance, replaceType.ItemType); @@ -186,14 +176,7 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV naConvCols.Add(new ConvertTransform.Column() { Name = tmpIsMissingColName, Source = tmpIsMissingColName, ResultType = replaceType.ItemType.RawKind }); // Add the NAReplaceTransform column. - replaceCols.Add( - new NAReplaceTransform.Column() - { - Kind = (NAReplaceTransform.ReplacementKind?)column.Kind, - Name = tmpReplacementColName, - Source = column.Source, - Slot = column.ImputeBySlot - }); + replaceCols.Add(new NAReplaceTransform.ColumnInfo(column.Source, tmpReplacementColName, (NAReplaceTransform.ColumnInfo.ReplacementMode)(column.Kind ?? args.ReplaceWith), column.ImputeBySlot ?? args.ImputeBySlot)); // Add the ConcatTransform column. if (replaceType.IsVector) @@ -237,15 +220,8 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV h.AssertValue(output); output = new ConvertTransform(h, new ConvertTransform.Arguments() { Column = naConvCols.ToArray() }, output); } - // Create the NAReplace transform. - output = new NAReplaceTransform(h, - new NAReplaceTransform.Arguments() - { - Column = replaceCols.ToArray(), - ReplacementKind = (NAReplaceTransform.ReplacementKind)args.ReplaceWith, - ImputeBySlot = args.ImputeBySlot - }, output ?? input); + output = NAReplaceTransform.Create(env, output ?? input, replaceCols.ToArray()); // Concat the NAReplaceTransform output and the NAIndicatorTransform output. if (naIndicatorCols.Count > 0) diff --git a/src/Microsoft.ML.Transforms/NAHandling.cs b/src/Microsoft.ML.Transforms/NAHandling.cs index 0870d16461..8f8fdaabb9 100644 --- a/src/Microsoft.ML.Transforms/NAHandling.cs +++ b/src/Microsoft.ML.Transforms/NAHandling.cs @@ -88,7 +88,7 @@ public static CommonOutputs.TransformOutput Indicator(IHostEnvironment env, NAIn public static CommonOutputs.TransformOutput Replace(IHostEnvironment env, NAReplaceTransform.Arguments input) { var h = EntryPointUtils.CheckArgsAndCreateHost(env, "NAReplace", input); - var xf = new NAReplaceTransform(h, input, input.Data); + var xf = NAReplaceTransform.Create(h, input, input.Data); return new CommonOutputs.TransformOutput() { Model = new TransformModel(h, xf, input.Data), diff --git a/src/Microsoft.ML.Transforms/NAIndicatorTransform.cs b/src/Microsoft.ML.Transforms/NAIndicatorTransform.cs index 7607e19c61..044751f459 100644 --- a/src/Microsoft.ML.Transforms/NAIndicatorTransform.cs +++ b/src/Microsoft.ML.Transforms/NAIndicatorTransform.cs @@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(NAIndicatorTransform).Assembly.FullName); } internal const string Summary = "Create a boolean output column with the same number of slots as the input column, where the output value" @@ -156,7 +157,7 @@ private ColumnType[] GetTypesAndMetadata() using (var bldr = md.BuildMetadata(iinfo, Source.Schema, Infos[iinfo].Source, MetadataUtils.Kinds.SlotNames)) { // Output is normalized. - bldr.AddPrimitive(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, DvBool.True); + bldr.AddPrimitive(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, true); } } md.Seal(); @@ -184,41 +185,41 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou /// /// Getter generator for single valued inputs. /// - private ValueGetter ComposeGetterOne(IRow input, int iinfo) + private ValueGetter ComposeGetterOne(IRow input, int iinfo) { - Func> func = ComposeGetterOne; + Func> func = ComposeGetterOne; return Utils.MarshalInvoke(func, Infos[iinfo].TypeSrc.RawType, input, iinfo); } /// /// Tests if a value is NA for scalars. /// - private ValueGetter ComposeGetterOne(IRow input, int iinfo) + private ValueGetter ComposeGetterOne(IRow input, int iinfo) { var getSrc = GetSrcGetter(input, iinfo); var isNA = Conversions.Instance.GetIsNAPredicate(input.Schema.GetColumnType(Infos[iinfo].Source)); T src = default(T); return - (ref DvBool dst) => + (ref bool dst) => { getSrc(ref src); - dst = isNA(ref src) ? DvBool.True : DvBool.False; + dst = isNA(ref src); }; } /// /// Getter generator for vector valued inputs. /// - private ValueGetter> ComposeGetterVec(IRow input, int iinfo) + private ValueGetter> ComposeGetterVec(IRow input, int iinfo) { - Func>> func = ComposeGetterVec; + Func>> func = ComposeGetterVec; return Utils.MarshalInvoke(func, Infos[iinfo].TypeSrc.ItemType.RawType, input, iinfo); } /// /// Tests if a value is NA for vectors. /// - private ValueGetter> ComposeGetterVec(IRow input, int iinfo) + private ValueGetter> ComposeGetterVec(IRow input, int iinfo) { var getSrc = GetSrcGetter>(input, iinfo); var isNA = Conversions.Instance.GetIsNAPredicate(input.Schema.GetColumnType(Infos[iinfo].Source).ItemType); @@ -227,7 +228,7 @@ private ValueGetter> ComposeGetterVec(IRow input, int iinfo) var src = default(VBuffer); var indices = new List(); return - (ref VBuffer dst) => + (ref VBuffer dst) => { // Sense indicates if the values added to the indices list represent NAs or non-NAs. bool sense; @@ -285,7 +286,7 @@ private void FindNAs(ref VBuffer src, RefPredicate isNA, bool defaultIs /// Fills indicator values for vectors. The indices is a list that either holds all of the NAs or all /// of the non-NAs, indicated by sense being true or false respectively. /// - private void FillValues(int srcLength, ref VBuffer dst, List indices, bool sense) + private void FillValues(int srcLength, ref VBuffer dst, List indices, bool sense) { var dstValues = dst.Values; var dstIndices = dst.Indices; @@ -295,15 +296,15 @@ private void FillValues(int srcLength, ref VBuffer dst, List indice if (sense) { // Return empty VBuffer. - dst = new VBuffer(srcLength, 0, dstValues, dstIndices); + dst = new VBuffer(srcLength, 0, dstValues, dstIndices); return; } // Return VBuffer filled with 1's. Utils.EnsureSize(ref dstValues, srcLength, false); for (int i = 0; i < srcLength; i++) - dstValues[i] = DvBool.True; - dst = new VBuffer(srcLength, dstValues, dstIndices); + dstValues[i] = true; + dst = new VBuffer(srcLength, dstValues, dstIndices); return; } @@ -316,10 +317,10 @@ private void FillValues(int srcLength, ref VBuffer dst, List indice indices.CopyTo(dstIndices); for (int ii = 0; ii < dstCount; ii++) - dstValues[ii] = DvBool.True; + dstValues[ii] = true; Host.Assert(dstCount <= srcLength); - dst = new VBuffer(srcLength, dstCount, dstValues, dstIndices); + dst = new VBuffer(srcLength, dstCount, dstValues, dstIndices); } else if (!sense && srcLength - indices.Count < srcLength / 2) { @@ -342,7 +343,7 @@ private void FillValues(int srcLength, ref VBuffer dst, List indice if (i < iNext) { Host.Assert(iiDst < dstCount); - dstValues[iiDst] = DvBool.True; + dstValues[iiDst] = true; dstIndices[iiDst++] = i; } else @@ -355,7 +356,7 @@ private void FillValues(int srcLength, ref VBuffer dst, List indice Host.Assert(srcLength == iiSrc + iiDst); Host.Assert(iiDst == dstCount); - dst = new VBuffer(srcLength, dstCount, dstValues, dstIndices); + dst = new VBuffer(srcLength, dstCount, dstValues, dstIndices); } else { @@ -367,24 +368,21 @@ private void FillValues(int srcLength, ref VBuffer dst, List indice indices.Add(srcLength); int ii = 0; - // Assigns values correctly depending on the sense. - DvBool hit = sense ? DvBool.True : DvBool.False; - DvBool miss = sense ? DvBool.False : DvBool.True; for (int i = 0; i < srcLength; i++) { Host.Assert(0 <= i && i <= indices[ii]); if (i == indices[ii]) { - dstValues[i] = hit; + dstValues[i] = sense; ii++; Host.Assert(ii < indices.Count); Host.Assert(indices[ii - 1] < indices[ii]); } else - dstValues[i] = miss; + dstValues[i] = !sense; } - dst = new VBuffer(srcLength, dstValues, dstIndices); + dst = new VBuffer(srcLength, dstValues, dstIndices); } } } diff --git a/src/Microsoft.ML.Transforms/NAReplaceTransform.cs b/src/Microsoft.ML.Transforms/NAReplaceTransform.cs index c9b309af89..16ebdcae6a 100644 --- a/src/Microsoft.ML.Transforms/NAReplaceTransform.cs +++ b/src/Microsoft.ML.Transforms/NAReplaceTransform.cs @@ -2,29 +2,37 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using System; -using System.Collections; -using System.Collections.Generic; -using System.Reflection; -using System.Text; -using System.IO; -using System.Linq; +using Microsoft.ML.Core.Data; +using Microsoft.ML.Data.StaticPipe.Runtime; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; -using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.Data.Conversion; +using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Model.Onnx; +using System; +using System.Collections; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Reflection; +using System.Text; + +[assembly: LoadableClass(NAReplaceTransform.Summary, typeof(IDataTransform), typeof(NAReplaceTransform), typeof(NAReplaceTransform.Arguments), typeof(SignatureDataTransform), + NAReplaceTransform.FriendlyName, NAReplaceTransform.LoadName, "NAReplace", NAReplaceTransform.ShortName, DocName = "transform/NAHandle.md")] -[assembly: LoadableClass(typeof(NAReplaceTransform), typeof(NAReplaceTransform.Arguments), typeof(SignatureDataTransform), - NAReplaceTransform.FriendlyName, NAReplaceTransform.LoadName, "NAReplace", NAReplaceTransform.ShortName, DocName = "transform/NAHandle.md")] +[assembly: LoadableClass(NAReplaceTransform.Summary, typeof(IDataTransform), typeof(NAReplaceTransform), null, typeof(SignatureLoadDataTransform), + NAReplaceTransform.FriendlyName, NAReplaceTransform.LoadName)] -[assembly: LoadableClass(typeof(NAReplaceTransform), null, typeof(SignatureLoadDataTransform), +[assembly: LoadableClass(NAReplaceTransform.Summary, typeof(NAReplaceTransform), null, typeof(SignatureLoadModel), NAReplaceTransform.FriendlyName, NAReplaceTransform.LoadName)] +[assembly: LoadableClass(typeof(IRowMapper), typeof(NAReplaceTransform), null, typeof(SignatureLoadRowMapper), + NAReplaceTransform.FriendlyName, NAReplaceTransform.LoadName)] + namespace Microsoft.ML.Runtime.Data { // This transform can transform either scalars or vectors (both fixed and variable size), @@ -33,16 +41,16 @@ namespace Microsoft.ML.Runtime.Data // Imputation modes are supported for vectors both by slot and across all slots. // REVIEW: May make sense to implement the transform template interface. /// - public sealed partial class NAReplaceTransform : OneToOneTransformBase + public sealed partial class NAReplaceTransform : OneToOneTransformerBase { - public enum ReplacementKind + public enum ReplacementKind : byte { // REVIEW: What should the full list of options for this transform be? - DefaultValue, - Mean, - Minimum, - Maximum, - SpecifiedValue, + DefaultValue = 0, + Mean = 1, + Minimum = 2, + Maximum = 3, + SpecifiedValue = 4, [HideEnumValue] Def = DefaultValue, @@ -112,14 +120,15 @@ public sealed class Arguments : TransformInputBase public Column[] Column; [Argument(ArgumentType.AtMostOnce, HelpText = "The replacement method to utilize", ShortName = "kind")] - public ReplacementKind ReplacementKind = ReplacementKind.DefaultValue; + public ReplacementKind ReplacementKind = (ReplacementKind)NAReplaceEstimator.Defaults.ReplacementMode; // Specifying by-slot imputation for vectors of unknown size will cause a warning, and the imputation will be global. [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to impute values by slot", ShortName = "slot")] - public bool ImputeBySlot = true; + public bool ImputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot; } public const string LoadName = "NAReplaceTransform"; + private static VersionInfo GetVersionInfo() { return new VersionInfo( @@ -129,16 +138,17 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x0010002, // Added imputation methods. verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoadName); + loaderSignature: LoadName, + loaderAssemblyName: typeof(NAReplaceTransform).Assembly.FullName); } internal const string Summary = "Create an output column of the same type and size of the input column, where missing values " - + "are replaced with either the default value or the mean/min/max value (for non-text columns only)."; + + "are replaced with either the default value or the mean/min/max value (for non-text columns only)."; internal const string FriendlyName = "NA Replace Transform"; internal const string ShortName = "NARep"; - private static string TestType(ColumnType type) + internal static string TestType(ColumnType type) { // Item type must have an NA value that exists and is not equal to its default value. Func func = TestType; @@ -149,8 +159,7 @@ private static string TestType(ColumnType type) private static string TestType(ColumnType type) { Contracts.Assert(type.ItemType.RawType == typeof(T)); - RefPredicate isNA; - if (!Conversions.Instance.TryGetIsNAPredicate(type.ItemType, out isNA)) + if (!Conversions.Instance.TryGetIsNAPredicate(type.ItemType, out RefPredicate isNA)) { return string.Format("Type '{0}' is not supported by {1} since it doesn't have an NA value", type, LoadName); @@ -165,8 +174,50 @@ private static string TestType(ColumnType type) return null; } + public class ColumnInfo + { + public enum ReplacementMode : byte + { + DefaultValue = 0, + Mean = 1, + Minimum = 2, + Maximum = 3, + } + + public readonly string Input; + public readonly string Output; + public readonly bool ImputeBySlot; + public readonly ReplacementMode Replacement; + + /// + /// Describes how the transformer handles one column pair. + /// + /// Name of input column. + /// Name of output column. + /// What to replace the missing value with. + /// If true, per-slot imputation of replacement is performed. + /// Otherwise, replacement value is imputed for the entire vector column. This setting is ignored for scalars and variable vectors, + /// where imputation is always for the entire column. + public ColumnInfo(string input, string output, ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, + bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Input = input; + Output = output; + ImputeBySlot = imputeBySlot; + Replacement = replacementMode; + } + + internal string ReplacementString { get; set; } + } + + private static (string input, string output)[] GetColumnPairs(ColumnInfo[] columns) + { + Contracts.CheckValue(columns, nameof(columns)); + return columns.Select(x => (x.Input, x.Output)).ToArray(); + } + // The output column types, parallel to Infos. - private readonly ColumnType[] _types; + private readonly ColumnType[] _replaceTypes; // The replacementValues for the columns, parallel to Infos. // The elements of this array can be either primitive values or arrays of primitive values. When replacing a scalar valued column in Infos, @@ -178,89 +229,56 @@ private static string TestType(ColumnType type) // Marks if the replacement values in given slots of _repValues are the default value. // REVIEW: Currently these arrays are constructed on load but could be changed to being constructed lazily. - private BitArray[] _repIsDefault; - - // The isNA delegates, parallel to Infos. - private readonly Delegate[] _isNAs; + private readonly BitArray[] _repIsDefault; - public override bool CanSaveOnnx => true; - - /// - /// Convenience constructor for public facing API. - /// - /// Host Environment. - /// Input . This is the output from previous transform or loader. - /// Name of the output column. - /// Name of the column to be transformed. If this is null '' will be used. - /// The replacement method to utilize. - public NAReplaceTransform(IHostEnvironment env, IDataView input, string name, string source = null, ReplacementKind replacementKind = ReplacementKind.DefaultValue) - : this(env, new Arguments() { Column = new[] { new Column() { Source = source ?? name, Name = name } }, ReplacementKind = replacementKind }, input) + protected override void CheckInputColumn(ISchema inputSchema, int col, int srcCol) { + var type = inputSchema.GetColumnType(srcCol); + string reason = TestType(type); + if (reason != null) + throw Host.ExceptParam(nameof(inputSchema), reason); } - /// - /// Public constructor corresponding to SignatureDataTransform. - /// - public NAReplaceTransform(IHostEnvironment env, Arguments args, IDataView input) - : base(env, LoadName, Contracts.CheckRef(args, nameof(args)).Column, - input, TestType) + public NAReplaceTransform(IHostEnvironment env, IDataView input, params ColumnInfo[] columns) + : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(NAReplaceTransform)), GetColumnPairs(columns)) { - Host.CheckValue(args, nameof(args)); - Host.AssertNonEmpty(Infos); - Host.Assert(Infos.Length == Utils.Size(args.Column)); - - GetInfoAndMetadata(out _types, out _isNAs); - GetReplacementValues(args, out _repValues, out _repIsDefault); + // Check that all the input columns are present and correct. + for (int i = 0; i < ColumnPairs.Length; i++) + { + if (!input.Schema.TryGetColumnIndex(ColumnPairs[i].input, out int srcCol)) + throw Host.ExceptSchemaMismatch(nameof(input), "input", ColumnPairs[i].input); + CheckInputColumn(input.Schema, i, srcCol); + } + GetReplacementValues(input, columns, out _repValues, out _repIsDefault, out _replaceTypes); } - private NAReplaceTransform(IHost host, ModelLoadContext ctx, IDataView input) - : base(host, ctx, input, TestType) + private NAReplaceTransform(IHost host, ModelLoadContext ctx) + : base(host, ctx) { - Host.AssertValue(ctx); - Host.AssertNonEmpty(Infos); - - GetInfoAndMetadata(out _types, out _isNAs); - - // *** Binary format *** - // - // for each column: - // type and value - _repValues = new object[Infos.Length]; - _repIsDefault = new BitArray[Infos.Length]; + var columnsLength = ColumnPairs.Length; + _repValues = new object[columnsLength]; + _repIsDefault = new BitArray[columnsLength]; + _replaceTypes = new ColumnType[columnsLength]; var saver = new BinarySaver(Host, new BinarySaver.Arguments()); - for (int iinfo = 0; iinfo < Infos.Length; iinfo++) + for (int i = 0; i < columnsLength; i++) { - object repValue; - ColumnType repType; - if (!saver.TryLoadTypeAndValue(ctx.Reader.BaseStream, out repType, out repValue)) + if (!saver.TryLoadTypeAndValue(ctx.Reader.BaseStream, out ColumnType savedType, out object repValue)) throw Host.ExceptDecode(); - if (!_types[iinfo].ItemType.Equals(repType.ItemType)) - throw Host.ExceptParam(nameof(input), "Decoded serialization of type '{0}' does not match expected ColumnType of '{1}'", repType.ItemType, _types[iinfo].ItemType); - // If type is a vector and the value is not either a scalar or a vector of the same size, throw an error. - if (repType.IsVector) + _replaceTypes[i] = savedType; + if (savedType.IsVector) { - if (!_types[iinfo].IsVector) - throw Host.ExceptParam(nameof(input), "Decoded serialization of type '{0}' cannot be a vector when Columntype is a scalar of type '{1}'", repType, _types[iinfo]); - if (!_types[iinfo].IsKnownSizeVector) - throw Host.ExceptParam(nameof(input), "Decoded serialization for unknown size vector '{0}' must be a scalar instead of type '{1}'", _types[iinfo], repType); - if (_types[iinfo].VectorSize != repType.VectorSize) - { - throw Host.ExceptParam(nameof(input), "Decoded serialization of type '{0}' must be a scalar or a vector of the same size as Columntype '{1}'", - repType, _types[iinfo]); - } - // REVIEW: The current implementation takes the serialized VBuffer, densifies it, and stores the values array. // It might be of value to consider storing the VBUffer in order to possibly benefit from sparsity. However, this would // necessitate a reimplementation of the FillValues code to accomodate sparse VBuffers. - object[] args = new object[] { repValue, _types[iinfo], iinfo }; + object[] args = new object[] { repValue, _replaceTypes[i], i }; Func, ColumnType, int, int[]> func = GetValuesArray; - var meth = func.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(repType.ItemType.RawType); - _repValues[iinfo] = meth.Invoke(this, args); + var meth = func.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(savedType.ItemType.RawType); + _repValues[i] = meth.Invoke(this, args); } else - _repValues[iinfo] = repValue; + _repValues[i] = repValue; - Host.Assert(repValue.GetType() == _types[iinfo].RawType || repValue.GetType() == _types[iinfo].ItemType.RawType); + Host.Assert(repValue.GetType() == _replaceTypes[i].RawType || repValue.GetType() == _replaceTypes[i].ItemType.RawType); } } @@ -282,105 +300,53 @@ private T[] GetValuesArray(VBuffer src, ColumnType srcType, int iinfo) return valReturn; } - public static NAReplaceTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) - { - Contracts.CheckValue(env, nameof(env)); - var h = env.Register(LoadName); - h.CheckValue(ctx, nameof(ctx)); - h.CheckValue(input, nameof(input)); - ctx.CheckAtModel(GetVersionInfo()); - return h.Apply("Loading Model", ch => new NAReplaceTransform(h, ctx, input)); - } - - public override void Save(ModelSaveContext ctx) - { - Host.CheckValue(ctx, nameof(ctx)); - ctx.CheckAtModel(); - ctx.SetVersionInfo(GetVersionInfo()); - - // *** Binary format *** - // - // for each column: - // type and value - SaveBase(ctx); - var saver = new BinarySaver(Host, new BinarySaver.Arguments()); - for (int iinfo = 0; iinfo < _types.Length; iinfo++) - { - var repValue = _repValues[iinfo]; - var repType = _types[iinfo].ItemType; - if (_repIsDefault[iinfo] != null) - { - Host.Assert(repValue is Array); - Func> function = CreateVBuffer; - var method = function.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(_types[iinfo].ItemType.RawType); - repValue = method.Invoke(this, new object[] { _repValues[iinfo] }); - repType = _types[iinfo]; - } - Host.Assert(!(repValue is Array)); - object[] args = new object[] { ctx.Writer.BaseStream, saver, repType, repValue }; - Action func = WriteTypeAndValue; - Host.Assert(repValue.GetType() == _types[iinfo].RawType || repValue.GetType() == _types[iinfo].ItemType.RawType); - var meth = func.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(repValue.GetType()); - meth.Invoke(this, args); - } - } - - private VBuffer CreateVBuffer(T[] array) - { - Host.AssertValue(array); - return new VBuffer(array.Length, array); - } - - private void WriteTypeAndValue(Stream stream, BinarySaver saver, ColumnType type, T rep) - { - Host.AssertValue(stream); - Host.AssertValue(saver); - Host.Assert(type.RawType == typeof(T) || type.ItemType.RawType == typeof(T)); - - int bytesWritten; - if (!saver.TryWriteTypeAndValue(stream, type, ref rep, out bytesWritten)) - throw Host.Except("We do not know how to serialize terms of type '{0}'", type); - } - /// /// Fill the repValues array with the correct replacement values based on the user-given replacement kinds. /// Vectors default to by-slot imputation unless otherwise specified, except for unknown sized vectors /// which force across-slot imputation. /// - private void GetReplacementValues(Arguments args, out object[] repValues, out BitArray[] slotIsDefault) + private void GetReplacementValues(IDataView input, ColumnInfo[] columns, out object[] repValues, out BitArray[] slotIsDefault, out ColumnType[] types) { - repValues = new object[Infos.Length]; - slotIsDefault = new BitArray[Infos.Length]; - - ReplacementKind?[] imputationModes = new ReplacementKind?[Infos.Length]; + repValues = new object[columns.Length]; + slotIsDefault = new BitArray[columns.Length]; + types = new ColumnType[columns.Length]; + var sources = new int[columns.Length]; + ReplacementKind[] imputationModes = new ReplacementKind[columns.Length]; List columnsToImpute = null; // REVIEW: Would like to get rid of the sourceColumns list but seems to be the best way to provide // the cursor with what columns to cursor through. HashSet sourceColumns = null; - for (int iinfo = 0; iinfo < Infos.Length; iinfo++) + for (int iinfo = 0; iinfo < columns.Length; iinfo++) { - ReplacementKind kind = args.Column[iinfo].Kind ?? args.ReplacementKind; + input.Schema.TryGetColumnIndex(columns[iinfo].Input, out int colSrc); + sources[iinfo] = colSrc; + var type = input.Schema.GetColumnType(colSrc); + if (type.IsVector) + type = new VectorType(type.ItemType.AsPrimitive, type.AsVector); + Delegate isNa = GetIsNADelegate(type); + types[iinfo] = type; + var kind = (ReplacementKind)columns[iinfo].Replacement; switch (kind) { - case ReplacementKind.SpecifiedValue: - repValues[iinfo] = GetSpecifiedValue(args.Column[iinfo].ReplacementString, _types[iinfo], _isNAs[iinfo]); - break; - case ReplacementKind.DefaultValue: - repValues[iinfo] = GetDefault(_types[iinfo]); - break; - case ReplacementKind.Mean: - case ReplacementKind.Min: - case ReplacementKind.Max: - if (!_types[iinfo].ItemType.IsNumber && !_types[iinfo].ItemType.IsTimeSpan && !_types[iinfo].ItemType.IsDateTime) - throw Host.Except("Cannot perform mean imputations on non-numeric '{0}'", _types[iinfo].ItemType); - imputationModes[iinfo] = kind; - Utils.Add(ref columnsToImpute, iinfo); - Utils.Add(ref sourceColumns, Infos[iinfo].Source); - break; - default: - Host.Assert(false); - throw Host.Except("Internal error, undefined ReplacementKind '{0}' assigned in NAReplaceTransform.", kind); + case ReplacementKind.SpecifiedValue: + repValues[iinfo] = GetSpecifiedValue(columns[iinfo].ReplacementString, _replaceTypes[iinfo], isNa); + break; + case ReplacementKind.DefaultValue: + repValues[iinfo] = GetDefault(type); + break; + case ReplacementKind.Mean: + case ReplacementKind.Minimum: + case ReplacementKind.Maximum: + if (!type.ItemType.IsNumber && !type.ItemType.IsTimeSpan && !type.ItemType.IsDateTime) + throw Host.Except("Cannot perform mean imputations on non-numeric '{0}'", type.ItemType); + imputationModes[iinfo] = kind; + Utils.Add(ref columnsToImpute, iinfo); + Utils.Add(ref sourceColumns, colSrc); + break; + default: + Host.Assert(false); + throw Host.Except("Internal error, undefined ReplacementKind '{0}' assigned in NAReplaceTransform.", columns[iinfo].Replacement); } } @@ -390,20 +356,21 @@ private void GetReplacementValues(Arguments args, out object[] repValues, out Bi // Impute values. using (var ch = Host.Start("Computing Statistics")) - using (var cursor = Source.GetRowCursor(sourceColumns.Contains)) + using (var cursor = input.GetRowCursor(sourceColumns.Contains)) { StatAggregator[] statAggregators = new StatAggregator[columnsToImpute.Count]; for (int ii = 0; ii < columnsToImpute.Count; ii++) { int iinfo = columnsToImpute[ii]; - bool bySlot = args.Column[ii].Slot ?? args.ImputeBySlot; - if (_types[iinfo].IsVector && !_types[iinfo].IsKnownSizeVector && bySlot) + bool bySlot = columns[ii].ImputeBySlot; + if (types[iinfo].IsVector && !types[iinfo].IsKnownSizeVector && bySlot) { ch.Warning("By-slot imputation can not be done on variable-length column"); bySlot = false; } - statAggregators[ii] = CreateStatAggregator(ch, _types[iinfo], imputationModes[iinfo], bySlot, - cursor, Infos[iinfo].Source); + + statAggregators[ii] = CreateStatAggregator(ch, types[iinfo], imputationModes[iinfo], bySlot, + cursor, sources[iinfo]); } while (cursor.MoveNext()) @@ -425,8 +392,8 @@ private void GetReplacementValues(Arguments args, out object[] repValues, out Bi if (repValues[slot] is Array) { Func func = ComputeDefaultSlots; - var meth = func.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(_types[slot].ItemType.RawType); - slotIsDefault[slot] = (BitArray)meth.Invoke(this, new object[] { _types[slot], repValues[slot] }); + var meth = func.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(types[slot].ItemType.RawType); + slotIsDefault[slot] = (BitArray)meth.Invoke(this, new object[] { types[slot], repValues[slot] }); } } } @@ -444,31 +411,6 @@ private BitArray ComputeDefaultSlots(ColumnType type, T[] values) return defaultSlots; } - private void GetInfoAndMetadata(out ColumnType[] types, out Delegate[] isNAs) - { - var md = Metadata; - types = new ColumnType[Infos.Length]; - isNAs = new Delegate[Infos.Length]; - for (int iinfo = 0; iinfo < Infos.Length; iinfo++) - { - var type = Infos[iinfo].TypeSrc; - - if (!type.IsVector) - types[iinfo] = type; - else - types[iinfo] = new VectorType(type.ItemType.AsPrimitive, type.AsVector); - - isNAs[iinfo] = GetIsNADelegate(type); - - // Pass through slot name metadata and normalization data. - using (var bldr = md.BuildMetadata(iinfo, Source.Schema, Infos[iinfo].Source, - MetadataUtils.Kinds.SlotNames, MetadataUtils.Kinds.IsNormalized)) - { - } - } - md.Seal(); - } - private object GetDefault(ColumnType type) { Func func = GetDefault; @@ -495,12 +437,6 @@ private Delegate GetIsNADelegate(ColumnType type) return Conversions.Instance.GetIsNAPredicate(type.ItemType); } - protected override ColumnType GetColumnTypeCore(int iinfo) - { - Host.Assert(0 <= iinfo & iinfo < Infos.Length); - return _types[iinfo]; - } - /// /// Converts a string to its respective value in the corresponding type. /// @@ -517,9 +453,8 @@ private object GetSpecifiedValue(string srcStr, ColumnType dstType, RefPredic if (!string.IsNullOrEmpty(srcStr)) { // Handles converting input strings to correct types. - DvText srcTxt = new DvText(srcStr); - bool identity; - var strToT = Conversions.Instance.GetStandardConversion(TextType.Instance, dstType.ItemType, out identity); + var srcTxt = srcStr.AsMemory(); + var strToT = Conversions.Instance.GetStandardConversion, T>(TextType.Instance, dstType.ItemType, out bool identity); strToT(ref srcTxt, ref val); // Make sure that the srcTxt can legitimately be converted to dstType, throw error otherwise. if (isNA(ref val)) @@ -529,367 +464,678 @@ private object GetSpecifiedValue(string srcStr, ColumnType dstType, RefPredic return val; } - protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + // Factory method for SignatureDataTransform. + public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input) { - Host.AssertValueOrNull(ch); - Host.AssertValue(input); - Host.Assert(0 <= iinfo && iinfo < Infos.Length); - disposer = null; + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(args, nameof(args)); + env.CheckValue(input, nameof(input)); - if (!Infos[iinfo].TypeSrc.IsVector) - return ComposeGetterOne(input, iinfo); - return ComposeGetterVec(input, iinfo); + env.CheckValue(args.Column, nameof(args.Column)); + var cols = new ColumnInfo[args.Column.Length]; + for (int i = 0; i < cols.Length; i++) + { + var item = args.Column[i]; + var kind = item.Kind ?? args.ReplacementKind; + if (!Enum.IsDefined(typeof(ReplacementKind), kind)) + throw env.ExceptUserArg(nameof(args.ReplacementKind), "Undefined sorting criteria '{0}' detected for column '{1}'", kind, item.Name); + + cols[i] = new ColumnInfo(item.Source, + item.Name, + (ColumnInfo.ReplacementMode)(item.Kind ?? args.ReplacementKind), + item.Slot ?? args.ImputeBySlot); + cols[i].ReplacementString = item.ReplacementString; + }; + return new NAReplaceTransform(env, input, cols).MakeDataTransform(input); } - /// - /// Getter generator for single valued inputs. - /// - private Delegate ComposeGetterOne(IRow input, int iinfo) - => Utils.MarshalInvoke(ComposeGetterOne, Infos[iinfo].TypeSrc.RawType, input, iinfo); + public static IDataTransform Create(IHostEnvironment env, IDataView input, params ColumnInfo[] columns) + { + return new NAReplaceTransform(env, input, columns).MakeDataTransform(input); + } - /// - /// Replaces NA values for scalars. - /// - private Delegate ComposeGetterOne(IRow input, int iinfo) + // Factory method for SignatureLoadModel. + public static NAReplaceTransform Create(IHostEnvironment env, ModelLoadContext ctx) { - var getSrc = GetSrcGetter(input, iinfo); - var src = default(T); - var isNA = (RefPredicate)_isNAs[iinfo]; - Host.Assert(_repValues[iinfo] is T); - T rep = (T)_repValues[iinfo]; - ValueGetter getter; + Contracts.CheckValue(env, nameof(env)); + var host = env.Register(LoadName); - return getter = - (ref T dst) => - { - getSrc(ref src); - dst = isNA(ref src) ? rep : src; - }; + host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + + return new NAReplaceTransform(host, ctx); } - /// - /// Getter generator for vector valued inputs. - /// - private Delegate ComposeGetterVec(IRow input, int iinfo) - => Utils.MarshalInvoke(ComposeGetterVec, Infos[iinfo].TypeSrc.ItemType.RawType, input, iinfo); + // Factory method for SignatureLoadDataTransform. + public static IDataTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + => Create(env, ctx).MakeDataTransform(input); - /// - /// Replaces NA values for vectors. - /// - private Delegate ComposeGetterVec(IRow input, int iinfo) + // Factory method for SignatureLoadRowMapper. + public static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, ISchema inputSchema) + => Create(env, ctx).MakeRowMapper(inputSchema); + + private VBuffer CreateVBuffer(T[] array) { - var getSrc = GetSrcGetter>(input, iinfo); - var isNA = (RefPredicate)_isNAs[iinfo]; - var isDefault = Conversions.Instance.GetIsDefaultPredicate(input.Schema.GetColumnType(Infos[iinfo].Source).ItemType); + Host.AssertValue(array); + return new VBuffer(array.Length, array); + } - var src = default(VBuffer); - ValueGetter> getter; + private void WriteTypeAndValue(Stream stream, BinarySaver saver, ColumnType type, T rep) + { + Host.AssertValue(stream); + Host.AssertValue(saver); + Host.Assert(type.RawType == typeof(T) || type.ItemType.RawType == typeof(T)); - if (_repIsDefault[iinfo] == null) - { - // One replacement value for all slots. - Host.Assert(_repValues[iinfo] is T); - T rep = (T)_repValues[iinfo]; - bool repIsDefault = isDefault(ref rep); - return getter = - (ref VBuffer dst) => - { - getSrc(ref src); - FillValues(ref src, ref dst, isNA, rep, repIsDefault); - }; - } + if (!saver.TryWriteTypeAndValue(stream, type, ref rep, out int bytesWritten)) + throw Host.Except("We do not know how to serialize terms of type '{0}'", type); + } - // Replacement values by slot. - Host.Assert(_repValues[iinfo] is T[]); - // The replacement array. - T[] repArray = (T[])_repValues[iinfo]; + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); - return getter = - (ref VBuffer dst) => + SaveColumns(ctx); + var saver = new BinarySaver(Host, new BinarySaver.Arguments()); + for (int iinfo = 0; iinfo < _replaceTypes.Length; iinfo++) + { + var repValue = _repValues[iinfo]; + var repType = _replaceTypes[iinfo].ItemType; + if (_repIsDefault[iinfo] != null) { - getSrc(ref src); - Host.Check(src.Length == repArray.Length); - FillValues(ref src, ref dst, isNA, repArray, _repIsDefault[iinfo]); - }; + Host.Assert(repValue is Array); + Func> function = CreateVBuffer; + var method = function.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(repType.RawType); + repValue = method.Invoke(this, new object[] { _repValues[iinfo] }); + repType = _replaceTypes[iinfo]; + } + Host.Assert(!(repValue is Array)); + object[] args = new object[] { ctx.Writer.BaseStream, saver, repType, repValue }; + Action func = WriteTypeAndValue; + Host.Assert(repValue.GetType() == _replaceTypes[iinfo].RawType || repValue.GetType() == _replaceTypes[iinfo].ItemType.RawType); + var meth = func.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(repValue.GetType()); + meth.Invoke(this, args); + } } - protected override bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string srcVariableName, string dstVariableName) + protected override IRowMapper MakeRowMapper(ISchema schema) + => new Mapper(this, schema); + + private sealed class Mapper : MapperBase, ISaveAsOnnx { - DataKind rawKind; - var type = Infos[iinfo].TypeSrc; - if (type.IsVector) - rawKind = type.AsVector.ItemType.RawKind; - else if (type.IsKey) - rawKind = type.AsKey.RawKind; - else - rawKind = type.RawKind; - if (rawKind != DataKind.R4) - return false; + private sealed class ColInfo + { + public readonly string Name; + public readonly string Source; + public readonly ColumnType TypeSrc; + + public ColInfo(string name, string source, ColumnType type) + { + Name = name; + Source = source; + TypeSrc = type; + } + } - string opType = "Imputer"; - var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType)); - node.AddAttribute("replaced_value_float", Single.NaN); + private readonly NAReplaceTransform _parent; + private readonly ColInfo[] _infos; + private readonly ColumnType[] _types; + // The isNA delegates, parallel to Infos. + private readonly Delegate[] _isNAs; + public bool CanSaveOnnx => true; - if (!Infos[iinfo].TypeSrc.IsVector) - node.AddAttribute("imputed_value_floats", Enumerable.Repeat((float)_repValues[iinfo], 1)); - else + public Mapper(NAReplaceTransform parent, ISchema inputSchema) + : base(parent.Host.Register(nameof(Mapper)), parent, inputSchema) { - if (_repIsDefault[iinfo] != null) - node.AddAttribute("imputed_value_floats", (float[])_repValues[iinfo]); - else - node.AddAttribute("imputed_value_floats", Enumerable.Repeat((float)_repValues[iinfo], 1)); + _parent = parent; + _infos = CreateInfos(inputSchema); + _types = new ColumnType[_parent.ColumnPairs.Length]; + _isNAs = new Delegate[_parent.ColumnPairs.Length]; + for (int i = 0; i < _parent.ColumnPairs.Length; i++) + { + var type = _infos[i].TypeSrc; + if (type.IsVector) + type = new VectorType(type.ItemType.AsPrimitive, type.AsVector); + var repType = _parent._repIsDefault[i] != null ? _parent._replaceTypes[i] : _parent._replaceTypes[i].ItemType; + if (!type.ItemType.Equals(repType.ItemType)) + throw Host.ExceptParam(nameof(InputSchema), "Column '{0}' item type '{1}' does not match expected ColumnType of '{2}'", + _infos[i].Source, _parent._replaceTypes[i].ItemType.ToString(), _infos[i].TypeSrc); + // If type is a vector and the value is not either a scalar or a vector of the same size, throw an error. + if (repType.IsVector) + { + if (!type.IsVector) + throw Host.ExceptParam(nameof(inputSchema), "Column '{0}' item type '{1}' cannot be a vector when Columntype is a scalar of type '{2}'", + _infos[i].Source, repType, type); + if (!type.IsKnownSizeVector) + throw Host.ExceptParam(nameof(inputSchema), "Column '{0}' is unknown size vector '{1}' must be a scalar instead of type '{2}'", _infos[i].Source, type, parent._replaceTypes[i]); + if (type.VectorSize != repType.VectorSize) + throw Host.ExceptParam(nameof(inputSchema), "Column '{0}' item type '{1}' must be a scalar or a vector of the same size as Columntype '{2}'", + _infos[i].Source, repType, type); + } + _types[i] = type; + _isNAs[i] = _parent.GetIsNADelegate(type); + } } - return true; - } - - protected override VectorType GetSlotTypeCore(int iinfo) - { - Host.Assert(0 <= iinfo && iinfo < Infos.Length); - return Infos[iinfo].SlotTypeSrc; - } + private ColInfo[] CreateInfos(ISchema inputSchema) + { + Host.AssertValue(inputSchema); + var infos = new ColInfo[_parent.ColumnPairs.Length]; + for (int i = 0; i < _parent.ColumnPairs.Length; i++) + { + if (!inputSchema.TryGetColumnIndex(_parent.ColumnPairs[i].input, out int colSrc)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.ColumnPairs[i].input); + _parent.CheckInputColumn(inputSchema, i, colSrc); + var type = inputSchema.GetColumnType(colSrc); + infos[i] = new ColInfo(_parent.ColumnPairs[i].output, _parent.ColumnPairs[i].input, type); + } + return infos; + } - protected override ISlotCursor GetSlotCursorCore(int iinfo) - { - Host.Assert(0 <= iinfo && iinfo < Infos.Length); - Host.AssertValue(Infos[iinfo].SlotTypeSrc); + public override RowMapperColumnInfo[] GetOutputColumns() + { + var result = new RowMapperColumnInfo[_parent.ColumnPairs.Length]; + for (int i = 0; i < _parent.ColumnPairs.Length; i++) + { + InputSchema.TryGetColumnIndex(_parent.ColumnPairs[i].input, out int colIndex); + Host.Assert(colIndex >= 0); + var colMetaInfo = new ColumnMetadataInfo(_parent.ColumnPairs[i].output); + var meta = RowColumnUtils.GetMetadataAsRow(InputSchema, colIndex, x => x == MetadataUtils.Kinds.SlotNames || x == MetadataUtils.Kinds.IsNormalized); + result[i] = new RowMapperColumnInfo(_parent.ColumnPairs[i].output, _types[i], meta); + } + return result; + } - ISlotCursor cursor = InputTranspose.GetSlotCursor(Infos[iinfo].Source); - var type = GetSlotTypeCore(iinfo); - Host.AssertValue(type); - return Utils.MarshalInvoke(GetSlotCursorCore, type.ItemType.RawType, this, iinfo, cursor, type); - } + protected override Delegate MakeGetter(IRow input, int iinfo, out Action disposer) + { + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < _infos.Length); + disposer = null; - private ISlotCursor GetSlotCursorCore(NAReplaceTransform parent, int iinfo, ISlotCursor cursor, VectorType type) - => new SlotCursor(parent, iinfo, cursor, type); + if (!_infos[iinfo].TypeSrc.IsVector) + return ComposeGetterOne(input, iinfo); + return ComposeGetterVec(input, iinfo); + } - private sealed class SlotCursor : SynchronizedCursorBase, ISlotCursor - { - private readonly ValueGetter> _getter; - private readonly VectorType _type; + /// + /// Getter generator for single valued inputs. + /// + private Delegate ComposeGetterOne(IRow input, int iinfo) + => Utils.MarshalInvoke(ComposeGetterOne, _infos[iinfo].TypeSrc.RawType, input, iinfo); - public SlotCursor(NAReplaceTransform parent, int iinfo, ISlotCursor cursor, VectorType type) - : base(parent.Host, cursor) + /// + /// Replaces NA values for scalars. + /// + private Delegate ComposeGetterOne(IRow input, int iinfo) { - Ch.Assert(0 <= iinfo && iinfo < parent.Infos.Length); - Ch.AssertValue(cursor); - Ch.AssertValue(type); - var srcGetter = cursor.GetGetter(); - _type = type; - _getter = CreateGetter(parent, iinfo, cursor, type); + var getSrc = input.GetGetter(ColMapNewToOld[iinfo]); + var src = default(T); + var isNA = (RefPredicate)_isNAs[iinfo]; + Host.Assert(_parent._repValues[iinfo] is T); + T rep = (T)_parent._repValues[iinfo]; + ValueGetter getter; + + return getter = + (ref T dst) => + { + getSrc(ref src); + dst = isNA(ref src) ? rep : src; + }; } - private ValueGetter> CreateGetter(NAReplaceTransform parent, int iinfo, ISlotCursor cursor, VectorType type) + /// + /// Getter generator for vector valued inputs. + /// + private Delegate ComposeGetterVec(IRow input, int iinfo) + => Utils.MarshalInvoke(ComposeGetterVec, _infos[iinfo].TypeSrc.ItemType.RawType, input, iinfo); + + /// + /// Replaces NA values for vectors. + /// + private Delegate ComposeGetterVec(IRow input, int iinfo) { + var getSrc = input.GetGetter>(ColMapNewToOld[iinfo]); + var isNA = (RefPredicate)_isNAs[iinfo]; + var isDefault = Conversions.Instance.GetIsDefaultPredicate(_infos[iinfo].TypeSrc.ItemType); + var src = default(VBuffer); ValueGetter> getter; - var getSrc = cursor.GetGetter(); - var isNA = (RefPredicate)parent._isNAs[iinfo]; - var isDefault = Conversions.Instance.GetIsDefaultPredicate(type.ItemType); - - if (parent._repIsDefault[iinfo] == null) + if (_parent._repIsDefault[iinfo] == null) { // One replacement value for all slots. - Ch.Assert(parent._repValues[iinfo] is T); - T rep = (T)parent._repValues[iinfo]; + Host.Assert(_parent._repValues[iinfo] is T); + T rep = (T)_parent._repValues[iinfo]; bool repIsDefault = isDefault(ref rep); - - return (ref VBuffer dst) => - { - getSrc(ref src); - parent.FillValues(ref src, ref dst, isNA, rep, repIsDefault); - }; + return getter = + (ref VBuffer dst) => + { + getSrc(ref src); + FillValues(ref src, ref dst, isNA, rep, repIsDefault); + }; } // Replacement values by slot. - Ch.Assert(parent._repValues[iinfo] is T[]); + Host.Assert(_parent._repValues[iinfo] is T[]); // The replacement array. - T[] repArray = (T[])parent._repValues[iinfo]; + T[] repArray = (T[])_parent._repValues[iinfo]; return getter = (ref VBuffer dst) => { getSrc(ref src); - Ch.Check(0 <= Position && Position < repArray.Length); - T rep = repArray[(int)Position]; - parent.FillValues(ref src, ref dst, isNA, rep, isDefault(ref rep)); + Host.Check(src.Length == repArray.Length); + FillValues(ref src, ref dst, isNA, repArray, _parent._repIsDefault[iinfo]); }; } - public VectorType GetSlotType() => _type; - - public ValueGetter> GetGetter() + /// + /// Fills values for vectors where there is one replacement value. + /// + private void FillValues(ref VBuffer src, ref VBuffer dst, RefPredicate isNA, T rep, bool repIsDefault) { - ValueGetter> getter = _getter as ValueGetter>; - if (getter == null) - throw Ch.Except("Invalid TValue: '{0}'", typeof(TValue)); - return getter; - } - } + Host.AssertValue(isNA); - /// - /// Fills values for vectors where there is one replacement value. - /// - private void FillValues(ref VBuffer src, ref VBuffer dst, RefPredicate isNA, T rep, bool repIsDefault) - { - Host.AssertValue(isNA); + int srcSize = src.Length; + int srcCount = src.Count; + var srcValues = src.Values; + Host.Assert(Utils.Size(srcValues) >= srcCount); + var srcIndices = src.Indices; - int srcSize = src.Length; - int srcCount = src.Count; - var srcValues = src.Values; - Host.Assert(Utils.Size(srcValues) >= srcCount); - var srcIndices = src.Indices; + var dstValues = dst.Values; + var dstIndices = dst.Indices; - var dstValues = dst.Values; - var dstIndices = dst.Indices; + // If the values array is not large enough, allocate sufficient space. + // Note: We can't set the max to srcSize as vectors can be of variable lengths. + Utils.EnsureSize(ref dstValues, srcCount, keepOld: false); - // If the values array is not large enough, allocate sufficient space. - // Note: We can't set the max to srcSize as vectors can be of variable lengths. - Utils.EnsureSize(ref dstValues, srcCount, keepOld: false); + int iivDst = 0; + if (src.IsDense) + { + // The source vector is dense. + Host.Assert(srcSize == srcCount); + + for (int ivSrc = 0; ivSrc < srcCount; ivSrc++) + { + var srcVal = srcValues[ivSrc]; + + // The output for dense inputs is always dense. + // Note: Theoretically, one could imagine a dataset with NA values that one wished to replace with + // the default value, resulting in more than half of the indices being the default value. + // In this case, changing the dst vector to be sparse would be more memory efficient -- the current decision + // is it is not worth handling this case at the expense of running checks that will almost always not be triggered. + dstValues[ivSrc] = isNA(ref srcVal) ? rep : srcVal; + } + iivDst = srcCount; + } + else + { + // The source vector is sparse. + Host.Assert(Utils.Size(srcIndices) >= srcCount); + Host.Assert(srcCount < srcSize); + + // Allocate more space if necessary. + // REVIEW: One thing that changing the code to simply ensure that there are srcCount indices in the arrays + // does is over-allocate space if the replacement value is the default value in a dataset with a + // signficiant amount of NA values -- is it worth handling allocation of memory for this case? + Utils.EnsureSize(ref dstIndices, srcCount, keepOld: false); + + // Note: ivPrev is only used for asserts. + int ivPrev = -1; + for (int iivSrc = 0; iivSrc < srcCount; iivSrc++) + { + Host.Assert(iivDst <= iivSrc); + var srcVal = srcValues[iivSrc]; + int iv = srcIndices[iivSrc]; + Host.Assert(ivPrev < iv & iv < srcSize); + ivPrev = iv; + + if (!isNA(ref srcVal)) + { + dstValues[iivDst] = srcVal; + dstIndices[iivDst++] = iv; + } + else if (!repIsDefault) + { + // Allow for further sparsification. + dstValues[iivDst] = rep; + dstIndices[iivDst++] = iv; + } + } + Host.Assert(iivDst <= srcCount); + } + Host.Assert(0 <= iivDst); + Host.Assert(repIsDefault || iivDst == srcCount); + dst = new VBuffer(srcSize, iivDst, dstValues, dstIndices); + } - int iivDst = 0; - if (src.IsDense) + /// + /// Fills values for vectors where there is slot-wise replacement values. + /// + private void FillValues(ref VBuffer src, ref VBuffer dst, RefPredicate isNA, T[] rep, BitArray repIsDefault) { - // The source vector is dense. - Host.Assert(srcSize == srcCount); + Host.AssertValue(rep); + Host.Assert(rep.Length == src.Length); + Host.AssertValue(repIsDefault); + Host.Assert(repIsDefault.Length == src.Length); + Host.AssertValue(isNA); + + int srcSize = src.Length; + int srcCount = src.Count; + var srcValues = src.Values; + Host.Assert(Utils.Size(srcValues) >= srcCount); + var srcIndices = src.Indices; + + var dstValues = dst.Values; + var dstIndices = dst.Indices; + + // If the values array is not large enough, allocate sufficient space. + Utils.EnsureSize(ref dstValues, srcCount, srcSize, keepOld: false); + + int iivDst = 0; + Host.Assert(Utils.Size(srcValues) >= srcCount); + if (src.IsDense) + { + // The source vector is dense. + Host.Assert(srcSize == srcCount); - for (int ivSrc = 0; ivSrc < srcCount; ivSrc++) + for (int ivSrc = 0; ivSrc < srcCount; ivSrc++) + { + var srcVal = srcValues[ivSrc]; + + // The output for dense inputs is always dense. + // Note: Theoretically, one could imagine a dataset with NA values that one wished to replace with + // the default value, resulting in more than half of the indices being the default value. + // In this case, changing the dst vector to be sparse would be more memory efficient -- the current decision + // is it is not worth handling this case at the expense of running checks that will almost always not be triggered. + dstValues[ivSrc] = isNA(ref srcVal) ? rep[ivSrc] : srcVal; + } + iivDst = srcCount; + } + else { - var srcVal = srcValues[ivSrc]; - - // The output for dense inputs is always dense. - // Note: Theoretically, one could imagine a dataset with NA values that one wished to replace with - // the default value, resulting in more than half of the indices being the default value. - // In this case, changing the dst vector to be sparse would be more memory efficient -- the current decision - // is it is not worth handling this case at the expense of running checks that will almost always not be triggered. - dstValues[ivSrc] = isNA(ref srcVal) ? rep : srcVal; + // The source vector is sparse. + Host.Assert(Utils.Size(srcIndices) >= srcCount); + Host.Assert(srcCount < srcSize); + + // Allocate more space if necessary. + // REVIEW: One thing that changing the code to simply ensure that there are srcCount indices in the arrays + // does is over-allocate space if the replacement value is the default value in a dataset with a + // signficiant amount of NA values -- is it worth handling allocation of memory for this case? + Utils.EnsureSize(ref dstIndices, srcCount, srcSize, keepOld: false); + + // Note: ivPrev is only used for asserts. + int ivPrev = -1; + for (int iivSrc = 0; iivSrc < srcCount; iivSrc++) + { + Host.Assert(iivDst <= iivSrc); + var srcVal = srcValues[iivSrc]; + int iv = srcIndices[iivSrc]; + Host.Assert(ivPrev < iv & iv < srcSize); + ivPrev = iv; + + if (!isNA(ref srcVal)) + { + dstValues[iivDst] = srcVal; + dstIndices[iivDst++] = iv; + } + else if (!repIsDefault[iv]) + { + // Allow for further sparsification. + dstValues[iivDst] = rep[iv]; + dstIndices[iivDst++] = iv; + } + } + Host.Assert(iivDst <= srcCount); } - iivDst = srcCount; + Host.Assert(0 <= iivDst); + dst = new VBuffer(srcSize, iivDst, dstValues, dstIndices); } - else - { - // The source vector is sparse. - Host.Assert(Utils.Size(srcIndices) >= srcCount); - Host.Assert(srcCount < srcSize); - // Allocate more space if necessary. - // REVIEW: One thing that changing the code to simply ensure that there are srcCount indices in the arrays - // does is over-allocate space if the replacement value is the default value in a dataset with a - // signficiant amount of NA values -- is it worth handling allocation of memory for this case? - Utils.EnsureSize(ref dstIndices, srcCount, keepOld: false); + public void SaveAsOnnx(OnnxContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); - // Note: ivPrev is only used for asserts. - int ivPrev = -1; - for (int iivSrc = 0; iivSrc < srcCount; iivSrc++) + for (int iinfo = 0; iinfo < _infos.Length; ++iinfo) { - Host.Assert(iivDst <= iivSrc); - var srcVal = srcValues[iivSrc]; - int iv = srcIndices[iivSrc]; - Host.Assert(ivPrev < iv & iv < srcSize); - ivPrev = iv; - - if (!isNA(ref srcVal)) + ColInfo info = _infos[iinfo]; + string sourceColumnName = info.Source; + if (!ctx.ContainsColumn(sourceColumnName)) { - dstValues[iivDst] = srcVal; - dstIndices[iivDst++] = iv; + ctx.RemoveColumn(info.Name, false); + continue; } - else if (!repIsDefault) + + if (!SaveAsOnnxCore(ctx, iinfo, info, ctx.GetVariableName(sourceColumnName), + ctx.AddIntermediateVariable(_parent._replaceTypes[iinfo], info.Name))) { - // Allow for further sparsification. - dstValues[iivDst] = rep; - dstIndices[iivDst++] = iv; + ctx.RemoveColumn(info.Name, true); } } - Host.Assert(iivDst <= srcCount); } - Host.Assert(0 <= iivDst); - Host.Assert(repIsDefault || iivDst == srcCount); - dst = new VBuffer(srcSize, iivDst, dstValues, dstIndices); + + private bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string srcVariableName, string dstVariableName) + { + DataKind rawKind; + var type = _infos[iinfo].TypeSrc; + if (type.IsVector) + rawKind = type.AsVector.ItemType.RawKind; + else if (type.IsKey) + rawKind = type.AsKey.RawKind; + else + rawKind = type.RawKind; + + if (rawKind != DataKind.R4) + return false; + + string opType = "Imputer"; + var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType)); + node.AddAttribute("replaced_value_float", Single.NaN); + + if (!_infos[iinfo].TypeSrc.IsVector) + node.AddAttribute("imputed_value_floats", Enumerable.Repeat((float)_parent._repValues[iinfo], 1)); + else + { + if (_parent._repIsDefault[iinfo] != null) + node.AddAttribute("imputed_value_floats", (float[])_parent._repValues[iinfo]); + else + node.AddAttribute("imputed_value_floats", Enumerable.Repeat((float)_parent._repValues[iinfo], 1)); + } + return true; + } } + } - /// - /// Fills values for vectors where there is slot-wise replacement values. - /// - private void FillValues(ref VBuffer src, ref VBuffer dst, RefPredicate isNA, T[] rep, BitArray repIsDefault) + public sealed class NAReplaceEstimator : IEstimator + { + public static class Defaults { - Host.AssertValue(rep); - Host.Assert(rep.Length == src.Length); - Host.AssertValue(repIsDefault); - Host.Assert(repIsDefault.Length == src.Length); - Host.AssertValue(isNA); + public const NAReplaceTransform.ColumnInfo.ReplacementMode ReplacementMode = NAReplaceTransform.ColumnInfo.ReplacementMode.DefaultValue; + public const bool ImputeBySlot = true; + } - int srcSize = src.Length; - int srcCount = src.Count; - var srcValues = src.Values; - Host.Assert(Utils.Size(srcValues) >= srcCount); - var srcIndices = src.Indices; + private readonly IHost _host; + private readonly NAReplaceTransform.ColumnInfo[] _columns; - var dstValues = dst.Values; - var dstIndices = dst.Indices; + public NAReplaceEstimator(IHostEnvironment env, string name, string source = null, NAReplaceTransform.ColumnInfo.ReplacementMode replacementKind = Defaults.ReplacementMode) + : this(env, new NAReplaceTransform.ColumnInfo(source ?? name, name, replacementKind)) + { + + } - // If the values array is not large enough, allocate sufficient space. - Utils.EnsureSize(ref dstValues, srcCount, srcSize, keepOld: false); + public NAReplaceEstimator(IHostEnvironment env, params NAReplaceTransform.ColumnInfo[] columns) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(nameof(NAReplaceEstimator)); + _columns = columns; + } - int iivDst = 0; - Host.Assert(Utils.Size(srcValues) >= srcCount); - if (src.IsDense) + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + _host.CheckValue(inputSchema, nameof(inputSchema)); + var result = inputSchema.Columns.ToDictionary(x => x.Name); + foreach (var colInfo in _columns) { - // The source vector is dense. - Host.Assert(srcSize == srcCount); + if (!inputSchema.TryFindColumn(colInfo.Input, out var col)) + throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", colInfo.Input); + string reason = NAReplaceTransform.TestType(col.ItemType); + if (reason != null) + throw _host.ExceptParam(nameof(inputSchema), reason); + var metadata = new List(); + if (col.Metadata.TryFindColumn(MetadataUtils.Kinds.SlotNames, out var slotMeta)) + metadata.Add(slotMeta); + if (col.Metadata.TryFindColumn(MetadataUtils.Kinds.IsNormalized, out var normalized)) + metadata.Add(normalized); + var type = !col.ItemType.IsVector ? col.ItemType : new VectorType(col.ItemType.ItemType.AsPrimitive, col.ItemType.AsVector); + result[colInfo.Output] = new SchemaShape.Column(colInfo.Output, col.Kind, type, false, new SchemaShape(metadata.ToArray())); + } + return new SchemaShape(result.Values); + } - for (int ivSrc = 0; ivSrc < srcCount; ivSrc++) - { - var srcVal = srcValues[ivSrc]; - - // The output for dense inputs is always dense. - // Note: Theoretically, one could imagine a dataset with NA values that one wished to replace with - // the default value, resulting in more than half of the indices being the default value. - // In this case, changing the dst vector to be sparse would be more memory efficient -- the current decision - // is it is not worth handling this case at the expense of running checks that will almost always not be triggered. - dstValues[ivSrc] = isNA(ref srcVal) ? rep[ivSrc] : srcVal; - } - iivDst = srcCount; + public NAReplaceTransform Fit(IDataView input) => new NAReplaceTransform(_host, input, _columns); + } + + /// + /// Extension methods for the static-pipeline over objects. + /// + public static class NAReplaceExtensions + { + private struct Config + { + public readonly bool ImputeBySlot; + public readonly NAReplaceTransform.ColumnInfo.ReplacementMode ReplacementMode; + + public Config(NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, + bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + ImputeBySlot = imputeBySlot; + ReplacementMode = replacementMode; } - else + } + + private interface IColInput + { + PipelineColumn Input { get; } + Config Config { get; } + } + + private sealed class OutScalar : Scalar, IColInput + { + public PipelineColumn Input { get; } + public Config Config { get; } + + public OutScalar(Scalar input, Config config) + : base(Reconciler.Inst, input) { - // The source vector is sparse. - Host.Assert(Utils.Size(srcIndices) >= srcCount); - Host.Assert(srcCount < srcSize); + Input = input; + Config = config; + } + } - // Allocate more space if necessary. - // REVIEW: One thing that changing the code to simply ensure that there are srcCount indices in the arrays - // does is over-allocate space if the replacement value is the default value in a dataset with a - // signficiant amount of NA values -- is it worth handling allocation of memory for this case? - Utils.EnsureSize(ref dstIndices, srcCount, srcSize, keepOld: false); + private sealed class OutVectorColumn : Vector, IColInput + { + public PipelineColumn Input { get; } + public Config Config { get; } - // Note: ivPrev is only used for asserts. - int ivPrev = -1; - for (int iivSrc = 0; iivSrc < srcCount; iivSrc++) - { - Host.Assert(iivDst <= iivSrc); - var srcVal = srcValues[iivSrc]; - int iv = srcIndices[iivSrc]; - Host.Assert(ivPrev < iv & iv < srcSize); - ivPrev = iv; + public OutVectorColumn(Vector input, Config config) + : base(Reconciler.Inst, input) + { + Input = input; + Config = config; + } - if (!isNA(ref srcVal)) - { - dstValues[iivDst] = srcVal; - dstIndices[iivDst++] = iv; - } - else if (!repIsDefault[iv]) - { - // Allow for further sparsification. - dstValues[iivDst] = rep[iv]; - dstIndices[iivDst++] = iv; - } + } + + private sealed class OutVarVectorColumn : VarVector, IColInput + { + public PipelineColumn Input { get; } + public Config Config { get; } + + public OutVarVectorColumn(VarVector input, Config config) + : base(Reconciler.Inst, input) + { + Input = input; + Config = config; + } + } + + private sealed class Reconciler : EstimatorReconciler + { + public static Reconciler Inst = new Reconciler(); + + private Reconciler() { } + + public override IEstimator Reconcile(IHostEnvironment env, + PipelineColumn[] toOutput, + IReadOnlyDictionary inputNames, + IReadOnlyDictionary outputNames, + IReadOnlyCollection usedNames) + { + var infos = new NAReplaceTransform.ColumnInfo[toOutput.Length]; + for (int i = 0; i < toOutput.Length; ++i) + { + var col = (IColInput)toOutput[i]; + infos[i] = new NAReplaceTransform.ColumnInfo(inputNames[col.Input], outputNames[toOutput[i]], col.Config.ReplacementMode, col.Config.ImputeBySlot); } - Host.Assert(iivDst <= srcCount); + return new NAReplaceEstimator(env, infos); } - Host.Assert(0 <= iivDst); - dst = new VBuffer(srcSize, iivDst, dstValues, dstIndices); + } + + public static Scalar ReplaceWithMissingValues(this Scalar input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutScalar(input, new Config(replacementMode, imputeBySlot)); + } + + public static Scalar ReplaceWithMissingValues(this Scalar input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutScalar(input, new Config(replacementMode, imputeBySlot)); + } + + public static Scalar ReplaceWithMissingValues(this Scalar input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutScalar(input, new Config(replacementMode, imputeBySlot)); + } + + public static Vector ReplaceWithMissingValues(this Vector input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutVectorColumn(input, new Config(replacementMode, imputeBySlot)); + } + + public static Vector ReplaceWithMissingValues(this Vector input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutVectorColumn(input, new Config(replacementMode, imputeBySlot)); + } + + public static Vector ReplaceWithMissingValues(this Vector input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutVectorColumn(input, new Config(replacementMode, imputeBySlot)); + } + + public static VarVector ReplaceWithMissingValues(this VarVector input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutVarVectorColumn(input, new Config(replacementMode, imputeBySlot)); + } + + public static VarVector NAReplace(this VarVector input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutVarVectorColumn(input, new Config(replacementMode, imputeBySlot)); + } + + public static VarVector ReplaceWithMissingValues(this VarVector input, NAReplaceTransform.ColumnInfo.ReplacementMode replacementMode = NAReplaceEstimator.Defaults.ReplacementMode, bool imputeBySlot = NAReplaceEstimator.Defaults.ImputeBySlot) + { + Contracts.CheckValue(input, nameof(input)); + return new OutVarVectorColumn(input, new Config(replacementMode, imputeBySlot)); } } } diff --git a/src/Microsoft.ML.Transforms/NAReplaceUtils.cs b/src/Microsoft.ML.Transforms/NAReplaceUtils.cs index 2340f9b413..cf4c32cc0e 100644 --- a/src/Microsoft.ML.Transforms/NAReplaceUtils.cs +++ b/src/Microsoft.ML.Transforms/NAReplaceUtils.cs @@ -14,7 +14,7 @@ public sealed partial class NAReplaceTransform { private static StatAggregator CreateStatAggregator(IChannel ch, ColumnType type, ReplacementKind? kind, bool bySlot, IRowCursor cursor, int col) { - ch.Assert(type.ItemType.IsNumber || type.ItemType.IsTimeSpan || type.ItemType.IsDateTime); + ch.Assert(type.ItemType.IsNumber); if (!type.IsVector) { // The type is a scalar. @@ -22,22 +22,10 @@ private static StatAggregator CreateStatAggregator(IChannel ch, ColumnType type, { switch (type.RawKind) { - case DataKind.I1: - return new I1.MeanAggregatorOne(ch, cursor, col); - case DataKind.I2: - return new I2.MeanAggregatorOne(ch, cursor, col); - case DataKind.I4: - return new I4.MeanAggregatorOne(ch, cursor, col); - case DataKind.I8: - return new Long.MeanAggregatorOne(ch, type, cursor, col); case DataKind.R4: return new R4.MeanAggregatorOne(ch, cursor, col); case DataKind.R8: return new R8.MeanAggregatorOne(ch, cursor, col); - case DataKind.TS: - return new Long.MeanAggregatorOne(ch, type, cursor, col); - case DataKind.DT: - return new Long.MeanAggregatorOne(ch, type, cursor, col); default: break; } @@ -46,22 +34,10 @@ private static StatAggregator CreateStatAggregator(IChannel ch, ColumnType type, { switch (type.RawKind) { - case DataKind.I1: - return new I1.MinMaxAggregatorOne(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.I2: - return new I2.MinMaxAggregatorOne(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.I4: - return new I4.MinMaxAggregatorOne(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.I8: - return new Long.MinMaxAggregatorOne(ch, type, cursor, col, kind == ReplacementKind.Max); case DataKind.R4: return new R4.MinMaxAggregatorOne(ch, cursor, col, kind == ReplacementKind.Max); case DataKind.R8: return new R8.MinMaxAggregatorOne(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.TS: - return new Long.MinMaxAggregatorOne(ch, type, cursor, col, kind == ReplacementKind.Max); - case DataKind.DT: - return new Long.MinMaxAggregatorOne(ch, type, cursor, col, kind == ReplacementKind.Max); default: break; } @@ -78,22 +54,10 @@ private static StatAggregator CreateStatAggregator(IChannel ch, ColumnType type, { switch (type.ItemType.RawKind) { - case DataKind.I1: - return new I1.MeanAggregatorBySlot(ch, type, cursor, col); - case DataKind.I2: - return new I2.MeanAggregatorBySlot(ch, type, cursor, col); - case DataKind.I4: - return new I4.MeanAggregatorBySlot(ch, type, cursor, col); - case DataKind.I8: - return new Long.MeanAggregatorBySlot(ch, type, cursor, col); case DataKind.R4: return new R4.MeanAggregatorBySlot(ch, type, cursor, col); case DataKind.R8: return new R8.MeanAggregatorBySlot(ch, type, cursor, col); - case DataKind.TS: - return new Long.MeanAggregatorBySlot(ch, type, cursor, col); - case DataKind.DT: - return new Long.MeanAggregatorBySlot(ch, type, cursor, col); default: break; } @@ -102,22 +66,10 @@ private static StatAggregator CreateStatAggregator(IChannel ch, ColumnType type, { switch (type.ItemType.RawKind) { - case DataKind.I1: - return new I1.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); - case DataKind.I2: - return new I2.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); - case DataKind.I4: - return new I4.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); - case DataKind.I8: - return new Long.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); case DataKind.R4: return new R4.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); case DataKind.R8: return new R8.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); - case DataKind.TS: - return new Long.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); - case DataKind.DT: - return new Long.MinMaxAggregatorBySlot(ch, type, cursor, col, kind == ReplacementKind.Max); default: break; } @@ -130,22 +82,10 @@ private static StatAggregator CreateStatAggregator(IChannel ch, ColumnType type, { switch (type.ItemType.RawKind) { - case DataKind.I1: - return new I1.MeanAggregatorAcrossSlots(ch, cursor, col); - case DataKind.I2: - return new I2.MeanAggregatorAcrossSlots(ch, cursor, col); - case DataKind.I4: - return new I4.MeanAggregatorAcrossSlots(ch, cursor, col); - case DataKind.I8: - return new Long.MeanAggregatorAcrossSlots(ch, type, cursor, col); case DataKind.R4: return new R4.MeanAggregatorAcrossSlots(ch, cursor, col); case DataKind.R8: return new R8.MeanAggregatorAcrossSlots(ch, cursor, col); - case DataKind.TS: - return new Long.MeanAggregatorAcrossSlots(ch, type, cursor, col); - case DataKind.DT: - return new Long.MeanAggregatorAcrossSlots(ch, type, cursor, col); default: break; } @@ -154,22 +94,10 @@ private static StatAggregator CreateStatAggregator(IChannel ch, ColumnType type, { switch (type.ItemType.RawKind) { - case DataKind.I1: - return new I1.MinMaxAggregatorAcrossSlots(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.I2: - return new I2.MinMaxAggregatorAcrossSlots(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.I4: - return new I4.MinMaxAggregatorAcrossSlots(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.I8: - return new Long.MinMaxAggregatorAcrossSlots(ch, type, cursor, col, kind == ReplacementKind.Max); case DataKind.R4: return new R4.MinMaxAggregatorAcrossSlots(ch, cursor, col, kind == ReplacementKind.Max); case DataKind.R8: return new R8.MinMaxAggregatorAcrossSlots(ch, cursor, col, kind == ReplacementKind.Max); - case DataKind.TS: - return new Long.MinMaxAggregatorAcrossSlots(ch, type, cursor, col, kind == ReplacementKind.Max); - case DataKind.DT: - return new Long.MinMaxAggregatorAcrossSlots(ch, type, cursor, col, kind == ReplacementKind.Max); default: break; } @@ -503,17 +431,17 @@ private void AssertValid(long valMax) Contracts.Assert(_cna >= 0); } - public void Update(long val, long valMax) + public void Update(long? val, long valMax) { AssertValid(valMax); - Contracts.Assert(-valMax - 1 <= val && val <= valMax); + Contracts.Assert(!val.HasValue || -valMax <= val && val <= valMax); - if (val >= 0) + if (!val.HasValue) + _cna++; + else if (val >= 0) IntUtils.Add(ref _sumHi, ref _sumLo, (ulong)val); - else if (val >= -valMax) - IntUtils.Sub(ref _sumHi, ref _sumLo, (ulong)(-val)); else - _cna++; + IntUtils.Sub(ref _sumHi, ref _sumLo, (ulong)(-val)); AssertValid(valMax); } @@ -928,800 +856,5 @@ public override object GetStat() } } } - - private static class I1 - { - // Utilizes MeanStatInt for the mean aggregators of all IX types, TS, and DT. - - private const long MaxVal = sbyte.MaxValue; - - public sealed class MeanAggregatorOne : StatAggregator - { - public MeanAggregatorOne(IChannel ch, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - } - - protected override void ProcessRow(ref DvInt1 val) - { - Stat.Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, RowCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - return (DvInt1)(sbyte)val; - } - } - - public sealed class MeanAggregatorAcrossSlots : StatAggregatorAcrossSlots - { - public MeanAggregatorAcrossSlots(IChannel ch, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - } - - protected override void ProcessValue(ref DvInt1 val) - { - Stat.Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, ValueCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - return (DvInt1)(sbyte)val; - } - } - - public sealed class MeanAggregatorBySlot : StatAggregatorBySlot - { - public MeanAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col) - : base(ch, type, cursor, col) - { - } - - protected override void ProcessValue(ref DvInt1 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - Stat[slot].Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - DvInt1[] stat = new DvInt1[Stat.Length]; - for (int slot = 0; slot < stat.Length; slot++) - { - long val = Stat[slot].GetCurrentValue(Ch, RowCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - stat[slot] = (DvInt1)(sbyte)val; - } - return stat; - } - } - - public sealed class MinMaxAggregatorOne : MinMaxAggregatorOne - { - public MinMaxAggregatorOne(IChannel ch, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = (sbyte)(ReturnMax ? -MaxVal : MaxVal); - } - - protected override void ProcessValueMin(ref DvInt1 val) - { - var raw = val.RawValue; - if (raw < Stat && raw != DvInt1.RawNA) - Stat = raw; - } - - protected override void ProcessValueMax(ref DvInt1 val) - { - var raw = val.RawValue; - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - return (DvInt1)Stat; - } - } - - public sealed class MinMaxAggregatorAcrossSlots : MinMaxAggregatorAcrossSlots - { - public MinMaxAggregatorAcrossSlots(IChannel ch, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = (sbyte)(ReturnMax ? -MaxVal : MaxVal); - } - - protected override void ProcessValueMin(ref DvInt1 val) - { - var raw = val.RawValue; - if (raw < Stat && raw != DvInt1.RawNA) - Stat = raw; - } - - protected override void ProcessValueMax(ref DvInt1 val) - { - var raw = val.RawValue; - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - // If sparsity occurred, fold in a zero. - if (ValueCount > (ulong)ValuesProcessed) - { - var def = default(DvInt1); - ProcValueDelegate(ref def); - } - return (DvInt1)Stat; - } - } - - public sealed class MinMaxAggregatorBySlot : MinMaxAggregatorBySlot - { - public MinMaxAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col, bool returnMax) - : base(ch, type, cursor, col, returnMax) - { - sbyte bound = (sbyte)(ReturnMax ? -MaxVal : MaxVal); - for (int i = 0; i < Stat.Length; i++) - Stat[i] = bound; - } - - protected override void ProcessValueMin(ref DvInt1 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = val.RawValue; - if (raw < Stat[slot] && raw != DvInt1.RawNA) - Stat[slot] = raw; - } - - protected override void ProcessValueMax(ref DvInt1 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = val.RawValue; - if (raw > Stat[slot]) - Stat[slot] = raw; - } - - public override object GetStat() - { - DvInt1[] stat = new DvInt1[Stat.Length]; - // Account for defaults resulting from sparsity. - for (int slot = 0; slot < Stat.Length; slot++) - { - if (GetValuesProcessed(slot) < RowCount) - { - var def = default(DvInt1); - ProcValueDelegate(ref def, slot); - } - stat[slot] = (DvInt1)Stat[slot]; - } - return stat; - } - } - } - - private static class I2 - { - private const long MaxVal = short.MaxValue; - - public sealed class MeanAggregatorOne : StatAggregator - { - public MeanAggregatorOne(IChannel ch, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - } - - protected override void ProcessRow(ref DvInt2 val) - { - Stat.Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, RowCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - return (DvInt2)(short)val; - } - } - - public sealed class MeanAggregatorAcrossSlots : StatAggregatorAcrossSlots - { - public MeanAggregatorAcrossSlots(IChannel ch, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - } - - protected override void ProcessValue(ref DvInt2 val) - { - Stat.Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, ValueCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - return (DvInt2)(short)val; - } - } - - public sealed class MeanAggregatorBySlot : StatAggregatorBySlot - { - public MeanAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col) - : base(ch, type, cursor, col) - { - } - - protected override void ProcessValue(ref DvInt2 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - Stat[slot].Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - DvInt2[] stat = new DvInt2[Stat.Length]; - for (int slot = 0; slot < stat.Length; slot++) - { - long val = Stat[slot].GetCurrentValue(Ch, RowCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - stat[slot] = (DvInt2)(short)val; - } - return stat; - } - } - - public sealed class MinMaxAggregatorOne : MinMaxAggregatorOne - { - public MinMaxAggregatorOne(IChannel ch, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = (short)(ReturnMax ? -MaxVal : MaxVal); - } - - protected override void ProcessValueMin(ref DvInt2 val) - { - var raw = val.RawValue; - if (raw < Stat && raw != DvInt2.RawNA) - Stat = raw; - } - - protected override void ProcessValueMax(ref DvInt2 val) - { - var raw = val.RawValue; - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - return (DvInt2)Stat; - } - } - - public sealed class MinMaxAggregatorAcrossSlots : MinMaxAggregatorAcrossSlots - { - public MinMaxAggregatorAcrossSlots(IChannel ch, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = (short)(ReturnMax ? -MaxVal : MaxVal); - } - - protected override void ProcessValueMin(ref DvInt2 val) - { - var raw = val.RawValue; - if (raw < Stat && raw != DvInt2.RawNA) - Stat = raw; - } - - protected override void ProcessValueMax(ref DvInt2 val) - { - var raw = val.RawValue; - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - // If sparsity occurred, fold in a zero. - if (ValueCount > (ulong)ValuesProcessed) - { - var def = default(DvInt2); - ProcValueDelegate(ref def); - } - return (DvInt2)Stat; - } - } - - public sealed class MinMaxAggregatorBySlot : MinMaxAggregatorBySlot - { - public MinMaxAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col, bool returnMax) - : base(ch, type, cursor, col, returnMax) - { - short bound = (short)(ReturnMax ? -MaxVal : MaxVal); - for (int i = 0; i < Stat.Length; i++) - Stat[i] = bound; - } - - protected override void ProcessValueMin(ref DvInt2 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = val.RawValue; - if (raw < Stat[slot] && raw != DvInt2.RawNA) - Stat[slot] = raw; - } - - protected override void ProcessValueMax(ref DvInt2 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = val.RawValue; - if (raw > Stat[slot]) - Stat[slot] = raw; - } - - public override object GetStat() - { - DvInt2[] stat = new DvInt2[Stat.Length]; - // Account for defaults resulting from sparsity. - for (int slot = 0; slot < Stat.Length; slot++) - { - if (GetValuesProcessed(slot) < RowCount) - { - var def = default(DvInt2); - ProcValueDelegate(ref def, slot); - } - stat[slot] = (DvInt2)Stat[slot]; - } - return stat; - } - } - } - - private static class I4 - { - private const long MaxVal = int.MaxValue; - - public sealed class MeanAggregatorOne : StatAggregator - { - public MeanAggregatorOne(IChannel ch, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - } - - protected override void ProcessRow(ref DvInt4 val) - { - Stat.Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, RowCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - return (DvInt4)(int)val; - } - } - - public sealed class MeanAggregatorAcrossSlots : StatAggregatorAcrossSlots - { - public MeanAggregatorAcrossSlots(IChannel ch, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - } - - protected override void ProcessValue(ref DvInt4 val) - { - Stat.Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, ValueCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - return (DvInt4)(int)val; - } - } - - public sealed class MeanAggregatorBySlot : StatAggregatorBySlot - { - public MeanAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col) - : base(ch, type, cursor, col) - { - } - - protected override void ProcessValue(ref DvInt4 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - Stat[slot].Update(val.RawValue, MaxVal); - } - - public override object GetStat() - { - DvInt4[] stat = new DvInt4[Stat.Length]; - for (int slot = 0; slot < stat.Length; slot++) - { - long val = Stat[slot].GetCurrentValue(Ch, RowCount, MaxVal); - Ch.Assert(-MaxVal - 1 <= val && val <= MaxVal); - stat[slot] = (DvInt4)(int)val; - } - return stat; - } - } - - public sealed class MinMaxAggregatorOne : MinMaxAggregatorOne - { - public MinMaxAggregatorOne(IChannel ch, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = (int)(ReturnMax ? -MaxVal : MaxVal); - } - - protected override void ProcessValueMin(ref DvInt4 val) - { - var raw = val.RawValue; - if (raw < Stat && raw != DvInt4.RawNA) - Stat = raw; - } - - protected override void ProcessValueMax(ref DvInt4 val) - { - var raw = val.RawValue; - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - return (DvInt4)Stat; - } - } - - public sealed class MinMaxAggregatorAcrossSlots : MinMaxAggregatorAcrossSlots - { - public MinMaxAggregatorAcrossSlots(IChannel ch, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = (int)(ReturnMax ? -MaxVal : MaxVal); - } - - protected override void ProcessValueMin(ref DvInt4 val) - { - var raw = val.RawValue; - if (raw < Stat && raw != DvInt4.RawNA) - Stat = raw; - } - - protected override void ProcessValueMax(ref DvInt4 val) - { - var raw = val.RawValue; - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - // If sparsity occurred, fold in a zero. - if (ValueCount > (ulong)ValuesProcessed) - { - var def = default(DvInt4); - ProcValueDelegate(ref def); - } - return (DvInt4)Stat; - } - } - - public sealed class MinMaxAggregatorBySlot : MinMaxAggregatorBySlot - { - public MinMaxAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col, bool returnMax) - : base(ch, type, cursor, col, returnMax) - { - int bound = (int)(ReturnMax ? -MaxVal : MaxVal); - for (int i = 0; i < Stat.Length; i++) - Stat[i] = bound; - } - - protected override void ProcessValueMin(ref DvInt4 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = val.RawValue; - if (raw < Stat[slot] && raw != DvInt4.RawNA) - Stat[slot] = raw; - } - - protected override void ProcessValueMax(ref DvInt4 val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = val.RawValue; - if (raw > Stat[slot]) - Stat[slot] = raw; - } - - public override object GetStat() - { - DvInt4[] stat = new DvInt4[Stat.Length]; - // Account for defaults resulting from sparsity. - for (int slot = 0; slot < Stat.Length; slot++) - { - if (GetValuesProcessed(slot) < RowCount) - { - var def = default(DvInt4); - ProcValueDelegate(ref def, slot); - } - stat[slot] = (DvInt4)Stat[slot]; - } - return stat; - } - } - } - - private static class Long - { - private const long MaxVal = long.MaxValue; - - public sealed class MeanAggregatorOne : StatAggregator - { - // Converts between TItem and long. - private Converter _converter; - - public MeanAggregatorOne(IChannel ch, ColumnType type, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - _converter = CreateConverter(type); - } - - protected override void ProcessRow(ref TItem val) - { - Stat.Update(_converter.ToLong(val), MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, RowCount, MaxVal); - return _converter.FromLong(val); - } - } - - public sealed class MeanAggregatorAcrossSlots : StatAggregatorAcrossSlots - { - private Converter _converter; - - public MeanAggregatorAcrossSlots(IChannel ch, ColumnType type, IRowCursor cursor, int col) - : base(ch, cursor, col) - { - _converter = CreateConverter(type); - } - - protected override void ProcessValue(ref TItem val) - { - Stat.Update(_converter.ToLong(val), MaxVal); - } - - public override object GetStat() - { - long val = Stat.GetCurrentValue(Ch, ValueCount, MaxVal); - return _converter.FromLong(val); - } - } - - public sealed class MeanAggregatorBySlot : StatAggregatorBySlot - { - private Converter _converter; - - public MeanAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col) - : base(ch, type, cursor, col) - { - _converter = CreateConverter(type); - } - - protected override void ProcessValue(ref TItem val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - Stat[slot].Update(_converter.ToLong(val), MaxVal); - } - - public override object GetStat() - { - TItem[] stat = new TItem[Stat.Length]; - for (int slot = 0; slot < stat.Length; slot++) - { - long val = Stat[slot].GetCurrentValue(Ch, RowCount, MaxVal); - stat[slot] = _converter.FromLong(val); - } - return stat; - } - } - - public sealed class MinMaxAggregatorOne : MinMaxAggregatorOne - { - private Converter _converter; - - public MinMaxAggregatorOne(IChannel ch, ColumnType type, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = ReturnMax ? -MaxVal : MaxVal; - _converter = CreateConverter(type); - } - - protected override void ProcessValueMin(ref TItem val) - { - var raw = _converter.ToLong(val); - if (raw < Stat && -MaxVal <= raw) - Stat = raw; - } - - protected override void ProcessValueMax(ref TItem val) - { - var raw = _converter.ToLong(val); - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - return _converter.FromLong(Stat); - } - } - - public sealed class MinMaxAggregatorAcrossSlots : MinMaxAggregatorAcrossSlots - { - private Converter _converter; - - public MinMaxAggregatorAcrossSlots(IChannel ch, ColumnType type, IRowCursor cursor, int col, bool returnMax) - : base(ch, cursor, col, returnMax) - { - Stat = ReturnMax ? -MaxVal : MaxVal; - _converter = CreateConverter(type); - } - - protected override void ProcessValueMin(ref TItem val) - { - var raw = _converter.ToLong(val); - if (raw < Stat && -MaxVal <= raw) - Stat = raw; - } - - protected override void ProcessValueMax(ref TItem val) - { - var raw = _converter.ToLong(val); - if (raw > Stat) - Stat = raw; - } - - public override object GetStat() - { - // If sparsity occurred, fold in a zero. - if (ValueCount > (ulong)ValuesProcessed) - { - TItem def = default(TItem); - ProcValueDelegate(ref def); - } - return _converter.FromLong(Stat); - } - } - - public sealed class MinMaxAggregatorBySlot : MinMaxAggregatorBySlot - { - private Converter _converter; - - public MinMaxAggregatorBySlot(IChannel ch, ColumnType type, IRowCursor cursor, int col, bool returnMax) - : base(ch, type, cursor, col, returnMax) - { - long bound = ReturnMax ? -MaxVal : MaxVal; - for (int i = 0; i < Stat.Length; i++) - Stat[i] = bound; - - _converter = CreateConverter(type); - } - - protected override void ProcessValueMin(ref TItem val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = _converter.ToLong(val); - if (raw < Stat[slot] && -MaxVal <= raw) - Stat[slot] = raw; - } - - protected override void ProcessValueMax(ref TItem val, int slot) - { - Ch.Assert(0 <= slot && slot < Stat.Length); - var raw = _converter.ToLong(val); - if (raw > Stat[slot]) - Stat[slot] = raw; - } - - public override object GetStat() - { - TItem[] stat = new TItem[Stat.Length]; - // Account for defaults resulting from sparsity. - for (int slot = 0; slot < Stat.Length; slot++) - { - if (GetValuesProcessed(slot) < RowCount) - { - var def = default(TItem); - ProcValueDelegate(ref def, slot); - } - stat[slot] = _converter.FromLong(Stat[slot]); - } - return stat; - } - } - - private static Converter CreateConverter(ColumnType type) - { - Contracts.AssertValue(type); - Contracts.Assert(typeof(TItem) == type.ItemType.RawType); - Converter converter; - if (type.ItemType.IsTimeSpan) - converter = new TSConverter(); - else if (type.ItemType.IsDateTime) - converter = new DTConverter(); - else - { - Contracts.Assert(type.ItemType.RawKind == DataKind.I8); - converter = new I8Converter(); - } - return (Converter)converter; - } - - /// - /// The base class for conversions from types to long. - /// - private abstract class Converter - { - } - - private abstract class Converter : Converter - { - public abstract long ToLong(T val); - public abstract T FromLong(long val); - } - - private sealed class I8Converter : Converter - { - public override long ToLong(DvInt8 val) - { - return val.RawValue; - } - - public override DvInt8 FromLong(long val) - { - Contracts.Assert(DvInt8.RawNA != val); - return (DvInt8)val; - } - } - - private sealed class TSConverter : Converter - { - public override long ToLong(DvTimeSpan val) - { - return val.Ticks.RawValue; - } - - public override DvTimeSpan FromLong(long val) - { - Contracts.Assert(DvInt8.RawNA != val); - return new DvTimeSpan(val); - } - } - - private sealed class DTConverter : Converter - { - public override long ToLong(DvDateTime val) - { - return val.Ticks.RawValue; - } - - public override DvDateTime FromLong(long val) - { - Contracts.Assert(0 <= val && val <= DvDateTime.MaxTicks); - return new DvDateTime(val); - } - } - } } } \ No newline at end of file diff --git a/src/Microsoft.ML.Transforms/OptionalColumnTransform.cs b/src/Microsoft.ML.Transforms/OptionalColumnTransform.cs index 5117496194..b78f008215 100644 --- a/src/Microsoft.ML.Transforms/OptionalColumnTransform.cs +++ b/src/Microsoft.ML.Transforms/OptionalColumnTransform.cs @@ -226,7 +226,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Save the input schema, for metadata verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(OptionalColumnTransform).Assembly.FullName); } private readonly Bindings _bindings; diff --git a/src/Microsoft.ML.Transforms/ProduceIdTransform.cs b/src/Microsoft.ML.Transforms/ProduceIdTransform.cs index 0934fd0086..b87476cefa 100644 --- a/src/Microsoft.ML.Transforms/ProduceIdTransform.cs +++ b/src/Microsoft.ML.Transforms/ProduceIdTransform.cs @@ -84,7 +84,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(ProduceIdTransform).Assembly.FullName); } private readonly Bindings _bindings; diff --git a/src/Microsoft.ML.Transforms/RffTransform.cs b/src/Microsoft.ML.Transforms/RffTransform.cs index 7cb51a6f65..866683bb99 100644 --- a/src/Microsoft.ML.Transforms/RffTransform.cs +++ b/src/Microsoft.ML.Transforms/RffTransform.cs @@ -220,7 +220,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(RffTransform).Assembly.FullName); } // These are parallel to Infos. diff --git a/src/Microsoft.ML.Transforms/TermLookupTransform.cs b/src/Microsoft.ML.Transforms/TermLookupTransform.cs index 96acf4aa6e..9cadc31d9f 100644 --- a/src/Microsoft.ML.Transforms/TermLookupTransform.cs +++ b/src/Microsoft.ML.Transforms/TermLookupTransform.cs @@ -115,7 +115,7 @@ public static VecValueMap CreateVector(VectorType type) public abstract void Train(IExceptionContext ectx, IRowCursor cursor, int colTerm, int colValue); - public abstract Delegate GetGetter(ValueGetter getSrc); + public abstract Delegate GetGetter(ValueGetter> getSrc); } /// @@ -146,22 +146,18 @@ public override void Train(IExceptionContext ectx, IRowCursor cursor, int colTer ectx.Assert(0 <= colValue && colValue < cursor.Schema.ColumnCount); ectx.Assert(cursor.Schema.GetColumnType(colValue).Equals(Type)); - var getTerm = cursor.GetGetter(colTerm); + var getTerm = cursor.GetGetter>(colTerm); var getValue = cursor.GetGetter(colValue); var terms = new NormStr.Pool(); var values = new List(); - DvText term = default(DvText); + ReadOnlyMemory term = default; while (cursor.MoveNext()) { getTerm(ref term); // REVIEW: Should we trim? - term = term.Trim(); - // REVIEW: Should we handle mapping "missing" to something? - if (term.IsNA) - throw ectx.Except("Missing term in lookup data around row: {0}", values.Count); - - var nstr = term.AddToPool(terms); + term = ReadOnlyMemoryUtils.TrimSpaces(term); + var nstr = ReadOnlyMemoryUtils.AddToPool(term, terms); if (nstr.Id != values.Count) throw ectx.Except("Duplicate term in lookup data: '{0}'", nstr); @@ -179,7 +175,7 @@ public override void Train(IExceptionContext ectx, IRowCursor cursor, int colTer /// /// Given the term getter, produce a value getter from this value map. /// - public override Delegate GetGetter(ValueGetter getTerm) + public override Delegate GetGetter(ValueGetter> getTerm) { Contracts.Assert(_terms != null); Contracts.Assert(_values != null); @@ -188,15 +184,15 @@ public override Delegate GetGetter(ValueGetter getTerm) return GetGetterCore(getTerm); } - private ValueGetter GetGetterCore(ValueGetter getTerm) + private ValueGetter GetGetterCore(ValueGetter> getTerm) { - var src = default(DvText); + var src = default(ReadOnlyMemory); return (ref TRes dst) => { getTerm(ref src); - src = src.Trim(); - var nstr = src.FindInPool(_terms); + src = ReadOnlyMemoryUtils.TrimSpaces(src); + var nstr = ReadOnlyMemoryUtils.FindInPool(src, _terms); if (nstr == null) GetMissing(ref dst); else @@ -225,11 +221,13 @@ public OneValueMap(PrimitiveType type) // REVIEW: This uses the fact that standard conversions map NA to NA to get the NA for TRes. // We should probably have a mapping from type to its bad value somewhere, perhaps in Conversions. bool identity; - ValueMapper conv; - if (Conversions.Instance.TryGetStandardConversion(TextType.Instance, type, + ValueMapper, TRes> conv; + if (Conversions.Instance.TryGetStandardConversion, TRes>(TextType.Instance, type, out conv, out identity)) { - var bad = DvText.NA; + //Empty string will map to NA for R4 and R8, the only two types that can + //handle missing values. + var bad = String.Empty.AsMemory(); conv(ref bad, ref _badValue); } } @@ -279,7 +277,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Dropped sizeof(Float). verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TermLookupTransform).Assembly.FullName); } // This is the byte array containing the binary .idv file contents for the lookup data. @@ -374,9 +373,9 @@ private static IComponentFactory GetLoaderFacto var data = TextLoader.ReadFile(host, txtArgs, new MultiFileSource(filename)); using (var cursor = data.GetRowCursor(c => true)) { - var getTerm = cursor.GetGetter(0); - var getVal = cursor.GetGetter(1); - DvText txt = default(DvText); + var getTerm = cursor.GetGetter>(0); + var getVal = cursor.GetGetter>(1); + ReadOnlyMemory txt = default; using (var ch = host.Start("Creating Text Lookup Loader")) { @@ -405,7 +404,7 @@ private static IComponentFactory GetLoaderFacto //If parsing as a ulong fails, we increment the counter for the non-key values. else { - var term = default(DvText); + var term = default(ReadOnlyMemory); getTerm(ref term); if (countNonKeys < 5) ch.Warning("Term '{0}' in mapping file is mapped to non key value '{1}'", term, txt); @@ -703,7 +702,7 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou Host.Assert(0 <= iinfo && iinfo < Infos.Length); disposer = null; - var getSrc = GetSrcGetter(input, iinfo); + var getSrc = GetSrcGetter>(input, iinfo); return _valueMap.GetGetter(getSrc); } } diff --git a/src/Microsoft.ML.Transforms/Text/CharTokenizeTransform.cs b/src/Microsoft.ML.Transforms/Text/CharTokenizeTransform.cs index e1ea2974b3..da10ce2b65 100644 --- a/src/Microsoft.ML.Transforms/Text/CharTokenizeTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/CharTokenizeTransform.cs @@ -75,7 +75,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Updated to use UnitSeparator character instead of using for vector inputs. verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CharTokenizeTransform).Assembly.FullName); } // Controls whether to mark the beginning/end of each row/slot with TextStartMarker/TextEndMarker. @@ -171,7 +172,7 @@ private void SetMetadata() // Slot names should propagate. using (var bldr = md.BuildMetadata(iinfo, Source.Schema, info.Source, MetadataUtils.Kinds.SlotNames)) { - bldr.AddGetter>(MetadataUtils.Kinds.KeyValues, + bldr.AddGetter>>(MetadataUtils.Kinds.KeyValues, MetadataUtils.GetNamesType(_type.ItemType.KeyCount), GetKeyValues); } } @@ -181,7 +182,7 @@ private void SetMetadata() /// /// Get the key values (chars) corresponding to keys in the output columns. /// - private void GetKeyValues(int iinfo, ref VBuffer dst) + private void GetKeyValues(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); @@ -211,10 +212,10 @@ private void GetKeyValues(int iinfo, ref VBuffer dst) var values = dst.Values; if (Utils.Size(values) < CharsCount) - values = new DvText[CharsCount]; + values = new ReadOnlyMemory[CharsCount]; for (int i = 0; i < CharsCount; i++) - values[i] = new DvText(keyValuesStr, keyValuesBoundaries[i], keyValuesBoundaries[i + 1]); - dst = new VBuffer(CharsCount, values, dst.Indices); + values[i] = keyValuesStr.AsMemory().Slice(keyValuesBoundaries[i], keyValuesBoundaries[i + 1] - keyValuesBoundaries[i]); + dst = new VBuffer>(CharsCount, values, dst.Indices); } private void AppendCharRepr(char c, StringBuilder bldr) @@ -368,14 +369,14 @@ private ValueGetter> MakeGetterOne(IRow input, int iinfo) Host.AssertValue(input); Host.Assert(Infos[iinfo].TypeSrc.IsText); - var getSrc = GetSrcGetter(input, iinfo); - var src = default(DvText); + var getSrc = GetSrcGetter>(input, iinfo); + var src = default(ReadOnlyMemory); return (ref VBuffer dst) => { getSrc(ref src); - var len = src.HasChars ? (_useMarkerChars ? src.Length + TextMarkersCount : src.Length) : 0; + var len = !src.IsEmpty ? (_useMarkerChars ? src.Length + TextMarkersCount : src.Length) : 0; var values = dst.Values; if (len > 0) { @@ -385,8 +386,9 @@ private ValueGetter> MakeGetterOne(IRow input, int iinfo) int index = 0; if (_useMarkerChars) values[index++] = TextStartMarker; + var span = src.Span; for (int ich = 0; ich < src.Length; ich++) - values[index++] = src[ich]; + values[index++] = span[ich]; if (_useMarkerChars) values[index++] = TextEndMarker; Contracts.Assert(index == len); @@ -405,8 +407,8 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) int cv = Infos[iinfo].TypeSrc.VectorSize; Contracts.Assert(cv >= 0); - var getSrc = GetSrcGetter>(input, iinfo); - var src = default(VBuffer); + var getSrc = GetSrcGetter>>(input, iinfo); + var src = default(VBuffer>); ValueGetter> getterWithStartEndSep = (ref VBuffer dst) => { @@ -415,7 +417,7 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) int len = 0; for (int i = 0; i < src.Count; i++) { - if (src.Values[i].HasChars) + if (!src.Values[i].IsEmpty) { len += src.Values[i].Length; if (_useMarkerChars) @@ -432,12 +434,13 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) int index = 0; for (int i = 0; i < src.Count; i++) { - if (!src.Values[i].HasChars) + if (src.Values[i].IsEmpty) continue; if (_useMarkerChars) values[index++] = TextStartMarker; + var span = src.Values[i].Span; for (int ich = 0; ich < src.Values[i].Length; ich++) - values[index++] = src.Values[i][ich]; + values[index++] = span[ich]; if (_useMarkerChars) values[index++] = TextEndMarker; } @@ -455,7 +458,7 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) for (int i = 0; i < src.Count; i++) { - if (src.Values[i].HasChars) + if (!src.Values[i].IsEmpty) { len += src.Values[i].Length; @@ -475,10 +478,10 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) int index = 0; - // VBuffer can be a result of either concatenating text columns together + // ReadOnlyMemory can be a result of either concatenating text columns together // or application of word tokenizer before char tokenizer in TextTransform. // - // Considering VBuffer as a single text stream. + // Considering VBuffer as a single text stream. // Therefore, prepend and append start and end markers only once i.e. at the start and at end of vector. // Insert UnitSeparator after every piece of text in the vector. if (_useMarkerChars) @@ -486,16 +489,15 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) for (int i = 0; i < src.Count; i++) { - if (!src.Values[i].HasChars) + if (src.Values[i].IsEmpty) continue; if (i > 0) values[index++] = UnitSeparator; + var span = src.Values[i].Span; for (int ich = 0; ich < src.Values[i].Length; ich++) - { - values[index++] = src.Values[i][ich]; - } + values[index++] = span[ich]; } if (_useMarkerChars) diff --git a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs index 0b3d52854f..f15546d2cc 100644 --- a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs @@ -291,7 +291,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LdaTransform).Assembly.FullName); } private readonly ColInfoEx[] _exes; @@ -403,7 +404,7 @@ public static LdaTransform Create(IHostEnvironment env, ModelLoadContext ctx, ID public string GetTopicSummary() { StringWriter writer = new StringWriter(); - VBuffer slotNames = default(VBuffer); + VBuffer> slotNames = default; for (int i = 0; i < _ldas.Length; i++) { GetSlotNames(i, ref slotNames); @@ -427,7 +428,7 @@ public override void Save(ModelSaveContext ctx) ctx.Writer.Write(sizeof(Float)); SaveBase(ctx); Host.Assert(_ldas.Length == Infos.Length); - VBuffer slotNames = default(VBuffer); + VBuffer> slotNames = default; for (int i = 0; i < _ldas.Length; i++) { GetSlotNames(i, ref slotNames); @@ -435,13 +436,13 @@ public override void Save(ModelSaveContext ctx) } } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); if (Source.Schema.HasSlotNames(Infos[iinfo].Source, Infos[iinfo].TypeSrc.ValueCount)) Source.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, Infos[iinfo].Source, ref dst); else - dst = default(VBuffer); + dst = default(VBuffer>); } private static string TestType(ColumnType t) @@ -691,7 +692,7 @@ public LdaState(IExceptionContext ectx, ModelLoadContext ctx) } } - public Action GetTopicSummaryWriter(VBuffer mapping) + public Action GetTopicSummaryWriter(VBuffer> mapping) { Action writeAction; @@ -715,7 +716,7 @@ public Action GetTopicSummaryWriter(VBuffer mapping) writeAction = writer => { - DvText slotName = default(DvText); + ReadOnlyMemory slotName = default; for (int i = 0; i < _ldaTrainer.NumTopic; i++) { KeyValuePair[] topicSummaryVector = _ldaTrainer.GetTopicSummary(i); @@ -733,7 +734,7 @@ public Action GetTopicSummaryWriter(VBuffer mapping) return writeAction; } - public void Save(ModelSaveContext ctx, bool saveText, VBuffer mapping) + public void Save(ModelSaveContext ctx, bool saveText, VBuffer> mapping) { Contracts.AssertValue(ctx); long memBlockSize = 0; diff --git a/src/Microsoft.ML.Transforms/Text/NgramHashTransform.cs b/src/Microsoft.ML.Transforms/Text/NgramHashTransform.cs index 548b80cb8c..72eb1d650a 100644 --- a/src/Microsoft.ML.Transforms/Text/NgramHashTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/NgramHashTransform.cs @@ -213,7 +213,7 @@ protected override void GetMetadataCore(string kind, int iinfo, ref TVal { if (kind == MetadataUtils.Kinds.SlotNames && _parent._slotNames != null && _parent._slotNames[iinfo].Length > 0) { - MetadataUtils.MetadataGetter> getTerms = _parent.GetTerms; + MetadataUtils.MetadataGetter>> getTerms = _parent.GetTerms; getTerms.Marshal(iinfo, ref value); return; } @@ -317,13 +317,14 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Invert hash key values, hash fix verReadableCur: 0x00010002, verWeCanReadBack: 0x00010002, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(NgramHashTransform).Assembly.FullName); } private readonly Bindings _bindings; private readonly ColInfoEx[] _exes; - private readonly VBuffer[] _slotNames; + private readonly VBuffer>[] _slotNames; private readonly ColumnType[] _slotNamesTypes; private const string RegistrationName = "NgramHash"; @@ -447,7 +448,7 @@ private static int GetAndVerifyInvertHashMaxCount(Arguments args, Column col, Co return invertHashMaxCount; } - private void GetTerms(int iinfo, ref VBuffer dst) + private void GetTerms(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < _exes.Length); Host.Assert(_slotNames[iinfo].Length > 0); @@ -1005,9 +1006,9 @@ public NgramIdFinder Decorate(int iinfo, NgramIdFinder finder) }; } - public VBuffer[] SlotNamesMetadata(out ColumnType[] types) + public VBuffer>[] SlotNamesMetadata(out ColumnType[] types) { - var values = new VBuffer[_iinfoToCollector.Length]; + var values = new VBuffer>[_iinfoToCollector.Length]; types = new ColumnType[_iinfoToCollector.Length]; for (int iinfo = 0; iinfo < _iinfoToCollector.Length; ++iinfo) { diff --git a/src/Microsoft.ML.Transforms/Text/NgramTransform.cs b/src/Microsoft.ML.Transforms/Text/NgramTransform.cs index 546c46479d..3f24f290f9 100644 --- a/src/Microsoft.ML.Transforms/Text/NgramTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/NgramTransform.cs @@ -200,7 +200,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010002, // Add support for TF-IDF verReadableCur: 0x00010002, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(NgramTransform).Assembly.FullName); } private readonly VectorType[] _types; @@ -303,7 +304,7 @@ public override void Save(ModelSaveContext ctx) ctx.Writer.Write(sizeof(Float)); SaveBase(ctx); - var ngramsNames = default(VBuffer); + var ngramsNames = default(VBuffer>); for (int i = 0; i < _exes.Length; i++) { _exes[i].Save(ctx); @@ -358,7 +359,7 @@ private void InitColumnTypeAndMetadata(out VectorType[] types, out VectorType[] if (_ngramMaps[iinfo].Count > 0) { slotNamesTypes[iinfo] = new VectorType(TextType.Instance, _ngramMaps[iinfo].Count); - bldr.AddGetter>(MetadataUtils.Kinds.SlotNames, + bldr.AddGetter>>(MetadataUtils.Kinds.SlotNames, slotNamesTypes[iinfo], GetSlotNames); } } @@ -366,7 +367,7 @@ private void InitColumnTypeAndMetadata(out VectorType[] types, out VectorType[] md.Seal(); } - private void GetSlotNames(int iinfo, ref VBuffer dst) + private void GetSlotNames(int iinfo, ref VBuffer> dst) { Host.Assert(0 <= iinfo && iinfo < Infos.Length); Host.Assert(_slotNamesTypes[iinfo] != null); @@ -374,7 +375,7 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) var keyCount = Infos[iinfo].TypeSrc.ItemType.KeyCount; Host.Assert(Source.Schema.HasKeyNames(Infos[iinfo].Source, keyCount)); - var unigramNames = new VBuffer(); + var unigramNames = new VBuffer>(); // Get the key values of the unigrams. Source.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues, Infos[iinfo].Source, ref unigramNames); @@ -397,13 +398,13 @@ private void GetSlotNames(int iinfo, ref VBuffer dst) // Get the unigrams composing the current ngram. ComposeNgramString(ngram, n, sb, keyCount, unigramNames.GetItemOrDefault); - values[slot] = new DvText(sb.ToString()); + values[slot] = sb.ToString().AsMemory(); } - dst = new VBuffer(ngramCount, values, dst.Indices); + dst = new VBuffer>(ngramCount, values, dst.Indices); } - private delegate void TermGetter(int index, ref DvText term); + private delegate void TermGetter(int index, ref ReadOnlyMemory term); private void ComposeNgramString(uint[] ngram, int count, StringBuilder sb, int keyCount, TermGetter termGetter) { @@ -412,7 +413,7 @@ private void ComposeNgramString(uint[] ngram, int count, StringBuilder sb, int k Host.Assert(keyCount > 0); sb.Clear(); - DvText term = default(DvText); + ReadOnlyMemory term = default; string sep = ""; for (int iterm = 0; iterm < count; iterm++) { @@ -424,7 +425,7 @@ private void ComposeNgramString(uint[] ngram, int count, StringBuilder sb, int k else { termGetter((int)unigram - 1, ref term); - term.AddToStringBuilder(sb); + sb.AppendMemory(term); } } } diff --git a/src/Microsoft.ML.Transforms/Text/StopWordsRemoverTransform.cs b/src/Microsoft.ML.Transforms/Text/StopWordsRemoverTransform.cs index b9ca08fe06..42fac1e2bb 100644 --- a/src/Microsoft.ML.Transforms/Text/StopWordsRemoverTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/StopWordsRemoverTransform.cs @@ -235,7 +235,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(StopWordsRemoverTransform).Assembly.FullName); } private readonly bool?[] _resourcesExist; @@ -244,7 +245,7 @@ private static VersionInfo GetVersionInfo() private static readonly ColumnType _outputType = new VectorType(TextType.Instance); private static volatile NormStr.Pool[] _stopWords; - private static volatile Dictionary _langsDictionary; + private static volatile Dictionary, Language> _langsDictionary; private const Language DefaultLanguage = Language.English; private const string RegistrationName = "StopWordsRemover"; @@ -270,14 +271,14 @@ private static NormStr.Pool[] StopWords } } - private static Dictionary LangsDictionary + private static Dictionary, Language> LangsDictionary { get { if (_langsDictionary == null) { var langsDictionary = Enum.GetValues(typeof(Language)).Cast() - .ToDictionary(lang => new DvText(lang.ToString())); + .ToDictionary(lang => lang.ToString().AsMemory()); Interlocked.CompareExchange(ref _langsDictionary, langsDictionary, null); } @@ -449,16 +450,16 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou var ex = _exes[iinfo]; Language stopWordslang = ex.Lang; - var lang = default(DvText); - var getLang = ex.LangsColIndex >= 0 ? input.GetGetter(ex.LangsColIndex) : null; + var lang = default(ReadOnlyMemory); + var getLang = ex.LangsColIndex >= 0 ? input.GetGetter>(ex.LangsColIndex) : null; - var getSrc = GetSrcGetter>(input, iinfo); - var src = default(VBuffer); + var getSrc = GetSrcGetter>>(input, iinfo); + var src = default(VBuffer>); var buffer = new StringBuilder(); - var list = new List(); + var list = new List>(); - ValueGetter> del = - (ref VBuffer dst) => + ValueGetter>> del = + (ref VBuffer> dst) => { var langToUse = stopWordslang; UpdateLanguage(ref langToUse, getLang, ref lang); @@ -468,10 +469,10 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou for (int i = 0; i < src.Count; i++) { - if (!src.Values[i].HasChars) + if (src.Values[i].IsEmpty) continue; buffer.Clear(); - src.Values[i].AddLowerCaseToStringBuilder(buffer); + ReadOnlyMemoryUtils.AddLowerCaseToStringBuilder(src.Values[i].Span, buffer); // REVIEW nihejazi: Consider using a trie for string matching (Aho-Corasick, etc.) if (StopWords[(int)langToUse].Get(buffer) == null) @@ -484,13 +485,13 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou return del; } - private void UpdateLanguage(ref Language langToUse, ValueGetter getLang, ref DvText langTxt) + private void UpdateLanguage(ref Language langToUse, ValueGetter> getLang, ref ReadOnlyMemory langTxt) { if (getLang != null) { getLang(ref langTxt); Language lang; - if (!langTxt.IsNA && LangsDictionary.TryGetValue(langTxt, out lang)) + if (LangsDictionary.TryGetValue(langTxt, out lang)) langToUse = lang; } @@ -602,10 +603,11 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(CustomStopWordsRemoverTransform).Assembly.FullName); } - public const string StopwrodsManagerLoaderSignature = "CustomStopWordsManager"; + public const string StopwordsManagerLoaderSignature = "CustomStopWordsManager"; private static VersionInfo GetStopwrodsManagerVersionInfo() { return new VersionInfo( @@ -613,7 +615,8 @@ private static VersionInfo GetStopwrodsManagerVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: StopwrodsManagerLoaderSignature); + loaderSignature: StopwordsManagerLoaderSignature, + loaderAssemblyName: typeof(CustomStopWordsRemoverTransform).Assembly.FullName); } private static readonly ColumnType _outputType = new VectorType(TextType.Instance); @@ -694,24 +697,24 @@ private void LoadStopWords(IHostEnvironment env, IChannel ch, ArgumentsBase load ch.Warning("Explicit stopwords list specified. Data file arguments will be ignored"); } - var src = default(DvText); + var src = default(ReadOnlyMemory); stopWordsMap = new NormStr.Pool(); var buffer = new StringBuilder(); - var stopwords = new DvText(loaderArgs.Stopwords); - stopwords = stopwords.Trim(); - if (stopwords.HasChars) + var stopwords = loaderArgs.Stopwords.AsMemory(); + stopwords = ReadOnlyMemoryUtils.TrimSpaces(stopwords); + if (!stopwords.IsEmpty) { bool warnEmpty = true; for (bool more = true; more;) { - DvText stopword; - more = stopwords.SplitOne(',', out stopword, out stopwords); - stopword = stopword.Trim(); - if (stopword.HasChars) + ReadOnlyMemory stopword; + more = ReadOnlyMemoryUtils.SplitOne(stopwords, ',', out stopword, out stopwords); + stopword = ReadOnlyMemoryUtils.TrimSpaces(stopword); + if (!stopword.IsEmpty) { buffer.Clear(); - stopword.AddLowerCaseToStringBuilder(buffer); + ReadOnlyMemoryUtils.AddLowerCaseToStringBuilder(stopwords.Span, buffer); stopWordsMap.Add(buffer); } else if (warnEmpty) @@ -727,12 +730,12 @@ private void LoadStopWords(IHostEnvironment env, IChannel ch, ArgumentsBase load bool warnEmpty = true; foreach (string word in loaderArgs.Stopword) { - var stopword = new DvText(word); - stopword = stopword.Trim(); - if (stopword.HasChars) + var stopword = word.AsSpan(); + stopword = stopword.Trim(' '); + if (!stopword.IsEmpty) { buffer.Clear(); - stopword.AddLowerCaseToStringBuilder(buffer); + ReadOnlyMemoryUtils.AddLowerCaseToStringBuilder(stopword, buffer); stopWordsMap.Add(buffer); } else if (warnEmpty) @@ -756,14 +759,14 @@ private void LoadStopWords(IHostEnvironment env, IChannel ch, ArgumentsBase load using (var cursor = loader.GetRowCursor(col => col == colSrc)) { bool warnEmpty = true; - var getter = cursor.GetGetter(colSrc); + var getter = cursor.GetGetter>(colSrc); while (cursor.MoveNext()) { getter(ref src); - if (src.HasChars) + if (!src.IsEmpty) { buffer.Clear(); - src.AddLowerCaseToStringBuilder(buffer); + ReadOnlyMemoryUtils.AddLowerCaseToStringBuilder(src.Span, buffer); stopWordsMap.Add(buffer); } else if (warnEmpty) @@ -902,7 +905,7 @@ public override void Save(ModelSaveContext ctx) foreach (var nstr in _stopWordsMap) { Host.Assert(nstr.Id == id); - ctx.SaveString(nstr); + ctx.SaveString(nstr.Value); id++; } @@ -928,23 +931,23 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou Host.Assert(Infos[iinfo].TypeSrc.IsVector & Infos[iinfo].TypeSrc.ItemType.IsText); disposer = null; - var getSrc = GetSrcGetter>(input, iinfo); - var src = default(VBuffer); + var getSrc = GetSrcGetter>>(input, iinfo); + var src = default(VBuffer>); var buffer = new StringBuilder(); - var list = new List(); + var list = new List>(); - ValueGetter> del = - (ref VBuffer dst) => + ValueGetter>> del = + (ref VBuffer> dst) => { getSrc(ref src); list.Clear(); for (int i = 0; i < src.Count; i++) { - if (!src.Values[i].HasChars) + if (src.Values[i].IsEmpty) continue; buffer.Clear(); - src.Values[i].AddLowerCaseToStringBuilder(buffer); + ReadOnlyMemoryUtils.AddLowerCaseToStringBuilder(src.Values[i].Span, buffer); // REVIEW nihejazi: Consider using a trie for string matching (Aho-Corasick, etc.) if (_stopWordsMap.Get(buffer) == null) diff --git a/src/Microsoft.ML.Transforms/Text/TextNormalizerTransform.cs b/src/Microsoft.ML.Transforms/Text/TextNormalizerTransform.cs index 9565b4b445..87a33fe2bf 100644 --- a/src/Microsoft.ML.Transforms/Text/TextNormalizerTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/TextNormalizerTransform.cs @@ -25,7 +25,7 @@ namespace Microsoft.ML.Runtime.TextAnalytics { /// /// A text normalization transform that allows normalizing text case, removing diacritical marks, punctuation marks and/or numbers. - /// The transform operates on text input as well as vector of tokens/text (vector of DvText). + /// The transform operates on text input as well as vector of tokens/text (vector of ReadOnlyMemory). /// public sealed class TextNormalizerTransform : OneToOneTransformBase { @@ -76,7 +76,7 @@ public sealed class Arguments } internal const string Summary = "A text normalization transform that allows normalizing text case, removing diacritical marks, punctuation marks and/or numbers." + - " The transform operates on text input as well as vector of tokens/text (vector of DvText)."; + " The transform operates on text input as well as vector of tokens/text (vector of ReadOnlyMemory)."; public const string LoaderSignature = "TextNormalizerTransform"; private static VersionInfo GetVersionInfo() @@ -86,7 +86,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(TextNormalizerTransform).Assembly.FullName); } private const string RegistrationName = "TextNormalizer"; @@ -256,31 +257,31 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou return MakeGetterOne(input, iinfo); } - private ValueGetter MakeGetterOne(IRow input, int iinfo) + private ValueGetter> MakeGetterOne(IRow input, int iinfo) { Contracts.Assert(Infos[iinfo].TypeSrc.IsText); - var getSrc = GetSrcGetter(input, iinfo); + var getSrc = GetSrcGetter>(input, iinfo); Host.AssertValue(getSrc); - var src = default(DvText); + var src = default(ReadOnlyMemory); var buffer = new StringBuilder(); return - (ref DvText dst) => + (ref ReadOnlyMemory dst) => { getSrc(ref src); NormalizeSrc(ref src, ref dst, buffer); }; } - private ValueGetter> MakeGetterVec(IRow input, int iinfo) + private ValueGetter>> MakeGetterVec(IRow input, int iinfo) { - var getSrc = GetSrcGetter>(input, iinfo); + var getSrc = GetSrcGetter>>(input, iinfo); Host.AssertValue(getSrc); - var src = default(VBuffer); + var src = default(VBuffer>); var buffer = new StringBuilder(); - var list = new List(); - var temp = default(DvText); + var list = new List>(); + var temp = default(ReadOnlyMemory); return - (ref VBuffer dst) => + (ref VBuffer> dst) => { getSrc(ref src); list.Clear(); @@ -295,11 +296,11 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) }; } - private void NormalizeSrc(ref DvText src, ref DvText dst, StringBuilder buffer) + private void NormalizeSrc(ref ReadOnlyMemory src, ref ReadOnlyMemory dst, StringBuilder buffer) { Host.AssertValue(buffer); - if (!src.HasChars) + if (src.IsEmpty) { dst = src; return; @@ -307,18 +308,16 @@ private void NormalizeSrc(ref DvText src, ref DvText dst, StringBuilder buffer) buffer.Clear(); - int ichMin; - int ichLim; - string text = src.GetRawUnderlyingBufferInfo(out ichMin, out ichLim); - int i = ichMin; - int min = ichMin; - while (i < ichLim) + int i = 0; + int min = 0; + var span = src.Span; + while (i < src.Length) { - char ch = text[i]; + char ch = span[i]; if (!_keepPunctuations && char.IsPunctuation(ch) || !_keepNumbers && char.IsNumber(ch)) { // Append everything before ch and ignore ch. - buffer.Append(text, min, i - min); + buffer.AppendSpan(span.Slice(min, i - min)); min = i + 1; i++; continue; @@ -328,7 +327,7 @@ private void NormalizeSrc(ref DvText src, ref DvText dst, StringBuilder buffer) { if (IsCombiningDiacritic(ch)) { - buffer.Append(text, min, i - min); + buffer.AppendSpan(span.Slice(min, i - min)); min = i + 1; i++; continue; @@ -343,26 +342,26 @@ private void NormalizeSrc(ref DvText src, ref DvText dst, StringBuilder buffer) else if (_case == CaseNormalizationMode.Upper) ch = CharUtils.ToUpperInvariant(ch); - if (ch != text[i]) + if (ch != src.Span[i]) { - buffer.Append(text, min, i - min).Append(ch); + buffer.AppendSpan(span.Slice(min, i - min)).Append(ch); min = i + 1; } i++; } - Host.Assert(i == ichLim); + Host.Assert(i == src.Length); int len = i - min; - if (ichMin == min) + if (min == 0) { Host.Assert(src.Length == len); dst = src; } else { - buffer.Append(text, min, len); - dst = new DvText(buffer.ToString()); + buffer.AppendSpan(span.Slice(min, len)); + dst = buffer.ToString().AsMemory(); } } diff --git a/src/Microsoft.ML.Transforms/Text/TextStaticExtensions.cs b/src/Microsoft.ML.Transforms/Text/TextStaticExtensions.cs new file mode 100644 index 0000000000..4e0821241b --- /dev/null +++ b/src/Microsoft.ML.Transforms/Text/TextStaticExtensions.cs @@ -0,0 +1,116 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Core.Data; +using Microsoft.ML.Data.StaticPipe.Runtime; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using System; +using System.Collections.Generic; + +namespace Microsoft.ML.Transforms.Text +{ + /// + /// Extensions for statically typed word tokenizer. + /// + public static class WordTokenizerExtensions + { + private sealed class OutPipelineColumn : VarVector + { + public readonly Scalar Input; + + public OutPipelineColumn(Scalar input, string separators) + : base(new Reconciler(separators), input) + { + Input = input; + } + } + + private sealed class Reconciler : EstimatorReconciler + { + private readonly string _separators; + + public Reconciler(string separators) + { + _separators = separators; + } + + public override IEstimator Reconcile(IHostEnvironment env, + PipelineColumn[] toOutput, + IReadOnlyDictionary inputNames, + IReadOnlyDictionary outputNames, + IReadOnlyCollection usedNames) + { + Contracts.Assert(toOutput.Length == 1); + + var pairs = new List<(string input, string output)>(); + foreach (var outCol in toOutput) + pairs.Add((inputNames[((OutPipelineColumn)outCol).Input], outputNames[outCol])); + + return new WordTokenizer(env, pairs.ToArray(), _separators); + } + } + + /// + /// Tokenize incoming text using and output the tokens. + /// + /// The column to apply to. + /// The separators to use (comma separated). + public static VarVector TokenizeText(this Scalar input, string separators = "space") => new OutPipelineColumn(input, separators); + } + + /// + /// Extensions for statically typed character tokenizer. + /// + public static class CharacterTokenizerExtensions + { + private sealed class OutPipelineColumn : VarVector> + { + public readonly Scalar Input; + + public OutPipelineColumn(Scalar input, bool useMarkerChars) + : base(new Reconciler(useMarkerChars), input) + { + Input = input; + } + } + + private sealed class Reconciler : EstimatorReconciler, IEquatable + { + private readonly bool _useMarker; + + public Reconciler(bool useMarkerChars) + { + _useMarker = useMarkerChars; + } + + public bool Equals(Reconciler other) + { + return _useMarker == other._useMarker; + } + + public override IEstimator Reconcile(IHostEnvironment env, + PipelineColumn[] toOutput, + IReadOnlyDictionary inputNames, + IReadOnlyDictionary outputNames, + IReadOnlyCollection usedNames) + { + Contracts.Assert(toOutput.Length == 1); + + var pairs = new List<(string input, string output)>(); + foreach (var outCol in toOutput) + pairs.Add((inputNames[((OutPipelineColumn)outCol).Input], outputNames[outCol])); + + return new CharacterTokenizer(env, pairs.ToArray(), _useMarker); + } + } + + /// + /// Tokenize incoming text into a sequence of characters. + /// + /// The column to apply to. + /// Whether to use marker characters to separate words. + public static VarVector> TokenizeIntoCharacters(this Scalar input, bool useMarkerCharacters = true) => new OutPipelineColumn(input, useMarkerCharacters); + } +} diff --git a/src/Microsoft.ML.Transforms/Text/TextTransform.cs b/src/Microsoft.ML.Transforms/Text/TextTransform.cs index 7a5cf4bc7b..77e7739ad7 100644 --- a/src/Microsoft.ML.Transforms/Text/TextTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/TextTransform.cs @@ -228,7 +228,7 @@ private bool UsesHashExtractors } // If we're performing language auto detection, or either of our extractors aren't hashing then - // we need all the input text concatenated into a single Vect, for the LanguageDetectionTransform + // we need all the input text concatenated into a single ReadOnlyMemory, for the LanguageDetectionTransform // to operate on the entire text vector, and for the Dictionary feature extractor to build its bound dictionary // correctly. public bool NeedInitialSourceColumnConcatTransform @@ -639,7 +639,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(Transformer).Assembly.FullName); } } diff --git a/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs b/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs index 1340855547..4341afbb92 100644 --- a/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs @@ -182,7 +182,7 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV } /// - /// A transform that turns a collection of tokenized text (vector of DvText), or vectors of keys into numerical + /// A transform that turns a collection of tokenized text (vector of ReadOnlyMemory), or vectors of keys into numerical /// feature vectors. The feature vectors are counts of ngrams (sequences of consecutive *tokens* -words or keys- /// of length 1-n). /// @@ -275,7 +275,7 @@ public sealed class Arguments : ArgumentsBase public Column[] Column; } - internal const string Summary = "A transform that turns a collection of tokenized text (vector of DvText), or vectors of keys into numerical " + + internal const string Summary = "A transform that turns a collection of tokenized text ReadOnlyMemory, or vectors of keys into numerical " + "feature vectors. The feature vectors are counts of ngrams (sequences of consecutive *tokens* -words or keys- of length 1-n)."; internal const string LoaderSignature = "NgramExtractor"; diff --git a/src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs b/src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs index 83e420afff..b9ef52a414 100644 --- a/src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -7,6 +7,8 @@ using System.IO; using System.Linq; using System.Text; +using Microsoft.ML.Core.Data; +using Microsoft.ML.Data.StaticPipe.Runtime; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; @@ -18,13 +20,19 @@ [assembly: LoadableClass(WordEmbeddingsTransform.Summary, typeof(IDataTransform), typeof(WordEmbeddingsTransform), typeof(WordEmbeddingsTransform.Arguments), typeof(SignatureDataTransform), WordEmbeddingsTransform.UserName, "WordEmbeddingsTransform", WordEmbeddingsTransform.ShortName, DocName = "transform/WordEmbeddingsTransform.md")] -[assembly: LoadableClass(typeof(WordEmbeddingsTransform), null, typeof(SignatureLoadDataTransform), +[assembly: LoadableClass(WordEmbeddingsTransform.Summary, typeof(IDataTransform), typeof(WordEmbeddingsTransform), null, typeof(SignatureLoadDataTransform), + WordEmbeddingsTransform.UserName, WordEmbeddingsTransform.LoaderSignature)] + +[assembly: LoadableClass(typeof(WordEmbeddingsTransform), null, typeof(SignatureLoadModel), + WordEmbeddingsTransform.UserName, WordEmbeddingsTransform.LoaderSignature)] + +[assembly: LoadableClass(typeof(IRowMapper), typeof(WordEmbeddingsTransform), null, typeof(SignatureLoadRowMapper), WordEmbeddingsTransform.UserName, WordEmbeddingsTransform.LoaderSignature)] namespace Microsoft.ML.Runtime.Data { /// - public sealed class WordEmbeddingsTransform : OneToOneTransformBase + public sealed class WordEmbeddingsTransform : OneToOneTransformerBase { public sealed class Column : OneToOneColumn { @@ -71,17 +79,18 @@ public static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, //Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(WordEmbeddingsTransform).Assembly.FullName); } private readonly PretrainedModelKind? _modelKind; private readonly string _modelFileNameWithPath; - private readonly Model _currentVocab; private static object _embeddingsLock = new object(); - private readonly VectorType _outputType; private readonly bool _customLookup; private readonly int _linesToSkip; + private readonly Model _currentVocab; private static Dictionary> _vocab = new Dictionary>(); + public IReadOnlyCollection<(string input, string output)> Columns => ColumnPairs.AsReadOnly(); private sealed class Model { @@ -106,12 +115,9 @@ public void AddWordVector(IChannel ch, string word, float[] wordVector) } } - public bool GetWordVector(ref DvText word, float[] wordVector) + public bool GetWordVector(ref ReadOnlyMemory word, float[] wordVector) { - if (word.IsNA) - return false; - string rawWord = word.GetRawUnderlyingBufferInfo(out int ichMin, out int ichLim); - NormStr str = _pool.Get(rawWord, ichMin, ichLim); + NormStr str = _pool.Get(word); if (str != null) { _wordVectors.CopyTo(str.Id * Dimension, wordVector, Dimension); @@ -121,46 +127,125 @@ public bool GetWordVector(ref DvText word, float[] wordVector) } } + /// + /// Information for each column pair. + /// + public sealed class ColumnInfo + { + public readonly string Input; + public readonly string Output; + + public ColumnInfo(string input, string output) + { + Contracts.CheckNonEmpty(input, nameof(input)); + Contracts.CheckNonEmpty(output, nameof(output)); + + Input = input; + Output = output; + } + } + private const string RegistrationName = "WordEmbeddings"; private const int Timeout = 10 * 60 * 1000; /// - /// Public constructor corresponding to . + /// Instantiates using the pretrained word embedding model specified by . + /// + /// Host Environment. + /// Name of the input column. + /// Name of the output column. + /// The pretrained word embedding model. + public WordEmbeddingsTransform(IHostEnvironment env, string inputColumn, string outputColumn, + PretrainedModelKind modelKind = PretrainedModelKind.Sswe) + : this(env, modelKind, new ColumnInfo(inputColumn, outputColumn)) + { + } + + /// + /// Instantiates using the custom word embedding model by loading it from the file specified by the . /// - public WordEmbeddingsTransform(IHostEnvironment env, Arguments args, IDataView input) - : base(env, RegistrationName, Contracts.CheckRef(args, nameof(args)).Column, - input, TestIsTextVector) + /// Host Environment. + /// Name of the input column. + /// Name of the output column. + /// Filename for custom word embedding model. + public WordEmbeddingsTransform(IHostEnvironment env, string inputColumn, string outputColumn, string customModelFile) + : this(env, customModelFile, new ColumnInfo(inputColumn, outputColumn)) { + } + + /// + /// Instantiates using the pretrained word embedding model specified by . + /// + /// Host Environment. + /// The pretrained word embedding model. + /// Input/Output columns. + public WordEmbeddingsTransform(IHostEnvironment env, PretrainedModelKind modelKind, params ColumnInfo[] columns) + : base(Contracts.CheckRef(env, nameof(env)).Register(RegistrationName), GetColumnPairs(columns)) + { + env.CheckUserArg(Enum.IsDefined(typeof(PretrainedModelKind), modelKind), nameof(modelKind)); + + _modelKind = modelKind; + _modelFileNameWithPath = EnsureModelFile(env, out _linesToSkip, (PretrainedModelKind)_modelKind); + _currentVocab = GetVocabularyDictionary(); + } + + /// + /// Instantiates using the custom word embedding model by loading it from the file specified by the . + /// + /// Host Environment. + /// Filename for custom word embedding model. + /// Input/Output columns. + public WordEmbeddingsTransform(IHostEnvironment env, string customModelFile, params ColumnInfo[] columns) + : base(Contracts.CheckRef(env, nameof(env)).Register(RegistrationName), GetColumnPairs(columns)) + { + env.CheckValue(customModelFile, nameof(customModelFile)); + Host.CheckNonWhiteSpace(customModelFile, nameof(customModelFile)); + + _modelKind = null; + _modelFileNameWithPath = customModelFile; + _currentVocab = GetVocabularyDictionary(); + } + + private static (string input, string output)[] GetColumnPairs(ColumnInfo[] columns) + { + Contracts.CheckValue(columns, nameof(columns)); + return columns.Select(x => (x.Input, x.Output)).ToArray(); + } + + // Factory method for SignatureDataTransform. + public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(args, nameof(args)); + env.CheckValue(input, nameof(input)); + if (args.ModelKind == null) args.ModelKind = PretrainedModelKind.Sswe; - Host.CheckUserArg(!args.ModelKind.HasValue || Enum.IsDefined(typeof(PretrainedModelKind), args.ModelKind), nameof(args.ModelKind)); - Host.AssertNonEmpty(Infos); - Host.Assert(Infos.Length == Utils.Size(args.Column)); + env.CheckUserArg(!args.ModelKind.HasValue || Enum.IsDefined(typeof(PretrainedModelKind), args.ModelKind), nameof(args.ModelKind)); - _customLookup = !string.IsNullOrWhiteSpace(args.CustomLookupTable); - if (_customLookup) - { - _modelKind = null; - _modelFileNameWithPath = args.CustomLookupTable; - } - else + env.CheckValue(args.Column, nameof(args.Column)); + + var cols = new ColumnInfo[args.Column.Length]; + for (int i = 0; i < cols.Length; i++) { - _modelKind = args.ModelKind; - _modelFileNameWithPath = EnsureModelFile(env, out _linesToSkip, (PretrainedModelKind)_modelKind); + var item = args.Column[i]; + cols[i] = new ColumnInfo( + item.Source ?? item.Name, + item.Name); } - Host.CheckNonWhiteSpace(_modelFileNameWithPath, nameof(_modelFileNameWithPath)); - _currentVocab = GetVocabularyDictionary(); - _outputType = new VectorType(NumberType.R4, 3 * _currentVocab.Dimension); - Metadata.Seal(); + bool customLookup = !string.IsNullOrWhiteSpace(args.CustomLookupTable); + if (customLookup) + return new WordEmbeddingsTransform(env, args.CustomLookupTable, cols).MakeDataTransform(input); + else + return new WordEmbeddingsTransform(env, args.ModelKind.Value, cols).MakeDataTransform(input); } - private WordEmbeddingsTransform(IHost host, ModelLoadContext ctx, IDataView input) - : base(host, ctx, input, TestIsTextVector) + private WordEmbeddingsTransform(IHost host, ModelLoadContext ctx) + : base(host, ctx) { Host.AssertValue(ctx); - Host.AssertNonEmpty(Infos); _customLookup = ctx.Reader.ReadBoolByte(); if (_customLookup) @@ -176,27 +261,32 @@ private WordEmbeddingsTransform(IHost host, ModelLoadContext ctx, IDataView inpu Host.CheckNonWhiteSpace(_modelFileNameWithPath, nameof(_modelFileNameWithPath)); _currentVocab = GetVocabularyDictionary(); - _outputType = new VectorType(NumberType.R4, 3 * _currentVocab.Dimension); - Metadata.Seal(); } - public static WordEmbeddingsTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + public static WordEmbeddingsTransform Create(IHostEnvironment env, ModelLoadContext ctx) { Contracts.CheckValue(env, nameof(env)); IHost h = env.Register(RegistrationName); h.CheckValue(ctx, nameof(ctx)); - h.CheckValue(input, nameof(input)); - return h.Apply("Loading Model", - ch => new WordEmbeddingsTransform(h, ctx, input)); + ctx.CheckAtModel(GetVersionInfo()); + return new WordEmbeddingsTransform(h, ctx); } + // Factory method for SignatureLoadDataTransform. + private static IDataTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + => Create(env, ctx).MakeDataTransform(input); + + // Factory method for SignatureLoadRowMapper. + private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, ISchema inputSchema) + => Create(env, ctx).MakeRowMapper(inputSchema); + public override void Save(ModelSaveContext ctx) { Host.CheckValue(ctx, nameof(ctx)); ctx.CheckAtModel(); ctx.SetVersionInfo(GetVersionInfo()); - SaveBase(ctx); + base.SaveColumns(ctx); ctx.Writer.WriteBoolByte(_customLookup); if (_customLookup) ctx.SaveString(_modelFileNameWithPath); @@ -204,81 +294,99 @@ public override void Save(ModelSaveContext ctx) ctx.Writer.Write((uint)_modelKind); } - protected override ColumnType GetColumnTypeCore(int iinfo) + protected override IRowMapper MakeRowMapper(ISchema schema) + => new Mapper(this, schema); + + protected override void CheckInputColumn(ISchema inputSchema, int col, int srcCol) { - Host.Assert(0 <= iinfo && iinfo < Infos.Length); - return _outputType; + var colType = inputSchema.GetColumnType(srcCol); + if (!(colType.IsVector && colType.ItemType.IsText)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", ColumnPairs[col].input, "Text", inputSchema.GetColumnType(srcCol).ToString()); } - protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + private sealed class Mapper : MapperBase { - Host.AssertValue(ch); - ch.AssertValue(input); - ch.Assert(0 <= iinfo && iinfo < Infos.Length); - disposer = null; + private readonly WordEmbeddingsTransform _parent; + private readonly VectorType _outputType; - var info = Infos[iinfo]; - if (!info.TypeSrc.IsVector) + public Mapper(WordEmbeddingsTransform parent, ISchema inputSchema) + : base(parent.Host.Register(nameof(Mapper)), parent, inputSchema) { - throw Host.ExceptParam(nameof(input), - "Text input given, expects a text vector"); + Host.CheckValue(inputSchema, nameof(inputSchema)); + Host.CheckValue(parent, nameof(parent)); + + _parent = parent; + for (int i = 0; i < _parent.ColumnPairs.Length; i++) + { + _parent.CheckInputColumn(inputSchema, i, ColMapNewToOld[i]); + } + _outputType = new VectorType(NumberType.R4, 3 * _parent._currentVocab.Dimension); } - return GetGetterVec(ch, input, iinfo); - } - private ValueGetter> GetGetterVec(IChannel ch, IRow input, int iinfo) - { - Host.AssertValue(ch); - ch.AssertValue(input); - ch.Assert(0 <= iinfo && iinfo < Infos.Length); + public override RowMapperColumnInfo[] GetOutputColumns() + => _parent.ColumnPairs.Select(x => new RowMapperColumnInfo(x.output, _outputType, null)).ToArray(); - var info = Infos[iinfo]; - ch.Assert(info.TypeSrc.IsVector); - ch.Assert(info.TypeSrc.ItemType.IsText); + protected override Delegate MakeGetter(IRow input, int iinfo, out Action disposer) + { + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < _parent.ColumnPairs.Length); + disposer = null; + return GetGetterVec(input, iinfo); + } - var srcGetter = input.GetGetter>(info.Source); - var src = default(VBuffer); - int dimension = _currentVocab.Dimension; - float[] wordVector = new float[_currentVocab.Dimension]; + private ValueGetter> GetGetterVec(IRow input, int iinfo) + { + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < _parent.ColumnPairs.Length); - return - (ref VBuffer dst) => - { - int deno = 0; - srcGetter(ref src); - var values = dst.Values; - if (Utils.Size(values) != 3 * dimension) - values = new float[3 * dimension]; - int offset = 2 * dimension; - for (int i = 0; i < dimension; i++) - { - values[i] = float.MaxValue; - values[i + dimension] = 0; - values[i + offset] = float.MinValue; - } - for (int word = 0; word < src.Count; word++) + var colType = input.Schema.GetColumnType(ColMapNewToOld[iinfo]); + Host.Assert(colType.IsVector); + Host.Assert(colType.ItemType.IsText); + + var srcGetter = input.GetGetter>>(ColMapNewToOld[iinfo]); + var src = default(VBuffer>); + int dimension = _parent._currentVocab.Dimension; + float[] wordVector = new float[_parent._currentVocab.Dimension]; + + return + (ref VBuffer dst) => { - if (_currentVocab.GetWordVector(ref src.Values[word], wordVector)) + int deno = 0; + srcGetter(ref src); + var values = dst.Values; + if (Utils.Size(values) != 3 * dimension) + values = new float[3 * dimension]; + int offset = 2 * dimension; + for (int i = 0; i < dimension; i++) { - deno++; - for (int i = 0; i < dimension; i++) + values[i] = float.MaxValue; + values[i + dimension] = 0; + values[i + offset] = float.MinValue; + } + for (int word = 0; word < src.Count; word++) + { + if (_parent._currentVocab.GetWordVector(ref src.Values[word], wordVector)) { - float currentTerm = wordVector[i]; - if (values[i] > currentTerm) - values[i] = currentTerm; - values[dimension + i] += currentTerm; - if (values[offset + i] < currentTerm) - values[offset + i] = currentTerm; + deno++; + for (int i = 0; i < dimension; i++) + { + float currentTerm = wordVector[i]; + if (values[i] > currentTerm) + values[i] = currentTerm; + values[dimension + i] += currentTerm; + if (values[offset + i] < currentTerm) + values[offset + i] = currentTerm; + } } } - } - if (deno != 0) - for (int index = 0; index < dimension; index++) - values[index + dimension] /= deno; + if (deno != 0) + for (int index = 0; index < dimension; index++) + values[index + dimension] /= deno; - dst = new VBuffer(values.Length, values, dst.Indices); - }; + dst = new VBuffer(values.Length, values, dst.Indices); + }; + } } public enum PretrainedModelKind @@ -442,4 +550,147 @@ private Model GetVocabularyDictionary() } } } + + public sealed class WordEmbeddingsEstimator : IEstimator + { + private readonly IHost _host; + private readonly WordEmbeddingsTransform.ColumnInfo[] _columns; + private readonly WordEmbeddingsTransform.PretrainedModelKind? _modelKind; + private readonly string _customLookupTable; + + public WordEmbeddingsEstimator(IHostEnvironment env, string inputColumn, string outputColumn, + WordEmbeddingsTransform.PretrainedModelKind modelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe) + : this(env, modelKind, new WordEmbeddingsTransform.ColumnInfo(inputColumn, outputColumn)) + { + } + + public WordEmbeddingsEstimator(IHostEnvironment env, string inputColumn, string outputColumn, string customModelFile) + : this(env, customModelFile, new WordEmbeddingsTransform.ColumnInfo(inputColumn, outputColumn)) + { + } + + public WordEmbeddingsEstimator(IHostEnvironment env, + WordEmbeddingsTransform.PretrainedModelKind modelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe, params WordEmbeddingsTransform.ColumnInfo[] columns) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(nameof(WordEmbeddingsEstimator)); + _modelKind = modelKind; + _customLookupTable = null; + _columns = columns; + } + + public WordEmbeddingsEstimator(IHostEnvironment env, string customModelFile, params WordEmbeddingsTransform.ColumnInfo[] columns) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(nameof(WordEmbeddingsEstimator)); + _modelKind = null; + _customLookupTable = customModelFile; + _columns = columns; + } + + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + _host.CheckValue(inputSchema, nameof(inputSchema)); + var result = inputSchema.Columns.ToDictionary(x => x.Name); + foreach (var colInfo in _columns) + { + if (!inputSchema.TryFindColumn(colInfo.Input, out var col)) + throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", colInfo.Input); + if (!(col.ItemType is TextType) || (col.Kind != SchemaShape.Column.VectorKind.VariableVector && col.Kind != SchemaShape.Column.VectorKind.Vector)) + throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", colInfo.Input, new VectorType(TextType.Instance).ToString(), col.GetTypeString()); + + result[colInfo.Output] = new SchemaShape.Column(colInfo.Output, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); + } + + return new SchemaShape(result.Values); + } + + public WordEmbeddingsTransform Fit(IDataView input) + { + bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable); + if (customLookup) + return new WordEmbeddingsTransform(_host, _customLookupTable, _columns); + else + return new WordEmbeddingsTransform(_host, _modelKind.Value, _columns); + } + } + + public static class WordEmbeddingsStaticExtensions + { + /// + /// Vector of tokenized text. + /// The pretrained word embedding model. + /// + public static Vector WordEmbeddings(this VarVector input, WordEmbeddingsTransform.PretrainedModelKind modelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe) + { + Contracts.CheckValue(input, nameof(input)); + return new OutColumn(input, modelKind); + } + + /// + /// Vector of tokenized text. + /// The custom word embedding model file. + public static Vector WordEmbeddings(this VarVector input, string customModelFile) + { + Contracts.CheckValue(input, nameof(input)); + return new OutColumn(input, customModelFile); + } + + private sealed class OutColumn : Vector + { + public PipelineColumn Input { get; } + + public OutColumn(VarVector input, WordEmbeddingsTransform.PretrainedModelKind modelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe) + : base(new Reconciler(modelKind), input) + { + Input = input; + } + + public OutColumn(VarVector input, string customModelFile = null) + : base(new Reconciler(customModelFile), input) + { + Input = input; + } + } + + private sealed class Reconciler : EstimatorReconciler + { + private readonly WordEmbeddingsTransform.PretrainedModelKind? _modelKind; + private readonly string _customLookupTable; + + public Reconciler(WordEmbeddingsTransform.PretrainedModelKind modelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe) + { + _modelKind = modelKind; + _customLookupTable = null; + } + + public Reconciler(string customModelFile) + { + _modelKind = null; + _customLookupTable = customModelFile; + } + + public override IEstimator Reconcile(IHostEnvironment env, + PipelineColumn[] toOutput, + IReadOnlyDictionary inputNames, + IReadOnlyDictionary outputNames, + IReadOnlyCollection usedNames) + { + Contracts.Assert(toOutput.Length == 1); + + var cols = new WordEmbeddingsTransform.ColumnInfo[toOutput.Length]; + for (int i = 0; i < toOutput.Length; ++i) + { + var outCol = (OutColumn)toOutput[i]; + cols[i] = new WordEmbeddingsTransform.ColumnInfo(inputNames[outCol.Input], outputNames[outCol]); + } + + bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable); + if (customLookup) + return new WordEmbeddingsEstimator(env, _customLookupTable, cols); + else + return new WordEmbeddingsEstimator(env, _modelKind.Value, cols); + } + } + } } diff --git a/src/Microsoft.ML.Transforms/Text/WordHashBagTransform.cs b/src/Microsoft.ML.Transforms/Text/WordHashBagTransform.cs index 9417ea6260..b51cccd220 100644 --- a/src/Microsoft.ML.Transforms/Text/WordHashBagTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/WordHashBagTransform.cs @@ -177,7 +177,7 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV } /// - /// A transform that turns a collection of tokenized text (vector of DvText) into numerical feature vectors + /// A transform that turns a collection of tokenized text (vector of ReadOnlyMemory) into numerical feature vectors /// using the hashing trick. /// public static class NgramHashExtractorTransform @@ -320,7 +320,7 @@ public sealed class Arguments : ArgumentsBase public Column[] Column; } - internal const string Summary = "A transform that turns a collection of tokenized text (vector of DvText) into numerical feature vectors using the hashing trick."; + internal const string Summary = "A transform that turns a collection of tokenized text (vector of ReadOnlyMemory) into numerical feature vectors using the hashing trick."; internal const string LoaderSignature = "NgramHashExtractor"; diff --git a/src/Microsoft.ML.Transforms/Text/WordTokenizeTransform.cs b/src/Microsoft.ML.Transforms/Text/WordTokenizeTransform.cs index 2e38af34e2..391cfa0327 100644 --- a/src/Microsoft.ML.Transforms/Text/WordTokenizeTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/WordTokenizeTransform.cs @@ -35,7 +35,7 @@ public interface ITokenizeTransform : IDataTransform { } - // The input for this transform is a DvText or a vector of DvTexts, and its output is a vector of DvTexts, + // The input for this transform is a ReadOnlyMemory or a vector of ReadOnlyMemory, and its output is a vector of ReadOnlyMemory, // corresponding to the tokens in the input text, split using a set of user specified separator characters. // Empty strings and strings containing only spaces are dropped. /// @@ -141,7 +141,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(DelimitedTokenizeTransform).Assembly.FullName); } public override bool CanSavePfa => true; @@ -160,7 +161,7 @@ public DelimitedTokenizeTransform(IHostEnvironment env, Arguments args, IDataVie : base(env, RegistrationName, Contracts.CheckRef(args, nameof(args)).Column, input, TestIsTextItem) { - // REVIEW: Need to decide whether to inject an NA token between slots in VBuffer inputs. + // REVIEW: Need to decide whether to inject an NA token between slots in ReadOnlyMemory inputs. Host.AssertNonEmpty(Infos); Host.Assert(Infos.Length == Utils.Size(args.Column)); @@ -182,7 +183,7 @@ public DelimitedTokenizeTransform(IHostEnvironment env, TokenizeArguments args, Host.CheckValue(args, nameof(args)); Host.CheckUserArg(Utils.Size(columns) > 0, nameof(Arguments.Column)); - // REVIEW: Need to decide whether to inject an NA token between slots in VBuffer inputs. + // REVIEW: Need to decide whether to inject an NA token between slots in ReadOnlyMemory inputs. Host.AssertNonEmpty(Infos); Host.Assert(Infos.Length == Utils.Size(columns)); @@ -294,18 +295,18 @@ protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, ou return MakeGetterVec(input, iinfo); } - private ValueGetter> MakeGetterOne(IRow input, int iinfo) + private ValueGetter>> MakeGetterOne(IRow input, int iinfo) { Host.AssertValue(input); Host.Assert(Infos[iinfo].TypeSrc.IsText); - var getSrc = GetSrcGetter(input, iinfo); - var src = default(DvText); - var terms = new List(); + var getSrc = GetSrcGetter>(input, iinfo); + var src = default(ReadOnlyMemory); + var terms = new List>(); var separators = _exes[iinfo].Separators; return - (ref VBuffer dst) => + (ref VBuffer> dst) => { getSrc(ref src); terms.Clear(); @@ -316,15 +317,15 @@ private ValueGetter> MakeGetterOne(IRow input, int iinfo) if (terms.Count > 0) { if (Utils.Size(values) < terms.Count) - values = new DvText[terms.Count]; + values = new ReadOnlyMemory[terms.Count]; terms.CopyTo(values); } - dst = new VBuffer(terms.Count, values, dst.Indices); + dst = new VBuffer>(terms.Count, values, dst.Indices); }; } - private ValueGetter> MakeGetterVec(IRow input, int iinfo) + private ValueGetter>> MakeGetterVec(IRow input, int iinfo) { Host.AssertValue(input); Host.Assert(Infos[iinfo].TypeSrc.IsVector); @@ -333,13 +334,13 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) int cv = Infos[iinfo].TypeSrc.VectorSize; Contracts.Assert(cv >= 0); - var getSrc = GetSrcGetter>(input, iinfo); - var src = default(VBuffer); - var terms = new List(); + var getSrc = GetSrcGetter>>(input, iinfo); + var src = default(VBuffer>); + var terms = new List>(); var separators = _exes[iinfo].Separators; return - (ref VBuffer dst) => + (ref VBuffer> dst) => { getSrc(ref src); terms.Clear(); @@ -351,39 +352,39 @@ private ValueGetter> MakeGetterVec(IRow input, int iinfo) if (terms.Count > 0) { if (Utils.Size(values) < terms.Count) - values = new DvText[terms.Count]; + values = new ReadOnlyMemory[terms.Count]; terms.CopyTo(values); } - dst = new VBuffer(terms.Count, values, dst.Indices); + dst = new VBuffer>(terms.Count, values, dst.Indices); }; } - private void AddTerms(DvText txt, char[] separators, List terms) + private void AddTerms(ReadOnlyMemory txt, char[] separators, List> terms) { Host.AssertNonEmpty(separators); var rest = txt; if (separators.Length > 1) { - while (rest.HasChars) + while (!rest.IsEmpty) { - DvText term; - rest.SplitOne(separators, out term, out rest); - term = term.Trim(); - if (term.HasChars) + ReadOnlyMemory term; + ReadOnlyMemoryUtils.SplitOne(rest, separators, out term, out rest); + term = ReadOnlyMemoryUtils.TrimSpaces(term); + if (!term.IsEmpty) terms.Add(term); } } else { var separator = separators[0]; - while (rest.HasChars) + while (!rest.IsEmpty) { - DvText term; - rest.SplitOne(separator, out term, out rest); - term = term.Trim(); - if (term.HasChars) + ReadOnlyMemory term; + ReadOnlyMemoryUtils.SplitOne(rest, separator, out term, out rest); + term = ReadOnlyMemoryUtils.TrimSpaces(term); + if (!term.IsEmpty) terms.Add(term); } } diff --git a/src/Microsoft.ML.Transforms/Text/WrappedTextTransformers.cs b/src/Microsoft.ML.Transforms/Text/WrappedTextTransformers.cs new file mode 100644 index 0000000000..8d4330212d --- /dev/null +++ b/src/Microsoft.ML.Transforms/Text/WrappedTextTransformers.cs @@ -0,0 +1,122 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.TextAnalytics; +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; + +namespace Microsoft.ML.Transforms +{ + /// + /// Word tokenizer splits text into tokens using the delimiter. + /// For each text input, the output column is a variable vector of text. + /// + public sealed class WordTokenizer : TrivialWrapperEstimator + { + /// + /// Tokenize incoming text in and output the tokens as . + /// + /// The environment. + /// The column containing text to tokenize. + /// The column containing output tokens. Null means is replaced. + /// The separators to use (comma separated). + public WordTokenizer(IHostEnvironment env, string inputColumn, string outputColumn = null, string separators = "space") + : this(env, new[] { (inputColumn, outputColumn ?? inputColumn) }, separators) + { + } + + /// + /// Tokenize incoming text in input columns and output the tokens as output columns. + /// + /// The environment. + /// Pairs of columns to run the tokenization on. + /// The separators to use (comma separated). + public WordTokenizer(IHostEnvironment env, (string input, string output)[] columns, string separators = "space") + : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(WordTokenizer)), MakeTransformer(env, columns, separators)) + { + } + + private static TransformWrapper MakeTransformer(IHostEnvironment env, (string input, string output)[] columns, string separators) + { + Contracts.AssertValue(env); + env.CheckNonEmpty(columns, nameof(columns)); + foreach (var (input, output) in columns) + { + env.CheckValue(input, nameof(input)); + env.CheckValue(output, nameof(input)); + } + + // Create arguments. + // REVIEW: enable multiple separators via something other than parsing strings. + var args = new DelimitedTokenizeTransform.Arguments + { + Column = columns.Select(x => new DelimitedTokenizeTransform.Column { Source = x.input, Name = x.output }).ToArray(), + TermSeparators = separators + }; + + // Create a valid instance of data. + var schema = new SimpleSchema(env, columns.Select(x => new KeyValuePair(x.input, TextType.Instance)).ToArray()); + var emptyData = new EmptyDataView(env, schema); + + return new TransformWrapper(env, new DelimitedTokenizeTransform(env, args, emptyData)); + } + } + + /// + /// Character tokenizer splits text into sequences of characters using a sliding window. + /// + public sealed class CharacterTokenizer : TrivialWrapperEstimator + { + /// + /// Tokenize incoming text in and output the tokens as . + /// + /// The environment. + /// The column containing text to tokenize. + /// The column containing output tokens. Null means is replaced. + /// Whether to use marker characters to separate words. + public CharacterTokenizer(IHostEnvironment env, string inputColumn, string outputColumn = null, bool useMarkerCharacters = true) + : this (env, new[] { (inputColumn, outputColumn ?? inputColumn) }, useMarkerCharacters) + { + } + + /// + /// Tokenize incoming text in input columns and output the tokens as output columns. + /// + /// The environment. + /// Pairs of columns to run the tokenization on. + /// Whether to use marker characters to separate words. + public CharacterTokenizer(IHostEnvironment env, (string input, string output)[] columns, bool useMarkerCharacters = true) + : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(CharacterTokenizer)), MakeTransformer(env, columns, useMarkerCharacters)) + { + } + + private static TransformWrapper MakeTransformer(IHostEnvironment env, (string input, string output)[] columns, bool useMarkerChars) + { + Contracts.AssertValue(env); + env.CheckNonEmpty(columns, nameof(columns)); + foreach (var (input, output) in columns) + { + env.CheckValue(input, nameof(input)); + env.CheckValue(output, nameof(input)); + } + + // Create arguments. + var args = new CharTokenizeTransform.Arguments + { + Column = columns.Select(x => new CharTokenizeTransform.Column { Source = x.input, Name = x.output }).ToArray(), + UseMarkerChars = useMarkerChars + }; + + // Create a valid instance of data. + var schema = new SimpleSchema(env, columns.Select(x => new KeyValuePair(x.input, TextType.Instance)).ToArray()); + var emptyData = new EmptyDataView(env, schema); + + return new TransformWrapper(env, new CharTokenizeTransform(env, args, emptyData)); + } + } +} diff --git a/src/Microsoft.ML.Transforms/Text/doc.xml b/src/Microsoft.ML.Transforms/Text/doc.xml index 1a39b0b788..e3495e4ca9 100644 --- a/src/Microsoft.ML.Transforms/Text/doc.xml +++ b/src/Microsoft.ML.Transforms/Text/doc.xml @@ -46,8 +46,8 @@ This transform splits the text into words using the separator character(s). - The input for this transform is a DvText or a vector of DvTexts, - and its output is a vector of DvTexts, corresponding to the tokens in the input text. + The input for this transform is a ReadOnlyMemory or a vector of ReadOnlyMemory, + and its output is a vector of ReadOnlyMemory, corresponding to the tokens in the input text. The output is generated by splitting the input text, using a set of user specified separator characters. Empty strings and strings containing only spaces are dropped. This transform is not typically used on its own, but it is one of the transforms composing the Text Featurizer. diff --git a/src/Microsoft.ML.Transforms/UngroupTransform.cs b/src/Microsoft.ML.Transforms/UngroupTransform.cs index d77cc48bbd..cc49ffe8f3 100644 --- a/src/Microsoft.ML.Transforms/UngroupTransform.cs +++ b/src/Microsoft.ML.Transforms/UngroupTransform.cs @@ -58,7 +58,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(UngroupTransform).Assembly.FullName); } /// diff --git a/src/Microsoft.ML.Transforms/WhiteningTransform.cs b/src/Microsoft.ML.Transforms/WhiteningTransform.cs index 5d1b3188b7..21c0870633 100644 --- a/src/Microsoft.ML.Transforms/WhiteningTransform.cs +++ b/src/Microsoft.ML.Transforms/WhiteningTransform.cs @@ -211,7 +211,8 @@ private static VersionInfo GetVersionInfo() verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, loaderSignature: LoaderSignature, - loaderSignatureAlt: LoaderSignatureOld); + loaderSignatureAlt: LoaderSignatureOld, + loaderAssemblyName: typeof(WhiteningTransform).Assembly.FullName); } private readonly ColInfoEx[] _exes; diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt index eb8fd43aae..730e9c8fa9 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 3.64133739 1 0 1 35 0 -2.796535 1E-15 1.4415419267167138E-15 0 37 0 -1.65404248 1E-15 1.4415419267167138E-15 0 -40 0 +40 0 ? ? ? 0 41 1 1.04337406 0.8947368 0.16046469748481262 1 44 1 4.33966541 1 0 1 45 0 -2.89273548 1E-15 1.4415419267167138E-15 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -2.35179329 1E-15 1.4415419267167138E-15 0 141 0 -2.904073 1E-15 1.4415419267167138E-15 0 144 0 -2.796535 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 147 0 -2.463921 1E-15 1.4415419267167138E-15 0 150 0 -2.8614285 1E-15 1.4415419267167138E-15 0 151 1 3.17632246 1 0 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -3.01920986 1E-15 1.4415419267167138E-15 0 156 0 -2.49565363 1E-15 1.4415419267167138E-15 0 161 0 -2.30924 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 167 1 2.38255262 0.8947368 0.16046469748481262 1 169 0 -3.03097248 1E-15 1.4415419267167138E-15 0 171 0 -2.804134 1E-15 1.4415419267167138E-15 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 7.227234 1 0 1 247 1 2.672493 0.8947368 0.16046469748481262 1 248 0 -1.711307 1E-15 1.4415419267167138E-15 0 -249 0 +249 0 ? ? ? 0 250 0 -2.60319138 1E-15 1.4415419267167138E-15 0 252 0 2.992918 0.8947368 3.2479272984652883 1 254 1 5.35164165 1 0 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -2.804134 1E-15 1.4415419267167138E-15 0 271 0 -2.34358668 1E-15 1.4415419267167138E-15 0 272 1 2.73419476 0.8947368 0.16046469748481262 1 -275 0 +275 0 ? ? ? 0 276 0 -2.68139815 1E-15 1.4415419267167138E-15 0 277 0 -2.91167164 1E-15 1.4415419267167138E-15 0 278 0 -2.804134 1E-15 1.4415419267167138E-15 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -2.804134 1E-15 1.4415419267167138E-15 0 293 1 3.413344 1 0 1 296 0 1.47483253 0.8947368 3.2479272984652883 1 -297 0 +297 0 ? ? ? 0 299 1 3.53376913 1 0 1 300 1 3.78027344 1 0 1 301 0 -2.804134 1E-15 1.4415419267167138E-15 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.45851088 1 0 1 317 1 5.48386 1 0 1 319 0 1.44604874 0.8947368 3.2479272984652883 1 -321 0 +321 0 ? ? ? 0 323 1 3.36483288 1 0 1 327 0 -2.91167164 1E-15 1.4415419267167138E-15 0 328 1 3.03614 0.8947368 0.16046469748481262 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 8.167599 1 0 1 613 0 -2.39343452 1E-15 1.4415419267167138E-15 0 614 0 -2.85382986 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -2.56626129 1E-15 1.4415419267167138E-15 0 619 0 -2.45112467 1E-15 1.4415419267167138E-15 0 621 0 -0.5349846 0.2 0.32192810026182023 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -2.27471757 1E-15 1.4415419267167138E-15 0 19 0 -2.00358748 1E-15 1.4415419267167138E-15 0 22 0 -2.5426836 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -2.68141246 1E-15 1.4415419267167138E-15 0 26 0 -2.33200574 1E-15 1.4415419267167138E-15 0 27 0 -2.27155375 1E-15 1.4415419267167138E-15 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -2.546271 1E-15 1.4415419267167138E-15 0 135 0 -1.52827668 1E-15 1.4415419267167138E-15 0 136 0 -2.40711856 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -2.67508459 1E-15 1.4415419267167138E-15 0 142 1 2.1587286 0.9117647 0.13326656969825684 1 143 0 -1.54474568 1E-15 1.4415419267167138E-15 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.83878517 1E-15 1.4415419267167138E-15 0 155 1 3.237853 0.9117647 0.13326656969825684 1 157 0 -2.54584742 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 6.77223158 1 0 1 160 1 5.42115831 1 0 1 162 0 -2.41028237 1E-15 1.4415419267167138E-15 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.187345 1 0 1 232 0 0.397937775 0.714285731 1.8073550080489322 1 234 0 -1.183644 1E-15 1.4415419267167138E-15 0 -235 0 +235 0 ? ? ? 0 236 1 5.6324296 1 0 1 238 1 6.77774 1 0 1 243 0 -1.01514125 1E-15 1.4415419267167138E-15 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 7.8132205 1 0 1 287 0 -2.546271 1E-15 1.4415419267167138E-15 0 289 1 5.25516939 1 0 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77293825 1 0 1 298 0 -1.80093193 1E-15 1.4415419267167138E-15 0 302 1 7.77011251 1 0 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -2.68141246 1E-15 1.4415419267167138E-15 0 310 0 -2.68183613 1E-15 1.4415419267167138E-15 0 313 0 -2.94621468 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 318 0 -2.69217515 1E-15 1.4415419267167138E-15 0 320 1 3.800508 0.9444444 0.082462200658479604 1 322 0 -2.41028237 1E-15 1.4415419267167138E-15 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.81381369 1E-15 1.4415419267167138E-15 0 408 0 -1.46855927 1E-15 1.4415419267167138E-15 0 410 0 -2.81381369 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 4.83788157 1 0 1 417 0 -2.81381369 1E-15 1.4415419267167138E-15 0 420 0 -1.04175115 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt index 3b51cd60d5..689d51fcc1 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 3.64133739 0.9844741 0.022574810341618422 1 35 0 -2.796535 0.0199539755 0.029078592776682857 0 37 0 -1.65404248 0.0782266259 0.11751599963260938 0 -40 0 +40 0 ? ? ? 0 41 1 1.04337406 0.7116856 0.49068805388019454 1 44 1 4.33966541 0.9934526 0.0094769477341525628 1 45 0 -2.89273548 0.0177341755 0.025814589979333783 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -2.35179329 0.03427408 0.050314293386144675 0 141 0 -2.904073 0.0174890943 0.025454673744601461 0 144 0 -2.796535 0.0199539755 0.029078592776682857 0 -145 0 +145 0 ? ? ? 0 147 0 -2.463921 0.0299276374 0.04383572570114834 0 150 0 -2.8614285 0.01842858 0.026834852844454766 0 151 1 3.17632246 0.9725775 0.040114860620962978 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -3.01920986 0.0151813291 0.022069981158425081 0 156 0 -2.49565363 0.0287977811 0.042156377165560456 0 161 0 -2.30924 0.0360781476 0.053011906638395674 0 -164 0 +164 0 ? ? ? 0 167 1 2.38255262 0.9293544 0.10569919063924076 1 169 0 -3.03097248 0.0149631584 0.021750410734051751 0 171 0 -2.804134 0.0197691489 0.028806541052408416 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 7.227234 0.999821365 0.00025773902362411926 1 247 1 2.672493 0.9497448 0.074388155670230882 1 248 0 -1.711307 0.07322063 0.10970216917333013 0 -249 0 +249 0 ? ? ? 0 250 0 -2.60319138 0.0252686124 0.036923393165046718 0 252 0 2.992918 0.9657571 4.8680501214754637 1 254 1 5.35164165 0.998142242 0.0026826702788205732 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -2.804134 0.0197691489 0.028806541052408416 0 271 0 -2.34358668 0.0346150957 0.050823827322496622 0 272 1 2.73419476 0.9532994 0.068998701675288501 1 -275 0 +275 0 ? ? ? 0 276 0 -2.68139815 0.0229703225 0.033525709865030048 0 277 0 -2.91167164 0.0173267 0.025216237008576704 0 278 0 -2.804134 0.0197691489 0.028806541052408416 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -2.804134 0.0197691489 0.028806541052408416 0 293 1 3.413344 0.9794621 0.029938444898768346 1 296 0 1.47483253 0.8088676 2.3873555377194497 1 -297 0 +297 0 ? ? ? 0 299 1 3.53376913 0.982280254 0.025793396317687449 1 300 1 3.78027344 0.986915946 0.019000876915907086 1 301 0 -2.804134 0.0197691489 0.028806541052408416 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.45851088 0.980567157 0.028311653515975352 1 317 1 5.48386 0.9984247 0.0022744566762884174 1 319 0 1.44604874 0.803245664 2.3455326626007635 1 -321 0 +321 0 ? ? ? 0 323 1 3.36483288 0.978206754 0.031788669528507826 1 327 0 -2.91167164 0.0173267 0.025216237008576704 0 328 1 3.03614 0.9674988 0.047668253892284254 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 8.167599 0.9999448 7.9630164919418245E-05 1 613 0 -2.39343452 0.03259308 0.047805235639227908 0 614 0 -2.85382986 0.0186011065 0.027088450049153875 0 -617 0 +617 0 ? ? ? 0 618 0 -2.56626129 0.02643034 0.038643886518904186 0 619 0 -2.45112467 0.0303953178 0.044531429350354604 0 621 0 -0.5349846 0.255690753 0.42602593625059765 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -2.27471757 0.0249303821 0.036422867055812318 0 19 0 -2.00358748 0.03410183 0.050056992617068342 0 22 0 -2.5426836 0.0182464458 0.026567179066779304 0 -23 1 +23 1 ? ? ? 0 24 0 -2.68141246 0.015512228 0.022554807909292037 0 26 0 -2.33200574 0.02332543 0.034050161954667077 0 27 0 -2.27155375 0.0250220858 0.036558556406276949 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -2.546271 0.0181701127 0.026455011357386121 0 135 0 -1.52827668 0.0585265532 0.087007689086403689 0 136 0 -2.40711856 0.0213731956 0.031169296396687675 0 -139 0 +139 0 ? ? ? 0 140 0 -2.67508459 0.015627671 0.022723991158114246 0 142 1 2.1587286 0.83348304 0.26277525125659912 1 143 0 -1.54474568 0.05745574 0.085367730161702821 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.83878517 0.0411879122 0.060679997683715341 0 155 1 3.237853 0.947596252 0.077655602601770859 1 157 0 -2.54584742 0.0181791112 0.026468233670711376 0 -158 0 +158 0 ? ? ? 0 159 1 6.77223158 0.999177039 0.0011877710421138682 1 160 1 5.42115831 0.995904 0.0059213730758132374 1 162 0 -2.41028237 0.0212945715 0.031053393109467146 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.187345 0.9945968 0.0078163354020312284 1 232 0 0.397937775 0.381006956 0.69200489802945642 1 234 0 -1.183644 0.085664086 0.12920380666492423 0 -235 0 +235 0 ? ? ? 0 236 1 5.6324296 0.9968118 0.0046069378176225089 1 238 1 6.77774 0.9991824 0.0011800254692933552 1 243 0 -1.01514125 0.102734588 0.15639329612707187 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 7.8132205 0.999761462 0.00034417833577817593 1 287 0 -2.546271 0.0181701127 0.026455011357386121 0 289 1 5.25516939 0.9950138 0.0072115986401722143 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77293825 0.9911819 0.012778237651616852 1 298 0 -1.80093193 0.0430044457 0.063415872213561694 0 302 1 7.77011251 0.999748945 0.00036224093577622284 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -2.68141246 0.015512228 0.022554807909292037 0 310 0 -2.68183613 0.0155045288 0.022543525273161929 0 313 0 -2.94621468 0.0113662509 0.016491938164477051 0 -315 0 +315 0 ? ? ? 0 318 0 -2.69217515 0.0153178023 0.022269919361612874 0 320 1 3.800508 0.9724724 0.040270746291192143 1 322 0 -2.41028237 0.0212945715 0.031053393109467146 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.81381369 0.0132805621 0.019288165348886492 0 408 0 -1.46855927 0.06256822 0.093214386421141548 0 410 0 -2.81381369 0.0132805621 0.019288165348886492 0 -411 0 +411 0 ? ? ? 0 412 1 4.83788157 0.991832554 0.011831516022545778 1 417 0 -2.81381369 0.0132805621 0.019288165348886492 0 420 0 -1.04175115 0.09985152 0.15176509864238413 0 diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt index 09a2fb9aa7..e51f951dee 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Assigned 32 1 3.64133739 1 35 0 -2.796535 0 37 0 -1.65404248 0 -40 0 +40 0 ? 0 41 1 1.04337406 1 44 1 4.33966541 1 45 0 -2.89273548 0 @@ -76,7 +76,7 @@ Instance Label Score Assigned 138 0 -2.35179329 0 141 0 -2.904073 0 144 0 -2.796535 0 -145 0 +145 0 ? 0 147 0 -2.463921 0 150 0 -2.8614285 0 151 1 3.17632246 1 @@ -84,7 +84,7 @@ Instance Label Score Assigned 154 0 -3.01920986 0 156 0 -2.49565363 0 161 0 -2.30924 0 -164 0 +164 0 ? 0 167 1 2.38255262 1 169 0 -3.03097248 0 171 0 -2.804134 0 @@ -130,7 +130,7 @@ Instance Label Score Assigned 246 1 7.227234 1 247 1 2.672493 1 248 0 -1.711307 0 -249 0 +249 0 ? 0 250 0 -2.60319138 0 252 0 2.992918 1 254 1 5.35164165 1 @@ -144,7 +144,7 @@ Instance Label Score Assigned 269 0 -2.804134 0 271 0 -2.34358668 0 272 1 2.73419476 1 -275 0 +275 0 ? 0 276 0 -2.68139815 0 277 0 -2.91167164 0 278 0 -2.804134 0 @@ -158,7 +158,7 @@ Instance Label Score Assigned 291 0 -2.804134 0 293 1 3.413344 1 296 0 1.47483253 1 -297 0 +297 0 ? 0 299 1 3.53376913 1 300 1 3.78027344 1 301 0 -2.804134 0 @@ -172,7 +172,7 @@ Instance Label Score Assigned 316 1 3.45851088 1 317 1 5.48386 1 319 0 1.44604874 1 -321 0 +321 0 ? 0 323 1 3.36483288 1 327 0 -2.91167164 0 328 1 3.03614 1 @@ -318,7 +318,7 @@ Instance Label Score Assigned 612 1 8.167599 1 613 0 -2.39343452 0 614 0 -2.85382986 0 -617 0 +617 0 ? 0 618 0 -2.56626129 0 619 0 -2.45112467 0 621 0 -0.5349846 0 @@ -375,7 +375,7 @@ Instance Label Score Assigned 17 0 -2.27471757 0 19 0 -2.00358748 0 22 0 -2.5426836 0 -23 1 +23 1 ? 0 24 0 -2.68141246 0 26 0 -2.33200574 0 27 0 -2.27155375 0 @@ -425,7 +425,7 @@ Instance Label Score Assigned 134 0 -2.546271 0 135 0 -1.52827668 0 136 0 -2.40711856 0 -139 0 +139 0 ? 0 140 0 -2.67508459 0 142 1 2.1587286 1 143 0 -1.54474568 0 @@ -435,7 +435,7 @@ Instance Label Score Assigned 153 0 -1.83878517 0 155 1 3.237853 1 157 0 -2.54584742 0 -158 0 +158 0 ? 0 159 1 6.77223158 1 160 1 5.42115831 1 162 0 -2.41028237 0 @@ -474,7 +474,7 @@ Instance Label Score Assigned 231 1 5.187345 1 232 0 0.397937775 1 234 0 -1.183644 0 -235 0 +235 0 ? 0 236 1 5.6324296 1 238 1 6.77774 1 243 0 -1.01514125 0 @@ -496,8 +496,8 @@ Instance Label Score Assigned 286 1 7.8132205 1 287 0 -2.546271 0 289 1 5.25516939 1 -292 1 -294 0 +292 1 ? 0 +294 0 ? 0 295 1 4.77293825 1 298 0 -1.80093193 0 302 1 7.77011251 1 @@ -506,7 +506,7 @@ Instance Label Score Assigned 307 0 -2.68141246 0 310 0 -2.68183613 0 313 0 -2.94621468 0 -315 0 +315 0 ? 0 318 0 -2.69217515 0 320 1 3.800508 1 322 0 -2.41028237 0 @@ -551,7 +551,7 @@ Instance Label Score Assigned 407 0 -2.81381369 0 408 0 -1.46855927 0 410 0 -2.81381369 0 -411 0 +411 0 ? 0 412 1 4.83788157 1 417 0 -2.81381369 0 420 0 -1.04175115 0 diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt index 693440edc2..7664c7d409 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 2.35438 0.996385 0.0052248235129257635 1 35 0 -1.83556986 0.00724350661 0.010488202773633042 0 37 0 -0.8828192 0.07420456 0.11123463299619094 0 -40 0 +40 0 ? ? ? 0 41 1 0.6366718 0.7855496 0.34822575879653411 1 44 1 2.72072053 0.998558342 0.002081372862402793 1 45 0 -1.87994158 0.00648348359 0.0093841435784006184 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.51871562 0.0159320254 0.02317012154973026 0 141 0 -1.93865037 0.00559835136 0.0080994066187532153 0 144 0 -1.83556986 0.00724350661 0.010488202773633042 0 -145 0 +145 0 ? ? ? 0 147 0 -1.69274938 0.010342177 0.014998299390129487 0 150 0 -1.91297352 0.00596963847 0.0086381769438633477 0 151 1 1.55168462 0.973401248 0.038893469912665984 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -2.0423522 0.004318493 0.0062437599081359638 0 156 0 -1.73049688 0.009414184 0.013646131293487658 0 161 0 -1.46562588 0.0181668717 0.026450249055215744 0 -164 0 +164 0 ? ? ? 0 167 1 2.31870866 0.99604696 0.0057143326416850991 1 169 0 -2.041339 0.00432946626 0.0062596597963016712 0 171 0 -1.8361913 0.00723227439 0.010471879968765333 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 3.8998096 0.9999256 0.00010732116581602008 1 247 1 0.9928844 0.8997386 0.15242216136874395 1 248 0 -1.22778583 0.03256034 0.047756414470681303 0 -249 0 +249 0 ? ? ? 0 250 0 -1.83357728 0.00727963867 0.010540711608751351 0 252 0 1.37199 0.958826959 4.6021561858199664 1 254 1 2.79375386 0.9988 0.0017323029622904203 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.8361913 0.00723227439 0.010471879968765333 0 271 0 -1.42138422 0.0202620383 0.02953215377914243 0 272 1 1.10222125 0.921965 0.11721610663727763 1 -275 0 +275 0 ? ? ? 0 276 0 -1.73186815 0.009382072 0.013599363941350218 0 277 0 -1.93927169 0.00558965746 0.0080867934194779039 0 278 0 -1.8361913 0.00723227439 0.010471879968765333 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.8361913 0.00723227439 0.010471879968765333 0 293 1 1.65592456 0.9794098 0.030015442677317709 1 296 0 0.553571463 0.748245 1.9899076684215853 1 -297 0 +297 0 ? ? ? 0 299 1 2.001486 0.991262555 0.012660861084033199 1 300 1 2.09417748 0.9930671 0.010036913891508983 1 301 0 -1.8361913 0.00723227439 0.010471879968765333 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 1.47151637 0.9676506 0.047441894201880302 1 317 1 3.06524873 0.999393463 0.00087531320929783803 1 319 0 0.8519552 0.8629285 2.8669995609516308 1 -321 0 +321 0 ? ? ? 0 323 1 1.75474 0.9838682 0.023463058122578043 1 327 0 -1.93927169 0.00558965746 0.0080867934194779039 0 328 1 1.43182349 0.964374959 0.052333903838185522 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 5.09431458 0.9999963 5.3314720279485219E-06 1 613 0 -1.63692176 0.0118829105 0.017246087060301379 0 614 0 -1.9123522 0.00597891957 0.0086516472088849234 0 -617 0 +617 0 ? ? ? 0 618 0 -1.62816632 0.0121443039 0.017627783540922228 0 619 0 -1.52446461 0.0157068819 0.022840087163427197 0 621 0 -0.188778639 0.31474337 0.54528371301135603 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.57970572 0.0144622317 0.021016934775320784 0 19 0 -1.32506859 0.0253080986 0.036981837697027814 0 22 0 -1.76987159 0.009491567 0.01375883626140059 0 -23 1 +23 1 ? ? ? 0 24 0 -1.96166122 0.00619609 0.0089668769102733102 0 26 0 -1.67495286 0.0117150256 0.017000988488892345 0 27 0 -1.51523459 0.0166727714 0.024256502854015347 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.75784481 0.009748338 0.014132877086598956 0 135 0 -1.07198834 0.04377895 0.06458392745656516 0 136 0 -1.64255309 0.0125861792 0.018273256294688772 0 -139 0 +139 0 ? ? ? 0 140 0 -1.832719 0.008254912 0.01195874763978641 0 142 1 1.20702124 0.8832422 0.17911900782229345 1 143 0 -1.436229 0.0198381469 0.028908095216224419 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.37983692 0.0224505328 0.032758385480089913 0 155 1 1.24272728 0.8912444 0.16610696969487637 1 157 0 -1.83434272 0.008225175 0.011915489581813862 0 -158 0 +158 0 ? ? ? 0 159 1 4.09360933 0.999795 0.00029575448112624953 1 160 1 3.04705143 0.997864842 0.0030836744978663621 1 162 0 -1.70702422 0.0109114433 0.015828398337213719 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 2.80366325 0.996321738 0.0053163942348769758 1 232 0 0.248882532 0.46910888 0.91351208341864276 1 234 0 -0.8223932 0.0741594 0.11116426368346338 0 -235 0 +235 0 ? ? ? 0 236 1 3.65436459 0.999451637 0.00079133718371403176 1 238 1 4.208801 0.99984163 0.00022849704563499302 1 243 0 -1.18483961 0.0343320966 0.050400968413634589 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 4.869809 0.999964 5.1939695512002597E-05 1 287 0 -1.75784481 0.009748338 0.014132877086598956 0 289 1 2.8387928 0.996599257 0.0049145964567919058 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 2.21859837 0.9864867 0.019628533461458434 1 298 0 -1.053101 0.0455854833 0.067312108231381534 0 302 1 4.847371 0.999962151 5.4605525036338512E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.96166122 0.00619609 0.0089668769102733102 0 310 0 -1.88516331 0.007346285 0.010637570327263628 0 313 0 -2.087356 0.00468213623 0.0067707579292392971 0 -315 0 +315 0 ? ? ? 0 318 0 -1.9255811 0.006714394 0.00971948958962956 0 320 1 1.98424911 0.9773634 0.03303300364958086 1 322 0 -1.70702422 0.0109114433 0.015828398337213719 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.02450848 0.00538646942 0.0077920373965741406 0 408 0 -1.276682 0.0281251986 0.041157619505191562 0 410 0 -2.02450848 0.00538646942 0.0077920373965741406 0 -411 0 +411 0 ? ? ? 0 412 1 2.820312 0.996455967 0.0051220399276606741 1 417 0 -2.02450848 0.00538646942 0.0077920373965741406 0 420 0 -1.02720916 0.0481775925 0.071235676455785449 0 diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt index 0c2f376b8f..43672feda2 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 4.309107 0.9756098 0.035623875334191583 1 21 1 6.161626 1 0 1 22 0 -3.35177135 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -3.57612 1E-15 1.4415419267167138E-15 0 25 1 1.68778992 0.8125 0.29956028185890782 1 26 0 -3.21128821 1E-15 1.4415419267167138E-15 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.20465565 0.0625 0.093109404391481479 0 38 1 4.3035 0.9756098 0.035623875334191583 1 39 1 2.444232 0.84 0.25153881203904033 1 -40 0 +40 0 ? ? ? 0 41 1 2.349327 0.84 0.25153881203904033 1 42 1 6.69547653 1 0 1 43 1 0.1725626 0.7777778 0.36257005481575838 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -3.11112714 1E-15 1.4415419267167138E-15 0 137 0 -3.60871148 1E-15 1.4415419267167138E-15 0 138 0 -2.89319158 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -3.60871148 1E-15 1.4415419267167138E-15 0 141 0 -3.8493557 1E-15 1.4415419267167138E-15 0 142 1 2.848053 0.84 0.25153881203904033 1 143 0 -2.73937 1E-15 1.4415419267167138E-15 0 144 0 -3.59241557 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 146 1 1.31008816 0.8125 0.29956028185890782 1 147 0 -3.43885779 1E-15 1.4415419267167138E-15 0 148 0 -0.152019978 0.4 0.73696560849809378 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.618639 0.84 0.25153881203904033 1 156 0 -3.41809654 1E-15 1.4415419267167138E-15 0 157 0 -3.33547568 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 9.536341 1 0 1 160 1 6.860572 1 0 1 161 0 -2.5829134 1E-15 1.4415419267167138E-15 0 162 0 -3.09483147 1E-15 1.4415419267167138E-15 0 163 0 -2.50633955 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 165 0 -2.245256 1E-15 1.4415419267167138E-15 0 166 1 6.456311 1 0 1 167 1 4.319108 0.9756098 0.035623875334191583 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.8359299 0.7777778 2.1699250874336404 1 233 1 3.89948845 0.9411765 0.087462835875881578 1 234 0 -1.06992626 0.0625 0.093109404391481479 0 -235 0 +235 0 ? ? ? 0 236 1 8.428113 1 0 1 237 1 5.33226967 1 0 1 238 1 9.183852 1 0 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.645902 1 0 1 247 1 2.597087 0.84 0.25153881203904033 1 248 0 -1.497818 0.0625 0.093109404391481479 0 -249 0 +249 0 ? ? ? 0 250 0 -3.67503667 1E-15 1.4415419267167138E-15 0 251 1 5.0781126 1 0 1 252 0 3.308917 0.84 2.643855953298599 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 2.38956165 0.84 0.25153881203904033 1 273 1 0.3306446 0.7777778 0.36257005481575838 1 274 0 -2.82355762 1E-15 1.4415419267167138E-15 0 -275 0 +275 0 ? ? ? 0 276 0 -3.35177135 1E-15 1.4415419267167138E-15 0 277 0 -3.83305979 1E-15 1.4415419267167138E-15 0 278 0 -3.57612 1E-15 1.4415419267167138E-15 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 6.52714539 1 0 1 290 0 -4.08999968 1E-15 1.4415419267167138E-15 0 291 0 -3.57612 1E-15 1.4415419267167138E-15 0 -292 1 +292 1 ? ? ? 0 293 1 3.63595963 0.9411765 0.087462835875881578 1 -294 0 +294 0 ? ? ? 0 295 1 5.25647163 1 0 1 296 0 1.97336864 0.8125 2.4150374992788439 1 -297 0 +297 0 ? ? ? 0 298 0 -2.03882861 1E-15 1.4415419267167138E-15 0 299 1 4.99616432 1 0 1 300 1 5.63767242 1 0 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.14314938 0.84 0.25153881203904033 1 313 0 -4.08999968 1E-15 1.4415419267167138E-15 0 314 0 -3.88960457 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 316 1 3.149722 0.84 0.25153881203904033 1 317 1 7.02619457 1 0 1 318 0 -3.406486 1E-15 1.4415419267167138E-15 0 319 0 1.82393885 0.8125 2.4150374992788439 1 320 1 5.15885925 1 0 1 -321 0 +321 0 ? ? ? 0 322 0 -3.09483147 1E-15 1.4415419267167138E-15 0 323 1 3.22143555 0.84 0.25153881203904033 1 324 0 -3.57612 1E-15 1.4415419267167138E-15 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -2.29045367 1E-15 1.4415419267167138E-15 0 409 0 -3.13383579 1E-15 1.4415419267167138E-15 0 410 0 -3.83305979 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 6.52325153 1 0 1 413 0 -2.39560747 1E-15 1.4415419267167138E-15 0 414 1 5.304202 1 0 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -3.64896035 1E-15 1.4415419267167138E-15 0 615 0 -2.675256 1E-15 1.4415419267167138E-15 0 616 0 -3.35177135 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -3.11112714 1E-15 1.4415419267167138E-15 0 619 0 -2.870483 1E-15 1.4415419267167138E-15 0 620 0 -3.35177135 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt index 69424d1f96..b1c0d65d4f 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 4.309107 0.982883155 0.024908174987257281 1 21 1 6.161626 0.997486651 0.0036305605660221312 1 22 0 -3.35177135 0.0190011337 0.027676625763894382 0 -23 1 +23 1 ? ? ? 0 24 0 -3.57612 0.0150948567 0.021943310344814629 0 25 1 1.68778992 0.7883411 0.34310809466132391 1 26 0 -3.21128821 0.02193545 0.031998411900987428 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.20465565 0.154014841 0.24129573984468006 0 38 1 4.3035 0.98278445 0.025053063812451211 1 39 1 2.444232 0.8913242 0.16597778265246793 1 -40 0 +40 0 ? ? ? 0 41 1 2.349327 0.881352544 0.18220887767190111 1 42 1 6.69547653 0.99855864 0.002080942285094773 1 43 1 0.1725626 0.43382585 1.2048120730945795 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -3.11112714 0.0242936034 0.035481008429150813 0 137 0 -3.60871148 0.0145974671 0.021214914966477344 0 138 0 -2.89319158 0.0303093083 0.044403459692558656 0 -139 0 +139 0 ? ? ? 0 140 0 -3.60871148 0.0145974671 0.021214914966477344 0 141 0 -3.8493557 0.01139268 0.016530506138550251 0 142 1 2.848053 0.9259271 0.11102947905485054 1 143 0 -2.73937 0.0353999846 0.051997261707361145 0 144 0 -3.59241557 0.0148441121 0.021576064827714625 0 -145 0 +145 0 ? ? ? 0 146 1 1.31008816 0.7152061 0.48356908051152397 1 147 0 -3.43885779 0.01737916 0.025293256640113757 0 148 0 -0.152019978 0.353206038 0.62862188502856431 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.618639 0.9077395 0.139649725798073 1 156 0 -3.41809654 0.0177530367 0.025842292400220613 0 157 0 -3.33547568 0.01932072 0.028146699492868216 0 -158 0 +158 0 ? ? ? 0 159 1 9.536341 0.999925554 0.0001074071635410925 1 160 1 6.860572 0.998786449 0.0017518465781146784 1 161 0 -2.5829134 0.0414183028 0.061026700952795158 0 162 0 -3.09483147 0.0246999636 0.036081984337152172 0 163 0 -2.50633955 0.04470976 0.065988970965053517 0 -164 0 +164 0 ? ? ? 0 165 0 -2.245256 0.0579013154 0.086049905177797967 0 166 1 6.456311 0.998150766 0.0026703506849678977 1 167 1 4.319108 0.983057857 0.024651767929287045 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.8359299 0.6049175 1.3397742465159499 1 233 1 3.89948845 0.9739913 0.038019246409892023 1 234 0 -1.06992626 0.17323719 0.27445460033191865 0 -235 0 +235 0 ? ? ? 0 236 1 8.428113 0.99976337 0.0003414259594434771 1 237 1 5.33226967 0.994048536 0.0086117997569089607 1 238 1 9.183852 0.9998925 0.00015513669190214178 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.645902 0.9998115 0.00027201620468366962 1 247 1 2.597087 0.9058386 0.14267406349976108 1 248 0 -1.497818 0.118221387 0.1815116096306757 0 -249 0 +249 0 ? ? ? 0 250 0 -3.67503667 0.0136346063 0.019805910679898876 0 251 1 5.0781126 0.9922549 0.011217294571842519 1 252 0 3.308917 0.952875 4.4073641261174119 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 2.38956165 0.8856733 0.17515349285073656 1 273 1 0.3306446 0.474698573 1.0749163809587898 1 274 0 -2.82355762 0.0325194858 0.047695490325124906 0 -275 0 +275 0 ? ? ? 0 276 0 -3.35177135 0.0190011337 0.027676625763894382 0 277 0 -3.83305979 0.0115858121 0.016812375308683684 0 278 0 -3.57612 0.0150948567 0.021943310344814629 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 6.52714539 0.9982823 0.0024802287545360943 1 290 0 -4.08999968 0.008885143 0.012875839136462838 0 291 0 -3.57612 0.0150948567 0.021943310344814629 0 -292 1 +292 1 ? ? ? 0 293 1 3.63595963 0.9660381 0.049847993866089353 1 -294 0 +294 0 ? ? ? 0 295 1 5.25647163 0.9935618 0.0093183820918222807 1 296 0 1.97336864 0.8338234 2.589210701355853 1 -297 0 +297 0 ? ? ? 0 298 0 -2.03882861 0.0708337 0.10599126176574966 0 299 1 4.99616432 0.9915693 0.012214518276517204 1 300 1 5.63767242 0.995665669 0.0062667080268442431 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.14314938 0.856945038 0.22272541817603111 1 313 0 -4.08999968 0.008885143 0.012875839136462838 0 314 0 -3.88960457 0.0109292 0.015854298528928425 0 -315 0 +315 0 ? ? ? 0 316 1 3.149722 0.9448283 0.081875960094708669 1 317 1 7.02619457 0.9989789 0.0014738699989607148 1 318 0 -3.406486 0.0179655552 0.026154467003641695 0 319 0 1.82393885 0.8110785 2.4041411141982105 1 320 1 5.15885925 0.992876351 0.01031403381058839 1 -321 0 +321 0 ? ? ? 0 322 0 -3.09483147 0.0246999636 0.036081984337152172 0 323 1 3.22143555 0.948601961 0.076125243844936963 1 324 0 -3.57612 0.0150948567 0.021943310344814629 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -2.29045367 0.05538148 0.082196274856991033 0 409 0 -3.13383579 0.0237381756 0.034659977772739645 0 410 0 -3.83305979 0.0115858121 0.016812375308683684 0 -411 0 +411 0 ? ? ? 0 412 1 6.52325153 0.99827534 0.0024903070861691909 1 413 0 -2.39560747 0.0499131419 0.073868682437534181 0 414 1 5.304202 0.9938727 0.0088670155127870496 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -3.64896035 0.0140054561 0.020348431589167357 0 615 0 -2.675256 0.0377570055 0.055526832252899339 0 616 0 -3.35177135 0.0190011337 0.027676625763894382 0 -617 0 +617 0 ? ? ? 0 618 0 -3.11112714 0.0242936034 0.035481008429150813 0 619 0 -2.870483 0.0310136 0.045451678527033197 0 620 0 -3.35177135 0.0190011337 0.027676625763894382 0 diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt index 0f08e7f566..55861f250c 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Assigned 20 1 4.309107 1 21 1 6.161626 1 22 0 -3.35177135 0 -23 1 +23 1 ? 0 24 0 -3.57612 0 25 1 1.68778992 1 26 0 -3.21128821 0 @@ -39,7 +39,7 @@ Instance Label Score Assigned 37 0 -1.20465565 0 38 1 4.3035 1 39 1 2.444232 1 -40 0 +40 0 ? 0 41 1 2.349327 1 42 1 6.69547653 1 43 1 0.1725626 1 @@ -138,13 +138,13 @@ Instance Label Score Assigned 136 0 -3.11112714 0 137 0 -3.60871148 0 138 0 -2.89319158 0 -139 0 +139 0 ? 0 140 0 -3.60871148 0 141 0 -3.8493557 0 142 1 2.848053 1 143 0 -2.73937 0 144 0 -3.59241557 0 -145 0 +145 0 ? 0 146 1 1.31008816 1 147 0 -3.43885779 0 148 0 -0.152019978 0 @@ -157,13 +157,13 @@ Instance Label Score Assigned 155 1 2.618639 1 156 0 -3.41809654 0 157 0 -3.33547568 0 -158 0 +158 0 ? 0 159 1 9.536341 1 160 1 6.860572 1 161 0 -2.5829134 0 162 0 -3.09483147 0 163 0 -2.50633955 0 -164 0 +164 0 ? 0 165 0 -2.245256 0 166 1 6.456311 1 167 1 4.319108 1 @@ -234,7 +234,7 @@ Instance Label Score Assigned 232 0 0.8359299 1 233 1 3.89948845 1 234 0 -1.06992626 0 -235 0 +235 0 ? 0 236 1 8.428113 1 237 1 5.33226967 1 238 1 9.183852 1 @@ -248,7 +248,7 @@ Instance Label Score Assigned 246 1 8.645902 1 247 1 2.597087 1 248 0 -1.497818 0 -249 0 +249 0 ? 0 250 0 -3.67503667 0 251 1 5.0781126 1 252 0 3.308917 1 @@ -274,7 +274,7 @@ Instance Label Score Assigned 272 1 2.38956165 1 273 1 0.3306446 1 274 0 -2.82355762 0 -275 0 +275 0 ? 0 276 0 -3.35177135 0 277 0 -3.83305979 0 278 0 -3.57612 0 @@ -291,12 +291,12 @@ Instance Label Score Assigned 289 1 6.52714539 1 290 0 -4.08999968 0 291 0 -3.57612 0 -292 1 +292 1 ? 0 293 1 3.63595963 1 -294 0 +294 0 ? 0 295 1 5.25647163 1 296 0 1.97336864 1 -297 0 +297 0 ? 0 298 0 -2.03882861 0 299 1 4.99616432 1 300 1 5.63767242 1 @@ -314,13 +314,13 @@ Instance Label Score Assigned 312 1 2.14314938 1 313 0 -4.08999968 0 314 0 -3.88960457 0 -315 0 +315 0 ? 0 316 1 3.149722 1 317 1 7.02619457 1 318 0 -3.406486 0 319 0 1.82393885 1 320 1 5.15885925 1 -321 0 +321 0 ? 0 322 0 -3.09483147 0 323 1 3.22143555 1 324 0 -3.57612 0 @@ -410,7 +410,7 @@ Instance Label Score Assigned 408 0 -2.29045367 0 409 0 -3.13383579 0 410 0 -3.83305979 0 -411 0 +411 0 ? 0 412 1 6.52325153 1 413 0 -2.39560747 0 414 1 5.304202 1 @@ -616,7 +616,7 @@ Instance Label Score Assigned 614 0 -3.64896035 0 615 0 -2.675256 0 616 0 -3.35177135 0 -617 0 +617 0 ? 0 618 0 -3.11112714 0 619 0 -2.870483 0 620 0 -3.35177135 0 diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt index f1da00a926..e07ceec647 100644 --- a/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 2.27614474 0.99046284 0.01382524609823586 1 21 1 2.71551442 0.9964734 0.0050968413754289565 1 22 0 -1.91610467 0.007339344 0.010627482294138626 0 -23 1 +23 1 ? ? ? 0 24 0 -2.12083673 0.00461633364 0.0066753814217522627 0 25 1 0.579404354 0.6851912 0.54542144241146051 1 26 0 -1.88108075 0.007944072 0.011506638331166432 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.9763708 0.0591678135 0.087990678318914578 0 38 1 1.97923112 0.9814142 0.027065950749429996 1 39 1 0.8320954 0.794680536 0.33155308613045453 1 -40 0 +40 0 ? ? ? 0 41 1 0.88030076 0.812019646 0.30041346219868831 1 42 1 3.29876113 0.9990636 0.0013515566126819322 1 43 1 0.21289444 0.485705882 1.0418451361573347 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.76776826 0.010259659 0.014878012048023576 0 137 0 -2.00804472 0.00596073642 0.0086252569347725905 0 138 0 -1.64871967 0.0134130223 0.019481850015569589 0 -139 0 +139 0 ? ? ? 0 140 0 -2.00804472 0.00596073642 0.0086252569347725905 0 141 0 -2.15638113 0.00425880356 0.0061572754121802442 0 142 1 1.42587113 0.93738085 0.093292772480968233 1 143 0 -1.57122457 0.0159614533 0.023213265078036949 0 144 0 -2.06444073 0.005245867 0.007588107121760313 0 -145 0 +145 0 ? ? ? 0 146 1 0.5954063 0.693000734 0.52907121478369279 1 147 0 -1.97373641 0.00644216966 0.0093241524569279595 0 148 0 -0.5180371 0.15157719 0.23714468564136862 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 1.38289 0.931381464 0.10255592440494538 1 156 0 -2.00303721 0.00602870947 0.0087239127220832425 0 157 0 -1.97250068 0.00646021264 0.0093503519898618208 0 -158 0 +158 0 ? ? ? 0 159 1 4.52930737 0.9999432 8.1952060728897203E-05 1 160 1 3.334266 0.9991363 0.0012465526889135054 1 161 0 -1.5841018 0.015507183 0.022547414886151074 0 162 0 -1.82416427 0.009033906 0.01309239897866022 0 163 0 -1.37575865 0.0246935654 0.03607251996647886 0 -164 0 +164 0 ? ? ? 0 165 0 -1.40072858 0.0233600326 0.034101275741716536 0 166 1 3.14331079 0.9986662 0.0019255122730899305 1 167 1 2.61804938 0.995600462 0.0063611951559849299 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.262052536 0.5136946 1.0400654369740645 1 233 1 2.16407657 0.987723053 0.017821513393369265 1 234 0 -1.00943446 0.0551115535 0.081784080061402753 0 -235 0 +235 0 ? ? ? 0 236 1 4.29103756 0.999902248 0.00014103266690546063 1 237 1 2.42226958 0.9931447 0.0099241759578403005 1 238 1 4.649124 0.9999568 6.2345058017421014E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 4.143937 0.9998633 0.00019719158494834585 1 247 1 1.19717526 0.898898065 0.15377057127992425 1 248 0 -1.217197 0.03506021 0.051489167959956395 0 -249 0 +249 0 ? ? ? 0 250 0 -2.09497738 0.004895074 0.0070794400974987601 0 251 1 2.700413 0.9963504 0.0052748803033137396 1 252 0 1.58717227 0.955785036 4.4993214783146023 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.980280161 0.8443483 0.2440898311411345 1 273 1 0.00625777245 0.3710024 1.4304995524008379 1 274 0 -1.73243809 0.0111099789 0.01611801356385726 0 -275 0 +275 0 ? ? ? 0 276 0 -1.91610467 0.007339344 0.010627482294138626 0 277 0 -2.21277714 0.00374727719 0.0054163328257276827 0 278 0 -2.12083673 0.00461633364 0.0066753814217522627 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 3.0956676 0.9985136 0.002146047033326764 1 290 0 -2.30471754 0.00304132653 0.0043943924919565489 0 291 0 -2.12083673 0.00461633364 0.0066753814217522627 0 -292 1 +292 1 ? ? ? 0 293 1 1.9506762 0.980189741 0.028867048302480647 1 -294 0 +294 0 ? ? ? 0 295 1 2.57708764 0.995172262 0.0069818201865888847 1 296 0 0.6443205 0.7161847 1.8169756066019795 1 -297 0 +297 0 ? ? ? 0 298 0 -1.00866961 0.0552023575 0.081922729978655878 0 299 1 2.32091022 0.9913795 0.012490669010981763 1 300 1 2.40809751 0.992921352 0.010248646031556275 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 1.19807649 0.899084449 0.15347146394878203 1 313 0 -2.30471754 0.00304132653 0.0043943924919565489 0 314 0 -2.26844478 0.00330244377 0.0047723037303054865 0 -315 0 +315 0 ? ? ? 0 316 1 1.47730184 0.943915665 0.083270128208594502 1 317 1 3.18132329 0.998776734 0.0017658802629188005 1 318 0 -1.9538343 0.00673895236 0.0097551599741469968 0 319 0 0.68000555 0.732414246 1.9019267823886503 1 320 1 2.448116 0.993534148 0.009358541174187586 1 -321 0 +321 0 ? ? ? 0 322 0 -1.82416427 0.009033906 0.01309239897866022 0 323 1 1.739836 0.9683619 0.046381755881038968 1 324 0 -2.12083673 0.00461633364 0.0066753814217522627 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -1.41561711 0.022598628 0.032976965091470092 0 409 0 -1.79705584 0.009603847 0.013922383925987339 0 410 0 -2.21277714 0.00374727719 0.0054163328257276827 0 -411 0 +411 0 ? ? ? 0 412 1 3.177976 0.9987674 0.0017793974994358322 1 413 0 -1.40844309 0.0229624342 0.033514061939899156 0 414 1 2.38908362 0.992610335 0.010700618410080708 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -2.1201086 0.00462396163 0.0066864373761273176 0 615 0 -1.529671 0.01751844 0.025497765300806271 0 616 0 -1.91610467 0.007339344 0.010627482294138626 0 -617 0 +617 0 ? ? ? 0 618 0 -1.76776826 0.010259659 0.014878012048023576 0 619 0 -1.61943209 0.0143251875 0.020816334525491278 0 620 0 -1.91610467 0.007339344 0.010627482294138626 0 diff --git a/test/BaselineOutput/SingleDebug/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt b/test/BaselineOutput/SingleDebug/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt index 3608e165c9..338a27b79c 100644 --- a/test/BaselineOutput/SingleDebug/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt +++ b/test/BaselineOutput/SingleDebug/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt @@ -38,6 +38,8 @@ DCG@2: 0.000000 (0.0000) DCG@3: 0.000000 (0.0000) --------------------------------------- +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' Physical memory usage(MB): %Number% Virtual memory usage(MB): %Number% %DateTime% Time elapsed(s): %Number% diff --git a/test/BaselineOutput/SingleDebug/Command/Datatypes-1-out.txt b/test/BaselineOutput/SingleDebug/Command/Datatypes-1-out.txt new file mode 100644 index 0000000000..fe04f014c2 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/Command/Datatypes-1-out.txt @@ -0,0 +1 @@ +Wrote 5 rows across 9 columns in %Time% diff --git a/test/BaselineOutput/SingleDebug/Command/DataTypes-out.txt b/test/BaselineOutput/SingleDebug/Command/Datatypes-2-out.txt similarity index 100% rename from test/BaselineOutput/SingleDebug/Command/DataTypes-out.txt rename to test/BaselineOutput/SingleDebug/Command/Datatypes-2-out.txt diff --git a/test/BaselineOutput/SingleDebug/Command/Datatypes-datatypes.txt b/test/BaselineOutput/SingleDebug/Command/Datatypes-datatypes.txt index e7d128e400..aaf1a3cb2e 100644 --- a/test/BaselineOutput/SingleDebug/Command/Datatypes-datatypes.txt +++ b/test/BaselineOutput/SingleDebug/Command/Datatypes-datatypes.txt @@ -14,6 +14,6 @@ bl i1 i2 i4 i8 ts dto dt tx 0 127 32767 2147483647 9223372036854775807 "2.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" foo 1 -127 -32767 -2147483647 -9223372036854775807 "7.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" xyz - "7.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" +0 -128 -32768 -2147483648 -9223372036854775808 "7.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" "" 9 0:0 - +0 -128 -32768 -2147483648 -9223372036854775808 "00:00:00" "0001-01-01T00:00:00.0000000+00:00" "0001-01-01T00:00:00.0000000" "" diff --git a/test/BaselineOutput/SingleRelease/Command/DataTypes-out.txt b/test/BaselineOutput/SingleDebug/Command/Datatypes-out.txt similarity index 100% rename from test/BaselineOutput/SingleRelease/Command/DataTypes-out.txt rename to test/BaselineOutput/SingleDebug/Command/Datatypes-out.txt diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt index 193a84b3cd..f6c942f824 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt @@ -46,6 +46,12 @@ Log-loss: 0.253074 (0.0597) Log-loss reduction: 76.713844 (5.4729) --------------------------------------- +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' Physical memory usage(MB): %Number% Virtual memory usage(MB): %Number% %DateTime% Time elapsed(s): %Number% diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt index e6f1c68e64..28327f69a4 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt @@ -50,6 +50,10 @@ Log-loss: 0.253074 (0.0597) Log-loss reduction: 76.713839 (5.4729) --------------------------------------- +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' Physical memory usage(MB): %Number% Virtual memory usage(MB): %Number% %DateTime% Time elapsed(s): %Number% diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-out.txt similarity index 63% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-out.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-out.txt index 9631870541..afa867d488 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-out.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-out.txt @@ -7,24 +7,24 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.524348 -L2(avg): 0.466735 -RMS(avg): 0.683180 -Loss-fn(avg): 0.466735 -R Squared: 0.400415 -L1(avg): 0.517508 -L2(avg): 0.458039 -RMS(avg): 0.676786 -Loss-fn(avg): 0.458039 -R Squared: 0.420159 +L1(avg): 27.477977 +L2(avg): 1,428.594095 +RMS(avg): 37.796747 +Loss-fn(avg): 1,428.594094 +R Squared: 0.920504 +L1(avg): 26.801569 +L2(avg): 1,413.398603 +RMS(avg): 37.595194 +Loss-fn(avg): 1,413.398596 +R Squared: 0.923322 OVERALL RESULTS --------------------------------------- -L1(avg): 0.520928 (0.0034) -L2(avg): 0.462387 (0.0043) -RMS(avg): 0.679983 (0.0032) -Loss-fn(avg): 0.462387 (0.0043) -R Squared: 0.410287 (0.0099) +L1(avg): 27.139773 (0.3382) +L2(avg): 1,420.996349 (7.5977) +RMS(avg): 37.695971 (0.1008) +Loss-fn(avg): 1,420.996345 (7.5977) +R Squared: 0.921913 (0.0014) --------------------------------------- Physical memory usage(MB): %Number% @@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l2: 0.189697165394939 +[2] (%Time%) Iteration: 50 Training-l2: 37.107605006517 [2] 'Training with LightGBM' finished in %Time%. [3] 'Loading data for LightGBM #2' started. [3] 'Loading data for LightGBM #2' finished in %Time%. [4] 'Training with LightGBM #2' started. -[4] (%Time%) Iteration: 50 Training-l2: 0.204982247876212 +[4] (%Time%) Iteration: 50 Training-l2: 27.7037679135951 [4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-rp.txt similarity index 89% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-rp.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-rp.txt index ede416ff06..368e4a4273 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-rp.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.520928 0.462387 0.679983 0.462387 0.410287 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 +27.13977 1420.996 37.69597 1420.996 0.921913 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt new file mode 100644 index 0000000000..3b188aa21f --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt @@ -0,0 +1,501 @@ +Instance Label Score L1-loss L2-loss +5 322.18 281.224945 40.955047607421875 1677.3159245261922 +6 425.31 428.151917 2.8419189453125 8.0765032917261124 +8 159.37 106.197723 53.172271728515625 2827.290480771102 +9 325.18 309.94397 15.23602294921875 232.13639530912042 +10 276.02 259.230316 16.7896728515625 281.89311446249485 +11 193.96 196.782562 2.8225555419921875 7.9668197876308113 +18 471.27 470.431122 0.8388671875 0.70369815826416016 +20 290.11 267.865662 22.24432373046875 494.80993822589517 +21 432.91 409.047363 23.862640380859375 569.42560594622046 +25 413.49 387.7714 25.718597412109375 661.44625284615904 +28 360.71 395.3086 34.598602294921875 1197.0632807621732 +31 372.81 338.0285 34.781494140625 1209.7523346543312 +32 349.87 351.383118 1.51312255859375 2.2895398773252964 +35 339.31 335.774963 3.5350341796875 12.496466651558876 +37 326.38 319.16214 7.217864990234375 52.097575017251074 +40 265.78 263.883759 1.896240234375 3.5957270264625549 +41 638.26 588.1356 50.1243896484375 2512.4544376283884 +44 476.58 479.588928 3.008941650390625 9.0537298554554582 +45 324.73 353.262177 28.53216552734375 814.08446967974305 +46 155.52 176.948944 21.428939819335938 459.19946178072132 +48 394.42 392.8226 1.597412109375 2.551725447177887 +50 378.13 420.66217 42.53216552734375 1808.9851044453681 +51 607.06 622.043335 14.98333740234375 224.50039971247315 +52 814.77 644.548 170.2220458984375 28975.544909849763 +54 406.3 396.773651 9.526336669921875 90.751090348698199 +56 381.53 316.025818 65.504180908203125 4290.7977164546028 +60 595.54 578.850037 16.68994140625 278.55414414405823 +63 565.44 544.2438 21.19622802734375 449.28008258715272 +64 503.88 459.8302 44.0498046875 1940.385293006897 +66 412.4 483.7573 71.3572998046875 5091.8642354160547 +68 284.83 320.5699 35.739898681640625 1277.3403577739373 +69 342.33 351.791443 9.461456298828125 89.519155294634402 +70 319.81 351.033051 31.223052978515625 974.87903729919344 +71 465.97 446.610535 19.359466552734375 374.78894520644099 +72 605.7 570.022339 35.67767333984375 1272.8963749445975 +73 325.76 372.722 46.96197509765625 2205.4271050728858 +74 231.07 185.792953 45.277053833007812 2050.0116037970874 +76 497.31 457.7382 39.571807861328125 1565.9279774138704 +77 175.58 219.618118 44.038116455078125 1939.3557009110227 +79 313.36 336.6784 23.31842041015625 543.74873042479157 +82 544.1 537.079163 7.02081298828125 49.291815016418695 +88 494.21 478.712341 15.497650146484375 240.17716006282717 +90 315.69 307.981171 7.708831787109375 59.42608752194792 +91 370.5 346.7038 23.79620361328125 566.25930640473962 +92 254.02 249.575821 4.444183349609375 19.750765644945204 +93 599.81 567.3842 32.42578125 1051.4312896728516 +95 298.6 294.3337 4.26629638671875 18.201284859329462 +96 603.69 624.9469 21.25689697265625 451.85566890612245 +97 274.36 265.9407 8.419281005859375 70.884292655624449 +98 164.33 195.256363 30.926361083984375 956.43980989698321 +99 414.57 400.773926 13.79608154296875 190.33186594024301 +100 120.26 127.97123 7.7112274169921875 59.463028276572004 +102 519.61 528.5951 8.985107421875 80.732155382633209 +104 339.79 303.827118 35.962890625 1293.3295021057129 +105 523.01 597.759949 74.74993896484375 5587.5533752478659 +106 372.35 353.298431 19.05157470703125 362.96249881759286 +108 368.77 368.545135 0.224853515625 0.050559103488922119 +109 400.39 402.3977 2.0076904296875 4.0308208614587784 +111 466.77 464.010742 2.759246826171875 7.6134430477395654 +112 195.44 197.507233 2.067230224609375 4.273440801538527 +113 594.45 506.632416 87.817596435546875 7711.9302437165752 +115 424.78 424.465149 0.314849853515625 0.09913043025881052 +117 256.09 247.309891 8.7801055908203125 77.090254185954109 +120 102.78 104.091949 1.31195068359375 1.7212145961821079 +121 443.92 423.8661 20.053924560546875 402.15989028010517 +122 356.3 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b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-out.txt @@ -3,19 +3,19 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.402080 -L2(avg): 0.272274 -RMS(avg): 0.521799 -Loss-fn(avg): 0.272274 -R Squared: 0.652798 +L1(avg): 3.472291 +L2(avg): 26.064428 +RMS(avg): 5.105333 +Loss-fn(avg): 26.064428 +R Squared: 0.998571 OVERALL RESULTS --------------------------------------- -L1(avg): 0.402080 (0.0000) -L2(avg): 0.272274 (0.0000) -RMS(avg): 0.521799 (0.0000) -Loss-fn(avg): 0.272274 (0.0000) -R Squared: 0.652798 (0.0000) +L1(avg): 3.472291 (0.0000) +L2(avg): 26.064428 (0.0000) +RMS(avg): 5.105333 (0.0000) +Loss-fn(avg): 26.064428 (0.0000) +R Squared: 0.998571 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% @@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l2: 0.272273893168108 +[2] (%Time%) Iteration: 50 Training-l2: 26.0644295080124 [2] 'Training with LightGBM' finished in %Time%. [3] 'Saving model' started. [3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt new file mode 100644 index 0000000000..58c87f276b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +3.472291 26.06443 5.105333 26.06443 0.998571 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 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a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -LightGBMR -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.40208 0.272274 0.521799 0.272274 0.652798 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 - diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine.txt deleted file mode 100644 index 34d954ed70..0000000000 --- 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0.19656626694109036 -4895 6 5.4413476 0.55865240097045898 0.31209250511005848 -4896 7 7.037857 0.0378570556640625 0.0014331566635519266 -4897 6 5.9355073 0.064492702484130859 0.0041593086737066187 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-out.txt similarity index 63% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-out.txt index 0209b5a363..c2530555e1 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-out.txt @@ -7,24 +7,24 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.529227 -L2(avg): 0.479877 -RMS(avg): 0.692731 -Loss-fn(avg): 0.479877 -R Squared: 0.383532 -L1(avg): 0.519622 -L2(avg): 0.463327 -RMS(avg): 0.680681 -Loss-fn(avg): 0.463327 -R Squared: 0.413465 +L1(avg): 27.482854 +L2(avg): 1,445.214986 +RMS(avg): 38.015983 +Loss-fn(avg): 1,445.214986 +R Squared: 0.919579 +L1(avg): 25.716714 +L2(avg): 1,341.437580 +RMS(avg): 36.625641 +Loss-fn(avg): 1,341.437567 +R Squared: 0.927226 OVERALL RESULTS --------------------------------------- -L1(avg): 0.524425 (0.0048) -L2(avg): 0.471602 (0.0083) -RMS(avg): 0.686706 (0.0060) -Loss-fn(avg): 0.471602 (0.0083) -R Squared: 0.398499 (0.0150) +L1(avg): 26.599784 (0.8831) +L2(avg): 1,393.326283 (51.8887) +RMS(avg): 37.320812 (0.6952) +Loss-fn(avg): 1,393.326277 (51.8887) +R Squared: 0.923402 (0.0038) --------------------------------------- Physical memory usage(MB): %Number% @@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l1: 0.344282018934789 +[2] (%Time%) Iteration: 50 Training-l1: 2.72125878362794 [2] 'Training with LightGBM' finished in %Time%. [3] 'Loading data for LightGBM #2' started. [3] 'Loading data for LightGBM #2' finished in %Time%. [4] 'Training with LightGBM #2' started. -[4] (%Time%) Iteration: 50 Training-l1: 0.345211959030736 +[4] (%Time%) Iteration: 50 Training-l1: 2.24116204430926 [4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-rp.txt index e2cc16093d..d7359a5e25 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae +26.59978 1393.326 37.32081 1393.326 0.923402 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE.txt new file mode 100644 index 0000000000..ed40bebf10 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE.txt @@ -0,0 +1,501 @@ +Instance Label Score L1-loss L2-loss +5 322.18 294.792328 27.387664794921875 750.08418291900307 +6 425.31 423.010223 2.299774169921875 5.2889612326398492 +8 159.37 114.656738 44.7132568359375 1999.2753368765116 +9 325.18 360.2713 35.09130859375 1231.3999388217926 +10 276.02 271.508362 4.511627197265625 20.354779967106879 +11 193.96 181.32164 12.63836669921875 159.72831282392144 +18 471.27 466.567 4.7030029296875 22.118236556649208 +20 290.11 249.322037 40.787948608398438 1663.656751681352 +21 432.91 432.3761 0.533905029296875 0.28505458030849695 +25 413.49 392.391418 21.09857177734375 445.14973104372621 +28 360.71 367.5943 6.884307861328125 47.393694729544222 +31 372.81 338.866943 33.94305419921875 1152.1309283711016 +32 349.87 340.980682 8.889312744140625 79.019881063140929 +35 339.31 327.5378 11.772186279296875 138.5843697944656 +37 326.38 292.9897 33.39031982421875 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0.022923946380615234 0.0005255073176613223 -4851 5 5.278072 0.27807188034057617 0.077323970636143713 -4852 5 6.418775 1.4187750816345215 2.0129227322670431 -4853 5 5.904184 0.90418386459350586 0.81754846099124734 -4854 6 7.04106474 1.0410647392272949 1.0838157912623956 -4856 6 5.81926346 0.18073654174804688 0.032665697523043491 -4859 6 6.03407764 0.034077644348144531 0.0011612858443186269 -4860 6 6.10150051 0.10150051116943359 0.010302353767656314 -4862 6 6.09098768 0.090987682342529297 0.0082787583380650176 -4866 6 5.99347734 0.0065226554870605469 4.254503460288106E-05 -4867 6 4.705145 1.2948551177978516 1.676649776087288 -4869 6 5.51711273 0.48288726806640625 0.23318011366063729 -4871 6 6.68095255 0.68095254898071289 0.46369637396333019 -4872 5 5.705171 0.70517110824584961 0.49726629190467975 -4876 7 6.81956148 0.18043851852416992 0.032558058967197212 -4878 4 4.51450825 0.51450824737548828 0.26471873661739664 -4879 6 5.44779968 0.5522003173828125 0.30492519051767886 -4880 6 5.44779968 0.5522003173828125 0.30492519051767886 -4881 6 5.75216627 0.2478337287902832 0.061421557126095649 -4882 5 5.97157431 0.97157430648803711 0.94395663302771027 -4883 6 6.152341 0.15234088897705078 0.023207746454318112 -4886 7 6.863344 0.13665580749511719 0.018674809722142527 -4887 7 4.93395472 2.0660452842712402 4.2685431166594299 -4888 5 6.00417376 1.004173755645752 1.0083649315276944 -4889 6 5.75216627 0.2478337287902832 0.061421557126095649 -4894 5 5.44779968 0.4477996826171875 0.20052455575205386 -4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-out.txt similarity index 67% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-out.txt index 4792a52b30..aaad5d20e5 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-out.txt @@ -3,19 +3,19 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.407028 -L2(avg): 0.274963 -RMS(avg): 0.524369 -Loss-fn(avg): 0.274963 -R Squared: 0.649369 +L1(avg): 3.428896 +L2(avg): 25.236013 +RMS(avg): 5.023546 +Loss-fn(avg): 25.236013 +R Squared: 0.998616 OVERALL RESULTS --------------------------------------- -L1(avg): 0.407028 (0.0000) -L2(avg): 0.274963 (0.0000) -RMS(avg): 0.524369 (0.0000) -Loss-fn(avg): 0.274963 (0.0000) -R Squared: 0.649369 (0.0000) +L1(avg): 3.428896 (0.0000) +L2(avg): 25.236013 (0.0000) +RMS(avg): 5.023546 (0.0000) +Loss-fn(avg): 25.236013 (0.0000) +R Squared: 0.998616 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% @@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l1: 0.407028426136092 +[2] (%Time%) Iteration: 50 Training-l1: 3.42889585604196 [2] 'Training with LightGBM' finished in %Time%. [3] 'Saving model' started. [3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-rp.txt index cf5f4d432b..59ac27dc61 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae +3.428896 25.23601 5.023546 25.23601 0.998616 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE.txt new file mode 100644 index 0000000000..5f5a7cc20b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE.txt @@ -0,0 +1,501 @@ +Instance Label Score L1-loss L2-loss +0 140.66 140.224045 0.4359588623046875 0.19006012962199748 +1 148.12 150.455109 2.335113525390625 5.4527551764622331 +2 402.2 399.137634 3.0623779296875 9.3781585842370987 +3 443.51 440.823761 2.686248779296875 7.2159325042739511 +4 329.59 325.1096 4.48040771484375 20.074053291231394 +5 322.18 316.19873 5.98126220703125 35.77549758926034 +6 425.31 421.0235 4.2864990234375 18.374073877930641 +7 421.76 423.108826 1.34881591796875 1.8193043805658817 +8 159.37 154.908264 4.46173095703125 19.907043132930994 +9 325.18 323.497437 1.68255615234375 2.8309952057898045 +10 276.02 282.882629 6.862640380859375 47.095832997001708 +11 193.96 185.404175 8.5558319091796875 73.202259658137336 +12 472.97 463.2464 9.723602294921875 94.548441589809954 +13 266.98 267.559174 0.57916259765625 0.33542931452393532 +14 331.77 331.464661 0.305328369140625 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0.16701602935791016 0.027894354062482307 -4887 7 6.50685453 0.49314546585083008 0.24319245048923221 -4888 5 5.990843 0.99084281921386719 0.98176949238768429 -4889 6 6.151292 0.15129184722900391 0.022889223037964257 -4890 6 6.509506 0.5095062255859375 0.25959659391082823 -4891 6 5.611698 0.38830184936523438 0.15077832622046117 -4892 5 5.985514 0.98551416397094727 0.97123816738735513 -4893 6 6.2242403 0.22424030303955078 0.050283713507269567 -4894 5 5.458216 0.45821619033813477 0.20996207708799375 -4895 6 5.30194473 0.69805526733398438 0.48728115625272039 -4896 7 6.68631124 0.31368875503540039 0.098400635035659434 -4897 6 6.04107332 0.041073322296142578 0.001687017804442803 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-out.txt similarity index 62% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-out.txt index 6483a88e76..483c724038 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-out.txt @@ -7,24 +7,24 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.529227 -L2(avg): 0.479877 -RMS(avg): 0.692731 -Loss-fn(avg): 0.479877 -R Squared: 0.383532 -L1(avg): 0.519622 -L2(avg): 0.463327 -RMS(avg): 0.680681 -Loss-fn(avg): 0.463327 -R Squared: 0.413465 +L1(avg): 27.482854 +L2(avg): 1,445.214986 +RMS(avg): 38.015983 +Loss-fn(avg): 1,445.214986 +R Squared: 0.919579 +L1(avg): 25.716714 +L2(avg): 1,341.437580 +RMS(avg): 36.625641 +Loss-fn(avg): 1,341.437567 +R Squared: 0.927226 OVERALL RESULTS --------------------------------------- -L1(avg): 0.524425 (0.0048) -L2(avg): 0.471602 (0.0083) -RMS(avg): 0.686706 (0.0060) -Loss-fn(avg): 0.471602 (0.0083) -R Squared: 0.398499 (0.0150) +L1(avg): 26.599784 (0.8831) +L2(avg): 1,393.326283 (51.8887) +RMS(avg): 37.320812 (0.6952) +Loss-fn(avg): 1,393.326277 (51.8887) +R Squared: 0.923402 (0.0038) --------------------------------------- Physical memory usage(MB): %Number% @@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-rmse: 0.444161678699535 +[2] (%Time%) Iteration: 50 Training-rmse: 6.01041394354288 [2] 'Training with LightGBM' finished in %Time%. [3] 'Loading data for LightGBM #2' started. [3] 'Loading data for LightGBM #2' finished in %Time%. [4] 'Training with LightGBM #2' started. -[4] (%Time%) Iteration: 50 Training-rmse: 0.447777922357938 +[4] (%Time%) Iteration: 50 Training-rmse: 4.74971028160668 [4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-rp.txt index 56cb0fac01..1b893d09dc 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse +26.59978 1393.326 37.32081 1393.326 0.923402 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE.txt new file mode 100644 index 0000000000..ed40bebf10 --- 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5.44779968 0.5522003173828125 0.30492519051767886 -4881 6 5.75216627 0.2478337287902832 0.061421557126095649 -4882 5 5.97157431 0.97157430648803711 0.94395663302771027 -4883 6 6.152341 0.15234088897705078 0.023207746454318112 -4886 7 6.863344 0.13665580749511719 0.018674809722142527 -4887 7 4.93395472 2.0660452842712402 4.2685431166594299 -4888 5 6.00417376 1.004173755645752 1.0083649315276944 -4889 6 5.75216627 0.2478337287902832 0.061421557126095649 -4894 5 5.44779968 0.4477996826171875 0.20052455575205386 -4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-out.txt similarity index 67% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-out.txt index d01de377a4..1ed592dd87 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-out.txt @@ -3,19 +3,19 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.407028 -L2(avg): 0.274963 -RMS(avg): 0.524369 -Loss-fn(avg): 0.274963 -R Squared: 0.649369 +L1(avg): 3.428896 +L2(avg): 25.236013 +RMS(avg): 5.023546 +Loss-fn(avg): 25.236013 +R Squared: 0.998616 OVERALL RESULTS --------------------------------------- -L1(avg): 0.407028 (0.0000) -L2(avg): 0.274963 (0.0000) -RMS(avg): 0.524369 (0.0000) -Loss-fn(avg): 0.274963 (0.0000) -R Squared: 0.649369 (0.0000) +L1(avg): 3.428896 (0.0000) +L2(avg): 25.236013 (0.0000) +RMS(avg): 5.023546 (0.0000) +Loss-fn(avg): 25.236013 (0.0000) +R Squared: 0.998616 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% @@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-rmse: 0.524369259872456 +[2] (%Time%) Iteration: 50 Training-rmse: 5.02354584275365 [2] 'Training with LightGBM' finished in %Time%. [3] 'Saving model' started. [3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt rename to test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-rp.txt index 2c813d3277..b8f135e448 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse +3.428896 25.23601 5.023546 25.23601 0.998616 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE.txt new file mode 100644 index 0000000000..5f5a7cc20b --- /dev/null +++ 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6 6.05903625 0.0590362548828125 0.0034852793905884027 -1996 6 6.00442839 0.0044283866882324219 1.9610608660514117E-05 -1997 6 6.05903625 0.0590362548828125 0.0034852793905884027 -1998 6 6.05903625 0.0590362548828125 0.0034852793905884027 -1999 6 6.65917635 0.65917634963989258 0.43451345992457391 -2000 5 5.15526772 0.15526771545410156 0.024108063462335849 -2001 5 5.116252 0.11625194549560547 0.013514514831513225 -2002 6 6.24034739 0.24034738540649414 0.057766865671737833 -2003 6 5.74645329 0.25354671478271484 0.06428593657710735 -2004 6 5.97547865 0.024521350860595703 0.00060129664802843763 -2005 6 6.00442839 0.0044283866882324219 1.9610608660514117E-05 -2006 6 6.05903625 0.0590362548828125 0.0034852793905884027 -2007 6 6.035142 0.035141944885253906 0.0012349562903182232 -2008 5 5.388008 0.38800811767578125 0.15055029938230291 -2009 7 6.72901726 0.27098274230957031 0.073431646629614988 -2010 6 6.07737875 0.077378749847412109 0.0059874709279483795 -2011 5 5.388008 0.38800811767578125 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0.16701602935791016 0.027894354062482307 -4887 7 6.50685453 0.49314546585083008 0.24319245048923221 -4888 5 5.990843 0.99084281921386719 0.98176949238768429 -4889 6 6.151292 0.15129184722900391 0.022889223037964257 -4890 6 6.509506 0.5095062255859375 0.25959659391082823 -4891 6 5.611698 0.38830184936523438 0.15077832622046117 -4892 5 5.985514 0.98551416397094727 0.97123816738735513 -4893 6 6.2242403 0.22424030303955078 0.050283713507269567 -4894 5 5.458216 0.45821619033813477 0.20996207708799375 -4895 6 5.30194473 0.69805526733398438 0.48728115625272039 -4896 7 6.68631124 0.31368875503540039 0.098400635035659434 -4897 6 6.04107332 0.041073322296142578 0.001687017804442803 diff --git a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt index b337fd78da..b113f448bc 100644 --- a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 1.25826859 1 0 1 35 0 -1.41764426 1E-15 1.4415419267167138E-15 0 37 0 -0.835641861 1E-15 1.4415419267167138E-15 0 -40 0 +40 0 ? ? ? 0 41 1 0.227130175 0.8666667 0.20645086423799175 1 44 1 1.61423349 1 0 1 45 0 -1.47288513 1E-15 1.4415419267167138E-15 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.19960022 1E-15 1.4415419267167138E-15 0 141 0 -1.47188914 1E-15 1.4415419267167138E-15 0 144 0 -1.41764426 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 147 0 -1.334388 1E-15 1.4415419267167138E-15 0 150 0 -1.48176765 1E-15 1.4415419267167138E-15 0 151 1 0.7797704 1 0 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -1.54539037 1E-15 1.4415419267167138E-15 0 156 0 -1.35846376 1E-15 1.4415419267167138E-15 0 161 0 -1.1599288 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 167 1 1.07981849 1 0 1 169 0 -1.5413084 1E-15 1.4415419267167138E-15 0 171 0 -1.43690062 1E-15 1.4415419267167138E-15 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.368261 1 0 1 247 1 0.4837153 0.8666667 0.20645086423799175 1 248 0 -0.934056163 1E-15 1.4415419267167138E-15 0 -249 0 +249 0 ? ? ? 0 250 0 -1.41270852 1E-15 1.4415419267167138E-15 0 252 0 0.706041336 0.8666667 2.9068906815998465 1 254 1 1.600352 1 0 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.43690062 1E-15 1.4415419267167138E-15 0 271 0 -1.1428957 1E-15 1.4415419267167138E-15 0 272 1 0.435359716 0.8666667 0.20645086423799175 1 -275 0 +275 0 ? ? ? 0 276 0 -1.34414315 1E-15 1.4415419267167138E-15 0 277 0 -1.49114561 1E-15 1.4415419267167138E-15 0 278 0 -1.43690062 1E-15 1.4415419267167138E-15 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.43690062 1E-15 1.4415419267167138E-15 0 293 1 0.9331081 1 0 1 296 0 0.221757889 0.8666667 2.9068906815998465 1 -297 0 +297 0 ? ? ? 0 299 1 1.13769388 1 0 1 300 1 1.24829745 1 0 1 301 0 -1.43690062 1E-15 1.4415419267167138E-15 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.7455783 0.9151356 0.12794252993534275 1 317 1 1.81665158 1 0 1 319 0 0.285441637 0.8666667 2.9068906815998465 1 -321 0 +321 0 ? ? ? 0 323 1 0.8990345 1 0 1 327 0 -1.49114561 1E-15 1.4415419267167138E-15 0 328 1 0.8467562 1 0 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 2.95104 1 0 1 613 0 -1.29436374 1E-15 1.4415419267167138E-15 0 614 0 -1.4625113 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -1.2706418 1E-15 1.4415419267167138E-15 0 619 0 -1.19714069 1E-15 1.4415419267167138E-15 0 621 0 -0.3866408 0.5799383 1.2513268180432666 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.13828516 1E-15 1.4415419267167138E-15 0 19 0 -0.9969288 1E-15 1.4415419267167138E-15 0 22 0 -1.259604 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -1.35031986 1E-15 1.4415419267167138E-15 0 26 0 -1.22985053 1E-15 1.4415419267167138E-15 0 27 0 -1.11824775 1E-15 1.4415419267167138E-15 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.22317672 1E-15 1.4415419267167138E-15 0 135 0 -0.8295188 1E-15 1.4415419267167138E-15 0 136 0 -1.18892586 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -1.3102448 1E-15 1.4415419267167138E-15 0 142 1 0.6019325 0.875 0.19264507794239591 1 143 0 -1.00445139 1E-15 1.4415419267167138E-15 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.01599169 1E-15 1.4415419267167138E-15 0 155 1 0.7134235 0.875 0.19264507794239591 1 157 0 -1.27964163 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 2.42447567 1 0 1 160 1 1.81060028 1 0 1 162 0 -1.20896339 1E-15 1.4415419267167138E-15 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1.72919893 1 0 1 232 0 -0.038654685 0.6923077 1.7004398041324202 0 234 0 -0.661043048 1E-15 1.4415419267167138E-15 0 -235 0 +235 0 ? ? ? 0 236 1 2.13174057 1 0 1 238 1 2.40485334 1 0 1 243 0 -0.8018886 1E-15 1.4415419267167138E-15 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 2.9424572 1 0 1 287 0 -1.22317672 1E-15 1.4415419267167138E-15 0 289 1 1.71858239 1 0 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 1.42958808 1 0 1 298 0 -0.7813908 1E-15 1.4415419267167138E-15 0 302 1 2.925787 1 0 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.35031986 1E-15 1.4415419267167138E-15 0 310 0 -1.293855 1E-15 1.4415419267167138E-15 0 313 0 -1.45160127 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 318 0 -1.24103785 1E-15 1.4415419267167138E-15 0 320 1 1.18153763 1 0 1 322 0 -1.20896339 1E-15 1.4415419267167138E-15 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -1.40096045 1E-15 1.4415419267167138E-15 0 408 0 -0.928058 1E-15 1.4415419267167138E-15 0 410 0 -1.40096045 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 1.731832 1 0 1 417 0 -1.40096045 1E-15 1.4415419267167138E-15 0 420 0 -0.7677182 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt index fed143de3d..000020d071 100644 --- a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Assigned 32 1 1.25826859 1 35 0 -1.41764426 0 37 0 -0.835641861 0 -40 0 +40 0 ? 0 41 1 0.227130175 1 44 1 1.61423349 1 45 0 -1.47288513 0 @@ -76,7 +76,7 @@ Instance Label Score Assigned 138 0 -1.19960022 0 141 0 -1.47188914 0 144 0 -1.41764426 0 -145 0 +145 0 ? 0 147 0 -1.334388 0 150 0 -1.48176765 0 151 1 0.7797704 1 @@ -84,7 +84,7 @@ Instance Label Score Assigned 154 0 -1.54539037 0 156 0 -1.35846376 0 161 0 -1.1599288 0 -164 0 +164 0 ? 0 167 1 1.07981849 1 169 0 -1.5413084 0 171 0 -1.43690062 0 @@ -130,7 +130,7 @@ Instance Label Score Assigned 246 1 2.368261 1 247 1 0.4837153 1 248 0 -0.934056163 0 -249 0 +249 0 ? 0 250 0 -1.41270852 0 252 0 0.706041336 1 254 1 1.600352 1 @@ -144,7 +144,7 @@ Instance Label Score Assigned 269 0 -1.43690062 0 271 0 -1.1428957 0 272 1 0.435359716 1 -275 0 +275 0 ? 0 276 0 -1.34414315 0 277 0 -1.49114561 0 278 0 -1.43690062 0 @@ -158,7 +158,7 @@ Instance Label Score Assigned 291 0 -1.43690062 0 293 1 0.9331081 1 296 0 0.221757889 1 -297 0 +297 0 ? 0 299 1 1.13769388 1 300 1 1.24829745 1 301 0 -1.43690062 0 @@ -172,7 +172,7 @@ Instance Label Score Assigned 316 1 0.7455783 1 317 1 1.81665158 1 319 0 0.285441637 1 -321 0 +321 0 ? 0 323 1 0.8990345 1 327 0 -1.49114561 0 328 1 0.8467562 1 @@ -318,7 +318,7 @@ Instance Label Score Assigned 612 1 2.95104 1 613 0 -1.29436374 0 614 0 -1.4625113 0 -617 0 +617 0 ? 0 618 0 -1.2706418 0 619 0 -1.19714069 0 621 0 -0.3866408 0 @@ -375,7 +375,7 @@ Instance Label Score Assigned 17 0 -1.13828516 0 19 0 -0.9969288 0 22 0 -1.259604 0 -23 1 +23 1 ? 0 24 0 -1.35031986 0 26 0 -1.22985053 0 27 0 -1.11824775 0 @@ -425,7 +425,7 @@ Instance Label Score Assigned 134 0 -1.22317672 0 135 0 -0.8295188 0 136 0 -1.18892586 0 -139 0 +139 0 ? 0 140 0 -1.3102448 0 142 1 0.6019325 1 143 0 -1.00445139 0 @@ -435,7 +435,7 @@ Instance Label Score Assigned 153 0 -1.01599169 0 155 1 0.7134235 1 157 0 -1.27964163 0 -158 0 +158 0 ? 0 159 1 2.42447567 1 160 1 1.81060028 1 162 0 -1.20896339 0 @@ -474,7 +474,7 @@ Instance Label Score Assigned 231 1 1.72919893 1 232 0 -0.038654685 0 234 0 -0.661043048 0 -235 0 +235 0 ? 0 236 1 2.13174057 1 238 1 2.40485334 1 243 0 -0.8018886 0 @@ -496,8 +496,8 @@ Instance Label Score Assigned 286 1 2.9424572 1 287 0 -1.22317672 0 289 1 1.71858239 1 -292 1 -294 0 +292 1 ? 0 +294 0 ? 0 295 1 1.42958808 1 298 0 -0.7813908 0 302 1 2.925787 1 @@ -506,7 +506,7 @@ Instance Label Score Assigned 307 0 -1.35031986 0 310 0 -1.293855 0 313 0 -1.45160127 0 -315 0 +315 0 ? 0 318 0 -1.24103785 0 320 1 1.18153763 1 322 0 -1.20896339 0 @@ -551,7 +551,7 @@ Instance Label Score Assigned 407 0 -1.40096045 0 408 0 -0.928058 0 410 0 -1.40096045 0 -411 0 +411 0 ? 0 412 1 1.731832 1 417 0 -1.40096045 0 420 0 -0.7677182 0 diff --git a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt index 9c673bc10a..905fbd46fd 100644 --- a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.133441 1 0 1 21 1 1.46698761 1 0 1 22 0 -1.29266214 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -1.38898408 1E-15 1.4415419267167138E-15 0 25 1 0.2072978 0.8125 0.29956028185890782 1 26 0 -1.27053475 1E-15 1.4415419267167138E-15 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.7899848 1E-15 1.4415419267167138E-15 0 38 1 1.07285547 0.955555558 0.065588337627980248 1 39 1 0.444084644 0.826086938 0.27563447429444238 1 -40 0 +40 0 ? ? ? 0 41 1 0.278235674 0.8125 0.29956028185890782 1 42 1 1.82501435 1 0 1 43 1 0.0648527145 0.8125 0.29956028185890782 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.21864986 1E-15 1.4415419267167138E-15 0 137 0 -1.34436476 1E-15 1.4415419267167138E-15 0 138 0 -1.15066063 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -1.34436476 1E-15 1.4415419267167138E-15 0 141 0 -1.418377 1E-15 1.4415419267167138E-15 0 142 1 0.605928659 0.826086938 0.27563447429444238 1 143 0 -1.04888356 1E-15 1.4415419267167138E-15 0 144 0 -1.36667442 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 146 1 0.263422 0.8125 0.29956028185890782 1 147 0 -1.28818154 1E-15 1.4415419267167138E-15 0 148 0 -0.38141644 0.333333343 0.5849625222189877 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.7075844 0.826086938 0.27563447429444238 1 156 0 -1.30894148 1E-15 1.4415419267167138E-15 0 157 0 -1.3149718 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 2.41813564 1 0 1 160 1 1.80749393 1 0 1 161 0 -1.10798192 1E-15 1.4415419267167138E-15 0 162 0 -1.24095953 1E-15 1.4415419267167138E-15 0 163 0 -1.14838839 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 165 0 -0.9971055 1E-15 1.4415419267167138E-15 0 166 1 1.77878284 1 0 1 167 1 1.0986836 0.955555558 0.065588337627980248 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.0348134041 0.727272749 1.8744692325712462 0 233 1 1.102045 1 0 1 234 0 -0.709003 0.1 0.15200309583369792 0 -235 0 +235 0 ? ? ? 0 236 1 2.13387632 1 0 1 237 1 1.230866 1 0 1 238 1 2.40226221 1 0 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.38478136 1 0 1 247 1 0.5292797 0.826086938 0.27563447429444238 1 248 0 -0.8721206 1E-15 1.4415419267167138E-15 0 -249 0 +249 0 ? ? ? 0 250 0 -1.3606441 1E-15 1.4415419267167138E-15 0 251 1 1.35184932 1 0 1 252 0 0.7461872 0.826086938 2.5235618055722013 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.451641083 0.826086938 0.27563447429444238 1 273 1 -0.184997916 0.727272749 0.45943157564163517 0 274 0 -1.1819942 1E-15 1.4415419267167138E-15 0 -275 0 +275 0 ? ? ? 0 276 0 -1.29266214 1E-15 1.4415419267167138E-15 0 277 0 -1.4406867 1E-15 1.4415419267167138E-15 0 278 0 -1.38898408 1E-15 1.4415419267167138E-15 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.698751 1 0 1 290 0 -1.49238932 1E-15 1.4415419267167138E-15 0 291 0 -1.38898408 1E-15 1.4415419267167138E-15 0 -292 1 +292 1 ? ? ? 0 293 1 0.9705 0.955555558 0.065588337627980248 1 -294 0 +294 0 ? ? ? 0 295 1 1.44576406 1 0 1 296 0 0.264410973 0.8125 2.4150374992788439 1 -297 0 +297 0 ? ? ? 0 298 0 -0.785661459 1E-15 1.4415419267167138E-15 0 299 1 1.16638494 1 0 1 300 1 1.27287531 1 0 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.6419792 0.826086938 0.27563447429444238 1 313 0 -1.49238932 1E-15 1.4415419267167138E-15 0 314 0 -1.4823153 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 316 1 0.773257732 0.826086938 0.27563447429444238 1 317 1 1.83582067 1 0 1 318 0 -1.26409817 1E-15 1.4415419267167138E-15 0 319 0 0.303462982 0.8125 2.4150374992788439 1 320 1 1.16069293 1 0 1 -321 0 +321 0 ? ? ? 0 322 0 -1.24095953 1E-15 1.4415419267167138E-15 0 323 1 0.909108639 0.955555558 0.065588337627980248 1 324 0 -1.38898408 1E-15 1.4415419267167138E-15 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -0.9617897 1E-15 1.4415419267167138E-15 0 409 0 -1.22467291 1E-15 1.4415419267167138E-15 0 410 0 -1.4406867 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 1.77363062 1 0 1 413 0 -1.02494574 1E-15 1.4415419267167138E-15 0 414 1 1.26032782 1 0 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.408303 1E-15 1.4415419267167138E-15 0 615 0 -1.0826714 1E-15 1.4415419267167138E-15 0 616 0 -1.29266214 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -1.21864986 1E-15 1.4415419267167138E-15 0 619 0 -1.14463758 1E-15 1.4415419267167138E-15 0 620 0 -1.29266214 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt index c5e41e5240..1ee365e6f7 100644 --- a/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Assigned 20 1 1.133441 1 21 1 1.46698761 1 22 0 -1.29266214 0 -23 1 +23 1 ? 0 24 0 -1.38898408 0 25 1 0.2072978 1 26 0 -1.27053475 0 @@ -39,7 +39,7 @@ Instance Label Score Assigned 37 0 -0.7899848 0 38 1 1.07285547 1 39 1 0.444084644 1 -40 0 +40 0 ? 0 41 1 0.278235674 1 42 1 1.82501435 1 43 1 0.0648527145 1 @@ -138,13 +138,13 @@ Instance Label Score Assigned 136 0 -1.21864986 0 137 0 -1.34436476 0 138 0 -1.15066063 0 -139 0 +139 0 ? 0 140 0 -1.34436476 0 141 0 -1.418377 0 142 1 0.605928659 1 143 0 -1.04888356 0 144 0 -1.36667442 0 -145 0 +145 0 ? 0 146 1 0.263422 1 147 0 -1.28818154 0 148 0 -0.38141644 0 @@ -157,13 +157,13 @@ Instance Label Score Assigned 155 1 0.7075844 1 156 0 -1.30894148 0 157 0 -1.3149718 0 -158 0 +158 0 ? 0 159 1 2.41813564 1 160 1 1.80749393 1 161 0 -1.10798192 0 162 0 -1.24095953 0 163 0 -1.14838839 0 -164 0 +164 0 ? 0 165 0 -0.9971055 0 166 1 1.77878284 1 167 1 1.0986836 1 @@ -234,7 +234,7 @@ Instance Label Score Assigned 232 0 -0.0348134041 0 233 1 1.102045 1 234 0 -0.709003 0 -235 0 +235 0 ? 0 236 1 2.13387632 1 237 1 1.230866 1 238 1 2.40226221 1 @@ -248,7 +248,7 @@ Instance Label Score Assigned 246 1 2.38478136 1 247 1 0.5292797 1 248 0 -0.8721206 0 -249 0 +249 0 ? 0 250 0 -1.3606441 0 251 1 1.35184932 1 252 0 0.7461872 1 @@ -274,7 +274,7 @@ Instance Label Score Assigned 272 1 0.451641083 1 273 1 -0.184997916 0 274 0 -1.1819942 0 -275 0 +275 0 ? 0 276 0 -1.29266214 0 277 0 -1.4406867 0 278 0 -1.38898408 0 @@ -291,12 +291,12 @@ Instance Label Score Assigned 289 1 1.698751 1 290 0 -1.49238932 0 291 0 -1.38898408 0 -292 1 +292 1 ? 0 293 1 0.9705 1 -294 0 +294 0 ? 0 295 1 1.44576406 1 296 0 0.264410973 1 -297 0 +297 0 ? 0 298 0 -0.785661459 0 299 1 1.16638494 1 300 1 1.27287531 1 @@ -314,13 +314,13 @@ Instance Label Score Assigned 312 1 0.6419792 1 313 0 -1.49238932 0 314 0 -1.4823153 0 -315 0 +315 0 ? 0 316 1 0.773257732 1 317 1 1.83582067 1 318 0 -1.26409817 0 319 0 0.303462982 1 320 1 1.16069293 1 -321 0 +321 0 ? 0 322 0 -1.24095953 0 323 1 0.909108639 1 324 0 -1.38898408 0 @@ -410,7 +410,7 @@ Instance Label Score Assigned 408 0 -0.9617897 0 409 0 -1.22467291 0 410 0 -1.4406867 0 -411 0 +411 0 ? 0 412 1 1.77363062 1 413 0 -1.02494574 0 414 1 1.26032782 1 @@ -616,7 +616,7 @@ Instance Label Score Assigned 614 0 -1.408303 0 615 0 -1.0826714 0 616 0 -1.29266214 0 -617 0 +617 0 ? 0 618 0 -1.21864986 0 619 0 -1.14463758 0 620 0 -1.29266214 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.txt index 55744d3dbb..c3e87fdfec 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 7.01161575 0.999099433 0.0012998283148002473 1 35 0 -5.546275 0.0038867984 0.0056183906346792553 0 37 0 -0.627175331 0.3481513 0.61739094617065504 0 -40 0 +40 0 ? ? ? 0 41 1 3.0365572 0.9541986 0.067638527006529184 1 44 1 6.61341858 0.998659551 0.0019351561965343684 1 45 0 -5.565003 0.00381495967 0.0055143486004666058 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.72209072 0.008818108 0.012778263407408707 0 141 0 -6.18090439 0.002064286 0.0029812133205916491 0 144 0 -5.546275 0.0038867984 0.0056183906346792553 0 -145 0 +145 0 ? ? ? 0 147 0 -5.305633 0.0049390397 0.0071431828757896592 0 150 0 -5.43029261 0.004362697 0.0063078105710409225 0 151 1 4.990402 0.993243039 0.0097813179599174817 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.58201027 0.00138314639 0.0019968397134892645 0 156 0 -5.6663394 0.00344857574 0.0049838416366178697 0 161 0 -3.992939 0.0181113519 0.026368671070599831 0 -164 0 +164 0 ? ? ? 0 167 1 6.79999542 0.9988875 0.0016059215653949079 1 169 0 -6.41288567 0.00163759827 0.0023644914709276374 0 171 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 10.4554977 0.9999712 4.1534408054175585E-05 1 247 1 2.7647686 0.940742 0.088128954823708028 1 248 0 -3.52696 0.0285547972 0.041795476162409473 0 -249 0 +249 0 ? ? ? 0 250 0 -6.30096865 0.00183116761 0.0026442381917314273 0 252 0 4.46767044 0.988656163 6.4619475094968903 1 254 1 5.268241 0.994873762 0.007414618780919654 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.312752 0.004904177 0.007092637921430613 0 271 0 -3.70832729 0.02393173 0.034946036680259647 0 272 1 3.75608158 0.9771588 0.033335080773514915 1 -275 0 +275 0 ? ? ? 0 276 0 -5.145169 0.005793728 0.0083828901690503332 0 277 0 -5.947381 0.002605866 0.0037643767357215252 0 278 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.312752 0.004904177 0.007092637921430613 0 293 1 4.889887 0.992533863 0.0108117709104356 1 296 0 1.11456966 0.752980053 2.0173005524167049 1 -297 0 +297 0 ? ? ? 0 299 1 7.92421341 0.999638259 0.00052197576964742038 1 300 1 5.01128674 0.993381739 0.009579868932513079 1 301 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.64061546 0.974434555 0.037362801100438832 1 317 1 8.889477 0.9998622 0.00019882564438936136 1 319 0 2.932189 0.9494149 4.3051439460642333 1 -321 0 +321 0 ? ? ? 0 323 1 5.11123276 0.994007468 0.0086714037349760222 1 327 0 -5.947381 0.002605866 0.0037643767357215252 0 328 1 3.24846554 0.962617934 0.054964793658751787 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 16.72465 0.99999994 8.5991327994145617E-08 1 613 0 -5.02813625 0.00650837226 0.0094202851039732072 0 614 0 -5.6638155 0.00345726055 0.0049964145907412149 0 -617 0 +617 0 ? ? ? 0 618 0 -4.744063 0.008628122 0.012501759455550775 0 619 0 -4.34295654 0.01283126 0.01863138517568386 0 621 0 0.373829842 0.5923841 1.2947177664921086 1 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.23449326 0.0142802689 0.020750590335179415 0 19 0 -3.10273981 0.04299438 0.063400697943788428 0 22 0 -5.130806 0.00587705 0.0085038042327941973 0 -23 1 +23 1 ? ? ? 0 24 0 -5.932124 0.002645822 0.0038221729188529165 0 26 0 -5.0699935 0.006243238 0.0090353231046563232 0 27 0 -3.99905157 0.01800297 0.026209433910245255 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.12534046 0.00590906851 0.0085502708566916 0 135 0 -2.97098112 0.0487542 0.072109916649189282 0 136 0 -4.56492853 0.0103033585 0.014941712057931327 0 -139 0 +139 0 ? ? ? 0 140 0 -5.461241 0.00423030974 0.0061159922992663762 0 142 1 3.05590534 0.9550368 0.066371741280747978 1 143 0 -5.35564375 0.004699242 0.0067955523991724961 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.276688 0.0136983329 0.019899122722508662 0 155 1 1.77740765 0.8553765 0.22536855303918271 1 157 0 -5.366247 0.004649908 0.0067240441834850199 0 -158 0 +158 0 ? ? ? 0 159 1 8.96132851 0.999871731 0.0001850652016644095 1 160 1 6.856139 0.998948157 0.001518287560141836 1 162 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.663686 0.9965423 0.0049970870434534837 1 232 0 0.928171158 0.71670413 1.8196185260804569 1 234 0 -3.391735 0.0325547643 0.047748098148187916 0 -235 0 +235 0 ? ? ? 0 236 1 7.88262749 0.9996229 0.00054416973071506056 1 238 1 8.651073 0.9998251 0.00025232061237670696 1 243 0 -4.03390265 0.0173970815 0.025319570708974903 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.9212322 0.99998194 2.6055606891255495E-05 1 287 0 -5.12534046 0.00590906851 0.0085502708566916 0 289 1 5.185871 0.9944361 0.0080494462952667781 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77450371 0.9916284 0.012128492162908918 1 298 0 -2.54918242 0.07248143 0.10855193009256793 0 302 1 11.1919193 0.999986231 1.9864132926342996E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.932124 0.002645822 0.0038221729188529165 0 310 0 -5.691217 0.00336412713 0.0048615916252809817 0 313 0 -6.59299469 0.0013680571 0.0019750404849533288 0 -315 0 +315 0 ? ? ? 0 318 0 -5.91572762 0.00268944423 0.0038852748092412133 0 320 1 4.1229 0.9840607 0.023180779648038535 1 322 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -6.26255941 0.00190273311 0.0027476785076986981 0 408 0 -4.386099 0.0122961234 0.017849522550602435 0 410 0 -6.26255941 0.00190273311 0.0027476785076986981 0 -411 0 +411 0 ? ? ? 0 412 1 7.037607 0.99912256 0.0012664339928697493 1 417 0 -6.26255941 0.00190273311 0.0027476785076986981 0 420 0 -3.338777 0.0342646 0.050300135586962377 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt index 0a4ce514a2..3b22d36b5d 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 7.01161575 0.999099433 0.0012998283148002473 1 35 0 -5.546275 0.0038867984 0.0056183906346792553 0 37 0 -0.627175331 0.3481513 0.61739094617065504 0 -40 0 +40 0 ? ? ? 0 41 1 3.0365572 0.9541986 0.067638527006529184 1 44 1 6.61341858 0.998659551 0.0019351561965343684 1 45 0 -5.565003 0.00381495967 0.0055143486004666058 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.72209072 0.008818108 0.012778263407408707 0 141 0 -6.18090439 0.002064286 0.0029812133205916491 0 144 0 -5.546275 0.0038867984 0.0056183906346792553 0 -145 0 +145 0 ? ? ? 0 147 0 -5.305633 0.0049390397 0.0071431828757896592 0 150 0 -5.43029261 0.004362697 0.0063078105710409225 0 151 1 4.990402 0.993243039 0.0097813179599174817 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.58201027 0.00138314639 0.0019968397134892645 0 156 0 -5.6663394 0.00344857574 0.0049838416366178697 0 161 0 -3.992939 0.0181113519 0.026368671070599831 0 -164 0 +164 0 ? ? ? 0 167 1 6.79999542 0.9988875 0.0016059215653949079 1 169 0 -6.41288567 0.00163759827 0.0023644914709276374 0 171 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 10.4554977 0.9999712 4.1534408054175585E-05 1 247 1 2.7647686 0.940742 0.088128954823708028 0 248 0 -3.52696 0.0285547972 0.041795476162409473 0 -249 0 +249 0 ? ? ? 0 250 0 -6.30096865 0.00183116761 0.0026442381917314273 0 252 0 4.46767044 0.988656163 6.4619475094968903 1 254 1 5.268241 0.994873762 0.007414618780919654 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.312752 0.004904177 0.007092637921430613 0 271 0 -3.70832729 0.02393173 0.034946036680259647 0 272 1 3.75608158 0.9771588 0.033335080773514915 1 -275 0 +275 0 ? ? ? 0 276 0 -5.145169 0.005793728 0.0083828901690503332 0 277 0 -5.947381 0.002605866 0.0037643767357215252 0 278 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.312752 0.004904177 0.007092637921430613 0 293 1 4.889887 0.992533863 0.0108117709104356 1 296 0 1.11456966 0.752980053 2.0173005524167049 0 -297 0 +297 0 ? ? ? 0 299 1 7.92421341 0.999638259 0.00052197576964742038 1 300 1 5.01128674 0.993381739 0.009579868932513079 1 301 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.64061546 0.974434555 0.037362801100438832 1 317 1 8.889477 0.9998622 0.00019882564438936136 1 319 0 2.932189 0.9494149 4.3051439460642333 0 -321 0 +321 0 ? ? ? 0 323 1 5.11123276 0.994007468 0.0086714037349760222 1 327 0 -5.947381 0.002605866 0.0037643767357215252 0 328 1 3.24846554 0.962617934 0.054964793658751787 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 16.72465 0.99999994 8.5991327994145617E-08 1 613 0 -5.02813625 0.00650837226 0.0094202851039732072 0 614 0 -5.6638155 0.00345726055 0.0049964145907412149 0 -617 0 +617 0 ? ? ? 0 618 0 -4.744063 0.008628122 0.012501759455550775 0 619 0 -4.34295654 0.01283126 0.01863138517568386 0 621 0 0.373829842 0.5923841 1.2947177664921086 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.23449326 0.0142802689 0.020750590335179415 0 19 0 -3.10273981 0.04299438 0.063400697943788428 0 22 0 -5.130806 0.00587705 0.0085038042327941973 0 -23 1 +23 1 ? ? ? 0 24 0 -5.932124 0.002645822 0.0038221729188529165 0 26 0 -5.0699935 0.006243238 0.0090353231046563232 0 27 0 -3.99905157 0.01800297 0.026209433910245255 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.12534046 0.00590906851 0.0085502708566916 0 135 0 -2.97098112 0.0487542 0.072109916649189282 0 136 0 -4.56492853 0.0103033585 0.014941712057931327 0 -139 0 +139 0 ? ? ? 0 140 0 -5.461241 0.00423030974 0.0061159922992663762 0 142 1 3.05590534 0.9550368 0.066371741280747978 1 143 0 -5.35564375 0.004699242 0.0067955523991724961 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.276688 0.0136983329 0.019899122722508662 0 155 1 1.77740765 0.8553765 0.22536855303918271 0 157 0 -5.366247 0.004649908 0.0067240441834850199 0 -158 0 +158 0 ? ? ? 0 159 1 8.96132851 0.999871731 0.0001850652016644095 1 160 1 6.856139 0.998948157 0.001518287560141836 1 162 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.663686 0.9965423 0.0049970870434534837 1 232 0 0.928171158 0.71670413 1.8196185260804569 0 234 0 -3.391735 0.0325547643 0.047748098148187916 0 -235 0 +235 0 ? ? ? 0 236 1 7.88262749 0.9996229 0.00054416973071506056 1 238 1 8.651073 0.9998251 0.00025232061237670696 1 243 0 -4.03390265 0.0173970815 0.025319570708974903 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.9212322 0.99998194 2.6055606891255495E-05 1 287 0 -5.12534046 0.00590906851 0.0085502708566916 0 289 1 5.185871 0.9944361 0.0080494462952667781 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77450371 0.9916284 0.012128492162908918 1 298 0 -2.54918242 0.07248143 0.10855193009256793 0 302 1 11.1919193 0.999986231 1.9864132926342996E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.932124 0.002645822 0.0038221729188529165 0 310 0 -5.691217 0.00336412713 0.0048615916252809817 0 313 0 -6.59299469 0.0013680571 0.0019750404849533288 0 -315 0 +315 0 ? ? ? 0 318 0 -5.91572762 0.00268944423 0.0038852748092412133 0 320 1 4.1229 0.9840607 0.023180779648038535 1 322 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -6.26255941 0.00190273311 0.0027476785076986981 0 408 0 -4.386099 0.0122961234 0.017849522550602435 0 410 0 -6.26255941 0.00190273311 0.0027476785076986981 0 -411 0 +411 0 ? ? ? 0 412 1 7.037607 0.99912256 0.0012664339928697493 1 417 0 -6.26255941 0.00190273311 0.0027476785076986981 0 420 0 -3.338777 0.0342646 0.050300135586962377 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt index 2748bad72f..db7cabc84e 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 5.917115 0.9973143 0.003879895635620469 1 35 0 -5.023262 0.006539965 0.0094661631818091386 0 37 0 -1.38112783 0.200827926 0.3234219246434899 0 -40 0 +40 0 ? ? ? 0 41 1 1.99977684 0.880773664 0.18315676412764836 1 44 1 6.1460495 0.997862637 0.0030868629883528767 1 45 0 -4.92853165 0.00718512246 0.010403360173725132 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.17745543 0.0151058007 0.021959341229968121 0 141 0 -5.50801945 0.00403775927 0.0058370475132605874 0 144 0 -5.023262 0.006539965 0.0094661631818091386 0 -145 0 +145 0 ? ? ? 0 147 0 -4.81479263 0.00804367848 0.011651498579728541 0 150 0 -4.92451668 0.00721382024 0.01044506254471955 0 151 1 4.048602 0.9828524 0.024953319944645107 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -5.80309772 0.00300910859 0.0043477707884351756 0 156 0 -4.933721 0.00714819832 0.010349705365439381 0 161 0 -3.81854677 0.0214878246 0.031338292831631254 0 -164 0 +164 0 ? ? ? 0 167 1 7.07203674 0.999152243 0.0012235733412714462 1 169 0 -5.652446 0.00349665456 0.0050534464360921993 0 171 0 -4.83358335 0.007895125 0.011435459622816726 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.218597 0.9999008 0.00014309666195095306 1 247 1 1.9463377 0.87504673 0.19256803177214576 1 248 0 -3.2993412 0.0355937965 0.052287163877458576 0 -249 0 +249 0 ? ? ? 0 250 0 -5.418478 0.004414317 0.006382611353481173 0 252 0 3.48067 0.970132768 5.0652926577610451 1 254 1 4.863985 0.9923395 0.011094325676462969 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -4.83358335 0.007895125 0.011435459622816726 0 271 0 -3.65327024 0.0252520833 0.036898928715096181 0 272 1 3.02218533 0.9535664 0.068594734962942897 1 -275 0 +275 0 ? ? ? 0 276 0 -4.72818375 0.008765012 0.012700983189760973 0 277 0 -5.3183403 0.00487697963 0.00705320751084503 0 278 0 -4.83358335 0.007895125 0.011435459622816726 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -4.83358335 0.007895125 0.011435459622816726 0 293 1 3.6722765 0.975211561 0.036212866280089365 1 296 0 0.9506779 0.7212515 1.8429639897619017 1 -297 0 +297 0 ? ? ? 0 299 1 5.993658 0.9975117 0.0035943536619517991 1 300 1 5.41301346 0.9955616 0.0064175103553020287 1 301 0 -4.83358335 0.007895125 0.011435459622816726 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 2.857479 0.945704 0.080539421519246923 1 317 1 7.483817 0.9994382 0.00081069597738820146 1 319 0 2.14308548 0.895020843 3.2518251711052208 1 -321 0 +321 0 ? ? ? 0 323 1 4.38443756 0.987683654 0.017879061305842645 1 327 0 -5.3183403 0.00487697963 0.00705320751084503 0 328 1 1.9153614 0.8716203 0.19822830198625993 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 15.0148582 0.9999997 4.2995669122556443E-07 1 613 0 -4.76532364 0.008448151 0.012239880753341762 0 614 0 -5.114196 0.005974896 0.00864580721752186 0 -617 0 +617 0 ? ? ? 0 618 0 -4.43310547 0.0117381271 0.017034712310540854 0 619 0 -4.138027 0.0157037526 0.022835500585175472 0 621 0 -0.147964478 0.463076234 0.89721082981393785 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.094967 0.0163834114 0.023832028779250634 0 19 0 -3.158533 0.0407563634 0.06003080556474015 0 22 0 -4.786547 0.008272208 0.011983909171361835 0 -23 1 +23 1 ? ? ? 0 24 0 -5.49961758 0.00407168828 0.0058861960152521283 0 26 0 -4.637971 0.009584561 0.013894290826829943 0 27 0 -3.85011339 0.02083403 0.030374676365783088 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -4.786547 0.008272208 0.011983909171361835 0 135 0 -2.91646814 0.0513454638 0.07604528657651706 0 136 0 -4.31833 0.01314697 0.019092852440504127 0 -139 0 +139 0 ? ? ? 0 140 0 -5.00991058 0.006627286 0.009592975357514695 0 142 1 2.566639 0.9286834 0.10674124733849408 1 143 0 -4.68576 0.009141385 0.013248879501449318 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -3.91140127 0.0196197983 0.028586744893093978 0 155 1 1.73489761 0.8500378 0.23440107518588552 1 157 0 -5.03140068 0.00648729829 0.0093896829482070059 0 -158 0 +158 0 ? ? ? 0 159 1 8.209714 0.9997281 0.00039234577165867646 1 160 1 6.214038 0.9980028 0.0028841924162386562 1 162 0 -4.563184 0.0103211654 0.014967669647872791 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.128908 0.994111836 0.0085199331359509835 1 232 0 0.387944221 0.5957877 1.3068148861794051 1 234 0 -3.05071926 0.04518643 0.066709024779774861 0 -235 0 +235 0 ? ? ? 0 236 1 7.45407867 0.999421239 0.00083521748895634131 1 238 1 8.351737 0.9997641 0.00034039381967173406 1 243 0 -3.79230738 0.02204652 0.032162254419967784 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.2351351 0.9999641 5.1767706679585828E-05 1 287 0 -4.786547 0.008272208 0.011983909171361835 0 289 1 5.007719 0.993358254 0.0096139756423385195 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.08445454 0.9834463 0.024081816959951673 1 298 0 -2.520249 0.07445079 0.11161839829444895 0 302 1 10.2925406 0.999966145 4.8843899665517181E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.49961758 0.00407168828 0.0058861960152521283 0 310 0 -5.254764 0.00519544445 0.0075149807235167431 0 313 0 -5.94634438 0.00260856142 0.0037682756571934027 0 -315 0 +315 0 ? ? ? 0 318 0 -5.49961758 0.00407168828 0.0058861960152521283 0 320 1 3.84560966 0.9790739 0.030510363432469476 1 322 0 -4.563184 0.0103211654 0.014967669647872791 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -5.722981 0.003259291 0.00470984242704696 0 408 0 -3.98740244 0.0182100739 0.026513731287650329 0 410 0 -5.722981 0.003259291 0.00470984242704696 0 -411 0 +411 0 ? ? ? 0 412 1 5.972435 0.997458458 0.0036713375244963246 1 417 0 -5.722981 0.003259291 0.00470984242704696 0 420 0 -3.14838743 0.0411548652 0.060630273661156198 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt index c81880f594..3e137abda1 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 5.678712 0.9965937 0.0049226209615764287 1 21 1 6.001464 0.997531 0.0035664232425213858 1 22 0 -5.02218437 0.00654697046 0.009476336418019371 0 -23 1 +23 1 ? ? ? 0 24 0 -5.557753 0.00384261133 0.0055543948499928858 0 25 1 0.7356682 0.676047862 0.56480270753052697 1 26 0 -5.01491547 0.006594418 0.0095452416271748757 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.86437464 0.134193972 0.20788424941725006 0 38 1 4.26738262 0.986175358 0.020083890425044392 1 39 1 0.948868752 0.7208876 0.47215375855428471 1 -40 0 +40 0 ? ? ? 0 41 1 2.21941185 0.9019792 0.14883391736209772 1 42 1 6.171118 0.997915447 0.00301051350343931 1 43 1 -0.406847954 0.399668157 1.3231254618912542 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.573848 0.0102128033 0.014809714349895977 0 137 0 -5.38329029 0.00457168929 0.0066106759896893642 0 138 0 -4.32377768 0.0130764758 0.018989799023276886 0 -139 0 +139 0 ? ? ? 0 140 0 -5.38329029 0.00457168929 0.0066106759896893642 0 141 0 -5.83162737 0.00292472052 0.0042256622529479159 0 142 1 3.1618576 0.9593734 0.059835633747694031 1 143 0 -4.83655453 0.007871887 0.011401667353256314 0 144 0 -5.470522 0.004191393 0.00605960993587892 0 -145 0 +145 0 ? ? ? 0 146 1 0.0557060242 0.5139229 0.96037607240274647 1 147 0 -5.47995234 0.00415221555 0.0060028519951750492 0 148 0 -1.59599066 0.168542728 0.26628596824729922 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.20912266 0.901065767 0.15029568586304221 1 156 0 -5.58346558 0.003745433 0.0054136621278108544 0 157 0 -5.109416 0.006003351 0.0086871066895572498 0 -158 0 +158 0 ? ? ? 0 159 1 9.879018 0.99994874 7.3954435339176224E-05 1 160 1 6.966673 0.9990581 0.0013594752512588889 1 161 0 -4.01959 0.0176434461 0.025681337989317106 0 162 0 -4.661079 0.009367671 0.01357839101244592 0 163 0 -3.39304066 0.03251367 0.04768681882905737 0 -164 0 +164 0 ? ? ? 0 165 0 -3.65873766 0.0251178537 0.036700273114887771 0 166 1 6.106388 0.9977764 0.0032115643460085509 1 167 1 6.96164227 0.999053359 0.0013663610592643267 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.434880257 0.607038438 1.3475398955993996 1 233 1 4.62145329 0.9902574 0.014124543390739288 1 234 0 -3.19655 0.0392957628 0.057835744186683809 0 -235 0 +235 0 ? ? ? 0 236 1 9.488486 0.9999243 0.00010921311695177715 1 237 1 5.185233 0.994432569 0.0080545481788837064 1 238 1 10.3383093 0.999967635 4.6694045347237877E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.706185 0.9998345 0.00023881767606976273 1 247 1 1.85991 0.865286469 0.20875025156266316 1 248 0 -3.24698353 0.03743543 0.055044774760243595 0 -249 0 +249 0 ? ? ? 0 250 0 -5.9445715 0.00261317822 0.0037749537255417041 0 251 1 6.851535 0.998943269 0.001525346290754842 1 252 0 3.15871048 0.959250569 4.6170762873605469 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.54124355 0.8236454 0.27990471757503477 1 273 1 -0.485281944 0.381005645 1.3921157227274092 0 274 0 -4.467927 0.0113409776 0.016455057773140165 0 -275 0 +275 0 ? ? ? 0 276 0 -5.02218437 0.00654697046 0.009476336418019371 0 277 0 -5.918859 0.00268105837 0.0038731439944547637 0 278 0 -5.557753 0.00384261133 0.0055543948499928858 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 6.391654 0.9983273 0.0024151950596217639 1 290 0 -6.27996445 0.00186996383 0.0027003130775063206 0 291 0 -5.557753 0.00384261133 0.0055543948499928858 0 -292 1 +292 1 ? ? ? 0 293 1 4.062559 0.98308605 0.024610393646554929 1 -294 0 +294 0 ? ? ? 0 295 1 5.48979473 0.9958883 0.0059441683338054239 1 296 0 0.7984762 0.689648449 1.6880247409542737 1 -297 0 +297 0 ? ? ? 0 298 0 -2.82211971 0.05614051 0.083355987427050499 0 299 1 5.92269325 0.9973292 0.0038583400725341575 1 300 1 5.553643 0.9961416 0.0055772429838620297 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.505681 0.924539149 0.11319368429557292 1 313 0 -6.27996445 0.00186996383 0.0027003130775063206 0 314 0 -5.996913 0.00248024915 0.0035826879721537609 0 -315 0 +315 0 ? ? ? 0 316 1 2.06765842 0.8877198 0.17182370265537225 1 317 1 6.799261 0.998886645 0.0016071267852858751 1 318 0 -5.323591 0.004851562 0.0070163583406618974 0 319 0 1.16352749 0.7619731 2.0708033667370818 1 320 1 5.47767448 0.995838344 0.006016528395062894 1 -321 0 +321 0 ? ? ? 0 322 0 -4.661079 0.009367671 0.01357839101244592 0 323 1 3.7019043 0.975917757 0.035168522105486828 1 324 0 -5.557753 0.00384261133 0.0055543948499928858 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.06660128 0.0168468487 0.024511924018010906 0 409 0 -4.77211475 0.008391453 0.012157388278315496 0 410 0 -5.918859 0.00268105837 0.0038731439944547637 0 -411 0 +411 0 ? ? ? 0 412 1 6.93106556 0.999024 0.0014087955764285343 1 413 0 -3.5143342 0.02890712 0.042318806680098388 0 414 1 4.808298 0.9919043 0.0117271336749766 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -5.548576 0.00387790077 0.0056055040477979955 0 615 0 -4.073707 0.0167295579 0.024339819893712551 0 616 0 -5.02218437 0.00654697046 0.009476336418019371 0 -617 0 +617 0 ? ? ? 0 618 0 -4.573848 0.0102128033 0.014809714349895977 0 619 0 -4.12551069 0.0158983991 0.023120824431946189 0 620 0 -5.02218437 0.00654697046 0.009476336418019371 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt index 4789a49c72..51deeac562 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 7.041827 0.999126256 0.0012610978584151201 1 21 1 7.16006565 0.9992236 0.0011205580676841706 1 22 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -23 1 +23 1 ? ? ? 0 24 0 -6.16573143 0.00209578 0.003026744365055557 0 25 1 1.140585 0.757787049 0.4001356116356401 1 26 0 -5.39192247 0.00453257374 0.006553986122637143 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.74917936 0.148150727 0.23132991391158644 0 38 1 5.25044537 0.9947822 0.0075473881419006704 1 39 1 1.14784718 0.7591175 0.39760491464649406 1 -40 0 +40 0 ? ? ? 0 41 1 2.83779049 0.9446841 0.082096136496848479 1 42 1 5.84602737 0.997117 0.0041653216557672994 1 43 1 -0.115566254 0.471140563 1.0857705467412089 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.81180668 0.008067538 0.011686200181083801 0 137 0 -5.771742 0.003104659 0.0044860436134326774 0 138 0 -4.708121 0.008941052 0.01295722303877558 0 -139 0 +139 0 ? ? ? 0 140 0 -5.771742 0.003104659 0.0044860436134326774 0 141 0 -6.35020638 0.00174334215 0.0025173059685722476 0 142 1 3.59143162 0.9731803 0.039220987499558525 1 143 0 -5.32383156 0.00485040154 0.0070146760362167319 0 144 0 -5.96873665 0.00255094632 0.0036849396417204128 0 -145 0 +145 0 ? ? ? 0 146 1 -0.228344917 0.443160534 1.1740986883981319 0 147 0 -6.02899933 0.00240211817 0.0034696929544304877 0 148 0 -1.17236042 0.236428589 0.38916500660996345 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.561039 0.928311646 0.10731887720885433 1 156 0 -6.32817459 0.0017821081 0.0025733321754180501 0 157 0 -5.587267 0.00373127544 0.0053931605303660906 0 -158 0 +158 0 ? ? ? 0 159 1 9.957122 0.9999526 6.836472348564471E-05 1 160 1 7.81574535 0.999596834 0.00058176260649806227 1 161 0 -4.194911 0.0148482891 0.02158218175164682 0 162 0 -5.008802 0.00663458835 0.0096035809636014526 0 163 0 -4.450941 0.0115330191 0.016735320470494343 0 -164 0 +164 0 ? ? ? 0 165 0 -3.79370117 0.022016488 0.0321179521831969 0 166 1 6.78795338 0.998874 0.0016253773809814112 1 167 1 6.650817 0.9987087 0.0018641198790525675 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.440856934 0.6084632 1.3527802863415537 1 233 1 5.59169 0.996285141 0.0053693888063565016 1 234 0 -3.380312 0.0329164639 0.048287580681296273 0 -235 0 +235 0 ? ? ? 0 236 1 9.42349148 0.9999192 0.00011660894974341421 1 237 1 5.527053 0.9960381 0.005727196256675989 1 238 1 10.3295126 0.999967337 4.7124015954602722E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.522981 0.999926865 0.00010551521477366078 1 247 1 2.22113419 0.9021314 0.14859054441592434 1 248 0 -3.25320721 0.037211813 0.0547096542674552 0 -249 0 +249 0 ? ? ? 0 250 0 -6.70964432 0.00121761323 0.0017577148937104486 0 251 1 8.275113 0.9997453 0.00036748773339945429 1 252 0 3.75820923 0.97720623 5.4552166396980901 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.25216866 0.777675033 0.36276067331990891 1 273 1 -0.4770708 0.382944047 1.3847944817499098 0 274 0 -4.773376 0.008380964 0.01214212852075712 0 -275 0 +275 0 ? ? ? 0 276 0 -5.39027166 0.00454002852 0.0065647900946516503 0 277 0 -6.547201 0.00143207016 0.0020675212884715419 0 278 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 7.07017231 0.999150634 0.0012258970788924348 1 290 0 -6.928671 0.0009783435 0.0014121422633917285 0 291 0 -6.16573143 0.00209578 0.003026744365055557 0 -292 1 +292 1 ? ? ? 0 293 1 5.206727 0.9945503 0.0078837745923848179 1 -294 0 +294 0 ? ? ? 0 295 1 6.0751133 0.9977059 0.0033135223937081622 1 296 0 1.04628563 0.740061 1.9437549070362801 1 -297 0 +297 0 ? ? ? 0 298 0 -2.41310167 0.08217907 0.12371538777024385 0 299 1 7.396323 0.999386847 0.00088486407096525718 1 300 1 5.69218445 0.996639132 0.0048568731086720204 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 3.90498447 0.9802564 0.02876897031816629 1 313 0 -6.928671 0.0009783435 0.0014121422633917285 0 314 0 -6.664193 0.00127416 0.0018393963949252371 0 -315 0 +315 0 ? ? ? 0 316 1 2.20020485 0.9002679 0.15157371666802275 1 317 1 7.75578 0.9995719 0.00061772192605728435 1 318 0 -5.81475639 0.002974334 0.0042974510254079716 0 319 0 1.38163567 0.7992536 2.3165538563686012 1 320 1 5.837037 0.997091 0.0042029227668842466 1 -321 0 +321 0 ? ? ? 0 322 0 -5.008802 0.00663458835 0.0096035809636014526 0 323 1 4.26100159 0.9860881 0.020211552173447847 1 324 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.4899044 0.0110971872 0.016099351806882618 0 409 0 -5.286586 0.00503353868 0.0072801992795113032 0 410 0 -6.547201 0.00143207016 0.0020675212884715419 0 -411 0 +411 0 ? ? ? 0 412 1 7.575248 0.9994873 0.00073988707471124668 1 413 0 -3.748186 0.0230181254 0.033596298028071243 0 414 1 5.274519 0.994905651 0.0073683771133106393 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.085728 0.00226994278 0.0032785576690230068 0 615 0 -4.604435 0.0099082 0.01436579907268937 0 616 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -617 0 +617 0 ? ? ? 0 618 0 -4.81180668 0.008067538 0.011686200181083801 0 619 0 -4.233342 0.0142964814 0.020774319020048612 0 620 0 -5.39027166 0.00454002852 0.0065647900946516503 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt index 92afd941f0..a1bd6936bc 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 7.041827 0.999126256 0.0012610978584151201 1 21 1 7.16006565 0.9992236 0.0011205580676841706 1 22 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -23 1 +23 1 ? ? ? 0 24 0 -6.16573143 0.00209578 0.003026744365055557 0 25 1 1.140585 0.757787049 0.4001356116356401 0 26 0 -5.39192247 0.00453257374 0.006553986122637143 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.74917936 0.148150727 0.23132991391158644 0 38 1 5.25044537 0.9947822 0.0075473881419006704 1 39 1 1.14784718 0.7591175 0.39760491464649406 0 -40 0 +40 0 ? ? ? 0 41 1 2.83779049 0.9446841 0.082096136496848479 0 42 1 5.84602737 0.997117 0.0041653216557672994 1 43 1 -0.115566254 0.471140563 1.0857705467412089 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.81180668 0.008067538 0.011686200181083801 0 137 0 -5.771742 0.003104659 0.0044860436134326774 0 138 0 -4.708121 0.008941052 0.01295722303877558 0 -139 0 +139 0 ? ? ? 0 140 0 -5.771742 0.003104659 0.0044860436134326774 0 141 0 -6.35020638 0.00174334215 0.0025173059685722476 0 142 1 3.59143162 0.9731803 0.039220987499558525 1 143 0 -5.32383156 0.00485040154 0.0070146760362167319 0 144 0 -5.96873665 0.00255094632 0.0036849396417204128 0 -145 0 +145 0 ? ? ? 0 146 1 -0.228344917 0.443160534 1.1740986883981319 0 147 0 -6.02899933 0.00240211817 0.0034696929544304877 0 148 0 -1.17236042 0.236428589 0.38916500660996345 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.561039 0.928311646 0.10731887720885433 0 156 0 -6.32817459 0.0017821081 0.0025733321754180501 0 157 0 -5.587267 0.00373127544 0.0053931605303660906 0 -158 0 +158 0 ? ? ? 0 159 1 9.957122 0.9999526 6.836472348564471E-05 1 160 1 7.81574535 0.999596834 0.00058176260649806227 1 161 0 -4.194911 0.0148482891 0.02158218175164682 0 162 0 -5.008802 0.00663458835 0.0096035809636014526 0 163 0 -4.450941 0.0115330191 0.016735320470494343 0 -164 0 +164 0 ? ? ? 0 165 0 -3.79370117 0.022016488 0.0321179521831969 0 166 1 6.78795338 0.998874 0.0016253773809814112 1 167 1 6.650817 0.9987087 0.0018641198790525675 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.440856934 0.6084632 1.3527802863415537 0 233 1 5.59169 0.996285141 0.0053693888063565016 1 234 0 -3.380312 0.0329164639 0.048287580681296273 0 -235 0 +235 0 ? ? ? 0 236 1 9.42349148 0.9999192 0.00011660894974341421 1 237 1 5.527053 0.9960381 0.005727196256675989 1 238 1 10.3295126 0.999967337 4.7124015954602722E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.522981 0.999926865 0.00010551521477366078 1 247 1 2.22113419 0.9021314 0.14859054441592434 0 248 0 -3.25320721 0.037211813 0.0547096542674552 0 -249 0 +249 0 ? ? ? 0 250 0 -6.70964432 0.00121761323 0.0017577148937104486 0 251 1 8.275113 0.9997453 0.00036748773339945429 1 252 0 3.75820923 0.97720623 5.4552166396980901 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.25216866 0.777675033 0.36276067331990891 0 273 1 -0.4770708 0.382944047 1.3847944817499098 0 274 0 -4.773376 0.008380964 0.01214212852075712 0 -275 0 +275 0 ? ? ? 0 276 0 -5.39027166 0.00454002852 0.0065647900946516503 0 277 0 -6.547201 0.00143207016 0.0020675212884715419 0 278 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 7.07017231 0.999150634 0.0012258970788924348 1 290 0 -6.928671 0.0009783435 0.0014121422633917285 0 291 0 -6.16573143 0.00209578 0.003026744365055557 0 -292 1 +292 1 ? ? ? 0 293 1 5.206727 0.9945503 0.0078837745923848179 1 -294 0 +294 0 ? ? ? 0 295 1 6.0751133 0.9977059 0.0033135223937081622 1 296 0 1.04628563 0.740061 1.9437549070362801 0 -297 0 +297 0 ? ? ? 0 298 0 -2.41310167 0.08217907 0.12371538777024385 0 299 1 7.396323 0.999386847 0.00088486407096525718 1 300 1 5.69218445 0.996639132 0.0048568731086720204 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 3.90498447 0.9802564 0.02876897031816629 1 313 0 -6.928671 0.0009783435 0.0014121422633917285 0 314 0 -6.664193 0.00127416 0.0018393963949252371 0 -315 0 +315 0 ? ? ? 0 316 1 2.20020485 0.9002679 0.15157371666802275 0 317 1 7.75578 0.9995719 0.00061772192605728435 1 318 0 -5.81475639 0.002974334 0.0042974510254079716 0 319 0 1.38163567 0.7992536 2.3165538563686012 0 320 1 5.837037 0.997091 0.0042029227668842466 1 -321 0 +321 0 ? ? ? 0 322 0 -5.008802 0.00663458835 0.0096035809636014526 0 323 1 4.26100159 0.9860881 0.020211552173447847 1 324 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.4899044 0.0110971872 0.016099351806882618 0 409 0 -5.286586 0.00503353868 0.0072801992795113032 0 410 0 -6.547201 0.00143207016 0.0020675212884715419 0 -411 0 +411 0 ? ? ? 0 412 1 7.575248 0.9994873 0.00073988707471124668 1 413 0 -3.748186 0.0230181254 0.033596298028071243 0 414 1 5.274519 0.994905651 0.0073683771133106393 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.085728 0.00226994278 0.0032785576690230068 0 615 0 -4.604435 0.0099082 0.01436579907268937 0 616 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -617 0 +617 0 ? ? ? 0 618 0 -4.81180668 0.008067538 0.011686200181083801 0 619 0 -4.233342 0.0142964814 0.020774319020048612 0 620 0 -5.39027166 0.00454002852 0.0065647900946516503 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt index 946584d7cb..6f9ac3e2b6 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 4.455554 0.98851943 0.016658771299360001 1 35 0 -5.20966339 0.005433825 0.0078607284616035548 0 37 0 -1.95550251 0.1239546 0.19092245979854006 0 -40 0 +40 0 ? ? ? 0 41 1 1.363338 0.796301663 0.32861302447496599 1 44 1 2.93189621 0.949400842 0.074910765340690438 1 45 0 -5.4227767 0.00439546537 0.0063552935777515963 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.283581 0.01360552 0.019763368834071083 0 141 0 -5.903559 0.0027222808 0.0039327765009182425 0 144 0 -5.20966339 0.005433825 0.0078607284616035548 0 -145 0 +145 0 ? ? ? 0 147 0 -4.808229 0.008096219 0.011727915268260366 0 150 0 -4.955528 0.00699508563 0.010127237230146099 0 151 1 2.9794178 0.9516356 0.071518853245330763 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.343319 0.00175536959 0.0025346883174766728 0 156 0 -4.870145 0.00761383865 0.011026477645071437 0 161 0 -3.876839 0.0202957559 0.029581804866342248 0 -164 0 +164 0 ? ? ? 0 167 1 1.63665962 0.8370799 0.25656278900168672 1 169 0 -6.067718 0.00231109979 0.0033380709957833601 0 171 0 -4.955528 0.00699508563 0.010127237230146099 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 6.373105 0.9982961 0.0024603307167519865 1 247 1 1.08481979 0.747405 0.42003789409759523 1 248 0 -3.67766953 0.0246584155 0.036020526273661445 0 -249 0 +249 0 ? ? ? 0 250 0 -5.56404066 0.00381861837 0.0055196472135604522 0 252 0 2.67256546 0.935388267 3.9520600195019679 1 254 1 2.64337635 0.933601558 0.099121125939627047 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -4.955528 0.00699508563 0.010127237230146099 0 271 0 -3.63624859 0.0256744642 0.037524217400354012 0 272 1 2.26432514 0.905879 0.14260970234578763 1 -275 0 +275 0 ? ? ? 0 276 0 -5.20966339 0.005433825 0.0078607284616035548 0 277 0 -5.6494236 0.003507201 0.0050687153327134771 0 278 0 -4.955528 0.00699508563 0.010127237230146099 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -4.955528 0.00699508563 0.010127237230146099 0 293 1 2.05892181 0.886846 0.17324448166666195 1 296 0 1.99554062 0.880328059 3.0628431682446857 1 -297 0 +297 0 ? ? ? 0 299 1 3.69336033 0.9757162 0.035466552037373179 1 300 1 4.02837372 0.9825081 0.025458759934045509 1 301 0 -4.955528 0.00699508563 0.010127237230146099 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 2.94207382 0.9498875 0.074171415588786135 1 317 1 4.312872 0.986782 0.019196674360521217 1 319 0 2.41989422 0.9183318 3.6140817927103961 1 -321 0 +321 0 ? ? ? 0 323 1 3.56970787 0.972607434 0.040070476517068486 1 327 0 -5.6494236 0.003507201 0.0050687153327134771 0 328 1 1.70264626 0.845880032 0.24147502978012392 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 7.478863 0.9994354 0.00081473984708772015 1 613 0 -4.91670847 0.00726995664 0.010526641032229434 0 614 0 -5.20966339 0.005433825 0.0078607284616035548 0 -617 0 +617 0 ? ? ? 0 618 0 -4.769904 0.008409867 0.01218417928555986 0 619 0 -4.330144 0.0129945707 0.018870074173474232 0 621 0 -0.284356117 0.429386139 0.80941330227154873 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -3.99180722 0.0181314889 0.026398258875503611 0 19 0 -3.49091744 0.0295717642 0.043306566793261343 0 22 0 -4.74231339 0.008643099 0.012523555687056959 0 -23 1 +23 1 ? ? ? 0 24 0 -4.99358654 0.00673562335 0.0097503246472711684 0 26 0 -4.230923 0.01433061 0.020824270758417435 0 27 0 -3.74053383 0.0231908429 0.0338513700113664 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -4.74265528 0.00864017 0.012519293184591884 0 135 0 -2.40284228 0.08295622 0.12493748028384198 0 136 0 -4.24142361 0.0141830426 0.020608297294341209 0 -139 0 +139 0 ? ? ? 0 140 0 -4.99192953 0.006746718 0.0097664397610658042 0 142 1 1.883389 0.8679999 0.20423319963131603 1 143 0 -3.93245554 0.0192188919 0.027996904861385053 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -3.71085787 0.02387269 0.034858773809671437 0 155 1 2.1317234 0.8939485 0.1617363815870177 1 157 0 -4.492697 0.0110665858 0.016054708591916572 0 -158 0 +158 0 ? ? ? 0 159 1 7.019025 0.9991061 0.0012901886373564615 1 160 1 5.00210667 0.9933211 0.0096679074407147667 1 162 0 -4.492697 0.0110665858 0.016054708591916572 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 4.06135273 0.983066 0.02463978413547805 1 232 0 0.582720757 0.641693234 1.4807328100816015 1 234 0 -2.76217723 0.0594026 0.08835075047841523 0 -235 0 +235 0 ? ? ? 0 236 1 5.42756176 0.995625436 0.0063250060270779312 1 238 1 6.00247669 0.9975335 0.0035628026721853763 1 243 0 -3.18360639 0.039787326 0.058574116415727635 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 7.122633 0.999194 0.0011632435375058345 1 287 0 -4.74265528 0.00864017 0.012519293184591884 0 289 1 5.24414349 0.994749367 0.0075950186704868833 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 3.85042763 0.979172349 0.030365276805999493 1 298 0 -2.97889614 0.0483884327 0.07155528637722855 0 302 1 7.28154945 0.999312341 0.00099242318488484949 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -4.99358654 0.00673562335 0.0097503246472711684 0 310 0 -4.74265528 0.00864017 0.012519293184591884 0 313 0 -5.49281931 0.004099349 0.005926265742136525 0 -315 0 +315 0 ? ? ? 0 318 0 -4.994271 0.006731047 0.0097436773674529319 0 320 1 3.98940563 0.9818257 0.026461150371251282 1 322 0 -4.492697 0.0110665858 0.016054708591916572 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -5.243203 0.00525554 0.0076021355607147146 0 408 0 -2.50673819 0.07538716 0.11307869372093513 0 410 0 -5.243203 0.00525554 0.0076021355607147146 0 -411 0 +411 0 ? ? ? 0 412 1 4.41673565 0.9880704 0.017314215991460075 1 417 0 -5.243203 0.00525554 0.0076021355607147146 0 420 0 -2.416037 0.08195794 0.12336783697816152 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt index fc9c09274b..27120c9ca1 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 3.60211229 0.973457634 0.038809901663734138 1 21 1 4.16481066 0.9847049 0.022236640568523543 1 22 0 -5.531984 0.003942524 0.0056991017626305011 0 -23 1 +23 1 ? ? ? 0 24 0 -5.641634 0.00353453052 0.0051082826744376919 0 25 1 1.07475233 0.7454996 0.42372049536525624 1 26 0 -5.05423546 0.00634177 0.0091783743058498103 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -2.63470244 0.06693815 0.099955373797007113 0 38 1 3.11990023 0.9577062 0.062344933763709205 1 39 1 1.3332 0.791369438 0.33757674381727276 1 -40 0 +40 0 ? ? ? 0 41 1 1.76657629 0.8540314 0.22763900783247884 1 42 1 5.77468061 0.996904433 0.0044728861234643764 1 43 1 -0.160024166 0.4600791 1.1200461625954479 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.98228741 0.00681164 0.0098607417433267518 0 137 0 -5.97203064 0.00254257885 0.0036728371153443573 0 138 0 -4.3374877 0.0129007176 0.018732896643962105 0 -139 0 +139 0 ? ? ? 0 140 0 -5.97203064 0.00254257885 0.0036728371153443573 0 141 0 -5.97203064 0.00254257885 0.0036728371153443573 0 142 1 3.62155676 0.973955452 0.03807230821683618 1 143 0 -4.06941748 0.0168002676 0.024443571870852193 0 144 0 -5.531984 0.003942524 0.0056991017626305011 0 -145 0 +145 0 ? ? ? 0 146 1 0.845272541 0.69957453 0.51545032982981998 1 147 0 -5.264244 0.00514667667 0.0074442579843589417 0 148 0 -3.53539133 0.0283218417 0.041449555087460649 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.63197279 0.9328912 0.1002192761113103 1 156 0 -4.95913267 0.00697009 0.010090922390098837 0 157 0 -5.091937 0.006108559 0.0088398149066821179 0 -158 0 +158 0 ? ? ? 0 159 1 7.637605 0.9995183 0.00069514934090023593 1 160 1 5.102776 0.9939569 0.00874476585784471 1 161 0 -4.27338028 0.013743096 0.019964600611336557 0 162 0 -5.091937 0.006108559 0.0088398149066821179 0 163 0 -3.67683315 0.0246785376 0.036050290649489301 0 -164 0 +164 0 ? ? ? 0 165 0 -3.45594358 0.0305921026 0.044824259180207618 0 166 1 4.88854 0.9925239 0.010826239558709188 1 167 1 2.59618044 0.930615366 0.10374308707957469 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.465872765 0.6144064 1.3748470144034199 1 233 1 3.621717 0.9739595 0.038066304453576054 1 234 0 -3.43166828 0.0313202776 0.045908353174351067 0 -235 0 +235 0 ? ? ? 0 236 1 6.647678 0.9987047 0.001869974861439897 1 237 1 4.42246 0.9881377 0.017215962974508822 1 238 1 6.44672251 0.9984168 0.0022859116128351314 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 6.46864176 0.998451054 0.0022363890442921863 1 247 1 1.5234189 0.8210414 0.28447311579312373 1 248 0 -4.00451946 0.0179065578 0.026067797200325149 0 -249 0 +249 0 ? ? ? 0 250 0 -5.39917946 0.00449994765 0.0065067030783201637 0 251 1 4.104112 0.983763337 0.023616805142920666 1 252 0 2.75052214 0.939942837 4.0575198580425882 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.62500715 0.8354845 0.2593150232737797 1 273 1 0.228233814 0.556812048 0.84473766721050514 1 274 0 -4.82307673 0.007977848 0.011555758238394069 0 -275 0 +275 0 ? ? ? 0 276 0 -5.531984 0.003942524 0.0056991017626305011 0 277 0 -6.0816803 0.00227912888 0.003291840622802274 0 278 0 -5.641634 0.00353453052 0.0051082826744376919 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 5.68027353 0.996598959 0.004915027880645002 1 290 0 -6.521727 0.00146896509 0.0021208267355545722 0 291 0 -5.641634 0.00353453052 0.0051082826744376919 0 -292 1 +292 1 ? ? ? 0 293 1 2.365685 0.9141729 0.12946106222132911 1 -294 0 +294 0 ? ? ? 0 295 1 4.049787 0.982872367 0.024924010557578272 1 296 0 1.7836585 0.856148 2.7973428610745215 1 -297 0 +297 0 ? ? ? 0 298 0 -3.845192 0.0209346656 0.030522958750931656 0 299 1 2.974937 0.95142895 0.071832170969213474 1 300 1 4.1471324 0.9844364 0.022630102350657457 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.437553883 0.607676 0.71862571139451958 1 313 0 -6.521727 0.00146896509 0.0021208267355545722 0 314 0 -6.0816803 0.00227912888 0.003291840622802274 0 -315 0 +315 0 ? ? ? 0 316 1 3.03096247 0.953953445 0.068009233653927595 1 317 1 3.97896051 0.9816384 0.026736450275361392 1 318 0 -5.641634 0.00353453052 0.0051082826744376919 0 319 0 1.61403608 0.833970964 2.5904925250318187 1 320 1 5.17757845 0.99439 0.0081162910482616441 1 -321 0 +321 0 ? ? ? 0 322 0 -5.091937 0.006108559 0.0088398149066821179 0 323 1 3.22697878 0.961837 0.056135679390163992 1 324 0 -5.641634 0.00353453052 0.0051082826744376919 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -2.996994 0.0475618578 0.070302697725435973 0 409 0 -4.887184 0.007486166 0.010840883662554117 0 410 0 -6.0816803 0.00227912888 0.003291840622802274 0 -411 0 +411 0 ? ? ? 0 412 1 4.5649085 0.989696443 0.014942001227255185 1 413 0 -3.34774446 0.0339691 0.049858759088118251 0 414 1 3.84952021 0.9791539 0.030392501392593196 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -5.531984 0.003942524 0.0056991017626305011 0 615 0 -3.69268847 0.02429977 0.035490127405006046 0 616 0 -5.531984 0.003942524 0.0056991017626305011 0 -617 0 +617 0 ? ? ? 0 618 0 -4.98228741 0.00681164 0.0098607417433267518 0 619 0 -4.43259048 0.0117441025 0.01704343535923129 0 620 0 -5.531984 0.003942524 0.0056991017626305011 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt index c637c69d47..777b0c9e2e 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 7.291877 0.999319434 0.00098218321186686951 1 35 0 -5.869519 0.002816277 0.0040687610645090766 0 37 0 -1.02334213 0.2643769 0.44296132773823416 0 -40 0 +40 0 ? ? ? 0 41 1 2.78505516 0.9418629 0.086411051010362436 1 44 1 7.821086 0.999599 0.00057866567352766714 1 45 0 -5.776886 0.00308877858 0.0044630618032660361 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.724151 0.008800117 0.01275207813790242 0 141 0 -6.55827045 0.00141632778 0.0020447774455947432 0 144 0 -5.869519 0.002816277 0.0040687610645090766 0 -145 0 +145 0 ? ? ? 0 147 0 -5.710598 0.00329976762 0.0047684300667823433 0 150 0 -5.60911131 0.003650946 0.0052768404018184306 0 151 1 5.118675 0.994051635 0.0086073014434059885 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.9242 0.000982723 0.0014184666437026428 0 156 0 -5.82082272 0.00295639853 0.0042714987401086318 0 161 0 -4.27493 0.0137221068 0.019933897944770858 0 -164 0 +164 0 ? ? ? 0 167 1 8.728978 0.9998382 0.00023348534112652949 1 169 0 -6.667038 0.00127054506 0.0018341745113224558 0 171 0 -5.546697 0.00388516486 0.0056160247410976614 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 11.3055124 0.9999877 1.7714321792245208E-05 1 247 1 2.306137 0.909384 0.1370384544406823 1 248 0 -3.57901382 0.027145749 0.039704412233602736 0 -249 0 +249 0 ? ? ? 0 250 0 -6.50957441 0.00148689921 0.0021467385111997687 0 252 0 4.227894 0.9856265 6.1204466632636887 1 254 1 5.1967 0.9944957 0.0079629764358839714 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.546697 0.00388516486 0.0056160247410976614 0 271 0 -4.08297968 0.01657771 0.024117038815588843 0 272 1 4.140359 0.984332263 0.022782712303270784 1 -275 0 +275 0 ? ? ? 0 276 0 -5.50359 0.004055611 0.0058629065586406487 0 277 0 -6.235449 0.00195492059 0.002823114622841595 0 278 0 -5.546697 0.00388516486 0.0056160247410976614 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.546697 0.00388516486 0.0056160247410976614 0 293 1 4.409566 0.9879857 0.017437977320811163 1 296 0 1.50613689 0.818488 2.4618631795162846 1 -297 0 +297 0 ? ? ? 0 299 1 7.93633366 0.9996426 0.00051569614495754321 1 300 1 7.85100842 0.9996108 0.00056163266102194308 1 301 0 -5.546697 0.00388516486 0.0056160247410976614 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.280983 0.9637706 0.05323826093940856 1 317 1 9.331164 0.999911368 0.00012787476790525535 1 319 0 2.956067 0.9505495 4.3378705942158717 1 -321 0 +321 0 ? ? ? 0 323 1 5.71478939 0.996714 0.0047485078589307303 1 327 0 -6.235449 0.00195492059 0.002823114622841595 0 328 1 1.37423134 0.7980629 0.32542559954710693 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 18.5483551 1 0 1 613 0 -5.86728668 0.00282255374 0.0040778419569695535 0 614 0 -5.931934 0.00264632422 0.0038228993852335589 0 -617 0 +617 0 ? ? ? 0 618 0 -5.1376605 0.00583713735 0.0084458830416492391 0 619 0 -4.771732 0.00839464 0.012162025043583415 0 621 0 0.3424616 0.5847883 1.2680810757859888 1 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.595036 0.01000083 0.014500778622910757 0 19 0 -3.32248163 0.03480794 0.051112046016137037 0 22 0 -5.58065033 0.00375595246 0.0054288957255828087 0 -23 1 +23 1 ? ? ? 0 24 0 -6.503868 0.00149539544 0.0021590142749859306 0 26 0 -5.548387 0.00387863023 0.0056065605311466102 0 27 0 -4.308096 0.0132804094 0.01928794203033625 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.58065033 0.00375595246 0.0054288957255828087 0 135 0 -3.21473217 0.038615074 0.056813911931726302 0 136 0 -4.944373 0.007072995 0.010240432848577178 0 -139 0 +139 0 ? ? ? 0 140 0 -5.929988 0.00265146513 0.0038303358354253489 0 142 1 3.36657238 0.966643333 0.048944424422816823 1 143 0 -5.92445755 0.0026661302 0.0038515494632620751 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.654668 0.009427353 0.013665310686569912 0 155 1 1.85960007 0.865250349 0.20881047650865889 1 157 0 -5.867591 0.00282169762 0.0040766033438554457 0 -158 0 +158 0 ? ? ? 0 159 1 9.591543 0.9999317 9.8549424786689596E-05 1 160 1 7.363619 0.9993665 0.00091420540184684017 1 162 0 -5.231313 0.00531806657 0.0076928219174680146 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.973879 0.9974621 0.0036660786976757281 1 232 0 1.10679436 0.751531 2.008862256839882 1 234 0 -3.7641573 0.0226616841 0.033070042201646284 0 -235 0 +235 0 ? ? ? 0 236 1 8.480452 0.9997926 0.00029928085261473974 1 238 1 9.448584 0.9999212 0.00011368501131740873 1 243 0 -4.52710867 0.0106962454 0.015514542524251164 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 11.9692574 0.9999937 9.115109290810302E-06 1 287 0 -5.58065033 0.00375595246 0.0054288957255828087 0 289 1 5.61943245 0.9963864 0.005222752234881816 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 5.155699 0.9942666 0.008295308421383105 1 298 0 -2.71633816 0.0620161332 0.092364986128816551 0 302 1 12.1483784 0.9999947 7.6532482629398447E-06 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -6.503868 0.00149539544 0.0021590142749859306 0 310 0 -6.216928 0.001991392 0.00287583593610438 0 313 0 -7.2025423 0.000744136 0.0010739609416730365 0 -315 0 +315 0 ? ? ? 0 318 0 -6.503868 0.00149539544 0.0021590142749859306 0 320 1 4.269491 0.9862041 0.02004186218454097 1 322 0 -5.231313 0.00531806657 0.0076928219174680146 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -6.853205 0.00105495122 0.0015227762658880663 0 408 0 -4.852546 0.00774797052 0.011221486878031036 0 410 0 -6.853205 0.00105495122 0.0015227762658880663 0 -411 0 +411 0 ? ? ? 0 412 1 7.587702 0.9994936 0.00073076734716613276 1 417 0 -6.853205 0.00105495122 0.0015227762658880663 0 420 0 -3.639381 0.0255962238 0.037408370576337828 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt index 3c3d96d974..157c16ab10 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 6.59719467 0.9986377 0.0019667577188335239 1 21 1 6.9093914 0.999002635 0.0014396108762453246 1 22 0 -5.50264263 0.00405943953 0.0058684526611357517 0 -23 1 +23 1 ? ? ? 0 24 0 -6.14459133 0.002140461 0.0030913421022840675 0 25 1 0.9981127 0.7306873 0.4526739227114121 1 26 0 -5.56536674 0.00381357735 0.0055123467049374769 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.79816341 0.142074779 0.22107619020053021 0 38 1 4.925646 0.9927943 0.010433298358924143 1 39 1 0.895828247 0.7100915 0.49392321501990555 1 -40 0 +40 0 ? ? ? 0 41 1 2.72234154 0.93833214 0.091829412491125792 1 42 1 6.45601654 0.998431444 0.0022647243580549464 1 43 1 -0.5776577 0.359471738 1.4760497444221736 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.965955 0.006923029 0.010022553277705419 0 137 0 -5.93407059 0.00264069065 0.0038147503192492542 0 138 0 -4.668453 0.009299485 0.013479092815885742 0 -139 0 +139 0 ? ? ? 0 140 0 -5.93407059 0.00264069065 0.0038147503192492542 0 141 0 -6.47075844 0.00154565845 0.0022316389032971582 0 142 1 3.64823437 0.9746237 0.037082819238846716 1 143 0 -5.466771 0.00420707744 0.0060823331671915537 0 144 0 -6.03933048 0.002377488 0.0034340739321673348 0 -145 0 +145 0 ? ? ? 0 146 1 -0.110341072 0.4724427 1.0817887726099731 0 147 0 -6.11751556 0.002199078 0.0031760924132441036 0 148 0 -1.55709267 0.174064219 0.2758984827724662 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.36491013 0.9141121 0.12955701132443528 1 156 0 -6.267907 0.00189260417 0.002733037741461919 0 157 0 -5.607903 0.003655344 0.0052832085489667481 0 -158 0 +158 0 ? ? ? 0 159 1 10.9176846 0.9999819 2.614159977229895E-05 1 160 1 7.75989342 0.9995737 0.00061514108379339587 1 161 0 -4.309515 0.0132618267 0.019260772318565499 0 162 0 -5.07121468 0.006235666 0.0090243302579491296 0 163 0 -3.76092339 0.02273342 0.033175939048272665 0 -164 0 +164 0 ? ? ? 0 165 0 -3.924346 0.0193723515 0.028222656292276067 0 166 1 6.74228859 0.998821437 0.001701309224545778 1 167 1 7.94067574 0.99964416 0.0005134595729021004 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.7225275 0.673163354 1.6133583443719168 1 233 1 5.29774761 0.995022058 0.0071995859979441072 1 234 0 -3.554101 0.027811477 0.040691992083778071 0 -235 0 +235 0 ? ? ? 0 236 1 10.4206543 0.9999702 4.2996303413732479E-05 1 237 1 5.828807 0.997067034 0.0042375925496323883 1 238 1 11.3587952 0.9999883 1.6854398235588073E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.599171 0.9999322 9.7775450197580527E-05 1 247 1 2.067935 0.887747347 0.17177895049852415 1 248 0 -3.4094696 0.0320008248 0.046922276673100262 0 -249 0 +249 0 ? ? ? 0 250 0 -6.699335 0.0012302153 0.0017759181240158319 0 251 1 7.9959507 0.9996633 0.00048584679587356108 1 252 0 3.6111412 0.9736899 5.2482402117884597 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.72366619 0.848600447 0.23684265601696217 1 273 1 -0.342484474 0.4152061 1.2681004399882052 0 274 0 -4.846204 0.007796883 0.011292605602753639 0 -275 0 +275 0 ? ? ? 0 276 0 -5.50264263 0.00405943953 0.0058684526611357517 0 277 0 -6.57601929 0.0013914461 0.0020088303041534395 0 278 0 -6.14459133 0.002140461 0.0030913421022840675 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 7.101452 0.999176741 0.0011882013529343118 1 290 0 -7.007447 0.0009042981 0.0013052166206428517 0 291 0 -6.14459133 0.002140461 0.0030913421022840675 0 -292 1 +292 1 ? ? ? 0 293 1 4.6855 0.9908563 0.013252265463070151 1 -294 0 +294 0 ? ? ? 0 295 1 6.2168417 0.99800843 0.0028760930785605436 1 296 0 1.02090549 0.735148966 1.9167469555439196 1 -297 0 +297 0 ? ? ? 0 298 0 -3.02001572 0.0465297773 0.068740211727588499 0 299 1 7.09773827 0.9991737 0.0011925905306341051 1 300 1 6.64691544 0.998703659 0.001871438610749495 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.90730953 0.9482066 0.076726654530730437 1 313 0 -7.007447 0.0009042981 0.0013052166206428517 0 314 0 -6.649353 0.001293185 0.0018668790298154393 0 -315 0 +315 0 ? ? ? 0 316 1 2.12067223 0.892896235 0.16343556787762994 1 317 1 7.5715065 0.9994854 0.00074264021133653045 1 318 0 -5.92458725 0.00266578537 0.0038510506602204577 0 319 0 1.35382557 0.7947544 2.2845767018893883 1 320 1 5.988476 0.9974988 0.0036129742419820916 1 -321 0 +321 0 ? ? ? 0 322 0 -5.07121468 0.006235666 0.0090243302579491296 0 323 1 4.28225327 0.986376643 0.019789456912749203 1 324 0 -6.14459133 0.002140461 0.0030913421022840675 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.58592224 0.0100914678 0.014632868867702545 0 409 0 -5.205141 0.00545831956 0.0078962603748794508 0 410 0 -6.57601929 0.0013914461 0.0020088303041534395 0 -411 0 +411 0 ? ? ? 0 412 1 7.70175171 0.9995482 0.00065196153701951257 1 413 0 -3.70033741 0.02411908 0.035222977147123087 0 414 1 5.41945839 0.99559 0.006376396588275309 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.112665 0.00220974674 0.003191518193469859 0 615 0 -4.37095165 0.012481451 0.018120247723782505 0 616 0 -5.50264263 0.00405943953 0.0058684526611357517 0 -617 0 +617 0 ? ? ? 0 618 0 -4.965955 0.006923029 0.010022553277705419 0 619 0 -4.429267 0.0117827384 0.017099838701332234 0 620 0 -5.50264263 0.00405943953 0.0058684526611357517 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt index 4a5221f10c..ca68c5efee 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 4.28022528 0.9863494 0.019829298264044417 1 35 0 -4.29419041 0.0134638669 0.019556202484947195 0 37 0 -1.95759869 0.123727158 0.19054794774570458 0 -40 0 +40 0 ? ? ? 0 41 1 1.34579754 0.7934417 0.33380384997661361 1 44 1 5.22175264 0.9946311 0.0077665361772382407 1 45 0 -4.55969143 0.0103568994 0.015019761369081966 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -3.50701833 0.0291131958 0.042624993467054707 0 141 0 -4.510108 0.0108776484 0.015779105577069866 0 144 0 -4.29419041 0.0134638669 0.019556202484947195 0 -145 0 +145 0 ? ? ? 0 147 0 -4.118532 0.0160079524 0.023281438797448952 0 150 0 -4.43784046 0.0116833262 0.016954714516170224 0 151 1 2.86513376 0.9460957 0.079941963974142813 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -4.792855 0.008220622 0.011908867541972762 0 156 0 -4.152423 0.0154827805 0.022511655407603286 0 161 0 -3.361469 0.0335215963 0.049190599682179155 0 -164 0 +164 0 ? ? ? 0 167 1 3.00652266 0.9528679 0.069651835538374979 1 169 0 -4.76463461 0.008453925 0.012248282163630898 0 171 0 -4.36102 0.0126044592 0.01829996516606797 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 7.761687 0.9995744 0.00061410874818056485 1 247 1 2.24705172 0.904395938 0.14497358262585658 1 248 0 -2.75623083 0.05973572 0.088861781570551343 0 -249 0 +249 0 ? ? ? 0 250 0 -4.36834049 0.0125136767 0.018167327858314108 0 252 0 2.47272253 0.9222073 3.6842213333365441 1 254 1 5.30986643 0.9950817 0.0071130806432035459 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -4.36102 0.0126044592 0.01829996516606797 0 271 0 -3.23003125 0.0380511023 0.055967840193562365 0 272 1 1.97665262 0.878323853 0.18717511071531998 1 -275 0 +275 0 ? ? ? 0 276 0 -4.011443 0.0177852046 0.025889540399059593 0 277 0 -4.57693768 0.010181617 0.014764258553784819 0 278 0 -4.36102 0.0126044592 0.01829996516606797 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -4.36102 0.0126044592 0.01829996516606797 0 293 1 3.50341845 0.9707849 0.042776422691022505 1 296 0 0.854511261 0.701512635 1.7442582303419563 1 -297 0 +297 0 ? ? ? 0 299 1 4.02451658 0.9824417 0.025556263019683039 1 300 1 4.165831 0.9847203 0.02221411037855156 1 301 0 -4.36102 0.0126044592 0.01829996516606797 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 2.756329 0.940269768 0.088853362072482428 1 317 1 5.80636454 0.9970007 0.004333585623359653 1 319 0 1.0757966 0.7456977 1.975383454841898 1 -321 0 +321 0 ? ? ? 0 323 1 3.082415 0.956161559 0.064673689924879513 1 327 0 -4.57693768 0.010181617 0.014764258553784819 0 328 1 2.805962 0.942997158 0.084674672619554123 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 9.154173 0.999894261 0.00015255667702522208 1 613 0 -3.88851619 0.0200648643 0.029241838018723456 0 614 0 -4.371011 0.0124807227 0.018119183737570529 0 -617 0 +617 0 ? ? ? 0 618 0 -3.72869563 0.0234605316 0.034249740786795546 0 619 0 -3.44594836 0.0308899172 0.045267541796380997 0 621 0 -0.782016754 0.3138854 0.54347850919806506 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -3.6590457 0.0251103118 0.036689112229595131 0 19 0 -2.927483 0.0508115776 0.075233591312011905 0 22 0 -4.14026976 0.0156691261 0.022784748886103094 0 -23 1 +23 1 ? ? ? 0 24 0 -4.75638962 0.008523319 0.012349253326730132 0 26 0 -3.93837214 0.0191076845 0.027833331950969452 0 27 0 -3.40870714 0.03202445 0.046957488715061987 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -4.14026976 0.0156691261 0.022784748886103094 0 135 0 -2.62711 0.06741392 0.1006911970043132 0 136 0 -3.77448845 0.0224339925 0.032733975062274864 0 -139 0 +139 0 ? ? ? 0 140 0 -4.255713 0.0139846308 0.020317960583175827 0 142 1 1.9862442 0.879345238 0.18549840430247841 1 143 0 -3.74331784 0.02312786 0.033758349764067219 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -3.267953 0.0366871059 0.053923618256310278 0 155 1 1.88906479 0.8686489 0.20315494639328185 1 157 0 -4.39060831 0.0122414771 0.017769705367455636 0 -158 0 +158 0 ? ? ? 0 159 1 6.88715744 0.9989802 0.001471976257367614 1 160 1 5.57244873 0.996213257 0.0054734847881935346 1 162 0 -4.024827 0.0175529048 0.025548374564996635 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 4.531541 0.989350557 0.015446292097008558 1 232 0 0.251759052 0.562609434 1.1930059918223119 1 234 0 -2.51516485 0.0748018846 0.11216576762239737 0 -235 0 +235 0 ? ? ? 0 236 1 5.772835 0.9968987 0.0044811669482472481 1 238 1 6.65452766 0.9987135 0.0018572316949005939 1 243 0 -3.28154564 0.036209736 0.053208867556931244 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 8.09413 0.999694765 0.00044042879818134223 1 287 0 -4.14026976 0.0156691261 0.022784748886103094 0 289 1 4.30420876 0.9866685 0.019362604522295365 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 3.601099 0.973431468 0.038848681676236112 1 298 0 -2.22490883 0.09753586 0.14805849380018501 0 302 1 8.018049 0.9996706 0.00047526633912005399 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -4.75638962 0.008523319 0.012349253326730132 0 310 0 -4.506051 0.0109213842 0.015842898366274911 0 313 0 -4.987275 0.00677798 0.0098118479335557937 0 -315 0 +315 0 ? ? ? 0 318 0 -4.75638962 0.008523319 0.012349253326730132 0 320 1 2.81792259 0.943636656 0.08369663336324043 1 322 0 -4.024827 0.0175529048 0.025548374564996635 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -4.87183237 0.00760109862 0.011007956774122585 0 408 0 -3.31706572 0.03499035 0.051384728090851442 0 410 0 -4.87183237 0.00760109862 0.011007956774122585 0 -411 0 +411 0 ? ? ? 0 412 1 5.23884 0.9947216 0.007635302704268032 1 417 0 -4.87183237 0.00760109862 0.011007956774122585 0 420 0 -2.70138979 0.0628913939 0.093711836328273818 0 diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt index ff73fc5189..1f68afb149 100644 --- a/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 3.96267843 0.9813426 0.027171185979008489 1 21 1 5.265054 0.9948575 0.0074382155674881083 1 22 0 -4.51155567 0.0108620832 0.015756402974357524 0 -23 1 +23 1 ? ? ? 0 24 0 -5.107146 0.00601691334 0.0087067913827365544 0 25 1 0.6726794 0.6621029 0.59487269328676762 1 26 0 -4.36255264 0.0125854 0.018272117354929882 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -2.04001045 0.115065664 0.17635768683092809 0 38 1 3.6194644 0.973902345 0.038150977482211336 1 39 1 0.673018932 0.6621788 0.59470724065030856 1 -40 0 +40 0 ? ? ? 0 41 1 1.75375986 0.8524264 0.23035280375662126 1 42 1 5.60796976 0.9963449 0.0052828205055256471 1 43 1 -0.5184488 0.37321502 1.4219210475645763 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.10382557 0.0162412636 0.023623552162962799 0 137 0 -4.73142624 0.008736885 0.01266004645418743 0 138 0 -3.874074 0.0203508064 0.029662873497674963 0 -139 0 +139 0 ? ? ? 0 140 0 -4.73142624 0.008736885 0.01266004645418743 0 141 0 -5.13915634 0.00582846347 0.0084332958699167727 0 142 1 2.53462648 0.9265339 0.11008436686792368 1 143 0 -4.1287837 0.0158472732 0.02304587572398243 0 144 0 -4.919286 0.00725137955 0.010499643937654568 0 -145 0 +145 0 ? ? ? 0 146 1 0.185849667 0.546329141 0.87215771785369722 1 147 0 -4.93812275 0.007117027 0.010304411526052911 0 148 0 -2.08798218 0.110270388 0.16856112656874939 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.24833822 0.9045071 0.14479626651911345 1 156 0 -4.913893 0.007290303 0.010556209568305783 0 157 0 -4.69941568 0.009018519 0.013069997580773615 0 -158 0 +158 0 ? ? ? 0 159 1 8.213823 0.9997292 0.0003907114930137318 1 160 1 6.409885 0.9983575 0.0023716112041259128 1 161 0 -3.70981741 0.0238969475 0.034894625912887001 0 162 0 -4.29168558 0.0134971775 0.019604916230060426 0 163 0 -4.20999336 0.0146292737 0.021261482597556794 0 -164 0 +164 0 ? ? ? 0 165 0 -3.35529184 0.0337223038 0.049490233625727506 0 166 1 5.13335562 0.9941378 0.0084822193431077515 1 167 1 4.31503153 0.986810148 0.019155543364578753 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.387140751 0.5955942 1.3061245037640643 1 233 1 4.22282743 0.9855546 0.020992330945643928 1 234 0 -2.79013348 0.0578596778 0.085986144354393876 0 -235 0 +235 0 ? ? ? 0 236 1 7.198673 0.999253 0.0010781320275837761 1 237 1 4.24964952 0.9859315 0.020440656601130012 1 238 1 7.93097258 0.9996407 0.00051844885378563097 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.043116 0.9996788 0.00046348169408102081 1 247 1 2.170814 0.8975978 0.15585897141376154 1 248 0 -3.01326466 0.0468302034 0.069194857755858583 0 -249 0 +249 0 ? ? ? 0 250 0 -5.133764 0.005859794 0.0084787621145538308 0 251 1 5.4693675 0.9958038 0.0060666126630314994 1 252 0 2.521253 0.925618351 3.7489094481346643 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.26803923 0.7804069 0.35770157547115156 1 273 1 -0.341918468 0.415343523 1.2676230380461255 0 274 0 -4.11754751 0.01602347 0.023304190486054636 0 -275 0 +275 0 ? ? ? 0 276 0 -4.51155567 0.0108620832 0.015756402974357524 0 277 0 -5.327016 0.00483505568 0.0069924288920724343 0 278 0 -5.107146 0.00601691334 0.0087067913827365544 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 5.13687468 0.9941583 0.0084524642023233402 1 290 0 -5.54688644 0.00388443237 0.005614963867042919 0 291 0 -5.107146 0.00601691334 0.0087067913827365544 0 -292 1 +292 1 ? ? ? 0 293 1 3.96790457 0.981438041 0.027030903251222888 1 -294 0 +294 0 ? ? ? 0 295 1 4.55162954 0.9895601 0.015140724489837425 1 296 0 0.6700988 0.661525249 1.5628798789895393 1 -297 0 +297 0 ? ? ? 0 298 0 -2.40630245 0.08269336 0.12452401321321592 0 299 1 4.497334 0.988984048 0.015980843395464565 1 300 1 4.62145948 0.990257442 0.014124456553393392 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 1.84912252 0.864024043 0.21085663625525078 1 313 0 -5.54688644 0.00388443237 0.005614963867042919 0 314 0 -5.36138344 0.004672473 0.0067567510712633252 0 -315 0 +315 0 ? ? ? 0 316 1 2.20764112 0.900933564 0.15050737158003294 1 317 1 5.890983 0.997243345 0.0039825045312741787 1 318 0 -5.00404263 0.006666029 0.0096492437155483984 0 319 0 0.6469283 0.656317949 1.540853588056835 1 320 1 3.99139547 0.9818612 0.026409039376297374 1 -321 0 +321 0 ? ? ? 0 322 0 -4.29168558 0.0134971775 0.019604916230060426 0 323 1 3.157802 0.959215045 0.060073807756361522 1 324 0 -5.107146 0.00601691334 0.0087067913827365544 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -3.57900143 0.0271460768 0.039704898382857935 0 409 0 -4.281804 0.0136293843 0.019798272915656116 0 410 0 -5.327016 0.00483505568 0.0069924288920724343 0 -411 0 +411 0 ? ? ? 0 412 1 6.193191 0.997960865 0.0029448527311883681 1 413 0 -3.24647379 0.0374538042 0.055072312735252281 0 414 1 4.55400229 0.9895846 0.015105009633995839 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -4.95365334 0.00700811762 0.010146170993954725 0 615 0 -3.64432216 0.0254732724 0.037226341208279841 0 616 0 -4.51155567 0.0108620832 0.015756402974357524 0 -617 0 +617 0 ? ? ? 0 618 0 -4.10382557 0.0162412636 0.023623552162962799 0 619 0 -3.696096 0.0242191125 0.035370869193417386 0 620 0 -4.51155567 0.0108620832 0.015756402974357524 0 diff --git a/test/BaselineOutput/SingleDebug/NAReplace/featurized.tsv b/test/BaselineOutput/SingleDebug/NAReplace/featurized.tsv new file mode 100644 index 0000000000..5ca9707e3e --- /dev/null +++ b/test/BaselineOutput/SingleDebug/NAReplace/featurized.tsv @@ -0,0 +1,13 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=A:R4:0 +#@ col=B:R8:1 +#@ col=C:R4:2-5 +#@ col=D:R8:6-9 +#@ } +A B 8 0:"" +5 5 5 1 1 1 5 1 1 1 +5 5 5 4 4 5 5 4 4 5 +3 3 3 1 1 1 3 1 1 1 +6 6 6 8 8 1 6 8 8 1 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-out.txt deleted file mode 100644 index 8e81e97339..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-out.txt +++ /dev/null @@ -1,33 +0,0 @@ -maml.exe CV tr=OLS threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 2409 examples -Coefficient of determination R2 = 0.291173667189042, or 0.287920813763543 (adjusted) -Not training a calibrator because it is not needed. -Not adding a normalizer. -Trainer solving for 12 parameters across 2489 examples -Coefficient of determination R2 = 0.280280855195625, or 0.277084686203761 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.586798 -L2(avg): 0.573048 -RMS(avg): 0.756999 -Loss-fn(avg): 0.573048 -R Squared: 0.263841 -L1(avg): 0.587999 -L2(avg): 0.571859 -RMS(avg): 0.756214 -Loss-fn(avg): 0.571859 -R Squared: 0.276072 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.587398 (0.0006) -L2(avg): 0.572454 (0.0006) -RMS(avg): 0.756606 (0.0004) -Loss-fn(avg): 0.572454 (0.0006) -R Squared: 0.269956 (0.0061) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-rp.txt deleted file mode 100644 index e3768ce894..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.587398 0.572454 0.756606 0.572454 0.269956 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine.txt deleted file mode 100644 index c49fe65f22..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine.txt +++ /dev/null @@ -1,4899 +0,0 @@ -Instance Label Score L1-loss L2-loss -5 6 5.72627258 0.2737274169921875 0.074926698813214898 -6 6 5.47047424 0.5295257568359375 0.28039752715267241 -8 6 5.164154 0.835845947265625 0.69863844756036997 -9 6 5.711914 0.2880859375 0.082993507385253906 -10 5 6.12764 1.1276397705078125 1.271571452030912 -11 5 5.514374 0.514373779296875 0.26458038482815027 -18 6 5.71618652 0.2838134765625 0.080550089478492737 -20 8 5.861252 2.1387481689453125 4.574243730166927 -21 7 5.85632324 1.1436767578125 1.3079965263605118 -25 6 6.04560852 0.0456085205078125 0.0020801371429115534 -28 6 5.98152161 0.0184783935546875 0.00034145102836191654 -31 6 5.786499 0.2135009765625 0.045582666993141174 -32 6 5.94306946 0.0569305419921875 0.0032410866115242243 -35 5 6.35475159 1.3547515869140625 1.8353518622461706 -37 6 5.89408875 0.1059112548828125 0.011217193910852075 -40 6 5.5723114 0.4276885986328125 0.18291753740049899 -41 6 5.56813049 0.4318695068359375 0.18651127093471587 -44 6 5.54307556 0.4569244384765625 0.20877994247712195 -45 7 5.77168274 1.2283172607421875 1.508763293037191 -46 4 5.47047424 1.4704742431640625 2.1622944998089224 -48 6 5.45797729 0.542022705078125 0.29378861282020807 -50 6 6.289795 0.289794921875 0.083981096744537354 -51 7 6.18988037 0.81011962890625 0.65629381313920021 -52 7 6.18954468 0.810455322265625 0.65683782938867807 -54 6 5.370331 0.629669189453125 0.39648328814655542 -56 6 5.90994263 0.090057373046875 0.0081103304401040077 -60 6 5.22787476 0.772125244140625 0.59617739263921976 -63 6 5.4642334 0.5357666015625 0.28704585134983063 -64 6 5.67704773 0.3229522705078125 0.1042981690261513 -66 7 6.84855652 0.1514434814453125 0.022935128072276711 -68 8 6.04177856 1.958221435546875 3.8346311906352639 -69 5 5.551605 0.551605224609375 0.30426832381635904 -70 6 5.46566772 0.534332275390625 0.28551098052412271 -71 5 5.44389343 0.4438934326171875 0.19704137952066958 -72 5 5.63322449 0.6332244873046875 0.40097325132228434 -73 6 5.06080627 0.9391937255859375 0.88208485417999327 -74 8 6.04177856 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+maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 500 examples +Coefficient of determination R2 = 0.99992945840165, or 0.999927868324639 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.892619 +L2(avg): 1.286446 +RMS(avg): 1.134216 +Loss-fn(avg): 1.286446 +R Squared: 0.999929 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.892619 (0.0000) +L2(avg): 1.286446 (0.0000) +RMS(avg): 1.134216 (0.0000) +Loss-fn(avg): 1.286446 (0.0000) +R Squared: 0.999929 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-generatedRegressionDataset-rp.txt similarity index 84% rename from test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-rp.txt rename to test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-generatedRegressionDataset-rp.txt index 705c51f87f..4be0272a5e 100644 --- a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-rp.txt +++ b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-generatedRegressionDataset-rp.txt @@ -1,4 +1,4 @@ OLS L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.583635 0.563155 0.750436 0.563155 0.281869 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +0.892619 1.286446 1.134216 1.286446 0.999929 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-generatedRegressionDataset.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-generatedRegressionDataset.txt new file mode 100644 index 0000000000..4a4acae0c9 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-generatedRegressionDataset.txt @@ -0,0 +1,501 @@ +Instance Label Score L1-loss L2-loss +0 140.66 142.00174 1.34173583984375 1.8002550639212132 +1 148.12 147.024979 1.0950164794921875 1.1990610903594643 +2 402.2 401.973938 0.22607421875 0.051109552383422852 +3 443.51 442.014343 1.49566650390625 2.2370182909071445 +4 329.59 331.785278 2.195281982421875 4.8192629823461175 +5 322.18 323.341 1.1610107421875 1.3479459434747696 +6 425.31 425.2812 0.02880859375 0.00082993507385253906 +7 421.76 421.1026 0.65740966796875 0.43218747153878212 +8 159.37 160.2427 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344.483429 0.3565673828125 0.12714029848575592 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-out.txt deleted file mode 100644 index 7d2d822e9a..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-out.txt +++ /dev/null @@ -1,24 +0,0 @@ -maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 4898 examples -Coefficient of determination R2 = 0.281869324119046, or 0.280252574746412 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.583635 -L2(avg): 0.563155 -RMS(avg): 0.750436 -Loss-fn(avg): 0.563155 -R Squared: 0.281869 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.583635 (0.0000) -L2(avg): 0.563155 (0.0000) -RMS(avg): 0.750436 (0.0000) -Loss-fn(avg): 0.563155 (0.0000) -R Squared: 0.281869 (0.0000) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine.txt deleted file mode 100644 index 7144734016..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine.txt +++ /dev/null @@ -1,4899 +0,0 @@ -Instance Label Score L1-loss L2-loss -0 6 5.5627594 0.4372406005859375 0.19117934280075133 -1 6 5.21707153 0.782928466796875 0.6129769841209054 -2 6 5.766144 0.233856201171875 0.05468872282654047 -3 6 5.77809143 0.2219085693359375 0.049243413144722581 -4 6 5.77809143 0.2219085693359375 0.049243413144722581 -5 6 5.766144 0.233856201171875 0.05468872282654047 -6 6 5.457855 0.542144775390625 0.29392095748335123 -7 6 5.5627594 0.4372406005859375 0.19117934280075133 -8 6 5.21707153 0.782928466796875 0.6129769841209054 -9 6 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b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-out.txt deleted file mode 100644 index 46f60adaa5..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-out.txt +++ /dev/null @@ -1,33 +0,0 @@ -maml.exe CV tr=OLS threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 xf=MinMax{col=Features} -Not adding a normalizer. -Trainer solving for 12 parameters across 2409 examples -Coefficient of determination R2 = 0.291172889085396, or 0.287920032089126 (adjusted) -Not training a calibrator because it is not needed. -Not adding a normalizer. -Trainer solving for 12 parameters across 2489 examples -Coefficient of determination R2 = 0.280282876617768, or 0.277086716602748 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.586799 -L2(avg): 0.573023 -RMS(avg): 0.756983 -Loss-fn(avg): 0.573023 -R Squared: 0.263873 -L1(avg): 0.588002 -L2(avg): 0.571858 -RMS(avg): 0.756213 -Loss-fn(avg): 0.571858 -R Squared: 0.276073 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.587400 (0.0006) -L2(avg): 0.572440 (0.0006) -RMS(avg): 0.756598 (0.0004) -Loss-fn(avg): 0.572440 (0.0006) -R Squared: 0.269973 (0.0061) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-rp.txt deleted file mode 100644 index c6ae0bb553..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.5874 0.57244 0.756598 0.57244 0.269973 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 xf=MinMax{col=Features} - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine.txt deleted file mode 100644 index 0ebf68c03b..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine.txt +++ /dev/null @@ -1,4899 +0,0 @@ -Instance Label Score L1-loss L2-loss -5 6 5.725769 0.27423095703125 0.075202617794275284 -6 6 5.47061157 0.529388427734375 0.28025210741907358 -8 6 5.16453552 0.8354644775390625 0.69800089322961867 -9 6 5.71276855 0.2872314453125 0.082501903176307678 -10 5 6.127655 1.127655029296875 1.271605865098536 -11 5 5.51383972 0.5138397216796875 0.26403125957585871 -18 6 5.71614075 0.2838592529296875 0.080576075473800302 -20 8 5.861252 2.1387481689453125 4.574243730166927 -21 7 5.856018 1.14398193359375 1.308694664388895 -25 6 6.04542542 0.0454254150390625 0.0020634683314710855 -28 6 5.981476 0.018524169921875 0.00034314487129449844 -31 6 5.785904 0.2140960693359375 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a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-out.txt +++ /dev/null @@ -1,24 +0,0 @@ -maml.exe TrainTest test=%Data% tr=OLS dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 xf=MinMax{col=Features} -Not adding a normalizer. -Trainer solving for 12 parameters across 4898 examples -Coefficient of determination R2 = 0.281870677180194, or 0.280253930853747 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.583635 -L2(avg): 0.563154 -RMS(avg): 0.750436 -Loss-fn(avg): 0.563154 -R Squared: 0.281871 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.583635 (0.0000) -L2(avg): 0.563154 (0.0000) -RMS(avg): 0.750436 (0.0000) -Loss-fn(avg): 0.563154 (0.0000) -R Squared: 0.281871 (0.0000) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-rp.txt deleted file mode 100644 index fe37f2c5f9..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.583635 0.563154 0.750436 0.563154 0.281871 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 xf=MinMax{col=Features} - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine.txt deleted file mode 100644 index e296b7b161..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine.txt +++ /dev/null @@ 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-1,33 +0,0 @@ -maml.exe CV tr=OLS{l2=0.1} threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 2409 examples -Coefficient of determination R2 = 0.269438870320579, or 0.266086274397978 (adjusted) -Not training a calibrator because it is not needed. -Not adding a normalizer. -Trainer solving for 12 parameters across 2489 examples -Coefficient of determination R2 = 0.255663034368455, or 0.252357541182364 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.596885 -L2(avg): 0.589177 -RMS(avg): 0.767579 -Loss-fn(avg): 0.589177 -R Squared: 0.243122 -L1(avg): 0.599038 -L2(avg): 0.587517 -RMS(avg): 0.766497 -Loss-fn(avg): 0.587517 -R Squared: 0.256250 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.597962 (0.0011) -L2(avg): 0.588347 (0.0008) -RMS(avg): 0.767038 (0.0005) -Loss-fn(avg): 0.588347 (0.0008) -R Squared: 0.249686 (0.0066) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-rp.txt deleted file mode 100644 index f063507bbb..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.597962 0.588347 0.767038 0.588347 0.249686 0.1 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS{l2=0.1} threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /l2:0.1 - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine.txt deleted file mode 100644 index 9373538bea..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine.txt +++ /dev/null @@ -1,4899 +0,0 @@ -Instance Label Score L1-loss L2-loss -5 6 5.68127632 0.31872367858886719 0.10158478329321952 -6 6 5.52236938 0.477630615234375 0.22813100460916758 -8 6 5.26242733 0.73757266998291016 0.5440134435057189 -9 6 5.86241531 0.13758468627929688 0.018929545898572542 -10 5 6.125346 1.1253461837768555 1.2664040333411322 -11 5 5.36850357 0.36850357055664063 0.13579488151299302 -18 6 5.720931 0.27906894683837891 0.077879477089481952 -20 8 6.28227329 1.7177267074584961 2.9505850415162058 -21 7 5.847972 1.1520280838012695 1.3271687058668249 -25 6 5.930442 0.069558143615722656 0.0048383353432654985 -28 6 5.96074 0.039259910583496094 0.0015413405790241086 -31 6 5.59461832 0.40538167953491211 0.16433430610254618 -32 6 5.894807 0.10519313812255859 0.01106559630807169 -35 5 6.42026043 1.4202604293823242 2.017139687269264 -37 6 5.911791 0.088209152221679688 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0.64340972900390625 0.41397607937688008 -4876 7 6.18003941 0.81996059417724609 0.67233537600350246 -4878 4 5.089946 1.0899457931518555 1.1879818320094273 -4879 6 5.58836651 0.41163349151611328 0.1694421313377461 -4880 6 5.58836651 0.41163349151611328 0.1694421313377461 -4881 6 5.69312 0.30687999725341797 0.09417533271425782 -4882 5 5.795699 0.79569911956787109 0.63313708888108522 -4883 6 6.033263 0.033263206481933594 0.001106440905459749 -4886 7 6.720747 0.27925300598144531 0.077982241349673131 -4887 7 6.57487 0.42512989044189453 0.18073542374713725 -4888 5 5.336135 0.33613491058349609 0.11298667811297491 -4889 6 5.69565964 0.30434036254882813 0.092623056276352145 -4894 5 5.579279 0.57927894592285156 0.33556409718948998 -4896 7 6.57025433 0.42974567413330078 0.18468134443628514 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-out.txt deleted file mode 100644 index 6edb443e88..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-out.txt +++ /dev/null @@ -1,24 +0,0 @@ -maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 4898 examples -Coefficient of determination R2 = 0.259289504308921, or 0.257621920303067 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.595209 -L2(avg): 0.580862 -RMS(avg): 0.762143 -Loss-fn(avg): 0.580862 -R Squared: 0.259290 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.595209 (0.0000) -L2(avg): 0.580862 (0.0000) -RMS(avg): 0.762143 (0.0000) -Loss-fn(avg): 0.580862 (0.0000) -R Squared: 0.259290 (0.0000) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-rp.txt deleted file mode 100644 index 1707c73c50..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.595209 0.580862 0.762143 0.580862 0.25929 0.1 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /l2:0.1 - diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine.txt deleted file mode 100644 index b7b381bd97..0000000000 --- a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine.txt +++ /dev/null @@ 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5.749995 0.25000476837158203 0.062502384208528383 -4890 6 6.16887569 0.16887569427490234 0.028519000116830284 -4891 6 5.945266 0.054734230041503906 0.0029958359382362687 -4892 5 5.570834 0.57083415985107422 0.32585163805288175 -4893 6 6.11876 0.11876010894775391 0.014103963477282377 -4894 5 5.64458752 0.64458751678466797 0.41549306679462461 -4895 6 5.38194847 0.61805152893066406 0.38198769241353148 -4896 7 6.55421925 0.44578075408935547 0.19872048071647441 -4897 6 6.25601768 0.25601768493652344 0.06554505500025698 diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..38585c7ede --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 134 | 0.0000 + negative || 0 | 228 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6298 | +OVERALL 0/1 ACCURACY: 0.629834 +LOG LOSS/instance: 0.959786 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -0.945203 +AUC: 0.500000 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 107 | 0.0000 + negative || 0 | 230 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6825 | +OVERALL 0/1 ACCURACY: 0.682493 +LOG LOSS/instance: 0.910421 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -0.972725 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.656163 (0.0263) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.656163 (0.0263) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.935104 (0.0247) +Log-loss reduction: -0.958964 (0.0138) +F1 Score: NaN (NaN) +AUPRC: 0.418968 (0.0212) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..83b563173d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.656163 0 0 0.656163 1 0.935104 -0.958964 NaN 0.418968 PriorPredictor %Data% %Output% 99 0 0 maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer.txt new file mode 100644 index 0000000000..56f9146f1d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 -0.364985168 0.317507416 1.6551378056300845 0 +6 0 -0.364985168 0.317507416 0.55111472519016635 0 +8 0 -0.364985168 0.317507416 0.55111472519016635 0 +9 0 -0.364985168 0.317507416 0.55111472519016635 0 +10 0 -0.364985168 0.317507416 0.55111472519016635 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diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..8e3bf3b17b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 241 | 0.0000 + negative || 0 | 458 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6552 | +OVERALL 0/1 ACCURACY: 0.655222 +LOG LOSS/instance: 0.929318 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 0.000000 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.655222 (0.0000) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.655222 (0.0000) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.929318 (0.0000) +Log-loss reduction: 0.000000 (0.0000) +F1 Score: NaN (0.0000) +AUPRC: 0.415719 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..60265c1608 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.655222 0 0 0.655222 1 0.929318 0 NaN 0.415719 PriorPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5bd2f17a17 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -0.310443461 0.34477827 0.60994489004156338 0 +1 0 -0.310443461 0.34477827 0.60994489004156338 0 +2 0 -0.310443461 0.34477827 0.60994489004156338 0 +3 0 -0.310443461 0.34477827 0.60994489004156338 0 +4 0 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0 -0.310443461 0.34477827 0.60994489004156338 0 +688 0 -0.310443461 0.34477827 0.60994489004156338 0 +689 0 -0.310443461 0.34477827 0.60994489004156338 0 +690 0 -0.310443461 0.34477827 0.60994489004156338 0 +691 1 -0.310443461 0.34477827 1.5362592468820475 0 +692 0 -0.310443461 0.34477827 0.60994489004156338 0 +693 0 -0.310443461 0.34477827 0.60994489004156338 0 +694 0 -0.310443461 0.34477827 0.60994489004156338 0 +695 0 -0.310443461 0.34477827 0.60994489004156338 0 +696 1 -0.310443461 0.34477827 1.5362592468820475 0 +697 1 -0.310443461 0.34477827 1.5362592468820475 0 +698 1 -0.310443461 0.34477827 1.5362592468820475 0 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..acd9af8ba2 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 67 | 67 | 0.5000 + negative || 116 | 112 | 0.4912 + ||====================== +Precision || 0.3661 | 0.6257 | +OVERALL 0/1 ACCURACY: 0.494475 +LOG LOSS/instance: 1.411189 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -48.421356 +AUC: 0.521242 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 50 | 57 | 0.4673 + negative || 113 | 117 | 0.5087 + ||====================== +Precision || 0.3067 | 0.6724 | +OVERALL 0/1 ACCURACY: 0.495549 +LOG LOSS/instance: 1.451172 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -60.946260 +AUC: 0.484884 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.503063 (0.0182) +Accuracy: 0.495012 (0.0005) +Positive precision: 0.336434 (0.0297) +Positive recall: 0.483645 (0.0164) +Negative precision: 0.649056 (0.0234) +Negative recall: 0.499962 (0.0087) +Log-loss: 1.431181 (0.0200) +Log-loss reduction: -54.683808 (6.2625) +F1 Score: 0.396542 (0.0262) +AUPRC: 0.348911 (0.0260) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..2692f38450 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.503063 0.495012 0.336434 0.483645 0.649056 0.499962 1.431181 -54.68381 0.396542 0.348911 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 + diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt new file mode 100644 index 0000000000..c0fc1e43b3 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 -0.5901826 0.2049087 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b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 n=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 113 | 128 | 0.4689 + negative || 231 | 227 | 0.4956 + ||====================== +Precision || 0.3285 | 0.6394 | +OVERALL 0/1 ACCURACY: 0.486409 +LOG LOSS/instance: 1.479534 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): -59.206457 +AUC: 0.471290 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.471290 (0.0000) +Accuracy: 0.486409 (0.0000) +Positive precision: 0.328488 (0.0000) +Positive recall: 0.468880 (0.0000) +Negative precision: 0.639437 (0.0000) +Negative recall: 0.495633 (0.0000) +Log-loss: 1.479534 (0.0000) +Log-loss reduction: -59.206457 (0.0000) +F1 Score: 0.386325 (0.0000) +AUPRC: 0.314886 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..4af687d0c8 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.47129 0.486409 0.328488 0.46888 0.639437 0.495633 1.479534 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0 +611 1 0.576089442 0.7880447 0.34365064568846004 1 +612 1 -0.979093552 0.0104532242 6.5799081953976843 0 +613 0 0.8082966 0.90414834 3.383052775845699 1 +614 0 -0.381685466 0.309157252 0.53357073875790595 0 +615 0 -0.127211764 0.436394125 0.82724144515031472 0 +616 0 0.5924155 0.7962078 2.2948291585469072 1 +617 0 -0.6395387 0.180230647 0.28671003949751844 0 +618 0 0.3540915 0.677045763 1.6305983450311048 1 +619 0 0.6115931 0.805796564 2.3643593658252904 1 +620 0 0.6400163 0.820008159 2.4739965814401228 1 +621 0 0.7968601 0.898430049 3.2994544502632865 1 +622 0 0.675995231 0.8379976 2.6259130452612465 1 +623 0 -0.7748064 0.11259681 0.17233835535887324 0 +624 0 -0.0532539561 0.473373026 0.92514667537194428 0 +625 0 -0.5945378 0.2027311 0.32686170647294815 0 +626 1 0.8289326 0.914466262 0.12899815043528098 1 +627 0 0.104072481 0.5520362 1.1585460253031501 1 +628 0 -0.9865577 0.006721139 0.0097292865309182072 0 +629 0 -0.463386655 0.268306673 0.45068899242407306 0 +630 0 -0.267027766 0.366486132 0.6585518940425763 0 +631 0 -0.3718826 0.314058721 0.54384301734651552 0 +632 0 -0.245580524 0.377209723 0.6831816736355687 0 +633 1 0.0679595545 0.5339798 0.90514299940335918 1 +634 0 0.09977744 0.54988873 1.1516464073052537 1 +635 0 0.95841974 0.9792099 5.5879594854483496 1 +636 1 0.737865 0.8689325 0.20268400818392096 1 +637 0 0.537909746 0.7689549 2.1137534336440491 1 +638 0 0.546947956 0.773474 2.1422513069005187 1 +639 0 0.155516088 0.5777581 1.2438582577750641 1 +640 0 0.797553837 0.8987769 3.3043893726547267 1 +641 0 0.165859073 0.582929552 1.2616370014547258 1 +642 0 -0.602790952 0.198604524 0.31941372957385827 0 +643 0 0.26200825 0.6310041 1.438323289389781 1 +644 0 0.47849384 0.7392469 1.9392437171910466 1 +645 0 0.8082667 0.9041333 3.3828267169922572 1 +646 0 -0.444251865 0.277874053 0.46967761241254513 0 +647 0 -0.6682002 0.1658999 0.26170756807246698 0 +648 1 0.324374646 0.662187338 0.59468867062038799 1 +649 0 0.614939749 0.807469845 2.3768436681748701 1 +650 0 -0.74318254 0.12840873 0.19827634867923982 0 +651 0 0.4591076 0.7295538 1.8865865681200769 1 +652 0 -0.124314286 0.437842846 0.83095459482826206 0 +653 0 -0.476671636 0.261664182 0.43765094683609368 0 +654 0 0.279933721 0.639966846 1.4737983282978608 1 +655 0 0.446524471 0.72326225 1.8534086406314256 1 +656 0 0.07063705 0.5353185 1.1056858666658542 1 +657 0 0.8181843 0.9090922 3.4594519127328351 1 +658 1 -0.163035512 0.418482244 1.2567616836178841 0 +659 0 0.7172431 0.858621538 2.8223657390864321 1 +660 0 0.9027938 0.951396942 4.3628091059314427 1 +661 0 0.452043056 0.7260215 1.8678655590413153 1 +662 0 -0.399452657 0.300273657 0.51513728792308566 0 +663 0 0.31736517 0.6586826 1.5508140671025619 1 +664 0 0.3395698 0.6697849 1.5985220173076775 1 +665 0 -0.9288423 0.0355788469 0.052264800342187279 0 +666 0 0.348564833 0.674282432 1.6183065576637607 1 +667 0 -0.959403038 0.020298481 0.029585817731858574 0 +668 1 -0.0620544665 0.468972772 1.092423929762264 0 +669 1 -0.1093397 0.445330143 1.167052828382644 0 +670 1 0.7400145 0.8700073 0.20090062751552754 1 +671 0 0.257556081 0.62877804 1.4296460394438404 1 +672 0 -0.0252051521 0.487397432 0.96408738964953067 0 +673 0 0.5220516 0.7610258 2.0650731416111654 1 +674 0 -0.150225192 0.424887419 0.79808369619187902 0 +675 0 -0.8285416 0.08572921 0.12930656935508233 0 +676 0 -0.128591537 0.435704231 0.82547656303306038 0 +677 0 -0.772794247 0.113602877 0.17397489553374348 0 +678 0 -0.328698128 0.335650921 0.58998659669085396 0 +679 0 -0.920071542 0.03996423 0.058839932936285387 0 +680 1 0.8711379 0.9355689 0.096084145903302609 1 +681 1 0.431411982 0.715706 0.48256103915508791 1 +682 0 -0.7243137 0.137843162 0.21397775564200355 0 +683 0 0.3713024 0.6856512 1.669561761695042 1 +684 0 -0.7297887 0.13510564 0.20940416465001679 0 +685 0 0.7174773 0.858738661 2.8235614161513505 1 +686 0 -0.994656265 0.00267186761 0.0038598489514670123 0 +687 0 -0.99033767 0.004831165 0.0069867886829129309 0 +688 0 0.4759514 0.7379757 1.9322275734663419 1 +689 0 -0.1828268 0.4085866 0.75776114229334535 0 +690 0 0.870642662 0.935321331 3.950566199919979 1 +691 1 -0.213670343 0.3931648 1.3467938821453134 0 +692 0 -0.6592799 0.170360059 0.26944274322608602 0 +693 0 0.396216482 0.698108256 1.7278967910194019 1 +694 0 0.366007477 0.6830037 1.6574622011524394 1 +695 0 0.410838276 0.7054191 1.7632643162514681 1 +696 1 -0.232817248 0.383591384 1.382357780264464 0 +697 1 0.855639338 0.927819669 0.10808366406515908 1 +698 1 -0.126234785 0.436882615 1.1946823972611171 0 diff --git a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-CV-breast-cancer.txt index 7cfbe77b4a..cb14fcc5fe 100644 --- a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 0.8960438 0.7101358 0.49383312022081494 1 35 0 -1.47149158 0.186716 0.298168872554196 0 37 0 -1.077555 0.253969 0.42269254099397019 0 -40 0 +40 0 ? ? ? 0 41 1 -0.110265851 0.472461432 1.0817315302478872 0 44 1 1.38623238 0.799990058 0.32194602419012386 1 45 0 -1.49285364 0.1834938 0.29246424185161274 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.329715 0.2092065 0.33862709419515019 0 141 0 -1.51333463 0.18044512 0.28708753442168577 0 144 0 -1.47149158 0.186716 0.298168872554196 0 -145 0 +145 0 ? ? ? 0 147 0 -1.36272907 0.203797117 0.3287919993163349 0 150 0 -1.490515 0.183844447 0.29308395009459498 0 151 1 0.488354921 0.6197188 0.69031438298058734 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -1.54631257 0.175619483 0.27861768553552435 0 156 0 -1.34795034 0.206205666 0.33316283039965772 0 161 0 -1.29642129 0.214767918 0.34880897708810421 0 -164 0 +164 0 ? ? ? 0 167 1 0.7817354 0.686054 0.54360597630084673 1 169 0 -1.52040219 0.1794023 0.28525299602441972 0 171 0 -1.46262646 0.188065946 0.3005655398637459 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.30008841 0.908884346 0.13783136852928218 1 247 1 0.283135176 0.5703147 0.81016986229628651 1 248 0 -1.02733588 0.263600916 0.44144026210768955 0 -249 0 +249 0 ? ? ? 0 250 0 -1.3897934 0.199440747 0.32091990872905618 0 252 0 0.575369835 0.6400013 1.4739363860396681 1 254 1 1.36448383 0.796487451 0.32827646395944027 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.46262646 0.188065946 0.3005655398637459 0 271 0 -1.330715 0.209041134 0.33832542505991492 0 272 1 0.3417368 0.58461237 0.77444774011663531 1 -275 0 +275 0 ? ? ? 0 276 0 -1.43851376 0.1917756 0.30717219734355156 0 277 0 -1.50446939 0.181759879 0.28940381583950159 0 278 0 -1.46262646 0.188065946 0.3005655398637459 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.46262646 0.188065946 0.3005655398637459 0 293 1 0.690094 0.665987849 0.58643223892348229 1 296 0 0.122398376 0.530561447 1.090991766168943 1 -297 0 +297 0 ? ? ? 0 299 1 0.8090725 0.6919118 0.53133991521179469 1 300 1 1.07804394 0.7461236 0.42251343016986392 1 301 0 -1.46262646 0.188065946 0.3005655398637459 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.633879662 0.653368652 0.61403085786632716 1 317 1 1.75457263 0.852528632 0.2301798078479787 1 319 0 0.1361382 0.5339821 1.1015427183713855 1 -321 0 +321 0 ? ? ? 0 323 1 0.8411846 0.6987147 0.51722465664834216 1 327 0 -1.50446939 0.181759879 0.28940381583950159 0 328 1 0.758497 0.681027353 0.55421535102487163 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 2.80018759 0.942685962 0.085150850944065576 1 613 0 -1.31009483 0.212470978 0.34459500515903269 0 614 0 -1.49938011 0.18251799 0.29074111357003468 0 -617 0 +617 0 ? ? ? 0 618 0 -1.40553582 0.196939126 0.31641874253214702 0 619 0 -1.37255788 0.2022069 0.32591343959007929 0 621 0 -0.609831333 0.35209766 0.62615172722986678 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.29125416 0.2156406 0.35041324245427652 0 19 0 -1.18682528 0.233827218 0.38425832019246531 0 22 0 -1.38527489 0.200163171 0.32222238184143043 0 -23 1 +23 1 ? ? ? 0 24 0 -1.44789767 0.19032532 0.30458573193898042 0 26 0 -1.321662 0.210541919 0.341065431407162 0 27 0 -1.28084588 0.217406273 0.35366454853946033 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.36420059 0.203558445 0.3283595980104454 0 135 0 -0.9835708 0.272183836 0.45835400169661483 0 136 0 -1.33306038 0.2086536 0.33761874206668624 0 -139 0 +139 0 ? ? ? 0 140 0 -1.42708111 0.1935539 0.31034997353186378 0 142 1 0.5200758 0.6271655 0.67308190344279184 1 143 0 -1.07713079 0.254049361 0.42284792669686094 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.147677 0.240913659 0.39766410362601251 0 155 1 0.775262833 0.6846582 0.54654409786019487 1 157 0 -1.39568317 0.198502019 0.31922920878555094 0 -158 0 +158 0 ? ? ? 0 159 1 2.4702127 0.922027051 0.11711901622998303 1 160 1 1.8089292 0.8592324 0.21887965615084526 1 162 0 -1.34346867 0.206940219 0.33449847387485032 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1.80702519 0.859001935 0.21926671447669044 1 232 0 -0.09305465 0.476753116 0.93443627885443414 0 234 0 -0.7166654 0.3281277 0.57374106872220532 0 -235 0 +235 0 ? ? ? 0 236 1 2.0308764 0.884000957 0.1778801634200064 1 238 1 2.5700233 0.9289072 0.10639359546534205 1 243 0 -0.9099059 0.2870191 0.48806467358561068 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 3.03633 0.954188645 0.067653576940859037 1 287 0 -1.36420059 0.203558445 0.3283595980104454 0 289 1 1.839293 0.8628651 0.21279308434601185 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 1.50403929 0.81817615 0.28951661133253503 1 298 0 -1.01809835 0.265398 0.44496526256029006 0 302 1 2.95740485 0.9506123 0.07307101604489151 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.44789767 0.19032532 0.30458573193898042 0 310 0 -1.4164151 0.1952242 0.31334116395889644 0 313 0 -1.53151011 0.17777285 0.28239108395025142 0 -315 0 +315 0 ? ? ? 0 318 0 -1.38467479 0.200259253 0.32239570012101715 0 320 1 1.03225207 0.737352252 0.43957409861257096 1 322 0 -1.34346867 0.206940219 0.33449847387485032 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -1.48970389 0.183966175 0.29329914054619283 0 408 0 -0.969954848 0.2748895 0.46372722780050113 0 410 0 -1.48970389 0.183966175 0.29329914054619283 0 -411 0 +411 0 ? ? ? 0 412 1 1.784699 0.8562761 0.22385204529486596 1 417 0 -1.48970389 0.183966175 0.29329914054619283 0 420 0 -0.827743053 0.3041225 0.52309474947909951 0 diff --git a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-CV-breast-cancer.txt index 0302f143a8..4462030630 100644 --- a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 5.83416653 0.997082651 0.0042149967260249437 1 35 0 -5.20735168 0.005446332 0.0078788712300762211 0 37 0 -2.71164179 0.0622898862 0.092786101770516077 0 -40 0 +40 0 ? ? ? 0 41 1 1.76036549 0.853255451 0.22895036937850363 1 44 1 7.40955639 0.999394953 0.00087316212305162816 1 45 0 -5.421812 0.004399689 0.0063614137834035649 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.322978 0.0130867995 0.019004890409612719 0 141 0 -5.4411335 0.00431586 0.0062399450406013428 0 144 0 -5.20735168 0.005446332 0.0078788712300762211 0 -145 0 +145 0 ? ? ? 0 147 0 -4.875398 0.00757424766 0.010968922848962245 0 150 0 -5.39204836 0.00453200564 0.006553162786217457 0 151 1 4.061223 0.9830638 0.024643020624724253 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -5.73325 0.00322609954 0.0046618015694471927 0 156 0 -4.91463 0.007284973 0.010548463613035559 0 161 0 -4.12887526 0.0158458445 0.023043781432081138 0 -164 0 +164 0 ? ? ? 0 167 1 4.796111 0.9918059 0.011870271192039138 1 169 0 -5.68914 0.00337109854 0.0048716832359580091 0 171 0 -5.265687 0.005139294 0.0074335520922891698 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 10.8528 0.9999806 2.7947451458241805E-05 1 247 1 3.03624249 0.95418483 0.06765934462197544 1 248 0 -3.08702755 0.04364554 0.064382659498109923 0 -249 0 +249 0 ? ? ? 0 250 0 -5.14841127 0.00577508053 0.0083558311280958943 0 252 0 3.84936237 0.979150653 5.5838539825714451 1 254 1 7.35057163 0.9993582 0.00092625186884288718 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.265687 0.005139294 0.0074335520922891698 0 271 0 -4.097221 0.016347127 0.023778810630558714 0 272 1 2.79984665 0.942667544 0.085179038039461821 1 -275 0 +275 0 ? ? ? 0 276 0 -4.915235 0.007280598 0.010542105680653375 0 277 0 -5.49946833 0.00407229364 0.0058870729354622087 0 278 0 -5.265687 0.005139294 0.0074335520922891698 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.265687 0.005139294 0.0074335520922891698 0 293 1 4.736758 0.991309166 0.012593024732102163 1 296 0 1.75365162 0.85241276 2.760360102190484 1 -297 0 +297 0 ? ? ? 0 299 1 5.53140068 0.9960552 0.0057024187918531082 1 300 1 6.04150867 0.9976277 0.0034266068641119179 1 301 0 -5.265687 0.005139294 0.0074335520922891698 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 4.025776 0.9824635 0.025524315591638584 1 317 1 8.423935 0.9997805 0.00031674081892699212 1 319 0 1.82892418 0.861633539 2.8534338101579171 1 -321 0 +321 0 ? ? ? 0 323 1 4.504982 0.9890671 0.015859728309184334 1 327 0 -5.49946833 0.00407229364 0.0058870729354622087 0 328 1 4.454892 0.9885119 0.016669732083571809 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 13.2412815 0.9999982 2.5797420694119618E-06 1 613 0 -4.637956 0.009584702 0.013894495676017315 0 614 0 -5.333713 0.00480293762 0.0069458679549985972 0 -617 0 +617 0 ? ? ? 0 618 0 -4.623119 0.009726578 0.014101176192227349 0 619 0 -4.331002 0.0129835671 0.018853990456118342 0 621 0 -0.969069 0.2750661 0.46407865469425469 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.77261448 0.00838729553 0.012151339639068351 0 19 0 -4.05630064 0.01701831 0.02476355221691592 0 22 0 -5.26293468 0.00515338546 0.0074539868149001397 0 -23 1 +23 1 ? ? ? 0 24 0 -5.847086 0.00287998538 0.0041609352152631573 0 26 0 -5.01217842 0.00661237258 0.0095713166824383505 0 27 0 -4.54662037 0.0104917344 0.015216336343473301 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.25242233 0.005207562 0.0075325538981779582 0 135 0 -3.511651 0.028982535 0.042430850287558594 0 136 0 -4.90477753 0.0073565715 0.010652520453755514 0 -139 0 +139 0 ? ? ? 0 140 0 -5.39509726 0.00451827142 0.0065332584338255633 0 142 1 2.2592926 0.9054491 0.14329456494743992 1 143 0 -4.372905 0.0124574006 0.018085112102952432 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.16768646 0.01525183 0.022173264390610525 0 155 1 2.62865639 0.9326832 0.10054091831709649 1 157 0 -5.488929 0.004115263 0.0059493194428234175 0 -158 0 +158 0 ? ? ? 0 159 1 8.939568 0.9998689 0.00018910731810054123 1 160 1 6.86695 0.9989594 0.0015020181784367139 1 162 0 -5.13077164 0.00587725034 0.0085040948177142958 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.89666176 0.9972589 0.0039599989302112676 1 232 0 -0.179828167 0.455163717 0.87610531376236689 0 234 0 -3.15223122 0.04100345 0.060402470875188088 0 -235 0 +235 0 ? ? ? 0 236 1 7.74873161 0.9995689 0.00062210936850165717 1 238 1 9.156269 0.99989444 0.00015229867579129541 1 243 0 -3.844254 0.0209539 0.030551300678084092 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.8717079 0.999981 2.7431493603031304E-05 1 287 0 -5.25242233 0.005207562 0.0075325538981779582 0 289 1 6.0516243 0.9976515 0.0033921289521523556 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.83148575 0.992088437 0.011459363337312383 1 298 0 -3.31150723 0.0351785272 0.051666079123835182 0 302 1 10.6722527 0.9999768 3.3451013395372324E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.847086 0.00287998538 0.0041609352152631573 0 310 0 -5.61057949 0.00364560937 0.005269112963217676 0 313 0 -6.111411 0.00221251347 0.0031955185815049345 0 -315 0 +315 0 ? ? ? 0 318 0 -5.81555 0.002971982 0.0042940476081927553 0 320 1 3.90395832 0.98023653 0.028798182473169599 1 322 0 -5.13077164 0.00587725034 0.0085040948177142958 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -5.97924852 0.002524339 0.0036464555896367997 0 408 0 -4.06157875 0.01693024 0.024634297974768089 0 410 0 -5.97924852 0.002524339 0.0036464555896367997 0 -411 0 +411 0 ? ? ? 0 412 1 6.142968 0.9978561 0.003096342326011223 1 417 0 -5.97924852 0.002524339 0.0036464555896367997 0 420 0 -3.33492 0.0343924649 0.050491160716171948 0 diff --git a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt index 22409b2a5a..a0b287a08e 100644 --- a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 4.078068 0.983342052 0.024234755458090462 1 21 1 5.07943726 0.993815064 0.0089506841986991444 1 22 0 -6.401946 0.00165558141 0.0023904784483303873 0 -23 1 +23 1 ? ? ? 0 24 0 -6.95376 0.000954126066 0.0013771700453364219 0 25 1 0.131910324 0.532929838 0.90798248584253316 1 26 0 -6.20803738 0.00200913986 0.0029014918436988269 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -3.97515774 0.018430287 0.026837360561959296 0 38 1 3.85910988 0.9793487 0.030105439588229164 1 39 1 0.289131165 0.5717834 0.806459299491597 1 -40 0 +40 0 ? ? ? 0 41 1 0.5163593 0.626296043 0.67508333009285992 1 42 1 6.25112057 0.9980754 0.0027792492014372186 1 43 1 -1.67306614 0.158015817 2.6618591152960773 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -6.01794958 0.00242874329 0.0035081978824050039 0 137 0 -6.618124 0.00133415242 0.0019260601909807822 0 138 0 -5.72209167 0.0032621813 0.0047140259759722502 0 -139 0 +139 0 ? ? ? 0 140 0 -6.618124 0.00133415242 0.0019260601909807822 0 141 0 -7.00212 0.00090912357 0.0013121846250039875 0 142 1 1.86599445 0.8659941 0.20757090594664257 1 143 0 -5.405919 0.00446985662 0.0064630953185838089 0 144 0 -6.785942 0.00112826866 0.001628666561573529 0 -145 0 +145 0 ? ? ? 0 146 1 -0.2344532 0.441653728 1.17901240396708 0 147 0 -6.632551 0.00131506776 0.0018984903241446914 0 148 0 -3.505578 0.0291539337 0.042685529480640867 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.2497673 0.9046305 0.14459948541676573 1 156 0 -6.63783455 0.0013081471 0.0018884928122825894 0 157 0 -6.569764 0.00140016491 0.0020214264686188826 0 -158 0 +158 0 ? ? ? 0 159 1 9.262019 0.99990505 0.00013699068516156971 1 160 1 6.81782627 0.9989071 0.001577599187795759 1 161 0 -5.57644749 0.00377171184 0.005451717595353108 0 162 0 -6.18576765 0.002054292 0.0029667652410511319 0 163 0 -5.441502 0.00431427639 0.0062376503223464629 0 -164 0 +164 0 ? ? ? 0 165 0 -5.081398 0.00617287867 0.0089331816190877534 0 166 1 5.99673557 0.9975193 0.0035833193575627669 1 167 1 4.04529858 0.982796669 0.025035126906624509 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.9520569 0.278471351 0.47087141475726779 0 233 1 4.02608871 0.982468843 0.025516438252352936 1 234 0 -4.248459 0.0140850125 0.020464842181276369 0 -235 0 +235 0 ? ? ? 0 236 1 8.120999 0.9997029 0.00042873045458892818 1 237 1 3.97880173 0.9816355 0.02674065507190734 1 238 1 9.321572 0.999910533 0.00012907875367543245 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.005473 0.9998773 0.00017706700464490148 1 247 1 1.74156952 0.8508863 0.23296175567699981 1 248 0 -4.675151 0.009237982 0.013389533098992946 0 -249 0 +249 0 ? ? ? 0 250 0 -6.85401249 0.00105410081 0.0015215480827833656 0 251 1 5.57425 0.996220052 0.0054636445480688526 1 252 0 2.525447 0.9259066 3.7545111242363967 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.04092026 0.73902756 0.43629992864352879 1 273 1 -1.54782867 0.1754001 2.5112785805368674 0 274 0 -5.9604435 0.002572135 0.0037155870547972859 0 -275 0 +275 0 ? ? ? 0 276 0 -6.401946 0.00165558141 0.0023904784483303873 0 277 0 -7.16993856 0.0007687785 0.0011095394740419171 0 278 0 -6.95376 0.000954126066 0.0013771700453364219 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 5.963811 0.997436464 0.0037031495249571483 1 290 0 -7.386117 0.0006194141 0.0008939025092919693 0 291 0 -6.95376 0.000954126066 0.0013771700453364219 0 -292 1 +292 1 ? ? ? 0 293 1 3.82977867 0.97874707 0.030992011604895862 1 -294 0 +294 0 ? ? ? 0 295 1 4.92571831 0.992794752 0.010432605435470237 1 296 0 -0.00817585 0.497956038 0.9941143927175089 0 -297 0 +297 0 ? ? ? 0 298 0 -4.33488274 0.0129339322 0.018781442391253584 0 299 1 4.3436203 0.987177134 0.018619117824464626 1 300 1 4.40119934 0.987885952 0.017583597598896144 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.9174366 0.7145195 0.48495470886222203 1 313 0 -7.386117 0.0006194141 0.0008939025092919693 0 314 0 -7.21227646 0.0007369329 0.001063561390310713 0 -315 0 +315 0 ? ? ? 0 316 1 2.16819382 0.897356749 0.15624644520733796 1 317 1 6.37115574 0.998292744 0.0024651544582865699 1 318 0 -6.826747 0.00108320534 0.0015635819620021878 0 319 0 -0.115743637 0.471096337 0.91892312744499838 0 320 1 4.11173058 0.9838846 0.023439022974424555 1 -321 0 +321 0 ? ? ? 0 322 0 -6.18576765 0.002054292 0.0029667652410511319 0 323 1 3.31629562 0.964983642 0.051423608112212277 1 324 0 -6.95376 0.000954126066 0.0013771700453364219 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -5.098096 0.00607128069 0.0087857038468610767 0 409 0 -6.106088 0.00222429563 0.0032125544404639546 0 410 0 -7.16993856 0.0007687785 0.0011095394740419171 0 -411 0 +411 0 ? ? ? 0 412 1 6.053849 0.9976567 0.0033846301153950954 1 413 0 -5.12191772 0.00592920836 0.0085794995279385791 0 414 1 4.639694 0.9904318 0.013870479681066349 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.82828045 0.00108154735 0.0015611874005433336 0 615 0 -5.42623472 0.00438035838 0.0063334027525860032 0 616 0 -6.401946 0.00165558141 0.0023904784483303873 0 -617 0 +617 0 ? ? ? 0 618 0 -6.01794958 0.00242874329 0.0035081978824050039 0 619 0 -5.633953 0.00356168649 0.0051475999586587254 0 620 0 -6.401946 0.00165558141 0.0023904784483303873 0 diff --git a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt index 5197b73592..ffa3b3b912 100644 --- a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 2.322781 0.998222351 0.0025668874786068312 1 35 0 -1.91525483 0.005915621 0.0085597800780180429 0 37 0 -0.682821751 0.142692313 0.22211501491184898 0 -40 0 +40 0 ? ? ? 0 41 1 0.809957266 0.9039319 0.14571398208584879 1 44 1 2.73944569 0.999422848 0.00083289438051421241 1 45 0 -1.94671428 0.0054360223 0.0078639160441893435 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.53363073 0.0164193157 0.023884691545080352 0 141 0 -2.06519866 0.00395226665 0.0057132130161921821 0 144 0 -1.91525483 0.005915621 0.0085597800780180429 0 -145 0 +145 0 ? ? ? 0 147 0 -1.84313476 0.007179688 0.010395463465996112 0 150 0 -1.97708547 0.005009752 0.0072457094989476683 0 151 1 1.45099306 0.9815549 0.026859095208834581 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -2.1974268 0.00276787742 0.0039987395951823247 0 156 0 -1.89696681 0.006213517 0.0089921755974289187 0 161 0 -1.42502642 0.0218984671 0.031943861389482818 0 -164 0 +164 0 ? ? ? 0 167 1 2.354064 0.998366237 0.0023589497379793915 1 169 0 -2.21602869 0.00263251015 0.0038029171463890205 0 171 0 -1.897539 0.006203974 0.008978322239894131 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 3.30174255 0.9998737 0.00018222712667875736 1 247 1 0.629675865 0.8525092 0.23021269060466767 1 248 0 -1.12171221 0.0483664 0.07152188601007381 0 -249 0 +249 0 ? ? ? 0 250 0 -2.04691076 0.00415170472 0.0060021119500057712 0 252 0 1.01602411 0.942604 4.12290612676055 1 254 1 2.17657 0.997363031 0.0038093670302838936 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.897539 0.006203974 0.008978322239894131 0 271 0 -1.368627 0.0254129283 0.037137010195002478 0 272 1 0.765766859 0.8930476 0.16319106749073262 1 -275 0 +275 0 ? ? ? 0 276 0 -1.78302681 0.008435549 0.012221545522817861 0 277 0 -2.047483 0.00414531538 0.0059928556832883097 0 278 0 -1.897539 0.006203974 0.008978322239894131 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.897539 0.006203974 0.008978322239894131 0 293 1 1.17371225 0.961760044 0.056251103565820429 1 296 0 0.4346769 0.773356 2.1415002561411738 1 -297 0 +297 0 ? ? ? 0 299 1 2.019999 0.9959794 0.0058121507954068162 1 300 1 2.2421217 0.9977903 0.0031914838554231535 1 301 0 -1.897539 0.006203974 0.008978322239894131 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.9578371 0.9334789 0.099310694790810672 1 317 1 2.6748023 0.999312758 0.00099182083151890563 1 319 0 0.702352047 0.87553966 3.0062420076587428 1 -321 0 +321 0 ? ? ? 0 323 1 1.138917 0.9581469 0.061681187824867326 1 327 0 -2.047483 0.00414531538 0.0059928556832883097 0 328 1 0.998517036 0.9399896 0.089283259387362562 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 5.011583 0.999998748 1.8058189642293044E-06 1 613 0 -1.848559 0.00707593327 0.010244702155254935 0 614 0 -1.9948014 0.0047766366 0.006907740990800825 0 -617 0 +617 0 ? ? ? 0 618 0 -1.6507988 0.0120159388 0.017440327322179375 0 619 0 -1.51857078 0.017089799 0.024868477463820319 0 621 0 -0.159297943 0.406589121 0.75289671799437707 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -2.01459932 0.02046752 0.029834763390847389 0 19 0 -1.722561 0.0706015 0.10563077935630806 0 22 0 -2.18885 0.009580599 0.013888519789490289 0 -23 1 +23 1 ? ? ? 0 24 0 -2.45265675 0.00300532184 0.0043422911736604322 0 26 0 -2.15108967 0.0113010341 0.016396771634848997 0 27 0 -1.8968116 0.0339725837 0.049863960916695586 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -2.18885 0.009580599 0.013888519789490289 0 135 0 -1.67432666 0.0859358 0.12963259925365803 0 136 0 -2.04283071 0.0181100331 0.026366733420336564 0 -139 0 +139 0 ? ? ? 0 140 0 -2.21708131 0.00846625 0.012266215273071623 0 142 1 -0.39607358 0.963921666 0.053012185419208942 0 143 0 -2.12454414 0.0126899984 0.018424952871546458 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.87877429 0.03668837 0.053925509586129468 0 155 1 -0.6914537 0.878659666 0.18662362559522522 0 157 0 -2.30663776 0.005714676 0.0082681821048535484 0 -158 0 +158 0 ? ? ? 0 159 1 0.826603651 0.9998317 0.00024285994311160547 1 160 1 0.465637445 0.999170542 0.0011971518470987805 1 162 0 -2.16061854 0.0108400183 0.015724220850716938 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 -0.0580048561 0.9916699 0.012068138193505744 0 232 0 -1.12658465 0.5141482 1.0414117089703063 0 234 0 -1.83982372 0.04328234 0.063834867527523209 0 -235 0 +235 0 ? ? ? 0 236 1 0.979896069 0.999914467 0.00012340282955575714 1 238 1 0.930060863 0.9998934 0.00015376068339360816 1 243 0 -2.01161838 0.02073334 0.030226326896041298 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 1.41079044 0.999987245 1.8402261006614678E-05 1 287 0 -2.18885 0.009580599 0.013888519789490289 0 289 1 0.0753440857 0.9953623 0.0067063762389963991 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 -0.367047548 0.9681271 0.046731673516829733 0 298 0 -1.44922507 0.202717274 0.32683668378040626 0 302 1 1.39901066 0.9999866 1.9348177961999343E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -2.45265675 0.00300532184 0.0043422911736604322 0 310 0 -2.33486915 0.00504769245 0.0073007223089671565 0 313 0 -2.50911975 0.002343144 0.0033844089083832525 0 -315 0 +315 0 ? ? ? 0 318 0 -2.45265675 0.00300532184 0.0043422911736604322 0 320 1 0.09479761 0.995742738 0.006155041546569217 1 322 0 -2.16061854 0.0108400183 0.015724220850716938 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.48088837 0.00265371148 0.0038335852528692412 0 408 0 -2.10281515 0.0139513118 0.020269210492385212 0 410 0 -2.48088837 0.00265371148 0.0038335852528692412 0 -411 0 +411 0 ? ? ? 0 412 1 -0.0120434761 0.9931908 0.0098571608082237746 0 417 0 -2.48088837 0.00265371148 0.0038335852528692412 0 420 0 -1.86625433 0.0386949927 0.056933846240527428 0 diff --git a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt index 1c905b24c8..b2ca41a554 100644 --- a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.51313376 0.997060657 0.0042468207167790329 1 21 1 1.88215756 0.9991858 0.0011751199613487565 1 22 0 -1.5913856 0.006754517 0.0097777676588594702 0 -23 1 +23 1 ? ? ? 0 24 0 -1.85515833 0.00270523666 0.0039081201011675363 0 25 1 0.183981657 0.767693 0.38139863321618261 1 26 0 -1.596403 0.006638234 0.0096088756803218642 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.48321867 0.244259775 0.40403768061841711 0 38 1 1.01125383 0.983338952 0.024239302762828055 1 39 1 -0.0106446743 0.62648654 0.67464458153125895 0 -40 0 +40 0 ? ? ? 0 41 1 0.8607874 0.9721748 0.040712322202366577 1 42 1 1.4849956 0.9967589 0.004683544378464086 1 43 1 -0.7279444 0.121085733 3.0458992041721875 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.404305 0.0128827207 0.018706593545987932 0 137 0 -1.701774 0.00460771844 0.0066628947272309403 0 138 0 -1.360993 0.0149499336 0.021731041715098105 0 -139 0 +139 0 ? ? ? 0 140 0 -1.701774 0.00460771844 0.0066628947272309403 0 141 0 -1.8888545 0.00240627048 0.0034756978965475691 0 142 1 0.881847143 0.974092543 0.037869253621396266 1 143 0 -1.545697 0.007910921 0.01145843020471164 0 144 0 -1.77846622 0.00353102176 0.0051032026604077695 0 -145 0 +145 0 ? ? ? 0 146 1 -0.216273546 0.450334132 1.1509322683430081 0 147 0 -1.82796979 0.00297325244 0.0042958860993499966 0 148 0 -0.7305672 0.12011648 0.18461554363807445 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.429970026 0.886195242 0.17430351324204957 1 156 0 -1.92223477 0.0021426254 0.0030944713637373776 0 157 0 -1.66807771 0.005178785 0.0074908208279084272 0 -158 0 +158 0 ? ? ? 0 159 1 2.79265332 0.999965847 4.9273870913609948E-05 1 160 1 2.08471417 0.999597847 0.00058030016510027748 1 161 0 -1.23200977 0.0232356917 0.033917610773834123 0 162 0 -1.48099709 0.009891603 0.014341615100451873 0 163 0 -1.20272636 0.02566766 0.037514142314967996 0 -164 0 +164 0 ? ? ? 0 165 0 -1.09945023 0.0363804549 0.053464438820388052 0 166 1 1.35837817 0.99497056 0.0072742561293771086 1 167 1 1.40636182 0.9957416 0.0061566823680882154 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.0236132145 0.6158536 1.3802718900263455 0 233 1 1.45269513 0.9963742 0.005240444497289571 1 234 0 -1.12313449 0.0335955 0.049300920379201259 0 -235 0 +235 0 ? ? ? 0 236 1 2.801736 0.9999669 4.7725975020197848E-05 1 237 1 1.18725443 0.9909072 0.013178153095375312 1 238 1 2.58363461 0.999929249 0.00010207531428030829 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.15890861 0.9996894 0.00044817040124598361 1 247 1 0.7686317 0.9620387 0.055833170607592193 1 248 0 -0.9843831 0.0533667132 0.079122441964183776 0 -249 0 +249 0 ? ? ? 0 250 0 -2.03262329 0.001459477 0.0021071182550953969 0 251 1 1.98737311 0.999435544 0.0008145677672951316 1 252 0 0.938287735 0.9786189 5.5475214513768956 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.364958525 0.861275733 0.21545291272160746 1 273 1 -0.0762293339 0.5716655 0.80675680440858633 0 274 0 -1.41909039 0.0122437514 0.017773027141160865 0 -275 0 +275 0 ? ? ? 0 276 0 -1.5913856 0.006754517 0.0097777676588594702 0 277 0 -1.96554685 0.00184303813 0.0026613952529717312 0 278 0 -1.85515833 0.00270523666 0.0039081201011675363 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.51208711 0.99705 0.0042622586236973218 1 290 0 -2.07593513 0.00125529012 0.0018121384446850468 0 291 0 -1.85515833 0.00270523666 0.0039081201011675363 0 -292 1 +292 1 ? ? ? 0 293 1 1.33046842 0.9944597 0.0080152036111407286 1 -294 0 +294 0 ? ? ? 0 295 1 1.2251215 0.9920222 0.011555664783429847 1 296 0 0.111403227 0.719591737 1.8343992308763424 1 -297 0 +297 0 ? ? ? 0 298 0 -0.7513188 0.112681925 0.17247673834356275 0 299 1 1.96303582 0.9993856 0.00088667099785278705 1 300 1 1.383044 0.995382845 0.0066765713113142704 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.17001605 0.7589018 0.39801492605707506 1 313 0 -2.07593513 0.00125529012 0.0018121384446850468 0 314 0 -1.97056413 0.00181115558 0.0026153142649758387 0 -315 0 +315 0 ? ? ? 0 316 1 0.541802645 0.9199835 0.12032009864475471 1 317 1 1.56700492 0.9975625 0.0035208219605770688 1 318 0 -1.84010613 0.00285049225 0.0041182633675105971 0 319 0 0.05099702 0.675234735 1.6225307564202673 1 320 1 1.7057507 0.9984955 0.0021721414278146082 1 -321 0 +321 0 ? ? ? 0 322 0 -1.48099709 0.009891603 0.014341615100451873 0 323 1 0.7110567 0.95399344 0.067948749601842412 1 324 0 -1.85515833 0.00270523666 0.0039081201011675363 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -1.45910537 0.0106672905 0.015472318523715401 0 409 0 -1.54807365 0.007846191 0.011364302031181555 0 410 0 -1.96554685 0.00184303813 0.0026613952529717312 0 -411 0 +411 0 ? ? ? 0 412 1 1.61123109 0.9979099 0.0030185274243607983 1 413 0 -1.063524 0.0410328656 0.060446722723610943 0 414 1 1.54281878 0.9973487 0.0038300596622238277 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.78348351 0.00347004039 0.0050149160869795298 0 615 0 -1.31768119 0.0173430257 0.025240206070453266 0 616 0 -1.5913856 0.006754517 0.0097777676588594702 0 -617 0 +617 0 ? ? ? 0 618 0 -1.404305 0.0128827207 0.018706593545987932 0 619 0 -1.21722436 0.024434112 0.035688781701501276 0 620 0 -1.5913856 0.006754517 0.0097777676588594702 0 diff --git a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-TrainTest-breast-cancer.txt index ff963fe7a2..9b8d818944 100644 --- a/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SDCA/BinarySDCA-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.37743354 0.7985785 0.32449386248842338 1 21 1 1.7630446 0.853590548 0.22838389385799779 1 22 0 -1.874893 0.132976577 0.20585712538187464 0 -23 1 +23 1 ? ? ? 0 24 0 -1.948468 0.124720506 0.19218432401279964 0 25 1 0.243255854 0.5605158 0.83517300013076445 1 26 0 -1.79869926 0.142009482 0.22096639052344227 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.30918431 0.212623373 0.34487420705553562 0 38 1 1.52097869 0.8206825 0.28510385981645775 1 39 1 0.568297148 0.638370156 0.64753488753370236 1 -40 0 +40 0 ? ? ? 0 41 1 0.08091617 0.520218 0.94281173541226504 1 42 1 2.4137857 0.9178725 0.12363434755323521 1 43 1 -0.08661461 0.478359878 1.0638317048217665 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.80893993 0.1407663 0.21887750444885776 0 137 0 -1.9332242 0.126394138 0.1949455575113978 0 138 0 -1.70428443 0.153906524 0.24111103479242962 0 -139 0 +139 0 ? ? ? 0 140 0 -1.9332242 0.126394138 0.1949455575113978 0 141 0 -1.99917722 0.119289339 0.18325996439997919 0 142 1 0.6176696 0.649688363 0.62218023037407078 1 143 0 -1.44733071 0.190412715 0.3047414631100317 0 144 0 -1.94084609 0.125554934 0.19356034047915699 0 -145 0 +145 0 ? ? ? 0 146 1 0.272925377 0.567810953 0.81651741701068026 1 147 0 -1.8198235 0.13945505 0.21667754281184065 0 148 0 -0.713562 0.328812242 0.57521169167023078 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.9670353 0.7245282 0.46488626460347809 1 156 0 -1.793424 0.142653435 0.22204959316054371 0 157 0 -1.882515 0.132100269 0.20439971838260004 0 -158 0 +158 0 ? ? ? 0 159 1 3.20693851 0.961094558 0.057249715926430703 1 160 1 2.3220613 0.910687745 0.13497162539860094 1 161 0 -1.65904641 0.159890041 0.25134992500211428 0 162 0 -1.81656182 0.139846936 0.21733468482291701 0 163 0 -1.60762358 0.166918814 0.26347099825296155 0 -164 0 +164 0 ? ? ? 0 165 0 -1.50459647 0.181740969 0.28937047546414252 0 166 1 2.457339 0.9210965 0.11857577878171396 1 167 1 1.31327176 0.7880601 0.34362238392184646 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.213132143 0.446917742 0.85443403273590568 0 233 1 1.23634028 0.774926364 0.36786886673349695 1 234 0 -1.00412917 0.268130362 0.4503413991221501 0 -235 0 +235 0 ? ? ? 0 236 1 2.65734458 0.934462249 0.097791712220334726 1 237 1 1.59322524 0.8310694 0.26695912082718543 1 238 1 3.30821967 0.9647097 0.051833223594515407 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 3.32132936 0.965153337 0.051169929428424521 1 247 1 0.572052956 0.639236748 0.64557774708968529 1 248 0 -1.29279661 0.215379834 0.34993367973529765 0 -249 0 +249 0 ? ? ? 0 250 0 -1.85175514 0.135666952 0.21034077001927648 0 251 1 1.686516 0.843765438 0.24508610192596253 1 252 0 0.9327009 0.717622936 1.8243051828654713 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.5505221 0.6342567 0.65686119381445462 1 273 1 -0.451063156 0.389108032 1.3617573340834921 0 274 0 -1.72499943 0.151228324 0.23655158068052121 0 -275 0 +275 0 ? ? ? 0 276 0 -1.874893 0.132976577 0.20585712538187464 0 277 0 -2.00679922 0.1184909 0.18195262731195211 0 278 0 -1.948468 0.124720506 0.19218432401279964 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 2.363632 0.914011657 0.12971552945936157 1 290 0 -2.06513023 0.112532459 0.17223374110934919 0 291 0 -1.948468 0.124720506 0.19218432401279964 0 -292 1 +292 1 ? ? ? 0 293 1 1.17569327 0.764172554 0.38802965202562639 1 -294 0 +294 0 ? ? ? 0 295 1 1.9321816 0.8734907 0.1951357665052601 1 296 0 0.2875533 0.571397066 1.222286369266439 1 -297 0 +297 0 ? ? ? 0 298 0 -1.39716244 0.198266774 0.31880583096618414 0 299 1 1.31489539 0.788331151 0.34312631094095036 1 300 1 1.6675427 0.8412479 0.24939706872810494 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.6733315 0.662248731 0.59455492152164024 1 313 0 -2.06513023 0.112532459 0.17223374110934919 0 314 0 -2.03932476 0.115135506 0.17647155325903272 0 -315 0 +315 0 ? ? ? 0 316 1 0.9730499 0.725726962 0.46250122508222125 1 317 1 2.53153682 0.9263233 0.11041230078378952 1 318 0 -1.85089087 0.135768339 0.21051001013383977 0 319 0 0.279238 0.5693594 1.2154438271457824 1 320 1 1.35926127 0.795639634 0.32981295026166818 1 -321 0 +321 0 ? ? ? 0 322 0 -1.81656182 0.139846936 0.21733468482291701 0 323 1 1.28926969 0.7840235 0.35103115421599534 1 324 0 -1.948468 0.124720506 0.19218432401279964 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -1.33084726 0.209019259 0.33828552617088203 0 409 0 -1.77023745 0.1455128 0.22686922143757757 0 410 0 -2.00679922 0.1184909 0.18195262731195211 0 -411 0 +411 0 ? ? ? 0 412 1 2.25751042 0.9052964 0.14353790094246915 1 413 0 -1.58000016 0.170795456 0.27020007178125394 0 414 1 1.60186315 0.8322786 0.2648615365498318 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.97337174 0.122027189 0.18775183095640005 0 615 0 -1.59962881 0.1680335 0.26540264924934215 0 616 0 -1.874893 0.132976577 0.20585712538187464 0 -617 0 +617 0 ? ? ? 0 618 0 -1.80893993 0.1407663 0.21887750444885776 0 619 0 -1.74298692 0.148933932 0.23265696296547442 0 620 0 -1.874893 0.132976577 0.20585712538187464 0 diff --git a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-CV-breast-cancer.txt index 8b3d215017..bee6e20c10 100644 --- a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 0.344691515 0.5853297 0.77267858291118818 1 35 0 -0.6512495 0.342708021 0.60539371596619707 0 37 0 -0.503962 0.376610041 0.68179317658398975 0 -40 0 +40 0 ? ? ? 0 41 1 -0.09442055 0.4764124 1.069717174215209 0 44 1 0.5798913 0.6410424 0.64150828626867074 1 45 0 -0.6560674 0.341623574 0.60301541680763648 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -0.597589 0.354895473 0.63239515287919279 0 141 0 -0.6678773 0.3389723 0.5972173671855312 0 144 0 -0.6512495 0.342708021 0.60539371596619707 0 -145 0 +145 0 ? ? ? 0 147 0 -0.6018609 0.353918076 0.63021098184538349 0 150 0 -0.656989932 0.3414161 0.60256083089734203 0 151 1 0.160812974 0.540116847 0.88865654739065503 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -0.67789495 0.336731285 0.59233461600767046 0 156 0 -0.5912131 0.356356561 0.63566639957530646 0 161 0 -0.5840918 0.3579916 0.63933593475596628 0 -164 0 +164 0 ? ? ? 0 167 1 0.315922141 0.5783301 0.79003490476906479 1 169 0 -0.6661818 0.33935234 0.59804704249396301 0 171 0 -0.644639254 0.344198585 0.60866907932442826 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 0.9649427 0.7241103 0.46571861194179465 1 247 1 0.07307994 0.51826185 0.94824689644610127 1 248 0 -0.4621131 0.386484653 0.70482866116462817 0 -249 0 +249 0 ? ? ? 0 250 0 -0.607840955 0.352551848 0.62716342858897578 0 252 0 0.210040927 0.552318037 1.1594538998856256 1 254 1 0.5551709 0.635334432 0.65441188637353787 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -0.644639254 0.344198585 0.60866907932442826 0 271 0 -0.6045689 0.3532991 0.62882950155653372 0 272 1 0.109180689 0.5272681 0.92339134731632821 1 -275 0 +275 0 ? ? ? 0 276 0 -0.6412319 0.3449681 0.61036295013192154 0 277 0 -0.6612671 0.340455025 0.60045705425766605 0 278 0 -0.644639254 0.344198585 0.60866907932442826 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -0.644639254 0.344198585 0.60866907932442826 0 293 1 0.2438618 0.560665131 0.83478874766701627 1 296 0 0.0298114419 0.5074523 1.0216646747888918 1 -297 0 +297 0 ? ? ? 0 299 1 0.29232645 0.5725656 0.80448706147450022 1 300 1 0.440484524 0.6083745 0.71696846696669991 1 301 0 -0.644639254 0.344198585 0.60866907932442826 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.23788023 0.5591912 0.83858636815416177 1 317 1 0.736536 0.676237941 0.56439713300164063 1 319 0 0.032892406 0.508222342 1.0239219003304794 1 -321 0 +321 0 ? ? ? 0 323 1 0.3295461 0.581648946 0.78177941761076775 1 327 0 -0.6612671 0.340455025 0.60045705425766605 0 328 1 0.309677362 0.5768065 0.79384070845234189 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 1.18309152 0.7655032 0.38551963408922602 1 613 0 -0.578392267 0.3593026 0.64228498181768678 0 614 0 -0.663600147 0.339931339 0.59931199209014285 0 -617 0 +617 0 ? ? ? 0 618 0 -0.6312144 0.347235233 0.61536490408676614 0 619 0 -0.621196747 0.349509329 0.62039972730149728 0 621 0 -0.2970261 0.426284641 0.80159295389429652 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -0.5587295 0.363841474 0.65254177576954708 0 19 0 -0.521061659 0.372604 0.67255179238253271 0 22 0 -0.597002566 0.355029762 0.63269550517202178 0 -23 1 +23 1 ? ? ? 0 24 0 -0.6152313 0.3508668 0.62341353797116505 0 26 0 -0.568058968 0.361684829 0.64765915777643768 0 27 0 -0.559334755 0.3637014 0.6522241546694586 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -0.586384356 0.35746488 0.6381527823333405 0 135 0 -0.4233432 0.395717025 0.72670379813371644 0 136 0 -0.578168631 0.3593541 0.6424009484072789 0 -139 0 +139 0 ? ? ? 0 140 0 -0.6164417 0.350591153 0.62280105585760936 0 142 1 0.253997982 0.5631603 0.82838245949919198 1 143 0 -0.455442041 0.388067663 0.70855595514156899 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -0.498274028 0.377946377 0.68488914380895782 0 155 1 0.381782949 0.5943031 0.75072925773026189 1 157 0 -0.5963973 0.355168372 0.63300558819075581 0 -158 0 +158 0 ? ? ? 0 159 1 1.14907432 0.7593418 0.3971787124185161 1 160 1 0.835970342 0.697615862 0.51949525142708575 1 162 0 -0.5775634 0.359493434 0.64271473576785443 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 0.867232 0.7041694 0.50600557448041072 1 232 0 -0.0153304338 0.496167481 0.98898385339386186 0 234 0 -0.28234 0.4298802 0.81066299300396294 0 -235 0 +235 0 ? ? ? 0 236 1 0.9360302 0.718297064 0.47734747567952907 1 238 1 1.21385264 0.770979941 0.37523476983545384 1 243 0 -0.3776855 0.406685263 0.7531304771823496 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 1.42419529 0.8059953 0.31115669483110281 1 287 0 -0.586384356 0.35746488 0.6381527823333405 0 289 1 0.8870376 0.7082785 0.49761139182067243 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 0.7293541 0.674663544 0.56775988879752004 1 298 0 -0.4508557 0.389157325 0.711127239010238 0 302 1 1.38025045 0.7990312 0.3236762608744207 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -0.6152313 0.3508668 0.62341353797116505 0 310 0 -0.6052183 0.353150755 0.62849857907527806 0 313 0 -0.654109538 0.342064053 0.60398095649539585 0 -315 0 +315 0 ? ? ? 0 318 0 -0.5833766 0.358156025 0.63970545866432549 0 320 1 0.483023465 0.618461549 0.69324419149565364 1 322 0 -0.5775634 0.359493434 0.64271473576785443 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -0.6346704 0.3464523 0.61363554879852178 0 408 0 -0.398791075 0.401602834 0.74082475334425091 0 410 0 -0.6346704 0.3464523 0.61363554879852178 0 -411 0 +411 0 ? ? ? 0 412 1 0.851157367 0.7008099 0.51290494780474771 1 417 0 -0.6346704 0.3464523 0.61363554879852178 0 420 0 -0.3393041 0.415978521 0.77590666598714197 0 diff --git a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-CV-breast-cancer.txt index a2364522f6..ca9f631c71 100644 --- a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 0.6522908 0.9885379 0.01663180464505477 1 35 0 -1.12420082 0.0107460516 0.015587176400536187 0 37 0 -0.865502357 0.0386134759 0.056811513682664551 0 -40 0 +40 0 ? ? ? 0 41 1 -0.1372056 0.6145622 0.70236902985400396 0 44 1 1.0547893 0.9984863 0.0021854902279334833 1 45 0 -1.13070083 0.0104023358 0.015085999817509936 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.03150129 0.0170594156 0.024823881934527593 0 141 0 -1.15380061 0.00926659 0.0134311903289673 0 144 0 -1.12420082 0.0107460516 0.015587176400536187 0 -145 0 +145 0 ? ? ? 0 147 0 -1.03270125 0.0169580057 0.024675046951938132 0 150 0 -1.13440084 0.0102115627 0.0148079061901775 0 151 1 0.335192084 0.945543051 0.080784949020458824 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -1.17080045 0.008510069 0.012329973523626827 0 156 0 -1.01700115 0.0183329377 0.026694285275120175 0 161 0 -1.00710154 0.0192555338 0.028050804915513337 0 -164 0 +164 0 ? ? ? 0 167 1 0.6017914 0.98525393 0.021432495728287648 1 169 0 -1.15050054 0.009420992 0.013656046492446974 0 171 0 -1.11160088 0.01144463 0.016606319988959024 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 1.7594862 0.999956965 6.208707291578748E-05 1 247 1 0.172593474 0.8841638 0.17761443218515088 1 248 0 -0.793102443 0.0547425 0.081220704071742325 0 -249 0 +249 0 ? ? ? 0 250 0 -1.04660094 0.0158256628 0.0230141968119793 0 252 0 0.423291922 0.964417338 4.8126817596940885 1 254 1 1.03148985 0.998297453 0.0024583495418655562 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.11160088 0.01144463 0.016606319988959024 0 271 0 -1.04360127 0.01606356 0.023362970756245272 0 272 1 0.243792772 0.9162424 0.12619873583154692 1 -275 0 +275 0 ? ? ? 0 276 0 -1.10720086 0.0116990069 0.016977604564195337 0 277 0 -1.14120078 0.009869945 0.014310057532988397 0 278 0 -1.11160088 0.01144463 0.016606319988959024 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.11160088 0.01144463 0.016606319988959024 0 293 1 0.478191853 0.97280544 0.039776797794310127 1 296 0 0.0886935 0.83318603 2.5836879844847194 1 -297 0 +297 0 ? ? ? 0 299 1 0.5653906 0.9823277 0.025723714124946184 1 300 1 0.8071904 0.994728446 0.0076253612609159995 1 301 0 -1.11160088 0.01144463 0.016606319988959024 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.472591877 0.9720465 0.040902773075660377 1 317 1 1.35288787 0.9996641 0.00048464251232972397 1 319 0 0.100693226 0.841446757 2.6569607115771978 1 -321 0 +321 0 ? ? ? 0 323 1 0.6351912 0.987516046 0.018123905012295945 1 327 0 -1.14120078 0.009869945 0.014310057532988397 0 328 1 0.5871916 0.9841424 0.023060980928491851 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 2.158784 0.9999943 8.2551908597278378E-06 1 613 0 -0.993201256 0.0206282046 0.030071445887912274 0 614 0 -1.14700067 0.009587526 0.013898610301947204 0 -617 0 +617 0 ? ? ? 0 618 0 -1.090201 0.0127353743 0.018491259389232038 0 619 0 -1.07320118 0.0138622615 0.020138926273650697 0 621 0 -0.4947039 0.207426473 0.33538331559891793 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.02980137 0.0147205992 0.021395199721883466 0 19 0 -0.9630018 0.0192868058 0.028096807322037363 0 22 0 -1.0968008 0.011213962 0.016269722836470393 0 -23 1 +23 1 ? ? ? 0 24 0 -1.13000059 0.00979631 0.014202769680513335 0 26 0 -1.04700089 0.0137287723 0.019943648017493942 0 27 0 -1.03000128 0.0147086745 0.021377739156459911 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.079601 0.0120263351 0.017455508586451673 0 135 0 -0.7955023 0.03769685 0.055436642942084668 0 136 0 -1.0634011 0.0128445318 0.018650780679196597 0 -139 0 +139 0 ? ? ? 0 140 0 -1.13040054 0.009780362 0.014179534189705549 0 142 1 0.3573923 0.8180728 0.2896988683892544 1 143 0 -0.8376017 0.03189309 0.046761717990888088 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -0.917201638 0.0231931154 0.033854726268903304 0 155 1 0.5948919 0.922758937 0.1159742899072022 1 157 0 -1.09660089 0.0112230852 0.016283034217764617 0 -158 0 +158 0 ? ? ? 0 159 1 1.88818586 0.9995909 0.00059036523296398255 1 160 1 1.36718786 0.996521652 0.0050269435856363917 1 162 0 -1.06320107 0.01285497 0.018666035936317264 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1.39838827 0.9969394 0.0044222533640880836 1 232 0 -0.118705273 0.38808772 0.70860324232262772 0 234 0 -0.57100296 0.08979461 0.13573597071280369 0 -235 0 +235 0 ? ? ? 0 236 1 1.52918732 0.9982108 0.0025835996006501579 1 238 1 1.96808636 0.9997055 0.00042494571667690465 1 243 0 -0.703202665 0.0541654155 0.080340199841784268 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 2.32438421 0.999932 9.8119438852589428E-05 1 287 0 -1.079601 0.0120263351 0.017455508586451673 0 289 1 1.42798841 0.99728936 0.0039159372565436843 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 1.171589 0.9922556 0.011216254621805593 1 298 0 -0.8448025 0.0309909787 0.045417997957015462 0 302 1 2.26218414 0.999912143 0.00012675678201834402 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.13000059 0.00979631 0.014202769680513335 0 310 0 -1.11300075 0.0104985284 0.015226241971828952 0 313 0 -1.19720018 0.007447739 0.010785028389452023 0 -315 0 +315 0 ? ? ? 0 318 0 -1.07840109 0.0120851332 0.017541371482030246 0 320 1 0.77709043 0.961948335 0.055968683838555341 1 322 0 -1.06320107 0.01285497 0.018666035936317264 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -1.16360033 0.008542378 0.012376987044267063 0 408 0 -0.7606021 0.0432654768 0.063809436873055653 0 410 0 -1.16360033 0.008542378 0.012376987044267063 0 -411 0 +411 0 ? ? ? 0 412 1 1.35098827 0.9962829 0.0053726686646499901 1 417 0 -1.16360033 0.008542378 0.012376987044267063 0 420 0 -0.6557027 0.0650944263 0.097107435980156739 0 diff --git a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt index 9dec61f554..8153302a29 100644 --- a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.222367 0.9828292 0.024987354577091989 1 21 1 1.49796534 0.9935609 0.0093196803205346252 1 22 0 -1.19740391 0.009379565 0.013595712679399167 0 -23 1 +23 1 ? ? ? 0 24 0 -1.23810339 0.008112156 0.01175109505221196 0 25 1 0.3802781 0.7344174 0.4453278964846259 1 26 0 -1.14100432 0.01146578 0.016637187062001499 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.79460907 0.0387752019 0.057054226638771945 0 38 1 1.34886718 0.989039242 0.015900330726329494 1 39 1 0.6641748 0.8848001 0.17657658460400094 1 -40 0 +40 0 ? ? ? 0 41 1 0.223279715 0.611168444 0.7103580388075188 1 42 1 2.04505968 0.999095738 0.0013051645925104319 1 43 1 0.169680834 0.56447953 0.82500683046719547 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.15510464 0.0109047387 0.015818618982627604 0 137 0 -1.24130344 0.008020028 0.011617101215948859 0 138 0 -1.0749054 0.0144998142 0.021071951694510416 0 -139 0 +139 0 ? ? ? 0 140 0 -1.24130344 0.008020028 0.011617101215948859 0 141 0 -1.28360271 0.00689550675 0.0099825705316781733 0 142 1 0.643275 0.8769101 0.18949916409473391 1 143 0 -0.8591082 0.0309929028 0.04542086264678518 0 144 0 -1.23970342 0.008065961 0.011683906942824285 0 -145 0 +145 0 ? ? ? 0 146 1 0.4436772 0.7764816 0.36497630408526505 1 147 0 -1.13820457 0.01158053 0.016804665017851262 0 148 0 -0.320714116 0.181648508 0.2892074628941344 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.9467714 0.9550218 0.066394431489110783 1 156 0 -1.11830425 0.0124295028 0.018044356997620709 0 157 0 -1.19580388 0.009433212 0.013673843835136703 0 -158 0 +158 0 ? ? ? 0 159 1 2.5887537 0.9998721 0.0001845491876153846 1 160 1 1.92586017 0.9986121 0.0020036987954509607 1 161 0 -1.04420578 0.01616599 0.023513167231551696 0 162 0 -1.15350461 0.0109670116 0.015909453114236246 0 163 0 -1.00530624 0.0185497329 0.027012930786301039 0 -164 0 +164 0 ? ? ? 0 165 0 -0.9275073 0.0243961066 0.035632579294711073 0 166 1 2.06175947 0.9991484 0.0012290814661556479 1 167 1 1.15397048 0.9781426 0.031883260676163518 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.0278829336 0.437610149 0.83035753638984611 1 233 1 1.11616874 0.975037336 0.036470630963644925 1 234 0 -0.544210553 0.09034561 0.13660958234622511 0 -235 0 +235 0 ? ? ? 0 236 1 2.15735817 0.999396145 0.00087144125636392123 1 237 1 1.37376881 0.9899692 0.014544463077068375 1 238 1 2.659554 0.9999008 0.00014309666195095306 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.70435286 0.9999156 0.00012176885553963592 1 247 1 0.616675138 0.8662006 0.207226880023576 1 248 0 -0.7762085 0.04131975 0.060878383940094143 0 -249 0 +249 0 ? ? ? 0 250 0 -1.16220379 0.0106326314 0.015421777653289784 0 251 1 1.43526506 0.9919444 0.011668877043853167 1 252 0 0.8921714 0.9457866 4.2052065773298537 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.6439743 0.8771814 0.18905285611569214 1 273 1 -0.149016142 0.29164663 1.7777066932129042 0 274 0 -1.08650517 0.0139153069 0.020216532376881539 0 -275 0 +275 0 ? ? ? 0 276 0 -1.19740391 0.009379565 0.013595712679399167 0 277 0 -1.28200269 0.00693504466 0.010040008870103924 0 278 0 -1.23810339 0.008112156 0.01175109505221196 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.98086047 0.998861 0.0016441447437435472 1 290 0 -1.32590222 0.005927712 0.0085773281383116576 0 291 0 -1.23810339 0.008112156 0.01175109505221196 0 -292 1 +292 1 ? ? ? 0 293 1 1.05756974 0.9693573 0.044899543967322132 1 -294 0 +294 0 ? ? ? 0 295 1 1.67846322 0.9966264 0.0048753374261991068 1 296 0 0.418977976 0.760678768 2.062979700875831 1 -297 0 +297 0 ? ? ? 0 298 0 -0.865008354 0.0303616133 0.044481280497859745 0 299 1 1.15566754 0.9782728 0.031691271742118471 1 300 1 1.43996739 0.9920784 0.011473925159666753 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.7111747 0.9009488 0.15048293737780147 1 313 0 -1.32590222 0.005927712 0.0085773281383116576 0 314 0 -1.3091023 0.006294775 0.0091101437491470354 0 -315 0 +315 0 ? ? ? 0 316 1 0.9520712 0.955833852 0.065168231187009498 1 317 1 2.1167593 0.999301255 0.0010084286664473847 1 318 0 -1.15680456 0.0108389612 0.015722679136914018 0 319 0 0.43477726 0.7708744 2.1257893169799549 1 320 1 1.20646763 0.9818366 0.026445122755562926 1 -321 0 +321 0 ? ? ? 0 322 0 -1.15350461 0.0109670116 0.015909453114236246 0 323 1 1.1870687 0.980549 0.028338400890888689 1 324 0 -1.23810339 0.008112156 0.01175109505221196 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -0.775208354 0.0414625444 0.061093287384022553 0 409 0 -1.11720467 0.0124781644 0.018115446186086449 0 410 0 -1.28200269 0.00693504466 0.010040008870103924 0 -411 0 +411 0 ? ? ? 0 412 1 1.89956212 0.9984746 0.0022023701778286324 1 413 0 -0.9887064 0.01966903 0.028659194266170487 0 414 1 1.3937664 0.9906591 0.013539378350893604 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.266803 0.00732205063 0.010602349145536318 0 615 0 -0.994706035 0.0192570463 0.028053029788189736 0 616 0 -1.19740391 0.009379565 0.013595712679399167 0 -617 0 +617 0 ? ? ? 0 618 0 -1.15510464 0.0109047387 0.015818618982627604 0 619 0 -1.11280513 0.0126747517 0.018402674010350579 0 620 0 -1.19740391 0.009379565 0.013595712679399167 0 diff --git a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-TrainTest-breast-cancer.txt index 29a93c0326..da39a0132a 100644 --- a/test/BaselineOutput/SingleDebug/SGD/BinarySGD-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SGD/BinarySGD-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 0.822916269 0.694855034 0.52521607106152413 1 21 1 1.02655149 0.736246765 0.441738705665011 1 22 0 -1.01600647 0.265806019 0.44576680836978433 0 -23 1 +23 1 ? ? ? 0 24 0 -1.03864312 0.261411875 0.43715802871945769 0 25 1 0.184996247 0.5461176 0.87271643290195555 1 26 0 -0.9708479 0.274711519 0.46337315957197994 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.7265403 0.325954378 0.56908185228017971 0 38 1 0.933466434 0.717778 0.47839033548395937 1 39 1 0.4073757 0.600458443 0.7358636922780708 1 -40 0 +40 0 ? ? ? 0 41 1 0.04465425 0.5111617 0.96814839425710286 1 42 1 1.43612671 0.8078541 0.30783330336923359 1 43 1 0.0479370356 0.511981964 0.96583510643055481 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -0.9885073 0.271207035 0.45641906112335939 0 137 0 -1.04836845 0.259538531 0.4335034307664255 0 138 0 -0.9260764 0.283721417 0.48140728969854896 0 -139 0 +139 0 ? ? ? 0 140 0 -1.04836845 0.259538531 0.4335034307664255 0 141 0 -1.07586777 0.2542888 0.42331107075059288 0 142 1 0.373193622 0.592230439 0.75576945149404562 1 143 0 -0.7654944 0.317454576 0.55100303407297502 0 144 0 -1.04350591 0.2604741 0.43532739229387868 0 -145 0 +145 0 ? ? ? 0 146 1 0.2147963 0.553493559 0.85336156547352904 1 147 0 -0.9662212 0.275634348 0.46520995753133637 0 148 0 -0.318193555 0.421116054 0.78865394770237895 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.600466 0.6457629 0.63092349091318589 1 156 0 -0.9430999 0.2802746 0.47448152072334643 0 157 0 -1.01114392 0.266756058 0.44763484776839352 0 -158 0 +158 0 ? ? ? 0 159 1 1.87013483 0.866473854 0.20677187910596315 1 160 1 1.34225249 0.7928601 0.33486178660539723 1 161 0 -0.901281357 0.288787246 0.49164689849299364 0 162 0 -0.9836446 0.2721692 0.45832499621527728 0 163 0 -0.855002642 0.2983845 0.51124744948014056 0 -164 0 +164 0 ? ? ? 0 165 0 -0.8168132 0.306440562 0.5279085681868505 0 166 1 1.49839211 0.817334533 0.29100140459599977 1 167 1 0.8153703 0.6932527 0.5285468008024331 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.08038044 0.4799157 0.9431826305616432 0 233 1 0.6980891 0.667763948 0.58258988829378422 1 234 0 -0.49562943 0.378568321 0.68633230772699549 0 -235 0 +235 0 ? ? ? 0 236 1 1.52862382 0.8218049 0.28313219549408758 1 237 1 0.9843018 0.727960944 0.45806704440223556 1 238 1 1.96605682 0.877186954 0.18904373922390869 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 1.97140193 0.8777616 0.18809893386755816 1 247 1 0.323040724 0.580065131 0.78571319608569612 1 248 0 -0.6818254 0.335854024 0.59042772056631165 0 -249 0 +249 0 ? ? ? 0 250 0 -0.9754619 0.273793161 0.46154757804170599 0 251 1 0.9712138 0.725361347 0.46322822535916075 1 252 0 0.5616691 0.6368387 1.4613175226151991 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.354397058 0.587683439 0.76688885070680657 1 273 1 -0.237432361 0.4409192 1.1814138244518058 0 274 0 -0.9287806 0.28317216 0.48030142611403009 0 -275 0 +275 0 ? ? ? 0 276 0 -1.01600647 0.265806019 0.44576680836978433 0 277 0 -1.07100511 0.255211979 0.42509822607281988 0 278 0 -1.03864312 0.261411875 0.43715802871945769 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.4364171 0.8078992 0.30775283385408775 1 290 0 -1.10336709 0.249109536 0.4133256254437066 0 291 0 -1.03864312 0.261411875 0.43715802871945769 0 -292 1 +292 1 ? ? ? 0 293 1 0.6671076 0.6608552 0.59759395409472371 1 -294 0 +294 0 ? ? ? 0 295 1 1.17509961 0.764065564 0.38823165513968877 1 296 0 0.224430084 0.5558732 1.1709565135958244 1 -297 0 +297 0 ? ? ? 0 298 0 -0.7844506 0.313361466 0.54237727145415437 0 299 1 0.748945951 0.678949 0.55862488926125509 1 300 1 1.01898217 0.7347743 0.44462694496525584 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.4770298 0.617046237 0.69654949630747753 1 313 0 -1.10336709 0.249109536 0.4133256254437066 0 314 0 -1.09132075 0.251369655 0.41767456748452503 0 -315 0 +315 0 ? ? ? 0 316 1 0.5936177 0.6441948 0.63443112049115613 1 317 1 1.53676748 0.8229943 0.28104567061447505 1 318 0 -0.977696 0.2733492 0.46066585861057346 0 319 0 0.228058934 0.5567689 1.1738689621124481 1 320 1 0.7848048 0.6867147 0.54221722972478248 1 -321 0 +321 0 ? ? ? 0 322 0 -0.9836446 0.2721692 0.45832499621527728 0 323 1 0.799038649 0.6897688 0.53581523979313106 1 324 0 -1.03864312 0.261411875 0.43715802871945769 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -0.6859037 0.334944934 0.58845429463916576 0 409 0 -0.9535757 0.2781663 0.47026158367983178 0 410 0 -1.07100511 0.255211979 0.42509822607281988 0 -411 0 +411 0 ? ? ? 0 412 1 1.353524 0.794705153 0.331508396641091 1 413 0 -0.8662151 0.296042472 0.50643970604072797 0 414 1 0.9272311 0.7165132 0.48093477882048502 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.06382155 0.256579816 0.42775023737254403 0 615 0 -0.863645554 0.296578258 0.50753816766843873 0 616 0 -1.01600647 0.265806019 0.44576680836978433 0 -617 0 +617 0 ? ? ? 0 618 0 -0.9885073 0.271207035 0.45641906112335939 0 619 0 -0.961007953 0.276676446 0.46728696355201749 0 620 0 -1.01600647 0.265806019 0.44576680836978433 0 diff --git a/test/BaselineOutput/SingleDebug/SavePipe/TestParquetPrimitiveDataTypes-Data.txt b/test/BaselineOutput/SingleDebug/SavePipe/TestParquetPrimitiveDataTypes-Data.txt index af1e19e1cc..85a3d35b4b 100644 --- a/test/BaselineOutput/SingleDebug/SavePipe/TestParquetPrimitiveDataTypes-Data.txt +++ b/test/BaselineOutput/SingleDebug/SavePipe/TestParquetPrimitiveDataTypes-Data.txt @@ -11,5 +11,5 @@ #@ col=string:TX:7 #@ } sbyte short int long bool DateTimeOffset Interval string - 1 "2018-09-01T19:53:18.2910000+00:00" "31.00:00:00.0010000" "" +-128 -32768 -2147483648 -9223372036854775808 1 "2018-09-01T19:53:18.2910000+00:00" "31.00:00:00.0010000" "" 127 32767 2147483647 9223372036854775807 0 "2018-09-01T19:53:18.3110000+00:00" "31.00:00:00.0010000" """""" diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer.txt index d0e7499c6d..cc1083e607 100644 --- a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 454.251282 1 0 1 35 0 -320.737427 0 0 0 37 0 -78.9787 5.011722E-35 0 0 -40 0 +40 0 ? ? ? 0 41 1 199.0091 1 0 1 44 1 656.8247 1 0 1 45 0 -322.804565 0 0 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -289.415222 0 0 0 141 0 -344.61084 0 0 0 144 0 -320.737427 0 0 0 -145 0 +145 0 ? ? ? 0 147 0 -309.023651 0 0 0 150 0 -272.79837 0 0 0 151 1 249.55658 1 0 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -349.126221 0 0 0 156 0 -227.212433 0 0 0 161 0 -274.6302 0 0 0 -164 0 +164 0 ? ? ? 0 167 1 283.316284 1 0 1 169 0 -331.047241 0 0 0 171 0 -301.379425 0 0 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 1044.54126 1 0 1 247 1 409.237671 1 0 1 248 0 -221.818024 0 0 0 -249 0 +249 0 ? ? ? 0 250 0 -251.085815 0 0 0 252 0 307.68988 1 Infinity 1 254 1 728.536743 1 0 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -301.379425 0 0 0 271 0 -283.3178 0 0 0 272 1 408.017578 1 0 1 -275 0 +275 0 ? ? ? 0 276 0 -316.222015 0 0 0 277 0 -325.252838 0 0 0 278 0 -301.379425 0 0 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -301.379425 0 0 0 293 1 386.6949 1 0 1 296 0 139.220642 1 Infinity 1 -297 0 +297 0 ? ? ? 0 299 1 227.814941 1 0 1 300 1 407.6792 1 0 1 301 0 -301.379425 0 0 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 466.170166 1 0 1 317 1 736.0132 1 0 1 319 0 161.598083 1 Infinity 1 -321 0 +321 0 ? ? ? 0 323 1 388.03302 1 0 1 327 0 -325.252838 0 0 0 328 1 584.984 1 0 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 1115.01685 1 0 1 613 0 -169.23941 0 0 0 614 0 -292.156342 0 0 0 -617 0 +617 0 ? ? ? 0 618 0 -311.7066 0 0 0 619 0 -307.1912 0 0 0 621 0 -15.8763733 1.27344038E-07 1.8371862313930792E-07 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -643.057739 0 0 0 19 0 -668.631836 0 0 0 22 0 -540.900146 0 0 0 -23 1 +23 1 ? ? ? 0 24 0 -604.696655 0 0 0 26 0 -270.657074 0 0 0 27 0 -566.4742 0 0 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -670.7141 0 0 0 135 0 -421.652374 0 0 0 136 0 -553.6872 0 0 0 -139 0 +139 0 ? ? ? 0 140 0 -451.529541 0 0 0 142 1 488.315338 1 0 1 143 0 -142.331116 0 0 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -322.504425 0 0 0 155 1 1089.60254 1 0 1 157 0 -617.483643 0 0 0 -158 0 +158 0 ? ? ? 0 159 1 1923.50525 1 0 1 160 1 1494.5094 1 0 1 162 0 -630.2707 0 0 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1347.16248 1 0 1 232 0 355.92923 1 Infinity 1 234 0 50.92752 1 Infinity 1 -235 0 +235 0 ? ? ? 0 236 1 1204.77161 1 0 1 238 1 2421.62354 1 0 1 243 0 -499.813416 0 0 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 1933.38391 1 0 1 287 0 -670.7141 0 0 0 289 1 1586.01746 1 0 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 906.5393 1 0 1 298 0 -764.4775 0 0 0 302 1 1682.504 1 0 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -604.696655 0 0 0 310 0 -657.927 0 0 0 313 0 -425.9555 0 0 0 -315 0 +315 0 ? ? ? 0 318 0 -994.1385 0 0 0 320 1 276.6519 1 0 1 322 0 -630.2707 0 0 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -515.32605 0 0 0 408 0 -106.253662 0 0 0 410 0 -515.32605 0 0 0 -411 0 +411 0 ? ? ? 0 412 1 1142.7179 1 0 1 417 0 -515.32605 0 0 0 420 0 -151.036346 0 0 0 diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer.txt index 4a57fddad1..f3130d53ec 100644 --- a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 10.1497192 0.9999609 5.6411412351548271E-05 1 21 1 -61.521698 1.91190378E-27 88.757048641689195 0 22 0 -407.94165 0 0 0 -23 1 +23 1 ? ? ? 0 24 0 -413.829132 0 0 0 25 1 -111.690765 0 Infinity 0 26 0 -333.49762 0 0 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -367.9438 0 0 0 38 1 125.049805 1 0 1 39 1 -120.420319 0 Infinity 0 -40 0 +40 0 ? ? ? 0 41 1 -214.114883 0 Infinity 0 42 1 86.67383 1 0 1 43 1 -299.954132 0 Infinity 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -408.326935 0 0 0 137 0 -401.2836 0 0 0 138 0 -411.7511 0 0 0 -139 0 +139 0 ? ? ? 0 140 0 -401.2836 0 0 0 141 0 -400.898315 0 0 0 142 1 -108.134125 0 Infinity 0 143 0 -305.7808 0 0 0 144 0 -407.556366 0 0 0 -145 0 +145 0 ? ? ? 0 146 1 -205.1228 0 Infinity 0 147 0 -408.638123 0 0 0 148 0 -448.917847 0 0 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 39.9500122 1 0 1 156 0 -361.30127 0 0 0 157 0 -414.214417 0 0 0 -158 0 +158 0 ? ? ? 0 159 1 214.183228 1 0 1 160 1 120.047485 1 0 1 161 0 -401.219147 0 0 0 162 0 -414.5997 0 0 0 163 0 -282.1694 0 0 0 -164 0 +164 0 ? ? ? 0 165 0 -377.536072 0 0 0 166 1 123.761658 1 0 1 167 1 -9.15014648 0.000106192965 13.201024182527696 0 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -256.504028 0 0 0 233 1 -84.9973755 1.21929513E-37 122.62529213957316 0 234 0 -275.7568 0 0 0 -235 0 +235 0 ? ? ? 0 236 1 116.098267 1 0 1 237 1 26.0986938 1 0 1 238 1 370.027954 1 0 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 321.109131 1 0 1 247 1 -61.9207153 1.28284749E-27 89.332708892981643 0 248 0 -346.1557 0 0 0 -249 0 +249 0 ? ? ? 0 250 0 -354.643219 0 0 0 251 1 108.2807 1 0 1 252 0 -4.193939 0.0148625113 0.021603009492489122 0 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 -151.574524 0 Infinity 0 273 1 -303.688629 0 Infinity 0 274 0 -400.833862 0 0 0 -275 0 +275 0 ? ? ? 0 276 0 -407.94165 0 0 0 277 0 -407.171082 0 0 0 278 0 -413.829132 0 0 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 175.671082 1 0 1 290 0 -400.513 0 0 0 291 0 -413.829132 0 0 0 -292 1 +292 1 ? ? ? 0 293 1 51.4936523 1 0 1 -294 0 +294 0 ? ? ? 0 295 1 5.8543396 0.997140765 0.0041309123233919023 1 296 0 -173.148254 0 0 0 -297 0 +297 0 ? ? ? 0 298 0 -429.3664 0 0 0 299 1 -112.8385 0 Infinity 0 300 1 -114.377228 0 Infinity 0 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 -175.547363 0 Infinity 0 313 0 -400.513 0 0 0 314 0 -382.020966 0 0 0 -315 0 +315 0 ? ? ? 0 316 1 -56.5515747 2.753995E-25 81.586676422594735 0 317 1 168.155884 1 0 1 318 0 -489.2794 0 0 0 319 0 -232.038055 0 0 0 320 1 16.8273315 0.99999994 8.5991327994145617E-08 1 -321 0 +321 0 ? ? ? 0 322 0 -414.5997 0 0 0 323 1 72.79901 1 0 1 324 0 -413.829132 0 0 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -278.598877 0 0 0 409 0 -411.365784 0 0 0 410 0 -407.171082 0 0 0 -411 0 +411 0 ? ? ? 0 412 1 21.296814 1 0 1 413 0 -418.794434 0 0 0 414 1 17.1500549 0.99999994 8.5991327994145617E-08 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -382.40625 0 0 0 615 0 -415.175232 0 0 0 616 0 -407.94165 0 0 0 -617 0 +617 0 ? ? ? 0 618 0 -408.326935 0 0 0 619 0 -408.712219 0 0 0 620 0 -407.94165 0 0 0 diff --git a/test/BaselineOutput/SingleDebug/Text/tokenized.tsv b/test/BaselineOutput/SingleDebug/Text/tokenized.tsv new file mode 100644 index 0000000000..eccf8ca0e6 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/Text/tokenized.tsv @@ -0,0 +1,12 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=text:TX:0 +#@ col=words:TX:1-** +#@ col={name=chars type=TX src={ min=-1 var=+}} +#@ } +text +==RUDE== Dude, you are rude upload that carl picture back, or else. ==RUDE== Dude, you are rude upload that carl picture back, or else. <␂> = = R U D E = = <␠> D u d e , <␠> y o u <␠> a r e <␠> r u d e <␠> u p l o a d <␠> t h a t <␠> c a r l <␠> p i c t u r e <␠> b a c k , <␠> o r <␠> e l s e . <␃> +== OK! == IM GOING TO VANDALIZE WILD ONES WIKI THEN!!! == OK! == IM GOING TO VANDALIZE WILD ONES WIKI THEN!!! <␂> = = <␠> O K ! <␠> = = <␠> <␠> I M <␠> G O I N G <␠> T O <␠> V A N D A L I Z E <␠> W I L D <␠> O N E S <␠> W I K I <␠> T H E N ! ! ! <␠> <␠> <␠> <␃> +Stop trolling, zapatancas, calling me a liar merely demonstartes that you arer Zapatancas. You may choose to chase every legitimate editor from this site and ignore me but I am an editor with a record that isnt 99% trolling and therefore my wishes are not to be completely ignored by a sockpuppet like yourself. The consensus is overwhelmingly against you and your trollin g lover Zapatancas, Stop trolling, zapatancas, calling me a liar merely demonstartes that you arer Zapatancas. You may choose to chase every legitimate editor from this site and ignore me but I am an editor with a record that isnt 99% trolling and therefore my wishes are not to be completely ignored by a sockpuppet like yourself. The consensus is overwhelmingly against you and your trollin g lover Zapatancas, <␂> S t o p <␠> t r o l l i n g , <␠> z a p a t a n c a s , <␠> c a l l i n g <␠> m e <␠> a <␠> l i a r <␠> m e r e l y <␠> d e m o n s t a r t e s <␠> t h a t <␠> y o u <␠> a r e r <␠> Z a p a t a n c a s . <␠> Y o u <␠> m a y <␠> c h o o s e <␠> t o <␠> c h a s e <␠> e v e r y <␠> l e g i t i m a t e <␠> e d i t o r <␠> f r o m <␠> t h i s <␠> s i t e <␠> a n d <␠> i g n o r e <␠> m e <␠> b u t <␠> I <␠> a m <␠> a n <␠> e d i t o r <␠> w i t h <␠> a <␠> r e c o r d <␠> t h a t <␠> i s n t <␠> 9 9 % <␠> t r o l l i n g <␠> a n d <␠> t h e r e f o r e <␠> m y <␠> w i s h e s <␠> a r e <␠> n o t <␠> t o <␠> b e <␠> c o m p l e t e l y <␠> i g n o r e d <␠> b y <␠> a <␠> s o c k p u p p e t <␠> l i k e <␠> y o u r s e l f . <␠> T h e <␠> c o n s e n s u s <␠> i s <␠> o v e r w h e l m i n g l y <␠> a g a i n s t <␠> y o u <␠> a n d <␠> y o u r <␠> t r o l l i n <␠> g <␠> l o v e r <␠> Z a p a t a n c a s , <␠> <␠> <␃> +==You're cool== You seem like a really cool guy... *bursts out laughing at sarcasm*. ==You're cool== You seem like a really cool guy... *bursts out laughing at sarcasm*. <␂> = = Y o u ' r e <␠> c o o l = = <␠> <␠> Y o u <␠> s e e m <␠> l i k e <␠> a <␠> r e a l l y <␠> c o o l <␠> g u y . . . <␠> * b u r s t s <␠> o u t <␠> l a u g h i n g <␠> a t <␠> s a r c a s m * . <␃> diff --git a/test/BaselineOutput/SingleDebug/Transform/Concat/Concat1.tsv b/test/BaselineOutput/SingleDebug/Transform/Concat/Concat1.tsv index 36b453f708..c548a80a00 100644 --- a/test/BaselineOutput/SingleDebug/Transform/Concat/Concat1.tsv +++ b/test/BaselineOutput/SingleDebug/Transform/Concat/Concat1.tsv @@ -7,13 +7,13 @@ #@ col=f4:R4:8-** #@ } float1 float1 float1 float4.age float4.fnlwgt float4.education-num float4.capital-gain float1 -25 25 25 25 226802 7 0 25 25 226802 7 0 0 40 0 25 -38 38 38 38 89814 9 0 38 38 89814 9 0 0 50 0 38 -28 28 28 28 336951 12 0 28 28 336951 12 0 0 40 1 28 -44 44 44 44 160323 10 7688 44 44 160323 10 7688 0 40 1 44 -18 18 18 18 103497 10 0 18 18 103497 10 0 0 30 0 18 -34 34 34 34 198693 6 0 34 34 198693 6 0 0 30 0 34 -29 29 29 29 227026 9 0 29 29 227026 9 0 0 40 0 29 -63 63 63 63 104626 15 3103 63 63 104626 15 3103 0 32 1 63 -24 24 24 24 369667 10 0 24 24 369667 10 0 0 40 0 24 -55 55 55 55 104996 4 0 55 55 104996 4 0 0 10 0 55 +25 25 25 25 226802 7 0 25 25 226802 7 0 0 40 ? 0 25 +38 38 38 38 89814 9 0 38 38 89814 9 0 0 50 ? 0 38 +28 28 28 28 336951 12 0 28 28 336951 12 0 0 40 ? 1 28 +44 44 44 44 160323 10 7688 44 44 160323 10 7688 0 40 ? 1 44 +18 18 18 18 103497 10 0 18 18 103497 10 0 0 30 ? 0 18 +34 34 34 34 198693 6 0 34 34 198693 6 0 0 30 ? 0 34 +29 29 29 29 227026 9 0 29 29 227026 9 0 0 40 ? 0 29 +63 63 63 63 104626 15 3103 63 63 104626 15 3103 0 32 ? 1 63 +24 24 24 24 369667 10 0 24 24 369667 10 0 0 40 ? 0 24 +55 55 55 55 104996 4 0 55 55 104996 4 0 0 10 ? 0 55 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt index eb8fd43aae..730e9c8fa9 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 3.64133739 1 0 1 35 0 -2.796535 1E-15 1.4415419267167138E-15 0 37 0 -1.65404248 1E-15 1.4415419267167138E-15 0 -40 0 +40 0 ? ? ? 0 41 1 1.04337406 0.8947368 0.16046469748481262 1 44 1 4.33966541 1 0 1 45 0 -2.89273548 1E-15 1.4415419267167138E-15 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -2.35179329 1E-15 1.4415419267167138E-15 0 141 0 -2.904073 1E-15 1.4415419267167138E-15 0 144 0 -2.796535 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 147 0 -2.463921 1E-15 1.4415419267167138E-15 0 150 0 -2.8614285 1E-15 1.4415419267167138E-15 0 151 1 3.17632246 1 0 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -3.01920986 1E-15 1.4415419267167138E-15 0 156 0 -2.49565363 1E-15 1.4415419267167138E-15 0 161 0 -2.30924 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 167 1 2.38255262 0.8947368 0.16046469748481262 1 169 0 -3.03097248 1E-15 1.4415419267167138E-15 0 171 0 -2.804134 1E-15 1.4415419267167138E-15 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 7.227234 1 0 1 247 1 2.672493 0.8947368 0.16046469748481262 1 248 0 -1.711307 1E-15 1.4415419267167138E-15 0 -249 0 +249 0 ? ? ? 0 250 0 -2.60319138 1E-15 1.4415419267167138E-15 0 252 0 2.992918 0.8947368 3.2479272984652883 1 254 1 5.35164165 1 0 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -2.804134 1E-15 1.4415419267167138E-15 0 271 0 -2.34358668 1E-15 1.4415419267167138E-15 0 272 1 2.73419476 0.8947368 0.16046469748481262 1 -275 0 +275 0 ? ? ? 0 276 0 -2.68139815 1E-15 1.4415419267167138E-15 0 277 0 -2.91167164 1E-15 1.4415419267167138E-15 0 278 0 -2.804134 1E-15 1.4415419267167138E-15 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -2.804134 1E-15 1.4415419267167138E-15 0 293 1 3.413344 1 0 1 296 0 1.47483253 0.8947368 3.2479272984652883 1 -297 0 +297 0 ? ? ? 0 299 1 3.53376913 1 0 1 300 1 3.78027344 1 0 1 301 0 -2.804134 1E-15 1.4415419267167138E-15 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.45851088 1 0 1 317 1 5.48386 1 0 1 319 0 1.44604874 0.8947368 3.2479272984652883 1 -321 0 +321 0 ? ? ? 0 323 1 3.36483288 1 0 1 327 0 -2.91167164 1E-15 1.4415419267167138E-15 0 328 1 3.03614 0.8947368 0.16046469748481262 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 8.167599 1 0 1 613 0 -2.39343452 1E-15 1.4415419267167138E-15 0 614 0 -2.85382986 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -2.56626129 1E-15 1.4415419267167138E-15 0 619 0 -2.45112467 1E-15 1.4415419267167138E-15 0 621 0 -0.5349846 0.2 0.32192810026182023 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -2.27471757 1E-15 1.4415419267167138E-15 0 19 0 -2.00358748 1E-15 1.4415419267167138E-15 0 22 0 -2.5426836 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -2.68141246 1E-15 1.4415419267167138E-15 0 26 0 -2.33200574 1E-15 1.4415419267167138E-15 0 27 0 -2.27155375 1E-15 1.4415419267167138E-15 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -2.546271 1E-15 1.4415419267167138E-15 0 135 0 -1.52827668 1E-15 1.4415419267167138E-15 0 136 0 -2.40711856 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -2.67508459 1E-15 1.4415419267167138E-15 0 142 1 2.1587286 0.9117647 0.13326656969825684 1 143 0 -1.54474568 1E-15 1.4415419267167138E-15 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.83878517 1E-15 1.4415419267167138E-15 0 155 1 3.237853 0.9117647 0.13326656969825684 1 157 0 -2.54584742 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 6.77223158 1 0 1 160 1 5.42115831 1 0 1 162 0 -2.41028237 1E-15 1.4415419267167138E-15 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.187345 1 0 1 232 0 0.397937775 0.714285731 1.8073550080489322 1 234 0 -1.183644 1E-15 1.4415419267167138E-15 0 -235 0 +235 0 ? ? ? 0 236 1 5.6324296 1 0 1 238 1 6.77774 1 0 1 243 0 -1.01514125 1E-15 1.4415419267167138E-15 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 7.8132205 1 0 1 287 0 -2.546271 1E-15 1.4415419267167138E-15 0 289 1 5.25516939 1 0 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77293825 1 0 1 298 0 -1.80093193 1E-15 1.4415419267167138E-15 0 302 1 7.77011251 1 0 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -2.68141246 1E-15 1.4415419267167138E-15 0 310 0 -2.68183613 1E-15 1.4415419267167138E-15 0 313 0 -2.94621468 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 318 0 -2.69217515 1E-15 1.4415419267167138E-15 0 320 1 3.800508 0.9444444 0.082462200658479604 1 322 0 -2.41028237 1E-15 1.4415419267167138E-15 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.81381369 1E-15 1.4415419267167138E-15 0 408 0 -1.46855927 1E-15 1.4415419267167138E-15 0 410 0 -2.81381369 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 4.83788157 1 0 1 417 0 -2.81381369 1E-15 1.4415419267167138E-15 0 420 0 -1.04175115 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt index 3b51cd60d5..689d51fcc1 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 3.64133739 0.9844741 0.022574810341618422 1 35 0 -2.796535 0.0199539755 0.029078592776682857 0 37 0 -1.65404248 0.0782266259 0.11751599963260938 0 -40 0 +40 0 ? ? ? 0 41 1 1.04337406 0.7116856 0.49068805388019454 1 44 1 4.33966541 0.9934526 0.0094769477341525628 1 45 0 -2.89273548 0.0177341755 0.025814589979333783 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -2.35179329 0.03427408 0.050314293386144675 0 141 0 -2.904073 0.0174890943 0.025454673744601461 0 144 0 -2.796535 0.0199539755 0.029078592776682857 0 -145 0 +145 0 ? ? ? 0 147 0 -2.463921 0.0299276374 0.04383572570114834 0 150 0 -2.8614285 0.01842858 0.026834852844454766 0 151 1 3.17632246 0.9725775 0.040114860620962978 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -3.01920986 0.0151813291 0.022069981158425081 0 156 0 -2.49565363 0.0287977811 0.042156377165560456 0 161 0 -2.30924 0.0360781476 0.053011906638395674 0 -164 0 +164 0 ? ? ? 0 167 1 2.38255262 0.9293544 0.10569919063924076 1 169 0 -3.03097248 0.0149631584 0.021750410734051751 0 171 0 -2.804134 0.0197691489 0.028806541052408416 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 7.227234 0.999821365 0.00025773902362411926 1 247 1 2.672493 0.9497448 0.074388155670230882 1 248 0 -1.711307 0.07322063 0.10970216917333013 0 -249 0 +249 0 ? ? ? 0 250 0 -2.60319138 0.0252686124 0.036923393165046718 0 252 0 2.992918 0.9657571 4.8680501214754637 1 254 1 5.35164165 0.998142242 0.0026826702788205732 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -2.804134 0.0197691489 0.028806541052408416 0 271 0 -2.34358668 0.0346150957 0.050823827322496622 0 272 1 2.73419476 0.9532994 0.068998701675288501 1 -275 0 +275 0 ? ? ? 0 276 0 -2.68139815 0.0229703225 0.033525709865030048 0 277 0 -2.91167164 0.0173267 0.025216237008576704 0 278 0 -2.804134 0.0197691489 0.028806541052408416 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -2.804134 0.0197691489 0.028806541052408416 0 293 1 3.413344 0.9794621 0.029938444898768346 1 296 0 1.47483253 0.8088676 2.3873555377194497 1 -297 0 +297 0 ? ? ? 0 299 1 3.53376913 0.982280254 0.025793396317687449 1 300 1 3.78027344 0.986915946 0.019000876915907086 1 301 0 -2.804134 0.0197691489 0.028806541052408416 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.45851088 0.980567157 0.028311653515975352 1 317 1 5.48386 0.9984247 0.0022744566762884174 1 319 0 1.44604874 0.803245664 2.3455326626007635 1 -321 0 +321 0 ? ? ? 0 323 1 3.36483288 0.978206754 0.031788669528507826 1 327 0 -2.91167164 0.0173267 0.025216237008576704 0 328 1 3.03614 0.9674988 0.047668253892284254 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 8.167599 0.9999448 7.9630164919418245E-05 1 613 0 -2.39343452 0.03259308 0.047805235639227908 0 614 0 -2.85382986 0.0186011065 0.027088450049153875 0 -617 0 +617 0 ? ? ? 0 618 0 -2.56626129 0.02643034 0.038643886518904186 0 619 0 -2.45112467 0.0303953178 0.044531429350354604 0 621 0 -0.5349846 0.255690753 0.42602593625059765 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -2.27471757 0.0249303821 0.036422867055812318 0 19 0 -2.00358748 0.03410183 0.050056992617068342 0 22 0 -2.5426836 0.0182464458 0.026567179066779304 0 -23 1 +23 1 ? ? ? 0 24 0 -2.68141246 0.015512228 0.022554807909292037 0 26 0 -2.33200574 0.02332543 0.034050161954667077 0 27 0 -2.27155375 0.0250220858 0.036558556406276949 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -2.546271 0.0181701127 0.026455011357386121 0 135 0 -1.52827668 0.0585265532 0.087007689086403689 0 136 0 -2.40711856 0.0213731956 0.031169296396687675 0 -139 0 +139 0 ? ? ? 0 140 0 -2.67508459 0.015627671 0.022723991158114246 0 142 1 2.1587286 0.83348304 0.26277525125659912 1 143 0 -1.54474568 0.05745574 0.085367730161702821 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.83878517 0.0411879122 0.060679997683715341 0 155 1 3.237853 0.947596252 0.077655602601770859 1 157 0 -2.54584742 0.0181791112 0.026468233670711376 0 -158 0 +158 0 ? ? ? 0 159 1 6.77223158 0.999177039 0.0011877710421138682 1 160 1 5.42115831 0.995904 0.0059213730758132374 1 162 0 -2.41028237 0.0212945715 0.031053393109467146 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.187345 0.9945968 0.0078163354020312284 1 232 0 0.397937775 0.381006956 0.69200489802945642 1 234 0 -1.183644 0.085664086 0.12920380666492423 0 -235 0 +235 0 ? ? ? 0 236 1 5.6324296 0.9968118 0.0046069378176225089 1 238 1 6.77774 0.9991824 0.0011800254692933552 1 243 0 -1.01514125 0.102734588 0.15639329612707187 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 7.8132205 0.999761462 0.00034417833577817593 1 287 0 -2.546271 0.0181701127 0.026455011357386121 0 289 1 5.25516939 0.9950138 0.0072115986401722143 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77293825 0.9911819 0.012778237651616852 1 298 0 -1.80093193 0.0430044457 0.063415872213561694 0 302 1 7.77011251 0.999748945 0.00036224093577622284 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -2.68141246 0.015512228 0.022554807909292037 0 310 0 -2.68183613 0.0155045288 0.022543525273161929 0 313 0 -2.94621468 0.0113662509 0.016491938164477051 0 -315 0 +315 0 ? ? ? 0 318 0 -2.69217515 0.0153178023 0.022269919361612874 0 320 1 3.800508 0.9724724 0.040270746291192143 1 322 0 -2.41028237 0.0212945715 0.031053393109467146 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.81381369 0.0132805621 0.019288165348886492 0 408 0 -1.46855927 0.06256822 0.093214386421141548 0 410 0 -2.81381369 0.0132805621 0.019288165348886492 0 -411 0 +411 0 ? ? ? 0 412 1 4.83788157 0.991832554 0.011831516022545778 1 417 0 -2.81381369 0.0132805621 0.019288165348886492 0 420 0 -1.04175115 0.09985152 0.15176509864238413 0 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt index 09a2fb9aa7..e51f951dee 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Assigned 32 1 3.64133739 1 35 0 -2.796535 0 37 0 -1.65404248 0 -40 0 +40 0 ? 0 41 1 1.04337406 1 44 1 4.33966541 1 45 0 -2.89273548 0 @@ -76,7 +76,7 @@ Instance Label Score Assigned 138 0 -2.35179329 0 141 0 -2.904073 0 144 0 -2.796535 0 -145 0 +145 0 ? 0 147 0 -2.463921 0 150 0 -2.8614285 0 151 1 3.17632246 1 @@ -84,7 +84,7 @@ Instance Label Score Assigned 154 0 -3.01920986 0 156 0 -2.49565363 0 161 0 -2.30924 0 -164 0 +164 0 ? 0 167 1 2.38255262 1 169 0 -3.03097248 0 171 0 -2.804134 0 @@ -130,7 +130,7 @@ Instance Label Score Assigned 246 1 7.227234 1 247 1 2.672493 1 248 0 -1.711307 0 -249 0 +249 0 ? 0 250 0 -2.60319138 0 252 0 2.992918 1 254 1 5.35164165 1 @@ -144,7 +144,7 @@ Instance Label Score Assigned 269 0 -2.804134 0 271 0 -2.34358668 0 272 1 2.73419476 1 -275 0 +275 0 ? 0 276 0 -2.68139815 0 277 0 -2.91167164 0 278 0 -2.804134 0 @@ -158,7 +158,7 @@ Instance Label Score Assigned 291 0 -2.804134 0 293 1 3.413344 1 296 0 1.47483253 1 -297 0 +297 0 ? 0 299 1 3.53376913 1 300 1 3.78027344 1 301 0 -2.804134 0 @@ -172,7 +172,7 @@ Instance Label Score Assigned 316 1 3.45851088 1 317 1 5.48386 1 319 0 1.44604874 1 -321 0 +321 0 ? 0 323 1 3.36483288 1 327 0 -2.91167164 0 328 1 3.03614 1 @@ -318,7 +318,7 @@ Instance Label Score Assigned 612 1 8.167599 1 613 0 -2.39343452 0 614 0 -2.85382986 0 -617 0 +617 0 ? 0 618 0 -2.56626129 0 619 0 -2.45112467 0 621 0 -0.5349846 0 @@ -375,7 +375,7 @@ Instance Label Score Assigned 17 0 -2.27471757 0 19 0 -2.00358748 0 22 0 -2.5426836 0 -23 1 +23 1 ? 0 24 0 -2.68141246 0 26 0 -2.33200574 0 27 0 -2.27155375 0 @@ -425,7 +425,7 @@ Instance Label Score Assigned 134 0 -2.546271 0 135 0 -1.52827668 0 136 0 -2.40711856 0 -139 0 +139 0 ? 0 140 0 -2.67508459 0 142 1 2.1587286 1 143 0 -1.54474568 0 @@ -435,7 +435,7 @@ Instance Label Score Assigned 153 0 -1.83878517 0 155 1 3.237853 1 157 0 -2.54584742 0 -158 0 +158 0 ? 0 159 1 6.77223158 1 160 1 5.42115831 1 162 0 -2.41028237 0 @@ -474,7 +474,7 @@ Instance Label Score Assigned 231 1 5.187345 1 232 0 0.397937775 1 234 0 -1.183644 0 -235 0 +235 0 ? 0 236 1 5.6324296 1 238 1 6.77774 1 243 0 -1.01514125 0 @@ -496,8 +496,8 @@ Instance Label Score Assigned 286 1 7.8132205 1 287 0 -2.546271 0 289 1 5.25516939 1 -292 1 -294 0 +292 1 ? 0 +294 0 ? 0 295 1 4.77293825 1 298 0 -1.80093193 0 302 1 7.77011251 1 @@ -506,7 +506,7 @@ Instance Label Score Assigned 307 0 -2.68141246 0 310 0 -2.68183613 0 313 0 -2.94621468 0 -315 0 +315 0 ? 0 318 0 -2.69217515 0 320 1 3.800508 1 322 0 -2.41028237 0 @@ -551,7 +551,7 @@ Instance Label Score Assigned 407 0 -2.81381369 0 408 0 -1.46855927 0 410 0 -2.81381369 0 -411 0 +411 0 ? 0 412 1 4.83788157 1 417 0 -2.81381369 0 420 0 -1.04175115 0 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt index 693440edc2..7664c7d409 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 2.35438 0.996385 0.0052248235129257635 1 35 0 -1.83556986 0.00724350661 0.010488202773633042 0 37 0 -0.8828192 0.07420456 0.11123463299619094 0 -40 0 +40 0 ? ? ? 0 41 1 0.6366718 0.7855496 0.34822575879653411 1 44 1 2.72072053 0.998558342 0.002081372862402793 1 45 0 -1.87994158 0.00648348359 0.0093841435784006184 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.51871562 0.0159320254 0.02317012154973026 0 141 0 -1.93865037 0.00559835136 0.0080994066187532153 0 144 0 -1.83556986 0.00724350661 0.010488202773633042 0 -145 0 +145 0 ? ? ? 0 147 0 -1.69274938 0.010342177 0.014998299390129487 0 150 0 -1.91297352 0.00596963847 0.0086381769438633477 0 151 1 1.55168462 0.973401248 0.038893469912665984 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -2.0423522 0.004318493 0.0062437599081359638 0 156 0 -1.73049688 0.009414184 0.013646131293487658 0 161 0 -1.46562588 0.0181668717 0.026450249055215744 0 -164 0 +164 0 ? ? ? 0 167 1 2.31870866 0.99604696 0.0057143326416850991 1 169 0 -2.041339 0.00432946626 0.0062596597963016712 0 171 0 -1.8361913 0.00723227439 0.010471879968765333 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 3.8998096 0.9999256 0.00010732116581602008 1 247 1 0.9928844 0.8997386 0.15242216136874395 1 248 0 -1.22778583 0.03256034 0.047756414470681303 0 -249 0 +249 0 ? ? ? 0 250 0 -1.83357728 0.00727963867 0.010540711608751351 0 252 0 1.37199 0.958826959 4.6021561858199664 1 254 1 2.79375386 0.9988 0.0017323029622904203 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.8361913 0.00723227439 0.010471879968765333 0 271 0 -1.42138422 0.0202620383 0.02953215377914243 0 272 1 1.10222125 0.921965 0.11721610663727763 1 -275 0 +275 0 ? ? ? 0 276 0 -1.73186815 0.009382072 0.013599363941350218 0 277 0 -1.93927169 0.00558965746 0.0080867934194779039 0 278 0 -1.8361913 0.00723227439 0.010471879968765333 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.8361913 0.00723227439 0.010471879968765333 0 293 1 1.65592456 0.9794098 0.030015442677317709 1 296 0 0.553571463 0.748245 1.9899076684215853 1 -297 0 +297 0 ? ? ? 0 299 1 2.001486 0.991262555 0.012660861084033199 1 300 1 2.09417748 0.9930671 0.010036913891508983 1 301 0 -1.8361913 0.00723227439 0.010471879968765333 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 1.47151637 0.9676506 0.047441894201880302 1 317 1 3.06524873 0.999393463 0.00087531320929783803 1 319 0 0.8519552 0.8629285 2.8669995609516308 1 -321 0 +321 0 ? ? ? 0 323 1 1.75474 0.9838682 0.023463058122578043 1 327 0 -1.93927169 0.00558965746 0.0080867934194779039 0 328 1 1.43182349 0.964374959 0.052333903838185522 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 5.09431458 0.9999963 5.3314720279485219E-06 1 613 0 -1.63692176 0.0118829105 0.017246087060301379 0 614 0 -1.9123522 0.00597891957 0.0086516472088849234 0 -617 0 +617 0 ? ? ? 0 618 0 -1.62816632 0.0121443039 0.017627783540922228 0 619 0 -1.52446461 0.0157068819 0.022840087163427197 0 621 0 -0.188778639 0.31474337 0.54528371301135603 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.57970572 0.0144622317 0.021016934775320784 0 19 0 -1.32506859 0.0253080986 0.036981837697027814 0 22 0 -1.76987159 0.009491567 0.01375883626140059 0 -23 1 +23 1 ? ? ? 0 24 0 -1.96166122 0.00619609 0.0089668769102733102 0 26 0 -1.67495286 0.0117150256 0.017000988488892345 0 27 0 -1.51523459 0.0166727714 0.024256502854015347 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.75784481 0.009748338 0.014132877086598956 0 135 0 -1.07198834 0.04377895 0.06458392745656516 0 136 0 -1.64255309 0.0125861792 0.018273256294688772 0 -139 0 +139 0 ? ? ? 0 140 0 -1.832719 0.008254912 0.01195874763978641 0 142 1 1.20702124 0.8832422 0.17911900782229345 1 143 0 -1.436229 0.0198381469 0.028908095216224419 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.37983692 0.0224505328 0.032758385480089913 0 155 1 1.24272728 0.8912444 0.16610696969487637 1 157 0 -1.83434272 0.008225175 0.011915489581813862 0 -158 0 +158 0 ? ? ? 0 159 1 4.09360933 0.999795 0.00029575448112624953 1 160 1 3.04705143 0.997864842 0.0030836744978663621 1 162 0 -1.70702422 0.0109114433 0.015828398337213719 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 2.80366325 0.996321738 0.0053163942348769758 1 232 0 0.248882532 0.46910888 0.91351208341864276 1 234 0 -0.8223932 0.0741594 0.11116426368346338 0 -235 0 +235 0 ? ? ? 0 236 1 3.65436459 0.999451637 0.00079133718371403176 1 238 1 4.208801 0.99984163 0.00022849704563499302 1 243 0 -1.18483961 0.0343320966 0.050400968413634589 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 4.869809 0.999964 5.1939695512002597E-05 1 287 0 -1.75784481 0.009748338 0.014132877086598956 0 289 1 2.8387928 0.996599257 0.0049145964567919058 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 2.21859837 0.9864867 0.019628533461458434 1 298 0 -1.053101 0.0455854833 0.067312108231381534 0 302 1 4.847371 0.999962151 5.4605525036338512E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.96166122 0.00619609 0.0089668769102733102 0 310 0 -1.88516331 0.007346285 0.010637570327263628 0 313 0 -2.087356 0.00468213623 0.0067707579292392971 0 -315 0 +315 0 ? ? ? 0 318 0 -1.9255811 0.006714394 0.00971948958962956 0 320 1 1.98424911 0.9773634 0.03303300364958086 1 322 0 -1.70702422 0.0109114433 0.015828398337213719 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.02450848 0.00538646942 0.0077920373965741406 0 408 0 -1.276682 0.0281251986 0.041157619505191562 0 410 0 -2.02450848 0.00538646942 0.0077920373965741406 0 -411 0 +411 0 ? ? ? 0 412 1 2.820312 0.996455967 0.0051220399276606741 1 417 0 -2.02450848 0.00538646942 0.0077920373965741406 0 420 0 -1.02720916 0.0481775925 0.071235676455785449 0 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt index 0c2f376b8f..43672feda2 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 4.309107 0.9756098 0.035623875334191583 1 21 1 6.161626 1 0 1 22 0 -3.35177135 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -3.57612 1E-15 1.4415419267167138E-15 0 25 1 1.68778992 0.8125 0.29956028185890782 1 26 0 -3.21128821 1E-15 1.4415419267167138E-15 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.20465565 0.0625 0.093109404391481479 0 38 1 4.3035 0.9756098 0.035623875334191583 1 39 1 2.444232 0.84 0.25153881203904033 1 -40 0 +40 0 ? ? ? 0 41 1 2.349327 0.84 0.25153881203904033 1 42 1 6.69547653 1 0 1 43 1 0.1725626 0.7777778 0.36257005481575838 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -3.11112714 1E-15 1.4415419267167138E-15 0 137 0 -3.60871148 1E-15 1.4415419267167138E-15 0 138 0 -2.89319158 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -3.60871148 1E-15 1.4415419267167138E-15 0 141 0 -3.8493557 1E-15 1.4415419267167138E-15 0 142 1 2.848053 0.84 0.25153881203904033 1 143 0 -2.73937 1E-15 1.4415419267167138E-15 0 144 0 -3.59241557 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 146 1 1.31008816 0.8125 0.29956028185890782 1 147 0 -3.43885779 1E-15 1.4415419267167138E-15 0 148 0 -0.152019978 0.4 0.73696560849809378 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.618639 0.84 0.25153881203904033 1 156 0 -3.41809654 1E-15 1.4415419267167138E-15 0 157 0 -3.33547568 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 9.536341 1 0 1 160 1 6.860572 1 0 1 161 0 -2.5829134 1E-15 1.4415419267167138E-15 0 162 0 -3.09483147 1E-15 1.4415419267167138E-15 0 163 0 -2.50633955 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 165 0 -2.245256 1E-15 1.4415419267167138E-15 0 166 1 6.456311 1 0 1 167 1 4.319108 0.9756098 0.035623875334191583 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.8359299 0.7777778 2.1699250874336404 1 233 1 3.89948845 0.9411765 0.087462835875881578 1 234 0 -1.06992626 0.0625 0.093109404391481479 0 -235 0 +235 0 ? ? ? 0 236 1 8.428113 1 0 1 237 1 5.33226967 1 0 1 238 1 9.183852 1 0 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.645902 1 0 1 247 1 2.597087 0.84 0.25153881203904033 1 248 0 -1.497818 0.0625 0.093109404391481479 0 -249 0 +249 0 ? ? ? 0 250 0 -3.67503667 1E-15 1.4415419267167138E-15 0 251 1 5.0781126 1 0 1 252 0 3.308917 0.84 2.643855953298599 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 2.38956165 0.84 0.25153881203904033 1 273 1 0.3306446 0.7777778 0.36257005481575838 1 274 0 -2.82355762 1E-15 1.4415419267167138E-15 0 -275 0 +275 0 ? ? ? 0 276 0 -3.35177135 1E-15 1.4415419267167138E-15 0 277 0 -3.83305979 1E-15 1.4415419267167138E-15 0 278 0 -3.57612 1E-15 1.4415419267167138E-15 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 6.52714539 1 0 1 290 0 -4.08999968 1E-15 1.4415419267167138E-15 0 291 0 -3.57612 1E-15 1.4415419267167138E-15 0 -292 1 +292 1 ? ? ? 0 293 1 3.63595963 0.9411765 0.087462835875881578 1 -294 0 +294 0 ? ? ? 0 295 1 5.25647163 1 0 1 296 0 1.97336864 0.8125 2.4150374992788439 1 -297 0 +297 0 ? ? ? 0 298 0 -2.03882861 1E-15 1.4415419267167138E-15 0 299 1 4.99616432 1 0 1 300 1 5.63767242 1 0 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.14314938 0.84 0.25153881203904033 1 313 0 -4.08999968 1E-15 1.4415419267167138E-15 0 314 0 -3.88960457 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 316 1 3.149722 0.84 0.25153881203904033 1 317 1 7.02619457 1 0 1 318 0 -3.406486 1E-15 1.4415419267167138E-15 0 319 0 1.82393885 0.8125 2.4150374992788439 1 320 1 5.15885925 1 0 1 -321 0 +321 0 ? ? ? 0 322 0 -3.09483147 1E-15 1.4415419267167138E-15 0 323 1 3.22143555 0.84 0.25153881203904033 1 324 0 -3.57612 1E-15 1.4415419267167138E-15 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -2.29045367 1E-15 1.4415419267167138E-15 0 409 0 -3.13383579 1E-15 1.4415419267167138E-15 0 410 0 -3.83305979 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 6.52325153 1 0 1 413 0 -2.39560747 1E-15 1.4415419267167138E-15 0 414 1 5.304202 1 0 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -3.64896035 1E-15 1.4415419267167138E-15 0 615 0 -2.675256 1E-15 1.4415419267167138E-15 0 616 0 -3.35177135 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -3.11112714 1E-15 1.4415419267167138E-15 0 619 0 -2.870483 1E-15 1.4415419267167138E-15 0 620 0 -3.35177135 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt index 69424d1f96..b1c0d65d4f 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 4.309107 0.982883155 0.024908174987257281 1 21 1 6.161626 0.997486651 0.0036305605660221312 1 22 0 -3.35177135 0.0190011337 0.027676625763894382 0 -23 1 +23 1 ? ? ? 0 24 0 -3.57612 0.0150948567 0.021943310344814629 0 25 1 1.68778992 0.7883411 0.34310809466132391 1 26 0 -3.21128821 0.02193545 0.031998411900987428 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.20465565 0.154014841 0.24129573984468006 0 38 1 4.3035 0.98278445 0.025053063812451211 1 39 1 2.444232 0.8913242 0.16597778265246793 1 -40 0 +40 0 ? ? ? 0 41 1 2.349327 0.881352544 0.18220887767190111 1 42 1 6.69547653 0.99855864 0.002080942285094773 1 43 1 0.1725626 0.43382585 1.2048120730945795 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -3.11112714 0.0242936034 0.035481008429150813 0 137 0 -3.60871148 0.0145974671 0.021214914966477344 0 138 0 -2.89319158 0.0303093083 0.044403459692558656 0 -139 0 +139 0 ? ? ? 0 140 0 -3.60871148 0.0145974671 0.021214914966477344 0 141 0 -3.8493557 0.01139268 0.016530506138550251 0 142 1 2.848053 0.9259271 0.11102947905485054 1 143 0 -2.73937 0.0353999846 0.051997261707361145 0 144 0 -3.59241557 0.0148441121 0.021576064827714625 0 -145 0 +145 0 ? ? ? 0 146 1 1.31008816 0.7152061 0.48356908051152397 1 147 0 -3.43885779 0.01737916 0.025293256640113757 0 148 0 -0.152019978 0.353206038 0.62862188502856431 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.618639 0.9077395 0.139649725798073 1 156 0 -3.41809654 0.0177530367 0.025842292400220613 0 157 0 -3.33547568 0.01932072 0.028146699492868216 0 -158 0 +158 0 ? ? ? 0 159 1 9.536341 0.999925554 0.0001074071635410925 1 160 1 6.860572 0.998786449 0.0017518465781146784 1 161 0 -2.5829134 0.0414183028 0.061026700952795158 0 162 0 -3.09483147 0.0246999636 0.036081984337152172 0 163 0 -2.50633955 0.04470976 0.065988970965053517 0 -164 0 +164 0 ? ? ? 0 165 0 -2.245256 0.0579013154 0.086049905177797967 0 166 1 6.456311 0.998150766 0.0026703506849678977 1 167 1 4.319108 0.983057857 0.024651767929287045 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.8359299 0.6049175 1.3397742465159499 1 233 1 3.89948845 0.9739913 0.038019246409892023 1 234 0 -1.06992626 0.17323719 0.27445460033191865 0 -235 0 +235 0 ? ? ? 0 236 1 8.428113 0.99976337 0.0003414259594434771 1 237 1 5.33226967 0.994048536 0.0086117997569089607 1 238 1 9.183852 0.9998925 0.00015513669190214178 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.645902 0.9998115 0.00027201620468366962 1 247 1 2.597087 0.9058386 0.14267406349976108 1 248 0 -1.497818 0.118221387 0.1815116096306757 0 -249 0 +249 0 ? ? ? 0 250 0 -3.67503667 0.0136346063 0.019805910679898876 0 251 1 5.0781126 0.9922549 0.011217294571842519 1 252 0 3.308917 0.952875 4.4073641261174119 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 2.38956165 0.8856733 0.17515349285073656 1 273 1 0.3306446 0.474698573 1.0749163809587898 1 274 0 -2.82355762 0.0325194858 0.047695490325124906 0 -275 0 +275 0 ? ? ? 0 276 0 -3.35177135 0.0190011337 0.027676625763894382 0 277 0 -3.83305979 0.0115858121 0.016812375308683684 0 278 0 -3.57612 0.0150948567 0.021943310344814629 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 6.52714539 0.9982823 0.0024802287545360943 1 290 0 -4.08999968 0.008885143 0.012875839136462838 0 291 0 -3.57612 0.0150948567 0.021943310344814629 0 -292 1 +292 1 ? ? ? 0 293 1 3.63595963 0.9660381 0.049847993866089353 1 -294 0 +294 0 ? ? ? 0 295 1 5.25647163 0.9935618 0.0093183820918222807 1 296 0 1.97336864 0.8338234 2.589210701355853 1 -297 0 +297 0 ? ? ? 0 298 0 -2.03882861 0.0708337 0.10599126176574966 0 299 1 4.99616432 0.9915693 0.012214518276517204 1 300 1 5.63767242 0.995665669 0.0062667080268442431 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.14314938 0.856945038 0.22272541817603111 1 313 0 -4.08999968 0.008885143 0.012875839136462838 0 314 0 -3.88960457 0.0109292 0.015854298528928425 0 -315 0 +315 0 ? ? ? 0 316 1 3.149722 0.9448283 0.081875960094708669 1 317 1 7.02619457 0.9989789 0.0014738699989607148 1 318 0 -3.406486 0.0179655552 0.026154467003641695 0 319 0 1.82393885 0.8110785 2.4041411141982105 1 320 1 5.15885925 0.992876351 0.01031403381058839 1 -321 0 +321 0 ? ? ? 0 322 0 -3.09483147 0.0246999636 0.036081984337152172 0 323 1 3.22143555 0.948601961 0.076125243844936963 1 324 0 -3.57612 0.0150948567 0.021943310344814629 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -2.29045367 0.05538148 0.082196274856991033 0 409 0 -3.13383579 0.0237381756 0.034659977772739645 0 410 0 -3.83305979 0.0115858121 0.016812375308683684 0 -411 0 +411 0 ? ? ? 0 412 1 6.52325153 0.99827534 0.0024903070861691909 1 413 0 -2.39560747 0.0499131419 0.073868682437534181 0 414 1 5.304202 0.9938727 0.0088670155127870496 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -3.64896035 0.0140054561 0.020348431589167357 0 615 0 -2.675256 0.0377570055 0.055526832252899339 0 616 0 -3.35177135 0.0190011337 0.027676625763894382 0 -617 0 +617 0 ? ? ? 0 618 0 -3.11112714 0.0242936034 0.035481008429150813 0 619 0 -2.870483 0.0310136 0.045451678527033197 0 620 0 -3.35177135 0.0190011337 0.027676625763894382 0 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt index 0f08e7f566..55861f250c 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Assigned 20 1 4.309107 1 21 1 6.161626 1 22 0 -3.35177135 0 -23 1 +23 1 ? 0 24 0 -3.57612 0 25 1 1.68778992 1 26 0 -3.21128821 0 @@ -39,7 +39,7 @@ Instance Label Score Assigned 37 0 -1.20465565 0 38 1 4.3035 1 39 1 2.444232 1 -40 0 +40 0 ? 0 41 1 2.349327 1 42 1 6.69547653 1 43 1 0.1725626 1 @@ -138,13 +138,13 @@ Instance Label Score Assigned 136 0 -3.11112714 0 137 0 -3.60871148 0 138 0 -2.89319158 0 -139 0 +139 0 ? 0 140 0 -3.60871148 0 141 0 -3.8493557 0 142 1 2.848053 1 143 0 -2.73937 0 144 0 -3.59241557 0 -145 0 +145 0 ? 0 146 1 1.31008816 1 147 0 -3.43885779 0 148 0 -0.152019978 0 @@ -157,13 +157,13 @@ Instance Label Score Assigned 155 1 2.618639 1 156 0 -3.41809654 0 157 0 -3.33547568 0 -158 0 +158 0 ? 0 159 1 9.536341 1 160 1 6.860572 1 161 0 -2.5829134 0 162 0 -3.09483147 0 163 0 -2.50633955 0 -164 0 +164 0 ? 0 165 0 -2.245256 0 166 1 6.456311 1 167 1 4.319108 1 @@ -234,7 +234,7 @@ Instance Label Score Assigned 232 0 0.8359299 1 233 1 3.89948845 1 234 0 -1.06992626 0 -235 0 +235 0 ? 0 236 1 8.428113 1 237 1 5.33226967 1 238 1 9.183852 1 @@ -248,7 +248,7 @@ Instance Label Score Assigned 246 1 8.645902 1 247 1 2.597087 1 248 0 -1.497818 0 -249 0 +249 0 ? 0 250 0 -3.67503667 0 251 1 5.0781126 1 252 0 3.308917 1 @@ -274,7 +274,7 @@ Instance Label Score Assigned 272 1 2.38956165 1 273 1 0.3306446 1 274 0 -2.82355762 0 -275 0 +275 0 ? 0 276 0 -3.35177135 0 277 0 -3.83305979 0 278 0 -3.57612 0 @@ -291,12 +291,12 @@ Instance Label Score Assigned 289 1 6.52714539 1 290 0 -4.08999968 0 291 0 -3.57612 0 -292 1 +292 1 ? 0 293 1 3.63595963 1 -294 0 +294 0 ? 0 295 1 5.25647163 1 296 0 1.97336864 1 -297 0 +297 0 ? 0 298 0 -2.03882861 0 299 1 4.99616432 1 300 1 5.63767242 1 @@ -314,13 +314,13 @@ Instance Label Score Assigned 312 1 2.14314938 1 313 0 -4.08999968 0 314 0 -3.88960457 0 -315 0 +315 0 ? 0 316 1 3.149722 1 317 1 7.02619457 1 318 0 -3.406486 0 319 0 1.82393885 1 320 1 5.15885925 1 -321 0 +321 0 ? 0 322 0 -3.09483147 0 323 1 3.22143555 1 324 0 -3.57612 0 @@ -410,7 +410,7 @@ Instance Label Score Assigned 408 0 -2.29045367 0 409 0 -3.13383579 0 410 0 -3.83305979 0 -411 0 +411 0 ? 0 412 1 6.52325153 1 413 0 -2.39560747 0 414 1 5.304202 1 @@ -616,7 +616,7 @@ Instance Label Score Assigned 614 0 -3.64896035 0 615 0 -2.675256 0 616 0 -3.35177135 0 -617 0 +617 0 ? 0 618 0 -3.11112714 0 619 0 -2.870483 0 620 0 -3.35177135 0 diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt index f1da00a926..e07ceec647 100644 --- a/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 2.27614474 0.99046284 0.01382524609823586 1 21 1 2.71551442 0.9964734 0.0050968413754289565 1 22 0 -1.91610467 0.007339344 0.010627482294138626 0 -23 1 +23 1 ? ? ? 0 24 0 -2.12083673 0.00461633364 0.0066753814217522627 0 25 1 0.579404354 0.6851912 0.54542144241146051 1 26 0 -1.88108075 0.007944072 0.011506638331166432 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.9763708 0.0591678135 0.087990678318914578 0 38 1 1.97923112 0.9814142 0.027065950749429996 1 39 1 0.8320954 0.794680536 0.33155308613045453 1 -40 0 +40 0 ? ? ? 0 41 1 0.88030076 0.812019646 0.30041346219868831 1 42 1 3.29876113 0.9990636 0.0013515566126819322 1 43 1 0.21289444 0.485705882 1.0418451361573347 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.76776826 0.010259659 0.014878012048023576 0 137 0 -2.00804472 0.00596073642 0.0086252569347725905 0 138 0 -1.64871967 0.0134130223 0.019481850015569589 0 -139 0 +139 0 ? ? ? 0 140 0 -2.00804472 0.00596073642 0.0086252569347725905 0 141 0 -2.15638113 0.00425880356 0.0061572754121802442 0 142 1 1.42587113 0.93738085 0.093292772480968233 1 143 0 -1.57122457 0.0159614533 0.023213265078036949 0 144 0 -2.06444073 0.005245867 0.007588107121760313 0 -145 0 +145 0 ? ? ? 0 146 1 0.5954063 0.693000734 0.52907121478369279 1 147 0 -1.97373641 0.00644216966 0.0093241524569279595 0 148 0 -0.5180371 0.15157719 0.23714468564136862 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 1.38289 0.931381464 0.10255592440494538 1 156 0 -2.00303721 0.00602870947 0.0087239127220832425 0 157 0 -1.97250068 0.00646021264 0.0093503519898618208 0 -158 0 +158 0 ? ? ? 0 159 1 4.52930737 0.9999432 8.1952060728897203E-05 1 160 1 3.334266 0.9991363 0.0012465526889135054 1 161 0 -1.5841018 0.015507183 0.022547414886151074 0 162 0 -1.82416427 0.009033906 0.01309239897866022 0 163 0 -1.37575865 0.0246935654 0.03607251996647886 0 -164 0 +164 0 ? ? ? 0 165 0 -1.40072858 0.0233600326 0.034101275741716536 0 166 1 3.14331079 0.9986662 0.0019255122730899305 1 167 1 2.61804938 0.995600462 0.0063611951559849299 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.262052536 0.5136946 1.0400654369740645 1 233 1 2.16407657 0.987723053 0.017821513393369265 1 234 0 -1.00943446 0.0551115535 0.081784080061402753 0 -235 0 +235 0 ? ? ? 0 236 1 4.29103756 0.999902248 0.00014103266690546063 1 237 1 2.42226958 0.9931447 0.0099241759578403005 1 238 1 4.649124 0.9999568 6.2345058017421014E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 4.143937 0.9998633 0.00019719158494834585 1 247 1 1.19717526 0.898898065 0.15377057127992425 1 248 0 -1.217197 0.03506021 0.051489167959956395 0 -249 0 +249 0 ? ? ? 0 250 0 -2.09497738 0.004895074 0.0070794400974987601 0 251 1 2.700413 0.9963504 0.0052748803033137396 1 252 0 1.58717227 0.955785036 4.4993214783146023 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.980280161 0.8443483 0.2440898311411345 1 273 1 0.00625777245 0.3710024 1.4304995524008379 1 274 0 -1.73243809 0.0111099789 0.01611801356385726 0 -275 0 +275 0 ? ? ? 0 276 0 -1.91610467 0.007339344 0.010627482294138626 0 277 0 -2.21277714 0.00374727719 0.0054163328257276827 0 278 0 -2.12083673 0.00461633364 0.0066753814217522627 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 3.0956676 0.9985136 0.002146047033326764 1 290 0 -2.30471754 0.00304132653 0.0043943924919565489 0 291 0 -2.12083673 0.00461633364 0.0066753814217522627 0 -292 1 +292 1 ? ? ? 0 293 1 1.9506762 0.980189741 0.028867048302480647 1 -294 0 +294 0 ? ? ? 0 295 1 2.57708764 0.995172262 0.0069818201865888847 1 296 0 0.6443205 0.7161847 1.8169756066019795 1 -297 0 +297 0 ? ? ? 0 298 0 -1.00866961 0.0552023575 0.081922729978655878 0 299 1 2.32091022 0.9913795 0.012490669010981763 1 300 1 2.40809751 0.992921352 0.010248646031556275 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 1.19807649 0.899084449 0.15347146394878203 1 313 0 -2.30471754 0.00304132653 0.0043943924919565489 0 314 0 -2.26844478 0.00330244377 0.0047723037303054865 0 -315 0 +315 0 ? ? ? 0 316 1 1.47730184 0.943915665 0.083270128208594502 1 317 1 3.18132329 0.998776734 0.0017658802629188005 1 318 0 -1.9538343 0.00673895236 0.0097551599741469968 0 319 0 0.68000555 0.732414246 1.9019267823886503 1 320 1 2.448116 0.993534148 0.009358541174187586 1 -321 0 +321 0 ? ? ? 0 322 0 -1.82416427 0.009033906 0.01309239897866022 0 323 1 1.739836 0.9683619 0.046381755881038968 1 324 0 -2.12083673 0.00461633364 0.0066753814217522627 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -1.41561711 0.022598628 0.032976965091470092 0 409 0 -1.79705584 0.009603847 0.013922383925987339 0 410 0 -2.21277714 0.00374727719 0.0054163328257276827 0 -411 0 +411 0 ? ? ? 0 412 1 3.177976 0.9987674 0.0017793974994358322 1 413 0 -1.40844309 0.0229624342 0.033514061939899156 0 414 1 2.38908362 0.992610335 0.010700618410080708 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -2.1201086 0.00462396163 0.0066864373761273176 0 615 0 -1.529671 0.01751844 0.025497765300806271 0 616 0 -1.91610467 0.007339344 0.010627482294138626 0 -617 0 +617 0 ? ? ? 0 618 0 -1.76776826 0.010259659 0.014878012048023576 0 619 0 -1.61943209 0.0143251875 0.020816334525491278 0 620 0 -1.91610467 0.007339344 0.010627482294138626 0 diff --git a/test/BaselineOutput/SingleRelease/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt b/test/BaselineOutput/SingleRelease/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt index 3608e165c9..338a27b79c 100644 --- a/test/BaselineOutput/SingleRelease/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt +++ b/test/BaselineOutput/SingleRelease/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt @@ -38,6 +38,8 @@ DCG@2: 0.000000 (0.0000) DCG@3: 0.000000 (0.0000) --------------------------------------- +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' Physical memory usage(MB): %Number% Virtual memory usage(MB): %Number% %DateTime% Time elapsed(s): %Number% diff --git a/test/BaselineOutput/SingleRelease/Command/Datatypes-1-out.txt b/test/BaselineOutput/SingleRelease/Command/Datatypes-1-out.txt new file mode 100644 index 0000000000..fe04f014c2 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/Command/Datatypes-1-out.txt @@ -0,0 +1 @@ +Wrote 5 rows across 9 columns in %Time% diff --git a/test/BaselineOutput/SingleRelease/Command/Datatypes-2-out.txt b/test/BaselineOutput/SingleRelease/Command/Datatypes-2-out.txt new file mode 100644 index 0000000000..a2aaab4439 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/Command/Datatypes-2-out.txt @@ -0,0 +1 @@ +Wrote 5 rows of length 9 diff --git a/test/BaselineOutput/SingleRelease/Command/Datatypes-datatypes.txt b/test/BaselineOutput/SingleRelease/Command/Datatypes-datatypes.txt index e7d128e400..aaf1a3cb2e 100644 --- a/test/BaselineOutput/SingleRelease/Command/Datatypes-datatypes.txt +++ b/test/BaselineOutput/SingleRelease/Command/Datatypes-datatypes.txt @@ -14,6 +14,6 @@ bl i1 i2 i4 i8 ts dto dt tx 0 127 32767 2147483647 9223372036854775807 "2.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" foo 1 -127 -32767 -2147483647 -9223372036854775807 "7.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" xyz - "7.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" +0 -128 -32768 -2147483648 -9223372036854775808 "7.00:00:00" "2008-11-30T00:00:00.0000000+00:00" "2013-08-05T00:00:00.0000000" "" 9 0:0 - +0 -128 -32768 -2147483648 -9223372036854775808 "00:00:00" "0001-01-01T00:00:00.0000000+00:00" "0001-01-01T00:00:00.0000000" "" diff --git a/test/BaselineOutput/SingleRelease/Command/Datatypes-out.txt b/test/BaselineOutput/SingleRelease/Command/Datatypes-out.txt new file mode 100644 index 0000000000..a2aaab4439 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/Command/Datatypes-out.txt @@ -0,0 +1 @@ +Wrote 5 rows of length 9 diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-out.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-out.txt index 193a84b3cd..f6c942f824 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-out.txt @@ -46,6 +46,12 @@ Log-loss: 0.253074 (0.0597) Log-loss reduction: 76.713844 (5.4729) --------------------------------------- +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' Physical memory usage(MB): %Number% Virtual memory usage(MB): %Number% %DateTime% Time elapsed(s): %Number% diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt index e6f1c68e64..28327f69a4 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt @@ -50,6 +50,10 @@ Log-loss: 0.253074 (0.0597) Log-loss reduction: 76.713839 (5.4729) --------------------------------------- +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' +Warning: There is no NA value for type 'Text'. The missing key value will be mapped to the default value of 'Text' Physical memory usage(MB): %Number% Virtual memory usage(MB): %Number% %DateTime% Time elapsed(s): %Number% diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-out.txt similarity index 63% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-out.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-out.txt index 9631870541..afa867d488 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-out.txt @@ -7,24 +7,24 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.524348 -L2(avg): 0.466735 -RMS(avg): 0.683180 -Loss-fn(avg): 0.466735 -R Squared: 0.400415 -L1(avg): 0.517508 -L2(avg): 0.458039 -RMS(avg): 0.676786 -Loss-fn(avg): 0.458039 -R Squared: 0.420159 +L1(avg): 27.477977 +L2(avg): 1,428.594095 +RMS(avg): 37.796747 +Loss-fn(avg): 1,428.594094 +R Squared: 0.920504 +L1(avg): 26.801569 +L2(avg): 1,413.398603 +RMS(avg): 37.595194 +Loss-fn(avg): 1,413.398596 +R Squared: 0.923322 OVERALL RESULTS --------------------------------------- -L1(avg): 0.520928 (0.0034) -L2(avg): 0.462387 (0.0043) -RMS(avg): 0.679983 (0.0032) -Loss-fn(avg): 0.462387 (0.0043) -R Squared: 0.410287 (0.0099) +L1(avg): 27.139773 (0.3382) +L2(avg): 1,420.996349 (7.5977) +RMS(avg): 37.695971 (0.1008) +Loss-fn(avg): 1,420.996345 (7.5977) +R Squared: 0.921913 (0.0014) --------------------------------------- Physical memory usage(MB): %Number% @@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l2: 0.189697165394939 +[2] (%Time%) Iteration: 50 Training-l2: 37.107605006517 [2] 'Training with LightGBM' finished in %Time%. [3] 'Loading data for LightGBM #2' started. [3] 'Loading data for LightGBM #2' finished in %Time%. [4] 'Training with LightGBM #2' started. -[4] (%Time%) Iteration: 50 Training-l2: 0.204982247876212 +[4] (%Time%) Iteration: 50 Training-l2: 27.7037679135951 [4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-rp.txt similarity index 89% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-rp.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-rp.txt index ede416ff06..368e4a4273 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-rp.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.520928 0.462387 0.679983 0.462387 0.410287 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 +27.13977 1420.996 37.69597 1420.996 0.921913 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt new file mode 100644 index 0000000000..3b188aa21f --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt @@ -0,0 +1,501 @@ +Instance Label Score L1-loss L2-loss +5 322.18 281.224945 40.955047607421875 1677.3159245261922 +6 425.31 428.151917 2.8419189453125 8.0765032917261124 +8 159.37 106.197723 53.172271728515625 2827.290480771102 +9 325.18 309.94397 15.23602294921875 232.13639530912042 +10 276.02 259.230316 16.7896728515625 281.89311446249485 +11 193.96 196.782562 2.8225555419921875 7.9668197876308113 +18 471.27 470.431122 0.8388671875 0.70369815826416016 +20 290.11 267.865662 22.24432373046875 494.80993822589517 +21 432.91 409.047363 23.862640380859375 569.42560594622046 +25 413.49 387.7714 25.718597412109375 661.44625284615904 +28 360.71 395.3086 34.598602294921875 1197.0632807621732 +31 372.81 338.0285 34.781494140625 1209.7523346543312 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0.42372608184814453 0.17954379243838048 -4878 4 4.537464 0.53746414184570312 0.28886770376993809 -4879 6 5.50284052 0.49715948104858398 0.24716754959649734 -4880 6 5.50284052 0.49715948104858398 0.24716754959649734 -4881 6 5.90926361 0.09073638916015625 0.0082330923178233206 -4882 5 5.77714348 0.77714347839355469 0.6039519860096334 -4883 6 6.174638 0.17463779449462891 0.030498359265948238 -4886 7 6.898373 0.10162687301635742 0.010328021319082836 -4887 7 4.873293 2.1267070770263672 4.5228829914740345 -4888 5 5.89248848 0.89248847961425781 0.79653568624416948 -4889 6 5.90926361 0.09073638916015625 0.0082330923178233206 -4894 5 5.370122 0.37012195587158203 0.13699026221820532 -4896 7 6.225442 0.77455806732177734 0.59994019965324696 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-out.txt similarity index 68% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-out.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-out.txt index 56988214d4..f15a4bb020 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-out.txt @@ -3,19 +3,19 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.402080 -L2(avg): 0.272274 -RMS(avg): 0.521799 -Loss-fn(avg): 0.272274 -R Squared: 0.652798 +L1(avg): 3.472291 +L2(avg): 26.064428 +RMS(avg): 5.105333 +Loss-fn(avg): 26.064428 +R Squared: 0.998571 OVERALL RESULTS --------------------------------------- -L1(avg): 0.402080 (0.0000) -L2(avg): 0.272274 (0.0000) -RMS(avg): 0.521799 (0.0000) -Loss-fn(avg): 0.272274 (0.0000) -R Squared: 0.652798 (0.0000) +L1(avg): 3.472291 (0.0000) +L2(avg): 26.064428 (0.0000) +RMS(avg): 5.105333 (0.0000) +Loss-fn(avg): 26.064428 (0.0000) +R Squared: 0.998571 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% @@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l2: 0.272273893168108 +[2] (%Time%) Iteration: 50 Training-l2: 26.0644295080124 [2] 'Training with LightGBM' finished in %Time%. [3] 'Saving model' started. [3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt new file mode 100644 index 0000000000..58c87f276b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +3.472291 26.06443 5.105333 26.06443 0.998571 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 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a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -LightGBMR -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.40208 0.272274 0.521799 0.272274 0.652798 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 - diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine.txt deleted file mode 100644 index 34d954ed70..0000000000 --- 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0.19656626694109036 -4895 6 5.4413476 0.55865240097045898 0.31209250511005848 -4896 7 7.037857 0.0378570556640625 0.0014331566635519266 -4897 6 5.9355073 0.064492702484130859 0.0041593086737066187 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-out.txt similarity index 63% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-out.txt index 0209b5a363..c2530555e1 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-out.txt @@ -7,24 +7,24 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.529227 -L2(avg): 0.479877 -RMS(avg): 0.692731 -Loss-fn(avg): 0.479877 -R Squared: 0.383532 -L1(avg): 0.519622 -L2(avg): 0.463327 -RMS(avg): 0.680681 -Loss-fn(avg): 0.463327 -R Squared: 0.413465 +L1(avg): 27.482854 +L2(avg): 1,445.214986 +RMS(avg): 38.015983 +Loss-fn(avg): 1,445.214986 +R Squared: 0.919579 +L1(avg): 25.716714 +L2(avg): 1,341.437580 +RMS(avg): 36.625641 +Loss-fn(avg): 1,341.437567 +R Squared: 0.927226 OVERALL RESULTS --------------------------------------- -L1(avg): 0.524425 (0.0048) -L2(avg): 0.471602 (0.0083) -RMS(avg): 0.686706 (0.0060) -Loss-fn(avg): 0.471602 (0.0083) -R Squared: 0.398499 (0.0150) +L1(avg): 26.599784 (0.8831) +L2(avg): 1,393.326283 (51.8887) +RMS(avg): 37.320812 (0.6952) +Loss-fn(avg): 1,393.326277 (51.8887) +R Squared: 0.923402 (0.0038) --------------------------------------- Physical memory usage(MB): %Number% @@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l1: 0.344282018934789 +[2] (%Time%) Iteration: 50 Training-l1: 2.72125878362794 [2] 'Training with LightGBM' finished in %Time%. [3] 'Loading data for LightGBM #2' started. [3] 'Loading data for LightGBM #2' finished in %Time%. [4] 'Training with LightGBM #2' started. -[4] (%Time%) Iteration: 50 Training-l1: 0.345211959030736 +[4] (%Time%) Iteration: 50 Training-l1: 2.24116204430926 [4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-rp.txt index e2cc16093d..d7359a5e25 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae +26.59978 1393.326 37.32081 1393.326 0.923402 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-generatedRegressionDataset.MAE.txt new file mode 100644 index 0000000000..ed40bebf10 --- /dev/null +++ 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5.44779968 0.5522003173828125 0.30492519051767886 -4881 6 5.75216627 0.2478337287902832 0.061421557126095649 -4882 5 5.97157431 0.97157430648803711 0.94395663302771027 -4883 6 6.152341 0.15234088897705078 0.023207746454318112 -4886 7 6.863344 0.13665580749511719 0.018674809722142527 -4887 7 4.93395472 2.0660452842712402 4.2685431166594299 -4888 5 6.00417376 1.004173755645752 1.0083649315276944 -4889 6 5.75216627 0.2478337287902832 0.061421557126095649 -4894 5 5.44779968 0.4477996826171875 0.20052455575205386 -4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-out.txt similarity index 67% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-out.txt index 4792a52b30..aaad5d20e5 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-out.txt @@ -3,19 +3,19 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.407028 -L2(avg): 0.274963 -RMS(avg): 0.524369 -Loss-fn(avg): 0.274963 -R Squared: 0.649369 +L1(avg): 3.428896 +L2(avg): 25.236013 +RMS(avg): 5.023546 +Loss-fn(avg): 25.236013 +R Squared: 0.998616 OVERALL RESULTS --------------------------------------- -L1(avg): 0.407028 (0.0000) -L2(avg): 0.274963 (0.0000) -RMS(avg): 0.524369 (0.0000) -Loss-fn(avg): 0.274963 (0.0000) -R Squared: 0.649369 (0.0000) +L1(avg): 3.428896 (0.0000) +L2(avg): 25.236013 (0.0000) +RMS(avg): 5.023546 (0.0000) +Loss-fn(avg): 25.236013 (0.0000) +R Squared: 0.998616 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% @@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-l1: 0.407028426136092 +[2] (%Time%) Iteration: 50 Training-l1: 3.42889585604196 [2] 'Training with LightGBM' finished in %Time%. [3] 'Saving model' started. [3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-rp.txt index cf5f4d432b..59ac27dc61 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae +3.428896 25.23601 5.023546 25.23601 0.998616 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-generatedRegressionDataset.MAE.txt new file mode 100644 index 0000000000..5f5a7cc20b --- /dev/null +++ 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6 6.05903625 0.0590362548828125 0.0034852793905884027 -1996 6 6.00442839 0.0044283866882324219 1.9610608660514117E-05 -1997 6 6.05903625 0.0590362548828125 0.0034852793905884027 -1998 6 6.05903625 0.0590362548828125 0.0034852793905884027 -1999 6 6.65917635 0.65917634963989258 0.43451345992457391 -2000 5 5.15526772 0.15526771545410156 0.024108063462335849 -2001 5 5.116252 0.11625194549560547 0.013514514831513225 -2002 6 6.24034739 0.24034738540649414 0.057766865671737833 -2003 6 5.74645329 0.25354671478271484 0.06428593657710735 -2004 6 5.97547865 0.024521350860595703 0.00060129664802843763 -2005 6 6.00442839 0.0044283866882324219 1.9610608660514117E-05 -2006 6 6.05903625 0.0590362548828125 0.0034852793905884027 -2007 6 6.035142 0.035141944885253906 0.0012349562903182232 -2008 5 5.388008 0.38800811767578125 0.15055029938230291 -2009 7 6.72901726 0.27098274230957031 0.073431646629614988 -2010 6 6.07737875 0.077378749847412109 0.0059874709279483795 -2011 5 5.388008 0.38800811767578125 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0.16701602935791016 0.027894354062482307 -4887 7 6.50685453 0.49314546585083008 0.24319245048923221 -4888 5 5.990843 0.99084281921386719 0.98176949238768429 -4889 6 6.151292 0.15129184722900391 0.022889223037964257 -4890 6 6.509506 0.5095062255859375 0.25959659391082823 -4891 6 5.611698 0.38830184936523438 0.15077832622046117 -4892 5 5.985514 0.98551416397094727 0.97123816738735513 -4893 6 6.2242403 0.22424030303955078 0.050283713507269567 -4894 5 5.458216 0.45821619033813477 0.20996207708799375 -4895 6 5.30194473 0.69805526733398438 0.48728115625272039 -4896 7 6.68631124 0.31368875503540039 0.098400635035659434 -4897 6 6.04107332 0.041073322296142578 0.001687017804442803 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-out.txt similarity index 62% rename from test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-out.txt index 6483a88e76..483c724038 100644 --- a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-out.txt @@ -7,24 +7,24 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.529227 -L2(avg): 0.479877 -RMS(avg): 0.692731 -Loss-fn(avg): 0.479877 -R Squared: 0.383532 -L1(avg): 0.519622 -L2(avg): 0.463327 -RMS(avg): 0.680681 -Loss-fn(avg): 0.463327 -R Squared: 0.413465 +L1(avg): 27.482854 +L2(avg): 1,445.214986 +RMS(avg): 38.015983 +Loss-fn(avg): 1,445.214986 +R Squared: 0.919579 +L1(avg): 25.716714 +L2(avg): 1,341.437580 +RMS(avg): 36.625641 +Loss-fn(avg): 1,341.437567 +R Squared: 0.927226 OVERALL RESULTS --------------------------------------- -L1(avg): 0.524425 (0.0048) -L2(avg): 0.471602 (0.0083) -RMS(avg): 0.686706 (0.0060) -Loss-fn(avg): 0.471602 (0.0083) -R Squared: 0.398499 (0.0150) +L1(avg): 26.599784 (0.8831) +L2(avg): 1,393.326283 (51.8887) +RMS(avg): 37.320812 (0.6952) +Loss-fn(avg): 1,393.326277 (51.8887) +R Squared: 0.923402 (0.0038) --------------------------------------- Physical memory usage(MB): %Number% @@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-rmse: 0.444161678699535 +[2] (%Time%) Iteration: 50 Training-rmse: 6.01041394354288 [2] 'Training with LightGBM' finished in %Time%. [3] 'Loading data for LightGBM #2' started. [3] 'Loading data for LightGBM #2' finished in %Time%. [4] 'Training with LightGBM #2' started. -[4] (%Time%) Iteration: 50 Training-rmse: 0.447777922357938 +[4] (%Time%) Iteration: 50 Training-rmse: 4.74971028160668 [4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-rp.txt index 56cb0fac01..1b893d09dc 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse +26.59978 1393.326 37.32081 1393.326 0.923402 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-generatedRegressionDataset.RMSE.txt new file mode 100644 index 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0.51450824737548828 0.26471873661739664 -4879 6 5.44779968 0.5522003173828125 0.30492519051767886 -4880 6 5.44779968 0.5522003173828125 0.30492519051767886 -4881 6 5.75216627 0.2478337287902832 0.061421557126095649 -4882 5 5.97157431 0.97157430648803711 0.94395663302771027 -4883 6 6.152341 0.15234088897705078 0.023207746454318112 -4886 7 6.863344 0.13665580749511719 0.018674809722142527 -4887 7 4.93395472 2.0660452842712402 4.2685431166594299 -4888 5 6.00417376 1.004173755645752 1.0083649315276944 -4889 6 5.75216627 0.2478337287902832 0.061421557126095649 -4894 5 5.44779968 0.4477996826171875 0.20052455575205386 -4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-out.txt similarity index 67% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-out.txt index d01de377a4..1ed592dd87 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-out.txt @@ -3,19 +3,19 @@ Not adding a normalizer. Auto-tuning parameters: UseCat = False LightGBM objective=regression Not training a calibrator because it is not needed. -L1(avg): 0.407028 -L2(avg): 0.274963 -RMS(avg): 0.524369 -Loss-fn(avg): 0.274963 -R Squared: 0.649369 +L1(avg): 3.428896 +L2(avg): 25.236013 +RMS(avg): 5.023546 +Loss-fn(avg): 25.236013 +R Squared: 0.998616 OVERALL RESULTS --------------------------------------- -L1(avg): 0.407028 (0.0000) -L2(avg): 0.274963 (0.0000) -RMS(avg): 0.524369 (0.0000) -Loss-fn(avg): 0.274963 (0.0000) -R Squared: 0.649369 (0.0000) +L1(avg): 3.428896 (0.0000) +L2(avg): 25.236013 (0.0000) +RMS(avg): 5.023546 (0.0000) +Loss-fn(avg): 25.236013 (0.0000) +R Squared: 0.998616 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% @@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number% [1] 'Loading data for LightGBM' started. [1] 'Loading data for LightGBM' finished in %Time%. [2] 'Training with LightGBM' started. -[2] (%Time%) Iteration: 50 Training-rmse: 0.524369259872456 +[2] (%Time%) Iteration: 50 Training-rmse: 5.02354584275365 [2] 'Training with LightGBM' finished in %Time%. [3] 'Saving model' started. [3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-rp.txt similarity index 88% rename from test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt rename to test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-rp.txt index 2c813d3277..b8f135e448 100644 --- a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE-rp.txt @@ -1,4 +1,4 @@ LightGBMR L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse +3.428896 25.23601 5.023546 25.23601 0.998616 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE.txt new file mode 100644 index 0000000000..5f5a7cc20b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-generatedRegressionDataset.RMSE.txt @@ -0,0 +1,501 @@ +Instance Label Score L1-loss L2-loss +0 140.66 140.224045 0.4359588623046875 0.19006012962199748 +1 148.12 150.455109 2.335113525390625 5.4527551764622331 +2 402.2 399.137634 3.0623779296875 9.3781585842370987 +3 443.51 440.823761 2.686248779296875 7.2159325042739511 +4 329.59 325.1096 4.48040771484375 20.074053291231394 +5 322.18 316.19873 5.98126220703125 35.77549758926034 +6 425.31 421.0235 4.2864990234375 18.374073877930641 +7 421.76 423.108826 1.34881591796875 1.8193043805658817 +8 159.37 154.908264 4.46173095703125 19.907043132930994 +9 325.18 323.497437 1.68255615234375 2.8309952057898045 +10 276.02 282.882629 6.862640380859375 47.095832997001708 +11 193.96 185.404175 8.5558319091796875 73.202259658137336 +12 472.97 463.2464 9.723602294921875 94.548441589809954 +13 266.98 267.559174 0.57916259765625 0.33542931452393532 +14 331.77 331.464661 0.305328369140625 0.093225413002073765 +15 187.16 187.073364 0.086639404296875 0.0075063863769173622 +16 287.02 291.93634 4.916351318359375 24.170510285533965 +17 404.13 402.2706 1.859405517578125 3.4573888787999749 +18 471.27 476.047943 4.7779541015625 22.828845396637917 +19 449.73 450.840942 1.110931396484375 1.2341685676947236 +20 290.11 283.6476 6.462371826171875 41.762249619700015 +21 432.91 428.31134 4.598663330078125 21.14770442340523 +22 522.87 531.2432 8.37322998046875 70.110980305820704 +23 324.79 330.6525 5.86248779296875 34.368763122707605 +24 456.12 461.731873 5.61187744140625 31.493168417364359 +25 413.49 410.420471 3.06951904296875 9.4219471551477909 +26 235.38 226.345764 9.03424072265625 81.617505434900522 +27 476.59 473.507233 3.082763671875 9.5034318566322327 +28 360.71 358.212372 2.49761962890625 6.238103810697794 +29 205.51 209.709946 4.199951171875 17.639589846134186 +30 237.69 240.792557 3.1025543212890625 9.6258433165494353 +31 372.81 374.748138 1.938140869140625 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0.45354270935058594 0.20570098920507007 -4836 5 5.27756 0.27756023406982422 0.077039683536895609 -4837 6 6.6851635 0.68516349792480469 0.46944901888855384 -4838 6 6.40559673 0.40559673309326172 0.16450870989592659 -4839 4 4.615171 0.61517095565795898 0.37843530468512654 -4840 7 6.953846 0.046154022216796875 0.0021301937667885795 -4841 6 6.42197466 0.42197465896606445 0.1780626128095264 -4842 6 5.909714 0.090285778045654297 0.0081515217173091514 -4843 5 5.849253 0.84925317764282227 0.72123095973643103 -4844 6 5.93484 0.065159797668457031 0.0042457992321942584 -4845 5 5.007286 0.00728607177734375 5.3086841944605112E-05 -4846 6 6.584194 0.58419418334960938 0.34128284385951702 -4847 7 6.47132969 0.52867031097412109 0.2794922977054739 -4848 6 6.12405062 0.12405061721801758 0.015388555632171119 -4849 5 5.252341 0.25234079360961914 0.063675876119532404 -4850 6 6.12405062 0.12405061721801758 0.015388555632171119 -4851 5 5.252341 0.25234079360961914 0.063675876119532404 -4852 5 5.93845654 0.93845653533935547 0.88070066872114694 -4853 5 5.962768 0.9627680778503418 0.9269223717276418 -4854 6 6.64682436 0.64682435989379883 0.41838175255202259 -4855 6 5.811765 0.18823480606079102 0.035432342212743606 -4856 6 5.811765 0.18823480606079102 0.035432342212743606 -4857 6 5.96738768 0.032612323760986328 0.0010635636610913934 -4858 5 5.23224735 0.23224735260009766 0.053938832789754088 -4859 6 6.204521 0.20452117919921875 0.041828912741038948 -4860 6 5.651828 0.34817218780517578 0.1212238723610426 -4861 6 6.31806231 0.31806230545043945 0.10116363014844865 -4862 6 6.010396 0.010396003723144531 0.00010807689341163496 -4863 7 6.99840736 0.0015926361083984375 2.5364897737745196E-06 -4864 5 5.177444 0.1774439811706543 0.031486366453691517 -4865 6 6.59354162 0.59354162216186523 0.35229165723853839 -4866 6 6.05234241 0.052342414855957031 0.0027397283929531113 -4867 6 5.65121841 0.34878158569335938 0.12164859451877419 -4868 6 6.40162659 0.4016265869140625 0.161303915316239 -4869 6 5.28494453 0.71505546569824219 0.51130431902493001 -4870 7 6.390793 0.6092071533203125 0.37113335565663874 -4871 6 6.365685 0.36568498611450195 0.13372550906956349 -4872 5 5.51866674 0.51866674423217773 0.26901519157240728 -4873 6 6.25817537 0.25817537307739258 0.066654523263650844 -4874 6 5.92614746 0.0738525390625 0.0054541975259780884 -4875 6 5.56610727 0.43389272689819336 0.18826289845515021 -4876 7 6.79692745 0.20307254791259766 0.041238459715714271 -4877 5 4.49993134 0.50006866455078125 0.25006866926560178 -4878 4 4.44558573 0.44558572769165039 0.19854664072249761 -4879 6 5.391604 0.60839605331420898 0.37014575768830582 -4880 6 5.391604 0.60839605331420898 0.37014575768830582 -4881 6 6.151292 0.15129184722900391 0.022889223037964257 -4882 5 5.638549 0.63854885101318359 0.40774463513025694 -4883 6 5.91789675 0.082103252410888672 0.0067409440564460965 -4884 5 5.34791565 0.3479156494140625 0.12104529910720885 -4885 6 5.391604 0.60839605331420898 0.37014575768830582 -4886 7 7.167016 0.16701602935791016 0.027894354062482307 -4887 7 6.50685453 0.49314546585083008 0.24319245048923221 -4888 5 5.990843 0.99084281921386719 0.98176949238768429 -4889 6 6.151292 0.15129184722900391 0.022889223037964257 -4890 6 6.509506 0.5095062255859375 0.25959659391082823 -4891 6 5.611698 0.38830184936523438 0.15077832622046117 -4892 5 5.985514 0.98551416397094727 0.97123816738735513 -4893 6 6.2242403 0.22424030303955078 0.050283713507269567 -4894 5 5.458216 0.45821619033813477 0.20996207708799375 -4895 6 5.30194473 0.69805526733398438 0.48728115625272039 -4896 7 6.68631124 0.31368875503540039 0.098400635035659434 -4897 6 6.04107332 0.041073322296142578 0.001687017804442803 diff --git a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt index b337fd78da..b113f448bc 100644 --- a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 1.25826859 1 0 1 35 0 -1.41764426 1E-15 1.4415419267167138E-15 0 37 0 -0.835641861 1E-15 1.4415419267167138E-15 0 -40 0 +40 0 ? ? ? 0 41 1 0.227130175 0.8666667 0.20645086423799175 1 44 1 1.61423349 1 0 1 45 0 -1.47288513 1E-15 1.4415419267167138E-15 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.19960022 1E-15 1.4415419267167138E-15 0 141 0 -1.47188914 1E-15 1.4415419267167138E-15 0 144 0 -1.41764426 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 147 0 -1.334388 1E-15 1.4415419267167138E-15 0 150 0 -1.48176765 1E-15 1.4415419267167138E-15 0 151 1 0.7797704 1 0 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -1.54539037 1E-15 1.4415419267167138E-15 0 156 0 -1.35846376 1E-15 1.4415419267167138E-15 0 161 0 -1.1599288 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 167 1 1.07981849 1 0 1 169 0 -1.5413084 1E-15 1.4415419267167138E-15 0 171 0 -1.43690062 1E-15 1.4415419267167138E-15 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.368261 1 0 1 247 1 0.4837153 0.8666667 0.20645086423799175 1 248 0 -0.934056163 1E-15 1.4415419267167138E-15 0 -249 0 +249 0 ? ? ? 0 250 0 -1.41270852 1E-15 1.4415419267167138E-15 0 252 0 0.706041336 0.8666667 2.9068906815998465 1 254 1 1.600352 1 0 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.43690062 1E-15 1.4415419267167138E-15 0 271 0 -1.1428957 1E-15 1.4415419267167138E-15 0 272 1 0.435359716 0.8666667 0.20645086423799175 1 -275 0 +275 0 ? ? ? 0 276 0 -1.34414315 1E-15 1.4415419267167138E-15 0 277 0 -1.49114561 1E-15 1.4415419267167138E-15 0 278 0 -1.43690062 1E-15 1.4415419267167138E-15 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.43690062 1E-15 1.4415419267167138E-15 0 293 1 0.9331081 1 0 1 296 0 0.221757889 0.8666667 2.9068906815998465 1 -297 0 +297 0 ? ? ? 0 299 1 1.13769388 1 0 1 300 1 1.24829745 1 0 1 301 0 -1.43690062 1E-15 1.4415419267167138E-15 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.7455783 0.9151356 0.12794252993534275 1 317 1 1.81665158 1 0 1 319 0 0.285441637 0.8666667 2.9068906815998465 1 -321 0 +321 0 ? ? ? 0 323 1 0.8990345 1 0 1 327 0 -1.49114561 1E-15 1.4415419267167138E-15 0 328 1 0.8467562 1 0 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 2.95104 1 0 1 613 0 -1.29436374 1E-15 1.4415419267167138E-15 0 614 0 -1.4625113 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -1.2706418 1E-15 1.4415419267167138E-15 0 619 0 -1.19714069 1E-15 1.4415419267167138E-15 0 621 0 -0.3866408 0.5799383 1.2513268180432666 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.13828516 1E-15 1.4415419267167138E-15 0 19 0 -0.9969288 1E-15 1.4415419267167138E-15 0 22 0 -1.259604 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -1.35031986 1E-15 1.4415419267167138E-15 0 26 0 -1.22985053 1E-15 1.4415419267167138E-15 0 27 0 -1.11824775 1E-15 1.4415419267167138E-15 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.22317672 1E-15 1.4415419267167138E-15 0 135 0 -0.8295188 1E-15 1.4415419267167138E-15 0 136 0 -1.18892586 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -1.3102448 1E-15 1.4415419267167138E-15 0 142 1 0.6019325 0.875 0.19264507794239591 1 143 0 -1.00445139 1E-15 1.4415419267167138E-15 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.01599169 1E-15 1.4415419267167138E-15 0 155 1 0.7134235 0.875 0.19264507794239591 1 157 0 -1.27964163 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 2.42447567 1 0 1 160 1 1.81060028 1 0 1 162 0 -1.20896339 1E-15 1.4415419267167138E-15 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1.72919893 1 0 1 232 0 -0.038654685 0.6923077 1.7004398041324202 0 234 0 -0.661043048 1E-15 1.4415419267167138E-15 0 -235 0 +235 0 ? ? ? 0 236 1 2.13174057 1 0 1 238 1 2.40485334 1 0 1 243 0 -0.8018886 1E-15 1.4415419267167138E-15 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 2.9424572 1 0 1 287 0 -1.22317672 1E-15 1.4415419267167138E-15 0 289 1 1.71858239 1 0 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 1.42958808 1 0 1 298 0 -0.7813908 1E-15 1.4415419267167138E-15 0 302 1 2.925787 1 0 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.35031986 1E-15 1.4415419267167138E-15 0 310 0 -1.293855 1E-15 1.4415419267167138E-15 0 313 0 -1.45160127 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 318 0 -1.24103785 1E-15 1.4415419267167138E-15 0 320 1 1.18153763 1 0 1 322 0 -1.20896339 1E-15 1.4415419267167138E-15 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -1.40096045 1E-15 1.4415419267167138E-15 0 408 0 -0.928058 1E-15 1.4415419267167138E-15 0 410 0 -1.40096045 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 1.731832 1 0 1 417 0 -1.40096045 1E-15 1.4415419267167138E-15 0 420 0 -0.7677182 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt index fed143de3d..000020d071 100644 --- a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt @@ -14,7 +14,7 @@ Instance Label Score Assigned 32 1 1.25826859 1 35 0 -1.41764426 0 37 0 -0.835641861 0 -40 0 +40 0 ? 0 41 1 0.227130175 1 44 1 1.61423349 1 45 0 -1.47288513 0 @@ -76,7 +76,7 @@ Instance Label Score Assigned 138 0 -1.19960022 0 141 0 -1.47188914 0 144 0 -1.41764426 0 -145 0 +145 0 ? 0 147 0 -1.334388 0 150 0 -1.48176765 0 151 1 0.7797704 1 @@ -84,7 +84,7 @@ Instance Label Score Assigned 154 0 -1.54539037 0 156 0 -1.35846376 0 161 0 -1.1599288 0 -164 0 +164 0 ? 0 167 1 1.07981849 1 169 0 -1.5413084 0 171 0 -1.43690062 0 @@ -130,7 +130,7 @@ Instance Label Score Assigned 246 1 2.368261 1 247 1 0.4837153 1 248 0 -0.934056163 0 -249 0 +249 0 ? 0 250 0 -1.41270852 0 252 0 0.706041336 1 254 1 1.600352 1 @@ -144,7 +144,7 @@ Instance Label Score Assigned 269 0 -1.43690062 0 271 0 -1.1428957 0 272 1 0.435359716 1 -275 0 +275 0 ? 0 276 0 -1.34414315 0 277 0 -1.49114561 0 278 0 -1.43690062 0 @@ -158,7 +158,7 @@ Instance Label Score Assigned 291 0 -1.43690062 0 293 1 0.9331081 1 296 0 0.221757889 1 -297 0 +297 0 ? 0 299 1 1.13769388 1 300 1 1.24829745 1 301 0 -1.43690062 0 @@ -172,7 +172,7 @@ Instance Label Score Assigned 316 1 0.7455783 1 317 1 1.81665158 1 319 0 0.285441637 1 -321 0 +321 0 ? 0 323 1 0.8990345 1 327 0 -1.49114561 0 328 1 0.8467562 1 @@ -318,7 +318,7 @@ Instance Label Score Assigned 612 1 2.95104 1 613 0 -1.29436374 0 614 0 -1.4625113 0 -617 0 +617 0 ? 0 618 0 -1.2706418 0 619 0 -1.19714069 0 621 0 -0.3866408 0 @@ -375,7 +375,7 @@ Instance Label Score Assigned 17 0 -1.13828516 0 19 0 -0.9969288 0 22 0 -1.259604 0 -23 1 +23 1 ? 0 24 0 -1.35031986 0 26 0 -1.22985053 0 27 0 -1.11824775 0 @@ -425,7 +425,7 @@ Instance Label Score Assigned 134 0 -1.22317672 0 135 0 -0.8295188 0 136 0 -1.18892586 0 -139 0 +139 0 ? 0 140 0 -1.3102448 0 142 1 0.6019325 1 143 0 -1.00445139 0 @@ -435,7 +435,7 @@ Instance Label Score Assigned 153 0 -1.01599169 0 155 1 0.7134235 1 157 0 -1.27964163 0 -158 0 +158 0 ? 0 159 1 2.42447567 1 160 1 1.81060028 1 162 0 -1.20896339 0 @@ -474,7 +474,7 @@ Instance Label Score Assigned 231 1 1.72919893 1 232 0 -0.038654685 0 234 0 -0.661043048 0 -235 0 +235 0 ? 0 236 1 2.13174057 1 238 1 2.40485334 1 243 0 -0.8018886 0 @@ -496,8 +496,8 @@ Instance Label Score Assigned 286 1 2.9424572 1 287 0 -1.22317672 0 289 1 1.71858239 1 -292 1 -294 0 +292 1 ? 0 +294 0 ? 0 295 1 1.42958808 1 298 0 -0.7813908 0 302 1 2.925787 1 @@ -506,7 +506,7 @@ Instance Label Score Assigned 307 0 -1.35031986 0 310 0 -1.293855 0 313 0 -1.45160127 0 -315 0 +315 0 ? 0 318 0 -1.24103785 0 320 1 1.18153763 1 322 0 -1.20896339 0 @@ -551,7 +551,7 @@ Instance Label Score Assigned 407 0 -1.40096045 0 408 0 -0.928058 0 410 0 -1.40096045 0 -411 0 +411 0 ? 0 412 1 1.731832 1 417 0 -1.40096045 0 420 0 -0.7677182 0 diff --git a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt index 9c673bc10a..905fbd46fd 100644 --- a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.133441 1 0 1 21 1 1.46698761 1 0 1 22 0 -1.29266214 1E-15 1.4415419267167138E-15 0 -23 1 +23 1 ? ? ? 0 24 0 -1.38898408 1E-15 1.4415419267167138E-15 0 25 1 0.2072978 0.8125 0.29956028185890782 1 26 0 -1.27053475 1E-15 1.4415419267167138E-15 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.7899848 1E-15 1.4415419267167138E-15 0 38 1 1.07285547 0.955555558 0.065588337627980248 1 39 1 0.444084644 0.826086938 0.27563447429444238 1 -40 0 +40 0 ? ? ? 0 41 1 0.278235674 0.8125 0.29956028185890782 1 42 1 1.82501435 1 0 1 43 1 0.0648527145 0.8125 0.29956028185890782 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.21864986 1E-15 1.4415419267167138E-15 0 137 0 -1.34436476 1E-15 1.4415419267167138E-15 0 138 0 -1.15066063 1E-15 1.4415419267167138E-15 0 -139 0 +139 0 ? ? ? 0 140 0 -1.34436476 1E-15 1.4415419267167138E-15 0 141 0 -1.418377 1E-15 1.4415419267167138E-15 0 142 1 0.605928659 0.826086938 0.27563447429444238 1 143 0 -1.04888356 1E-15 1.4415419267167138E-15 0 144 0 -1.36667442 1E-15 1.4415419267167138E-15 0 -145 0 +145 0 ? ? ? 0 146 1 0.263422 0.8125 0.29956028185890782 1 147 0 -1.28818154 1E-15 1.4415419267167138E-15 0 148 0 -0.38141644 0.333333343 0.5849625222189877 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.7075844 0.826086938 0.27563447429444238 1 156 0 -1.30894148 1E-15 1.4415419267167138E-15 0 157 0 -1.3149718 1E-15 1.4415419267167138E-15 0 -158 0 +158 0 ? ? ? 0 159 1 2.41813564 1 0 1 160 1 1.80749393 1 0 1 161 0 -1.10798192 1E-15 1.4415419267167138E-15 0 162 0 -1.24095953 1E-15 1.4415419267167138E-15 0 163 0 -1.14838839 1E-15 1.4415419267167138E-15 0 -164 0 +164 0 ? ? ? 0 165 0 -0.9971055 1E-15 1.4415419267167138E-15 0 166 1 1.77878284 1 0 1 167 1 1.0986836 0.955555558 0.065588337627980248 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.0348134041 0.727272749 1.8744692325712462 0 233 1 1.102045 1 0 1 234 0 -0.709003 0.1 0.15200309583369792 0 -235 0 +235 0 ? ? ? 0 236 1 2.13387632 1 0 1 237 1 1.230866 1 0 1 238 1 2.40226221 1 0 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.38478136 1 0 1 247 1 0.5292797 0.826086938 0.27563447429444238 1 248 0 -0.8721206 1E-15 1.4415419267167138E-15 0 -249 0 +249 0 ? ? ? 0 250 0 -1.3606441 1E-15 1.4415419267167138E-15 0 251 1 1.35184932 1 0 1 252 0 0.7461872 0.826086938 2.5235618055722013 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.451641083 0.826086938 0.27563447429444238 1 273 1 -0.184997916 0.727272749 0.45943157564163517 0 274 0 -1.1819942 1E-15 1.4415419267167138E-15 0 -275 0 +275 0 ? ? ? 0 276 0 -1.29266214 1E-15 1.4415419267167138E-15 0 277 0 -1.4406867 1E-15 1.4415419267167138E-15 0 278 0 -1.38898408 1E-15 1.4415419267167138E-15 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.698751 1 0 1 290 0 -1.49238932 1E-15 1.4415419267167138E-15 0 291 0 -1.38898408 1E-15 1.4415419267167138E-15 0 -292 1 +292 1 ? ? ? 0 293 1 0.9705 0.955555558 0.065588337627980248 1 -294 0 +294 0 ? ? ? 0 295 1 1.44576406 1 0 1 296 0 0.264410973 0.8125 2.4150374992788439 1 -297 0 +297 0 ? ? ? 0 298 0 -0.785661459 1E-15 1.4415419267167138E-15 0 299 1 1.16638494 1 0 1 300 1 1.27287531 1 0 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.6419792 0.826086938 0.27563447429444238 1 313 0 -1.49238932 1E-15 1.4415419267167138E-15 0 314 0 -1.4823153 1E-15 1.4415419267167138E-15 0 -315 0 +315 0 ? ? ? 0 316 1 0.773257732 0.826086938 0.27563447429444238 1 317 1 1.83582067 1 0 1 318 0 -1.26409817 1E-15 1.4415419267167138E-15 0 319 0 0.303462982 0.8125 2.4150374992788439 1 320 1 1.16069293 1 0 1 -321 0 +321 0 ? ? ? 0 322 0 -1.24095953 1E-15 1.4415419267167138E-15 0 323 1 0.909108639 0.955555558 0.065588337627980248 1 324 0 -1.38898408 1E-15 1.4415419267167138E-15 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -0.9617897 1E-15 1.4415419267167138E-15 0 409 0 -1.22467291 1E-15 1.4415419267167138E-15 0 410 0 -1.4406867 1E-15 1.4415419267167138E-15 0 -411 0 +411 0 ? ? ? 0 412 1 1.77363062 1 0 1 413 0 -1.02494574 1E-15 1.4415419267167138E-15 0 414 1 1.26032782 1 0 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.408303 1E-15 1.4415419267167138E-15 0 615 0 -1.0826714 1E-15 1.4415419267167138E-15 0 616 0 -1.29266214 1E-15 1.4415419267167138E-15 0 -617 0 +617 0 ? ? ? 0 618 0 -1.21864986 1E-15 1.4415419267167138E-15 0 619 0 -1.14463758 1E-15 1.4415419267167138E-15 0 620 0 -1.29266214 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt index c5e41e5240..1ee365e6f7 100644 --- a/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt @@ -22,7 +22,7 @@ Instance Label Score Assigned 20 1 1.133441 1 21 1 1.46698761 1 22 0 -1.29266214 0 -23 1 +23 1 ? 0 24 0 -1.38898408 0 25 1 0.2072978 1 26 0 -1.27053475 0 @@ -39,7 +39,7 @@ Instance Label Score Assigned 37 0 -0.7899848 0 38 1 1.07285547 1 39 1 0.444084644 1 -40 0 +40 0 ? 0 41 1 0.278235674 1 42 1 1.82501435 1 43 1 0.0648527145 1 @@ -138,13 +138,13 @@ Instance Label Score Assigned 136 0 -1.21864986 0 137 0 -1.34436476 0 138 0 -1.15066063 0 -139 0 +139 0 ? 0 140 0 -1.34436476 0 141 0 -1.418377 0 142 1 0.605928659 1 143 0 -1.04888356 0 144 0 -1.36667442 0 -145 0 +145 0 ? 0 146 1 0.263422 1 147 0 -1.28818154 0 148 0 -0.38141644 0 @@ -157,13 +157,13 @@ Instance Label Score Assigned 155 1 0.7075844 1 156 0 -1.30894148 0 157 0 -1.3149718 0 -158 0 +158 0 ? 0 159 1 2.41813564 1 160 1 1.80749393 1 161 0 -1.10798192 0 162 0 -1.24095953 0 163 0 -1.14838839 0 -164 0 +164 0 ? 0 165 0 -0.9971055 0 166 1 1.77878284 1 167 1 1.0986836 1 @@ -234,7 +234,7 @@ Instance Label Score Assigned 232 0 -0.0348134041 0 233 1 1.102045 1 234 0 -0.709003 0 -235 0 +235 0 ? 0 236 1 2.13387632 1 237 1 1.230866 1 238 1 2.40226221 1 @@ -248,7 +248,7 @@ Instance Label Score Assigned 246 1 2.38478136 1 247 1 0.5292797 1 248 0 -0.8721206 0 -249 0 +249 0 ? 0 250 0 -1.3606441 0 251 1 1.35184932 1 252 0 0.7461872 1 @@ -274,7 +274,7 @@ Instance Label Score Assigned 272 1 0.451641083 1 273 1 -0.184997916 0 274 0 -1.1819942 0 -275 0 +275 0 ? 0 276 0 -1.29266214 0 277 0 -1.4406867 0 278 0 -1.38898408 0 @@ -291,12 +291,12 @@ Instance Label Score Assigned 289 1 1.698751 1 290 0 -1.49238932 0 291 0 -1.38898408 0 -292 1 +292 1 ? 0 293 1 0.9705 1 -294 0 +294 0 ? 0 295 1 1.44576406 1 296 0 0.264410973 1 -297 0 +297 0 ? 0 298 0 -0.785661459 0 299 1 1.16638494 1 300 1 1.27287531 1 @@ -314,13 +314,13 @@ Instance Label Score Assigned 312 1 0.6419792 1 313 0 -1.49238932 0 314 0 -1.4823153 0 -315 0 +315 0 ? 0 316 1 0.773257732 1 317 1 1.83582067 1 318 0 -1.26409817 0 319 0 0.303462982 1 320 1 1.16069293 1 -321 0 +321 0 ? 0 322 0 -1.24095953 0 323 1 0.909108639 1 324 0 -1.38898408 0 @@ -410,7 +410,7 @@ Instance Label Score Assigned 408 0 -0.9617897 0 409 0 -1.22467291 0 410 0 -1.4406867 0 -411 0 +411 0 ? 0 412 1 1.77363062 1 413 0 -1.02494574 0 414 1 1.26032782 1 @@ -616,7 +616,7 @@ Instance Label Score Assigned 614 0 -1.408303 0 615 0 -1.0826714 0 616 0 -1.29266214 0 -617 0 +617 0 ? 0 618 0 -1.21864986 0 619 0 -1.14463758 0 620 0 -1.29266214 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.txt index 55744d3dbb..c3e87fdfec 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 7.01161575 0.999099433 0.0012998283148002473 1 35 0 -5.546275 0.0038867984 0.0056183906346792553 0 37 0 -0.627175331 0.3481513 0.61739094617065504 0 -40 0 +40 0 ? ? ? 0 41 1 3.0365572 0.9541986 0.067638527006529184 1 44 1 6.61341858 0.998659551 0.0019351561965343684 1 45 0 -5.565003 0.00381495967 0.0055143486004666058 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.72209072 0.008818108 0.012778263407408707 0 141 0 -6.18090439 0.002064286 0.0029812133205916491 0 144 0 -5.546275 0.0038867984 0.0056183906346792553 0 -145 0 +145 0 ? ? ? 0 147 0 -5.305633 0.0049390397 0.0071431828757896592 0 150 0 -5.43029261 0.004362697 0.0063078105710409225 0 151 1 4.990402 0.993243039 0.0097813179599174817 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.58201027 0.00138314639 0.0019968397134892645 0 156 0 -5.6663394 0.00344857574 0.0049838416366178697 0 161 0 -3.992939 0.0181113519 0.026368671070599831 0 -164 0 +164 0 ? ? ? 0 167 1 6.79999542 0.9988875 0.0016059215653949079 1 169 0 -6.41288567 0.00163759827 0.0023644914709276374 0 171 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 10.4554977 0.9999712 4.1534408054175585E-05 1 247 1 2.7647686 0.940742 0.088128954823708028 1 248 0 -3.52696 0.0285547972 0.041795476162409473 0 -249 0 +249 0 ? ? ? 0 250 0 -6.30096865 0.00183116761 0.0026442381917314273 0 252 0 4.46767044 0.988656163 6.4619475094968903 1 254 1 5.268241 0.994873762 0.007414618780919654 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.312752 0.004904177 0.007092637921430613 0 271 0 -3.70832729 0.02393173 0.034946036680259647 0 272 1 3.75608158 0.9771588 0.033335080773514915 1 -275 0 +275 0 ? ? ? 0 276 0 -5.145169 0.005793728 0.0083828901690503332 0 277 0 -5.947381 0.002605866 0.0037643767357215252 0 278 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.312752 0.004904177 0.007092637921430613 0 293 1 4.889887 0.992533863 0.0108117709104356 1 296 0 1.11456966 0.752980053 2.0173005524167049 1 -297 0 +297 0 ? ? ? 0 299 1 7.92421341 0.999638259 0.00052197576964742038 1 300 1 5.01128674 0.993381739 0.009579868932513079 1 301 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.64061546 0.974434555 0.037362801100438832 1 317 1 8.889477 0.9998622 0.00019882564438936136 1 319 0 2.932189 0.9494149 4.3051439460642333 1 -321 0 +321 0 ? ? ? 0 323 1 5.11123276 0.994007468 0.0086714037349760222 1 327 0 -5.947381 0.002605866 0.0037643767357215252 0 328 1 3.24846554 0.962617934 0.054964793658751787 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 16.72465 0.99999994 8.5991327994145617E-08 1 613 0 -5.02813625 0.00650837226 0.0094202851039732072 0 614 0 -5.6638155 0.00345726055 0.0049964145907412149 0 -617 0 +617 0 ? ? ? 0 618 0 -4.744063 0.008628122 0.012501759455550775 0 619 0 -4.34295654 0.01283126 0.01863138517568386 0 621 0 0.373829842 0.5923841 1.2947177664921086 1 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.23449326 0.0142802689 0.020750590335179415 0 19 0 -3.10273981 0.04299438 0.063400697943788428 0 22 0 -5.130806 0.00587705 0.0085038042327941973 0 -23 1 +23 1 ? ? ? 0 24 0 -5.932124 0.002645822 0.0038221729188529165 0 26 0 -5.0699935 0.006243238 0.0090353231046563232 0 27 0 -3.99905157 0.01800297 0.026209433910245255 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.12534046 0.00590906851 0.0085502708566916 0 135 0 -2.97098112 0.0487542 0.072109916649189282 0 136 0 -4.56492853 0.0103033585 0.014941712057931327 0 -139 0 +139 0 ? ? ? 0 140 0 -5.461241 0.00423030974 0.0061159922992663762 0 142 1 3.05590534 0.9550368 0.066371741280747978 1 143 0 -5.35564375 0.004699242 0.0067955523991724961 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.276688 0.0136983329 0.019899122722508662 0 155 1 1.77740765 0.8553765 0.22536855303918271 1 157 0 -5.366247 0.004649908 0.0067240441834850199 0 -158 0 +158 0 ? ? ? 0 159 1 8.96132851 0.999871731 0.0001850652016644095 1 160 1 6.856139 0.998948157 0.001518287560141836 1 162 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.663686 0.9965423 0.0049970870434534837 1 232 0 0.928171158 0.71670413 1.8196185260804569 1 234 0 -3.391735 0.0325547643 0.047748098148187916 0 -235 0 +235 0 ? ? ? 0 236 1 7.88262749 0.9996229 0.00054416973071506056 1 238 1 8.651073 0.9998251 0.00025232061237670696 1 243 0 -4.03390265 0.0173970815 0.025319570708974903 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.9212322 0.99998194 2.6055606891255495E-05 1 287 0 -5.12534046 0.00590906851 0.0085502708566916 0 289 1 5.185871 0.9944361 0.0080494462952667781 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77450371 0.9916284 0.012128492162908918 1 298 0 -2.54918242 0.07248143 0.10855193009256793 0 302 1 11.1919193 0.999986231 1.9864132926342996E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.932124 0.002645822 0.0038221729188529165 0 310 0 -5.691217 0.00336412713 0.0048615916252809817 0 313 0 -6.59299469 0.0013680571 0.0019750404849533288 0 -315 0 +315 0 ? ? ? 0 318 0 -5.91572762 0.00268944423 0.0038852748092412133 0 320 1 4.1229 0.9840607 0.023180779648038535 1 322 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -6.26255941 0.00190273311 0.0027476785076986981 0 408 0 -4.386099 0.0122961234 0.017849522550602435 0 410 0 -6.26255941 0.00190273311 0.0027476785076986981 0 -411 0 +411 0 ? ? ? 0 412 1 7.037607 0.99912256 0.0012664339928697493 1 417 0 -6.26255941 0.00190273311 0.0027476785076986981 0 420 0 -3.338777 0.0342646 0.050300135586962377 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt index 0a4ce514a2..3b22d36b5d 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 7.01161575 0.999099433 0.0012998283148002473 1 35 0 -5.546275 0.0038867984 0.0056183906346792553 0 37 0 -0.627175331 0.3481513 0.61739094617065504 0 -40 0 +40 0 ? ? ? 0 41 1 3.0365572 0.9541986 0.067638527006529184 1 44 1 6.61341858 0.998659551 0.0019351561965343684 1 45 0 -5.565003 0.00381495967 0.0055143486004666058 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.72209072 0.008818108 0.012778263407408707 0 141 0 -6.18090439 0.002064286 0.0029812133205916491 0 144 0 -5.546275 0.0038867984 0.0056183906346792553 0 -145 0 +145 0 ? ? ? 0 147 0 -5.305633 0.0049390397 0.0071431828757896592 0 150 0 -5.43029261 0.004362697 0.0063078105710409225 0 151 1 4.990402 0.993243039 0.0097813179599174817 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.58201027 0.00138314639 0.0019968397134892645 0 156 0 -5.6663394 0.00344857574 0.0049838416366178697 0 161 0 -3.992939 0.0181113519 0.026368671070599831 0 -164 0 +164 0 ? ? ? 0 167 1 6.79999542 0.9988875 0.0016059215653949079 1 169 0 -6.41288567 0.00163759827 0.0023644914709276374 0 171 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 10.4554977 0.9999712 4.1534408054175585E-05 1 247 1 2.7647686 0.940742 0.088128954823708028 0 248 0 -3.52696 0.0285547972 0.041795476162409473 0 -249 0 +249 0 ? ? ? 0 250 0 -6.30096865 0.00183116761 0.0026442381917314273 0 252 0 4.46767044 0.988656163 6.4619475094968903 1 254 1 5.268241 0.994873762 0.007414618780919654 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.312752 0.004904177 0.007092637921430613 0 271 0 -3.70832729 0.02393173 0.034946036680259647 0 272 1 3.75608158 0.9771588 0.033335080773514915 1 -275 0 +275 0 ? ? ? 0 276 0 -5.145169 0.005793728 0.0083828901690503332 0 277 0 -5.947381 0.002605866 0.0037643767357215252 0 278 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.312752 0.004904177 0.007092637921430613 0 293 1 4.889887 0.992533863 0.0108117709104356 1 296 0 1.11456966 0.752980053 2.0173005524167049 0 -297 0 +297 0 ? ? ? 0 299 1 7.92421341 0.999638259 0.00052197576964742038 1 300 1 5.01128674 0.993381739 0.009579868932513079 1 301 0 -5.312752 0.004904177 0.007092637921430613 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.64061546 0.974434555 0.037362801100438832 1 317 1 8.889477 0.9998622 0.00019882564438936136 1 319 0 2.932189 0.9494149 4.3051439460642333 0 -321 0 +321 0 ? ? ? 0 323 1 5.11123276 0.994007468 0.0086714037349760222 1 327 0 -5.947381 0.002605866 0.0037643767357215252 0 328 1 3.24846554 0.962617934 0.054964793658751787 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 16.72465 0.99999994 8.5991327994145617E-08 1 613 0 -5.02813625 0.00650837226 0.0094202851039732072 0 614 0 -5.6638155 0.00345726055 0.0049964145907412149 0 -617 0 +617 0 ? ? ? 0 618 0 -4.744063 0.008628122 0.012501759455550775 0 619 0 -4.34295654 0.01283126 0.01863138517568386 0 621 0 0.373829842 0.5923841 1.2947177664921086 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.23449326 0.0142802689 0.020750590335179415 0 19 0 -3.10273981 0.04299438 0.063400697943788428 0 22 0 -5.130806 0.00587705 0.0085038042327941973 0 -23 1 +23 1 ? ? ? 0 24 0 -5.932124 0.002645822 0.0038221729188529165 0 26 0 -5.0699935 0.006243238 0.0090353231046563232 0 27 0 -3.99905157 0.01800297 0.026209433910245255 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.12534046 0.00590906851 0.0085502708566916 0 135 0 -2.97098112 0.0487542 0.072109916649189282 0 136 0 -4.56492853 0.0103033585 0.014941712057931327 0 -139 0 +139 0 ? ? ? 0 140 0 -5.461241 0.00423030974 0.0061159922992663762 0 142 1 3.05590534 0.9550368 0.066371741280747978 1 143 0 -5.35564375 0.004699242 0.0067955523991724961 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.276688 0.0136983329 0.019899122722508662 0 155 1 1.77740765 0.8553765 0.22536855303918271 0 157 0 -5.366247 0.004649908 0.0067240441834850199 0 -158 0 +158 0 ? ? ? 0 159 1 8.96132851 0.999871731 0.0001850652016644095 1 160 1 6.856139 0.998948157 0.001518287560141836 1 162 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.663686 0.9965423 0.0049970870434534837 1 232 0 0.928171158 0.71670413 1.8196185260804569 0 234 0 -3.391735 0.0325547643 0.047748098148187916 0 -235 0 +235 0 ? ? ? 0 236 1 7.88262749 0.9996229 0.00054416973071506056 1 238 1 8.651073 0.9998251 0.00025232061237670696 1 243 0 -4.03390265 0.0173970815 0.025319570708974903 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.9212322 0.99998194 2.6055606891255495E-05 1 287 0 -5.12534046 0.00590906851 0.0085502708566916 0 289 1 5.185871 0.9944361 0.0080494462952667781 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.77450371 0.9916284 0.012128492162908918 1 298 0 -2.54918242 0.07248143 0.10855193009256793 0 302 1 11.1919193 0.999986231 1.9864132926342996E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.932124 0.002645822 0.0038221729188529165 0 310 0 -5.691217 0.00336412713 0.0048615916252809817 0 313 0 -6.59299469 0.0013680571 0.0019750404849533288 0 -315 0 +315 0 ? ? ? 0 318 0 -5.91572762 0.00268944423 0.0038852748092412133 0 320 1 4.1229 0.9840607 0.023180779648038535 1 322 0 -4.80037 0.008159574 0.011820066323398703 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -6.26255941 0.00190273311 0.0027476785076986981 0 408 0 -4.386099 0.0122961234 0.017849522550602435 0 410 0 -6.26255941 0.00190273311 0.0027476785076986981 0 -411 0 +411 0 ? ? ? 0 412 1 7.037607 0.99912256 0.0012664339928697493 1 417 0 -6.26255941 0.00190273311 0.0027476785076986981 0 420 0 -3.338777 0.0342646 0.050300135586962377 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt index 2748bad72f..db7cabc84e 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 5.917115 0.9973143 0.003879895635620469 1 35 0 -5.023262 0.006539965 0.0094661631818091386 0 37 0 -1.38112783 0.200827926 0.3234219246434899 0 -40 0 +40 0 ? ? ? 0 41 1 1.99977684 0.880773664 0.18315676412764836 1 44 1 6.1460495 0.997862637 0.0030868629883528767 1 45 0 -4.92853165 0.00718512246 0.010403360173725132 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.17745543 0.0151058007 0.021959341229968121 0 141 0 -5.50801945 0.00403775927 0.0058370475132605874 0 144 0 -5.023262 0.006539965 0.0094661631818091386 0 -145 0 +145 0 ? ? ? 0 147 0 -4.81479263 0.00804367848 0.011651498579728541 0 150 0 -4.92451668 0.00721382024 0.01044506254471955 0 151 1 4.048602 0.9828524 0.024953319944645107 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -5.80309772 0.00300910859 0.0043477707884351756 0 156 0 -4.933721 0.00714819832 0.010349705365439381 0 161 0 -3.81854677 0.0214878246 0.031338292831631254 0 -164 0 +164 0 ? ? ? 0 167 1 7.07203674 0.999152243 0.0012235733412714462 1 169 0 -5.652446 0.00349665456 0.0050534464360921993 0 171 0 -4.83358335 0.007895125 0.011435459622816726 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.218597 0.9999008 0.00014309666195095306 1 247 1 1.9463377 0.87504673 0.19256803177214576 1 248 0 -3.2993412 0.0355937965 0.052287163877458576 0 -249 0 +249 0 ? ? ? 0 250 0 -5.418478 0.004414317 0.006382611353481173 0 252 0 3.48067 0.970132768 5.0652926577610451 1 254 1 4.863985 0.9923395 0.011094325676462969 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -4.83358335 0.007895125 0.011435459622816726 0 271 0 -3.65327024 0.0252520833 0.036898928715096181 0 272 1 3.02218533 0.9535664 0.068594734962942897 1 -275 0 +275 0 ? ? ? 0 276 0 -4.72818375 0.008765012 0.012700983189760973 0 277 0 -5.3183403 0.00487697963 0.00705320751084503 0 278 0 -4.83358335 0.007895125 0.011435459622816726 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -4.83358335 0.007895125 0.011435459622816726 0 293 1 3.6722765 0.975211561 0.036212866280089365 1 296 0 0.9506779 0.7212515 1.8429639897619017 1 -297 0 +297 0 ? ? ? 0 299 1 5.993658 0.9975117 0.0035943536619517991 1 300 1 5.41301346 0.9955616 0.0064175103553020287 1 301 0 -4.83358335 0.007895125 0.011435459622816726 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 2.857479 0.945704 0.080539421519246923 1 317 1 7.483817 0.9994382 0.00081069597738820146 1 319 0 2.14308548 0.895020843 3.2518251711052208 1 -321 0 +321 0 ? ? ? 0 323 1 4.38443756 0.987683654 0.017879061305842645 1 327 0 -5.3183403 0.00487697963 0.00705320751084503 0 328 1 1.9153614 0.8716203 0.19822830198625993 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 15.0148582 0.9999997 4.2995669122556443E-07 1 613 0 -4.76532364 0.008448151 0.012239880753341762 0 614 0 -5.114196 0.005974896 0.00864580721752186 0 -617 0 +617 0 ? ? ? 0 618 0 -4.43310547 0.0117381271 0.017034712310540854 0 619 0 -4.138027 0.0157037526 0.022835500585175472 0 621 0 -0.147964478 0.463076234 0.89721082981393785 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.094967 0.0163834114 0.023832028779250634 0 19 0 -3.158533 0.0407563634 0.06003080556474015 0 22 0 -4.786547 0.008272208 0.011983909171361835 0 -23 1 +23 1 ? ? ? 0 24 0 -5.49961758 0.00407168828 0.0058861960152521283 0 26 0 -4.637971 0.009584561 0.013894290826829943 0 27 0 -3.85011339 0.02083403 0.030374676365783088 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -4.786547 0.008272208 0.011983909171361835 0 135 0 -2.91646814 0.0513454638 0.07604528657651706 0 136 0 -4.31833 0.01314697 0.019092852440504127 0 -139 0 +139 0 ? ? ? 0 140 0 -5.00991058 0.006627286 0.009592975357514695 0 142 1 2.566639 0.9286834 0.10674124733849408 1 143 0 -4.68576 0.009141385 0.013248879501449318 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -3.91140127 0.0196197983 0.028586744893093978 0 155 1 1.73489761 0.8500378 0.23440107518588552 1 157 0 -5.03140068 0.00648729829 0.0093896829482070059 0 -158 0 +158 0 ? ? ? 0 159 1 8.209714 0.9997281 0.00039234577165867646 1 160 1 6.214038 0.9980028 0.0028841924162386562 1 162 0 -4.563184 0.0103211654 0.014967669647872791 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.128908 0.994111836 0.0085199331359509835 1 232 0 0.387944221 0.5957877 1.3068148861794051 1 234 0 -3.05071926 0.04518643 0.066709024779774861 0 -235 0 +235 0 ? ? ? 0 236 1 7.45407867 0.999421239 0.00083521748895634131 1 238 1 8.351737 0.9997641 0.00034039381967173406 1 243 0 -3.79230738 0.02204652 0.032162254419967784 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.2351351 0.9999641 5.1767706679585828E-05 1 287 0 -4.786547 0.008272208 0.011983909171361835 0 289 1 5.007719 0.993358254 0.0096139756423385195 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.08445454 0.9834463 0.024081816959951673 1 298 0 -2.520249 0.07445079 0.11161839829444895 0 302 1 10.2925406 0.999966145 4.8843899665517181E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.49961758 0.00407168828 0.0058861960152521283 0 310 0 -5.254764 0.00519544445 0.0075149807235167431 0 313 0 -5.94634438 0.00260856142 0.0037682756571934027 0 -315 0 +315 0 ? ? ? 0 318 0 -5.49961758 0.00407168828 0.0058861960152521283 0 320 1 3.84560966 0.9790739 0.030510363432469476 1 322 0 -4.563184 0.0103211654 0.014967669647872791 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -5.722981 0.003259291 0.00470984242704696 0 408 0 -3.98740244 0.0182100739 0.026513731287650329 0 410 0 -5.722981 0.003259291 0.00470984242704696 0 -411 0 +411 0 ? ? ? 0 412 1 5.972435 0.997458458 0.0036713375244963246 1 417 0 -5.722981 0.003259291 0.00470984242704696 0 420 0 -3.14838743 0.0411548652 0.060630273661156198 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt index c81880f594..3e137abda1 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 5.678712 0.9965937 0.0049226209615764287 1 21 1 6.001464 0.997531 0.0035664232425213858 1 22 0 -5.02218437 0.00654697046 0.009476336418019371 0 -23 1 +23 1 ? ? ? 0 24 0 -5.557753 0.00384261133 0.0055543948499928858 0 25 1 0.7356682 0.676047862 0.56480270753052697 1 26 0 -5.01491547 0.006594418 0.0095452416271748757 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.86437464 0.134193972 0.20788424941725006 0 38 1 4.26738262 0.986175358 0.020083890425044392 1 39 1 0.948868752 0.7208876 0.47215375855428471 1 -40 0 +40 0 ? ? ? 0 41 1 2.21941185 0.9019792 0.14883391736209772 1 42 1 6.171118 0.997915447 0.00301051350343931 1 43 1 -0.406847954 0.399668157 1.3231254618912542 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.573848 0.0102128033 0.014809714349895977 0 137 0 -5.38329029 0.00457168929 0.0066106759896893642 0 138 0 -4.32377768 0.0130764758 0.018989799023276886 0 -139 0 +139 0 ? ? ? 0 140 0 -5.38329029 0.00457168929 0.0066106759896893642 0 141 0 -5.83162737 0.00292472052 0.0042256622529479159 0 142 1 3.1618576 0.9593734 0.059835633747694031 1 143 0 -4.83655453 0.007871887 0.011401667353256314 0 144 0 -5.470522 0.004191393 0.00605960993587892 0 -145 0 +145 0 ? ? ? 0 146 1 0.0557060242 0.5139229 0.96037607240274647 1 147 0 -5.47995234 0.00415221555 0.0060028519951750492 0 148 0 -1.59599066 0.168542728 0.26628596824729922 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.20912266 0.901065767 0.15029568586304221 1 156 0 -5.58346558 0.003745433 0.0054136621278108544 0 157 0 -5.109416 0.006003351 0.0086871066895572498 0 -158 0 +158 0 ? ? ? 0 159 1 9.879018 0.99994874 7.3954435339176224E-05 1 160 1 6.966673 0.9990581 0.0013594752512588889 1 161 0 -4.01959 0.0176434461 0.025681337989317106 0 162 0 -4.661079 0.009367671 0.01357839101244592 0 163 0 -3.39304066 0.03251367 0.04768681882905737 0 -164 0 +164 0 ? ? ? 0 165 0 -3.65873766 0.0251178537 0.036700273114887771 0 166 1 6.106388 0.9977764 0.0032115643460085509 1 167 1 6.96164227 0.999053359 0.0013663610592643267 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.434880257 0.607038438 1.3475398955993996 1 233 1 4.62145329 0.9902574 0.014124543390739288 1 234 0 -3.19655 0.0392957628 0.057835744186683809 0 -235 0 +235 0 ? ? ? 0 236 1 9.488486 0.9999243 0.00010921311695177715 1 237 1 5.185233 0.994432569 0.0080545481788837064 1 238 1 10.3383093 0.999967635 4.6694045347237877E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.706185 0.9998345 0.00023881767606976273 1 247 1 1.85991 0.865286469 0.20875025156266316 1 248 0 -3.24698353 0.03743543 0.055044774760243595 0 -249 0 +249 0 ? ? ? 0 250 0 -5.9445715 0.00261317822 0.0037749537255417041 0 251 1 6.851535 0.998943269 0.001525346290754842 1 252 0 3.15871048 0.959250569 4.6170762873605469 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.54124355 0.8236454 0.27990471757503477 1 273 1 -0.485281944 0.381005645 1.3921157227274092 0 274 0 -4.467927 0.0113409776 0.016455057773140165 0 -275 0 +275 0 ? ? ? 0 276 0 -5.02218437 0.00654697046 0.009476336418019371 0 277 0 -5.918859 0.00268105837 0.0038731439944547637 0 278 0 -5.557753 0.00384261133 0.0055543948499928858 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 6.391654 0.9983273 0.0024151950596217639 1 290 0 -6.27996445 0.00186996383 0.0027003130775063206 0 291 0 -5.557753 0.00384261133 0.0055543948499928858 0 -292 1 +292 1 ? ? ? 0 293 1 4.062559 0.98308605 0.024610393646554929 1 -294 0 +294 0 ? ? ? 0 295 1 5.48979473 0.9958883 0.0059441683338054239 1 296 0 0.7984762 0.689648449 1.6880247409542737 1 -297 0 +297 0 ? ? ? 0 298 0 -2.82211971 0.05614051 0.083355987427050499 0 299 1 5.92269325 0.9973292 0.0038583400725341575 1 300 1 5.553643 0.9961416 0.0055772429838620297 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.505681 0.924539149 0.11319368429557292 1 313 0 -6.27996445 0.00186996383 0.0027003130775063206 0 314 0 -5.996913 0.00248024915 0.0035826879721537609 0 -315 0 +315 0 ? ? ? 0 316 1 2.06765842 0.8877198 0.17182370265537225 1 317 1 6.799261 0.998886645 0.0016071267852858751 1 318 0 -5.323591 0.004851562 0.0070163583406618974 0 319 0 1.16352749 0.7619731 2.0708033667370818 1 320 1 5.47767448 0.995838344 0.006016528395062894 1 -321 0 +321 0 ? ? ? 0 322 0 -4.661079 0.009367671 0.01357839101244592 0 323 1 3.7019043 0.975917757 0.035168522105486828 1 324 0 -5.557753 0.00384261133 0.0055543948499928858 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.06660128 0.0168468487 0.024511924018010906 0 409 0 -4.77211475 0.008391453 0.012157388278315496 0 410 0 -5.918859 0.00268105837 0.0038731439944547637 0 -411 0 +411 0 ? ? ? 0 412 1 6.93106556 0.999024 0.0014087955764285343 1 413 0 -3.5143342 0.02890712 0.042318806680098388 0 414 1 4.808298 0.9919043 0.0117271336749766 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -5.548576 0.00387790077 0.0056055040477979955 0 615 0 -4.073707 0.0167295579 0.024339819893712551 0 616 0 -5.02218437 0.00654697046 0.009476336418019371 0 -617 0 +617 0 ? ? ? 0 618 0 -4.573848 0.0102128033 0.014809714349895977 0 619 0 -4.12551069 0.0158983991 0.023120824431946189 0 620 0 -5.02218437 0.00654697046 0.009476336418019371 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt index 4789a49c72..51deeac562 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 7.041827 0.999126256 0.0012610978584151201 1 21 1 7.16006565 0.9992236 0.0011205580676841706 1 22 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -23 1 +23 1 ? ? ? 0 24 0 -6.16573143 0.00209578 0.003026744365055557 0 25 1 1.140585 0.757787049 0.4001356116356401 1 26 0 -5.39192247 0.00453257374 0.006553986122637143 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.74917936 0.148150727 0.23132991391158644 0 38 1 5.25044537 0.9947822 0.0075473881419006704 1 39 1 1.14784718 0.7591175 0.39760491464649406 1 -40 0 +40 0 ? ? ? 0 41 1 2.83779049 0.9446841 0.082096136496848479 1 42 1 5.84602737 0.997117 0.0041653216557672994 1 43 1 -0.115566254 0.471140563 1.0857705467412089 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.81180668 0.008067538 0.011686200181083801 0 137 0 -5.771742 0.003104659 0.0044860436134326774 0 138 0 -4.708121 0.008941052 0.01295722303877558 0 -139 0 +139 0 ? ? ? 0 140 0 -5.771742 0.003104659 0.0044860436134326774 0 141 0 -6.35020638 0.00174334215 0.0025173059685722476 0 142 1 3.59143162 0.9731803 0.039220987499558525 1 143 0 -5.32383156 0.00485040154 0.0070146760362167319 0 144 0 -5.96873665 0.00255094632 0.0036849396417204128 0 -145 0 +145 0 ? ? ? 0 146 1 -0.228344917 0.443160534 1.1740986883981319 0 147 0 -6.02899933 0.00240211817 0.0034696929544304877 0 148 0 -1.17236042 0.236428589 0.38916500660996345 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.561039 0.928311646 0.10731887720885433 1 156 0 -6.32817459 0.0017821081 0.0025733321754180501 0 157 0 -5.587267 0.00373127544 0.0053931605303660906 0 -158 0 +158 0 ? ? ? 0 159 1 9.957122 0.9999526 6.836472348564471E-05 1 160 1 7.81574535 0.999596834 0.00058176260649806227 1 161 0 -4.194911 0.0148482891 0.02158218175164682 0 162 0 -5.008802 0.00663458835 0.0096035809636014526 0 163 0 -4.450941 0.0115330191 0.016735320470494343 0 -164 0 +164 0 ? ? ? 0 165 0 -3.79370117 0.022016488 0.0321179521831969 0 166 1 6.78795338 0.998874 0.0016253773809814112 1 167 1 6.650817 0.9987087 0.0018641198790525675 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.440856934 0.6084632 1.3527802863415537 1 233 1 5.59169 0.996285141 0.0053693888063565016 1 234 0 -3.380312 0.0329164639 0.048287580681296273 0 -235 0 +235 0 ? ? ? 0 236 1 9.42349148 0.9999192 0.00011660894974341421 1 237 1 5.527053 0.9960381 0.005727196256675989 1 238 1 10.3295126 0.999967337 4.7124015954602722E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.522981 0.999926865 0.00010551521477366078 1 247 1 2.22113419 0.9021314 0.14859054441592434 1 248 0 -3.25320721 0.037211813 0.0547096542674552 0 -249 0 +249 0 ? ? ? 0 250 0 -6.70964432 0.00121761323 0.0017577148937104486 0 251 1 8.275113 0.9997453 0.00036748773339945429 1 252 0 3.75820923 0.97720623 5.4552166396980901 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.25216866 0.777675033 0.36276067331990891 1 273 1 -0.4770708 0.382944047 1.3847944817499098 0 274 0 -4.773376 0.008380964 0.01214212852075712 0 -275 0 +275 0 ? ? ? 0 276 0 -5.39027166 0.00454002852 0.0065647900946516503 0 277 0 -6.547201 0.00143207016 0.0020675212884715419 0 278 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 7.07017231 0.999150634 0.0012258970788924348 1 290 0 -6.928671 0.0009783435 0.0014121422633917285 0 291 0 -6.16573143 0.00209578 0.003026744365055557 0 -292 1 +292 1 ? ? ? 0 293 1 5.206727 0.9945503 0.0078837745923848179 1 -294 0 +294 0 ? ? ? 0 295 1 6.0751133 0.9977059 0.0033135223937081622 1 296 0 1.04628563 0.740061 1.9437549070362801 1 -297 0 +297 0 ? ? ? 0 298 0 -2.41310167 0.08217907 0.12371538777024385 0 299 1 7.396323 0.999386847 0.00088486407096525718 1 300 1 5.69218445 0.996639132 0.0048568731086720204 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 3.90498447 0.9802564 0.02876897031816629 1 313 0 -6.928671 0.0009783435 0.0014121422633917285 0 314 0 -6.664193 0.00127416 0.0018393963949252371 0 -315 0 +315 0 ? ? ? 0 316 1 2.20020485 0.9002679 0.15157371666802275 1 317 1 7.75578 0.9995719 0.00061772192605728435 1 318 0 -5.81475639 0.002974334 0.0042974510254079716 0 319 0 1.38163567 0.7992536 2.3165538563686012 1 320 1 5.837037 0.997091 0.0042029227668842466 1 -321 0 +321 0 ? ? ? 0 322 0 -5.008802 0.00663458835 0.0096035809636014526 0 323 1 4.26100159 0.9860881 0.020211552173447847 1 324 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.4899044 0.0110971872 0.016099351806882618 0 409 0 -5.286586 0.00503353868 0.0072801992795113032 0 410 0 -6.547201 0.00143207016 0.0020675212884715419 0 -411 0 +411 0 ? ? ? 0 412 1 7.575248 0.9994873 0.00073988707471124668 1 413 0 -3.748186 0.0230181254 0.033596298028071243 0 414 1 5.274519 0.994905651 0.0073683771133106393 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.085728 0.00226994278 0.0032785576690230068 0 615 0 -4.604435 0.0099082 0.01436579907268937 0 616 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -617 0 +617 0 ? ? ? 0 618 0 -4.81180668 0.008067538 0.011686200181083801 0 619 0 -4.233342 0.0142964814 0.020774319020048612 0 620 0 -5.39027166 0.00454002852 0.0065647900946516503 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt index 92afd941f0..a1bd6936bc 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 7.041827 0.999126256 0.0012610978584151201 1 21 1 7.16006565 0.9992236 0.0011205580676841706 1 22 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -23 1 +23 1 ? ? ? 0 24 0 -6.16573143 0.00209578 0.003026744365055557 0 25 1 1.140585 0.757787049 0.4001356116356401 0 26 0 -5.39192247 0.00453257374 0.006553986122637143 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.74917936 0.148150727 0.23132991391158644 0 38 1 5.25044537 0.9947822 0.0075473881419006704 1 39 1 1.14784718 0.7591175 0.39760491464649406 0 -40 0 +40 0 ? ? ? 0 41 1 2.83779049 0.9446841 0.082096136496848479 0 42 1 5.84602737 0.997117 0.0041653216557672994 1 43 1 -0.115566254 0.471140563 1.0857705467412089 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.81180668 0.008067538 0.011686200181083801 0 137 0 -5.771742 0.003104659 0.0044860436134326774 0 138 0 -4.708121 0.008941052 0.01295722303877558 0 -139 0 +139 0 ? ? ? 0 140 0 -5.771742 0.003104659 0.0044860436134326774 0 141 0 -6.35020638 0.00174334215 0.0025173059685722476 0 142 1 3.59143162 0.9731803 0.039220987499558525 1 143 0 -5.32383156 0.00485040154 0.0070146760362167319 0 144 0 -5.96873665 0.00255094632 0.0036849396417204128 0 -145 0 +145 0 ? ? ? 0 146 1 -0.228344917 0.443160534 1.1740986883981319 0 147 0 -6.02899933 0.00240211817 0.0034696929544304877 0 148 0 -1.17236042 0.236428589 0.38916500660996345 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.561039 0.928311646 0.10731887720885433 0 156 0 -6.32817459 0.0017821081 0.0025733321754180501 0 157 0 -5.587267 0.00373127544 0.0053931605303660906 0 -158 0 +158 0 ? ? ? 0 159 1 9.957122 0.9999526 6.836472348564471E-05 1 160 1 7.81574535 0.999596834 0.00058176260649806227 1 161 0 -4.194911 0.0148482891 0.02158218175164682 0 162 0 -5.008802 0.00663458835 0.0096035809636014526 0 163 0 -4.450941 0.0115330191 0.016735320470494343 0 -164 0 +164 0 ? ? ? 0 165 0 -3.79370117 0.022016488 0.0321179521831969 0 166 1 6.78795338 0.998874 0.0016253773809814112 1 167 1 6.650817 0.9987087 0.0018641198790525675 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.440856934 0.6084632 1.3527802863415537 0 233 1 5.59169 0.996285141 0.0053693888063565016 1 234 0 -3.380312 0.0329164639 0.048287580681296273 0 -235 0 +235 0 ? ? ? 0 236 1 9.42349148 0.9999192 0.00011660894974341421 1 237 1 5.527053 0.9960381 0.005727196256675989 1 238 1 10.3295126 0.999967337 4.7124015954602722E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.522981 0.999926865 0.00010551521477366078 1 247 1 2.22113419 0.9021314 0.14859054441592434 0 248 0 -3.25320721 0.037211813 0.0547096542674552 0 -249 0 +249 0 ? ? ? 0 250 0 -6.70964432 0.00121761323 0.0017577148937104486 0 251 1 8.275113 0.9997453 0.00036748773339945429 1 252 0 3.75820923 0.97720623 5.4552166396980901 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.25216866 0.777675033 0.36276067331990891 0 273 1 -0.4770708 0.382944047 1.3847944817499098 0 274 0 -4.773376 0.008380964 0.01214212852075712 0 -275 0 +275 0 ? ? ? 0 276 0 -5.39027166 0.00454002852 0.0065647900946516503 0 277 0 -6.547201 0.00143207016 0.0020675212884715419 0 278 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 7.07017231 0.999150634 0.0012258970788924348 1 290 0 -6.928671 0.0009783435 0.0014121422633917285 0 291 0 -6.16573143 0.00209578 0.003026744365055557 0 -292 1 +292 1 ? ? ? 0 293 1 5.206727 0.9945503 0.0078837745923848179 1 -294 0 +294 0 ? ? ? 0 295 1 6.0751133 0.9977059 0.0033135223937081622 1 296 0 1.04628563 0.740061 1.9437549070362801 0 -297 0 +297 0 ? ? ? 0 298 0 -2.41310167 0.08217907 0.12371538777024385 0 299 1 7.396323 0.999386847 0.00088486407096525718 1 300 1 5.69218445 0.996639132 0.0048568731086720204 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 3.90498447 0.9802564 0.02876897031816629 1 313 0 -6.928671 0.0009783435 0.0014121422633917285 0 314 0 -6.664193 0.00127416 0.0018393963949252371 0 -315 0 +315 0 ? ? ? 0 316 1 2.20020485 0.9002679 0.15157371666802275 0 317 1 7.75578 0.9995719 0.00061772192605728435 1 318 0 -5.81475639 0.002974334 0.0042974510254079716 0 319 0 1.38163567 0.7992536 2.3165538563686012 0 320 1 5.837037 0.997091 0.0042029227668842466 1 -321 0 +321 0 ? ? ? 0 322 0 -5.008802 0.00663458835 0.0096035809636014526 0 323 1 4.26100159 0.9860881 0.020211552173447847 1 324 0 -6.16573143 0.00209578 0.003026744365055557 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.4899044 0.0110971872 0.016099351806882618 0 409 0 -5.286586 0.00503353868 0.0072801992795113032 0 410 0 -6.547201 0.00143207016 0.0020675212884715419 0 -411 0 +411 0 ? ? ? 0 412 1 7.575248 0.9994873 0.00073988707471124668 1 413 0 -3.748186 0.0230181254 0.033596298028071243 0 414 1 5.274519 0.994905651 0.0073683771133106393 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.085728 0.00226994278 0.0032785576690230068 0 615 0 -4.604435 0.0099082 0.01436579907268937 0 616 0 -5.39027166 0.00454002852 0.0065647900946516503 0 -617 0 +617 0 ? ? ? 0 618 0 -4.81180668 0.008067538 0.011686200181083801 0 619 0 -4.233342 0.0142964814 0.020774319020048612 0 620 0 -5.39027166 0.00454002852 0.0065647900946516503 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt index 946584d7cb..6f9ac3e2b6 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 4.455554 0.98851943 0.016658771299360001 1 35 0 -5.20966339 0.005433825 0.0078607284616035548 0 37 0 -1.95550251 0.1239546 0.19092245979854006 0 -40 0 +40 0 ? ? ? 0 41 1 1.363338 0.796301663 0.32861302447496599 1 44 1 2.93189621 0.949400842 0.074910765340690438 1 45 0 -5.4227767 0.00439546537 0.0063552935777515963 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.283581 0.01360552 0.019763368834071083 0 141 0 -5.903559 0.0027222808 0.0039327765009182425 0 144 0 -5.20966339 0.005433825 0.0078607284616035548 0 -145 0 +145 0 ? ? ? 0 147 0 -4.808229 0.008096219 0.011727915268260366 0 150 0 -4.955528 0.00699508563 0.010127237230146099 0 151 1 2.9794178 0.9516356 0.071518853245330763 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.343319 0.00175536959 0.0025346883174766728 0 156 0 -4.870145 0.00761383865 0.011026477645071437 0 161 0 -3.876839 0.0202957559 0.029581804866342248 0 -164 0 +164 0 ? ? ? 0 167 1 1.63665962 0.8370799 0.25656278900168672 1 169 0 -6.067718 0.00231109979 0.0033380709957833601 0 171 0 -4.955528 0.00699508563 0.010127237230146099 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 6.373105 0.9982961 0.0024603307167519865 1 247 1 1.08481979 0.747405 0.42003789409759523 1 248 0 -3.67766953 0.0246584155 0.036020526273661445 0 -249 0 +249 0 ? ? ? 0 250 0 -5.56404066 0.00381861837 0.0055196472135604522 0 252 0 2.67256546 0.935388267 3.9520600195019679 1 254 1 2.64337635 0.933601558 0.099121125939627047 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -4.955528 0.00699508563 0.010127237230146099 0 271 0 -3.63624859 0.0256744642 0.037524217400354012 0 272 1 2.26432514 0.905879 0.14260970234578763 1 -275 0 +275 0 ? ? ? 0 276 0 -5.20966339 0.005433825 0.0078607284616035548 0 277 0 -5.6494236 0.003507201 0.0050687153327134771 0 278 0 -4.955528 0.00699508563 0.010127237230146099 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -4.955528 0.00699508563 0.010127237230146099 0 293 1 2.05892181 0.886846 0.17324448166666195 1 296 0 1.99554062 0.880328059 3.0628431682446857 1 -297 0 +297 0 ? ? ? 0 299 1 3.69336033 0.9757162 0.035466552037373179 1 300 1 4.02837372 0.9825081 0.025458759934045509 1 301 0 -4.955528 0.00699508563 0.010127237230146099 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 2.94207382 0.9498875 0.074171415588786135 1 317 1 4.312872 0.986782 0.019196674360521217 1 319 0 2.41989422 0.9183318 3.6140817927103961 1 -321 0 +321 0 ? ? ? 0 323 1 3.56970787 0.972607434 0.040070476517068486 1 327 0 -5.6494236 0.003507201 0.0050687153327134771 0 328 1 1.70264626 0.845880032 0.24147502978012392 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 7.478863 0.9994354 0.00081473984708772015 1 613 0 -4.91670847 0.00726995664 0.010526641032229434 0 614 0 -5.20966339 0.005433825 0.0078607284616035548 0 -617 0 +617 0 ? ? ? 0 618 0 -4.769904 0.008409867 0.01218417928555986 0 619 0 -4.330144 0.0129945707 0.018870074173474232 0 621 0 -0.284356117 0.429386139 0.80941330227154873 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -3.99180722 0.0181314889 0.026398258875503611 0 19 0 -3.49091744 0.0295717642 0.043306566793261343 0 22 0 -4.74231339 0.008643099 0.012523555687056959 0 -23 1 +23 1 ? ? ? 0 24 0 -4.99358654 0.00673562335 0.0097503246472711684 0 26 0 -4.230923 0.01433061 0.020824270758417435 0 27 0 -3.74053383 0.0231908429 0.0338513700113664 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -4.74265528 0.00864017 0.012519293184591884 0 135 0 -2.40284228 0.08295622 0.12493748028384198 0 136 0 -4.24142361 0.0141830426 0.020608297294341209 0 -139 0 +139 0 ? ? ? 0 140 0 -4.99192953 0.006746718 0.0097664397610658042 0 142 1 1.883389 0.8679999 0.20423319963131603 1 143 0 -3.93245554 0.0192188919 0.027996904861385053 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -3.71085787 0.02387269 0.034858773809671437 0 155 1 2.1317234 0.8939485 0.1617363815870177 1 157 0 -4.492697 0.0110665858 0.016054708591916572 0 -158 0 +158 0 ? ? ? 0 159 1 7.019025 0.9991061 0.0012901886373564615 1 160 1 5.00210667 0.9933211 0.0096679074407147667 1 162 0 -4.492697 0.0110665858 0.016054708591916572 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 4.06135273 0.983066 0.02463978413547805 1 232 0 0.582720757 0.641693234 1.4807328100816015 1 234 0 -2.76217723 0.0594026 0.08835075047841523 0 -235 0 +235 0 ? ? ? 0 236 1 5.42756176 0.995625436 0.0063250060270779312 1 238 1 6.00247669 0.9975335 0.0035628026721853763 1 243 0 -3.18360639 0.039787326 0.058574116415727635 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 7.122633 0.999194 0.0011632435375058345 1 287 0 -4.74265528 0.00864017 0.012519293184591884 0 289 1 5.24414349 0.994749367 0.0075950186704868833 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 3.85042763 0.979172349 0.030365276805999493 1 298 0 -2.97889614 0.0483884327 0.07155528637722855 0 302 1 7.28154945 0.999312341 0.00099242318488484949 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -4.99358654 0.00673562335 0.0097503246472711684 0 310 0 -4.74265528 0.00864017 0.012519293184591884 0 313 0 -5.49281931 0.004099349 0.005926265742136525 0 -315 0 +315 0 ? ? ? 0 318 0 -4.994271 0.006731047 0.0097436773674529319 0 320 1 3.98940563 0.9818257 0.026461150371251282 1 322 0 -4.492697 0.0110665858 0.016054708591916572 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -5.243203 0.00525554 0.0076021355607147146 0 408 0 -2.50673819 0.07538716 0.11307869372093513 0 410 0 -5.243203 0.00525554 0.0076021355607147146 0 -411 0 +411 0 ? ? ? 0 412 1 4.41673565 0.9880704 0.017314215991460075 1 417 0 -5.243203 0.00525554 0.0076021355607147146 0 420 0 -2.416037 0.08195794 0.12336783697816152 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt index fc9c09274b..27120c9ca1 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 3.60211229 0.973457634 0.038809901663734138 1 21 1 4.16481066 0.9847049 0.022236640568523543 1 22 0 -5.531984 0.003942524 0.0056991017626305011 0 -23 1 +23 1 ? ? ? 0 24 0 -5.641634 0.00353453052 0.0051082826744376919 0 25 1 1.07475233 0.7454996 0.42372049536525624 1 26 0 -5.05423546 0.00634177 0.0091783743058498103 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -2.63470244 0.06693815 0.099955373797007113 0 38 1 3.11990023 0.9577062 0.062344933763709205 1 39 1 1.3332 0.791369438 0.33757674381727276 1 -40 0 +40 0 ? ? ? 0 41 1 1.76657629 0.8540314 0.22763900783247884 1 42 1 5.77468061 0.996904433 0.0044728861234643764 1 43 1 -0.160024166 0.4600791 1.1200461625954479 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.98228741 0.00681164 0.0098607417433267518 0 137 0 -5.97203064 0.00254257885 0.0036728371153443573 0 138 0 -4.3374877 0.0129007176 0.018732896643962105 0 -139 0 +139 0 ? ? ? 0 140 0 -5.97203064 0.00254257885 0.0036728371153443573 0 141 0 -5.97203064 0.00254257885 0.0036728371153443573 0 142 1 3.62155676 0.973955452 0.03807230821683618 1 143 0 -4.06941748 0.0168002676 0.024443571870852193 0 144 0 -5.531984 0.003942524 0.0056991017626305011 0 -145 0 +145 0 ? ? ? 0 146 1 0.845272541 0.69957453 0.51545032982981998 1 147 0 -5.264244 0.00514667667 0.0074442579843589417 0 148 0 -3.53539133 0.0283218417 0.041449555087460649 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.63197279 0.9328912 0.1002192761113103 1 156 0 -4.95913267 0.00697009 0.010090922390098837 0 157 0 -5.091937 0.006108559 0.0088398149066821179 0 -158 0 +158 0 ? ? ? 0 159 1 7.637605 0.9995183 0.00069514934090023593 1 160 1 5.102776 0.9939569 0.00874476585784471 1 161 0 -4.27338028 0.013743096 0.019964600611336557 0 162 0 -5.091937 0.006108559 0.0088398149066821179 0 163 0 -3.67683315 0.0246785376 0.036050290649489301 0 -164 0 +164 0 ? ? ? 0 165 0 -3.45594358 0.0305921026 0.044824259180207618 0 166 1 4.88854 0.9925239 0.010826239558709188 1 167 1 2.59618044 0.930615366 0.10374308707957469 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.465872765 0.6144064 1.3748470144034199 1 233 1 3.621717 0.9739595 0.038066304453576054 1 234 0 -3.43166828 0.0313202776 0.045908353174351067 0 -235 0 +235 0 ? ? ? 0 236 1 6.647678 0.9987047 0.001869974861439897 1 237 1 4.42246 0.9881377 0.017215962974508822 1 238 1 6.44672251 0.9984168 0.0022859116128351314 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 6.46864176 0.998451054 0.0022363890442921863 1 247 1 1.5234189 0.8210414 0.28447311579312373 1 248 0 -4.00451946 0.0179065578 0.026067797200325149 0 -249 0 +249 0 ? ? ? 0 250 0 -5.39917946 0.00449994765 0.0065067030783201637 0 251 1 4.104112 0.983763337 0.023616805142920666 1 252 0 2.75052214 0.939942837 4.0575198580425882 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.62500715 0.8354845 0.2593150232737797 1 273 1 0.228233814 0.556812048 0.84473766721050514 1 274 0 -4.82307673 0.007977848 0.011555758238394069 0 -275 0 +275 0 ? ? ? 0 276 0 -5.531984 0.003942524 0.0056991017626305011 0 277 0 -6.0816803 0.00227912888 0.003291840622802274 0 278 0 -5.641634 0.00353453052 0.0051082826744376919 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 5.68027353 0.996598959 0.004915027880645002 1 290 0 -6.521727 0.00146896509 0.0021208267355545722 0 291 0 -5.641634 0.00353453052 0.0051082826744376919 0 -292 1 +292 1 ? ? ? 0 293 1 2.365685 0.9141729 0.12946106222132911 1 -294 0 +294 0 ? ? ? 0 295 1 4.049787 0.982872367 0.024924010557578272 1 296 0 1.7836585 0.856148 2.7973428610745215 1 -297 0 +297 0 ? ? ? 0 298 0 -3.845192 0.0209346656 0.030522958750931656 0 299 1 2.974937 0.95142895 0.071832170969213474 1 300 1 4.1471324 0.9844364 0.022630102350657457 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.437553883 0.607676 0.71862571139451958 1 313 0 -6.521727 0.00146896509 0.0021208267355545722 0 314 0 -6.0816803 0.00227912888 0.003291840622802274 0 -315 0 +315 0 ? ? ? 0 316 1 3.03096247 0.953953445 0.068009233653927595 1 317 1 3.97896051 0.9816384 0.026736450275361392 1 318 0 -5.641634 0.00353453052 0.0051082826744376919 0 319 0 1.61403608 0.833970964 2.5904925250318187 1 320 1 5.17757845 0.99439 0.0081162910482616441 1 -321 0 +321 0 ? ? ? 0 322 0 -5.091937 0.006108559 0.0088398149066821179 0 323 1 3.22697878 0.961837 0.056135679390163992 1 324 0 -5.641634 0.00353453052 0.0051082826744376919 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -2.996994 0.0475618578 0.070302697725435973 0 409 0 -4.887184 0.007486166 0.010840883662554117 0 410 0 -6.0816803 0.00227912888 0.003291840622802274 0 -411 0 +411 0 ? ? ? 0 412 1 4.5649085 0.989696443 0.014942001227255185 1 413 0 -3.34774446 0.0339691 0.049858759088118251 0 414 1 3.84952021 0.9791539 0.030392501392593196 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -5.531984 0.003942524 0.0056991017626305011 0 615 0 -3.69268847 0.02429977 0.035490127405006046 0 616 0 -5.531984 0.003942524 0.0056991017626305011 0 -617 0 +617 0 ? ? ? 0 618 0 -4.98228741 0.00681164 0.0098607417433267518 0 619 0 -4.43259048 0.0117441025 0.01704343535923129 0 620 0 -5.531984 0.003942524 0.0056991017626305011 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt index c637c69d47..777b0c9e2e 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 7.291877 0.999319434 0.00098218321186686951 1 35 0 -5.869519 0.002816277 0.0040687610645090766 0 37 0 -1.02334213 0.2643769 0.44296132773823416 0 -40 0 +40 0 ? ? ? 0 41 1 2.78505516 0.9418629 0.086411051010362436 1 44 1 7.821086 0.999599 0.00057866567352766714 1 45 0 -5.776886 0.00308877858 0.0044630618032660361 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.724151 0.008800117 0.01275207813790242 0 141 0 -6.55827045 0.00141632778 0.0020447774455947432 0 144 0 -5.869519 0.002816277 0.0040687610645090766 0 -145 0 +145 0 ? ? ? 0 147 0 -5.710598 0.00329976762 0.0047684300667823433 0 150 0 -5.60911131 0.003650946 0.0052768404018184306 0 151 1 5.118675 0.994051635 0.0086073014434059885 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -6.9242 0.000982723 0.0014184666437026428 0 156 0 -5.82082272 0.00295639853 0.0042714987401086318 0 161 0 -4.27493 0.0137221068 0.019933897944770858 0 -164 0 +164 0 ? ? ? 0 167 1 8.728978 0.9998382 0.00023348534112652949 1 169 0 -6.667038 0.00127054506 0.0018341745113224558 0 171 0 -5.546697 0.00388516486 0.0056160247410976614 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 11.3055124 0.9999877 1.7714321792245208E-05 1 247 1 2.306137 0.909384 0.1370384544406823 1 248 0 -3.57901382 0.027145749 0.039704412233602736 0 -249 0 +249 0 ? ? ? 0 250 0 -6.50957441 0.00148689921 0.0021467385111997687 0 252 0 4.227894 0.9856265 6.1204466632636887 1 254 1 5.1967 0.9944957 0.0079629764358839714 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.546697 0.00388516486 0.0056160247410976614 0 271 0 -4.08297968 0.01657771 0.024117038815588843 0 272 1 4.140359 0.984332263 0.022782712303270784 1 -275 0 +275 0 ? ? ? 0 276 0 -5.50359 0.004055611 0.0058629065586406487 0 277 0 -6.235449 0.00195492059 0.002823114622841595 0 278 0 -5.546697 0.00388516486 0.0056160247410976614 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.546697 0.00388516486 0.0056160247410976614 0 293 1 4.409566 0.9879857 0.017437977320811163 1 296 0 1.50613689 0.818488 2.4618631795162846 1 -297 0 +297 0 ? ? ? 0 299 1 7.93633366 0.9996426 0.00051569614495754321 1 300 1 7.85100842 0.9996108 0.00056163266102194308 1 301 0 -5.546697 0.00388516486 0.0056160247410976614 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 3.280983 0.9637706 0.05323826093940856 1 317 1 9.331164 0.999911368 0.00012787476790525535 1 319 0 2.956067 0.9505495 4.3378705942158717 1 -321 0 +321 0 ? ? ? 0 323 1 5.71478939 0.996714 0.0047485078589307303 1 327 0 -6.235449 0.00195492059 0.002823114622841595 0 328 1 1.37423134 0.7980629 0.32542559954710693 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 18.5483551 1 0 1 613 0 -5.86728668 0.00282255374 0.0040778419569695535 0 614 0 -5.931934 0.00264632422 0.0038228993852335589 0 -617 0 +617 0 ? ? ? 0 618 0 -5.1376605 0.00583713735 0.0084458830416492391 0 619 0 -4.771732 0.00839464 0.012162025043583415 0 621 0 0.3424616 0.5847883 1.2680810757859888 1 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.595036 0.01000083 0.014500778622910757 0 19 0 -3.32248163 0.03480794 0.051112046016137037 0 22 0 -5.58065033 0.00375595246 0.0054288957255828087 0 -23 1 +23 1 ? ? ? 0 24 0 -6.503868 0.00149539544 0.0021590142749859306 0 26 0 -5.548387 0.00387863023 0.0056065605311466102 0 27 0 -4.308096 0.0132804094 0.01928794203033625 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.58065033 0.00375595246 0.0054288957255828087 0 135 0 -3.21473217 0.038615074 0.056813911931726302 0 136 0 -4.944373 0.007072995 0.010240432848577178 0 -139 0 +139 0 ? ? ? 0 140 0 -5.929988 0.00265146513 0.0038303358354253489 0 142 1 3.36657238 0.966643333 0.048944424422816823 1 143 0 -5.92445755 0.0026661302 0.0038515494632620751 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.654668 0.009427353 0.013665310686569912 0 155 1 1.85960007 0.865250349 0.20881047650865889 1 157 0 -5.867591 0.00282169762 0.0040766033438554457 0 -158 0 +158 0 ? ? ? 0 159 1 9.591543 0.9999317 9.8549424786689596E-05 1 160 1 7.363619 0.9993665 0.00091420540184684017 1 162 0 -5.231313 0.00531806657 0.0076928219174680146 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.973879 0.9974621 0.0036660786976757281 1 232 0 1.10679436 0.751531 2.008862256839882 1 234 0 -3.7641573 0.0226616841 0.033070042201646284 0 -235 0 +235 0 ? ? ? 0 236 1 8.480452 0.9997926 0.00029928085261473974 1 238 1 9.448584 0.9999212 0.00011368501131740873 1 243 0 -4.52710867 0.0106962454 0.015514542524251164 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 11.9692574 0.9999937 9.115109290810302E-06 1 287 0 -5.58065033 0.00375595246 0.0054288957255828087 0 289 1 5.61943245 0.9963864 0.005222752234881816 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 5.155699 0.9942666 0.008295308421383105 1 298 0 -2.71633816 0.0620161332 0.092364986128816551 0 302 1 12.1483784 0.9999947 7.6532482629398447E-06 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -6.503868 0.00149539544 0.0021590142749859306 0 310 0 -6.216928 0.001991392 0.00287583593610438 0 313 0 -7.2025423 0.000744136 0.0010739609416730365 0 -315 0 +315 0 ? ? ? 0 318 0 -6.503868 0.00149539544 0.0021590142749859306 0 320 1 4.269491 0.9862041 0.02004186218454097 1 322 0 -5.231313 0.00531806657 0.0076928219174680146 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -6.853205 0.00105495122 0.0015227762658880663 0 408 0 -4.852546 0.00774797052 0.011221486878031036 0 410 0 -6.853205 0.00105495122 0.0015227762658880663 0 -411 0 +411 0 ? ? ? 0 412 1 7.587702 0.9994936 0.00073076734716613276 1 417 0 -6.853205 0.00105495122 0.0015227762658880663 0 420 0 -3.639381 0.0255962238 0.037408370576337828 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt index 3c3d96d974..157c16ab10 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 6.59719467 0.9986377 0.0019667577188335239 1 21 1 6.9093914 0.999002635 0.0014396108762453246 1 22 0 -5.50264263 0.00405943953 0.0058684526611357517 0 -23 1 +23 1 ? ? ? 0 24 0 -6.14459133 0.002140461 0.0030913421022840675 0 25 1 0.9981127 0.7306873 0.4526739227114121 1 26 0 -5.56536674 0.00381357735 0.0055123467049374769 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.79816341 0.142074779 0.22107619020053021 0 38 1 4.925646 0.9927943 0.010433298358924143 1 39 1 0.895828247 0.7100915 0.49392321501990555 1 -40 0 +40 0 ? ? ? 0 41 1 2.72234154 0.93833214 0.091829412491125792 1 42 1 6.45601654 0.998431444 0.0022647243580549464 1 43 1 -0.5776577 0.359471738 1.4760497444221736 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.965955 0.006923029 0.010022553277705419 0 137 0 -5.93407059 0.00264069065 0.0038147503192492542 0 138 0 -4.668453 0.009299485 0.013479092815885742 0 -139 0 +139 0 ? ? ? 0 140 0 -5.93407059 0.00264069065 0.0038147503192492542 0 141 0 -6.47075844 0.00154565845 0.0022316389032971582 0 142 1 3.64823437 0.9746237 0.037082819238846716 1 143 0 -5.466771 0.00420707744 0.0060823331671915537 0 144 0 -6.03933048 0.002377488 0.0034340739321673348 0 -145 0 +145 0 ? ? ? 0 146 1 -0.110341072 0.4724427 1.0817887726099731 0 147 0 -6.11751556 0.002199078 0.0031760924132441036 0 148 0 -1.55709267 0.174064219 0.2758984827724662 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.36491013 0.9141121 0.12955701132443528 1 156 0 -6.267907 0.00189260417 0.002733037741461919 0 157 0 -5.607903 0.003655344 0.0052832085489667481 0 -158 0 +158 0 ? ? ? 0 159 1 10.9176846 0.9999819 2.614159977229895E-05 1 160 1 7.75989342 0.9995737 0.00061514108379339587 1 161 0 -4.309515 0.0132618267 0.019260772318565499 0 162 0 -5.07121468 0.006235666 0.0090243302579491296 0 163 0 -3.76092339 0.02273342 0.033175939048272665 0 -164 0 +164 0 ? ? ? 0 165 0 -3.924346 0.0193723515 0.028222656292276067 0 166 1 6.74228859 0.998821437 0.001701309224545778 1 167 1 7.94067574 0.99964416 0.0005134595729021004 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.7225275 0.673163354 1.6133583443719168 1 233 1 5.29774761 0.995022058 0.0071995859979441072 1 234 0 -3.554101 0.027811477 0.040691992083778071 0 -235 0 +235 0 ? ? ? 0 236 1 10.4206543 0.9999702 4.2996303413732479E-05 1 237 1 5.828807 0.997067034 0.0042375925496323883 1 238 1 11.3587952 0.9999883 1.6854398235588073E-05 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.599171 0.9999322 9.7775450197580527E-05 1 247 1 2.067935 0.887747347 0.17177895049852415 1 248 0 -3.4094696 0.0320008248 0.046922276673100262 0 -249 0 +249 0 ? ? ? 0 250 0 -6.699335 0.0012302153 0.0017759181240158319 0 251 1 7.9959507 0.9996633 0.00048584679587356108 1 252 0 3.6111412 0.9736899 5.2482402117884597 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.72366619 0.848600447 0.23684265601696217 1 273 1 -0.342484474 0.4152061 1.2681004399882052 0 274 0 -4.846204 0.007796883 0.011292605602753639 0 -275 0 +275 0 ? ? ? 0 276 0 -5.50264263 0.00405943953 0.0058684526611357517 0 277 0 -6.57601929 0.0013914461 0.0020088303041534395 0 278 0 -6.14459133 0.002140461 0.0030913421022840675 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 7.101452 0.999176741 0.0011882013529343118 1 290 0 -7.007447 0.0009042981 0.0013052166206428517 0 291 0 -6.14459133 0.002140461 0.0030913421022840675 0 -292 1 +292 1 ? ? ? 0 293 1 4.6855 0.9908563 0.013252265463070151 1 -294 0 +294 0 ? ? ? 0 295 1 6.2168417 0.99800843 0.0028760930785605436 1 296 0 1.02090549 0.735148966 1.9167469555439196 1 -297 0 +297 0 ? ? ? 0 298 0 -3.02001572 0.0465297773 0.068740211727588499 0 299 1 7.09773827 0.9991737 0.0011925905306341051 1 300 1 6.64691544 0.998703659 0.001871438610749495 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 2.90730953 0.9482066 0.076726654530730437 1 313 0 -7.007447 0.0009042981 0.0013052166206428517 0 314 0 -6.649353 0.001293185 0.0018668790298154393 0 -315 0 +315 0 ? ? ? 0 316 1 2.12067223 0.892896235 0.16343556787762994 1 317 1 7.5715065 0.9994854 0.00074264021133653045 1 318 0 -5.92458725 0.00266578537 0.0038510506602204577 0 319 0 1.35382557 0.7947544 2.2845767018893883 1 320 1 5.988476 0.9974988 0.0036129742419820916 1 -321 0 +321 0 ? ? ? 0 322 0 -5.07121468 0.006235666 0.0090243302579491296 0 323 1 4.28225327 0.986376643 0.019789456912749203 1 324 0 -6.14459133 0.002140461 0.0030913421022840675 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -4.58592224 0.0100914678 0.014632868867702545 0 409 0 -5.205141 0.00545831956 0.0078962603748794508 0 410 0 -6.57601929 0.0013914461 0.0020088303041534395 0 -411 0 +411 0 ? ? ? 0 412 1 7.70175171 0.9995482 0.00065196153701951257 1 413 0 -3.70033741 0.02411908 0.035222977147123087 0 414 1 5.41945839 0.99559 0.006376396588275309 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.112665 0.00220974674 0.003191518193469859 0 615 0 -4.37095165 0.012481451 0.018120247723782505 0 616 0 -5.50264263 0.00405943953 0.0058684526611357517 0 -617 0 +617 0 ? ? ? 0 618 0 -4.965955 0.006923029 0.010022553277705419 0 619 0 -4.429267 0.0117827384 0.017099838701332234 0 620 0 -5.50264263 0.00405943953 0.0058684526611357517 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt index 4a5221f10c..ca68c5efee 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 4.28022528 0.9863494 0.019829298264044417 1 35 0 -4.29419041 0.0134638669 0.019556202484947195 0 37 0 -1.95759869 0.123727158 0.19054794774570458 0 -40 0 +40 0 ? ? ? 0 41 1 1.34579754 0.7934417 0.33380384997661361 1 44 1 5.22175264 0.9946311 0.0077665361772382407 1 45 0 -4.55969143 0.0103568994 0.015019761369081966 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -3.50701833 0.0291131958 0.042624993467054707 0 141 0 -4.510108 0.0108776484 0.015779105577069866 0 144 0 -4.29419041 0.0134638669 0.019556202484947195 0 -145 0 +145 0 ? ? ? 0 147 0 -4.118532 0.0160079524 0.023281438797448952 0 150 0 -4.43784046 0.0116833262 0.016954714516170224 0 151 1 2.86513376 0.9460957 0.079941963974142813 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -4.792855 0.008220622 0.011908867541972762 0 156 0 -4.152423 0.0154827805 0.022511655407603286 0 161 0 -3.361469 0.0335215963 0.049190599682179155 0 -164 0 +164 0 ? ? ? 0 167 1 3.00652266 0.9528679 0.069651835538374979 1 169 0 -4.76463461 0.008453925 0.012248282163630898 0 171 0 -4.36102 0.0126044592 0.01829996516606797 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 7.761687 0.9995744 0.00061410874818056485 1 247 1 2.24705172 0.904395938 0.14497358262585658 1 248 0 -2.75623083 0.05973572 0.088861781570551343 0 -249 0 +249 0 ? ? ? 0 250 0 -4.36834049 0.0125136767 0.018167327858314108 0 252 0 2.47272253 0.9222073 3.6842213333365441 1 254 1 5.30986643 0.9950817 0.0071130806432035459 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -4.36102 0.0126044592 0.01829996516606797 0 271 0 -3.23003125 0.0380511023 0.055967840193562365 0 272 1 1.97665262 0.878323853 0.18717511071531998 1 -275 0 +275 0 ? ? ? 0 276 0 -4.011443 0.0177852046 0.025889540399059593 0 277 0 -4.57693768 0.010181617 0.014764258553784819 0 278 0 -4.36102 0.0126044592 0.01829996516606797 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -4.36102 0.0126044592 0.01829996516606797 0 293 1 3.50341845 0.9707849 0.042776422691022505 1 296 0 0.854511261 0.701512635 1.7442582303419563 1 -297 0 +297 0 ? ? ? 0 299 1 4.02451658 0.9824417 0.025556263019683039 1 300 1 4.165831 0.9847203 0.02221411037855156 1 301 0 -4.36102 0.0126044592 0.01829996516606797 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 2.756329 0.940269768 0.088853362072482428 1 317 1 5.80636454 0.9970007 0.004333585623359653 1 319 0 1.0757966 0.7456977 1.975383454841898 1 -321 0 +321 0 ? ? ? 0 323 1 3.082415 0.956161559 0.064673689924879513 1 327 0 -4.57693768 0.010181617 0.014764258553784819 0 328 1 2.805962 0.942997158 0.084674672619554123 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 9.154173 0.999894261 0.00015255667702522208 1 613 0 -3.88851619 0.0200648643 0.029241838018723456 0 614 0 -4.371011 0.0124807227 0.018119183737570529 0 -617 0 +617 0 ? ? ? 0 618 0 -3.72869563 0.0234605316 0.034249740786795546 0 619 0 -3.44594836 0.0308899172 0.045267541796380997 0 621 0 -0.782016754 0.3138854 0.54347850919806506 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -3.6590457 0.0251103118 0.036689112229595131 0 19 0 -2.927483 0.0508115776 0.075233591312011905 0 22 0 -4.14026976 0.0156691261 0.022784748886103094 0 -23 1 +23 1 ? ? ? 0 24 0 -4.75638962 0.008523319 0.012349253326730132 0 26 0 -3.93837214 0.0191076845 0.027833331950969452 0 27 0 -3.40870714 0.03202445 0.046957488715061987 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -4.14026976 0.0156691261 0.022784748886103094 0 135 0 -2.62711 0.06741392 0.1006911970043132 0 136 0 -3.77448845 0.0224339925 0.032733975062274864 0 -139 0 +139 0 ? ? ? 0 140 0 -4.255713 0.0139846308 0.020317960583175827 0 142 1 1.9862442 0.879345238 0.18549840430247841 1 143 0 -3.74331784 0.02312786 0.033758349764067219 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -3.267953 0.0366871059 0.053923618256310278 0 155 1 1.88906479 0.8686489 0.20315494639328185 1 157 0 -4.39060831 0.0122414771 0.017769705367455636 0 -158 0 +158 0 ? ? ? 0 159 1 6.88715744 0.9989802 0.001471976257367614 1 160 1 5.57244873 0.996213257 0.0054734847881935346 1 162 0 -4.024827 0.0175529048 0.025548374564996635 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 4.531541 0.989350557 0.015446292097008558 1 232 0 0.251759052 0.562609434 1.1930059918223119 1 234 0 -2.51516485 0.0748018846 0.11216576762239737 0 -235 0 +235 0 ? ? ? 0 236 1 5.772835 0.9968987 0.0044811669482472481 1 238 1 6.65452766 0.9987135 0.0018572316949005939 1 243 0 -3.28154564 0.036209736 0.053208867556931244 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 8.09413 0.999694765 0.00044042879818134223 1 287 0 -4.14026976 0.0156691261 0.022784748886103094 0 289 1 4.30420876 0.9866685 0.019362604522295365 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 3.601099 0.973431468 0.038848681676236112 1 298 0 -2.22490883 0.09753586 0.14805849380018501 0 302 1 8.018049 0.9996706 0.00047526633912005399 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -4.75638962 0.008523319 0.012349253326730132 0 310 0 -4.506051 0.0109213842 0.015842898366274911 0 313 0 -4.987275 0.00677798 0.0098118479335557937 0 -315 0 +315 0 ? ? ? 0 318 0 -4.75638962 0.008523319 0.012349253326730132 0 320 1 2.81792259 0.943636656 0.08369663336324043 1 322 0 -4.024827 0.0175529048 0.025548374564996635 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -4.87183237 0.00760109862 0.011007956774122585 0 408 0 -3.31706572 0.03499035 0.051384728090851442 0 410 0 -4.87183237 0.00760109862 0.011007956774122585 0 -411 0 +411 0 ? ? ? 0 412 1 5.23884 0.9947216 0.007635302704268032 1 417 0 -4.87183237 0.00760109862 0.011007956774122585 0 420 0 -2.70138979 0.0628913939 0.093711836328273818 0 diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt index ff73fc5189..1f68afb149 100644 --- a/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 3.96267843 0.9813426 0.027171185979008489 1 21 1 5.265054 0.9948575 0.0074382155674881083 1 22 0 -4.51155567 0.0108620832 0.015756402974357524 0 -23 1 +23 1 ? ? ? 0 24 0 -5.107146 0.00601691334 0.0087067913827365544 0 25 1 0.6726794 0.6621029 0.59487269328676762 1 26 0 -4.36255264 0.0125854 0.018272117354929882 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -2.04001045 0.115065664 0.17635768683092809 0 38 1 3.6194644 0.973902345 0.038150977482211336 1 39 1 0.673018932 0.6621788 0.59470724065030856 1 -40 0 +40 0 ? ? ? 0 41 1 1.75375986 0.8524264 0.23035280375662126 1 42 1 5.60796976 0.9963449 0.0052828205055256471 1 43 1 -0.5184488 0.37321502 1.4219210475645763 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -4.10382557 0.0162412636 0.023623552162962799 0 137 0 -4.73142624 0.008736885 0.01266004645418743 0 138 0 -3.874074 0.0203508064 0.029662873497674963 0 -139 0 +139 0 ? ? ? 0 140 0 -4.73142624 0.008736885 0.01266004645418743 0 141 0 -5.13915634 0.00582846347 0.0084332958699167727 0 142 1 2.53462648 0.9265339 0.11008436686792368 1 143 0 -4.1287837 0.0158472732 0.02304587572398243 0 144 0 -4.919286 0.00725137955 0.010499643937654568 0 -145 0 +145 0 ? ? ? 0 146 1 0.185849667 0.546329141 0.87215771785369722 1 147 0 -4.93812275 0.007117027 0.010304411526052911 0 148 0 -2.08798218 0.110270388 0.16856112656874939 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.24833822 0.9045071 0.14479626651911345 1 156 0 -4.913893 0.007290303 0.010556209568305783 0 157 0 -4.69941568 0.009018519 0.013069997580773615 0 -158 0 +158 0 ? ? ? 0 159 1 8.213823 0.9997292 0.0003907114930137318 1 160 1 6.409885 0.9983575 0.0023716112041259128 1 161 0 -3.70981741 0.0238969475 0.034894625912887001 0 162 0 -4.29168558 0.0134971775 0.019604916230060426 0 163 0 -4.20999336 0.0146292737 0.021261482597556794 0 -164 0 +164 0 ? ? ? 0 165 0 -3.35529184 0.0337223038 0.049490233625727506 0 166 1 5.13335562 0.9941378 0.0084822193431077515 1 167 1 4.31503153 0.986810148 0.019155543364578753 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.387140751 0.5955942 1.3061245037640643 1 233 1 4.22282743 0.9855546 0.020992330945643928 1 234 0 -2.79013348 0.0578596778 0.085986144354393876 0 -235 0 +235 0 ? ? ? 0 236 1 7.198673 0.999253 0.0010781320275837761 1 237 1 4.24964952 0.9859315 0.020440656601130012 1 238 1 7.93097258 0.9996407 0.00051844885378563097 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 8.043116 0.9996788 0.00046348169408102081 1 247 1 2.170814 0.8975978 0.15585897141376154 1 248 0 -3.01326466 0.0468302034 0.069194857755858583 0 -249 0 +249 0 ? ? ? 0 250 0 -5.133764 0.005859794 0.0084787621145538308 0 251 1 5.4693675 0.9958038 0.0060666126630314994 1 252 0 2.521253 0.925618351 3.7489094481346643 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.26803923 0.7804069 0.35770157547115156 1 273 1 -0.341918468 0.415343523 1.2676230380461255 0 274 0 -4.11754751 0.01602347 0.023304190486054636 0 -275 0 +275 0 ? ? ? 0 276 0 -4.51155567 0.0108620832 0.015756402974357524 0 277 0 -5.327016 0.00483505568 0.0069924288920724343 0 278 0 -5.107146 0.00601691334 0.0087067913827365544 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 5.13687468 0.9941583 0.0084524642023233402 1 290 0 -5.54688644 0.00388443237 0.005614963867042919 0 291 0 -5.107146 0.00601691334 0.0087067913827365544 0 -292 1 +292 1 ? ? ? 0 293 1 3.96790457 0.981438041 0.027030903251222888 1 -294 0 +294 0 ? ? ? 0 295 1 4.55162954 0.9895601 0.015140724489837425 1 296 0 0.6700988 0.661525249 1.5628798789895393 1 -297 0 +297 0 ? ? ? 0 298 0 -2.40630245 0.08269336 0.12452401321321592 0 299 1 4.497334 0.988984048 0.015980843395464565 1 300 1 4.62145948 0.990257442 0.014124456553393392 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 1.84912252 0.864024043 0.21085663625525078 1 313 0 -5.54688644 0.00388443237 0.005614963867042919 0 314 0 -5.36138344 0.004672473 0.0067567510712633252 0 -315 0 +315 0 ? ? ? 0 316 1 2.20764112 0.900933564 0.15050737158003294 1 317 1 5.890983 0.997243345 0.0039825045312741787 1 318 0 -5.00404263 0.006666029 0.0096492437155483984 0 319 0 0.6469283 0.656317949 1.540853588056835 1 320 1 3.99139547 0.9818612 0.026409039376297374 1 -321 0 +321 0 ? ? ? 0 322 0 -4.29168558 0.0134971775 0.019604916230060426 0 323 1 3.157802 0.959215045 0.060073807756361522 1 324 0 -5.107146 0.00601691334 0.0087067913827365544 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -3.57900143 0.0271460768 0.039704898382857935 0 409 0 -4.281804 0.0136293843 0.019798272915656116 0 410 0 -5.327016 0.00483505568 0.0069924288920724343 0 -411 0 +411 0 ? ? ? 0 412 1 6.193191 0.997960865 0.0029448527311883681 1 413 0 -3.24647379 0.0374538042 0.055072312735252281 0 414 1 4.55400229 0.9895846 0.015105009633995839 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -4.95365334 0.00700811762 0.010146170993954725 0 615 0 -3.64432216 0.0254732724 0.037226341208279841 0 616 0 -4.51155567 0.0108620832 0.015756402974357524 0 -617 0 +617 0 ? ? ? 0 618 0 -4.10382557 0.0162412636 0.023623552162962799 0 619 0 -3.696096 0.0242191125 0.035370869193417386 0 620 0 -4.51155567 0.0108620832 0.015756402974357524 0 diff --git a/test/BaselineOutput/SingleRelease/NAReplace/featurized.tsv b/test/BaselineOutput/SingleRelease/NAReplace/featurized.tsv new file mode 100644 index 0000000000..5ca9707e3e --- /dev/null +++ b/test/BaselineOutput/SingleRelease/NAReplace/featurized.tsv @@ -0,0 +1,13 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=A:R4:0 +#@ col=B:R8:1 +#@ col=C:R4:2-5 +#@ col=D:R8:6-9 +#@ } +A B 8 0:"" +5 5 5 1 1 1 5 1 1 1 +5 5 5 4 4 5 5 4 4 5 +3 3 3 1 1 1 3 1 1 1 +6 6 6 8 8 1 6 8 8 1 diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-out.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-out.txt deleted file mode 100644 index 8e81e97339..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-out.txt +++ /dev/null @@ -1,33 +0,0 @@ -maml.exe CV tr=OLS threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 2409 examples -Coefficient of determination R2 = 0.291173667189042, or 0.287920813763543 (adjusted) -Not training a calibrator because it is not needed. -Not adding a normalizer. -Trainer solving for 12 parameters across 2489 examples -Coefficient of determination R2 = 0.280280855195625, or 0.277084686203761 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.586798 -L2(avg): 0.573048 -RMS(avg): 0.756999 -Loss-fn(avg): 0.573048 -R Squared: 0.263841 -L1(avg): 0.587999 -L2(avg): 0.571859 -RMS(avg): 0.756214 -Loss-fn(avg): 0.571859 -R Squared: 0.276072 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.587398 (0.0006) -L2(avg): 0.572454 (0.0006) -RMS(avg): 0.756606 (0.0004) -Loss-fn(avg): 0.572454 (0.0006) -R Squared: 0.269956 (0.0061) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-rp.txt deleted file mode 100644 index e3768ce894..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.587398 0.572454 0.756606 0.572454 0.269956 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine.txt deleted file mode 100644 index c49fe65f22..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine.txt +++ /dev/null @@ -1,4899 +0,0 @@ -Instance Label Score L1-loss L2-loss -5 6 5.72627258 0.2737274169921875 0.074926698813214898 -6 6 5.47047424 0.5295257568359375 0.28039752715267241 -8 6 5.164154 0.835845947265625 0.69863844756036997 -9 6 5.711914 0.2880859375 0.082993507385253906 -10 5 6.12764 1.1276397705078125 1.271571452030912 -11 5 5.514374 0.514373779296875 0.26458038482815027 -18 6 5.71618652 0.2838134765625 0.080550089478492737 -20 8 5.861252 2.1387481689453125 4.574243730166927 -21 7 5.85632324 1.1436767578125 1.3079965263605118 -25 6 6.04560852 0.0456085205078125 0.0020801371429115534 -28 6 5.98152161 0.0184783935546875 0.00034145102836191654 -31 6 5.786499 0.2135009765625 0.045582666993141174 -32 6 5.94306946 0.0569305419921875 0.0032410866115242243 -35 5 6.35475159 1.3547515869140625 1.8353518622461706 -37 6 5.89408875 0.1059112548828125 0.011217193910852075 -40 6 5.5723114 0.4276885986328125 0.18291753740049899 -41 6 5.56813049 0.4318695068359375 0.18651127093471587 -44 6 5.54307556 0.4569244384765625 0.20877994247712195 -45 7 5.77168274 1.2283172607421875 1.508763293037191 -46 4 5.47047424 1.4704742431640625 2.1622944998089224 -48 6 5.45797729 0.542022705078125 0.29378861282020807 -50 6 6.289795 0.289794921875 0.083981096744537354 -51 7 6.18988037 0.81011962890625 0.65629381313920021 -52 7 6.18954468 0.810455322265625 0.65683782938867807 -54 6 5.370331 0.629669189453125 0.39648328814655542 -56 6 5.90994263 0.090057373046875 0.0081103304401040077 -60 6 5.22787476 0.772125244140625 0.59617739263921976 -63 6 5.4642334 0.5357666015625 0.28704585134983063 -64 6 5.67704773 0.3229522705078125 0.1042981690261513 -66 7 6.84855652 0.1514434814453125 0.022935128072276711 -68 8 6.04177856 1.958221435546875 3.8346311906352639 -69 5 5.551605 0.551605224609375 0.30426832381635904 -70 6 5.46566772 0.534332275390625 0.28551098052412271 -71 5 5.44389343 0.4438934326171875 0.19704137952066958 -72 5 5.63322449 0.6332244873046875 0.40097325132228434 -73 6 5.06080627 0.9391937255859375 0.88208485417999327 -74 8 6.04177856 1.958221435546875 3.8346311906352639 -76 7 6.69897461 0.301025390625 0.090616285800933838 -77 7 6.207245 0.792755126953125 0.62846069131046534 -79 5 4.77238464 0.2276153564453125 0.051808750489726663 -82 5 5.591614 0.59161376953125 0.35000685229897499 -88 5 5.215561 0.2155609130859375 0.046466507250443101 -90 6 5.21696472 0.7830352783203125 0.61314424709416926 -91 5 5.517044 0.5170440673828125 0.26733456761576235 -92 7 6.657852 0.3421478271484375 0.11706513562239707 -93 7 6.7482605 0.251739501953125 0.063372776843607426 -95 6 5.74305725 0.2569427490234375 0.066019576275721192 -96 6 5.4642334 0.5357666015625 0.28704585134983063 -97 7 5.82597351 1.1740264892578125 1.3783381974790245 -98 4 5.39360046 1.3936004638671875 1.9421222528908402 -99 6 5.4642334 0.5357666015625 0.28704585134983063 -100 5 5.69007874 0.6900787353515625 0.47620866098441184 -102 5 5.91423035 0.9142303466796875 0.83581712679006159 -104 5 5.69007874 0.6900787353515625 0.47620866098441184 -105 6 5.996109 0.0038909912109375 1.5139812603592873E-05 -106 5 6.18110657 1.1811065673828125 1.3950127235148102 -108 6 5.96624756 0.03375244140625 0.0011392273008823395 -109 5 5.66407776 0.6640777587890625 0.44099926971830428 -111 5 5.62986755 0.6298675537109375 0.39673313521780074 -112 5 5.56929 0.5692901611328125 0.32409128756262362 -113 5 5.135269 0.1352691650390625 0.018297747010365129 -115 4 4.919159 0.919158935546875 0.84485314879566431 -117 6 6.310257 0.3102569580078125 0.096259379992261529 -120 5 5.6348877 0.6348876953125 0.40308238565921783 -121 5 5.44267273 0.4426727294921875 0.19595914543606341 -122 5 5.784134 0.7841339111328125 0.61486599058844149 -123 6 5.33546448 0.6645355224609375 0.44160746061243117 -125 6 6.38046265 0.380462646484375 0.1447518253698945 -128 7 6.008789 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/dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-generatedRegressionDataset-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 500 examples +Coefficient of determination R2 = 0.99992945840165, or 0.999927868324639 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.892619 +L2(avg): 1.286446 +RMS(avg): 1.134216 +Loss-fn(avg): 1.286446 +R Squared: 0.999929 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.892619 (0.0000) +L2(avg): 1.286446 (0.0000) +RMS(avg): 1.134216 (0.0000) +Loss-fn(avg): 1.286446 (0.0000) +R Squared: 0.999929 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-generatedRegressionDataset-rp.txt similarity index 84% rename from test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-rp.txt rename to test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-generatedRegressionDataset-rp.txt index 705c51f87f..4be0272a5e 100644 --- a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-rp.txt +++ b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-generatedRegressionDataset-rp.txt @@ -1,4 +1,4 @@ OLS L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.583635 0.563155 0.750436 0.563155 0.281869 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +0.892619 1.286446 1.134216 1.286446 0.999929 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-generatedRegressionDataset.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-generatedRegressionDataset.txt new file mode 100644 index 0000000000..4a4acae0c9 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-generatedRegressionDataset.txt @@ -0,0 +1,501 @@ +Instance Label Score L1-loss L2-loss +0 140.66 142.00174 1.34173583984375 1.8002550639212132 +1 148.12 147.024979 1.0950164794921875 1.1990610903594643 +2 402.2 401.973938 0.22607421875 0.051109552383422852 +3 443.51 442.014343 1.49566650390625 2.2370182909071445 +4 329.59 331.785278 2.195281982421875 4.8192629823461175 +5 322.18 323.341 1.1610107421875 1.3479459434747696 +6 425.31 425.2812 0.02880859375 0.00082993507385253906 +7 421.76 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412.827423 1.172576904296875 1.3749365964904428 +499 344.84 344.483429 0.3565673828125 0.12714029848575592 diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-out.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-out.txt deleted file mode 100644 index 7d2d822e9a..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-out.txt +++ /dev/null @@ -1,24 +0,0 @@ -maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 4898 examples -Coefficient of determination R2 = 0.281869324119046, or 0.280252574746412 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.583635 -L2(avg): 0.563155 -RMS(avg): 0.750436 -Loss-fn(avg): 0.563155 -R Squared: 0.281869 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.583635 (0.0000) -L2(avg): 0.563155 (0.0000) -RMS(avg): 0.750436 (0.0000) -Loss-fn(avg): 0.563155 (0.0000) -R Squared: 0.281869 (0.0000) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine.txt deleted file mode 100644 index 7144734016..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine.txt +++ /dev/null @@ -1,4899 +0,0 @@ -Instance Label Score L1-loss L2-loss -0 6 5.5627594 0.4372406005859375 0.19117934280075133 -1 6 5.21707153 0.782928466796875 0.6129769841209054 -2 6 5.766144 0.233856201171875 0.05468872282654047 -3 6 5.77809143 0.2219085693359375 0.049243413144722581 -4 6 5.77809143 0.2219085693359375 0.049243413144722581 -5 6 5.766144 0.233856201171875 0.05468872282654047 -6 6 5.457855 0.542144775390625 0.29392095748335123 -7 6 5.5627594 0.4372406005859375 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b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-out.txt deleted file mode 100644 index 46f60adaa5..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-out.txt +++ /dev/null @@ -1,33 +0,0 @@ -maml.exe CV tr=OLS threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 xf=MinMax{col=Features} -Not adding a normalizer. -Trainer solving for 12 parameters across 2409 examples -Coefficient of determination R2 = 0.291172889085396, or 0.287920032089126 (adjusted) -Not training a calibrator because it is not needed. -Not adding a normalizer. -Trainer solving for 12 parameters across 2489 examples -Coefficient of determination R2 = 0.280282876617768, or 0.277086716602748 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.586799 -L2(avg): 0.573023 -RMS(avg): 0.756983 -Loss-fn(avg): 0.573023 -R Squared: 0.263873 -L1(avg): 0.588002 -L2(avg): 0.571858 -RMS(avg): 0.756213 -Loss-fn(avg): 0.571858 -R Squared: 0.276073 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.587400 (0.0006) -L2(avg): 0.572440 (0.0006) -RMS(avg): 0.756598 (0.0004) -Loss-fn(avg): 0.572440 (0.0006) -R Squared: 0.269973 (0.0061) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-rp.txt deleted file mode 100644 index c6ae0bb553..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.5874 0.57244 0.756598 0.57244 0.269973 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 xf=MinMax{col=Features} - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine.txt deleted file mode 100644 index 0ebf68c03b..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine.txt +++ /dev/null @@ -1,4899 +0,0 @@ -Instance Label Score L1-loss L2-loss -5 6 5.725769 0.27423095703125 0.075202617794275284 -6 6 5.47061157 0.529388427734375 0.28025210741907358 -8 6 5.16453552 0.8354644775390625 0.69800089322961867 -9 6 5.71276855 0.2872314453125 0.082501903176307678 -10 5 6.127655 1.127655029296875 1.271605865098536 -11 5 5.51383972 0.5138397216796875 0.26403125957585871 -18 6 5.71614075 0.2838592529296875 0.080576075473800302 -20 8 5.861252 2.1387481689453125 4.574243730166927 -21 7 5.856018 1.14398193359375 1.308694664388895 -25 6 6.04542542 0.0454254150390625 0.0020634683314710855 -28 6 5.981476 0.018524169921875 0.00034314487129449844 -31 6 5.785904 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a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine-out.txt +++ /dev/null @@ -1,24 +0,0 @@ -maml.exe TrainTest test=%Data% tr=OLS dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 xf=MinMax{col=Features} -Not adding a normalizer. -Trainer solving for 12 parameters across 4898 examples -Coefficient of determination R2 = 0.281870677180194, or 0.280253930853747 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.583635 -L2(avg): 0.563154 -RMS(avg): 0.750436 -Loss-fn(avg): 0.563154 -R Squared: 0.281871 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.583635 (0.0000) -L2(avg): 0.563154 (0.0000) -RMS(avg): 0.750436 (0.0000) -Loss-fn(avg): 0.563154 (0.0000) -R Squared: 0.281871 (0.0000) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine-rp.txt deleted file mode 100644 index fe37f2c5f9..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.583635 0.563154 0.750436 0.563154 0.281871 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 xf=MinMax{col=Features} - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine.txt deleted file mode 100644 index e296b7b161..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine.txt +++ 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@@ -1,33 +0,0 @@ -maml.exe CV tr=OLS{l2=0.1} threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 2409 examples -Coefficient of determination R2 = 0.269438870320579, or 0.266086274397978 (adjusted) -Not training a calibrator because it is not needed. -Not adding a normalizer. -Trainer solving for 12 parameters across 2489 examples -Coefficient of determination R2 = 0.255663034368455, or 0.252357541182364 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.596885 -L2(avg): 0.589177 -RMS(avg): 0.767579 -Loss-fn(avg): 0.589177 -R Squared: 0.243122 -L1(avg): 0.599038 -L2(avg): 0.587517 -RMS(avg): 0.766497 -Loss-fn(avg): 0.587517 -R Squared: 0.256250 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.597962 (0.0011) -L2(avg): 0.588347 (0.0008) -RMS(avg): 0.767038 (0.0005) -Loss-fn(avg): 0.588347 (0.0008) -R Squared: 0.249686 (0.0066) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-rp.txt deleted file mode 100644 index f063507bbb..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.597962 0.588347 0.767038 0.588347 0.249686 0.1 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS{l2=0.1} threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /l2:0.1 - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine.txt deleted 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a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-out.txt +++ /dev/null @@ -1,24 +0,0 @@ -maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 -Not adding a normalizer. -Trainer solving for 12 parameters across 4898 examples -Coefficient of determination R2 = 0.259289504308921, or 0.257621920303067 (adjusted) -Not training a calibrator because it is not needed. -L1(avg): 0.595209 -L2(avg): 0.580862 -RMS(avg): 0.762143 -Loss-fn(avg): 0.580862 -R Squared: 0.259290 - -OVERALL RESULTS ---------------------------------------- -L1(avg): 0.595209 (0.0000) -L2(avg): 0.580862 (0.0000) -RMS(avg): 0.762143 (0.0000) -Loss-fn(avg): 0.580862 (0.0000) -R Squared: 0.259290 (0.0000) - ---------------------------------------- -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-rp.txt deleted file mode 100644 index 1707c73c50..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-rp.txt +++ /dev/null @@ -1,4 +0,0 @@ -OLS -L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.595209 0.580862 0.762143 0.580862 0.25929 0.1 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /l2:0.1 - diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine.txt deleted file mode 100644 index b7b381bd97..0000000000 --- a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine.txt +++ 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5.749995 0.25000476837158203 0.062502384208528383 -4890 6 6.16887569 0.16887569427490234 0.028519000116830284 -4891 6 5.945266 0.054734230041503906 0.0029958359382362687 -4892 5 5.570834 0.57083415985107422 0.32585163805288175 -4893 6 6.11876 0.11876010894775391 0.014103963477282377 -4894 5 5.64458752 0.64458751678466797 0.41549306679462461 -4895 6 5.38194847 0.61805152893066406 0.38198769241353148 -4896 7 6.55421925 0.44578075408935547 0.19872048071647441 -4897 6 6.25601768 0.25601768493652344 0.06554505500025698 diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..38585c7ede --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 134 | 0.0000 + negative || 0 | 228 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6298 | +OVERALL 0/1 ACCURACY: 0.629834 +LOG LOSS/instance: 0.959786 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -0.945203 +AUC: 0.500000 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 107 | 0.0000 + negative || 0 | 230 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6825 | +OVERALL 0/1 ACCURACY: 0.682493 +LOG LOSS/instance: 0.910421 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -0.972725 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.656163 (0.0263) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.656163 (0.0263) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.935104 (0.0247) +Log-loss reduction: -0.958964 (0.0138) +F1 Score: NaN (NaN) +AUPRC: 0.418968 (0.0212) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..83b563173d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.656163 0 0 0.656163 1 0.935104 -0.958964 NaN 0.418968 PriorPredictor %Data% %Output% 99 0 0 maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer.txt new file mode 100644 index 0000000000..56f9146f1d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 -0.364985168 0.317507416 1.6551378056300845 0 +6 0 -0.364985168 0.317507416 0.55111472519016635 0 +8 0 -0.364985168 0.317507416 0.55111472519016635 0 +9 0 -0.364985168 0.317507416 0.55111472519016635 0 +10 0 -0.364985168 0.317507416 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diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..8e3bf3b17b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 241 | 0.0000 + negative || 0 | 458 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6552 | +OVERALL 0/1 ACCURACY: 0.655222 +LOG LOSS/instance: 0.929318 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 0.000000 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.655222 (0.0000) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.655222 (0.0000) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.929318 (0.0000) +Log-loss reduction: 0.000000 (0.0000) +F1 Score: NaN (0.0000) +AUPRC: 0.415719 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..60265c1608 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.655222 0 0 0.655222 1 0.929318 0 NaN 0.415719 PriorPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5bd2f17a17 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -0.310443461 0.34477827 0.60994489004156338 0 +1 0 -0.310443461 0.34477827 0.60994489004156338 0 +2 0 -0.310443461 0.34477827 0.60994489004156338 0 +3 0 -0.310443461 0.34477827 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b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 67 | 67 | 0.5000 + negative || 116 | 112 | 0.4912 + ||====================== +Precision || 0.3661 | 0.6257 | +OVERALL 0/1 ACCURACY: 0.494475 +LOG LOSS/instance: 1.411189 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -48.421356 +AUC: 0.521242 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 50 | 57 | 0.4673 + negative || 113 | 117 | 0.5087 + ||====================== +Precision || 0.3067 | 0.6724 | +OVERALL 0/1 ACCURACY: 0.495549 +LOG LOSS/instance: 1.451172 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -60.946260 +AUC: 0.484884 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.503063 (0.0182) +Accuracy: 0.495012 (0.0005) +Positive precision: 0.336434 (0.0297) +Positive recall: 0.483645 (0.0164) +Negative precision: 0.649056 (0.0234) +Negative recall: 0.499962 (0.0087) +Log-loss: 1.431181 (0.0200) +Log-loss reduction: -54.683808 (6.2625) +F1 Score: 0.396542 (0.0262) +AUPRC: 0.348911 (0.0260) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..2692f38450 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.503063 0.495012 0.336434 0.483645 0.649056 0.499962 1.431181 -54.68381 0.396542 0.348911 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer.txt new file mode 100644 index 0000000000..c0fc1e43b3 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer.txt @@ -0,0 +1,700 @@ 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mode 100644 index 0000000000..9d0392fe3b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 n=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 113 | 128 | 0.4689 + negative || 231 | 227 | 0.4956 + ||====================== +Precision || 0.3285 | 0.6394 | +OVERALL 0/1 ACCURACY: 0.486409 +LOG LOSS/instance: 1.479534 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): -59.206457 +AUC: 0.471290 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.471290 (0.0000) +Accuracy: 0.486409 (0.0000) +Positive precision: 0.328488 (0.0000) +Positive recall: 0.468880 (0.0000) +Negative precision: 0.639437 (0.0000) +Negative recall: 0.495633 (0.0000) +Log-loss: 1.479534 (0.0000) +Log-loss reduction: -59.206457 (0.0000) +F1 Score: 0.386325 (0.0000) +AUPRC: 0.314886 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..4af687d0c8 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.47129 0.486409 0.328488 0.46888 0.639437 0.495633 1.479534 -59.20646 0.386325 0.314886 RandomPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 n=1 + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..1980e349e8 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 0.6896762 0.844838142 2.6881541417898371 1 +1 0 -0.958645165 0.0206774175 0.030143942420402223 0 +2 0 -0.981372237 0.009313881 0.013500057520067015 0 +3 0 0.912489057 0.9562445 4.514392749742357 1 +4 0 0.9971796 0.9985898 9.4698985241832379 1 +5 1 -0.6345405 0.182729751 2.4522165515584184 0 +6 0 -0.6535362 0.1732319 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b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 0.8960438 0.7101358 0.49383312022081494 1 35 0 -1.47149158 0.186716 0.298168872554196 0 37 0 -1.077555 0.253969 0.42269254099397019 0 -40 0 +40 0 ? ? ? 0 41 1 -0.110265851 0.472461432 1.0817315302478872 0 44 1 1.38623238 0.799990058 0.32194602419012386 1 45 0 -1.49285364 0.1834938 0.29246424185161274 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.329715 0.2092065 0.33862709419515019 0 141 0 -1.51333463 0.18044512 0.28708753442168577 0 144 0 -1.47149158 0.186716 0.298168872554196 0 -145 0 +145 0 ? ? ? 0 147 0 -1.36272907 0.203797117 0.3287919993163349 0 150 0 -1.490515 0.183844447 0.29308395009459498 0 151 1 0.488354921 0.6197188 0.69031438298058734 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -1.54631257 0.175619483 0.27861768553552435 0 156 0 -1.34795034 0.206205666 0.33316283039965772 0 161 0 -1.29642129 0.214767918 0.34880897708810421 0 -164 0 +164 0 ? ? ? 0 167 1 0.7817354 0.686054 0.54360597630084673 1 169 0 -1.52040219 0.1794023 0.28525299602441972 0 171 0 -1.46262646 0.188065946 0.3005655398637459 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.30008841 0.908884346 0.13783136852928218 1 247 1 0.283135176 0.5703147 0.81016986229628651 1 248 0 -1.02733588 0.263600916 0.44144026210768955 0 -249 0 +249 0 ? ? ? 0 250 0 -1.3897934 0.199440747 0.32091990872905618 0 252 0 0.575369835 0.6400013 1.4739363860396681 1 254 1 1.36448383 0.796487451 0.32827646395944027 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.46262646 0.188065946 0.3005655398637459 0 271 0 -1.330715 0.209041134 0.33832542505991492 0 272 1 0.3417368 0.58461237 0.77444774011663531 1 -275 0 +275 0 ? ? ? 0 276 0 -1.43851376 0.1917756 0.30717219734355156 0 277 0 -1.50446939 0.181759879 0.28940381583950159 0 278 0 -1.46262646 0.188065946 0.3005655398637459 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.46262646 0.188065946 0.3005655398637459 0 293 1 0.690094 0.665987849 0.58643223892348229 1 296 0 0.122398376 0.530561447 1.090991766168943 1 -297 0 +297 0 ? ? ? 0 299 1 0.8090725 0.6919118 0.53133991521179469 1 300 1 1.07804394 0.7461236 0.42251343016986392 1 301 0 -1.46262646 0.188065946 0.3005655398637459 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.633879662 0.653368652 0.61403085786632716 1 317 1 1.75457263 0.852528632 0.2301798078479787 1 319 0 0.1361382 0.5339821 1.1015427183713855 1 -321 0 +321 0 ? ? ? 0 323 1 0.8411846 0.6987147 0.51722465664834216 1 327 0 -1.50446939 0.181759879 0.28940381583950159 0 328 1 0.758497 0.681027353 0.55421535102487163 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 2.80018759 0.942685962 0.085150850944065576 1 613 0 -1.31009483 0.212470978 0.34459500515903269 0 614 0 -1.49938011 0.18251799 0.29074111357003468 0 -617 0 +617 0 ? ? ? 0 618 0 -1.40553582 0.196939126 0.31641874253214702 0 619 0 -1.37255788 0.2022069 0.32591343959007929 0 621 0 -0.609831333 0.35209766 0.62615172722986678 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.29125416 0.2156406 0.35041324245427652 0 19 0 -1.18682528 0.233827218 0.38425832019246531 0 22 0 -1.38527489 0.200163171 0.32222238184143043 0 -23 1 +23 1 ? ? ? 0 24 0 -1.44789767 0.19032532 0.30458573193898042 0 26 0 -1.321662 0.210541919 0.341065431407162 0 27 0 -1.28084588 0.217406273 0.35366454853946033 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.36420059 0.203558445 0.3283595980104454 0 135 0 -0.9835708 0.272183836 0.45835400169661483 0 136 0 -1.33306038 0.2086536 0.33761874206668624 0 -139 0 +139 0 ? ? ? 0 140 0 -1.42708111 0.1935539 0.31034997353186378 0 142 1 0.5200758 0.6271655 0.67308190344279184 1 143 0 -1.07713079 0.254049361 0.42284792669686094 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.147677 0.240913659 0.39766410362601251 0 155 1 0.775262833 0.6846582 0.54654409786019487 1 157 0 -1.39568317 0.198502019 0.31922920878555094 0 -158 0 +158 0 ? ? ? 0 159 1 2.4702127 0.922027051 0.11711901622998303 1 160 1 1.8089292 0.8592324 0.21887965615084526 1 162 0 -1.34346867 0.206940219 0.33449847387485032 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1.80702519 0.859001935 0.21926671447669044 1 232 0 -0.09305465 0.476753116 0.93443627885443414 0 234 0 -0.7166654 0.3281277 0.57374106872220532 0 -235 0 +235 0 ? ? ? 0 236 1 2.0308764 0.884000957 0.1778801634200064 1 238 1 2.5700233 0.9289072 0.10639359546534205 1 243 0 -0.9099059 0.2870191 0.48806467358561068 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 3.03633 0.954188645 0.067653576940859037 1 287 0 -1.36420059 0.203558445 0.3283595980104454 0 289 1 1.839293 0.8628651 0.21279308434601185 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 1.50403929 0.81817615 0.28951661133253503 1 298 0 -1.01809835 0.265398 0.44496526256029006 0 302 1 2.95740485 0.9506123 0.07307101604489151 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.44789767 0.19032532 0.30458573193898042 0 310 0 -1.4164151 0.1952242 0.31334116395889644 0 313 0 -1.53151011 0.17777285 0.28239108395025142 0 -315 0 +315 0 ? ? ? 0 318 0 -1.38467479 0.200259253 0.32239570012101715 0 320 1 1.03225207 0.737352252 0.43957409861257096 1 322 0 -1.34346867 0.206940219 0.33449847387485032 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -1.48970389 0.183966175 0.29329914054619283 0 408 0 -0.969954848 0.2748895 0.46372722780050113 0 410 0 -1.48970389 0.183966175 0.29329914054619283 0 -411 0 +411 0 ? ? ? 0 412 1 1.784699 0.8562761 0.22385204529486596 1 417 0 -1.48970389 0.183966175 0.29329914054619283 0 420 0 -0.827743053 0.3041225 0.52309474947909951 0 diff --git a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-CV-breast-cancer.txt index 0302f143a8..4462030630 100644 --- a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 5.83416653 0.997082651 0.0042149967260249437 1 35 0 -5.20735168 0.005446332 0.0078788712300762211 0 37 0 -2.71164179 0.0622898862 0.092786101770516077 0 -40 0 +40 0 ? ? ? 0 41 1 1.76036549 0.853255451 0.22895036937850363 1 44 1 7.40955639 0.999394953 0.00087316212305162816 1 45 0 -5.421812 0.004399689 0.0063614137834035649 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -4.322978 0.0130867995 0.019004890409612719 0 141 0 -5.4411335 0.00431586 0.0062399450406013428 0 144 0 -5.20735168 0.005446332 0.0078788712300762211 0 -145 0 +145 0 ? ? ? 0 147 0 -4.875398 0.00757424766 0.010968922848962245 0 150 0 -5.39204836 0.00453200564 0.006553162786217457 0 151 1 4.061223 0.9830638 0.024643020624724253 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -5.73325 0.00322609954 0.0046618015694471927 0 156 0 -4.91463 0.007284973 0.010548463613035559 0 161 0 -4.12887526 0.0158458445 0.023043781432081138 0 -164 0 +164 0 ? ? ? 0 167 1 4.796111 0.9918059 0.011870271192039138 1 169 0 -5.68914 0.00337109854 0.0048716832359580091 0 171 0 -5.265687 0.005139294 0.0074335520922891698 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 10.8528 0.9999806 2.7947451458241805E-05 1 247 1 3.03624249 0.95418483 0.06765934462197544 1 248 0 -3.08702755 0.04364554 0.064382659498109923 0 -249 0 +249 0 ? ? ? 0 250 0 -5.14841127 0.00577508053 0.0083558311280958943 0 252 0 3.84936237 0.979150653 5.5838539825714451 1 254 1 7.35057163 0.9993582 0.00092625186884288718 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -5.265687 0.005139294 0.0074335520922891698 0 271 0 -4.097221 0.016347127 0.023778810630558714 0 272 1 2.79984665 0.942667544 0.085179038039461821 1 -275 0 +275 0 ? ? ? 0 276 0 -4.915235 0.007280598 0.010542105680653375 0 277 0 -5.49946833 0.00407229364 0.0058870729354622087 0 278 0 -5.265687 0.005139294 0.0074335520922891698 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -5.265687 0.005139294 0.0074335520922891698 0 293 1 4.736758 0.991309166 0.012593024732102163 1 296 0 1.75365162 0.85241276 2.760360102190484 1 -297 0 +297 0 ? ? ? 0 299 1 5.53140068 0.9960552 0.0057024187918531082 1 300 1 6.04150867 0.9976277 0.0034266068641119179 1 301 0 -5.265687 0.005139294 0.0074335520922891698 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 4.025776 0.9824635 0.025524315591638584 1 317 1 8.423935 0.9997805 0.00031674081892699212 1 319 0 1.82892418 0.861633539 2.8534338101579171 1 -321 0 +321 0 ? ? ? 0 323 1 4.504982 0.9890671 0.015859728309184334 1 327 0 -5.49946833 0.00407229364 0.0058870729354622087 0 328 1 4.454892 0.9885119 0.016669732083571809 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 13.2412815 0.9999982 2.5797420694119618E-06 1 613 0 -4.637956 0.009584702 0.013894495676017315 0 614 0 -5.333713 0.00480293762 0.0069458679549985972 0 -617 0 +617 0 ? ? ? 0 618 0 -4.623119 0.009726578 0.014101176192227349 0 619 0 -4.331002 0.0129835671 0.018853990456118342 0 621 0 -0.969069 0.2750661 0.46407865469425469 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -4.77261448 0.00838729553 0.012151339639068351 0 19 0 -4.05630064 0.01701831 0.02476355221691592 0 22 0 -5.26293468 0.00515338546 0.0074539868149001397 0 -23 1 +23 1 ? ? ? 0 24 0 -5.847086 0.00287998538 0.0041609352152631573 0 26 0 -5.01217842 0.00661237258 0.0095713166824383505 0 27 0 -4.54662037 0.0104917344 0.015216336343473301 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -5.25242233 0.005207562 0.0075325538981779582 0 135 0 -3.511651 0.028982535 0.042430850287558594 0 136 0 -4.90477753 0.0073565715 0.010652520453755514 0 -139 0 +139 0 ? ? ? 0 140 0 -5.39509726 0.00451827142 0.0065332584338255633 0 142 1 2.2592926 0.9054491 0.14329456494743992 1 143 0 -4.372905 0.0124574006 0.018085112102952432 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -4.16768646 0.01525183 0.022173264390610525 0 155 1 2.62865639 0.9326832 0.10054091831709649 1 157 0 -5.488929 0.004115263 0.0059493194428234175 0 -158 0 +158 0 ? ? ? 0 159 1 8.939568 0.9998689 0.00018910731810054123 1 160 1 6.86695 0.9989594 0.0015020181784367139 1 162 0 -5.13077164 0.00587725034 0.0085040948177142958 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 5.89666176 0.9972589 0.0039599989302112676 1 232 0 -0.179828167 0.455163717 0.87610531376236689 0 234 0 -3.15223122 0.04100345 0.060402470875188088 0 -235 0 +235 0 ? ? ? 0 236 1 7.74873161 0.9995689 0.00062210936850165717 1 238 1 9.156269 0.99989444 0.00015229867579129541 1 243 0 -3.844254 0.0209539 0.030551300678084092 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 10.8717079 0.999981 2.7431493603031304E-05 1 287 0 -5.25242233 0.005207562 0.0075325538981779582 0 289 1 6.0516243 0.9976515 0.0033921289521523556 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 4.83148575 0.992088437 0.011459363337312383 1 298 0 -3.31150723 0.0351785272 0.051666079123835182 0 302 1 10.6722527 0.9999768 3.3451013395372324E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -5.847086 0.00287998538 0.0041609352152631573 0 310 0 -5.61057949 0.00364560937 0.005269112963217676 0 313 0 -6.111411 0.00221251347 0.0031955185815049345 0 -315 0 +315 0 ? ? ? 0 318 0 -5.81555 0.002971982 0.0042940476081927553 0 320 1 3.90395832 0.98023653 0.028798182473169599 1 322 0 -5.13077164 0.00587725034 0.0085040948177142958 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -5.97924852 0.002524339 0.0036464555896367997 0 408 0 -4.06157875 0.01693024 0.024634297974768089 0 410 0 -5.97924852 0.002524339 0.0036464555896367997 0 -411 0 +411 0 ? ? ? 0 412 1 6.142968 0.9978561 0.003096342326011223 1 417 0 -5.97924852 0.002524339 0.0036464555896367997 0 420 0 -3.33492 0.0343924649 0.050491160716171948 0 diff --git a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt index 22409b2a5a..a0b287a08e 100644 --- a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 4.078068 0.983342052 0.024234755458090462 1 21 1 5.07943726 0.993815064 0.0089506841986991444 1 22 0 -6.401946 0.00165558141 0.0023904784483303873 0 -23 1 +23 1 ? ? ? 0 24 0 -6.95376 0.000954126066 0.0013771700453364219 0 25 1 0.131910324 0.532929838 0.90798248584253316 1 26 0 -6.20803738 0.00200913986 0.0029014918436988269 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -3.97515774 0.018430287 0.026837360561959296 0 38 1 3.85910988 0.9793487 0.030105439588229164 1 39 1 0.289131165 0.5717834 0.806459299491597 1 -40 0 +40 0 ? ? ? 0 41 1 0.5163593 0.626296043 0.67508333009285992 1 42 1 6.25112057 0.9980754 0.0027792492014372186 1 43 1 -1.67306614 0.158015817 2.6618591152960773 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -6.01794958 0.00242874329 0.0035081978824050039 0 137 0 -6.618124 0.00133415242 0.0019260601909807822 0 138 0 -5.72209167 0.0032621813 0.0047140259759722502 0 -139 0 +139 0 ? ? ? 0 140 0 -6.618124 0.00133415242 0.0019260601909807822 0 141 0 -7.00212 0.00090912357 0.0013121846250039875 0 142 1 1.86599445 0.8659941 0.20757090594664257 1 143 0 -5.405919 0.00446985662 0.0064630953185838089 0 144 0 -6.785942 0.00112826866 0.001628666561573529 0 -145 0 +145 0 ? ? ? 0 146 1 -0.2344532 0.441653728 1.17901240396708 0 147 0 -6.632551 0.00131506776 0.0018984903241446914 0 148 0 -3.505578 0.0291539337 0.042685529480640867 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 2.2497673 0.9046305 0.14459948541676573 1 156 0 -6.63783455 0.0013081471 0.0018884928122825894 0 157 0 -6.569764 0.00140016491 0.0020214264686188826 0 -158 0 +158 0 ? ? ? 0 159 1 9.262019 0.99990505 0.00013699068516156971 1 160 1 6.81782627 0.9989071 0.001577599187795759 1 161 0 -5.57644749 0.00377171184 0.005451717595353108 0 162 0 -6.18576765 0.002054292 0.0029667652410511319 0 163 0 -5.441502 0.00431427639 0.0062376503223464629 0 -164 0 +164 0 ? ? ? 0 165 0 -5.081398 0.00617287867 0.0089331816190877534 0 166 1 5.99673557 0.9975193 0.0035833193575627669 1 167 1 4.04529858 0.982796669 0.025035126906624509 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.9520569 0.278471351 0.47087141475726779 0 233 1 4.02608871 0.982468843 0.025516438252352936 1 234 0 -4.248459 0.0140850125 0.020464842181276369 0 -235 0 +235 0 ? ? ? 0 236 1 8.120999 0.9997029 0.00042873045458892818 1 237 1 3.97880173 0.9816355 0.02674065507190734 1 238 1 9.321572 0.999910533 0.00012907875367543245 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 9.005473 0.9998773 0.00017706700464490148 1 247 1 1.74156952 0.8508863 0.23296175567699981 1 248 0 -4.675151 0.009237982 0.013389533098992946 0 -249 0 +249 0 ? ? ? 0 250 0 -6.85401249 0.00105410081 0.0015215480827833656 0 251 1 5.57425 0.996220052 0.0054636445480688526 1 252 0 2.525447 0.9259066 3.7545111242363967 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 1.04092026 0.73902756 0.43629992864352879 1 273 1 -1.54782867 0.1754001 2.5112785805368674 0 274 0 -5.9604435 0.002572135 0.0037155870547972859 0 -275 0 +275 0 ? ? ? 0 276 0 -6.401946 0.00165558141 0.0023904784483303873 0 277 0 -7.16993856 0.0007687785 0.0011095394740419171 0 278 0 -6.95376 0.000954126066 0.0013771700453364219 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 5.963811 0.997436464 0.0037031495249571483 1 290 0 -7.386117 0.0006194141 0.0008939025092919693 0 291 0 -6.95376 0.000954126066 0.0013771700453364219 0 -292 1 +292 1 ? ? ? 0 293 1 3.82977867 0.97874707 0.030992011604895862 1 -294 0 +294 0 ? ? ? 0 295 1 4.92571831 0.992794752 0.010432605435470237 1 296 0 -0.00817585 0.497956038 0.9941143927175089 0 -297 0 +297 0 ? ? ? 0 298 0 -4.33488274 0.0129339322 0.018781442391253584 0 299 1 4.3436203 0.987177134 0.018619117824464626 1 300 1 4.40119934 0.987885952 0.017583597598896144 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.9174366 0.7145195 0.48495470886222203 1 313 0 -7.386117 0.0006194141 0.0008939025092919693 0 314 0 -7.21227646 0.0007369329 0.001063561390310713 0 -315 0 +315 0 ? ? ? 0 316 1 2.16819382 0.897356749 0.15624644520733796 1 317 1 6.37115574 0.998292744 0.0024651544582865699 1 318 0 -6.826747 0.00108320534 0.0015635819620021878 0 319 0 -0.115743637 0.471096337 0.91892312744499838 0 320 1 4.11173058 0.9838846 0.023439022974424555 1 -321 0 +321 0 ? ? ? 0 322 0 -6.18576765 0.002054292 0.0029667652410511319 0 323 1 3.31629562 0.964983642 0.051423608112212277 1 324 0 -6.95376 0.000954126066 0.0013771700453364219 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -5.098096 0.00607128069 0.0087857038468610767 0 409 0 -6.106088 0.00222429563 0.0032125544404639546 0 410 0 -7.16993856 0.0007687785 0.0011095394740419171 0 -411 0 +411 0 ? ? ? 0 412 1 6.053849 0.9976567 0.0033846301153950954 1 413 0 -5.12191772 0.00592920836 0.0085794995279385791 0 414 1 4.639694 0.9904318 0.013870479681066349 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -6.82828045 0.00108154735 0.0015611874005433336 0 615 0 -5.42623472 0.00438035838 0.0063334027525860032 0 616 0 -6.401946 0.00165558141 0.0023904784483303873 0 -617 0 +617 0 ? ? ? 0 618 0 -6.01794958 0.00242874329 0.0035081978824050039 0 619 0 -5.633953 0.00356168649 0.0051475999586587254 0 620 0 -6.401946 0.00165558141 0.0023904784483303873 0 diff --git a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt index 5197b73592..ffa3b3b912 100644 --- a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 2.322781 0.998222351 0.0025668874786068312 1 35 0 -1.91525483 0.005915621 0.0085597800780180429 0 37 0 -0.682821751 0.142692313 0.22211501491184898 0 -40 0 +40 0 ? ? ? 0 41 1 0.809957266 0.9039319 0.14571398208584879 1 44 1 2.73944569 0.999422848 0.00083289438051421241 1 45 0 -1.94671428 0.0054360223 0.0078639160441893435 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.53363073 0.0164193157 0.023884691545080352 0 141 0 -2.06519866 0.00395226665 0.0057132130161921821 0 144 0 -1.91525483 0.005915621 0.0085597800780180429 0 -145 0 +145 0 ? ? ? 0 147 0 -1.84313476 0.007179688 0.010395463465996112 0 150 0 -1.97708547 0.005009752 0.0072457094989476683 0 151 1 1.45099306 0.9815549 0.026859095208834581 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -2.1974268 0.00276787742 0.0039987395951823247 0 156 0 -1.89696681 0.006213517 0.0089921755974289187 0 161 0 -1.42502642 0.0218984671 0.031943861389482818 0 -164 0 +164 0 ? ? ? 0 167 1 2.354064 0.998366237 0.0023589497379793915 1 169 0 -2.21602869 0.00263251015 0.0038029171463890205 0 171 0 -1.897539 0.006203974 0.008978322239894131 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 3.30174255 0.9998737 0.00018222712667875736 1 247 1 0.629675865 0.8525092 0.23021269060466767 1 248 0 -1.12171221 0.0483664 0.07152188601007381 0 -249 0 +249 0 ? ? ? 0 250 0 -2.04691076 0.00415170472 0.0060021119500057712 0 252 0 1.01602411 0.942604 4.12290612676055 1 254 1 2.17657 0.997363031 0.0038093670302838936 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.897539 0.006203974 0.008978322239894131 0 271 0 -1.368627 0.0254129283 0.037137010195002478 0 272 1 0.765766859 0.8930476 0.16319106749073262 1 -275 0 +275 0 ? ? ? 0 276 0 -1.78302681 0.008435549 0.012221545522817861 0 277 0 -2.047483 0.00414531538 0.0059928556832883097 0 278 0 -1.897539 0.006203974 0.008978322239894131 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.897539 0.006203974 0.008978322239894131 0 293 1 1.17371225 0.961760044 0.056251103565820429 1 296 0 0.4346769 0.773356 2.1415002561411738 1 -297 0 +297 0 ? ? ? 0 299 1 2.019999 0.9959794 0.0058121507954068162 1 300 1 2.2421217 0.9977903 0.0031914838554231535 1 301 0 -1.897539 0.006203974 0.008978322239894131 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.9578371 0.9334789 0.099310694790810672 1 317 1 2.6748023 0.999312758 0.00099182083151890563 1 319 0 0.702352047 0.87553966 3.0062420076587428 1 -321 0 +321 0 ? ? ? 0 323 1 1.138917 0.9581469 0.061681187824867326 1 327 0 -2.047483 0.00414531538 0.0059928556832883097 0 328 1 0.998517036 0.9399896 0.089283259387362562 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 5.011583 0.999998748 1.8058189642293044E-06 1 613 0 -1.848559 0.00707593327 0.010244702155254935 0 614 0 -1.9948014 0.0047766366 0.006907740990800825 0 -617 0 +617 0 ? ? ? 0 618 0 -1.6507988 0.0120159388 0.017440327322179375 0 619 0 -1.51857078 0.017089799 0.024868477463820319 0 621 0 -0.159297943 0.406589121 0.75289671799437707 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -2.01459932 0.02046752 0.029834763390847389 0 19 0 -1.722561 0.0706015 0.10563077935630806 0 22 0 -2.18885 0.009580599 0.013888519789490289 0 -23 1 +23 1 ? ? ? 0 24 0 -2.45265675 0.00300532184 0.0043422911736604322 0 26 0 -2.15108967 0.0113010341 0.016396771634848997 0 27 0 -1.8968116 0.0339725837 0.049863960916695586 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -2.18885 0.009580599 0.013888519789490289 0 135 0 -1.67432666 0.0859358 0.12963259925365803 0 136 0 -2.04283071 0.0181100331 0.026366733420336564 0 -139 0 +139 0 ? ? ? 0 140 0 -2.21708131 0.00846625 0.012266215273071623 0 142 1 -0.39607358 0.963921666 0.053012185419208942 0 143 0 -2.12454414 0.0126899984 0.018424952871546458 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -1.87877429 0.03668837 0.053925509586129468 0 155 1 -0.6914537 0.878659666 0.18662362559522522 0 157 0 -2.30663776 0.005714676 0.0082681821048535484 0 -158 0 +158 0 ? ? ? 0 159 1 0.826603651 0.9998317 0.00024285994311160547 1 160 1 0.465637445 0.999170542 0.0011971518470987805 1 162 0 -2.16061854 0.0108400183 0.015724220850716938 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 -0.0580048561 0.9916699 0.012068138193505744 0 232 0 -1.12658465 0.5141482 1.0414117089703063 0 234 0 -1.83982372 0.04328234 0.063834867527523209 0 -235 0 +235 0 ? ? ? 0 236 1 0.979896069 0.999914467 0.00012340282955575714 1 238 1 0.930060863 0.9998934 0.00015376068339360816 1 243 0 -2.01161838 0.02073334 0.030226326896041298 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 1.41079044 0.999987245 1.8402261006614678E-05 1 287 0 -2.18885 0.009580599 0.013888519789490289 0 289 1 0.0753440857 0.9953623 0.0067063762389963991 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 -0.367047548 0.9681271 0.046731673516829733 0 298 0 -1.44922507 0.202717274 0.32683668378040626 0 302 1 1.39901066 0.9999866 1.9348177961999343E-05 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -2.45265675 0.00300532184 0.0043422911736604322 0 310 0 -2.33486915 0.00504769245 0.0073007223089671565 0 313 0 -2.50911975 0.002343144 0.0033844089083832525 0 -315 0 +315 0 ? ? ? 0 318 0 -2.45265675 0.00300532184 0.0043422911736604322 0 320 1 0.09479761 0.995742738 0.006155041546569217 1 322 0 -2.16061854 0.0108400183 0.015724220850716938 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -2.48088837 0.00265371148 0.0038335852528692412 0 408 0 -2.10281515 0.0139513118 0.020269210492385212 0 410 0 -2.48088837 0.00265371148 0.0038335852528692412 0 -411 0 +411 0 ? ? ? 0 412 1 -0.0120434761 0.9931908 0.0098571608082237746 0 417 0 -2.48088837 0.00265371148 0.0038335852528692412 0 420 0 -1.86625433 0.0386949927 0.056933846240527428 0 diff --git a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt index 1c905b24c8..b2ca41a554 100644 --- a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.51313376 0.997060657 0.0042468207167790329 1 21 1 1.88215756 0.9991858 0.0011751199613487565 1 22 0 -1.5913856 0.006754517 0.0097777676588594702 0 -23 1 +23 1 ? ? ? 0 24 0 -1.85515833 0.00270523666 0.0039081201011675363 0 25 1 0.183981657 0.767693 0.38139863321618261 1 26 0 -1.596403 0.006638234 0.0096088756803218642 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.48321867 0.244259775 0.40403768061841711 0 38 1 1.01125383 0.983338952 0.024239302762828055 1 39 1 -0.0106446743 0.62648654 0.67464458153125895 0 -40 0 +40 0 ? ? ? 0 41 1 0.8607874 0.9721748 0.040712322202366577 1 42 1 1.4849956 0.9967589 0.004683544378464086 1 43 1 -0.7279444 0.121085733 3.0458992041721875 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.404305 0.0128827207 0.018706593545987932 0 137 0 -1.701774 0.00460771844 0.0066628947272309403 0 138 0 -1.360993 0.0149499336 0.021731041715098105 0 -139 0 +139 0 ? ? ? 0 140 0 -1.701774 0.00460771844 0.0066628947272309403 0 141 0 -1.8888545 0.00240627048 0.0034756978965475691 0 142 1 0.881847143 0.974092543 0.037869253621396266 1 143 0 -1.545697 0.007910921 0.01145843020471164 0 144 0 -1.77846622 0.00353102176 0.0051032026604077695 0 -145 0 +145 0 ? ? ? 0 146 1 -0.216273546 0.450334132 1.1509322683430081 0 147 0 -1.82796979 0.00297325244 0.0042958860993499966 0 148 0 -0.7305672 0.12011648 0.18461554363807445 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.429970026 0.886195242 0.17430351324204957 1 156 0 -1.92223477 0.0021426254 0.0030944713637373776 0 157 0 -1.66807771 0.005178785 0.0074908208279084272 0 -158 0 +158 0 ? ? ? 0 159 1 2.79265332 0.999965847 4.9273870913609948E-05 1 160 1 2.08471417 0.999597847 0.00058030016510027748 1 161 0 -1.23200977 0.0232356917 0.033917610773834123 0 162 0 -1.48099709 0.009891603 0.014341615100451873 0 163 0 -1.20272636 0.02566766 0.037514142314967996 0 -164 0 +164 0 ? ? ? 0 165 0 -1.09945023 0.0363804549 0.053464438820388052 0 166 1 1.35837817 0.99497056 0.0072742561293771086 1 167 1 1.40636182 0.9957416 0.0061566823680882154 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.0236132145 0.6158536 1.3802718900263455 0 233 1 1.45269513 0.9963742 0.005240444497289571 1 234 0 -1.12313449 0.0335955 0.049300920379201259 0 -235 0 +235 0 ? ? ? 0 236 1 2.801736 0.9999669 4.7725975020197848E-05 1 237 1 1.18725443 0.9909072 0.013178153095375312 1 238 1 2.58363461 0.999929249 0.00010207531428030829 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.15890861 0.9996894 0.00044817040124598361 1 247 1 0.7686317 0.9620387 0.055833170607592193 1 248 0 -0.9843831 0.0533667132 0.079122441964183776 0 -249 0 +249 0 ? ? ? 0 250 0 -2.03262329 0.001459477 0.0021071182550953969 0 251 1 1.98737311 0.999435544 0.0008145677672951316 1 252 0 0.938287735 0.9786189 5.5475214513768956 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.364958525 0.861275733 0.21545291272160746 1 273 1 -0.0762293339 0.5716655 0.80675680440858633 0 274 0 -1.41909039 0.0122437514 0.017773027141160865 0 -275 0 +275 0 ? ? ? 0 276 0 -1.5913856 0.006754517 0.0097777676588594702 0 277 0 -1.96554685 0.00184303813 0.0026613952529717312 0 278 0 -1.85515833 0.00270523666 0.0039081201011675363 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.51208711 0.99705 0.0042622586236973218 1 290 0 -2.07593513 0.00125529012 0.0018121384446850468 0 291 0 -1.85515833 0.00270523666 0.0039081201011675363 0 -292 1 +292 1 ? ? ? 0 293 1 1.33046842 0.9944597 0.0080152036111407286 1 -294 0 +294 0 ? ? ? 0 295 1 1.2251215 0.9920222 0.011555664783429847 1 296 0 0.111403227 0.719591737 1.8343992308763424 1 -297 0 +297 0 ? ? ? 0 298 0 -0.7513188 0.112681925 0.17247673834356275 0 299 1 1.96303582 0.9993856 0.00088667099785278705 1 300 1 1.383044 0.995382845 0.0066765713113142704 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.17001605 0.7589018 0.39801492605707506 1 313 0 -2.07593513 0.00125529012 0.0018121384446850468 0 314 0 -1.97056413 0.00181115558 0.0026153142649758387 0 -315 0 +315 0 ? ? ? 0 316 1 0.541802645 0.9199835 0.12032009864475471 1 317 1 1.56700492 0.9975625 0.0035208219605770688 1 318 0 -1.84010613 0.00285049225 0.0041182633675105971 0 319 0 0.05099702 0.675234735 1.6225307564202673 1 320 1 1.7057507 0.9984955 0.0021721414278146082 1 -321 0 +321 0 ? ? ? 0 322 0 -1.48099709 0.009891603 0.014341615100451873 0 323 1 0.7110567 0.95399344 0.067948749601842412 1 324 0 -1.85515833 0.00270523666 0.0039081201011675363 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -1.45910537 0.0106672905 0.015472318523715401 0 409 0 -1.54807365 0.007846191 0.011364302031181555 0 410 0 -1.96554685 0.00184303813 0.0026613952529717312 0 -411 0 +411 0 ? ? ? 0 412 1 1.61123109 0.9979099 0.0030185274243607983 1 413 0 -1.063524 0.0410328656 0.060446722723610943 0 414 1 1.54281878 0.9973487 0.0038300596622238277 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.78348351 0.00347004039 0.0050149160869795298 0 615 0 -1.31768119 0.0173430257 0.025240206070453266 0 616 0 -1.5913856 0.006754517 0.0097777676588594702 0 -617 0 +617 0 ? ? ? 0 618 0 -1.404305 0.0128827207 0.018706593545987932 0 619 0 -1.21722436 0.024434112 0.035688781701501276 0 620 0 -1.5913856 0.006754517 0.0097777676588594702 0 diff --git a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-TrainTest-breast-cancer.txt index ff963fe7a2..9b8d818944 100644 --- a/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SDCA/BinarySDCA-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.37743354 0.7985785 0.32449386248842338 1 21 1 1.7630446 0.853590548 0.22838389385799779 1 22 0 -1.874893 0.132976577 0.20585712538187464 0 -23 1 +23 1 ? ? ? 0 24 0 -1.948468 0.124720506 0.19218432401279964 0 25 1 0.243255854 0.5605158 0.83517300013076445 1 26 0 -1.79869926 0.142009482 0.22096639052344227 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -1.30918431 0.212623373 0.34487420705553562 0 38 1 1.52097869 0.8206825 0.28510385981645775 1 39 1 0.568297148 0.638370156 0.64753488753370236 1 -40 0 +40 0 ? ? ? 0 41 1 0.08091617 0.520218 0.94281173541226504 1 42 1 2.4137857 0.9178725 0.12363434755323521 1 43 1 -0.08661461 0.478359878 1.0638317048217665 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.80893993 0.1407663 0.21887750444885776 0 137 0 -1.9332242 0.126394138 0.1949455575113978 0 138 0 -1.70428443 0.153906524 0.24111103479242962 0 -139 0 +139 0 ? ? ? 0 140 0 -1.9332242 0.126394138 0.1949455575113978 0 141 0 -1.99917722 0.119289339 0.18325996439997919 0 142 1 0.6176696 0.649688363 0.62218023037407078 1 143 0 -1.44733071 0.190412715 0.3047414631100317 0 144 0 -1.94084609 0.125554934 0.19356034047915699 0 -145 0 +145 0 ? ? ? 0 146 1 0.272925377 0.567810953 0.81651741701068026 1 147 0 -1.8198235 0.13945505 0.21667754281184065 0 148 0 -0.713562 0.328812242 0.57521169167023078 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.9670353 0.7245282 0.46488626460347809 1 156 0 -1.793424 0.142653435 0.22204959316054371 0 157 0 -1.882515 0.132100269 0.20439971838260004 0 -158 0 +158 0 ? ? ? 0 159 1 3.20693851 0.961094558 0.057249715926430703 1 160 1 2.3220613 0.910687745 0.13497162539860094 1 161 0 -1.65904641 0.159890041 0.25134992500211428 0 162 0 -1.81656182 0.139846936 0.21733468482291701 0 163 0 -1.60762358 0.166918814 0.26347099825296155 0 -164 0 +164 0 ? ? ? 0 165 0 -1.50459647 0.181740969 0.28937047546414252 0 166 1 2.457339 0.9210965 0.11857577878171396 1 167 1 1.31327176 0.7880601 0.34362238392184646 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.213132143 0.446917742 0.85443403273590568 0 233 1 1.23634028 0.774926364 0.36786886673349695 1 234 0 -1.00412917 0.268130362 0.4503413991221501 0 -235 0 +235 0 ? ? ? 0 236 1 2.65734458 0.934462249 0.097791712220334726 1 237 1 1.59322524 0.8310694 0.26695912082718543 1 238 1 3.30821967 0.9647097 0.051833223594515407 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 3.32132936 0.965153337 0.051169929428424521 1 247 1 0.572052956 0.639236748 0.64557774708968529 1 248 0 -1.29279661 0.215379834 0.34993367973529765 0 -249 0 +249 0 ? ? ? 0 250 0 -1.85175514 0.135666952 0.21034077001927648 0 251 1 1.686516 0.843765438 0.24508610192596253 1 252 0 0.9327009 0.717622936 1.8243051828654713 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.5505221 0.6342567 0.65686119381445462 1 273 1 -0.451063156 0.389108032 1.3617573340834921 0 274 0 -1.72499943 0.151228324 0.23655158068052121 0 -275 0 +275 0 ? ? ? 0 276 0 -1.874893 0.132976577 0.20585712538187464 0 277 0 -2.00679922 0.1184909 0.18195262731195211 0 278 0 -1.948468 0.124720506 0.19218432401279964 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 2.363632 0.914011657 0.12971552945936157 1 290 0 -2.06513023 0.112532459 0.17223374110934919 0 291 0 -1.948468 0.124720506 0.19218432401279964 0 -292 1 +292 1 ? ? ? 0 293 1 1.17569327 0.764172554 0.38802965202562639 1 -294 0 +294 0 ? ? ? 0 295 1 1.9321816 0.8734907 0.1951357665052601 1 296 0 0.2875533 0.571397066 1.222286369266439 1 -297 0 +297 0 ? ? ? 0 298 0 -1.39716244 0.198266774 0.31880583096618414 0 299 1 1.31489539 0.788331151 0.34312631094095036 1 300 1 1.6675427 0.8412479 0.24939706872810494 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.6733315 0.662248731 0.59455492152164024 1 313 0 -2.06513023 0.112532459 0.17223374110934919 0 314 0 -2.03932476 0.115135506 0.17647155325903272 0 -315 0 +315 0 ? ? ? 0 316 1 0.9730499 0.725726962 0.46250122508222125 1 317 1 2.53153682 0.9263233 0.11041230078378952 1 318 0 -1.85089087 0.135768339 0.21051001013383977 0 319 0 0.279238 0.5693594 1.2154438271457824 1 320 1 1.35926127 0.795639634 0.32981295026166818 1 -321 0 +321 0 ? ? ? 0 322 0 -1.81656182 0.139846936 0.21733468482291701 0 323 1 1.28926969 0.7840235 0.35103115421599534 1 324 0 -1.948468 0.124720506 0.19218432401279964 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -1.33084726 0.209019259 0.33828552617088203 0 409 0 -1.77023745 0.1455128 0.22686922143757757 0 410 0 -2.00679922 0.1184909 0.18195262731195211 0 -411 0 +411 0 ? ? ? 0 412 1 2.25751042 0.9052964 0.14353790094246915 1 413 0 -1.58000016 0.170795456 0.27020007178125394 0 414 1 1.60186315 0.8322786 0.2648615365498318 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.97337174 0.122027189 0.18775183095640005 0 615 0 -1.59962881 0.1680335 0.26540264924934215 0 616 0 -1.874893 0.132976577 0.20585712538187464 0 -617 0 +617 0 ? ? ? 0 618 0 -1.80893993 0.1407663 0.21887750444885776 0 619 0 -1.74298692 0.148933932 0.23265696296547442 0 620 0 -1.874893 0.132976577 0.20585712538187464 0 diff --git a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-CV-breast-cancer.txt index 8b3d215017..bee6e20c10 100644 --- a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 0.344691515 0.5853297 0.77267858291118818 1 35 0 -0.6512495 0.342708021 0.60539371596619707 0 37 0 -0.503962 0.376610041 0.68179317658398975 0 -40 0 +40 0 ? ? ? 0 41 1 -0.09442055 0.4764124 1.069717174215209 0 44 1 0.5798913 0.6410424 0.64150828626867074 1 45 0 -0.6560674 0.341623574 0.60301541680763648 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -0.597589 0.354895473 0.63239515287919279 0 141 0 -0.6678773 0.3389723 0.5972173671855312 0 144 0 -0.6512495 0.342708021 0.60539371596619707 0 -145 0 +145 0 ? ? ? 0 147 0 -0.6018609 0.353918076 0.63021098184538349 0 150 0 -0.656989932 0.3414161 0.60256083089734203 0 151 1 0.160812974 0.540116847 0.88865654739065503 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -0.67789495 0.336731285 0.59233461600767046 0 156 0 -0.5912131 0.356356561 0.63566639957530646 0 161 0 -0.5840918 0.3579916 0.63933593475596628 0 -164 0 +164 0 ? ? ? 0 167 1 0.315922141 0.5783301 0.79003490476906479 1 169 0 -0.6661818 0.33935234 0.59804704249396301 0 171 0 -0.644639254 0.344198585 0.60866907932442826 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 0.9649427 0.7241103 0.46571861194179465 1 247 1 0.07307994 0.51826185 0.94824689644610127 1 248 0 -0.4621131 0.386484653 0.70482866116462817 0 -249 0 +249 0 ? ? ? 0 250 0 -0.607840955 0.352551848 0.62716342858897578 0 252 0 0.210040927 0.552318037 1.1594538998856256 1 254 1 0.5551709 0.635334432 0.65441188637353787 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -0.644639254 0.344198585 0.60866907932442826 0 271 0 -0.6045689 0.3532991 0.62882950155653372 0 272 1 0.109180689 0.5272681 0.92339134731632821 1 -275 0 +275 0 ? ? ? 0 276 0 -0.6412319 0.3449681 0.61036295013192154 0 277 0 -0.6612671 0.340455025 0.60045705425766605 0 278 0 -0.644639254 0.344198585 0.60866907932442826 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -0.644639254 0.344198585 0.60866907932442826 0 293 1 0.2438618 0.560665131 0.83478874766701627 1 296 0 0.0298114419 0.5074523 1.0216646747888918 1 -297 0 +297 0 ? ? ? 0 299 1 0.29232645 0.5725656 0.80448706147450022 1 300 1 0.440484524 0.6083745 0.71696846696669991 1 301 0 -0.644639254 0.344198585 0.60866907932442826 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.23788023 0.5591912 0.83858636815416177 1 317 1 0.736536 0.676237941 0.56439713300164063 1 319 0 0.032892406 0.508222342 1.0239219003304794 1 -321 0 +321 0 ? ? ? 0 323 1 0.3295461 0.581648946 0.78177941761076775 1 327 0 -0.6612671 0.340455025 0.60045705425766605 0 328 1 0.309677362 0.5768065 0.79384070845234189 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 1.18309152 0.7655032 0.38551963408922602 1 613 0 -0.578392267 0.3593026 0.64228498181768678 0 614 0 -0.663600147 0.339931339 0.59931199209014285 0 -617 0 +617 0 ? ? ? 0 618 0 -0.6312144 0.347235233 0.61536490408676614 0 619 0 -0.621196747 0.349509329 0.62039972730149728 0 621 0 -0.2970261 0.426284641 0.80159295389429652 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -0.5587295 0.363841474 0.65254177576954708 0 19 0 -0.521061659 0.372604 0.67255179238253271 0 22 0 -0.597002566 0.355029762 0.63269550517202178 0 -23 1 +23 1 ? ? ? 0 24 0 -0.6152313 0.3508668 0.62341353797116505 0 26 0 -0.568058968 0.361684829 0.64765915777643768 0 27 0 -0.559334755 0.3637014 0.6522241546694586 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -0.586384356 0.35746488 0.6381527823333405 0 135 0 -0.4233432 0.395717025 0.72670379813371644 0 136 0 -0.578168631 0.3593541 0.6424009484072789 0 -139 0 +139 0 ? ? ? 0 140 0 -0.6164417 0.350591153 0.62280105585760936 0 142 1 0.253997982 0.5631603 0.82838245949919198 1 143 0 -0.455442041 0.388067663 0.70855595514156899 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -0.498274028 0.377946377 0.68488914380895782 0 155 1 0.381782949 0.5943031 0.75072925773026189 1 157 0 -0.5963973 0.355168372 0.63300558819075581 0 -158 0 +158 0 ? ? ? 0 159 1 1.14907432 0.7593418 0.3971787124185161 1 160 1 0.835970342 0.697615862 0.51949525142708575 1 162 0 -0.5775634 0.359493434 0.64271473576785443 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 0.867232 0.7041694 0.50600557448041072 1 232 0 -0.0153304338 0.496167481 0.98898385339386186 0 234 0 -0.28234 0.4298802 0.81066299300396294 0 -235 0 +235 0 ? ? ? 0 236 1 0.9360302 0.718297064 0.47734747567952907 1 238 1 1.21385264 0.770979941 0.37523476983545384 1 243 0 -0.3776855 0.406685263 0.7531304771823496 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 1.42419529 0.8059953 0.31115669483110281 1 287 0 -0.586384356 0.35746488 0.6381527823333405 0 289 1 0.8870376 0.7082785 0.49761139182067243 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 0.7293541 0.674663544 0.56775988879752004 1 298 0 -0.4508557 0.389157325 0.711127239010238 0 302 1 1.38025045 0.7990312 0.3236762608744207 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -0.6152313 0.3508668 0.62341353797116505 0 310 0 -0.6052183 0.353150755 0.62849857907527806 0 313 0 -0.654109538 0.342064053 0.60398095649539585 0 -315 0 +315 0 ? ? ? 0 318 0 -0.5833766 0.358156025 0.63970545866432549 0 320 1 0.483023465 0.618461549 0.69324419149565364 1 322 0 -0.5775634 0.359493434 0.64271473576785443 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -0.6346704 0.3464523 0.61363554879852178 0 408 0 -0.398791075 0.401602834 0.74082475334425091 0 410 0 -0.6346704 0.3464523 0.61363554879852178 0 -411 0 +411 0 ? ? ? 0 412 1 0.851157367 0.7008099 0.51290494780474771 1 417 0 -0.6346704 0.3464523 0.61363554879852178 0 420 0 -0.3393041 0.415978521 0.77590666598714197 0 diff --git a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-CV-breast-cancer.txt index a2364522f6..ca9f631c71 100644 --- a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 0.6522908 0.9885379 0.01663180464505477 1 35 0 -1.12420082 0.0107460516 0.015587176400536187 0 37 0 -0.865502357 0.0386134759 0.056811513682664551 0 -40 0 +40 0 ? ? ? 0 41 1 -0.1372056 0.6145622 0.70236902985400396 0 44 1 1.0547893 0.9984863 0.0021854902279334833 1 45 0 -1.13070083 0.0104023358 0.015085999817509936 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -1.03150129 0.0170594156 0.024823881934527593 0 141 0 -1.15380061 0.00926659 0.0134311903289673 0 144 0 -1.12420082 0.0107460516 0.015587176400536187 0 -145 0 +145 0 ? ? ? 0 147 0 -1.03270125 0.0169580057 0.024675046951938132 0 150 0 -1.13440084 0.0102115627 0.0148079061901775 0 151 1 0.335192084 0.945543051 0.080784949020458824 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -1.17080045 0.008510069 0.012329973523626827 0 156 0 -1.01700115 0.0183329377 0.026694285275120175 0 161 0 -1.00710154 0.0192555338 0.028050804915513337 0 -164 0 +164 0 ? ? ? 0 167 1 0.6017914 0.98525393 0.021432495728287648 1 169 0 -1.15050054 0.009420992 0.013656046492446974 0 171 0 -1.11160088 0.01144463 0.016606319988959024 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 1.7594862 0.999956965 6.208707291578748E-05 1 247 1 0.172593474 0.8841638 0.17761443218515088 1 248 0 -0.793102443 0.0547425 0.081220704071742325 0 -249 0 +249 0 ? ? ? 0 250 0 -1.04660094 0.0158256628 0.0230141968119793 0 252 0 0.423291922 0.964417338 4.8126817596940885 1 254 1 1.03148985 0.998297453 0.0024583495418655562 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -1.11160088 0.01144463 0.016606319988959024 0 271 0 -1.04360127 0.01606356 0.023362970756245272 0 272 1 0.243792772 0.9162424 0.12619873583154692 1 -275 0 +275 0 ? ? ? 0 276 0 -1.10720086 0.0116990069 0.016977604564195337 0 277 0 -1.14120078 0.009869945 0.014310057532988397 0 278 0 -1.11160088 0.01144463 0.016606319988959024 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -1.11160088 0.01144463 0.016606319988959024 0 293 1 0.478191853 0.97280544 0.039776797794310127 1 296 0 0.0886935 0.83318603 2.5836879844847194 1 -297 0 +297 0 ? ? ? 0 299 1 0.5653906 0.9823277 0.025723714124946184 1 300 1 0.8071904 0.994728446 0.0076253612609159995 1 301 0 -1.11160088 0.01144463 0.016606319988959024 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 0.472591877 0.9720465 0.040902773075660377 1 317 1 1.35288787 0.9996641 0.00048464251232972397 1 319 0 0.100693226 0.841446757 2.6569607115771978 1 -321 0 +321 0 ? ? ? 0 323 1 0.6351912 0.987516046 0.018123905012295945 1 327 0 -1.14120078 0.009869945 0.014310057532988397 0 328 1 0.5871916 0.9841424 0.023060980928491851 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 2.158784 0.9999943 8.2551908597278378E-06 1 613 0 -0.993201256 0.0206282046 0.030071445887912274 0 614 0 -1.14700067 0.009587526 0.013898610301947204 0 -617 0 +617 0 ? ? ? 0 618 0 -1.090201 0.0127353743 0.018491259389232038 0 619 0 -1.07320118 0.0138622615 0.020138926273650697 0 621 0 -0.4947039 0.207426473 0.33538331559891793 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -1.02980137 0.0147205992 0.021395199721883466 0 19 0 -0.9630018 0.0192868058 0.028096807322037363 0 22 0 -1.0968008 0.011213962 0.016269722836470393 0 -23 1 +23 1 ? ? ? 0 24 0 -1.13000059 0.00979631 0.014202769680513335 0 26 0 -1.04700089 0.0137287723 0.019943648017493942 0 27 0 -1.03000128 0.0147086745 0.021377739156459911 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -1.079601 0.0120263351 0.017455508586451673 0 135 0 -0.7955023 0.03769685 0.055436642942084668 0 136 0 -1.0634011 0.0128445318 0.018650780679196597 0 -139 0 +139 0 ? ? ? 0 140 0 -1.13040054 0.009780362 0.014179534189705549 0 142 1 0.3573923 0.8180728 0.2896988683892544 1 143 0 -0.8376017 0.03189309 0.046761717990888088 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -0.917201638 0.0231931154 0.033854726268903304 0 155 1 0.5948919 0.922758937 0.1159742899072022 1 157 0 -1.09660089 0.0112230852 0.016283034217764617 0 -158 0 +158 0 ? ? ? 0 159 1 1.88818586 0.9995909 0.00059036523296398255 1 160 1 1.36718786 0.996521652 0.0050269435856363917 1 162 0 -1.06320107 0.01285497 0.018666035936317264 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1.39838827 0.9969394 0.0044222533640880836 1 232 0 -0.118705273 0.38808772 0.70860324232262772 0 234 0 -0.57100296 0.08979461 0.13573597071280369 0 -235 0 +235 0 ? ? ? 0 236 1 1.52918732 0.9982108 0.0025835996006501579 1 238 1 1.96808636 0.9997055 0.00042494571667690465 1 243 0 -0.703202665 0.0541654155 0.080340199841784268 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 2.32438421 0.999932 9.8119438852589428E-05 1 287 0 -1.079601 0.0120263351 0.017455508586451673 0 289 1 1.42798841 0.99728936 0.0039159372565436843 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 1.171589 0.9922556 0.011216254621805593 1 298 0 -0.8448025 0.0309909787 0.045417997957015462 0 302 1 2.26218414 0.999912143 0.00012675678201834402 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -1.13000059 0.00979631 0.014202769680513335 0 310 0 -1.11300075 0.0104985284 0.015226241971828952 0 313 0 -1.19720018 0.007447739 0.010785028389452023 0 -315 0 +315 0 ? ? ? 0 318 0 -1.07840109 0.0120851332 0.017541371482030246 0 320 1 0.77709043 0.961948335 0.055968683838555341 1 322 0 -1.06320107 0.01285497 0.018666035936317264 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -1.16360033 0.008542378 0.012376987044267063 0 408 0 -0.7606021 0.0432654768 0.063809436873055653 0 410 0 -1.16360033 0.008542378 0.012376987044267063 0 -411 0 +411 0 ? ? ? 0 412 1 1.35098827 0.9962829 0.0053726686646499901 1 417 0 -1.16360033 0.008542378 0.012376987044267063 0 420 0 -0.6557027 0.0650944263 0.097107435980156739 0 diff --git a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt index 9dec61f554..8153302a29 100644 --- a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 1.222367 0.9828292 0.024987354577091989 1 21 1 1.49796534 0.9935609 0.0093196803205346252 1 22 0 -1.19740391 0.009379565 0.013595712679399167 0 -23 1 +23 1 ? ? ? 0 24 0 -1.23810339 0.008112156 0.01175109505221196 0 25 1 0.3802781 0.7344174 0.4453278964846259 1 26 0 -1.14100432 0.01146578 0.016637187062001499 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.79460907 0.0387752019 0.057054226638771945 0 38 1 1.34886718 0.989039242 0.015900330726329494 1 39 1 0.6641748 0.8848001 0.17657658460400094 1 -40 0 +40 0 ? ? ? 0 41 1 0.223279715 0.611168444 0.7103580388075188 1 42 1 2.04505968 0.999095738 0.0013051645925104319 1 43 1 0.169680834 0.56447953 0.82500683046719547 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -1.15510464 0.0109047387 0.015818618982627604 0 137 0 -1.24130344 0.008020028 0.011617101215948859 0 138 0 -1.0749054 0.0144998142 0.021071951694510416 0 -139 0 +139 0 ? ? ? 0 140 0 -1.24130344 0.008020028 0.011617101215948859 0 141 0 -1.28360271 0.00689550675 0.0099825705316781733 0 142 1 0.643275 0.8769101 0.18949916409473391 1 143 0 -0.8591082 0.0309929028 0.04542086264678518 0 144 0 -1.23970342 0.008065961 0.011683906942824285 0 -145 0 +145 0 ? ? ? 0 146 1 0.4436772 0.7764816 0.36497630408526505 1 147 0 -1.13820457 0.01158053 0.016804665017851262 0 148 0 -0.320714116 0.181648508 0.2892074628941344 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.9467714 0.9550218 0.066394431489110783 1 156 0 -1.11830425 0.0124295028 0.018044356997620709 0 157 0 -1.19580388 0.009433212 0.013673843835136703 0 -158 0 +158 0 ? ? ? 0 159 1 2.5887537 0.9998721 0.0001845491876153846 1 160 1 1.92586017 0.9986121 0.0020036987954509607 1 161 0 -1.04420578 0.01616599 0.023513167231551696 0 162 0 -1.15350461 0.0109670116 0.015909453114236246 0 163 0 -1.00530624 0.0185497329 0.027012930786301039 0 -164 0 +164 0 ? ? ? 0 165 0 -0.9275073 0.0243961066 0.035632579294711073 0 166 1 2.06175947 0.9991484 0.0012290814661556479 1 167 1 1.15397048 0.9781426 0.031883260676163518 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 0.0278829336 0.437610149 0.83035753638984611 1 233 1 1.11616874 0.975037336 0.036470630963644925 1 234 0 -0.544210553 0.09034561 0.13660958234622511 0 -235 0 +235 0 ? ? ? 0 236 1 2.15735817 0.999396145 0.00087144125636392123 1 237 1 1.37376881 0.9899692 0.014544463077068375 1 238 1 2.659554 0.9999008 0.00014309666195095306 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 2.70435286 0.9999156 0.00012176885553963592 1 247 1 0.616675138 0.8662006 0.207226880023576 1 248 0 -0.7762085 0.04131975 0.060878383940094143 0 -249 0 +249 0 ? ? ? 0 250 0 -1.16220379 0.0106326314 0.015421777653289784 0 251 1 1.43526506 0.9919444 0.011668877043853167 1 252 0 0.8921714 0.9457866 4.2052065773298537 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.6439743 0.8771814 0.18905285611569214 1 273 1 -0.149016142 0.29164663 1.7777066932129042 0 274 0 -1.08650517 0.0139153069 0.020216532376881539 0 -275 0 +275 0 ? ? ? 0 276 0 -1.19740391 0.009379565 0.013595712679399167 0 277 0 -1.28200269 0.00693504466 0.010040008870103924 0 278 0 -1.23810339 0.008112156 0.01175109505221196 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.98086047 0.998861 0.0016441447437435472 1 290 0 -1.32590222 0.005927712 0.0085773281383116576 0 291 0 -1.23810339 0.008112156 0.01175109505221196 0 -292 1 +292 1 ? ? ? 0 293 1 1.05756974 0.9693573 0.044899543967322132 1 -294 0 +294 0 ? ? ? 0 295 1 1.67846322 0.9966264 0.0048753374261991068 1 296 0 0.418977976 0.760678768 2.062979700875831 1 -297 0 +297 0 ? ? ? 0 298 0 -0.865008354 0.0303616133 0.044481280497859745 0 299 1 1.15566754 0.9782728 0.031691271742118471 1 300 1 1.43996739 0.9920784 0.011473925159666753 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.7111747 0.9009488 0.15048293737780147 1 313 0 -1.32590222 0.005927712 0.0085773281383116576 0 314 0 -1.3091023 0.006294775 0.0091101437491470354 0 -315 0 +315 0 ? ? ? 0 316 1 0.9520712 0.955833852 0.065168231187009498 1 317 1 2.1167593 0.999301255 0.0010084286664473847 1 318 0 -1.15680456 0.0108389612 0.015722679136914018 0 319 0 0.43477726 0.7708744 2.1257893169799549 1 320 1 1.20646763 0.9818366 0.026445122755562926 1 -321 0 +321 0 ? ? ? 0 322 0 -1.15350461 0.0109670116 0.015909453114236246 0 323 1 1.1870687 0.980549 0.028338400890888689 1 324 0 -1.23810339 0.008112156 0.01175109505221196 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -0.775208354 0.0414625444 0.061093287384022553 0 409 0 -1.11720467 0.0124781644 0.018115446186086449 0 410 0 -1.28200269 0.00693504466 0.010040008870103924 0 -411 0 +411 0 ? ? ? 0 412 1 1.89956212 0.9984746 0.0022023701778286324 1 413 0 -0.9887064 0.01966903 0.028659194266170487 0 414 1 1.3937664 0.9906591 0.013539378350893604 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.266803 0.00732205063 0.010602349145536318 0 615 0 -0.994706035 0.0192570463 0.028053029788189736 0 616 0 -1.19740391 0.009379565 0.013595712679399167 0 -617 0 +617 0 ? ? ? 0 618 0 -1.15510464 0.0109047387 0.015818618982627604 0 619 0 -1.11280513 0.0126747517 0.018402674010350579 0 620 0 -1.19740391 0.009379565 0.013595712679399167 0 diff --git a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-TrainTest-breast-cancer.txt index 29a93c0326..da39a0132a 100644 --- a/test/BaselineOutput/SingleRelease/SGD/BinarySGD-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SGD/BinarySGD-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 0.822916269 0.694855034 0.52521607106152413 1 21 1 1.02655149 0.736246765 0.441738705665011 1 22 0 -1.01600647 0.265806019 0.44576680836978433 0 -23 1 +23 1 ? ? ? 0 24 0 -1.03864312 0.261411875 0.43715802871945769 0 25 1 0.184996247 0.5461176 0.87271643290195555 1 26 0 -0.9708479 0.274711519 0.46337315957197994 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -0.7265403 0.325954378 0.56908185228017971 0 38 1 0.933466434 0.717778 0.47839033548395937 1 39 1 0.4073757 0.600458443 0.7358636922780708 1 -40 0 +40 0 ? ? ? 0 41 1 0.04465425 0.5111617 0.96814839425710286 1 42 1 1.43612671 0.8078541 0.30783330336923359 1 43 1 0.0479370356 0.511981964 0.96583510643055481 1 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -0.9885073 0.271207035 0.45641906112335939 0 137 0 -1.04836845 0.259538531 0.4335034307664255 0 138 0 -0.9260764 0.283721417 0.48140728969854896 0 -139 0 +139 0 ? ? ? 0 140 0 -1.04836845 0.259538531 0.4335034307664255 0 141 0 -1.07586777 0.2542888 0.42331107075059288 0 142 1 0.373193622 0.592230439 0.75576945149404562 1 143 0 -0.7654944 0.317454576 0.55100303407297502 0 144 0 -1.04350591 0.2604741 0.43532739229387868 0 -145 0 +145 0 ? ? ? 0 146 1 0.2147963 0.553493559 0.85336156547352904 1 147 0 -0.9662212 0.275634348 0.46520995753133637 0 148 0 -0.318193555 0.421116054 0.78865394770237895 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 0.600466 0.6457629 0.63092349091318589 1 156 0 -0.9430999 0.2802746 0.47448152072334643 0 157 0 -1.01114392 0.266756058 0.44763484776839352 0 -158 0 +158 0 ? ? ? 0 159 1 1.87013483 0.866473854 0.20677187910596315 1 160 1 1.34225249 0.7928601 0.33486178660539723 1 161 0 -0.901281357 0.288787246 0.49164689849299364 0 162 0 -0.9836446 0.2721692 0.45832499621527728 0 163 0 -0.855002642 0.2983845 0.51124744948014056 0 -164 0 +164 0 ? ? ? 0 165 0 -0.8168132 0.306440562 0.5279085681868505 0 166 1 1.49839211 0.817334533 0.29100140459599977 1 167 1 0.8153703 0.6932527 0.5285468008024331 1 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -0.08038044 0.4799157 0.9431826305616432 0 233 1 0.6980891 0.667763948 0.58258988829378422 1 234 0 -0.49562943 0.378568321 0.68633230772699549 0 -235 0 +235 0 ? ? ? 0 236 1 1.52862382 0.8218049 0.28313219549408758 1 237 1 0.9843018 0.727960944 0.45806704440223556 1 238 1 1.96605682 0.877186954 0.18904373922390869 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 1.97140193 0.8777616 0.18809893386755816 1 247 1 0.323040724 0.580065131 0.78571319608569612 1 248 0 -0.6818254 0.335854024 0.59042772056631165 0 -249 0 +249 0 ? ? ? 0 250 0 -0.9754619 0.273793161 0.46154757804170599 0 251 1 0.9712138 0.725361347 0.46322822535916075 1 252 0 0.5616691 0.6368387 1.4613175226151991 1 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 0.354397058 0.587683439 0.76688885070680657 1 273 1 -0.237432361 0.4409192 1.1814138244518058 0 274 0 -0.9287806 0.28317216 0.48030142611403009 0 -275 0 +275 0 ? ? ? 0 276 0 -1.01600647 0.265806019 0.44576680836978433 0 277 0 -1.07100511 0.255211979 0.42509822607281988 0 278 0 -1.03864312 0.261411875 0.43715802871945769 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 1.4364171 0.8078992 0.30775283385408775 1 290 0 -1.10336709 0.249109536 0.4133256254437066 0 291 0 -1.03864312 0.261411875 0.43715802871945769 0 -292 1 +292 1 ? ? ? 0 293 1 0.6671076 0.6608552 0.59759395409472371 1 -294 0 +294 0 ? ? ? 0 295 1 1.17509961 0.764065564 0.38823165513968877 1 296 0 0.224430084 0.5558732 1.1709565135958244 1 -297 0 +297 0 ? ? ? 0 298 0 -0.7844506 0.313361466 0.54237727145415437 0 299 1 0.748945951 0.678949 0.55862488926125509 1 300 1 1.01898217 0.7347743 0.44462694496525584 1 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 0.4770298 0.617046237 0.69654949630747753 1 313 0 -1.10336709 0.249109536 0.4133256254437066 0 314 0 -1.09132075 0.251369655 0.41767456748452503 0 -315 0 +315 0 ? ? ? 0 316 1 0.5936177 0.6441948 0.63443112049115613 1 317 1 1.53676748 0.8229943 0.28104567061447505 1 318 0 -0.977696 0.2733492 0.46066585861057346 0 319 0 0.228058934 0.5567689 1.1738689621124481 1 320 1 0.7848048 0.6867147 0.54221722972478248 1 -321 0 +321 0 ? ? ? 0 322 0 -0.9836446 0.2721692 0.45832499621527728 0 323 1 0.799038649 0.6897688 0.53581523979313106 1 324 0 -1.03864312 0.261411875 0.43715802871945769 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -0.6859037 0.334944934 0.58845429463916576 0 409 0 -0.9535757 0.2781663 0.47026158367983178 0 410 0 -1.07100511 0.255211979 0.42509822607281988 0 -411 0 +411 0 ? ? ? 0 412 1 1.353524 0.794705153 0.331508396641091 1 413 0 -0.8662151 0.296042472 0.50643970604072797 0 414 1 0.9272311 0.7165132 0.48093477882048502 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -1.06382155 0.256579816 0.42775023737254403 0 615 0 -0.863645554 0.296578258 0.50753816766843873 0 616 0 -1.01600647 0.265806019 0.44576680836978433 0 -617 0 +617 0 ? ? ? 0 618 0 -0.9885073 0.271207035 0.45641906112335939 0 619 0 -0.961007953 0.276676446 0.46728696355201749 0 620 0 -1.01600647 0.265806019 0.44576680836978433 0 diff --git a/test/BaselineOutput/SingleRelease/SavePipe/TestParquetPrimitiveDataTypes-Data.txt b/test/BaselineOutput/SingleRelease/SavePipe/TestParquetPrimitiveDataTypes-Data.txt index af1e19e1cc..85a3d35b4b 100644 --- a/test/BaselineOutput/SingleRelease/SavePipe/TestParquetPrimitiveDataTypes-Data.txt +++ b/test/BaselineOutput/SingleRelease/SavePipe/TestParquetPrimitiveDataTypes-Data.txt @@ -11,5 +11,5 @@ #@ col=string:TX:7 #@ } sbyte short int long bool DateTimeOffset Interval string - 1 "2018-09-01T19:53:18.2910000+00:00" "31.00:00:00.0010000" "" +-128 -32768 -2147483648 -9223372036854775808 1 "2018-09-01T19:53:18.2910000+00:00" "31.00:00:00.0010000" "" 127 32767 2147483647 9223372036854775807 0 "2018-09-01T19:53:18.3110000+00:00" "31.00:00:00.0010000" """""" diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer.txt index d0e7499c6d..cc1083e607 100644 --- a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer.txt @@ -14,7 +14,7 @@ Instance Label Score Probability Log-loss Assigned 32 1 454.251282 1 0 1 35 0 -320.737427 0 0 0 37 0 -78.9787 5.011722E-35 0 0 -40 0 +40 0 ? ? ? 0 41 1 199.0091 1 0 1 44 1 656.8247 1 0 1 45 0 -322.804565 0 0 0 @@ -76,7 +76,7 @@ Instance Label Score Probability Log-loss Assigned 138 0 -289.415222 0 0 0 141 0 -344.61084 0 0 0 144 0 -320.737427 0 0 0 -145 0 +145 0 ? ? ? 0 147 0 -309.023651 0 0 0 150 0 -272.79837 0 0 0 151 1 249.55658 1 0 1 @@ -84,7 +84,7 @@ Instance Label Score Probability Log-loss Assigned 154 0 -349.126221 0 0 0 156 0 -227.212433 0 0 0 161 0 -274.6302 0 0 0 -164 0 +164 0 ? ? ? 0 167 1 283.316284 1 0 1 169 0 -331.047241 0 0 0 171 0 -301.379425 0 0 0 @@ -130,7 +130,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 1044.54126 1 0 1 247 1 409.237671 1 0 1 248 0 -221.818024 0 0 0 -249 0 +249 0 ? ? ? 0 250 0 -251.085815 0 0 0 252 0 307.68988 1 Infinity 1 254 1 728.536743 1 0 1 @@ -144,7 +144,7 @@ Instance Label Score Probability Log-loss Assigned 269 0 -301.379425 0 0 0 271 0 -283.3178 0 0 0 272 1 408.017578 1 0 1 -275 0 +275 0 ? ? ? 0 276 0 -316.222015 0 0 0 277 0 -325.252838 0 0 0 278 0 -301.379425 0 0 0 @@ -158,7 +158,7 @@ Instance Label Score Probability Log-loss Assigned 291 0 -301.379425 0 0 0 293 1 386.6949 1 0 1 296 0 139.220642 1 Infinity 1 -297 0 +297 0 ? ? ? 0 299 1 227.814941 1 0 1 300 1 407.6792 1 0 1 301 0 -301.379425 0 0 0 @@ -172,7 +172,7 @@ Instance Label Score Probability Log-loss Assigned 316 1 466.170166 1 0 1 317 1 736.0132 1 0 1 319 0 161.598083 1 Infinity 1 -321 0 +321 0 ? ? ? 0 323 1 388.03302 1 0 1 327 0 -325.252838 0 0 0 328 1 584.984 1 0 1 @@ -318,7 +318,7 @@ Instance Label Score Probability Log-loss Assigned 612 1 1115.01685 1 0 1 613 0 -169.23941 0 0 0 614 0 -292.156342 0 0 0 -617 0 +617 0 ? ? ? 0 618 0 -311.7066 0 0 0 619 0 -307.1912 0 0 0 621 0 -15.8763733 1.27344038E-07 1.8371862313930792E-07 0 @@ -375,7 +375,7 @@ Instance Label Score Probability Log-loss Assigned 17 0 -643.057739 0 0 0 19 0 -668.631836 0 0 0 22 0 -540.900146 0 0 0 -23 1 +23 1 ? ? ? 0 24 0 -604.696655 0 0 0 26 0 -270.657074 0 0 0 27 0 -566.4742 0 0 0 @@ -425,7 +425,7 @@ Instance Label Score Probability Log-loss Assigned 134 0 -670.7141 0 0 0 135 0 -421.652374 0 0 0 136 0 -553.6872 0 0 0 -139 0 +139 0 ? ? ? 0 140 0 -451.529541 0 0 0 142 1 488.315338 1 0 1 143 0 -142.331116 0 0 0 @@ -435,7 +435,7 @@ Instance Label Score Probability Log-loss Assigned 153 0 -322.504425 0 0 0 155 1 1089.60254 1 0 1 157 0 -617.483643 0 0 0 -158 0 +158 0 ? ? ? 0 159 1 1923.50525 1 0 1 160 1 1494.5094 1 0 1 162 0 -630.2707 0 0 0 @@ -474,7 +474,7 @@ Instance Label Score Probability Log-loss Assigned 231 1 1347.16248 1 0 1 232 0 355.92923 1 Infinity 1 234 0 50.92752 1 Infinity 1 -235 0 +235 0 ? ? ? 0 236 1 1204.77161 1 0 1 238 1 2421.62354 1 0 1 243 0 -499.813416 0 0 0 @@ -496,8 +496,8 @@ Instance Label Score Probability Log-loss Assigned 286 1 1933.38391 1 0 1 287 0 -670.7141 0 0 0 289 1 1586.01746 1 0 1 -292 1 -294 0 +292 1 ? ? ? 0 +294 0 ? ? ? 0 295 1 906.5393 1 0 1 298 0 -764.4775 0 0 0 302 1 1682.504 1 0 1 @@ -506,7 +506,7 @@ Instance Label Score Probability Log-loss Assigned 307 0 -604.696655 0 0 0 310 0 -657.927 0 0 0 313 0 -425.9555 0 0 0 -315 0 +315 0 ? ? ? 0 318 0 -994.1385 0 0 0 320 1 276.6519 1 0 1 322 0 -630.2707 0 0 0 @@ -551,7 +551,7 @@ Instance Label Score Probability Log-loss Assigned 407 0 -515.32605 0 0 0 408 0 -106.253662 0 0 0 410 0 -515.32605 0 0 0 -411 0 +411 0 ? ? ? 0 412 1 1142.7179 1 0 1 417 0 -515.32605 0 0 0 420 0 -151.036346 0 0 0 diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer.txt index 4a57fddad1..f3130d53ec 100644 --- a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer.txt @@ -22,7 +22,7 @@ Instance Label Score Probability Log-loss Assigned 20 1 10.1497192 0.9999609 5.6411412351548271E-05 1 21 1 -61.521698 1.91190378E-27 88.757048641689195 0 22 0 -407.94165 0 0 0 -23 1 +23 1 ? ? ? 0 24 0 -413.829132 0 0 0 25 1 -111.690765 0 Infinity 0 26 0 -333.49762 0 0 0 @@ -39,7 +39,7 @@ Instance Label Score Probability Log-loss Assigned 37 0 -367.9438 0 0 0 38 1 125.049805 1 0 1 39 1 -120.420319 0 Infinity 0 -40 0 +40 0 ? ? ? 0 41 1 -214.114883 0 Infinity 0 42 1 86.67383 1 0 1 43 1 -299.954132 0 Infinity 0 @@ -138,13 +138,13 @@ Instance Label Score Probability Log-loss Assigned 136 0 -408.326935 0 0 0 137 0 -401.2836 0 0 0 138 0 -411.7511 0 0 0 -139 0 +139 0 ? ? ? 0 140 0 -401.2836 0 0 0 141 0 -400.898315 0 0 0 142 1 -108.134125 0 Infinity 0 143 0 -305.7808 0 0 0 144 0 -407.556366 0 0 0 -145 0 +145 0 ? ? ? 0 146 1 -205.1228 0 Infinity 0 147 0 -408.638123 0 0 0 148 0 -448.917847 0 0 0 @@ -157,13 +157,13 @@ Instance Label Score Probability Log-loss Assigned 155 1 39.9500122 1 0 1 156 0 -361.30127 0 0 0 157 0 -414.214417 0 0 0 -158 0 +158 0 ? ? ? 0 159 1 214.183228 1 0 1 160 1 120.047485 1 0 1 161 0 -401.219147 0 0 0 162 0 -414.5997 0 0 0 163 0 -282.1694 0 0 0 -164 0 +164 0 ? ? ? 0 165 0 -377.536072 0 0 0 166 1 123.761658 1 0 1 167 1 -9.15014648 0.000106192965 13.201024182527696 0 @@ -234,7 +234,7 @@ Instance Label Score Probability Log-loss Assigned 232 0 -256.504028 0 0 0 233 1 -84.9973755 1.21929513E-37 122.62529213957316 0 234 0 -275.7568 0 0 0 -235 0 +235 0 ? ? ? 0 236 1 116.098267 1 0 1 237 1 26.0986938 1 0 1 238 1 370.027954 1 0 1 @@ -248,7 +248,7 @@ Instance Label Score Probability Log-loss Assigned 246 1 321.109131 1 0 1 247 1 -61.9207153 1.28284749E-27 89.332708892981643 0 248 0 -346.1557 0 0 0 -249 0 +249 0 ? ? ? 0 250 0 -354.643219 0 0 0 251 1 108.2807 1 0 1 252 0 -4.193939 0.0148625113 0.021603009492489122 0 @@ -274,7 +274,7 @@ Instance Label Score Probability Log-loss Assigned 272 1 -151.574524 0 Infinity 0 273 1 -303.688629 0 Infinity 0 274 0 -400.833862 0 0 0 -275 0 +275 0 ? ? ? 0 276 0 -407.94165 0 0 0 277 0 -407.171082 0 0 0 278 0 -413.829132 0 0 0 @@ -291,12 +291,12 @@ Instance Label Score Probability Log-loss Assigned 289 1 175.671082 1 0 1 290 0 -400.513 0 0 0 291 0 -413.829132 0 0 0 -292 1 +292 1 ? ? ? 0 293 1 51.4936523 1 0 1 -294 0 +294 0 ? ? ? 0 295 1 5.8543396 0.997140765 0.0041309123233919023 1 296 0 -173.148254 0 0 0 -297 0 +297 0 ? ? ? 0 298 0 -429.3664 0 0 0 299 1 -112.8385 0 Infinity 0 300 1 -114.377228 0 Infinity 0 @@ -314,13 +314,13 @@ Instance Label Score Probability Log-loss Assigned 312 1 -175.547363 0 Infinity 0 313 0 -400.513 0 0 0 314 0 -382.020966 0 0 0 -315 0 +315 0 ? ? ? 0 316 1 -56.5515747 2.753995E-25 81.586676422594735 0 317 1 168.155884 1 0 1 318 0 -489.2794 0 0 0 319 0 -232.038055 0 0 0 320 1 16.8273315 0.99999994 8.5991327994145617E-08 1 -321 0 +321 0 ? ? ? 0 322 0 -414.5997 0 0 0 323 1 72.79901 1 0 1 324 0 -413.829132 0 0 0 @@ -410,7 +410,7 @@ Instance Label Score Probability Log-loss Assigned 408 0 -278.598877 0 0 0 409 0 -411.365784 0 0 0 410 0 -407.171082 0 0 0 -411 0 +411 0 ? ? ? 0 412 1 21.296814 1 0 1 413 0 -418.794434 0 0 0 414 1 17.1500549 0.99999994 8.5991327994145617E-08 1 @@ -616,7 +616,7 @@ Instance Label Score Probability Log-loss Assigned 614 0 -382.40625 0 0 0 615 0 -415.175232 0 0 0 616 0 -407.94165 0 0 0 -617 0 +617 0 ? ? ? 0 618 0 -408.326935 0 0 0 619 0 -408.712219 0 0 0 620 0 -407.94165 0 0 0 diff --git a/test/BaselineOutput/SingleRelease/Text/tokenized.tsv b/test/BaselineOutput/SingleRelease/Text/tokenized.tsv new file mode 100644 index 0000000000..eccf8ca0e6 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/Text/tokenized.tsv @@ -0,0 +1,12 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=text:TX:0 +#@ col=words:TX:1-** +#@ col={name=chars type=TX src={ min=-1 var=+}} +#@ } +text +==RUDE== Dude, you are rude upload that carl picture back, or else. ==RUDE== Dude, you are rude upload that carl picture back, or else. <␂> = = R U D E = = <␠> D u d e , <␠> y o u <␠> a r e <␠> r u d e <␠> u p l o a d <␠> t h a t <␠> c a r l <␠> p i c t u r e <␠> b a c k , <␠> o r <␠> e l s e . <␃> +== OK! == IM GOING TO VANDALIZE WILD ONES WIKI THEN!!! == OK! == IM GOING TO VANDALIZE WILD ONES WIKI THEN!!! <␂> = = <␠> O K ! <␠> = = <␠> <␠> I M <␠> G O I N G <␠> T O <␠> V A N D A L I Z E <␠> W I L D <␠> O N E S <␠> W I K I <␠> T H E N ! ! ! <␠> <␠> <␠> <␃> +Stop trolling, zapatancas, calling me a liar merely demonstartes that you arer Zapatancas. You may choose to chase every legitimate editor from this site and ignore me but I am an editor with a record that isnt 99% trolling and therefore my wishes are not to be completely ignored by a sockpuppet like yourself. The consensus is overwhelmingly against you and your trollin g lover Zapatancas, Stop trolling, zapatancas, calling me a liar merely demonstartes that you arer Zapatancas. You may choose to chase every legitimate editor from this site and ignore me but I am an editor with a record that isnt 99% trolling and therefore my wishes are not to be completely ignored by a sockpuppet like yourself. The consensus is overwhelmingly against you and your trollin g lover Zapatancas, <␂> S t o p <␠> t r o l l i n g , <␠> z a p a t a n c a s , <␠> c a l l i n g <␠> m e <␠> a <␠> l i a r <␠> m e r e l y <␠> d e m o n s t a r t e s <␠> t h a t <␠> y o u <␠> a r e r <␠> Z a p a t a n c a s . <␠> Y o u <␠> m a y <␠> c h o o s e <␠> t o <␠> c h a s e <␠> e v e r y <␠> l e g i t i m a t e <␠> e d i t o r <␠> f r o m <␠> t h i s <␠> s i t e <␠> a n d <␠> i g n o r e <␠> m e <␠> b u t <␠> I <␠> a m <␠> a n <␠> e d i t o r <␠> w i t h <␠> a <␠> r e c o r d <␠> t h a t <␠> i s n t <␠> 9 9 % <␠> t r o l l i n g <␠> a n d <␠> t h e r e f o r e <␠> m y <␠> w i s h e s <␠> a r e <␠> n o t <␠> t o <␠> b e <␠> c o m p l e t e l y <␠> i g n o r e d <␠> b y <␠> a <␠> s o c k p u p p e t <␠> l i k e <␠> y o u r s e l f . <␠> T h e <␠> c o n s e n s u s <␠> i s <␠> o v e r w h e l m i n g l y <␠> a g a i n s t <␠> y o u <␠> a n d <␠> y o u r <␠> t r o l l i n <␠> g <␠> l o v e r <␠> Z a p a t a n c a s , <␠> <␠> <␃> +==You're cool== You seem like a really cool guy... *bursts out laughing at sarcasm*. ==You're cool== You seem like a really cool guy... *bursts out laughing at sarcasm*. <␂> = = Y o u ' r e <␠> c o o l = = <␠> <␠> Y o u <␠> s e e m <␠> l i k e <␠> a <␠> r e a l l y <␠> c o o l <␠> g u y . . . <␠> * b u r s t s <␠> o u t <␠> l a u g h i n g <␠> a t <␠> s a r c a s m * . <␃> diff --git a/test/BaselineOutput/SingleRelease/Transform/Concat/Concat1.tsv b/test/BaselineOutput/SingleRelease/Transform/Concat/Concat1.tsv index 36b453f708..c548a80a00 100644 --- a/test/BaselineOutput/SingleRelease/Transform/Concat/Concat1.tsv +++ b/test/BaselineOutput/SingleRelease/Transform/Concat/Concat1.tsv @@ -7,13 +7,13 @@ #@ col=f4:R4:8-** #@ } float1 float1 float1 float4.age float4.fnlwgt float4.education-num float4.capital-gain float1 -25 25 25 25 226802 7 0 25 25 226802 7 0 0 40 0 25 -38 38 38 38 89814 9 0 38 38 89814 9 0 0 50 0 38 -28 28 28 28 336951 12 0 28 28 336951 12 0 0 40 1 28 -44 44 44 44 160323 10 7688 44 44 160323 10 7688 0 40 1 44 -18 18 18 18 103497 10 0 18 18 103497 10 0 0 30 0 18 -34 34 34 34 198693 6 0 34 34 198693 6 0 0 30 0 34 -29 29 29 29 227026 9 0 29 29 227026 9 0 0 40 0 29 -63 63 63 63 104626 15 3103 63 63 104626 15 3103 0 32 1 63 -24 24 24 24 369667 10 0 24 24 369667 10 0 0 40 0 24 -55 55 55 55 104996 4 0 55 55 104996 4 0 0 10 0 55 +25 25 25 25 226802 7 0 25 25 226802 7 0 0 40 ? 0 25 +38 38 38 38 89814 9 0 38 38 89814 9 0 0 50 ? 0 38 +28 28 28 28 336951 12 0 28 28 336951 12 0 0 40 ? 1 28 +44 44 44 44 160323 10 7688 44 44 160323 10 7688 0 40 ? 1 44 +18 18 18 18 103497 10 0 18 18 103497 10 0 0 30 ? 0 18 +34 34 34 34 198693 6 0 34 34 198693 6 0 0 30 ? 0 34 +29 29 29 29 227026 9 0 29 29 227026 9 0 0 40 ? 0 29 +63 63 63 63 104626 15 3103 63 63 104626 15 3103 0 32 ? 1 63 +24 24 24 24 369667 10 0 24 24 369667 10 0 0 40 ? 0 24 +55 55 55 55 104996 4 0 55 55 104996 4 0 0 10 ? 0 55 diff --git a/test/Microsoft.ML.Benchmarks/Helpers.cs b/test/Microsoft.ML.Benchmarks/Helpers.cs new file mode 100644 index 0000000000..55832fa13f --- /dev/null +++ b/test/Microsoft.ML.Benchmarks/Helpers.cs @@ -0,0 +1,22 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.IO; +using System.Text; + +namespace Microsoft.ML.Benchmarks +{ + internal class Helpers + { + public static string DatasetNotFound = "Could not find {0} Please ensure you have run 'build.cmd -- /t:DownloadExternalTestFiles /p:IncludeBenchmarkData=true' from the root"; + } + + // Adding this class to not print anything to the console. + // This is required for the current version of BenchmarkDotNet + internal class EmptyWriter : TextWriter + { + internal static readonly EmptyWriter Instance = new EmptyWriter(); + public override Encoding Encoding => null; + } +} diff --git a/test/Microsoft.ML.Benchmarks/Microsoft.ML.Benchmarks.csproj b/test/Microsoft.ML.Benchmarks/Microsoft.ML.Benchmarks.csproj index d78a5881fa..44eaec093e 100644 --- a/test/Microsoft.ML.Benchmarks/Microsoft.ML.Benchmarks.csproj +++ b/test/Microsoft.ML.Benchmarks/Microsoft.ML.Benchmarks.csproj @@ -1,4 +1,5 @@  + Exe 7.2 @@ -22,6 +23,7 @@ + @@ -34,9 +36,13 @@ PreserveNewest - + + + external\%(Identity) + + + PreserveNewest diff --git a/test/Microsoft.ML.Benchmarks/Numeric/Ranking.cs b/test/Microsoft.ML.Benchmarks/Numeric/Ranking.cs new file mode 100644 index 0000000000..a8c008ab04 --- /dev/null +++ b/test/Microsoft.ML.Benchmarks/Numeric/Ranking.cs @@ -0,0 +1,105 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using BenchmarkDotNet.Attributes; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.Runtime.Tools; +using System.IO; + +namespace Microsoft.ML.Benchmarks +{ + public class Ranking + { + private string _mslrWeb10k_Validate; + private string _mslrWeb10k_Train; + private string _mslrWeb10k_Test; + private string _modelPath_MSLR; + + [GlobalSetup(Targets = new string[] { + nameof(TrainTest_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking), + nameof(TrainTest_Ranking_MSLRWeb10K_RawNumericFeatures_LightGBMRanking) })] + public void SetupTrainingSpeedTests() + { + _mslrWeb10k_Validate = Path.GetFullPath(TestDatasets.MSLRWeb.validFilename); + _mslrWeb10k_Train = Path.GetFullPath(TestDatasets.MSLRWeb.trainFilename); + + if (!File.Exists(_mslrWeb10k_Validate)) + throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _mslrWeb10k_Validate)); + + if (!File.Exists(_mslrWeb10k_Train)) + throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _mslrWeb10k_Train)); + } + + [GlobalSetup(Target = nameof(Test_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking))] + public void SetupScoringSpeedTests() + { + _mslrWeb10k_Test = Path.GetFullPath(TestDatasets.MSLRWeb.testFilename); + if (!File.Exists(_mslrWeb10k_Test)) + throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _mslrWeb10k_Test)); + + SetupTrainingSpeedTests(); + _modelPath_MSLR = Path.Combine(Directory.GetCurrentDirectory(), @"FastTreeRankingModel.zip"); + + string cmd = @"TrainTest test=" + _mslrWeb10k_Validate + + " eval=RankingEvaluator{t=10}" + + " data=" + _mslrWeb10k_Train + + " loader=TextLoader{col=Label:R4:0 col=GroupId:TX:1 col=Features:R4:2-138}" + + " xf=HashTransform{col=GroupId}" + + " xf=NAHandleTransform{col=Features}" + + " tr=FastTreeRanking{}" + + " out={" + _modelPath_MSLR + "}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + { + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); + } + } + + [Benchmark] + public void TrainTest_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking() + { + string cmd = @"TrainTest test=" + _mslrWeb10k_Validate + + " eval=RankingEvaluator{t=10}" + + " data=" + _mslrWeb10k_Train + + " loader=TextLoader{col=Label:R4:0 col=GroupId:TX:1 col=Features:R4:2-138}" + + " xf=HashTransform{col=GroupId} xf=NAHandleTransform{col=Features}" + + " tr=FastTreeRanking{}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + { + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); + } + } + + [Benchmark] + public void TrainTest_Ranking_MSLRWeb10K_RawNumericFeatures_LightGBMRanking() + { + string cmd = @"TrainTest test=" + _mslrWeb10k_Validate + + " eval=RankingEvaluator{t=10}" + + " data=" + _mslrWeb10k_Train + + " loader=TextLoader{col=Label:R4:0 col=GroupId:TX:1 col=Features:R4:2-138}" + + " xf=HashTransform{col=GroupId}" + + " xf=NAHandleTransform{col=Features}" + + " tr=LightGBMRanking{}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + { + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); + } + } + + [Benchmark] + public void Test_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking() + { + // This benchmark is profiling bulk scoring speed and not training speed. + string cmd = @"Test data=" + _mslrWeb10k_Test + " in="+ _modelPath_MSLR; + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + { + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); + } + } + } +} diff --git a/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs b/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs index 7be306ff87..8b3d8333e0 100644 --- a/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs +++ b/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs @@ -107,7 +107,7 @@ public void TrainSentiment() WordFeatureExtractor = null, }, loader); - var trans = new WordEmbeddingsTransform(env, + var trans = WordEmbeddingsTransform.Create(env, new WordEmbeddingsTransform.Arguments() { Column = new WordEmbeddingsTransform.Column[1] @@ -122,7 +122,7 @@ public void TrainSentiment() }, text); // Train - var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments() { MaxIterations = 20 }); + var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments() { MaxIterations = 20 }, "Features", "Label"); var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); var predicted = trainer.Train(trainRoles); diff --git a/test/Microsoft.ML.Benchmarks/Text/MultiClassClassification.cs b/test/Microsoft.ML.Benchmarks/Text/MultiClassClassification.cs index 0a93b28c32..364a32f0bf 100644 --- a/test/Microsoft.ML.Benchmarks/Text/MultiClassClassification.cs +++ b/test/Microsoft.ML.Benchmarks/Text/MultiClassClassification.cs @@ -8,18 +8,9 @@ using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.Runtime.Tools; using System.IO; -using System.Text; namespace Microsoft.ML.Benchmarks { - // Adding this class to not print anything to the console. - // This is required for the current version of BenchmarkDotNet - internal class EmptyWriter : TextWriter - { - internal static readonly EmptyWriter Instance = new EmptyWriter(); - public override Encoding Encoding => null; - } - public class MultiClassClassification { private string _dataPath_Wiki; @@ -35,9 +26,7 @@ public void SetupTrainingSpeedTests() _dataPath_Wiki = Path.GetFullPath(TestDatasets.WikiDetox.trainFilename); if (!File.Exists(_dataPath_Wiki)) - { - throw new FileNotFoundException($"Could not find {_dataPath_Wiki} Please ensure you have run 'build.cmd -- /t:DownloadExternalTestFiles /p:IncludeBenchmarkData=true' from the root"); - } + throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _dataPath_Wiki)); } [GlobalSetup(Target = nameof(Test_Multiclass_WikiDetox_BigramsAndTrichar_OVAAveragedPerceptron))] @@ -45,30 +34,51 @@ public void SetupScoringSpeedTests() { SetupTrainingSpeedTests(); _modelPath_Wiki = Path.Combine(Directory.GetCurrentDirectory(), @"WikiModel.zip"); - string cmd = @"CV k=5 data=" + _dataPath_Wiki + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}} xf=Concat{col=Features:FeaturesText,logged_in,ns} tr=OVA{p=AveragedPerceptron{iter=10}} out={" + _modelPath_Wiki + "}"; - using (var tlc = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + + string cmd = @"CV k=5 data=" + _dataPath_Wiki + + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} xf=Convert{col=logged_in type=R4}" + + " xf=CategoricalTransform{col=ns}" + + " xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}}" + + " xf=Concat{col=Features:FeaturesText,logged_in,ns}" + + " tr=OVA{p=AveragedPerceptron{iter=10}}" + + " out={" + _modelPath_Wiki + "}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) { - Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false); + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); } } [Benchmark] public void CV_Multiclass_WikiDetox_BigramsAndTrichar_OVAAveragedPerceptron() { - string cmd = @"CV k=5 data=" + _dataPath_Wiki + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}} xf=Concat{col=Features:FeaturesText,logged_in,ns} tr=OVA{p=AveragedPerceptron{iter=10}}"; - using (var tlc = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + string cmd = @"CV k=5 data=" + _dataPath_Wiki + + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" + + " xf=Convert{col=logged_in type=R4}" + + " xf=CategoricalTransform{col=ns}" + + " xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}}" + + " xf=Concat{col=Features:FeaturesText,logged_in,ns}" + + " tr=OVA{p=AveragedPerceptron{iter=10}}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) { - Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false); + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); } } [Benchmark] public void CV_Multiclass_WikiDetox_BigramsAndTrichar_LightGBMMulticlass() { - string cmd = @"CV k=5 data=" + _dataPath_Wiki + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}} xf=Concat{col=Features:FeaturesText,logged_in,ns} tr=LightGBMMulticlass{}"; - using (var tlc = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + string cmd = @"CV k=5 data=" + _dataPath_Wiki + + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" + + " xf=Convert{col=logged_in type=R4}" + + " xf=CategoricalTransform{col=ns}" + + " xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}}" + + " xf=Concat{col=Features:FeaturesText,logged_in,ns} tr=LightGBMMulticlass{}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) { - Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false); + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); } } @@ -78,29 +88,45 @@ public void Test_Multiclass_WikiDetox_BigramsAndTrichar_OVAAveragedPerceptron() // This benchmark is profiling bulk scoring speed and not training speed. string modelpath = Path.Combine(Directory.GetCurrentDirectory(), @"WikiModel.fold000.zip"); string cmd = @"Test data=" + _dataPath_Wiki + " in=" + modelpath; - using (var tlc = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) { - Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false); + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); } } [Benchmark] public void CV_Multiclass_WikiDetox_WordEmbeddings_OVAAveragedPerceptron() { - string cmd = @"CV tr=OVA{p=AveragedPerceptron{iter=10}} k=5 loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} data=" + _dataPath_Wiki + " xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor=NGramExtractorTransform{ngram=2}} xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D} xf=Concat{col=Features:FeaturesText,FeaturesWordEmbedding,logged_in,ns}"; - using (var tlc = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + string cmd = @"CV k=5 data=" + _dataPath_Wiki + + " tr=OVA{p=AveragedPerceptron{iter=10}}" + + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" + + " xf=Convert{col=logged_in type=R4}" + + " xf=CategoricalTransform{col=ns}" + + " xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor=NGramExtractorTransform{ngram=2}}" + + " xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D}" + + " xf=Concat{col=Features:FeaturesText,FeaturesWordEmbedding,logged_in,ns}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) { - Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false); + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); } } [Benchmark] public void CV_Multiclass_WikiDetox_WordEmbeddings_SDCAMC() { - string cmd = @"CV tr=SDCAMC k=5 loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} data=" + _dataPath_Wiki + " xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor={} charExtractor={}} xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D} xf=Concat{col=Features:FeaturesWordEmbedding,logged_in,ns}"; - using (var tlc = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) + string cmd = @"CV k=5 data=" + _dataPath_Wiki + + " tr=SDCAMC" + + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" + + " xf=Convert{col=logged_in type=R4}" + + " xf=CategoricalTransform{col=ns}" + + " xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor={} charExtractor={}}" + + " xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D}" + + " xf=Concat{col=Features:FeaturesWordEmbedding,logged_in,ns}"; + + using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance)) { - Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false); + Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false); } } } diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/CoreBaseTestClass.cs b/test/Microsoft.ML.Core.Tests/UnitTests/CoreBaseTestClass.cs index 35859783ad..114d8c48b9 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/CoreBaseTestClass.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/CoreBaseTestClass.cs @@ -153,19 +153,19 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ switch (type.RawKind) { case DataKind.I1: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U1: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.I2: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U2: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.I4: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U4: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.I8: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U8: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.R4: @@ -176,15 +176,15 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ else return GetComparerOne(r1, r2, col, EqualWithEps); case DataKind.Text: - return GetComparerOne(r1, r2, col, DvText.Identical); + return GetComparerOne>(r1, r2, col, (a, b) => a.Span.SequenceEqual(b.Span)); case DataKind.Bool: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.TimeSpan: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x.Ticks == y.Ticks); case DataKind.DT: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x.Ticks == y.Ticks); case DataKind.DZ: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); case DataKind.UG: return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); } @@ -196,19 +196,19 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ switch (type.ItemType.RawKind) { case DataKind.I1: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U1: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.I2: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U2: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.I4: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U4: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.I8: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U8: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.R4: @@ -219,15 +219,15 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ else return GetComparerVec(r1, r2, col, size, EqualWithEps); case DataKind.Text: - return GetComparerVec(r1, r2, col, size, DvText.Identical); + return GetComparerVec>(r1, r2, col, size, (a,b) => a.Span.SequenceEqual(b.Span)); case DataKind.Bool: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.TimeSpan: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x.Ticks == y.Ticks); case DataKind.DT: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x.Ticks == y.Ticks); case DataKind.DZ: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); case DataKind.UG: return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); } diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/DataTypes.cs b/test/Microsoft.ML.Core.Tests/UnitTests/DataTypes.cs new file mode 100644 index 0000000000..7ae2384203 --- /dev/null +++ b/test/Microsoft.ML.Core.Tests/UnitTests/DataTypes.cs @@ -0,0 +1,239 @@ +using System; +using System.IO; +using System.Linq; +using System.Text; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.Conversion; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Runtime.RunTests +{ + public class DataTypesTest : TestDataViewBase + { + public DataTypesTest(ITestOutputHelper helper) + : base(helper) + { + } + + private readonly static Conversions _conv = Conversions.Instance; + + [Fact] + public void R4ToSBtoR4() + { + var r4ToSB = Conversions.Instance.GetStringConversion(NumberType.FromKind(DataKind.R4)); + + var txToR4 = Conversions.Instance.GetStandardConversion< ReadOnlyMemory, float>( + TextType.Instance, NumberType.FromKind(DataKind.R4), out bool identity2); + + Assert.NotNull(r4ToSB); + Assert.NotNull(txToR4); + + float fVal = float.NaN; + StringBuilder textFVal = default; + r4ToSB(ref fVal, ref textFVal); + + Assert.True("?" == textFVal.ToString()); + + fVal = 0; + var fValTX = textFVal.ToString().AsMemory(); + txToR4(ref fValTX, ref fVal); + + Assert.Equal(fVal, float.NaN); + } + + [Fact] + public void R8ToSBtoR8() + { + var r8ToSB = Conversions.Instance.GetStringConversion(NumberType.FromKind(DataKind.R8)); + + var txToR8 = Conversions.Instance.GetStandardConversion, double>( + TextType.Instance, NumberType.FromKind(DataKind.R8), out bool identity2); + + Assert.NotNull(r8ToSB); + Assert.NotNull(txToR8); + + double dVal = double.NaN; + StringBuilder textDVal = default; + r8ToSB(ref dVal, ref textDVal); + + Assert.True("?" == textDVal.ToString()); + + dVal = 0; + var dValTX = textDVal.ToString().AsMemory(); + txToR8(ref dValTX, ref dVal); + + Assert.Equal(dVal, double.NaN); + } + + [Fact] + public void TXToSByte() + { + var mapper = GetMapper, sbyte>(); + + Assert.NotNull(mapper); + + //1. sbyte.MinValue in text to sbyte. + sbyte minValue = sbyte.MinValue; + sbyte maxValue = sbyte.MaxValue; + ReadOnlyMemory src = minValue.ToString().AsMemory(); + sbyte dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, minValue); + + //2. sbyte.MaxValue in text to sbyte. + src = maxValue.ToString().AsMemory(); + dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, maxValue); + + //3. ERROR condition: sbyte.MinValue - 1 in text to sbyte. + src = (sbyte.MinValue - 1).ToString().AsMemory(); + dst = 0; + var ex = Assert.ThrowsAny(() => mapper(ref src, ref dst)); + Assert.Equal("Value could not be parsed from text to sbyte.", ex.Message); + + //4. ERROR condition: sbyte.MaxValue + 1 in text to sbyte. + src = (sbyte.MaxValue + 1).ToString().AsMemory(); + dst = 0; + ex = Assert.ThrowsAny(() => mapper(ref src, ref dst)); + Assert.Equal("Value could not be parsed from text to sbyte.", ex.Message); + + //5. Empty string in text to sbyte. + src = default; + dst = -1; + mapper(ref src, ref dst); + Assert.Equal(default, dst); + } + + [Fact] + public void TXToShort() + { + var mapper = GetMapper, short>(); + + Assert.NotNull(mapper); + + //1. short.MinValue in text to short. + short minValue = short.MinValue; + short maxValue = short.MaxValue; + ReadOnlyMemory src = minValue.ToString().AsMemory(); + short dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, minValue); + + //2. short.MaxValue in text to short. + src = maxValue.ToString().AsMemory(); + dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, maxValue); + + //3. ERROR condition: short.MinValue - 1 in text to short. + src = (minValue - 1).ToString().AsMemory(); + dst = 0; + var ex = Assert.ThrowsAny(() => mapper(ref src, ref dst)); + Assert.Equal("Value could not be parsed from text to short.", ex.Message); + + //4. ERROR condition: short.MaxValue + 1 in text to short. + src = (maxValue + 1).ToString().AsMemory(); + dst = 0; + ex = Assert.ThrowsAny(() => mapper(ref src, ref dst)); + Assert.Equal("Value could not be parsed from text to short.", ex.Message); + + //5. Empty value in text to short. + src = default; + dst = -1; + mapper(ref src, ref dst); + Assert.Equal(default, dst); + } + + [Fact] + public void TXToInt() + { + var mapper = GetMapper, int>(); + + Assert.NotNull(mapper); + + //1. int.MinValue in text to int. + int minValue = int.MinValue; + int maxValue = int.MaxValue; + ReadOnlyMemory src = minValue.ToString().AsMemory(); + int dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, minValue); + + //2. int.MaxValue in text to int. + src = maxValue.ToString().AsMemory(); + dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, maxValue); + + //3. ERROR condition: int.MinValue - 1 in text to int. + src = ((long)minValue - 1).ToString().AsMemory(); + dst = 0; + var ex = Assert.ThrowsAny(() => mapper(ref src, ref dst)); + Assert.Equal("Value could not be parsed from text to int.", ex.Message); + + //4. ERROR condition: int.MaxValue + 1 in text to int. + src = ((long)maxValue + 1).ToString().AsMemory(); + dst = 0; + ex = Assert.ThrowsAny(() => mapper(ref src, ref dst)); + Assert.Equal("Value could not be parsed from text to int.", ex.Message); + + //5. Empty value in text to int. + src = default; + dst = -1; + mapper(ref src, ref dst); + Assert.Equal(default, dst); + } + + [Fact] + public void TXToLong() + { + var mapper = GetMapper, long>(); + + Assert.NotNull(mapper); + + //1. long.MinValue in text to long. + var minValue = long.MinValue; + var maxValue = long.MaxValue; + ReadOnlyMemory src = minValue.ToString().AsMemory(); + var dst = default(long); + mapper(ref src, ref dst); + Assert.Equal(dst, minValue); + + //2. long.MaxValue in text to long. + src = maxValue.ToString().AsMemory(); + dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, maxValue); + + //3. long.MinValue - 1 in text to long. + src = (minValue - 1).ToString().AsMemory(); + dst = 0; + mapper(ref src, ref dst); + Assert.Equal(dst, (long)minValue - 1); + + //4. ERROR condition: long.MaxValue + 1 in text to long. + src = ((ulong)maxValue + 1).ToString().AsMemory(); + dst = 0; + var ex = Assert.ThrowsAny(() => mapper(ref src, ref dst)); + Assert.Equal("Value could not be parsed from text to long.", ex.Message); + + //5. Empty value in text to long. + src = default; + dst = -1; + mapper(ref src, ref dst); + Assert.Equal(default, dst); + } + + public ValueMapper GetMapper() + { + Assert.True(typeof(TDst).TryGetDataKind(out DataKind dstDataKind)); + + return Conversions.Instance.GetStandardConversion( + TextType.Instance, NumberType.FromKind(dstDataKind), out bool identity); + } + } +} + + diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/DvTypes.cs b/test/Microsoft.ML.Core.Tests/UnitTests/DvTypes.cs deleted file mode 100644 index a3f5d8231b..0000000000 --- a/test/Microsoft.ML.Core.Tests/UnitTests/DvTypes.cs +++ /dev/null @@ -1,133 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using Microsoft.ML.Runtime; -using Microsoft.ML.Runtime.Data; -using Xunit; -namespace Microsoft.ML.Runtime.RunTests -{ - public sealed class DvTypeTests - { - [Fact] - public void TestComparableDvInt4() - { - const int count = 100; - - var rand = RandomUtils.Create(42); - var values = new DvInt4[2 * count]; - for (int i = 0; i < count; i++) - { - var v = values[i] = rand.Next(); - values[values.Length - i - 1] = v; - } - - // Assign two NA's at random. - int iv1 = rand.Next(values.Length); - int iv2 = rand.Next(values.Length - 1); - if (iv2 >= iv1) - iv2++; - values[iv1] = DvInt4.NA; - values[iv2] = DvInt4.NA; - Array.Sort(values); - - Assert.True(values[0].IsNA); - Assert.True(values[1].IsNA); - Assert.True(!values[2].IsNA); - - Assert.True((values[0] == values[1]).IsNA); - Assert.True((values[0] != values[1]).IsNA); - Assert.True((values[0] <= values[1]).IsNA); - Assert.True(values[0].Equals(values[1])); - Assert.True(values[0].CompareTo(values[1]) == 0); - - Assert.True((values[1] == values[2]).IsNA); - Assert.True((values[1] != values[2]).IsNA); - Assert.True((values[1] <= values[2]).IsNA); - Assert.True(!values[1].Equals(values[2])); - Assert.True(values[1].CompareTo(values[2]) < 0); - - for (int i = 3; i < values.Length; i++) - { - DvBool eq = values[i - 1] == values[i]; - DvBool ne = values[i - 1] != values[i]; - DvBool le = values[i - 1] <= values[i]; - bool feq = values[i - 1].Equals(values[i]); - int cmp = values[i - 1].CompareTo(values[i]); - Assert.True(!eq.IsNA); - Assert.True(!ne.IsNA); - Assert.True(eq.IsTrue == ne.IsFalse); - Assert.True(le.IsTrue); - Assert.True(feq == eq.IsTrue); - Assert.True(cmp <= 0); - Assert.True(feq == (cmp == 0)); - } - } - - [Fact] - public void TestComparableDvText() - { - const int count = 100; - - var rand = RandomUtils.Create(42); - var chars = new char[2000]; - for (int i = 0; i < chars.Length; i++) - chars[i] = (char)rand.Next(128); - var str = new string(chars); - - var values = new DvText[2 * count]; - for (int i = 0; i < count; i++) - { - int len = rand.Next(20); - int ich = rand.Next(str.Length - len + 1); - var v = values[i] = new DvText(str, ich, ich + len); - values[values.Length - i - 1] = v; - } - - // Assign two NA's and an empty at random. - int iv1 = rand.Next(values.Length); - int iv2 = rand.Next(values.Length - 1); - if (iv2 >= iv1) - iv2++; - int iv3 = rand.Next(values.Length - 2); - if (iv3 >= iv1) - iv3++; - if (iv3 >= iv2) - iv3++; - - values[iv1] = DvText.NA; - values[iv2] = DvText.NA; - values[iv3] = DvText.Empty; - Array.Sort(values); - - Assert.True(values[0].IsNA); - Assert.True(values[1].IsNA); - Assert.True(values[2].IsEmpty); - - Assert.True((values[0] == values[1]).IsNA); - Assert.True((values[0] != values[1]).IsNA); - Assert.True(values[0].Equals(values[1])); - Assert.True(values[0].CompareTo(values[1]) == 0); - - Assert.True((values[1] == values[2]).IsNA); - Assert.True((values[1] != values[2]).IsNA); - Assert.True(!values[1].Equals(values[2])); - Assert.True(values[1].CompareTo(values[2]) < 0); - - for (int i = 3; i < values.Length; i++) - { - DvBool eq = values[i - 1] == values[i]; - DvBool ne = values[i - 1] != values[i]; - bool feq = values[i - 1].Equals(values[i]); - int cmp = values[i - 1].CompareTo(values[i]); - Assert.True(!eq.IsNA); - Assert.True(!ne.IsNA); - Assert.True(eq.IsTrue == ne.IsFalse); - Assert.True(feq == eq.IsTrue); - Assert.True(cmp <= 0); - Assert.True(feq == (cmp == 0)); - } - } - } -} diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs index 92754a551b..db18eb309b 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs @@ -6,6 +6,8 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.TestFramework; +using Microsoft.ML.Transforms; +using System; using System.Collections.Generic; using System.Linq; using Xunit; @@ -54,7 +56,7 @@ public void TestSimpleExperiment() public void TestSimpleTrainExperiment() { var dataPath = GetDataPath("adult.tiny.with-schema.txt"); - using (var env = new ConsoleEnvironment()) + using (var env = CreateConsoleEnvironment()) { var experiment = env.CreateExperiment(); @@ -123,7 +125,7 @@ public void TestSimpleTrainExperiment() public void TestTrainTestMacro() { var dataPath = GetDataPath("adult.tiny.with-schema.txt"); - using (var env = new ConsoleEnvironment()) + using (var env = CreateConsoleEnvironment()) { var subGraph = env.CreateExperiment(); @@ -195,7 +197,7 @@ public void TestTrainTestMacro() public void TestCrossValidationBinaryMacro() { var dataPath = GetDataPath("adult.tiny.with-schema.txt"); - using (var env = new ConsoleEnvironment()) + using (var env = CreateConsoleEnvironment()) { var subGraph = env.CreateExperiment(); @@ -263,7 +265,7 @@ public void TestCrossValidationBinaryMacro() [Fact] public void TestCrossValidationMacro() { - var dataPath = GetDataPath(TestDatasets.winequalitymacro.trainFilename); + var dataPath = GetDataPath(TestDatasets.generatedRegressionDatasetmacro.trainFilename); using (var env = new ConsoleEnvironment(42)) { var subGraph = env.CreateExperiment(); @@ -344,11 +346,10 @@ public void TestCrossValidationMacro() using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol || col == isWeightedCol)) { var getter = cursor.GetGetter(metricCol); - var foldGetter = cursor.GetGetter(foldCol); - var isWeightedGetter = cursor.GetGetter(isWeightedCol); - DvText fold = default; - DvBool isWeighted = default; - + var foldGetter = cursor.GetGetter>(foldCol); + ReadOnlyMemory fold = default; + var isWeightedGetter = cursor.GetGetter(isWeightedCol); + bool isWeighted = default; double avg = 0; double weightedAvg = 0; for (int w = 0; w < 2; w++) @@ -361,9 +362,9 @@ public void TestCrossValidationMacro() else getter(ref avg); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Average")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold)); isWeightedGetter(ref isWeighted); - Assert.True(isWeighted.IsTrue == (w == 1)); + Assert.True(isWeighted == (w == 1)); // Get the standard deviation. b = cursor.MoveNext(); @@ -371,13 +372,13 @@ public void TestCrossValidationMacro() double stdev = 0; getter(ref stdev); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Standard Deviation")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold)); if (w == 1) - Assert.Equal(0.004557, stdev, 6); + Assert.Equal(1.584696, stdev, 6); else - Assert.Equal(0.000393, stdev, 6); + Assert.Equal(1.385165, stdev, 6); isWeightedGetter(ref isWeighted); - Assert.True(isWeighted.IsTrue == (w == 1)); + Assert.True(isWeighted == (w == 1)); } double sum = 0; double weightedSum = 0; @@ -394,9 +395,9 @@ public void TestCrossValidationMacro() weightedSum += val; else sum += val; - Assert.True(fold.EqualsStr("Fold " + f)); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold)); isWeightedGetter(ref isWeighted); - Assert.True(isWeighted.IsTrue == (w == 1)); + Assert.True(isWeighted == (w == 1)); } } Assert.Equal(weightedAvg, weightedSum / 2); @@ -411,7 +412,7 @@ public void TestCrossValidationMacro() public void TestCrossValidationMacroWithMultiClass() { var dataPath = GetDataPath(@"Train-Tiny-28x28.txt"); - using (var env = new ConsoleEnvironment(42)) + using (var env = CreateConsoleEnvironment(42)) { var subGraph = env.CreateExperiment(); @@ -460,16 +461,16 @@ public void TestCrossValidationMacroWithMultiClass() using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol)) { var getter = cursor.GetGetter(metricCol); - var foldGetter = cursor.GetGetter(foldCol); - DvText fold = default; + var foldGetter = cursor.GetGetter>(foldCol); + ReadOnlyMemory fold = default; - // Get the verage. + // Get the average. b = cursor.MoveNext(); Assert.True(b); double avg = 0; getter(ref avg); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Average")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold)); // Get the standard deviation. b = cursor.MoveNext(); @@ -477,7 +478,7 @@ public void TestCrossValidationMacroWithMultiClass() double stdev = 0; getter(ref stdev); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Standard Deviation")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold)); Assert.Equal(0.025, stdev, 3); double sum = 0; @@ -489,7 +490,7 @@ public void TestCrossValidationMacroWithMultiClass() getter(ref val); foldGetter(ref fold); sum += val; - Assert.True(fold.EqualsStr("Fold " + f)); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold)); } Assert.Equal(avg, sum / 2); b = cursor.MoveNext(); @@ -504,15 +505,15 @@ public void TestCrossValidationMacroWithMultiClass() Assert.True(b); var type = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, countCol); Assert.True(type != null && type.ItemType.IsText && type.VectorSize == 10); - var slotNames = default(VBuffer); + var slotNames = default(VBuffer>); schema.GetMetadata(MetadataUtils.Kinds.SlotNames, countCol, ref slotNames); - Assert.True(slotNames.Values.Select((s, i) => s.EqualsStr(i.ToString())).All(x => x)); + Assert.True(slotNames.Values.Select((s, i) => ReadOnlyMemoryUtils.EqualsStr(i.ToString(), s)).All(x => x)); using (var curs = confusion.GetRowCursor(col => true)) { var countGetter = curs.GetGetter>(countCol); - var foldGetter = curs.GetGetter(foldCol); + var foldGetter = curs.GetGetter>(foldCol); var confCount = default(VBuffer); - var foldIndex = default(DvText); + var foldIndex = default(ReadOnlyMemory); int rowCount = 0; var foldCur = "Fold 0"; while (curs.MoveNext()) @@ -520,7 +521,7 @@ public void TestCrossValidationMacroWithMultiClass() countGetter(ref confCount); foldGetter(ref foldIndex); rowCount++; - Assert.True(foldIndex.EqualsStr(foldCur)); + Assert.True(ReadOnlyMemoryUtils.EqualsStr(foldCur, foldIndex)); if (rowCount == 10) { rowCount = 0; @@ -540,7 +541,7 @@ public void TestCrossValidationMacroWithMultiClass() public void TestCrossValidationMacroMultiClassWithWarnings() { var dataPath = GetDataPath(@"Train-Tiny-28x28.txt"); - using (var env = new ConsoleEnvironment(42)) + using (var env = CreateConsoleEnvironment(42)) { var subGraph = env.CreateExperiment(); @@ -598,11 +599,11 @@ public void TestCrossValidationMacroMultiClassWithWarnings() Assert.True(b); using (var cursor = warnings.GetRowCursor(col => col == warningCol)) { - var getter = cursor.GetGetter(warningCol); + var getter = cursor.GetGetter>(warningCol); b = cursor.MoveNext(); Assert.True(b); - var warning = default(DvText); + var warning = default(ReadOnlyMemory); getter(ref warning); Assert.Contains("test instances with class values not seen in the training set.", warning.ToString()); b = cursor.MoveNext(); @@ -619,7 +620,7 @@ public void TestCrossValidationMacroMultiClassWithWarnings() public void TestCrossValidationMacroWithStratification() { var dataPath = GetDataPath(@"breast-cancer.txt"); - using (var env = new ConsoleEnvironment(42)) + using (var env = CreateConsoleEnvironment(42)) { var subGraph = env.CreateExperiment(); @@ -673,8 +674,8 @@ public void TestCrossValidationMacroWithStratification() using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol)) { var getter = cursor.GetGetter(metricCol); - var foldGetter = cursor.GetGetter(foldCol); - DvText fold = default; + var foldGetter = cursor.GetGetter>(foldCol); + ReadOnlyMemory fold = default; // Get the verage. b = cursor.MoveNext(); @@ -682,7 +683,7 @@ public void TestCrossValidationMacroWithStratification() double avg = 0; getter(ref avg); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Average")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold)); // Get the standard deviation. b = cursor.MoveNext(); @@ -690,7 +691,7 @@ public void TestCrossValidationMacroWithStratification() double stdev = 0; getter(ref stdev); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Standard Deviation")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold)); Assert.Equal(0.00485, stdev, 5); double sum = 0; @@ -702,7 +703,7 @@ public void TestCrossValidationMacroWithStratification() getter(ref val); foldGetter(ref fold); sum += val; - Assert.True(fold.EqualsStr("Fold " + f)); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold)); } Assert.Equal(avg, sum / 2); b = cursor.MoveNext(); @@ -715,7 +716,7 @@ public void TestCrossValidationMacroWithStratification() public void TestCrossValidationMacroWithNonDefaultNames() { string dataPath = GetDataPath(@"adult.tiny.with-schema.txt"); - using (var env = new ConsoleEnvironment(42)) + using (var env = CreateConsoleEnvironment(42)) { var subGraph = env.CreateExperiment(); @@ -781,8 +782,8 @@ public void TestCrossValidationMacroWithNonDefaultNames() using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol)) { var getter = cursor.GetGetter>(metricCol); - var foldGetter = cursor.GetGetter(foldCol); - DvText fold = default; + var foldGetter = cursor.GetGetter>(foldCol); + ReadOnlyMemory fold = default; // Get the verage. b = cursor.MoveNext(); @@ -790,7 +791,7 @@ public void TestCrossValidationMacroWithNonDefaultNames() var avg = default(VBuffer); getter(ref avg); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Average")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold)); // Get the standard deviation. b = cursor.MoveNext(); @@ -798,7 +799,7 @@ public void TestCrossValidationMacroWithNonDefaultNames() var stdev = default(VBuffer); getter(ref stdev); foldGetter(ref fold); - Assert.True(fold.EqualsStr("Standard Deviation")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold)); Assert.Equal(2.462, stdev.Values[0], 3); Assert.Equal(2.763, stdev.Values[1], 3); Assert.Equal(3.273, stdev.Values[2], 3); @@ -813,7 +814,7 @@ public void TestCrossValidationMacroWithNonDefaultNames() getter(ref val); foldGetter(ref fold); sumBldr.AddFeatures(0, ref val); - Assert.True(fold.EqualsStr("Fold " + f)); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold)); } var sum = default(VBuffer); sumBldr.GetResult(ref sum); @@ -827,12 +828,12 @@ public void TestCrossValidationMacroWithNonDefaultNames() Assert.True(data.Schema.TryGetColumnIndex("Instance", out int nameCol)); using (var cursor = data.GetRowCursor(col => col == nameCol)) { - var getter = cursor.GetGetter(nameCol); + var getter = cursor.GetGetter>(nameCol); while (cursor.MoveNext()) { - DvText name = default; + ReadOnlyMemory name = default; getter(ref name); - Assert.Subset(new HashSet() { new DvText("Private"), new DvText("?"), new DvText("Federal-gov") }, new HashSet() { name }); + Assert.Subset(new HashSet() { "Private", "?", "Federal-gov" }, new HashSet() { name.ToString() }); if (cursor.Position > 4) break; } @@ -844,7 +845,7 @@ public void TestCrossValidationMacroWithNonDefaultNames() public void TestOvaMacro() { var dataPath = GetDataPath(@"iris.txt"); - using (var env = new ConsoleEnvironment(42)) + using (var env = CreateConsoleEnvironment(42)) { // Specify subgraph for OVA var subGraph = env.CreateExperiment(); @@ -903,7 +904,7 @@ public void TestOvaMacro() public void TestOvaMacroWithUncalibratedLearner() { var dataPath = GetDataPath(@"iris.txt"); - using (var env = new ConsoleEnvironment(42)) + using (var env = CreateConsoleEnvironment(42)) { // Specify subgraph for OVA var subGraph = env.CreateExperiment(); @@ -962,8 +963,10 @@ public void TestOvaMacroWithUncalibratedLearner() public void TestTensorFlowEntryPoint() { var dataPath = GetDataPath("Train-Tiny-28x28.txt"); - using (var env = new ConsoleEnvironment(42)) + using (var env = CreateConsoleEnvironment(42)) { + env.ComponentCatalog.RegisterAssembly(typeof(TensorFlowTransform).Assembly); + var experiment = env.CreateExperiment(); var importInput = new Legacy.Data.TextLoader(dataPath); diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEarlyStoppingCriteria.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEarlyStoppingCriteria.cs index 53b77440dc..3798246fa8 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEarlyStoppingCriteria.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEarlyStoppingCriteria.cs @@ -13,8 +13,10 @@ public sealed class TestEarlyStoppingCriteria { private IEarlyStoppingCriterion CreateEarlyStoppingCriterion(string name, string args, bool lowerIsBetter) { + var env = new ConsoleEnvironment() + .AddStandardComponents(); var sub = new SubComponent(name, args); - return sub.CreateInstance(new ConsoleEnvironment(), lowerIsBetter); + return sub.CreateInstance(env, lowerIsBetter); } [Fact] diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs index 295cb4e9a1..d94192f12e 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs @@ -15,11 +15,17 @@ using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.EntryPoints.JsonUtils; using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.ImageAnalytics; using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.LightGBM; +using Microsoft.ML.Runtime.Model.Onnx; using Microsoft.ML.Runtime.PCA; +using Microsoft.ML.Runtime.PipelineInference; +using Microsoft.ML.Runtime.SymSgd; using Microsoft.ML.Runtime.TextAnalytics; +using Microsoft.ML.Transforms; using Newtonsoft.Json; using Newtonsoft.Json.Linq; using Xunit; @@ -237,37 +243,19 @@ private string GetBuildPrefix() [Fact(Skip = "Execute this test if you want to regenerate ep-list and _manifest.json")] public void RegenerateEntryPointCatalog() { + var (epListContents, jObj) = BuildManifests(); + var buildPrefix = GetBuildPrefix(); var epListFile = buildPrefix + "_ep-list.tsv"; - var manifestFile = buildPrefix + "_manifest.json"; var entryPointsSubDir = Path.Combine("..", "Common", "EntryPoints"); var catalog = ModuleCatalog.CreateInstance(Env); var epListPath = GetBaselinePath(entryPointsSubDir, epListFile); DeleteOutputPath(epListPath); - var regex = new Regex(@"\r\n?|\n", RegexOptions.Compiled); - File.WriteAllLines(epListPath, catalog.AllEntryPoints() - .Select(x => string.Join("\t", - x.Name, - regex.Replace(x.Description, ""), - x.Method.DeclaringType, - x.Method.Name, - x.InputType, - x.OutputType) - .Replace(Environment.NewLine, "")) - .OrderBy(x => x)); - + File.WriteAllLines(epListPath, epListContents); - var jObj = JsonManifestUtils.BuildAllManifests(Env, catalog); - - //clean up the description from the new line characters - if (jObj[FieldNames.TopEntryPoints] != null && jObj[FieldNames.TopEntryPoints] is JArray) - { - foreach (JToken entry in jObj[FieldNames.TopEntryPoints].Children()) - if (entry[FieldNames.Desc] != null) - entry[FieldNames.Desc] = regex.Replace(entry[FieldNames.Desc].ToString(), ""); - } + var manifestFile = buildPrefix + "_manifest.json"; var manifestPath = GetBaselinePath(entryPointsSubDir, manifestFile); DeleteOutputPath(manifestPath); @@ -280,20 +268,49 @@ public void RegenerateEntryPointCatalog() } } - [Fact] public void EntryPointCatalog() { + var (epListContents, jObj) = BuildManifests(); + var buildPrefix = GetBuildPrefix(); var epListFile = buildPrefix + "_ep-list.tsv"; - var manifestFile = buildPrefix + "_manifest.json"; var entryPointsSubDir = Path.Combine("..", "Common", "EntryPoints"); var catalog = ModuleCatalog.CreateInstance(Env); var path = DeleteOutputPath(entryPointsSubDir, epListFile); + File.WriteAllLines(path, epListContents); + + CheckEquality(entryPointsSubDir, epListFile); + + var manifestFile = buildPrefix + "_manifest.json"; + var jPath = DeleteOutputPath(entryPointsSubDir, manifestFile); + using (var file = File.OpenWrite(jPath)) + using (var writer = new StreamWriter(file)) + using (var jw = new JsonTextWriter(writer)) + { + jw.Formatting = Formatting.Indented; + jObj.WriteTo(jw); + } + + CheckEquality(entryPointsSubDir, manifestFile); + Done(); + } + + private (IEnumerable epListContents, JObject manifest) BuildManifests() + { + Env.ComponentCatalog.RegisterAssembly(typeof(LightGbmBinaryPredictor).Assembly); + Env.ComponentCatalog.RegisterAssembly(typeof(TensorFlowTransform).Assembly); + Env.ComponentCatalog.RegisterAssembly(typeof(ImageLoaderTransform).Assembly); + Env.ComponentCatalog.RegisterAssembly(typeof(SymSgdClassificationTrainer).Assembly); + Env.ComponentCatalog.RegisterAssembly(typeof(AutoInference).Assembly); + Env.ComponentCatalog.RegisterAssembly(typeof(SaveOnnxCommand).Assembly); + + var catalog = ModuleCatalog.CreateInstance(Env); + var regex = new Regex(@"\r\n?|\n", RegexOptions.Compiled); - File.WriteAllLines(path, catalog.AllEntryPoints() + var epListContents = catalog.AllEntryPoints() .Select(x => string.Join("\t", x.Name, regex.Replace(x.Description, ""), @@ -302,39 +319,27 @@ public void EntryPointCatalog() x.InputType, x.OutputType) .Replace(Environment.NewLine, "")) - .OrderBy(x => x)); - - CheckEquality(entryPointsSubDir, epListFile); + .OrderBy(x => x); - var jObj = JsonManifestUtils.BuildAllManifests(Env, catalog); + var manifest = JsonManifestUtils.BuildAllManifests(Env, catalog); //clean up the description from the new line characters - if (jObj[FieldNames.TopEntryPoints] != null && jObj[FieldNames.TopEntryPoints] is JArray) + if (manifest[FieldNames.TopEntryPoints] != null && manifest[FieldNames.TopEntryPoints] is JArray) { - foreach (JToken entry in jObj[FieldNames.TopEntryPoints].Children()) + foreach (JToken entry in manifest[FieldNames.TopEntryPoints].Children()) if (entry[FieldNames.Desc] != null) entry[FieldNames.Desc] = regex.Replace(entry[FieldNames.Desc].ToString(), ""); } - var jPath = DeleteOutputPath(entryPointsSubDir, manifestFile); - using (var file = File.OpenWrite(jPath)) - using (var writer = new StreamWriter(file)) - using (var jw = new JsonTextWriter(writer)) - { - jw.Formatting = Formatting.Indented; - jObj.WriteTo(jw); - } - - CheckEquality(entryPointsSubDir, manifestFile); - Done(); + return (epListContents, manifest); } [Fact] public void EntryPointInputBuilderOptionals() { - var catelog = ModuleCatalog.CreateInstance(Env); + var catalog = ModuleCatalog.CreateInstance(Env); - InputBuilder ib1 = new InputBuilder(Env, typeof(LogisticRegression.Arguments), catelog); + InputBuilder ib1 = new InputBuilder(Env, typeof(LogisticRegression.Arguments), catalog); // Ensure that InputBuilder unwraps the Optional correctly. var weightType = ib1.GetFieldTypeOrNull("WeightColumn"); Assert.True(weightType.Equals(typeof(string))); @@ -699,7 +704,7 @@ public void EntryPointCalibrate() calibratedLrModel = Calibrate.Pav(Env, input).PredictorModel; // This tests that the SchemaBindableCalibratedPredictor doesn't get confused if its sub-predictor is already calibrated. - var fastForest = new FastForestClassification(Env, new FastForestClassification.Arguments()); + var fastForest = new FastForestClassification(Env, "Label", "Features"); var rmd = new RoleMappedData(splitOutput.TrainData[0], "Label", "Features"); var ffModel = new PredictorModel(Env, rmd, splitOutput.TrainData[0], fastForest.Train(rmd)); var calibratedFfModel = Calibrate.Platt(Env, @@ -825,9 +830,9 @@ public void EntryPointPipelineEnsemble() Assert.True(hasScoreCol, "Data scored with binary ensemble does not have a score column"); var type = binaryScored.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex); Assert.True(type != null && type.IsText, "Binary ensemble scored data does not have correct type of metadata."); - var kind = default(DvText); + var kind = default(ReadOnlyMemory); binaryScored.Schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex, ref kind); - Assert.True(kind.EqualsStr(MetadataUtils.Const.ScoreColumnKind.BinaryClassification), + Assert.True(ReadOnlyMemoryUtils.EqualsStr(MetadataUtils.Const.ScoreColumnKind.BinaryClassification, kind), $"Binary ensemble scored data column type should be '{MetadataUtils.Const.ScoreColumnKind.BinaryClassification}', but is instead '{kind}'"); hasScoreCol = regressionScored.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out scoreIndex); @@ -835,7 +840,7 @@ public void EntryPointPipelineEnsemble() type = regressionScored.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex); Assert.True(type != null && type.IsText, "Regression ensemble scored data does not have correct type of metadata."); regressionScored.Schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex, ref kind); - Assert.True(kind.EqualsStr(MetadataUtils.Const.ScoreColumnKind.Regression), + Assert.True(ReadOnlyMemoryUtils.EqualsStr(MetadataUtils.Const.ScoreColumnKind.Regression, kind), $"Regression ensemble scored data column type should be '{MetadataUtils.Const.ScoreColumnKind.Regression}', but is instead '{kind}'"); hasScoreCol = anomalyScored.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out scoreIndex); @@ -843,7 +848,7 @@ public void EntryPointPipelineEnsemble() type = anomalyScored.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex); Assert.True(type != null && type.IsText, "Anomaly detection ensemble scored data does not have correct type of metadata."); anomalyScored.Schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex, ref kind); - Assert.True(kind.EqualsStr(MetadataUtils.Const.ScoreColumnKind.AnomalyDetection), + Assert.True(ReadOnlyMemoryUtils.EqualsStr(MetadataUtils.Const.ScoreColumnKind.AnomalyDetection, kind), $"Anomaly detection ensemble scored data column type should be '{MetadataUtils.Const.ScoreColumnKind.AnomalyDetection}', but is instead '{kind}'"); var modelPath = DeleteOutputPath("SavePipe", "PipelineEnsembleModel.zip"); @@ -975,13 +980,13 @@ public void EntryPointPipelineEnsembleText() InputFile = inputFile }).Data; - ValueMapper labelToBinary = - (ref DvText src, ref DvBool dst) => + ValueMapper, bool> labelToBinary = + (ref ReadOnlyMemory src, ref bool dst) => { - if (src.EqualsStr("Sport")) - dst = DvBool.True; + if (ReadOnlyMemoryUtils.EqualsStr("Sport", src)) + dst = true; else - dst = DvBool.False; + dst = false; }; dataView = LambdaColumnMapper.Create(Env, "TextToBinaryLabel", dataView, "Label", "Label", TextType.Instance, BoolType.Instance, labelToBinary); @@ -1535,16 +1540,16 @@ public void EntryPointTextToKeyToText() { using (var cursor = loader.GetRowCursor(col => true)) { - DvText cat = default(DvText); - DvText catValue = default(DvText); + ReadOnlyMemory cat = default; + ReadOnlyMemory catValue = default; uint catKey = 0; bool success = loader.Schema.TryGetColumnIndex("Cat", out int catCol); Assert.True(success); - var catGetter = cursor.GetGetter(catCol); + var catGetter = cursor.GetGetter>(catCol); success = loader.Schema.TryGetColumnIndex("CatValue", out int catValueCol); Assert.True(success); - var catValueGetter = cursor.GetGetter(catValueCol); + var catValueGetter = cursor.GetGetter>(catValueCol); success = loader.Schema.TryGetColumnIndex("Key", out int keyCol); Assert.True(success); var keyGetter = cursor.GetGetter(keyCol); @@ -1698,13 +1703,13 @@ public void EntryPointEvaluateMultiClass() [Fact] public void EntryPointEvaluateRegression() { - var dataPath = GetDataPath(TestDatasets.winequalitymacro.trainFilename); + var dataPath = GetDataPath(TestDatasets.generatedRegressionDatasetmacro.trainFilename); var warningsPath = DeleteOutputPath("warnings.idv"); var overallMetricsPath = DeleteOutputPath("overall.idv"); var instanceMetricsPath = DeleteOutputPath("instance.idv"); RunTrainScoreEvaluate("Trainers.StochasticDualCoordinateAscentRegressor", "Models.RegressionEvaluator", - dataPath, warningsPath, overallMetricsPath, instanceMetricsPath, loader: TestDatasets.winequalitymacro.loaderSettings); + dataPath, warningsPath, overallMetricsPath, instanceMetricsPath, loader: TestDatasets.generatedRegressionDatasetmacro.loaderSettings); using (var loader = new BinaryLoader(Env, new BinaryLoader.Arguments(), warningsPath)) Assert.Equal(0, CountRows(loader)); @@ -1713,7 +1718,7 @@ public void EntryPointEvaluateRegression() Assert.Equal(1, CountRows(loader)); using (var loader = new BinaryLoader(Env, new BinaryLoader.Arguments(), instanceMetricsPath)) - Assert.Equal(975, CountRows(loader)); + Assert.Equal(103, CountRows(loader)); } [Fact] @@ -1794,12 +1799,14 @@ public void EntryPointEvaluateRanking() [Fact] public void EntryPointLightGbmBinary() { + Env.ComponentCatalog.RegisterAssembly(typeof(LightGbmBinaryPredictor).Assembly); TestEntryPointRoutine("breast-cancer.txt", "Trainers.LightGbmBinaryClassifier"); } [Fact] public void EntryPointLightGbmMultiClass() { + Env.ComponentCatalog.RegisterAssembly(typeof(LightGbmBinaryPredictor).Assembly); TestEntryPointRoutine(GetDataPath(@"iris.txt"), "Trainers.LightGbmClassifier"); } @@ -1818,7 +1825,7 @@ public void EntryPointSDCAMultiClass() [Fact()] public void EntryPointSDCARegression() { - TestEntryPointRoutine(TestDatasets.winequalitymacro.trainFilename, "Trainers.StochasticDualCoordinateAscentRegressor", loader: TestDatasets.winequalitymacro.loaderSettings); + TestEntryPointRoutine(TestDatasets.generatedRegressionDatasetmacro.trainFilename, "Trainers.StochasticDualCoordinateAscentRegressor", loader: TestDatasets.generatedRegressionDatasetmacro.loaderSettings); } [Fact] @@ -1925,7 +1932,7 @@ public void EntryPointClassificationEnsemble() [Fact] public void EntryPointRegressionEnsemble() { - TestEntryPointRoutine(TestDatasets.winequalitymacro.trainFilename, "Trainers.EnsembleRegression", loader: TestDatasets.winequalitymacro.loaderSettings); + TestEntryPointRoutine(TestDatasets.generatedRegressionDatasetmacro.trainFilename, "Trainers.EnsembleRegression", loader: TestDatasets.generatedRegressionDatasetmacro.loaderSettings); } [Fact] @@ -1943,7 +1950,7 @@ public void EntryPointHogwildSGD() [Fact] public void EntryPointPoissonRegression() { - TestEntryPointRoutine(TestDatasets.winequalitymacro.trainFilename, "Trainers.PoissonRegressor", loader: TestDatasets.winequalitymacro.loaderSettings); + TestEntryPointRoutine(TestDatasets.generatedRegressionDatasetmacro.trainFilename, "Trainers.PoissonRegressor", loader: TestDatasets.generatedRegressionDatasetmacro.loaderSettings); } [Fact] @@ -1968,7 +1975,6 @@ public void EntryPointConvert() { "Transforms.ColumnTypeConverter", "Transforms.ColumnTypeConverter", - "Transforms.ColumnTypeConverter", }, new[] { @@ -1984,7 +1990,7 @@ public void EntryPointConvert() { 'Name': 'Feat', 'Source': 'FT', - 'Type': 'I1' + 'Type': 'R4' }, { 'Name': 'Key1', @@ -1994,18 +2000,11 @@ public void EntryPointConvert() ]", @"'Column': [ { - 'Name': 'Ints', + 'Name': 'Doubles', 'Source': 'Feat' } ], - 'Type': 'I4'", - @"'Column': [ - { - 'Name': 'Floats', - 'Source': 'Ints' - } - ], - 'Type': 'Num'", + 'Type': 'R8'", }); } @@ -3606,18 +3605,18 @@ public void EntryPointPrepareLabelConvertPredictedLabel() { using (var cursor = loader.GetRowCursor(col => true)) { - DvText predictedLabel = default(DvText); + ReadOnlyMemory predictedLabel = default; var success = loader.Schema.TryGetColumnIndex("PredictedLabel", out int predictedLabelCol); Assert.True(success); - var predictedLabelGetter = cursor.GetGetter(predictedLabelCol); + var predictedLabelGetter = cursor.GetGetter>(predictedLabelCol); while (cursor.MoveNext()) { predictedLabelGetter(ref predictedLabel); - Assert.True(predictedLabel.EqualsStr("Iris-setosa") - || predictedLabel.EqualsStr("Iris-versicolor") - || predictedLabel.EqualsStr("Iris-virginica")); + Assert.True(ReadOnlyMemoryUtils.EqualsStr("Iris-setosa", predictedLabel) + || ReadOnlyMemoryUtils.EqualsStr("Iris-versicolor", predictedLabel) + || ReadOnlyMemoryUtils.EqualsStr("Iris-virginica", predictedLabel)); } } } @@ -3736,6 +3735,8 @@ public void EntryPointWordEmbeddings() [Fact] public void EntryPointTensorFlowTransform() { + Env.ComponentCatalog.RegisterAssembly(typeof(TensorFlowTransform).Assembly); + TestEntryPointPipelineRoutine(GetDataPath("Train-Tiny-28x28.txt"), "col=Label:R4:0 col=Placeholder:R4:1-784", new[] { "Transforms.TensorFlowScorer" }, new[] diff --git a/test/Microsoft.ML.FSharp.Tests/SmokeTests.fs b/test/Microsoft.ML.FSharp.Tests/SmokeTests.fs index b0921cc2e3..f4688c8016 100644 --- a/test/Microsoft.ML.FSharp.Tests/SmokeTests.fs +++ b/test/Microsoft.ML.FSharp.Tests/SmokeTests.fs @@ -128,6 +128,7 @@ module SmokeTest1 = Assert.Equal(predictionResults, [ false; true; true ]) module SmokeTest2 = + open System [] type SentimentData = @@ -199,7 +200,7 @@ module SmokeTest3 = type SentimentData() = [] - member val SentimentText = "" with get, set + member val SentimentText = "".AsMemory() with get, set [] member val Sentiment = 0.0 with get, set @@ -253,9 +254,9 @@ module SmokeTest3 = let model = pipeline.Train() let predictions = - [ SentimentData(SentimentText = "This is a gross exaggeration. Nobody is setting a kangaroo court. There was a simple addition.") - SentimentData(SentimentText = "Sort of ok") - SentimentData(SentimentText = "Joe versus the Volcano Coffee Company is a great film.") ] + [ SentimentData(SentimentText = "This is a gross exaggeration. Nobody is setting a kangaroo court. There was a simple addition.".AsMemory()) + SentimentData(SentimentText = "Sort of ok".AsMemory()) + SentimentData(SentimentText = "Joe versus the Volcano Coffee Company is a great film.".AsMemory()) ] |> model.Predict let predictionResults = [ for p in predictions -> p.Sentiment ] diff --git a/test/Microsoft.ML.InferenceTesting/GenerateSweepCandidatesCommand.cs b/test/Microsoft.ML.InferenceTesting/GenerateSweepCandidatesCommand.cs index b964d4dae6..534ababbc7 100644 --- a/test/Microsoft.ML.InferenceTesting/GenerateSweepCandidatesCommand.cs +++ b/test/Microsoft.ML.InferenceTesting/GenerateSweepCandidatesCommand.cs @@ -85,7 +85,7 @@ public GenerateSweepCandidatesCommand(IHostEnvironment env, Arguments args) if (!string.IsNullOrWhiteSpace(args.Sweeper)) { - var info = ComponentCatalog.GetLoadableClassInfo(args.Sweeper); + var info = env.ComponentCatalog.GetLoadableClassInfo(args.Sweeper); _host.CheckUserArg(info?.SignatureTypes[0] == typeof(SignatureSweeper), nameof(args.Sweeper), "Please specify a valid sweeper."); _sweeper = args.Sweeper; @@ -95,7 +95,7 @@ public GenerateSweepCandidatesCommand(IHostEnvironment env, Arguments args) if (!string.IsNullOrWhiteSpace(args.Mode)) { - var info = ComponentCatalog.GetLoadableClassInfo(args.Mode); + var info = env.ComponentCatalog.GetLoadableClassInfo(args.Mode); _host.CheckUserArg(info?.Type == typeof(TrainCommand) || info?.Type == typeof(TrainTestCommand) || info?.Type == typeof(CrossValidationCommand), nameof(args.Mode), "Invalid mode."); diff --git a/test/Microsoft.ML.Predictor.Tests/CmdLine/CmdLineReverseTest.cs b/test/Microsoft.ML.Predictor.Tests/CmdLine/CmdLineReverseTest.cs index 2369136f95..6d95829454 100644 --- a/test/Microsoft.ML.Predictor.Tests/CmdLine/CmdLineReverseTest.cs +++ b/test/Microsoft.ML.Predictor.Tests/CmdLine/CmdLineReverseTest.cs @@ -113,9 +113,9 @@ private class SimpleArg /// ToString is overrided by CmdParser.GetSettings which is of primary for this test /// /// - public string ToString(IExceptionContext ectx) + public string ToString(IHostEnvironment env) { - return CmdParser.GetSettings(ectx, this, new SimpleArg(), SettingsFlags.None); + return CmdParser.GetSettings(env, this, new SimpleArg(), SettingsFlags.None); } public override bool Equals(object obj) diff --git a/test/Microsoft.ML.Predictor.Tests/Test-API.cs b/test/Microsoft.ML.Predictor.Tests/Test-API.cs index 40efdf25df..acd01cd151 100644 --- a/test/Microsoft.ML.Predictor.Tests/Test-API.cs +++ b/test/Microsoft.ML.Predictor.Tests/Test-API.cs @@ -317,7 +317,7 @@ public void FactoryExampleTest() List originalOutputs = new List(); List originalProbabilities = new List(); - var env = new TlcEnvironment(SysRandom.Wrap(RunExperiments.GetRandom(cmd))); + var env = new LocalEnvironment(SysRandom.Wrap(RunExperiments.GetRandom(cmd))); Instances instances = RunExperiments.CreateTestData(cmd, testDataFilename, dataModel, null, env); foreach (Instance instance in instances) { diff --git a/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs b/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs index 1b991300ea..66d33fe4e8 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs @@ -26,7 +26,7 @@ public TestAutoInference(ITestOutputHelper helper) [TestCategory("EntryPoints")] public void TestLearn() { - using (var env = new ConsoleEnvironment()) + using (var env = CreateConsoleEnvironment()) { string pathData = GetDataPath("adult.train"); string pathDataTest = GetDataPath("adult.test"); @@ -96,7 +96,7 @@ public void TestTextDatasetLearn() [Fact] public void TestPipelineNodeCloning() { - using (var env = new ConsoleEnvironment()) + using (var env = CreateConsoleEnvironment()) { var lr1 = RecipeInference .AllowedLearners(env, MacroUtils.TrainerKinds.SignatureBinaryClassifierTrainer) diff --git a/test/Microsoft.ML.Predictor.Tests/TestPipelineSweeper.cs b/test/Microsoft.ML.Predictor.Tests/TestPipelineSweeper.cs index 5f65ca4c28..cd86e83414 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPipelineSweeper.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPipelineSweeper.cs @@ -22,6 +22,12 @@ public TestPipelineSweeper(ITestOutputHelper helper) { } + protected override void InitializeCore() + { + base.InitializeCore(); + Env.ComponentCatalog.RegisterAssembly(typeof(AutoInference).Assembly); + } + [Fact] public void PipelineSweeperBasic() { diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index b3f9938dfd..fdf2f50168 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -7,7 +7,6 @@ using System; using System.Collections.Generic; using System.IO; -using Microsoft.ML.Runtime.CommandLine; namespace Microsoft.ML.Runtime.RunTests { @@ -15,6 +14,8 @@ namespace Microsoft.ML.Runtime.RunTests using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.FastTree; using Microsoft.ML.Runtime.FastTree.Internal; + using Microsoft.ML.Runtime.LightGBM; + using Microsoft.ML.Runtime.SymSgd; using Microsoft.ML.TestFramework; using System.Linq; using System.Runtime.InteropServices; @@ -27,6 +28,20 @@ namespace Microsoft.ML.Runtime.RunTests /// public sealed partial class TestPredictors : BaseTestPredictors { + protected override void InitializeCore() + { + base.InitializeCore(); + InitializeEnvironment(Env); + } + + protected override void InitializeEnvironment(IHostEnvironment environment) + { + base.InitializeEnvironment(environment); + + environment.ComponentCatalog.RegisterAssembly(typeof(LightGbmBinaryPredictor).Assembly); + environment.ComponentCatalog.RegisterAssembly(typeof(SymSgdClassificationTrainer).Assembly); + } + /// /// Get a list of datasets for binary classifier base test. /// @@ -97,6 +112,30 @@ public void BinaryClassifierPerceptronTest() Done(); } + [Fact] + [TestCategory("Binary")] + [TestCategory("SimpleLearners")] + public void BinaryPriorTest() + { + var predictors = new[] { + TestLearners.binaryPrior}; + var datasets = GetDatasetsForBinaryClassifierBaseTest(); + RunAllTests(predictors, datasets); + Done(); + } + + [Fact] + [TestCategory("Binary")] + [TestCategory("SimpleLearners")] + public void BinaryRandomTest() + { + var predictors = new[] { + TestLearners.binaryRandom}; + var datasets = GetDatasetsForBinaryClassifierBaseTest(); + RunAllTests(predictors, datasets, extraSettings: new[] { "n=1" }); + Done(); + } + /// ///A test for binary classifiers /// @@ -495,7 +534,7 @@ public void MultiClassifierLightGBMKeyLabelU404Test() public void RegressorLightGBMTest() { var regPredictors = new[] { TestLearners.LightGBMReg }; - var regDatasets = new[] { TestDatasets.winequality }; + var regDatasets = new[] { TestDatasets.generatedRegressionDataset }; RunAllTests(regPredictors, regDatasets); Done(); } @@ -509,7 +548,7 @@ public void RegressorLightGBMTest() public void RegressorLightGBMMAETest() { var regPredictors = new[] { TestLearners.LightGBMRegMae }; - var regDatasets = new[] { TestDatasets.winequality }; + var regDatasets = new[] { TestDatasets.generatedRegressionDataset }; RunAllTests(regPredictors, regDatasets, extraTag: "MAE"); Done(); } @@ -523,7 +562,7 @@ public void RegressorLightGBMMAETest() public void RegressorLightGBMRMSETest() { var regPredictors = new[] { TestLearners.LightGBMRegRmse }; - var regDatasets = new[] { TestDatasets.winequality }; + var regDatasets = new[] { TestDatasets.generatedRegressionDataset }; RunAllTests(regPredictors, regDatasets, extraTag: "RMSE"); Done(); } @@ -631,23 +670,23 @@ private void CombineAndTestTreeEnsembles(IDataView idv, IPredictorModel[] fastTr { var scoreGetter = curs.GetGetter(scoreCol); var probGetter = curs.GetGetter(probCol); - var predGetter = curs.GetGetter(predCol); + var predGetter = curs.GetGetter(predCol); var scoreGetters = new ValueGetter[3]; var probGetters = new ValueGetter[3]; - var predGetters = new ValueGetter[3]; + var predGetters = new ValueGetter[3]; for (int i = 0; i < 3; i++) { scoreGetters[i] = cursors[i].GetGetter(scoreColArray[i]); probGetters[i] = cursors[i].GetGetter(probColArray[i]); - predGetters[i] = cursors[i].GetGetter(predColArray[i]); + predGetters[i] = cursors[i].GetGetter(predColArray[i]); } float score = 0; float prob = 0; - var pred = default(DvBool); + bool pred = default; var scores = new float[3]; var probs = new float[3]; - var preds = new DvBool[3]; + var preds = new bool[3]; while (curs.MoveNext()) { scoreGetter(ref score); @@ -662,7 +701,7 @@ private void CombineAndTestTreeEnsembles(IDataView idv, IPredictorModel[] fastTr } Assert.Equal(score, 0.4 * scores.Sum() / 3, 5); Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6); - Assert.True(pred.IsTrue == score > 0); + Assert.True(pred == score > 0); } } } @@ -898,7 +937,7 @@ public void RegressorOlsTest() [TestCategory("Regressor")] public void RegressorOlsTestOne() { - Run_TrainTest(TestLearners.Ols, TestDatasets.winequality); + Run_TrainTest(TestLearners.Ols, TestDatasets.generatedRegressionDataset); Done(); } @@ -911,7 +950,7 @@ public void RegressorOlsTestOne() public void RegressorSdcaTest() { var regressionPredictors = new[] { TestLearners.Sdcar, TestLearners.SdcarNorm, TestLearners.SdcarReg }; - RunAllTests(regressionPredictors, new[] { TestDatasets.winequality }); + RunAllTests(regressionPredictors, new[] { TestDatasets.generatedRegressionDataset }); Done(); } @@ -1532,7 +1571,7 @@ public void OneClassSvmLibsvmWrapperDenseTest() [TestCategory("Anomaly")] public void CompareSvmPredictorResultsToLibSvm() { - using (var env = new TlcEnvironment(1, conc: 1)) + using (var env = new LocalEnvironment(1, conc: 1)) { IDataView trainView = new TextLoader(env, new TextLoader.Arguments(), new MultiFileSource(GetDataPath(TestDatasets.mnistOneClass.trainFilename))); trainView = diff --git a/test/Microsoft.ML.Predictor.Tests/TestTransposer.cs b/test/Microsoft.ML.Predictor.Tests/TestTransposer.cs index b5b53677ab..4ce87f070b 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestTransposer.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestTransposer.cs @@ -149,19 +149,19 @@ public void TransposerTest() ArrayDataViewBuilder builder = new ArrayDataViewBuilder(Env); // A is to check the splitting of a sparse-ish column. - var dataA = GenerateHelper(rowCount, 0.1, rgen, () => (DvInt4)rgen.Next(), 50, 5, 10, 15); - dataA[rowCount / 2] = new VBuffer(50, 0, null, null); // Coverage for the null vbuffer case. + var dataA = GenerateHelper(rowCount, 0.1, rgen, () => (int)rgen.Next(), 50, 5, 10, 15); + dataA[rowCount / 2] = new VBuffer(50, 0, null, null); // Coverage for the null vbuffer case. builder.AddColumn("A", NumberType.I4, dataA); // B is to check the splitting of a dense-ish column. builder.AddColumn("B", NumberType.R8, GenerateHelper(rowCount, 0.8, rgen, rgen.NextDouble, 50, 0, 25, 49)); // C is to just have some column we do nothing with. - builder.AddColumn("C", NumberType.I2, GenerateHelper(rowCount, 0.1, rgen, () => (DvInt2)1, 30, 3, 10, 24)); + builder.AddColumn("C", NumberType.I2, GenerateHelper(rowCount, 0.1, rgen, () => (short)1, 30, 3, 10, 24)); // D is to check some column we don't have to split because it's sufficiently small. builder.AddColumn("D", NumberType.R8, GenerateHelper(rowCount, 0.1, rgen, rgen.NextDouble, 3, 1)); // E is to check a sparse scalar column. builder.AddColumn("E", NumberType.U4, GenerateHelper(rowCount, 0.1, rgen, () => (uint)rgen.Next(int.MinValue, int.MaxValue))); // F is to check a dense-ish scalar column. - builder.AddColumn("F", NumberType.I4, GenerateHelper(rowCount, 0.8, rgen, () => (DvInt4)rgen.Next())); + builder.AddColumn("F", NumberType.I4, GenerateHelper(rowCount, 0.8, rgen, () => rgen.Next())); IDataView view = builder.GetDataView(); @@ -182,11 +182,11 @@ public void TransposerTest() } // Check the contents Assert.Null(trans.TransposeSchema.GetSlotType(2)); // C check to see that it's not transposable. - TransposeCheckHelper(view, 0, trans); // A check. + TransposeCheckHelper(view, 0, trans); // A check. TransposeCheckHelper(view, 1, trans); // B check. TransposeCheckHelper(view, 3, trans); // D check. TransposeCheckHelper(view, 4, trans); // E check. - TransposeCheckHelper(view, 5, trans); // F check. + TransposeCheckHelper(view, 5, trans); // F check. } // Force save. Recheck columns that would have previously been passthrough columns. @@ -201,7 +201,7 @@ public void TransposerTest() Assert.Null(trans.TransposeSchema.GetSlotType(2)); TransposeCheckHelper(view, 3, trans); // D check. TransposeCheckHelper(view, 4, trans); // E check. - TransposeCheckHelper(view, 5, trans); // F check. + TransposeCheckHelper(view, 5, trans); // F check. } } @@ -214,19 +214,19 @@ public void TransposerSaverLoaderTest() ArrayDataViewBuilder builder = new ArrayDataViewBuilder(Env); // A is to check the splitting of a sparse-ish column. - var dataA = GenerateHelper(rowCount, 0.1, rgen, () => (DvInt4)rgen.Next(), 50, 5, 10, 15); - dataA[rowCount / 2] = new VBuffer(50, 0, null, null); // Coverage for the null vbuffer case. + var dataA = GenerateHelper(rowCount, 0.1, rgen, () => (int)rgen.Next(), 50, 5, 10, 15); + dataA[rowCount / 2] = new VBuffer(50, 0, null, null); // Coverage for the null vbuffer case. builder.AddColumn("A", NumberType.I4, dataA); // B is to check the splitting of a dense-ish column. builder.AddColumn("B", NumberType.R8, GenerateHelper(rowCount, 0.8, rgen, rgen.NextDouble, 50, 0, 25, 49)); // C is to just have some column we do nothing with. - builder.AddColumn("C", NumberType.I2, GenerateHelper(rowCount, 0.1, rgen, () => (DvInt2)1, 30, 3, 10, 24)); + builder.AddColumn("C", NumberType.I2, GenerateHelper(rowCount, 0.1, rgen, () => (short)1, 30, 3, 10, 24)); // D is to check some column we don't have to split because it's sufficiently small. builder.AddColumn("D", NumberType.R8, GenerateHelper(rowCount, 0.1, rgen, rgen.NextDouble, 3, 1)); // E is to check a sparse scalar column. builder.AddColumn("E", NumberType.U4, GenerateHelper(rowCount, 0.1, rgen, () => (uint)rgen.Next(int.MinValue, int.MaxValue))); // F is to check a dense-ish scalar column. - builder.AddColumn("F", NumberType.I4, GenerateHelper(rowCount, 0.8, rgen, () => (DvInt4)rgen.Next())); + builder.AddColumn("F", NumberType.I4, GenerateHelper(rowCount, 0.8, rgen, () => (int)rgen.Next())); IDataView view = builder.GetDataView(); @@ -241,12 +241,12 @@ public void TransposerSaverLoaderTest() // First check whether this as an IDataView yields the same values. CheckSameValues(view, loader); - TransposeCheckHelper(view, 0, loader); // A + TransposeCheckHelper(view, 0, loader); // A TransposeCheckHelper(view, 1, loader); // B - TransposeCheckHelper(view, 2, loader); // C + TransposeCheckHelper(view, 2, loader); // C TransposeCheckHelper(view, 3, loader); // D TransposeCheckHelper(view, 4, loader); // E - TransposeCheckHelper(view, 5, loader); // F + TransposeCheckHelper(view, 5, loader); // F Done(); } diff --git a/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs b/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs index 0095d2f4cd..8d7fb18cd4 100644 --- a/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs +++ b/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs @@ -1,13 +1,13 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. using Microsoft.ML.Data.StaticPipe; -using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.TestFramework; +using Microsoft.ML.Transforms.Text; using System; using System.Collections.Generic; using System.Collections.Immutable; @@ -80,12 +80,11 @@ public void SimpleTextLoaderCopyColumnsTest() // Next actually inspect the data. using (var cursor = textData.GetRowCursor(c => true)) { - var labelGetter = cursor.GetGetter(labelIdx); - var textGetter = cursor.GetGetter(textIdx); + var textGetter = cursor.GetGetter>(textIdx); var numericFeaturesGetter = cursor.GetGetter>(numericFeaturesIdx); - - DvBool labelVal = default; - DvText textVal = default; + ReadOnlyMemory textVal = default; + var labelGetter = cursor.GetGetter(labelIdx); + bool labelVal = default; VBuffer numVal = default; void CheckValuesSame(bool bl, string tx, float v0, float v1, float v2) @@ -93,9 +92,8 @@ void CheckValuesSame(bool bl, string tx, float v0, float v1, float v2) labelGetter(ref labelVal); textGetter(ref textVal); numericFeaturesGetter(ref numVal); - - Assert.Equal((DvBool)bl, labelVal); - Assert.Equal(new DvText(tx), textVal); + Assert.True(tx.AsSpan().SequenceEqual(textVal.Span)); + Assert.Equal((bool)bl, labelVal); Assert.Equal(3, numVal.Length); Assert.Equal(v0, numVal.GetItemOrDefault(0)); Assert.Equal(v1, numVal.GetItemOrDefault(1)); @@ -159,13 +157,13 @@ public void AssertStaticKeys() var counted = new MetaCounted(); // We'll test a few things here. First, the case where the key-value metadata is text. - var metaValues1 = new VBuffer(3, new[] { new DvText("a"), new DvText("b"), new DvText("c") }); + var metaValues1 = new VBuffer>(3, new[] { "a".AsMemory(), "b".AsMemory(), "c".AsMemory() }); var meta1 = RowColumnUtils.GetColumn(MetadataUtils.Kinds.KeyValues, new VectorType(TextType.Instance, 3), ref metaValues1); uint value1 = 2; var col1 = RowColumnUtils.GetColumn("stay", new KeyType(DataKind.U4, 0, 3), ref value1, RowColumnUtils.GetRow(counted, meta1)); // Next the case where those values are ints. - var metaValues2 = new VBuffer(3, new DvInt4[] { 1, 2, 3, 4 }); + var metaValues2 = new VBuffer(3, new int[] { 1, 2, 3, 4 }); var meta2 = RowColumnUtils.GetColumn(MetadataUtils.Kinds.KeyValues, new VectorType(NumberType.I4, 4), ref metaValues2); var value2 = new VBuffer(2, 0, null, null); var col2 = RowColumnUtils.GetColumn("awhile", new VectorType(new KeyType(DataKind.U1, 2, 4), 2), ref value2, RowColumnUtils.GetRow(counted, meta2)); @@ -263,7 +261,7 @@ public void AssertStaticKeys() public void Normalizer() { var env = new ConsoleEnvironment(seed: 0); - var dataPath = GetDataPath("external", "winequality-white.csv"); + var dataPath = GetDataPath("generated_regression_dataset.csv"); var dataSource = new MultiFileSource(dataPath); var reader = TextLoader.CreateReader(env, @@ -288,7 +286,7 @@ public void Normalizer() public void NormalizerWithOnFit() { var env = new ConsoleEnvironment(seed: 0); - var dataPath = GetDataPath("external", "winequality-white.csv"); + var dataPath = GetDataPath("generated_regression_dataset.csv"); var dataSource = new MultiFileSource(dataPath); var reader = TextLoader.CreateReader(env, @@ -403,5 +401,35 @@ public void ConcatWith() Assert.Equal(NumberType.Float, types[2].ItemType); Assert.Equal(NumberType.Float, types[3].ItemType); } + + [Fact] + public void Tokenize() + { + var env = new ConsoleEnvironment(seed: 0); + var dataPath = GetDataPath("wikipedia-detox-250-line-data.tsv"); + var reader = TextLoader.CreateReader(env, ctx => ( + label: ctx.LoadBool(0), + text: ctx.LoadText(1)), hasHeader: true); + var dataSource = new MultiFileSource(dataPath); + var data = reader.Read(dataSource); + + var est = data.MakeNewEstimator() + .Append(r => ( + r.label, + tokens: r.text.TokenizeText(), + chars: r.text.TokenizeIntoCharacters())); + + var tdata = est.Fit(data).Transform(data); + var schema = tdata.AsDynamic.Schema; + + Assert.True(schema.TryGetColumnIndex("tokens", out int tokensCol)); + var type = schema.GetColumnType(tokensCol); + Assert.True(type.IsVector && !type.IsKnownSizeVector && type.ItemType.IsText); + + Assert.True(schema.TryGetColumnIndex("chars", out int charsCol)); + type = schema.GetColumnType(charsCol); + Assert.True(type.IsVector && !type.IsKnownSizeVector && type.ItemType.IsKey); + Assert.True(type.ItemType.AsKey.RawKind == DataKind.U2); + } } } diff --git a/test/Microsoft.ML.StaticPipelineTesting/Training.cs b/test/Microsoft.ML.StaticPipelineTesting/Training.cs index 3385ad3e78..1f243e1381 100644 --- a/test/Microsoft.ML.StaticPipelineTesting/Training.cs +++ b/test/Microsoft.ML.StaticPipelineTesting/Training.cs @@ -23,7 +23,7 @@ public Training(ITestOutputHelper output) : base(output) public void SdcaRegression() { var env = new ConsoleEnvironment(seed: 0); - var dataPath = GetDataPath("external", "winequality-white.csv"); + var dataPath = GetDataPath("generated_regression_dataset.csv"); var dataSource = new MultiFileSource(dataPath); var reader = TextLoader.CreateReader(env, @@ -63,7 +63,7 @@ public void SdcaRegression() public void SdcaRegressionNameCollision() { var env = new ConsoleEnvironment(seed: 0); - var dataPath = GetDataPath("external", "winequality-white.csv"); + var dataPath = GetDataPath("generated_regression_dataset.csv"); var dataSource = new MultiFileSource(dataPath); // Here we introduce another column called "Score" to collide with the name of the default output. Heh heh heh... diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs index 99fb602490..e4ce5fac50 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs @@ -108,7 +108,8 @@ public void Init() string logPath = Path.Combine(logDir, FullTestName + LogSuffix); LogWriter = OpenWriter(logPath); _passed = true; - Env = new ConsoleEnvironment(42, outWriter: LogWriter, errWriter: LogWriter); + Env = new ConsoleEnvironment(42, outWriter: LogWriter, errWriter: LogWriter) + .AddStandardComponents(); InitializeCore(); } @@ -133,7 +134,7 @@ protected virtual void InitializeCore() } // This method is used by subclass to dispose of disposable objects - // such as TlcEnvironment. + // such as LocalEnvironment. // It is called as a first step in test clean up. protected virtual void CleanupCore() { diff --git a/test/Microsoft.ML.TestFramework/BaseTestClass.cs b/test/Microsoft.ML.TestFramework/BaseTestClass.cs index b5d7a23db3..9c5fceff16 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestClass.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestClass.cs @@ -2,7 +2,12 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.FastTree; using Microsoft.ML.Runtime.Internal.Internallearn.Test; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Transforms; using System.Globalization; using System.IO; using System.Threading; @@ -84,5 +89,17 @@ protected string DeleteOutputPath(string name) File.Delete(path); return path; } + + protected ConsoleEnvironment CreateConsoleEnvironment(int? seed = null, bool verbose = false) + { + return new ConsoleEnvironment(seed, verbose). + AddStandardComponents(); + } + + protected LocalEnvironment CreateLocalEnvironment(int? seed = null, int conc = 0) + { + return new LocalEnvironment(seed, conc). + AddStandardComponents(); + } } } diff --git a/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs b/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs index ddd9dfb8da..1b9eed9fe5 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs @@ -295,7 +295,7 @@ protected void RunResultProcessorTest(string[] dataFiles, string outPath, string if (extraArgs != null) args.AddRange(extraArgs); - ResultProcessor.Main(args.ToArray()); + ResultProcessor.Main(Env, args.ToArray()); } private static string GetNamePrefix(string testType, PredictorAndArgs predictor, TestDataset dataset, string extraTag = "") diff --git a/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs b/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs index f5be433b3e..b3b68c5a8a 100644 --- a/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs +++ b/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs @@ -2,24 +2,19 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - -using System; -using System.Collections.Generic; -using System.IO; -using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; -using Microsoft.ML.Runtime.Data.IO; -using Microsoft.ML.Runtime.Internal.Utilities; -using Microsoft.ML.Runtime.Model; -using Microsoft.ML.Runtime.TextAnalytics; +using System; using Xunit; -using System.Runtime.InteropServices; namespace Microsoft.ML.Runtime.RunTests { public sealed partial class TestParquet : TestDataPipeBase { + protected override void InitializeCore() + { + base.InitializeCore(); + Env.ComponentCatalog.RegisterAssembly(typeof(ParquetLoader).Assembly); + } [Fact] public void TestParquetPrimitiveDataTypes() @@ -33,7 +28,8 @@ public void TestParquetPrimitiveDataTypes() public void TestParquetNull() { string pathData = GetDataPath(@"Parquet", "test-null.parquet"); - TestCore(pathData, false, new[] { "loader=Parquet{bigIntDates=+}" }, forceDense: true); + var ex = Assert.Throws(() => TestCore(pathData, false, new[] { "loader=Parquet{bigIntDates=+}" }, forceDense: true)); + Assert.Equal("Nullable object must have a value.", ex.Message); Done(); } } diff --git a/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs index 42766d3934..e1b4986f06 100644 --- a/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs +++ b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs @@ -8,6 +8,7 @@ using System.Linq; using System.Reflection; using Microsoft.ML.Core.Data; +using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; @@ -310,7 +311,7 @@ protected IDataLoader TestCore(string pathData, bool keepHidden, string[] argsPi protected IDataLoader CreatePipeDataLoader(IHostEnvironment env, string pathData, string[] argsPipe, out MultiFileSource files) { - VerifyArgParsing(argsPipe); + VerifyArgParsing(env, argsPipe); // Default to breast-cancer.txt. if (string.IsNullOrEmpty(pathData)) @@ -349,7 +350,7 @@ protected void TestApplyTransformsToData(IHostEnvironment env, IDataLoader pipe, Failed(); } - protected void VerifyArgParsing(string[] strs) + protected void VerifyArgParsing(IHostEnvironment env, string[] strs) { string str = CmdParser.CombineSettings(strs); var args = new CompositeDataLoader.Arguments(); @@ -360,18 +361,18 @@ protected void VerifyArgParsing(string[] strs) } // For the loader and each transform, verify that custom unparsing is correct. - VerifyCustArgs(args.Loader); + VerifyCustArgs(env, args.Loader); foreach (var kvp in args.Transform) - VerifyCustArgs(kvp.Value); + VerifyCustArgs(env, kvp.Value); } - protected void VerifyCustArgs(IComponentFactory factory) + protected void VerifyCustArgs(IHostEnvironment env, IComponentFactory factory) where TRes : class { if (factory is ICommandLineComponentFactory commandLineFactory) { var str = commandLineFactory.GetSettingsString(); - var info = ComponentCatalog.GetLoadableClassInfo(commandLineFactory.Name, commandLineFactory.SignatureType); + var info = env.ComponentCatalog.GetLoadableClassInfo(commandLineFactory.Name, commandLineFactory.SignatureType); Assert.NotNull(info); var def = info.CreateArguments(); @@ -611,8 +612,8 @@ protected bool CheckSameSchemas(ISchema sch1, ISchema sch2, bool exactTypes = tr protected bool CheckMetadataNames(string kind, int size, ISchema sch1, ISchema sch2, int col, bool exactTypes, bool mustBeText) { - var names1 = default(VBuffer); - var names2 = default(VBuffer); + var names1 = default(VBuffer>); + var names2 = default(VBuffer>); var t1 = sch1.GetMetadataTypeOrNull(kind, col); var t2 = sch2.GetMetadataTypeOrNull(kind, col); @@ -653,7 +654,7 @@ protected bool CheckMetadataNames(string kind, int size, ISchema sch1, ISchema s sch1.GetMetadata(kind, col, ref names1); sch2.GetMetadata(kind, col, ref names2); - if (!CompareVec(ref names1, ref names2, size, DvText.Identical)) + if (!CompareVec(ref names1, ref names2, size, (a, b) => a.Span.SequenceEqual(b.Span))) { Fail("Different {0} metadata values", kind); return Failed(); @@ -661,7 +662,7 @@ protected bool CheckMetadataNames(string kind, int size, ISchema sch1, ISchema s return true; } - protected bool CheckMetadataCallFailure(string kind, ISchema sch, int col, ref VBuffer names) + protected bool CheckMetadataCallFailure(string kind, ISchema sch, int col, ref VBuffer> names) { try { @@ -780,6 +781,36 @@ protected bool SaveLoadTransposed(IDataView view, IHostEnvironment env, string s public abstract partial class TestDataViewBase : BaseTestBaseline { + + public class SentimentData + { + [ColumnName("Label")] + public bool Sentiment; + public string SentimentText; + } + + public class SentimentPrediction + { + [ColumnName("PredictedLabel")] + public bool Sentiment; + + public float Score; + } + + private static TextLoader.Arguments MakeSentimentTextLoaderArgs() + { + return new TextLoader.Arguments() + { + Separator = "tab", + HasHeader = true, + Column = new[] + { + new TextLoader.Column("Label", DataKind.BL, 0), + new TextLoader.Column("SentimentText", DataKind.Text, 1) + } + }; + } + protected bool Failed() { Contracts.Assert(!IsPassing); @@ -1028,19 +1059,19 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ switch (type.RawKind) { case DataKind.I1: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U1: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.I2: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U2: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.I4: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U4: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.I8: - return GetComparerOne(r1, r2, col, (x, y) => x.RawValue == y.RawValue); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.U8: return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.R4: @@ -1051,15 +1082,15 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ else return GetComparerOne(r1, r2, col, EqualWithEps); case DataKind.Text: - return GetComparerOne(r1, r2, col, DvText.Identical); + return GetComparerOne>(r1, r2, col, (a ,b) => a.Span.SequenceEqual(b.Span)); case DataKind.Bool: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.TimeSpan: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.DT: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x == y); case DataKind.DZ: - return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); + return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); case DataKind.UG: return GetComparerOne(r1, r2, col, (x, y) => x.Equals(y)); case (DataKind)0: @@ -1074,19 +1105,19 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ switch (type.ItemType.RawKind) { case DataKind.I1: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U1: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.I2: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U2: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.I4: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U4: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.I8: - return GetComparerVec(r1, r2, col, size, (x, y) => x.RawValue == y.RawValue); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.U8: return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.R4: @@ -1097,15 +1128,15 @@ protected Func GetColumnComparer(IRow r1, IRow r2, int col, ColumnType typ else return GetComparerVec(r1, r2, col, size, EqualWithEps); case DataKind.Text: - return GetComparerVec(r1, r2, col, size, DvText.Identical); + return GetComparerVec>(r1, r2, col, size, (a, b) => a.Span.SequenceEqual(b.Span)); case DataKind.Bool: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.TimeSpan: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.DT: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x == y); case DataKind.DZ: - return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); + return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); case DataKind.UG: return GetComparerVec(r1, r2, col, size, (x, y) => x.Equals(y)); } diff --git a/test/Microsoft.ML.TestFramework/Datasets.cs b/test/Microsoft.ML.TestFramework/Datasets.cs index 6e327b5c66..b9d0cad9a5 100644 --- a/test/Microsoft.ML.TestFramework/Datasets.cs +++ b/test/Microsoft.ML.TestFramework/Datasets.cs @@ -2,11 +2,6 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - -using System; -using Microsoft.ML.Runtime.Numeric; - namespace Microsoft.ML.Runtime.RunTests { public class TestDataset @@ -152,11 +147,11 @@ public static class TestDatasets testFilename = "housing.txt" }; - public static TestDataset winequalitymacro = new TestDataset + public static TestDataset generatedRegressionDatasetmacro = new TestDataset { - name = "wine", - trainFilename = "external/winequality-white.csv", - testFilename = "external/winequality-white.csv", + name = "generatedRegressionDataset", + trainFilename = "generated_regression_dataset.csv", + testFilename = "generated_regression_dataset.csv", loaderSettings = "col=Label:R4:11 col=Features:R4:0-10 sep=; header+" }; @@ -167,11 +162,26 @@ public static class TestDatasets testFilename = "external/WikiDetoxAnnotated160kRows.tsv" }; - public static TestDataset winequality = new TestDataset + public static TestDataset MSLRWeb = new TestDataset + { + name = "MSLRWeb", + trainFilename = "external/MSLRWeb10KTrain720kRows.tsv", + validFilename = "external/MSLRWeb10KValidate240kRows.tsv", + testFilename = "external/MSLRWeb10KTest240kRows.tsv" + }; + + public static TestDataset Sentiment = new TestDataset + { + name = "sentiment", + trainFilename = "wikipedia-detox-250-line-data.tsv", + testFilename = "wikipedia-detox-250-line-test.tsv" + }; + + public static TestDataset generatedRegressionDataset = new TestDataset { - name = "wine", - trainFilename = "external/winequality-white.csv", - testFilename = "external/winequality-white.csv", + name = "generatedRegressionDataset", + trainFilename = "generated_regression_dataset.csv", + testFilename = "generated_regression_dataset.csv", loaderSettings = "loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+}" }; @@ -304,6 +314,13 @@ public static class TestDatasets extraSettings = @"/inst Text{header+ sep=, label=14 attr=5-9,1,13,3 threads-}" }; + public static TestDataset adultRanking = new TestDataset + { + name = "adultRanking", + trainFilename = "adult.tiny.with-schema.txt", + loaderSettings = "loader=Text{header+ sep=tab, col=Label:R4:0 col=Workclass:TX:1 col=Categories:TX:2-8 col=NumericFeatures:R4:9-14}", + }; + public static TestDataset displayPoisson = new TestDataset { name = "DisplayPoisson", @@ -362,6 +379,13 @@ public static class TestDatasets mamlExtraSettings = new[] { "xf=Term{col=Label}" }, }; + public static TestDataset irisData = new TestDataset() + { + name = "iris", + trainFilename = @"iris.data", + loaderSettings = "loader=Text{col=Label:TX:4 col=Features:0-3}" + }; + public static TestDataset irisLabelName = new TestDataset() { name = "iris-label-name", diff --git a/test/Microsoft.ML.TestFramework/EnvironmentExtensions.cs b/test/Microsoft.ML.TestFramework/EnvironmentExtensions.cs new file mode 100644 index 0000000000..986e82a9c4 --- /dev/null +++ b/test/Microsoft.ML.TestFramework/EnvironmentExtensions.cs @@ -0,0 +1,31 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.KMeans; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.PCA; + +namespace Microsoft.ML.TestFramework +{ + public static class EnvironmentExtensions + { + public static TEnvironment AddStandardComponents(this TEnvironment env) + where TEnvironment : IHostEnvironment + { + env.ComponentCatalog.RegisterAssembly(typeof(TextLoader).Assembly); // ML.Data + env.ComponentCatalog.RegisterAssembly(typeof(LinearPredictor).Assembly); // ML.StandardLearners + env.ComponentCatalog.RegisterAssembly(typeof(CategoricalTransform).Assembly); // ML.Transforms + env.ComponentCatalog.RegisterAssembly(typeof(FastTreeBinaryPredictor).Assembly); // ML.FastTree + env.ComponentCatalog.RegisterAssembly(typeof(EnsemblePredictor).Assembly); // ML.Ensemble + env.ComponentCatalog.RegisterAssembly(typeof(KMeansPredictor).Assembly); // ML.KMeansClustering + env.ComponentCatalog.RegisterAssembly(typeof(PcaPredictor).Assembly); // ML.PCA + env.ComponentCatalog.RegisterAssembly(typeof(Experiment).Assembly); // ML.Legacy + return env; + } + } +} diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs index 6f7a0c428f..530feccfbf 100644 --- a/test/Microsoft.ML.TestFramework/Learners.cs +++ b/test/Microsoft.ML.TestFramework/Learners.cs @@ -42,6 +42,20 @@ static TestLearnersBase() Contracts.Check(ok, "Missing assemblies!"); } + // New. + public static PredictorAndArgs binaryPrior = new PredictorAndArgs + { + Trainer = new SubComponent("PriorPredictor"), + Tag = "BinaryPrior" + }; + + // New. + public static PredictorAndArgs binaryRandom = new PredictorAndArgs + { + Trainer = new SubComponent("RandomPredictor"), + Tag = "BinaryRandom" + }; + // New. public static PredictorAndArgs binarySdca = new PredictorAndArgs { diff --git a/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj b/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj index ad50df8042..b191c08a91 100644 --- a/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj +++ b/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj @@ -7,11 +7,15 @@ + + + + diff --git a/test/Microsoft.ML.TestFramework/TestCommandBase.cs b/test/Microsoft.ML.TestFramework/TestCommandBase.cs index 5338b1e2db..10b0e9552c 100644 --- a/test/Microsoft.ML.TestFramework/TestCommandBase.cs +++ b/test/Microsoft.ML.TestFramework/TestCommandBase.cs @@ -14,6 +14,7 @@ using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Tools; +using Microsoft.ML.TestFramework; using Xunit; using Xunit.Abstractions; @@ -270,6 +271,11 @@ public virtual OutputPath MetricsPath() } } + protected virtual void InitializeEnvironment(IHostEnvironment environment) + { + environment.AddStandardComponents(); + } + /// /// Runs a command with some arguments. Note that the input /// objects are used for comparison only. @@ -283,6 +289,8 @@ protected bool TestCore(RunContextBase ctx, string cmdName, string args, params using (var newWriter = OpenWriter(outputPath.Path)) using (var env = new ConsoleEnvironment(42, outWriter: newWriter, errWriter: newWriter)) { + InitializeEnvironment(env); + int res; res = MainForTest(env, newWriter, string.Format("{0} {1}", cmdName, args), ctx.BaselineProgress); if (res != 0) @@ -2031,15 +2039,16 @@ public void CommandTrainingBinaryFieldAwareFactorizationMachineWithValidationAnd } [Fact] - public void DataTypes() + public void Datatypes() { - //Skip for linux because DATE/TIME format is different. - if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux)) - return; - string idvPath = GetDataPath("datatypes.idv"); + OutputPath intermediateData = CreateOutputPath("intermediateDatatypes.idv"); OutputPath textOutputPath = CreateOutputPath("datatypes.txt"); TestCore("savedata", idvPath, "loader=binary", "saver=text", textOutputPath.Arg("dout")); + _step++; + TestCore("savedata", idvPath, "loader=binary", "saver=binary", intermediateData.ArgOnly("dout")); + _step++; + TestCore("savedata", intermediateData.Path, "loader=binary", "saver=text", textOutputPath.Arg("dout")); Done(); } } diff --git a/test/Microsoft.ML.TestFramework/TestSparseDataView.cs b/test/Microsoft.ML.TestFramework/TestSparseDataView.cs index ad21acb4ac..5c8301fb7a 100644 --- a/test/Microsoft.ML.TestFramework/TestSparseDataView.cs +++ b/test/Microsoft.ML.TestFramework/TestSparseDataView.cs @@ -1,9 +1,10 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; +using System; using Xunit; using Xunit.Abstractions; @@ -34,11 +35,11 @@ private class SparseExample public void SparseDataView() { GenericSparseDataView(new[] { 1f, 2f, 3f }, new[] { 1f, 10f, 100f }); - GenericSparseDataView(new DvInt4[] { 1, 2, 3 }, new DvInt4[] { 1, 10, 100 }); - GenericSparseDataView(new DvBool[] { true, true, true }, new DvBool[] { false, false, false }); + GenericSparseDataView(new int[] { 1, 2, 3 }, new int[] { 1, 10, 100 }); + GenericSparseDataView(new bool[] { true, true, true }, new bool[] { false, false, false }); GenericSparseDataView(new double[] { 1, 2, 3 }, new double[] { 1, 10, 100 }); - GenericSparseDataView(new DvText[] { new DvText("a"), new DvText("b"), new DvText("c") }, - new DvText[] { new DvText("aa"), new DvText("bb"), new DvText("cc") }); + GenericSparseDataView(new ReadOnlyMemory[] { "a".AsMemory(), "b".AsMemory(), "c".AsMemory() }, + new ReadOnlyMemory[] { "aa".AsMemory(), "bb".AsMemory(), "cc".AsMemory() }); } private void GenericSparseDataView(T[] v1, T[] v2) @@ -76,11 +77,11 @@ private void GenericSparseDataView(T[] v1, T[] v2) public void DenseDataView() { GenericDenseDataView(new[] { 1f, 2f, 3f }, new[] { 1f, 10f, 100f }); - GenericDenseDataView(new DvInt4[] { 1, 2, 3 }, new DvInt4[] { 1, 10, 100 }); - GenericDenseDataView(new DvBool[] { true, true, true }, new DvBool[] { false, false, false }); + GenericDenseDataView(new int[] { 1, 2, 3 }, new int[] { 1, 10, 100 }); + GenericDenseDataView(new bool[] { true, true, true }, new bool[] { false, false, false }); GenericDenseDataView(new double[] { 1, 2, 3 }, new double[] { 1, 10, 100 }); - GenericDenseDataView(new DvText[] { new DvText("a"), new DvText("b"), new DvText("c") }, - new DvText[] { new DvText("aa"), new DvText("bb"), new DvText("cc") }); + GenericDenseDataView(new ReadOnlyMemory[] { "a".AsMemory(), "b".AsMemory(), "c".AsMemory() }, + new ReadOnlyMemory[] { "aa".AsMemory(), "bb".AsMemory(), "cc".AsMemory() }); } private void GenericDenseDataView(T[] v1, T[] v2) diff --git a/test/Microsoft.ML.Tests/CSharpCodeGen.cs b/test/Microsoft.ML.Tests/CSharpCodeGen.cs index 5bf4edf26b..d16a9924f1 100644 --- a/test/Microsoft.ML.Tests/CSharpCodeGen.cs +++ b/test/Microsoft.ML.Tests/CSharpCodeGen.cs @@ -3,7 +3,6 @@ // See the LICENSE file in the project root for more information. using Microsoft.ML.Runtime.RunTests; -using Microsoft.ML.TestFramework; using System.IO; using Xunit; using Xunit.Abstractions; diff --git a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs b/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs index 3749676dcd..2e3b71ab01 100644 --- a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs +++ b/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -94,7 +94,7 @@ public void CanSuccessfullyEnumerated() using (var cursor = data.GetRowCursor((a => true))) { var IDGetter = cursor.GetGetter(0); - var TextGetter = cursor.GetGetter(1); + var TextGetter = cursor.GetGetter>(1); Assert.True(cursor.MoveNext()); @@ -102,7 +102,7 @@ public void CanSuccessfullyEnumerated() IDGetter(ref ID); Assert.Equal(1, ID); - DvText Text = new DvText(); + ReadOnlyMemory Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("1", Text.ToString()); @@ -112,7 +112,7 @@ public void CanSuccessfullyEnumerated() IDGetter(ref ID); Assert.Equal(2, ID); - Text = new DvText(); + Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("2", Text.ToString()); @@ -122,7 +122,7 @@ public void CanSuccessfullyEnumerated() IDGetter(ref ID); Assert.Equal(3, ID); - Text = new DvText(); + Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("3", Text.ToString()); @@ -294,29 +294,13 @@ public class ConversionSimpleClass public float fFloat; public double fDouble; public bool fBool; - public string fString; - } - - public class ConversionNullalbeClass - { - public int? fInt; - public uint? fuInt; - public short? fShort; - public ushort? fuShort; - public sbyte? fsByte; - public byte? fByte; - public long? fLong; - public ulong? fuLong; - public float? fFloat; - public double? fDouble; - public bool? fBool; - public string fString; + public string fString=""; } public bool CompareObjectValues(object x, object y, Type type) { - // By default behaviour for DvText is to be empty string, while for string is null. - // So if we do roundtrip string-> DvText -> string all null string become empty strings. + // By default behaviour for ReadOnlyMemory is to be empty string, while for string is null. + // So if we do roundtrip string-> ReadOnlyMemory -> string all null string become empty strings. // Therefore replace all null values to empty string if field is string. if (type == typeof(string) && x == null) x = ""; @@ -434,56 +418,6 @@ public void RoundTripConversionWithBasicTypes() new ConversionSimpleClass() }; - var dataNullable = new List - { - new ConversionNullalbeClass() - { - fInt = int.MaxValue - 1, - fuInt = uint.MaxValue - 1, - fBool = true, - fsByte = sbyte.MaxValue - 1, - fByte = byte.MaxValue - 1, - fDouble = double.MaxValue - 1, - fFloat = float.MaxValue - 1, - fLong = long.MaxValue - 1, - fuLong = ulong.MaxValue - 1, - fShort = short.MaxValue - 1, - fuShort = ushort.MaxValue - 1, - fString = "ha" - }, - new ConversionNullalbeClass() - { - fInt = int.MaxValue, - fuInt = uint.MaxValue, - fBool = true, - fsByte = sbyte.MaxValue, - fByte = byte.MaxValue, - fDouble = double.MaxValue, - fFloat = float.MaxValue, - fLong = long.MaxValue, - fuLong = ulong.MaxValue, - fShort = short.MaxValue, - fuShort = ushort.MaxValue, - fString = "ooh" - }, - new ConversionNullalbeClass() - { - fInt = int.MinValue + 1, - fuInt = uint.MinValue, - fBool = false, - fsByte = sbyte.MinValue + 1, - fByte = byte.MinValue, - fDouble = double.MinValue + 1, - fFloat = float.MinValue + 1, - fLong = long.MinValue + 1, - fuLong = ulong.MinValue, - fShort = short.MinValue + 1, - fuShort = ushort.MinValue, - fString = "" - }, - new ConversionNullalbeClass() - }; - using (var env = new ConsoleEnvironment()) { var dataView = ComponentCreation.CreateDataView(env, data); @@ -494,15 +428,6 @@ public void RoundTripConversionWithBasicTypes() Assert.True(CompareThroughReflection(enumeratorSimple.Current, originalEnumerator.Current)); } Assert.True(!enumeratorSimple.MoveNext() && !originalEnumerator.MoveNext()); - - dataView = ComponentCreation.CreateDataView(env, dataNullable); - var enumeratorNullable = dataView.AsEnumerable(env, false).GetEnumerator(); - var originalNullableEnumerator = dataNullable.GetEnumerator(); - while (enumeratorNullable.MoveNext() && originalNullableEnumerator.MoveNext()) - { - Assert.True(CompareThroughReflection(enumeratorNullable.Current, originalNullableEnumerator.Current)); - } - Assert.True(!enumeratorNullable.MoveNext() && !originalNullableEnumerator.MoveNext()); } } @@ -542,38 +467,6 @@ public void ConversionExceptionsBehavior() } } - public class ConversionLossMinValueClass - { - public int? fInt; - public long? fLong; - public short? fShort; - public sbyte? fSByte; - } - - [Fact] - public void ConversionMinValueToNullBehavior() - { - using (var env = new ConsoleEnvironment()) - { - - var data = new List - { - new ConversionLossMinValueClass() { fSByte = null, fInt = null, fLong = null, fShort = null }, - new ConversionLossMinValueClass() { fSByte = sbyte.MinValue, fInt = int.MinValue, fLong = long.MinValue, fShort = short.MinValue } - }; - foreach (var field in typeof(ConversionLossMinValueClass).GetFields()) - { - var dataView = ComponentCreation.CreateDataView(env, data); - var enumerator = dataView.AsEnumerable(env, false).GetEnumerator(); - while (enumerator.MoveNext()) - { - Assert.True(enumerator.Current.fInt == null && enumerator.Current.fLong == null && - enumerator.Current.fSByte == null && enumerator.Current.fShort == null); - } - } - } - } - public class ConversionLossMinValueClassProperties { private int? _fInt; @@ -586,30 +479,6 @@ public class ConversionLossMinValueClassProperties public long? LongProp { get { return _fLong; } set { _fLong = value; } } } - [Fact] - public void ConversionMinValueToNullBehaviorProperties() - { - using (var env = new ConsoleEnvironment()) - { - - var data = new List - { - new ConversionLossMinValueClassProperties() { SByteProp = null, IntProp = null, LongProp = null, ShortProp = null }, - new ConversionLossMinValueClassProperties() { SByteProp = sbyte.MinValue, IntProp = int.MinValue, LongProp = long.MinValue, ShortProp = short.MinValue } - }; - foreach (var field in typeof(ConversionLossMinValueClassProperties).GetFields()) - { - var dataView = ComponentCreation.CreateDataView(env, data); - var enumerator = dataView.AsEnumerable(env, false).GetEnumerator(); - while (enumerator.MoveNext()) - { - Assert.True(enumerator.Current.IntProp == null && enumerator.Current.LongProp == null && - enumerator.Current.SByteProp == null && enumerator.Current.ShortProp == null); - } - } - } - } - public class ClassWithConstField { public const string ConstString = "N"; @@ -625,7 +494,6 @@ public void ClassWithConstFieldsConversion() { new ClassWithConstField(){ fInt=1, fString ="lala" }, new ClassWithConstField(){ fInt=-1, fString ="" }, - new ClassWithConstField(){ fInt=0, fString =null } }; using (var env = new ConsoleEnvironment()) @@ -654,7 +522,6 @@ public void ClassWithMixOfFieldsAndPropertiesConversion() { new ClassWithMixOfFieldsAndProperties(){ IntProp=1, fString ="lala" }, new ClassWithMixOfFieldsAndProperties(){ IntProp=-1, fString ="" }, - new ClassWithMixOfFieldsAndProperties(){ IntProp=0, fString =null } }; using (var env = new ConsoleEnvironment()) @@ -744,7 +611,6 @@ public void ClassWithInheritedPropertiesConversion() { new ClassWithInheritedProperties(){ IntProp=1, StringProp ="lala", LongProp=17, ByteProp=3 }, new ClassWithInheritedProperties(){ IntProp=-1, StringProp ="", LongProp=2, ByteProp=4 }, - new ClassWithInheritedProperties(){ IntProp=0, StringProp =null, LongProp=18, ByteProp=5 } }; using (var env = new ConsoleEnvironment()) @@ -774,22 +640,6 @@ public class ClassWithArrays public bool[] fBool; } - public class ClassWithNullableArrays - { - public string[] fString; - public int?[] fInt; - public uint?[] fuInt; - public short?[] fShort; - public ushort?[] fuShort; - public sbyte?[] fsByte; - public byte?[] fByte; - public long?[] fLong; - public ulong?[] fuLong; - public float?[] fFloat; - public double?[] fDouble; - public bool?[] fBool; - } - [Fact] public void RoundTripConversionWithArrays() { @@ -801,7 +651,7 @@ public void RoundTripConversionWithArrays() fInt = new int[3] { 0, 1, 2 }, fFloat = new float[3] { -0.99f, 0f, 0.99f }, fString = new string[2] { "hola", "lola" }, - fBool = new bool[2] { true, false }, + fByte = new byte[3] { 0, 124, 255 }, fDouble = new double[3] { -1, 0, 1 }, fLong = new long[] { 0, 1, 2 }, @@ -815,26 +665,6 @@ public void RoundTripConversionWithArrays() new ClassWithArrays() }; - var nullableData = new List - { - new ClassWithNullableArrays() - { - fInt = new int?[3] { null, -1, 1 }, - fFloat = new float?[3] { -0.99f, null, 0.99f }, - fString = new string[2] { null, "" }, - fBool = new bool?[3] { true, null, false }, - fByte = new byte?[4] { 0, 125, null, 255 }, - fDouble = new double?[3] { -1, null, 1 }, - fLong = new long?[] { null, -1, 1 }, - fsByte = new sbyte?[3] { -127, 127, null }, - fShort = new short?[3] { 0, null, 32767 }, - fuInt = new uint?[4] { null, 42, 0, uint.MaxValue }, - fuLong = new ulong?[3] { ulong.MaxValue, null, 0 }, - fuShort = new ushort?[3] { 0, null, ushort.MaxValue } - }, - new ClassWithNullableArrays() { fInt = new int?[3] { -2, 1, 0 }, fFloat = new float?[3] { 0.99f, 0f, -0.99f }, fString = new string[2] { "lola", "hola" } }, - new ClassWithNullableArrays() - }; using (var env = new ConsoleEnvironment()) { @@ -846,15 +676,6 @@ public void RoundTripConversionWithArrays() Assert.True(CompareThroughReflection(enumeratorSimple.Current, originalEnumerator.Current)); } Assert.True(!enumeratorSimple.MoveNext() && !originalEnumerator.MoveNext()); - - var nullableDataView = ComponentCreation.CreateDataView(env, nullableData); - var enumeratorNullable = nullableDataView.AsEnumerable(env, false).GetEnumerator(); - var originalNullalbleEnumerator = nullableData.GetEnumerator(); - while (enumeratorNullable.MoveNext() && originalNullalbleEnumerator.MoveNext()) - { - Assert.True(CompareThroughReflection(enumeratorNullable.Current, originalNullalbleEnumerator.Current)); - } - Assert.True(!enumeratorNullable.MoveNext() && !originalNullalbleEnumerator.MoveNext()); } } public class ClassWithArrayProperties @@ -885,35 +706,6 @@ public class ClassWithArrayProperties public bool[] BoolProp { get { return _fBool; } set { _fBool = value; } } } - public class ClassWithNullableArrayProperties - { - private string[] _fString; - private int?[] _fInt; - private uint?[] _fuInt; - private short?[] _fShort; - private ushort?[] _fuShort; - private sbyte?[] _fsByte; - private byte?[] _fByte; - private long?[] _fLong; - private ulong?[] _fuLong; - private float?[] _fFloat; - private double?[] _fDouble; - private bool?[] _fBool; - - public string[] StringProp { get { return _fString; } set { _fString = value; } } - public int?[] IntProp { get { return _fInt; } set { _fInt = value; } } - public uint?[] UIntProp { get { return _fuInt; } set { _fuInt = value; } } - public short?[] ShortProp { get { return _fShort; } set { _fShort = value; } } - public ushort?[] UShortProp { get { return _fuShort; } set { _fuShort = value; } } - public sbyte?[] SByteProp { get { return _fsByte; } set { _fsByte = value; } } - public byte?[] ByteProp { get { return _fByte; } set { _fByte = value; } } - public long?[] LongProp { get { return _fLong; } set { _fLong = value; } } - public ulong?[] ULongProp { get { return _fuLong; } set { _fuLong = value; } } - public float?[] SingleProp { get { return _fFloat; } set { _fFloat = value; } } - public double?[] DoubleProp { get { return _fDouble; } set { _fDouble = value; } } - public bool?[] BoolProp { get { return _fBool; } set { _fBool = value; } } - } - [Fact] public void RoundTripConversionWithArrayPropertiess() { @@ -939,27 +731,6 @@ public void RoundTripConversionWithArrayPropertiess() new ClassWithArrayProperties() }; - var nullableData = new List - { - new ClassWithNullableArrayProperties() - { - IntProp = new int?[3] { null, -1, 1 }, - SingleProp = new float?[3] { -0.99f, null, 0.99f }, - StringProp = new string[2] { null, "" }, - BoolProp = new bool?[3] { true, null, false }, - ByteProp = new byte?[4] { 0, 125, null, 255 }, - DoubleProp = new double?[3] { -1, null, 1 }, - LongProp = new long?[] { null, -1, 1 }, - SByteProp = new sbyte?[3] { -127, 127, null }, - ShortProp = new short?[3] { 0, null, 32767 }, - UIntProp = new uint?[4] { null, 42, 0, uint.MaxValue }, - ULongProp = new ulong?[3] { ulong.MaxValue, null, 0 }, - UShortProp = new ushort?[3] { 0, null, ushort.MaxValue } - }, - new ClassWithNullableArrayProperties() { IntProp = new int?[3] { -2, 1, 0 }, SingleProp = new float?[3] { 0.99f, 0f, -0.99f }, StringProp = new string[2] { "lola", "hola" } }, - new ClassWithNullableArrayProperties() - }; - using (var env = new ConsoleEnvironment()) { var dataView = ComponentCreation.CreateDataView(env, data); @@ -970,15 +741,6 @@ public void RoundTripConversionWithArrayPropertiess() Assert.True(CompareThroughReflection(enumeratorSimple.Current, originalEnumerator.Current)); } Assert.True(!enumeratorSimple.MoveNext() && !originalEnumerator.MoveNext()); - - var nullableDataView = ComponentCreation.CreateDataView(env, nullableData); - var enumeratorNullable = nullableDataView.AsEnumerable(env, false).GetEnumerator(); - var originalNullalbleEnumerator = nullableData.GetEnumerator(); - while (enumeratorNullable.MoveNext() && originalNullalbleEnumerator.MoveNext()) - { - Assert.True(CompareThroughReflection(enumeratorNullable.Current, originalNullalbleEnumerator.Current)); - } - Assert.True(!enumeratorNullable.MoveNext() && !originalNullalbleEnumerator.MoveNext()); } } diff --git a/test/Microsoft.ML.Tests/CopyColumnEstimatorTests.cs b/test/Microsoft.ML.Tests/CopyColumnEstimatorTests.cs index 958e7052d4..a94c9dd727 100644 --- a/test/Microsoft.ML.Tests/CopyColumnEstimatorTests.cs +++ b/test/Microsoft.ML.Tests/CopyColumnEstimatorTests.cs @@ -147,14 +147,14 @@ void TestMetadataCopy() var result = transformer.Transform(term); result.Schema.TryGetColumnIndex("T", out int termIndex); result.Schema.TryGetColumnIndex("T1", out int copyIndex); - var names1 = default(VBuffer); - var names2 = default(VBuffer); + var names1 = default(VBuffer>); + var names2 = default(VBuffer>); var type1 = result.Schema.GetColumnType(termIndex); int size = type1.ItemType.IsKey ? type1.ItemType.KeyCount : -1; var type2 = result.Schema.GetColumnType(copyIndex); result.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues, termIndex, ref names1); result.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues, copyIndex, ref names2); - Assert.True(CompareVec(ref names1, ref names2, size, DvText.Identical)); + Assert.True(CompareVec(ref names1, ref names2, size, (a, b) => a.Span.SequenceEqual(b.Span))); } } @@ -171,16 +171,16 @@ private void ValidateCopyColumnTransformer(IDataView result) { using (var cursor = result.GetRowCursor(x => true)) { - DvInt4 avalue = 0; - DvInt4 bvalue = 0; - DvInt4 dvalue = 0; - DvInt4 evalue = 0; - DvInt4 fvalue = 0; - var aGetter = cursor.GetGetter(0); - var bGetter = cursor.GetGetter(1); - var dGetter = cursor.GetGetter(3); - var eGetter = cursor.GetGetter(4); - var fGetter = cursor.GetGetter(5); + int avalue = 0; + int bvalue = 0; + int dvalue = 0; + int evalue = 0; + int fvalue = 0; + var aGetter = cursor.GetGetter(0); + var bGetter = cursor.GetGetter(1); + var dGetter = cursor.GetGetter(3); + var eGetter = cursor.GetGetter(4); + var fGetter = cursor.GetGetter(5); while (cursor.MoveNext()) { aGetter(ref avalue); diff --git a/test/Microsoft.ML.Tests/ImagesTests.cs b/test/Microsoft.ML.Tests/ImagesTests.cs index d4ef47b2a9..c5aabae99d 100644 --- a/test/Microsoft.ML.Tests/ImagesTests.cs +++ b/test/Microsoft.ML.Tests/ImagesTests.cs @@ -7,7 +7,7 @@ using Microsoft.ML.Runtime.ImageAnalytics; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.RunTests; -using Microsoft.ML.TestFramework; +using System; using System.Drawing; using System.IO; using System.Linq; @@ -29,8 +29,21 @@ public void TestEstimatorChain() { var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); - var invalidData = env.CreateLoader("Text{col=ImagePath:R4:0}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); + var invalidData = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.R4, 0), + } + }, new MultiFileSource(dataFile)); var pipe = new ImageLoaderEstimator(env, imageFolder, ("ImagePath", "ImageReal")) .Append(new ImageResizerEstimator(env, "ImageReal", "ImageReal", 100, 100)) @@ -49,7 +62,14 @@ public void TestEstimatorSaveLoad() { var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var pipe = new ImageLoaderEstimator(env, imageFolder, ("ImagePath", "ImageReal")) .Append(new ImageResizerEstimator(env, "ImageReal", "ImageReal", 100, 100)) @@ -82,7 +102,14 @@ public void TestSaveImages() { var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -103,8 +130,8 @@ public void TestSaveImages() cropped.Schema.TryGetColumnIndex("ImageCropped", out int cropBitmapColumn); using (var cursor = cropped.GetRowCursor((x) => true)) { - var pathGetter = cursor.GetGetter(pathColumn); - DvText path = default; + var pathGetter = cursor.GetGetter>(pathColumn); + ReadOnlyMemory path = default; var bitmapCropGetter = cursor.GetGetter(cropBitmapColumn); Bitmap bitmap = default; while (cursor.MoveNext()) @@ -129,7 +156,14 @@ public void TestGreyscaleTransformImages() var imageWidth = 100; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -192,7 +226,14 @@ public void TestBackAndForthConversionWithAlphaInterleave() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -275,7 +316,14 @@ public void TestBackAndForthConversionWithoutAlphaInterleave() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -358,7 +406,14 @@ public void TestBackAndForthConversionWithAlphaNoInterleave() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -441,7 +496,14 @@ public void TestBackAndForthConversionWithoutAlphaNoInterleave() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -524,7 +586,14 @@ public void TestBackAndForthConversionWithAlphaInterleaveNoOffset() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -603,7 +672,14 @@ public void TestBackAndForthConversionWithoutAlphaInterleaveNoOffset() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -682,7 +758,14 @@ public void TestBackAndForthConversionWithAlphaNoInterleaveNoOffset() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -761,7 +844,14 @@ public void TestBackAndForthConversionWithoutAlphaNoInterleaveNoOffset() var imageWidth = 130; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] diff --git a/test/Microsoft.ML.Tests/LearningPipelineTests.cs b/test/Microsoft.ML.Tests/LearningPipelineTests.cs index 265c6201c0..a459456a03 100644 --- a/test/Microsoft.ML.Tests/LearningPipelineTests.cs +++ b/test/Microsoft.ML.Tests/LearningPipelineTests.cs @@ -94,7 +94,7 @@ public class Data public class Prediction { [ColumnName("PredictedLabel")] - public DvBool PredictedLabel; + public bool PredictedLabel; } [Fact] @@ -137,36 +137,6 @@ public void BooleanLabelPipeline() var model = pipeline.Train(); } - public class NullableBooleanLabelData - { - [ColumnName("Features")] - [VectorType(2)] - public float[] Features; - - [ColumnName("Label")] - public bool? Label; - } - - [Fact] - public void NullableBooleanLabelPipeline() - { - var data = new NullableBooleanLabelData[2]; - data[0] = new NullableBooleanLabelData - { - Features = new float[] { 0.0f, 1.0f }, - Label = null - }; - data[1] = new NullableBooleanLabelData - { - Features = new float[] { 1.0f, 0.0f }, - Label = false - }; - var pipeline = new Legacy.LearningPipeline(); - pipeline.Add(CollectionDataSource.Create(data)); - pipeline.Add(new FastForestBinaryClassifier()); - var model = pipeline.Train(); - } - [Fact] public void AppendPipeline() { diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj index 3494d971a8..62b713b46d 100644 --- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj +++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj @@ -1,4 +1,4 @@ - + @@ -25,6 +25,7 @@ + diff --git a/test/Microsoft.ML.Tests/OnnxTests.cs b/test/Microsoft.ML.Tests/OnnxTests.cs index 0240e9e32a..a9fbb6417a 100644 --- a/test/Microsoft.ML.Tests/OnnxTests.cs +++ b/test/Microsoft.ML.Tests/OnnxTests.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -9,6 +9,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.RunTests; +using System; using System.IO; using System.Text.RegularExpressions; using Xunit; @@ -27,7 +28,7 @@ public class BreastCancerData public float Label; public float F1; - public DvText F2; + public ReadOnlyMemory F2; } public class BreastCancerDataAllColumns @@ -41,7 +42,7 @@ public class BreastCancerDataAllColumns public class BreastCancerPrediction { [ColumnName("PredictedLabel")] - public DvBool Cancerous; + public bool Cancerous; } public class BreastCancerMCPrediction diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/ApiScenariosTests.cs b/test/Microsoft.ML.Tests/Scenarios/Api/ApiScenariosTests.cs index a8b7aaa7ad..efc4587bd2 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/ApiScenariosTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/ApiScenariosTests.cs @@ -17,10 +17,6 @@ public ApiScenariosTests(ITestOutputHelper output) : base(output) { } - public const string IrisDataPath = "iris.data"; - public const string SentimentDataPath = "wikipedia-detox-250-line-data.tsv"; - public const string SentimentTestPath = "wikipedia-detox-250-line-test.tsv"; - public class IrisData : IrisDataNoLabel { public string Label; diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/AspirationalExamples.cs b/test/Microsoft.ML.Tests/Scenarios/Api/AspirationalExamples.cs index fd2c5f0142..ebf6a24e7e 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/AspirationalExamples.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/AspirationalExamples.cs @@ -168,7 +168,7 @@ public class GithubClassification { public void ClassifyGithubIssues() { - var env = new TlcEnvironment(new SysRandom(0), verbose: true); + var env = new LocalEnvironment(new SysRandom(0), verbose: true); string dataPath = "corefx-issues-train.tsv"; @@ -228,7 +228,7 @@ public class SimpleTransform { public void ScaleData() { - var env = new TlcEnvironment(new SysRandom(0), verbose: true); + var env = new LocalEnvironment(new SysRandom(0), verbose: true); string dataPath = "iris.txt"; diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/AutoNormalizationAndCaching.cs b/test/Microsoft.ML.Tests/Scenarios/Api/AutoNormalizationAndCaching.cs index 00fb528307..54f53528f0 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/AutoNormalizationAndCaching.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/AutoNormalizationAndCaching.cs @@ -4,6 +4,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -18,13 +19,12 @@ public partial class ApiScenariosTests [Fact] public void AutoNormalizationAndCaching() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); + var data = GetDataPath(TestDatasets.Sentiment.trainFilename); using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline. - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(data)); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(false), loader); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/CrossValidation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/CrossValidation.cs index e55fe53743..0b74889515 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/CrossValidation.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/CrossValidation.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Legacy.Models; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using System; using System.Collections.Generic; using System.Linq; @@ -25,14 +26,13 @@ public partial class ApiScenariosTests [Fact] void CrossValidation() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); + var dataset = TestDatasets.Sentiment; int numFolds = 5; using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline. - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(dataset.trainFilename))); var text = TextTransform.Create(env, MakeSentimentTextTransformArgs(false), loader); IDataView trans = new GenerateNumberTransform(env, text, "StratificationColumn"); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs index c883151590..68857999e8 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs @@ -5,6 +5,8 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.TestFramework; using System.Linq; using Xunit; @@ -24,13 +26,13 @@ public partial class ApiScenariosTests [Fact] void DecomposableTrainAndPredict() { - var dataPath = GetDataPath(IrisDataPath); - using (var env = new LocalEnvironment()) + using (var env = new LocalEnvironment() + .AddStandardComponents()) // ScoreUtils.GetScorer requires scorers to be registered in the ComponentCatalog { - var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename))); var term = TermTransform.Create(env, loader, "Label"); var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term); - var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }); + var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label"); IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat; var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features"); @@ -47,8 +49,7 @@ void DecomposableTrainAndPredict() var keyToValue = new KeyToValueTransform(env, "PredictedLabel").Transform(newScorer); var model = env.CreatePredictionEngine(keyToValue); - var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); - var testData = testLoader.AsEnumerable(env, false); + var testData = loader.AsEnumerable(env, false); foreach (var input in testData.Take(20)) { var prediction = model.Predict(input); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs index 4073054051..2d0cb435bd 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs @@ -4,6 +4,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -21,14 +22,11 @@ public partial class ApiScenariosTests [Fact] void New_CrossValidation() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var data = new TextLoader(env, MakeSentimentTextLoaderArgs()) - .Read(new MultiFileSource(dataPath)); + .Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); // Pipeline. var pipeline = new TextTransform(env, "SentimentText", "Features") .Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs index 9d1d68dc4b..6e937ce238 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using System.Linq; using Xunit; @@ -23,26 +24,35 @@ public partial class ApiScenariosTests [Fact] void New_DecomposableTrainAndPredict() { - var dataPath = GetDataPath(IrisDataPath); + var dataPath = GetDataPath(TestDatasets.irisData.trainFilename); using (var env = new LocalEnvironment()) { - var data = new TextLoader(env, MakeIrisTextLoaderArgs()) - .Read(new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var term = TermTransform.Create(env, loader, "Label"); + var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term); + var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label"); - var pipeline = new ConcatEstimator(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") - .Append(new TermEstimator(env, "Label"), TransformerScope.TrainTest) - .Append(new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label")) - .Append(new KeyToValueEstimator(env, "PredictedLabel")); + IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat; + var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features"); - var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring); - var engine = model.MakePredictionFunction(env); + // Auto-normalization. + NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + // Cut out term transform from pipeline. + var newScorer = ApplyTransformUtils.ApplyAllTransformsToData(env, scorer, loader, term); + var keyToValue = new KeyToValueTransform(env, "PredictedLabel").Transform(newScorer); + var model = env.CreatePredictionEngine(keyToValue); var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); - var testData = testLoader.AsEnumerable(env, false); + var testData = testLoader.AsEnumerable(env, false); foreach (var input in testData.Take(20)) { - var prediction = engine.Predict(input); - Assert.True(prediction.PredictedLabel == input.Label); + var prediction = model.Predict(input); + Assert.True(prediction.PredictedLabel == "Iris-setosa"); } } } diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs index cf62d68912..41deccc3fb 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs @@ -4,6 +4,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -19,9 +20,6 @@ public partial class ApiScenariosTests [Fact] public void New_Evaluation() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var reader = new TextLoader(env, MakeSentimentTextLoaderArgs()); @@ -32,10 +30,10 @@ public void New_Evaluation() .Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments { NumThreads = 1 }, "Features", "Label")); // Train. - var readerModel = pipeline.Fit(new MultiFileSource(dataPath)); + var readerModel = pipeline.Fit(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); // Evaluate on the test set. - var dataEval = readerModel.Read(new MultiFileSource(testDataPath)); + var dataEval = readerModel.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.validFilename))); var evaluator = new MyBinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments() { }); var metrics = evaluator.Evaluate(dataEval); } diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs index 4c5cddf767..ee4bef02e1 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using System; using System.Linq; using Xunit; @@ -22,7 +23,8 @@ public partial class ApiScenariosTests [Fact] void New_Extensibility() { - var dataPath = GetDataPath(IrisDataPath); + var dataPath = GetDataPath(TestDatasets.irisData.trainFilename); + using (var env = new LocalEnvironment()) { var data = new TextLoader(env, MakeIrisTextLoaderArgs()) diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs index c291b37ced..bfeb59506a 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs @@ -6,6 +6,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -22,14 +23,11 @@ public partial class ApiScenariosTests [Fact] void New_FileBasedSavingOfData() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var trainData = new TextLoader(env, MakeSentimentTextLoaderArgs()) .Append(new TextTransform(env, "SentimentText", "Features")) - .FitAndRead(new MultiFileSource(dataPath)); + .FitAndRead(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); using (var file = env.CreateOutputFile("i.idv")) trainData.SaveAsBinary(env, file.CreateWriteStream()); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs index 17f452ff09..68eba74127 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs @@ -7,6 +7,7 @@ using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.Runtime.TextAnalytics; using System.Collections.Generic; using Xunit; @@ -32,13 +33,10 @@ public partial class ApiScenariosTests [Fact] public void New_IntrospectiveTraining() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var data = new TextLoader(env, MakeSentimentTextLoaderArgs()) - .Read(new MultiFileSource(dataPath)); + .Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var pipeline = new TextTransform(env, "SentimentText", "Features") .Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments { NumThreads = 1 }, "Features", "Label")); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs index 05d7b33445..e69de29bb2 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs @@ -1,39 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using Microsoft.ML.Runtime.Data; -using Microsoft.ML.Runtime.Internal.Calibration; -using Microsoft.ML.Runtime.Learners; -using System.Linq; -using Xunit; - -namespace Microsoft.ML.Tests.Scenarios.Api -{ - public partial class ApiScenariosTests - { - /// - /// Meta-components: Meta-components (e.g., components that themselves instantiate components) should not be booby-trapped. - /// When specifying what trainer OVA should use, a user will be able to specify any binary classifier. - /// If they specify a regression or multi-class classifier ideally that should be a compile error. - /// - [Fact] - public void New_Metacomponents() - { - var dataPath = GetDataPath(IrisDataPath); - using (var env = new LocalEnvironment()) - { - var data = new TextLoader(env, MakeIrisTextLoaderArgs()) - .Read(new MultiFileSource(dataPath)); - - var sdcaTrainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label"); - var pipeline = new ConcatEstimator(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") - .Append(new TermEstimator(env, "Label"), TransformerScope.TrainTest) - .Append(new Ova(env, sdcaTrainer)) - .Append(new KeyToValueEstimator(env, "PredictedLabel")); - - var model = pipeline.Fit(data); - } - } - } -} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs index 2e35acd71a..dcabc9ec43 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using System.Threading.Tasks; using Xunit; @@ -24,13 +25,10 @@ public partial class ApiScenariosTests [Fact] void New_MultithreadedPrediction() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var reader = new TextLoader(env, MakeSentimentTextLoaderArgs()); - var data = reader.Read(new MultiFileSource(dataPath)); + var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); // Pipeline. var pipeline = new TextTransform(env, "SentimentText", "Features") @@ -43,7 +41,7 @@ void New_MultithreadedPrediction() var engine = model.MakePredictionFunction(env); // Take a couple examples out of the test data and run predictions on top. - var testData = reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath))) + var testData = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename))) .AsEnumerable(env, false); Parallel.ForEach(testData, (input) => diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs index 42697089c3..0516e5ed4f 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -20,15 +21,12 @@ public partial class ApiScenariosTests [Fact] public void New_ReconfigurablePrediction() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var dataReader = new TextLoader(env, MakeSentimentTextLoaderArgs()); - var data = dataReader.Read(new MultiFileSource(dataPath)); - var testData = dataReader.Read(new MultiFileSource(testDataPath)); + var data = dataReader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); + var testData = dataReader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename))); // Pipeline. var pipeline = new TextTransform(env, "SentimentText", "Features") diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs index 1257196253..565255305a 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs @@ -7,6 +7,7 @@ using Microsoft.ML.Runtime.Learners; using Xunit; using System.Linq; +using Microsoft.ML.Runtime.RunTests; namespace Microsoft.ML.Tests.Scenarios.Api { @@ -21,13 +22,10 @@ public partial class ApiScenariosTests [Fact] public void New_SimpleTrainAndPredict() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var reader = new TextLoader(env, MakeSentimentTextLoaderArgs()); - var data = reader.Read(new MultiFileSource(dataPath)); + var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); // Pipeline. var pipeline = new TextTransform(env, "SentimentText", "Features") .Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments { NumThreads = 1 }, "Features", "Label")); @@ -39,7 +37,7 @@ public void New_SimpleTrainAndPredict() var engine = model.MakePredictionFunction(env); // Take a couple examples out of the test data and run predictions on top. - var testData = reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath))) + var testData = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename))) .AsEnumerable(env, false); foreach (var input in testData.Take(5)) { diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs index 013c269e92..b14e26b66c 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs @@ -6,6 +6,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using System.Linq; using Xunit; @@ -22,13 +23,10 @@ public partial class ApiScenariosTests [Fact] public void New_TrainSaveModelAndPredict() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var reader = new TextLoader(env, MakeSentimentTextLoaderArgs()); - var data = reader.Read(new MultiFileSource(dataPath)); + var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); // Pipeline. var pipeline = new TextTransform(env, "SentimentText", "Features") @@ -52,7 +50,7 @@ public void New_TrainSaveModelAndPredict() var engine = loadedModel.MakePredictionFunction(env); // Take a couple examples out of the test data and run predictions on top. - var testData = reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath))) + var testData = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename))) .AsEnumerable(env, false); foreach (var input in testData.Take(5)) { diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs index 09bca124e0..afc456dac6 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -18,11 +19,10 @@ public partial class ApiScenariosTests [Fact] public void New_TrainWithInitialPredictor() { - var dataPath = GetDataPath(SentimentDataPath); using (var env = new LocalEnvironment(seed: 1, conc: 1)) { - var data = new TextLoader(env, MakeSentimentTextLoaderArgs()).Read(new MultiFileSource(dataPath)); + var data = new TextLoader(env, MakeSentimentTextLoaderArgs()).Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); // Pipeline. var pipeline = new TextTransform(env, "SentimentText", "Features"); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithValidationSet.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithValidationSet.cs index 74431f7279..a689cab502 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithValidationSet.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithValidationSet.cs @@ -4,6 +4,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -17,9 +18,6 @@ public partial class ApiScenariosTests [Fact] public void New_TrainWithValidationSet() { - var dataPath = GetDataPath(SentimentDataPath); - var validationDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline. @@ -27,10 +25,10 @@ public void New_TrainWithValidationSet() var pipeline = new TextTransform(env, "SentimentText", "Features"); // Train the pipeline, prepare train and validation set. - var data = reader.Read(new MultiFileSource(dataPath)); + var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var preprocess = pipeline.Fit(data); var trainData = preprocess.Transform(data); - var validData = preprocess.Transform(reader.Read(new MultiFileSource(validationDataPath))); + var validData = preprocess.Transform(reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename)))); // Train model with validation set. var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments(), "Features", "Label"); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs index 7d0e0e7236..0759d820f4 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs @@ -1,8 +1,10 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -20,13 +22,12 @@ public partial class ApiScenariosTests [Fact] void New_Visibility() { - var dataPath = GetDataPath(SentimentDataPath); using (var env = new LocalEnvironment(seed: 1, conc: 1)) { var pipeline = new TextLoader(env, MakeSentimentTextLoaderArgs()) .Append(new TextTransform(env, "SentimentText", "Features", s => s.OutputTokens = true)); - var data = pipeline.FitAndRead(new MultiFileSource(dataPath)); + var data = pipeline.FitAndRead(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); // In order to find out available column names, you can go through schema and check // column names and appropriate type for getter. for (int i = 0; i < data.Schema.ColumnCount; i++) @@ -41,11 +42,11 @@ void New_Visibility() Assert.True(cursor.Schema.TryGetColumnIndex("Features_TransformedText", out int transformedTextColumn)); Assert.True(cursor.Schema.TryGetColumnIndex("Features", out int featureColumn)); - var originalTextGettter = cursor.GetGetter(textColumn); - var transformedTextGettter = cursor.GetGetter>(transformedTextColumn); + var originalTextGettter = cursor.GetGetter>(textColumn); + var transformedTextGettter = cursor.GetGetter>>(transformedTextColumn); var featureGettter = cursor.GetGetter>(featureColumn); - DvText text = default; - VBuffer transformedText = default; + ReadOnlyMemory text = default; + VBuffer> transformedText = default; VBuffer features = default; while (cursor.MoveNext()) { diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Wrappers.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Wrappers.cs index 86799ce445..aa425e3f4e 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Wrappers.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Wrappers.cs @@ -6,29 +6,16 @@ using Microsoft.ML.Legacy.Models; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Api; -using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; -using Microsoft.ML.Runtime.Internal.Internallearn; -using Microsoft.ML.Runtime.Learners; using Microsoft.ML.Runtime.Model; -using Microsoft.ML.Runtime.Training; -using Microsoft.ML.Tests.Scenarios.Api; using System; using System.Collections.Generic; using System.IO; using System.Linq; -[assembly: LoadableClass(typeof(TransformWrapper), null, typeof(SignatureLoadModel), - "Transform wrapper", TransformWrapper.LoaderSignature)] -[assembly: LoadableClass(typeof(LoaderWrapper), null, typeof(SignatureLoadModel), - "Loader wrapper", LoaderWrapper.LoaderSignature)] - namespace Microsoft.ML.Tests.Scenarios.Api { - using TScalarPredictor = IPredictorProducing; - using TWeightsPredictor = IPredictorWithFeatureWeights; - public sealed class LoaderWrapper : IDataReader, ICanSaveModel { public const string LoaderSignature = "LoaderWrapper"; @@ -65,7 +52,8 @@ private static VersionInfo GetVersionInfo() verWrittenCur: 0x00010001, // Initial verReadableCur: 0x00010001, verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LoaderWrapper).Assembly.FullName); } public LoaderWrapper(IHostEnvironment env, ModelLoadContext ctx) @@ -93,212 +81,6 @@ public LoaderWrapper(IHostEnvironment env, ModelLoadContext ctx) } } - public class TransformWrapper : ITransformer, ICanSaveModel - { - public const string LoaderSignature = "TransformWrapper"; - private const string TransformDirTemplate = "Step_{0:000}"; - - protected readonly IHostEnvironment _env; - protected readonly IDataView _xf; - - public TransformWrapper(IHostEnvironment env, IDataView xf) - { - _env = env; - _xf = xf; - } - - public ISchema GetOutputSchema(ISchema inputSchema) - { - var dv = new EmptyDataView(_env, inputSchema); - var output = ApplyTransformUtils.ApplyAllTransformsToData(_env, _xf, dv); - return output.Schema; - } - - public void Save(ModelSaveContext ctx) - { - ctx.CheckAtModel(); - ctx.SetVersionInfo(GetVersionInfo()); - - var dataPipe = _xf; - var transforms = new List(); - while (dataPipe is IDataTransform xf) - { - // REVIEW: a malicious user could construct a loop in the Source chain, that would - // cause this method to iterate forever (and throw something when the list overflows). There's - // no way to insulate from ALL malicious behavior. - transforms.Add(xf); - dataPipe = xf.Source; - Contracts.AssertValue(dataPipe); - } - transforms.Reverse(); - - ctx.SaveSubModel("Loader", c => BinaryLoader.SaveInstance(_env, c, dataPipe.Schema)); - - ctx.Writer.Write(transforms.Count); - for (int i = 0; i < transforms.Count; i++) - { - var dirName = string.Format(TransformDirTemplate, i); - ctx.SaveModel(transforms[i], dirName); - } - } - - private static VersionInfo GetVersionInfo() - { - return new VersionInfo( - modelSignature: "XF WRPR", - verWrittenCur: 0x00010001, // Initial - verReadableCur: 0x00010001, - verWeCanReadBack: 0x00010001, - loaderSignature: LoaderSignature); - } - - public TransformWrapper(IHostEnvironment env, ModelLoadContext ctx) - { - ctx.CheckAtModel(GetVersionInfo()); - int n = ctx.Reader.ReadInt32(); - - ctx.LoadModel(env, out var loader, "Loader", new MultiFileSource(null)); - - IDataView data = loader; - for (int i = 0; i < n; i++) - { - var dirName = string.Format(TransformDirTemplate, i); - ctx.LoadModel(env, out var xf, dirName, data); - data = xf; - } - - _env = env; - _xf = data; - } - - public IDataView Transform(IDataView input) => ApplyTransformUtils.ApplyAllTransformsToData(_env, _xf, input); - } - - public class ScorerWrapper : TransformWrapper, IPredictionTransformer - where TModel : IPredictor - { - protected readonly string _featureColumn; - - public ScorerWrapper(IHostEnvironment env, IDataView scorer, TModel trainedModel, string featureColumn) - : base(env, scorer) - { - _featureColumn = featureColumn; - Model = trainedModel; - } - - public TModel Model { get; } - - public string FeatureColumn => _featureColumn; - - public ColumnType FeatureColumnType => throw _env.ExceptNotSupp(); - } - - public class BinaryScorerWrapper : ScorerWrapper - where TModel : IPredictor - { - public BinaryScorerWrapper(IHostEnvironment env, TModel model, ISchema inputSchema, string featureColumn, BinaryClassifierScorer.Arguments args) - : base(env, MakeScorer(env, inputSchema, featureColumn, model, args), model, featureColumn) - { - } - - private static IDataView MakeScorer(IHostEnvironment env, ISchema schema, string featureColumn, TModel model, BinaryClassifierScorer.Arguments args) - { - var settings = $"Binary{{{CmdParser.GetSettings(env, args, new BinaryClassifierScorer.Arguments())}}}"; - - var scorerFactorySettings = CmdParser.CreateComponentFactory( - typeof(IComponentFactory), - typeof(SignatureDataScorer), - settings); - - var bindable = ScoreUtils.GetSchemaBindableMapper(env, model, scorerFactorySettings: scorerFactorySettings); - var edv = new EmptyDataView(env, schema); - var data = new RoleMappedData(edv, "Label", featureColumn, opt: true); - - return new BinaryClassifierScorer(env, args, data.Data, bindable.Bind(env, data.Schema), data.Schema); - } - - public BinaryScorerWrapper Clone(BinaryClassifierScorer.Arguments scorerArgs) - { - var scorer = _xf as IDataScorerTransform; - return new BinaryScorerWrapper(_env, Model, scorer.Source.Schema, _featureColumn, scorerArgs); - } - } - - public abstract class TrainerBase : ITrainerEstimator - where TTransformer : ScorerWrapper - where TModel : IPredictor - { - protected readonly IHostEnvironment _env; - protected readonly string _featureCol; - protected readonly string _labelCol; - - public abstract PredictionKind PredictionKind { get; } - - public TrainerInfo Info { get; } - - protected TrainerBase(IHostEnvironment env, TrainerInfo trainerInfo, string featureColumn, string labelColumn) - { - _env = env; - _featureCol = featureColumn; - _labelCol = labelColumn; - Info = trainerInfo; - } - - public TTransformer Fit(IDataView input) - { - return TrainTransformer(input); - } - - protected TTransformer TrainTransformer(IDataView trainSet, - IDataView validationSet = null, IPredictor initPredictor = null) - { - var cachedTrain = Info.WantCaching ? new CacheDataView(_env, trainSet, prefetch: null) : trainSet; - - var trainRoles = new RoleMappedData(cachedTrain, label: _labelCol, feature: _featureCol); - var emptyData = new EmptyDataView(_env, trainSet.Schema); - IDataView normalizer = emptyData; - - if (Info.NeedNormalization && trainRoles.Schema.FeaturesAreNormalized() == false) - { - var view = NormalizeTransform.CreateMinMaxNormalizer(_env, trainRoles.Data, name: trainRoles.Schema.Feature.Name); - normalizer = ApplyTransformUtils.ApplyAllTransformsToData(_env, view, emptyData, cachedTrain); - - trainRoles = new RoleMappedData(view, trainRoles.Schema.GetColumnRoleNames()); - } - - RoleMappedData validRoles; - - if (validationSet == null) - validRoles = null; - else - { - var cachedValid = Info.WantCaching ? new CacheDataView(_env, validationSet, prefetch: null) : validationSet; - cachedValid = ApplyTransformUtils.ApplyAllTransformsToData(_env, normalizer, cachedValid); - validRoles = new RoleMappedData(cachedValid, label: _labelCol, feature: _featureCol); - } - - var pred = TrainCore(new TrainContext(trainRoles, validRoles, initPredictor)); - - var scoreRoles = new RoleMappedData(normalizer, label: _labelCol, feature: _featureCol); - return MakeScorer(pred, scoreRoles); - } - - public SchemaShape GetOutputSchema(SchemaShape inputSchema) - { - throw new NotImplementedException(); - } - - protected abstract TModel TrainCore(TrainContext trainContext); - - protected abstract TTransformer MakeScorer(TModel predictor, RoleMappedData data); - - protected ScorerWrapper MakeScorerBasic(TModel predictor, RoleMappedData data) - { - var scorer = ScoreUtils.GetScorer(predictor, data, _env, data.Schema); - return (TTransformer)(new ScorerWrapper(_env, scorer, predictor, data.Schema.Feature.Name)); - } - } - public sealed class MyBinaryClassifierEvaluator { private readonly IHostEnvironment _env; diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Evaluation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Evaluation.cs index 3004155784..e90af95d3c 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Evaluation.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Evaluation.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -20,13 +21,10 @@ public partial class ApiScenariosTests [Fact] public void Evaluation() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); @@ -46,7 +44,7 @@ public void Evaluation() var model = env.CreatePredictionEngine(scorer); // Take a couple examples out of the test data and run predictions on top. - var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.validFilename))); var testData = testLoader.AsEnumerable(env, false); var dataEval = new RoleMappedData(scorer, label: "Label", feature: "Features", opt: true); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs index 00d0ee2a34..15e0824c7d 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs @@ -1,6 +1,8 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.TestFramework; using System; using System.Linq; using Xunit; @@ -18,10 +20,10 @@ public partial class ApiScenariosTests [Fact] void Extensibility() { - var dataPath = GetDataPath(IrisDataPath); - using (var env = new LocalEnvironment()) + using (var env = new LocalEnvironment() + .AddStandardComponents()) // ScoreUtils.GetScorer requires scorers to be registered in the ComponentCatalog { - var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename))); Action action = (i, j) => { j.Label = i.Label; @@ -35,7 +37,7 @@ void Extensibility() var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") .Transform(term); - var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }); + var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label"); IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat; var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features"); @@ -50,7 +52,7 @@ void Extensibility() var keyToValue = new KeyToValueTransform(env, "PredictedLabel").Transform(scorer); var model = env.CreatePredictionEngine(keyToValue); - var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename))); var testData = testLoader.AsEnumerable(env, false); foreach (var input in testData.Take(20)) { diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/FileBasedSavingOfData.cs b/test/Microsoft.ML.Tests/Scenarios/Api/FileBasedSavingOfData.cs index da6c4fcedb..facba8eeb3 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/FileBasedSavingOfData.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/FileBasedSavingOfData.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -21,13 +22,10 @@ public partial class ApiScenariosTests [Fact] void FileBasedSavingOfData() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); var saver = new BinarySaver(env, new BinarySaver.Arguments()); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/IntrospectiveTraining.cs b/test/Microsoft.ML.Tests/Scenarios/Api/IntrospectiveTraining.cs index 254485cebc..198b3c312b 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/IntrospectiveTraining.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/IntrospectiveTraining.cs @@ -7,6 +7,7 @@ using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.Runtime.TextAnalytics; using System.Collections.Generic; using Xunit; @@ -40,12 +41,11 @@ private TOut GetValue(Dictionary keyValues, string key) [Fact] public void IntrospectiveTraining() { - var dataPath = GetDataPath(SentimentDataPath); using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var words = WordBagTransform.Create(env, new WordBagTransform.Arguments() { @@ -75,12 +75,8 @@ public void IntrospectiveTraining() linearPredictor.GetFeatureWeights(ref weights); var topicSummary = lda.GetTopicSummary(); - var treeTrainer = new FastTreeBinaryClassificationTrainer(env, - new FastTreeBinaryClassificationTrainer.Arguments - { - NumTrees = 2 - } - ); + var treeTrainer = new FastTreeBinaryClassificationTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features, + advancedSettings: s =>{ s.NumTrees = 2; }); var ftPredictor = treeTrainer.Train(new Runtime.TrainContext(trainRoles)); FastTreeBinaryPredictor treePredictor; if (ftPredictor is CalibratedPredictorBase calibrator) diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Metacomponents.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Metacomponents.cs index ed48171f4d..994b47310b 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Metacomponents.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Metacomponents.cs @@ -4,8 +4,10 @@ using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.FastTree; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -20,10 +22,9 @@ public partial class ApiScenariosTests [Fact] public void Metacomponents() { - var dataPath = GetDataPath(IrisDataPath); using (var env = new LocalEnvironment()) { - var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename))); var term = TermTransform.Create(env, loader, "Label"); var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term); var trainer = new Ova(env, new Ova.Arguments diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/MultithreadedPrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/MultithreadedPrediction.cs index cb6b4dd6f0..e70c46a73f 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/MultithreadedPrediction.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/MultithreadedPrediction.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using System.Linq; using System.Threading.Tasks; using Xunit; @@ -25,13 +26,10 @@ public partial class ApiScenariosTests [Fact] void MultithreadedPrediction() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); @@ -52,7 +50,7 @@ void MultithreadedPrediction() var model = env.CreatePredictionEngine(scorer); // Take a couple examples out of the test data and run predictions on top. - var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename))); var testData = testLoader.AsEnumerable(env, false); Parallel.ForEach(testData, (input) => diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/ReconfigurablePrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/ReconfigurablePrediction.cs index 307f5e086a..fdfcad6653 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/ReconfigurablePrediction.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/ReconfigurablePrediction.cs @@ -6,6 +6,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -21,13 +22,12 @@ public partial class ApiScenariosTests [Fact] void ReconfigurablePrediction() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); + var data = GetDataPath(TestDatasets.Sentiment.trainFilename); using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(data)); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/SimpleTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/SimpleTrainAndPredict.cs index 26b9869aa8..2bb45ed61a 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/SimpleTrainAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/SimpleTrainAndPredict.cs @@ -7,6 +7,8 @@ using Microsoft.ML.Runtime.Learners; using Xunit; using System.Linq; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.RunTests; namespace Microsoft.ML.Tests.Scenarios.Api { @@ -21,13 +23,12 @@ public partial class ApiScenariosTests [Fact] public void SimpleTrainAndPredict() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); + var dataset = TestDatasets.Sentiment; using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(dataset.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); @@ -48,7 +49,7 @@ public void SimpleTrainAndPredict() var model = env.CreatePredictionEngine(scorer); // Take a couple examples out of the test data and run predictions on top. - var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(dataset.testFilename))); var testData = testLoader.AsEnumerable(env, false); foreach (var input in testData.Take(5)) { diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/TrainSaveModelAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/TrainSaveModelAndPredict.cs index 7cfe2aa73b..150f72936b 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/TrainSaveModelAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/TrainSaveModelAndPredict.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using System.Linq; using Xunit; @@ -21,13 +22,10 @@ public partial class ApiScenariosTests [Fact] public void TrainSaveModelAndPredict() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); @@ -55,7 +53,7 @@ public void TrainSaveModelAndPredict() } // Take a couple examples out of the test data and run predictions on top. - var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename))); var testData = testLoader.AsEnumerable(env, false); foreach (var input in testData.Take(5)) { diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithInitialPredictor.cs b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithInitialPredictor.cs index 9683af241c..6a37b1f408 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithInitialPredictor.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithInitialPredictor.cs @@ -5,6 +5,7 @@ using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -18,12 +19,11 @@ public partial class ApiScenariosTests [Fact] public void TrainWithInitialPredictor() { - var dataPath = GetDataPath(SentimentDataPath); using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); var trainData = trans; diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithValidationSet.cs b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithValidationSet.cs index c2fabcd970..f2460df8f7 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithValidationSet.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithValidationSet.cs @@ -4,6 +4,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -17,13 +18,11 @@ public partial class ApiScenariosTests [Fact] public void TrainWithValidationSet() { - var dataPath = GetDataPath(SentimentDataPath); - var validationDataPath = GetDataPath(SentimentTestPath); using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); var trainData = trans; @@ -33,7 +32,7 @@ public void TrainWithValidationSet() // to create another loader, or to save the loader to model file and then reload. // A new one is not always feasible, but this time it is. - var validLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(validationDataPath)); + var validLoader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.testFilename))); var validData = ApplyTransformUtils.ApplyAllTransformsToData(env, trainData, validLoader); // Cache both datasets. @@ -41,10 +40,7 @@ public void TrainWithValidationSet() var cachedValid = new CacheDataView(env, validData, prefetch: null); // Train. - var trainer = new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments - { - NumTrees = 3 - }); + var trainer = new FastTreeBinaryClassificationTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features, advancedSettings:s => { s.NumTrees = 3; }); var trainRoles = new RoleMappedData(cachedTrain, label: "Label", feature: "Features"); var validRoles = new RoleMappedData(cachedValid, label: "Label", feature: "Features"); trainer.Train(new Runtime.TrainContext(trainRoles, validRoles)); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Visibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Visibility.cs index 47a91e44c2..fd9c92b282 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Visibility.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Visibility.cs @@ -1,8 +1,10 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.RunTests; using Xunit; namespace Microsoft.ML.Tests.Scenarios.Api @@ -20,13 +22,10 @@ public partial class ApiScenariosTests [Fact] void Visibility() { - var dataPath = GetDataPath(SentimentDataPath); - var testDataPath = GetDataPath(SentimentTestPath); - using (var env = new LocalEnvironment(seed: 1, conc: 1)) { // Pipeline. - var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + var loader = TextLoader.ReadFile(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(false), loader); @@ -44,11 +43,11 @@ void Visibility() Assert.True(cursor.Schema.TryGetColumnIndex("Features_TransformedText", out int transformedTextColumn)); Assert.True(cursor.Schema.TryGetColumnIndex("Features", out int featureColumn)); - var originalTextGettter = cursor.GetGetter(textColumn); - var transformedTextGettter = cursor.GetGetter>(transformedTextColumn); + var originalTextGettter = cursor.GetGetter>(textColumn); + var transformedTextGettter = cursor.GetGetter>>(transformedTextColumn); var featureGettter = cursor.GetGetter>(featureColumn); - DvText text = default; - VBuffer transformedText = default; + ReadOnlyMemory text = default; + VBuffer> transformedText = default; VBuffer features = default; while (cursor.MoveNext()) { diff --git a/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs b/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs index aeaf0216cd..f841439322 100644 --- a/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs @@ -3,7 +3,6 @@ // See the LICENSE file in the project root for more information. using Microsoft.ML.Runtime.Api; -using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.TestFramework; using Xunit; using Xunit.Abstractions; diff --git a/test/Microsoft.ML.Tests/Scenarios/PipelineApi/MultithreadedPrediction.cs b/test/Microsoft.ML.Tests/Scenarios/PipelineApi/MultithreadedPrediction.cs index a239faa2c0..77cdc9cc3f 100644 --- a/test/Microsoft.ML.Tests/Scenarios/PipelineApi/MultithreadedPrediction.cs +++ b/test/Microsoft.ML.Tests/Scenarios/PipelineApi/MultithreadedPrediction.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. diff --git a/test/Microsoft.ML.Tests/Scenarios/PipelineApi/SimpleTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/PipelineApi/SimpleTrainAndPredict.cs index 5c6e5a2347..f3e0a2f70e 100644 --- a/test/Microsoft.ML.Tests/Scenarios/PipelineApi/SimpleTrainAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/PipelineApi/SimpleTrainAndPredict.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. diff --git a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs index 3e33fcea5a..e0517bbdd1 100644 --- a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs @@ -178,13 +178,13 @@ public void CrossValidateSentimentModelTest() var sentiments = GetTestData(); var predictions = cv.PredictorModels[0].Predict(sentiments); Assert.Equal(2, predictions.Count()); - Assert.True(predictions.ElementAt(0).Sentiment.IsTrue); - Assert.True(predictions.ElementAt(1).Sentiment.IsTrue); + Assert.True(predictions.ElementAt(0).Sentiment); + Assert.True(predictions.ElementAt(1).Sentiment); predictions = cv.PredictorModels[1].Predict(sentiments); Assert.Equal(2, predictions.Count()); - Assert.True(predictions.ElementAt(0).Sentiment.IsTrue); - Assert.True(predictions.ElementAt(1).Sentiment.IsTrue); + Assert.True(predictions.ElementAt(0).Sentiment); + Assert.True(predictions.ElementAt(1).Sentiment); } private void ValidateBinaryMetricsSymSGD(BinaryClassificationMetrics metrics) @@ -438,8 +438,8 @@ private void ValidateExamples(PredictionModel model) @@ -459,8 +459,8 @@ private void ValidateExamplesSymSGD(PredictionModel { - NumLeaves = 5, - NumTrees = 5, - MinDocumentsInLeafs = 2 + s.NumLeaves = 5; + s.NumTrees = 5; + s.MinDocumentsInLeafs = 2; }); var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); @@ -75,8 +75,8 @@ public void TrainAndPredictSentimentModelWithDirectionInstantiationTest() var sentiments = GetTestData(); var predictions = model.Predict(sentiments, false); Assert.Equal(2, predictions.Count()); - Assert.True(predictions.ElementAt(0).Sentiment.IsTrue); - Assert.True(predictions.ElementAt(1).Sentiment.IsTrue); + Assert.True(predictions.ElementAt(0).Sentiment); + Assert.True(predictions.ElementAt(1).Sentiment); // Get feature importance based on feature gain during training var summary = ((FeatureWeightsCalibratedPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema); @@ -123,7 +123,7 @@ public void TrainAndPredictSentimentModelWithDirectionInstantiationTestWithWordE }, loader); - var trans = new WordEmbeddingsTransform(env, new WordEmbeddingsTransform.Arguments() + var trans = WordEmbeddingsTransform.Create(env, new WordEmbeddingsTransform.Arguments() { Column = new WordEmbeddingsTransform.Column[1] { @@ -136,12 +136,12 @@ public void TrainAndPredictSentimentModelWithDirectionInstantiationTestWithWordE ModelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe, }, text); // Train - var trainer = new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments() - { - NumLeaves = 5, - NumTrees = 5, - MinDocumentsInLeafs = 2 - }); + var trainer = new FastTreeBinaryClassificationTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features, advancedSettings: s=> + { + s.NumLeaves = 5; + s.NumTrees = 5; + s.MinDocumentsInLeafs = 2; + }); var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); var pred = trainer.Train(trainRoles); @@ -158,8 +158,8 @@ public void TrainAndPredictSentimentModelWithDirectionInstantiationTestWithWordE var sentiments = GetTestData(); var predictions = model.Predict(sentiments, false); Assert.Equal(2, predictions.Count()); - Assert.True(predictions.ElementAt(0).Sentiment.IsTrue); - Assert.True(predictions.ElementAt(1).Sentiment.IsTrue); + Assert.True(predictions.ElementAt(0).Sentiment); + Assert.True(predictions.ElementAt(1).Sentiment); // Get feature importance based on feature gain during training var summary = ((FeatureWeightsCalibratedPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema); diff --git a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs index da9102fbb2..609ccf9c1c 100644 --- a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs +++ b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs @@ -10,6 +10,7 @@ using Microsoft.ML.Runtime.LightGBM; using Microsoft.ML.Transforms; using Microsoft.ML.Transforms.TensorFlow; +using System; using System.Collections.Generic; using System.IO; using Xunit; @@ -181,6 +182,95 @@ public void TensorFlowTransformInceptionTest() } } + [Fact] + public void TensorFlowInputsOutputsSchemaTest() + { + using (var env = new ConsoleEnvironment(seed: 1, conc: 1)) + { + var model_location = "mnist_model/frozen_saved_model.pb"; + var schema = TensorFlowUtils.GetModelSchema(env, model_location); + Assert.Equal(54, schema.ColumnCount); + Assert.True(schema.TryGetColumnIndex("Placeholder", out int col)); + var type = schema.GetColumnType(col).AsVector; + Assert.Equal(2, type.DimCount); + Assert.Equal(28, type.GetDim(0)); + Assert.Equal(28, type.GetDim(1)); + var metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col); + Assert.NotNull(metadataType); + Assert.True(metadataType.IsText); + ReadOnlyMemory opType = default; + schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType); + Assert.Equal("Placeholder", opType.ToString()); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col); + Assert.Null(metadataType); + + Assert.True(schema.TryGetColumnIndex("conv2d/Conv2D/ReadVariableOp", out col)); + type = schema.GetColumnType(col).AsVector; + Assert.Equal(4, type.DimCount); + Assert.Equal(5, type.GetDim(0)); + Assert.Equal(5, type.GetDim(1)); + Assert.Equal(1, type.GetDim(2)); + Assert.Equal(32, type.GetDim(3)); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col); + Assert.NotNull(metadataType); + Assert.True(metadataType.IsText); + schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType); + Assert.Equal("Identity", opType.ToString()); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col); + Assert.NotNull(metadataType); + VBuffer> inputOps = default; + schema.GetMetadata(TensorFlowUtils.InputOps, col, ref inputOps); + Assert.Equal(1, inputOps.Length); + Assert.Equal("conv2d/kernel", inputOps.Values[0].ToString()); + + Assert.True(schema.TryGetColumnIndex("conv2d/Conv2D", out col)); + type = schema.GetColumnType(col).AsVector; + Assert.Equal(3, type.DimCount); + Assert.Equal(28, type.GetDim(0)); + Assert.Equal(28, type.GetDim(1)); + Assert.Equal(32, type.GetDim(2)); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col); + Assert.NotNull(metadataType); + Assert.True(metadataType.IsText); + schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType); + Assert.Equal("Conv2D", opType.ToString()); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col); + Assert.NotNull(metadataType); + schema.GetMetadata(TensorFlowUtils.InputOps, col, ref inputOps); + Assert.Equal(2, inputOps.Length); + Assert.Equal("reshape/Reshape", inputOps.Values[0].ToString()); + Assert.Equal("conv2d/Conv2D/ReadVariableOp", inputOps.Values[1].ToString()); + + Assert.True(schema.TryGetColumnIndex("Softmax", out col)); + type = schema.GetColumnType(col).AsVector; + Assert.Equal(1, type.DimCount); + Assert.Equal(10, type.GetDim(0)); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col); + Assert.NotNull(metadataType); + Assert.True(metadataType.IsText); + schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType); + Assert.Equal("Softmax", opType.ToString()); + metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col); + Assert.NotNull(metadataType); + schema.GetMetadata(TensorFlowUtils.InputOps, col, ref inputOps); + Assert.Equal(1, inputOps.Length); + Assert.Equal("sequential/dense_1/BiasAdd", inputOps.Values[0].ToString()); + + model_location = "model_matmul/frozen_saved_model.pb"; + schema = TensorFlowUtils.GetModelSchema(env, model_location); + char name = 'a'; + for (int i = 0; i < schema.ColumnCount; i++) + { + Assert.Equal(name.ToString(), schema.GetColumnName(i)); + type = schema.GetColumnType(i).AsVector; + Assert.Equal(2, type.DimCount); + Assert.Equal(2, type.GetDim(0)); + Assert.Equal(2, type.GetDim(1)); + name++; + } + } + } + [Fact] public void TensorFlowTransformMNISTConvTest() { @@ -330,7 +420,14 @@ public void TensorFlowTransformCifar() var imageWidth = 32; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] @@ -384,8 +481,14 @@ public void TensorFlowTransformCifarInvalidShape() var imageWidth = 28; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); - var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); - + var data = TextLoader.Create(env, new TextLoader.Arguments() + { + Column = new[] + { + new TextLoader.Column("ImagePath", DataKind.TX, 0), + new TextLoader.Column("Name", DataKind.TX, 1), + } + }, new MultiFileSource(dataFile)); var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments() { Column = new ImageLoaderTransform.Column[1] diff --git a/test/Microsoft.ML.Tests/TensorFlowEstimatorTests.cs b/test/Microsoft.ML.Tests/TensorFlowEstimatorTests.cs index d102ea758c..6ea74d8dbb 100644 --- a/test/Microsoft.ML.Tests/TensorFlowEstimatorTests.cs +++ b/test/Microsoft.ML.Tests/TensorFlowEstimatorTests.cs @@ -134,7 +134,7 @@ void TestCommandLine() { using (var env = new ConsoleEnvironment()) { - Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=a:R4:0-3 col=b:R4:0-3} xf=TFTransform{inputs=a inputs=b outputs=c model={model_matmul/frozen_saved_model.pb}} in=f:\2.txt" }), (int)0); + Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=a:R4:0-3 col=b:R4:0-3} xf=TFTransform{inputs=a inputs=b outputs=c model={model_matmul/frozen_saved_model.pb}} in=f:\2.txt" })); } } diff --git a/test/Microsoft.ML.Tests/TermEstimatorTests.cs b/test/Microsoft.ML.Tests/TermEstimatorTests.cs index 040abe6d47..93c827ff30 100644 --- a/test/Microsoft.ML.Tests/TermEstimatorTests.cs +++ b/test/Microsoft.ML.Tests/TermEstimatorTests.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -8,6 +8,7 @@ using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.Runtime.Tools; +using System; using System.IO; using Xunit; using Xunit.Abstractions; @@ -137,11 +138,11 @@ void TestMetadataCopy() var dataView = ComponentCreation.CreateDataView(Env, data); var termEst = new TermEstimator(Env, new[] { new TermTransform.ColumnInfo("Term" ,"T") }); + var termTransformer = termEst.Fit(dataView); var result = termTransformer.Transform(dataView); - result.Schema.TryGetColumnIndex("T", out int termIndex); - var names1 = default(VBuffer); + var names1 = default(VBuffer>); var type1 = result.Schema.GetColumnType(termIndex); int size = type1.ItemType.IsKey ? type1.ItemType.KeyCount : -1; result.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues, termIndex, ref names1); @@ -153,7 +154,7 @@ void TestCommandLine() { using (var env = new ConsoleEnvironment()) { - Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} in=f:\2.txt" }), (int)0); + Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} in=f:\2.txt" })); } } diff --git a/test/Microsoft.ML.Tests/TextLoaderTests.cs b/test/Microsoft.ML.Tests/TextLoaderTests.cs index 39a83d0f61..c3d73aba79 100644 --- a/test/Microsoft.ML.Tests/TextLoaderTests.cs +++ b/test/Microsoft.ML.Tests/TextLoaderTests.cs @@ -2,18 +2,134 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Microsoft.ML; -using Microsoft.ML.Data; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.TestFramework; using System; +using System.IO; using Xunit; using Xunit.Abstractions; namespace Microsoft.ML.EntryPoints.Tests { + public sealed class TextLoaderTestPipe : TestDataPipeBase + { + public TextLoaderTestPipe(ITestOutputHelper output) + : base(output) + { + + } + + [Fact] + public void TestTextLoaderDataTypes() + { + string pathData = DeleteOutputPath("SavePipe", "TextInput.txt"); + File.WriteAllLines(pathData, new string[] { + string.Format("{0},{1},{2},{3}", sbyte.MinValue, short.MinValue, int.MinValue, long.MinValue), + string.Format("{0},{1},{2},{3}", sbyte.MaxValue, short.MaxValue, int.MaxValue, long.MaxValue), + "\"\",\"\",\"\",\"\"", + }); + + var data = TestCore(pathData, true, + new[] { + "loader=Text{col=DvInt1:I1:0 col=DvInt2:I2:1 col=DvInt4:I4:2 col=DvInt8:I8:3 sep=comma}", + }, logCurs: true); + + using (var cursor = data.GetRowCursor((a => true))) + { + var col1 = cursor.GetGetter(0); + var col2 = cursor.GetGetter(1); + var col3 = cursor.GetGetter(2); + var col4 = cursor.GetGetter(3); + + Assert.True(cursor.MoveNext()); + + sbyte[] sByteTargets = new sbyte[] { sbyte.MinValue, sbyte.MaxValue, default}; + short[] shortTargets = new short[] { short.MinValue, short.MaxValue, default }; + int[] intTargets = new int[] { int.MinValue, int.MaxValue, default }; + long[] longTargets = new long[] { long.MinValue, long.MaxValue, default }; + + int i = 0; + for (; i < sByteTargets.Length; i++) + { + sbyte sbyteValue = -1; + col1(ref sbyteValue); + Assert.Equal(sByteTargets[i], sbyteValue); + + short shortValue = -1; + col2(ref shortValue); + Assert.Equal(shortTargets[i], shortValue); + + int intValue = -1; + col3(ref intValue); + Assert.Equal(intTargets[i], intValue); + + long longValue = -1; + col4(ref longValue); + Assert.Equal(longTargets[i], longValue); + + if (i < sByteTargets.Length - 1) + Assert.True(cursor.MoveNext()); + else + Assert.False(cursor.MoveNext()); + } + + Assert.Equal(i, sByteTargets.Length); + } + } + + [Fact] + public void TestTextLoaderInvalidLongMin() + { + string pathData = DeleteOutputPath("SavePipe", "TextInput.txt"); + File.WriteAllLines(pathData, new string[] { + "-9223372036854775809" + + }); + + try + { + var data = TestCore(pathData, true, + new[] { + "loader=Text{col=DvInt8:I8:0 sep=comma}", + }, logCurs: true); + } + catch(Exception ex) + { + Assert.Equal("Value could not be parsed from text to long.", ex.Message); + return; + } + + Assert.True(false, "Test failed."); + } + + [Fact] + public void TestTextLoaderInvalidLongMax() + { + string pathData = DeleteOutputPath("SavePipe", "TextInput.txt"); + File.WriteAllLines(pathData, new string[] { + "9223372036854775808" + }); + + try + { + var data = TestCore(pathData, true, + new[] { + "loader=Text{col=DvInt8:I8:0 sep=comma}", + }, logCurs: true); + } + catch (Exception ex) + { + Assert.Equal("Value could not be parsed from text to long.", ex.Message); + return; + } + + Assert.True(false, "Test failed."); + } + } + public class TextLoaderTests : BaseTestClass { public TextLoaderTests(ITestOutputHelper output) @@ -74,7 +190,7 @@ public void CanSuccessfullyRetrieveQuotedData() using (var cursor = data.GetRowCursor((a => true))) { var IDGetter = cursor.GetGetter(0); - var TextGetter = cursor.GetGetter(1); + var TextGetter = cursor.GetGetter>(1); Assert.True(cursor.MoveNext()); @@ -82,7 +198,7 @@ public void CanSuccessfullyRetrieveQuotedData() IDGetter(ref ID); Assert.Equal(1, ID); - DvText Text = new DvText(); + ReadOnlyMemory Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("This text contains comma, within quotes.", Text.ToString()); @@ -92,7 +208,7 @@ public void CanSuccessfullyRetrieveQuotedData() IDGetter(ref ID); Assert.Equal(2, ID); - Text = new DvText(); + Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("This text contains extra punctuations and special characters.;*<>?!@#$%^&*()_+=-{}|[]:;'", Text.ToString()); @@ -102,7 +218,7 @@ public void CanSuccessfullyRetrieveQuotedData() IDGetter(ref ID); Assert.Equal(3, ID); - Text = new DvText(); + Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("This text has no quotes", Text.ToString()); @@ -197,7 +313,7 @@ public void CanSuccessfullyTrimSpaces() using (var cursor = data.GetRowCursor((a => true))) { var IDGetter = cursor.GetGetter(0); - var TextGetter = cursor.GetGetter(1); + var TextGetter = cursor.GetGetter>(1); Assert.True(cursor.MoveNext()); @@ -205,7 +321,7 @@ public void CanSuccessfullyTrimSpaces() IDGetter(ref ID); Assert.Equal(1, ID); - DvText Text = new DvText(); + ReadOnlyMemory Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("There is a space at the end", Text.ToString()); @@ -215,7 +331,7 @@ public void CanSuccessfullyTrimSpaces() IDGetter(ref ID); Assert.Equal(2, ID); - Text = new DvText(); + Text = new ReadOnlyMemory(); TextGetter(ref Text); Assert.Equal("There is no space at the end", Text.ToString()); diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/FAFMEstimator.cs b/test/Microsoft.ML.Tests/TrainerEstimators/FAFMEstimator.cs new file mode 100644 index 0000000000..87fa345d40 --- /dev/null +++ b/test/Microsoft.ML.Tests/TrainerEstimators/FAFMEstimator.cs @@ -0,0 +1,53 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Core.Data; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.FactorizationMachine; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests.TrainerEstimators +{ + public partial class TrainerEstimators : TestDataPipeBase + { + [Fact] + public void FieldAwareFactorizationMachine_Estimator() + { + var data = new TextLoader(Env, GetFafmBCLoaderArgs()) + .Read(new MultiFileSource(GetDataPath(TestDatasets.breastCancer.trainFilename))); + + var est = new FieldAwareFactorizationMachineTrainer(Env, "Label", new[] { "Feature1", "Feature2", "Feature3", "Feature4" }, + advancedSettings:s=> + { + s.Shuffle = false; + s.Iters = 3; + s.LatentDim = 7; + }); + + TestEstimatorCore(est, data); + + Done(); + } + + private TextLoader.Arguments GetFafmBCLoaderArgs() + { + return new TextLoader.Arguments() + { + Separator = "\t", + HasHeader = false, + Column = new[] + { + new TextLoader.Column("Feature1", DataKind.R4, new [] { new TextLoader.Range(1, 2) }), + new TextLoader.Column("Feature2", DataKind.R4, new [] { new TextLoader.Range(3, 4) }), + new TextLoader.Column("Feature3", DataKind.R4, new [] { new TextLoader.Range(5, 6) }), + new TextLoader.Column("Feature4", DataKind.R4, new [] { new TextLoader.Range(7, 9) }), + new TextLoader.Column("Label", DataKind.BL, 0) + } + }; + } + } +} diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs index eb4e845a6c..6b8f68fb62 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs @@ -12,14 +12,8 @@ namespace Microsoft.ML.Tests.TrainerEstimators { - public partial class MetalinearEstimators : TestDataPipeBase + public partial class TrainerEstimators { - - public MetalinearEstimators(ITestOutputHelper output) : base(output) - { - } - - /// /// OVA with calibrator argument /// diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/OnlineLinearTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/OnlineLinearTests.cs index cd0cb3fe94..2dea99e131 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/OnlineLinearTests.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/OnlineLinearTests.cs @@ -4,19 +4,14 @@ using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.FactorizationMachine; using Microsoft.ML.Runtime.Learners; -using Microsoft.ML.Runtime.RunTests; using Xunit; -using Xunit.Abstractions; -namespace Microsoft.ML.Tests.Transformers +namespace Microsoft.ML.Tests.TrainerEstimators { - public sealed class OnlineLinearTests : TestDataPipeBase + public partial class TrainerEstimators { - public OnlineLinearTests(ITestOutputHelper helper) : base(helper) - { - } - [Fact(Skip = "AP is now uncalibrated but advertises as calibrated")] public void OnlineLinearWorkout() { diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs new file mode 100644 index 0000000000..b510ab3bb0 --- /dev/null +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -0,0 +1,67 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Core.Data; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests +{ + public class SimpleEstimatorTests : TestDataPipeBase + { + private IDataView GetBreastCancerDataviewWithTextColumns() + { + var dataPath = GetDataPath("breast-cancer.txt"); + var inputFile = new SimpleFileHandle(Env, dataPath, false, false); + return ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + { + Arguments = + { + HasHeader = true, + Column = new[] + { + new TextLoader.Column("Label", type: null, 0), + new TextLoader.Column("F1", DataKind.Text, 1), + new TextLoader.Column("F2", DataKind.I4, 2), + new TextLoader.Column("Rest", type: null, new [] { new TextLoader.Range(3, 9) }) + } + }, + + InputFile = inputFile + }).Data; + } + + public SimpleEstimatorTests(ITestOutputHelper output) : base(output) + { + } + + [Fact] + public void TestEstimatorRandom() + { + var dataView = GetBreastCancerDataviewWithTextColumns(); + var pipe = new RandomTrainer(Env); + + // Test only that the schema propagation works. + // REVIEW: the save/load is not preserving the full state of the random predictor. This is unfortunate, but we don't care too much at this point. + TestEstimatorCore(pipe, new EmptyDataView(Env, dataView.Schema)); + Done(); + } + + [Fact] + public void TestEstimatorPrior() + { + var dataView = GetBreastCancerDataviewWithTextColumns(); + + var pipe = new PriorTrainer(Contracts.CheckRef(Env, nameof(Env)).Register("PriorPredictor"), "Label"); + TestEstimatorCore(pipe, dataView); + Done(); + } + } +} diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs index 6a31a38237..0eab4c7e1b 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs @@ -9,14 +9,10 @@ using Xunit; using Xunit.Abstractions; -namespace Microsoft.ML.Tests.Transformers +namespace Microsoft.ML.Tests.TrainerEstimators { - public sealed class SdcaTests : TestDataPipeBase + public partial class TrainerEstimators { - public SdcaTests(ITestOutputHelper helper) : base(helper) - { - } - [Fact] public void SdcaWorkout() { diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs new file mode 100644 index 0000000000..dc28fccc97 --- /dev/null +++ b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs @@ -0,0 +1,20 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Core.Data; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests.TrainerEstimators +{ + public partial class TrainerEstimators : TestDataPipeBase + { + public TrainerEstimators(ITestOutputHelper helper) : base(helper) + { + } + } +} diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs new file mode 100644 index 0000000000..fa6784bd69 --- /dev/null +++ b/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs @@ -0,0 +1,123 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.RunTests; +using System.Linq; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests.TrainerEstimators +{ + public partial class TreeEstimators : TestDataPipeBase + { + + public TreeEstimators(ITestOutputHelper output) : base(output) + { + } + + + /// + /// FastTreeBinaryClassification TrainerEstimator test + /// + [Fact] + public void FastTreeBinaryEstimator() + { + using (var env = new LocalEnvironment(seed: 1, conc: 1)) + { + var reader = new TextLoader(env, + new TextLoader.Arguments() + { + Separator = "\t", + HasHeader = true, + Column = new[] + { + new TextLoader.Column("Label", DataKind.BL, 0), + new TextLoader.Column("SentimentText", DataKind.Text, 1) + } + }); + + var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename))); + + // Pipeline. + var pipeline = new TextTransform(env, "SentimentText", "Features") + .Append(new FastTreeBinaryClassificationTrainer(env, "Label", "Features", advancedSettings: s => { + s.NumTrees = 10; + s.NumThreads = 1; + s.NumLeaves = 5; + })); + + TestEstimatorCore(pipeline, data); + } + } + + /// + /// FastTreeBinaryClassification TrainerEstimator test + /// + [Fact] + public void FastTreeRankerEstimator() + { + using (var env = new LocalEnvironment(seed: 1, conc: 1)) + { + var reader = new TextLoader(env, new TextLoader.Arguments + { + HasHeader = true, + Separator ="\t", + Column = new[] + { + new TextLoader.Column("Label", DataKind.R4, 0), + new TextLoader.Column("Workclass", DataKind.Text, 1), + new TextLoader.Column("NumericFeatures", DataKind.R4, new [] { new TextLoader.Range(9, 14) }) + } + }); + var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.adultRanking.trainFilename))); + + + // Pipeline. + var pipeline = new TermEstimator(env, new[]{ + new TermTransform.ColumnInfo("Workclass", "Group"), + new TermTransform.ColumnInfo("Label", "Label0") }) + .Append(new FastTreeRankingTrainer(env, "Label0", "NumericFeatures", "Group", + advancedSettings: s => { s.NumTrees = 10; })); + + TestEstimatorCore(pipeline, data); + } + } + + /// + /// FastTreeRegressor TrainerEstimator test + /// + [Fact] + public void FastTreeRegressorEstimator() + { + using (var env = new LocalEnvironment(seed: 1, conc: 1)) + { + // "loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+}" + var reader = new TextLoader(env, + new TextLoader.Arguments() + { + Separator = ";", + HasHeader = true, + Column = new[] + { + new TextLoader.Column("Label", DataKind.R4, 11), + new TextLoader.Column("Features", DataKind.R4, new [] { new TextLoader.Range(0, 10) } ) + } + }); + + var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.generatedRegressionDatasetmacro.trainFilename))); + + // Pipeline. + var pipeline = new FastTreeRegressionTrainer(env, "Label", "Features", advancedSettings: s => { + s.NumTrees = 10; + s.NumThreads = 1; + s.NumLeaves = 5; + }); + + TestEstimatorCore(pipeline, data); + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Transformers/CategoricalTests.cs b/test/Microsoft.ML.Tests/Transformers/CategoricalTests.cs index 89e95ec08e..e4746ccffd 100644 --- a/test/Microsoft.ML.Tests/Transformers/CategoricalTests.cs +++ b/test/Microsoft.ML.Tests/Transformers/CategoricalTests.cs @@ -8,6 +8,7 @@ using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.Runtime.Tools; +using System; using System.IO; using System.Linq; using Xunit; @@ -147,8 +148,8 @@ private void ValidateBinMetadata(IDataView result) Assert.True(result.Schema.TryGetColumnIndex("CatF", out int colH)); var types = result.Schema.GetMetadataTypes(colA); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.SlotNames }); - VBuffer slots = default; - DvBool normalized = default; + VBuffer> slots = default; + bool normalized = default; result.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colA, ref slots); Assert.True(slots.Length == 6); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[6] { "[0].Bit2", "[0].Bit1", "[0].Bit0", "[1].Bit2", "[1].Bit1", "[1].Bit0" }); @@ -159,7 +160,7 @@ private void ValidateBinMetadata(IDataView result) Assert.True(slots.Length == 2); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[2] { "Bit1", "Bit0" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colB, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colC); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.SlotNames }); @@ -174,7 +175,7 @@ private void ValidateBinMetadata(IDataView result) Assert.True(slots.Length == 3); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[3] { "Bit2", "Bit1", "Bit0" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colD, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colE); @@ -189,7 +190,7 @@ private void ValidateBinMetadata(IDataView result) Assert.True(slots.Length == 8); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[8] { "[0].Bit3", "[0].Bit2", "[0].Bit1", "[0].Bit0", "[1].Bit3", "[1].Bit2", "[1].Bit1", "[1].Bit0" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colF, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colG); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.SlotNames }); @@ -204,7 +205,7 @@ private void ValidateBinMetadata(IDataView result) Assert.True(slots.Length == 3); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[3] { "Bit2", "Bit1", "Bit0" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colH, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); } private void ValidateBagMetadata(IDataView result) @@ -219,9 +220,9 @@ private void ValidateBagMetadata(IDataView result) Assert.True(result.Schema.TryGetColumnIndex("CatF", out int colH)); var types = result.Schema.GetMetadataTypes(colA); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.SlotNames }); - VBuffer slots = default; - VBuffer slotRanges = default; - DvBool normalized = default; + VBuffer> slots = default; + VBuffer slotRanges = default; + bool normalized = default; result.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colA, ref slots); Assert.True(slots.Length == 2); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[2] { "A", "B" }); @@ -232,7 +233,7 @@ private void ValidateBagMetadata(IDataView result) Assert.True(slots.Length == 1); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[1] { "C" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colB, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colC); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.SlotNames }); @@ -247,7 +248,7 @@ private void ValidateBagMetadata(IDataView result) Assert.True(slots.Length == 2); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[2] { "6", "1" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colD, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colE); @@ -259,7 +260,7 @@ private void ValidateBagMetadata(IDataView result) Assert.True(slotRanges.Length == 4); Assert.Equal(slotRanges.Items().Select(x => x.Value.ToString()), new string[4] { "0", "5", "6", "11" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colE, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colF); Assert.Equal(types.Select(x => x.Key), new string[3] { MetadataUtils.Kinds.SlotNames, MetadataUtils.Kinds.CategoricalSlotRanges, MetadataUtils.Kinds.IsNormalized }); @@ -270,7 +271,7 @@ private void ValidateBagMetadata(IDataView result) Assert.True(slotRanges.Length == 2); Assert.Equal(slotRanges.Items().Select(x => x.Value.ToString()), new string[2] { "0", "1" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colF, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colG); Assert.Equal(types.Select(x => x.Key), new string[3] { MetadataUtils.Kinds.SlotNames, MetadataUtils.Kinds.CategoricalSlotRanges, MetadataUtils.Kinds.IsNormalized }); @@ -297,7 +298,7 @@ private void ValidateBagMetadata(IDataView result) [Fact] public void TestCommandLine() { - Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Cat{col=B:A} in=f:\2.txt" }), (int)0); + Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Cat{col=B:A} in=f:\2.txt" })); } [Fact] diff --git a/test/Microsoft.ML.Tests/Transformers/KeyToBinaryVectorEstimatorTest.cs b/test/Microsoft.ML.Tests/Transformers/KeyToBinaryVectorEstimatorTest.cs index fdd240c514..c5a9620c2b 100644 --- a/test/Microsoft.ML.Tests/Transformers/KeyToBinaryVectorEstimatorTest.cs +++ b/test/Microsoft.ML.Tests/Transformers/KeyToBinaryVectorEstimatorTest.cs @@ -8,6 +8,7 @@ using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.Runtime.Tools; +using System; using System.IO; using System.Linq; using Xunit; @@ -124,8 +125,8 @@ private void ValidateMetadata(IDataView result) Assert.True(result.Schema.TryGetColumnIndex("CatD", out int colD)); var types = result.Schema.GetMetadataTypes(colA); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.SlotNames }); - VBuffer slots = default; - DvBool normalized = default; + VBuffer> slots = default; + bool normalized = default; result.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colA, ref slots); Assert.True(slots.Length == 6); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[6] { "[0].Bit2", "[0].Bit1", "[0].Bit0", "[1].Bit2", "[1].Bit1", "[1].Bit0" }); @@ -136,7 +137,7 @@ private void ValidateMetadata(IDataView result) Assert.True(slots.Length == 2); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[2] { "Bit1", "Bit0" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colB, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colC); Assert.Equal(types.Select(x => x.Key), new string[0]); @@ -144,16 +145,13 @@ private void ValidateMetadata(IDataView result) types = result.Schema.GetMetadataTypes(colD); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.IsNormalized }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colD, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); } [Fact] public void TestCommandLine() { - using (var env = new ConsoleEnvironment()) - { - Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} xf=KeyToBinary{col=C:B} in=f:\2.txt" }), (int)0); - } + Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} xf=KeyToBinary{col=C:B} in=f:\2.txt" })); } [Fact] diff --git a/test/Microsoft.ML.Tests/Transformers/KeyToVectorEstimatorTests.cs b/test/Microsoft.ML.Tests/Transformers/KeyToVectorEstimatorTests.cs index 45c6f938c5..a460d9482b 100644 --- a/test/Microsoft.ML.Tests/Transformers/KeyToVectorEstimatorTests.cs +++ b/test/Microsoft.ML.Tests/Transformers/KeyToVectorEstimatorTests.cs @@ -8,6 +8,7 @@ using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.Runtime.Tools; +using System; using System.IO; using System.Linq; using Xunit; @@ -147,9 +148,9 @@ private void ValidateMetadata(IDataView result) Assert.True(result.Schema.TryGetColumnIndex("CatF", out int colH)); var types = result.Schema.GetMetadataTypes(colA); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.SlotNames }); - VBuffer slots = default; - VBuffer slotRanges = default; - DvBool normalized = default; + VBuffer> slots = default; + VBuffer slotRanges = default; + bool normalized = default; result.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colA, ref slots); Assert.True(slots.Length == 2); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[2] { "A", "B" }); @@ -161,9 +162,9 @@ private void ValidateMetadata(IDataView result) Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[1] { "C" }); result.Schema.GetMetadata(MetadataUtils.Kinds.CategoricalSlotRanges, colB, ref slotRanges); Assert.True(slotRanges.Length == 2); - Assert.Equal(slotRanges.Items().Select(x => x.Value.RawValue), new int[2] { 0, 0 }); + Assert.Equal(slotRanges.Items().Select(x => x.Value), new int[2] { 0, 0 }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colB, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colC); Assert.Equal(types.Select(x => x.Key), new string[3] { MetadataUtils.Kinds.SlotNames, MetadataUtils.Kinds.CategoricalSlotRanges, MetadataUtils.Kinds.IsNormalized }); @@ -172,9 +173,9 @@ private void ValidateMetadata(IDataView result) Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[4] { "[0].3", "[0].5", "[1].3", "[1].5" }); result.Schema.GetMetadata(MetadataUtils.Kinds.CategoricalSlotRanges, colC, ref slotRanges); Assert.True(slotRanges.Length == 4); - Assert.Equal(slotRanges.Items().Select(x => x.Value.RawValue), new int[4] { 0, 1, 2, 3 }); + Assert.Equal(slotRanges.Items().Select(x => x.Value), new int[4] { 0, 1, 2, 3 }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colC, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colD); Assert.Equal(types.Select(x => x.Key), new string[2] { MetadataUtils.Kinds.SlotNames, MetadataUtils.Kinds.IsNormalized }); @@ -182,43 +183,40 @@ private void ValidateMetadata(IDataView result) Assert.True(slots.Length == 2); Assert.Equal(slots.Items().Select(x => x.Value.ToString()), new string[2] { "6", "1" }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colD, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colE); Assert.Equal(types.Select(x => x.Key), new string[2] { MetadataUtils.Kinds.CategoricalSlotRanges, MetadataUtils.Kinds.IsNormalized }); result.Schema.GetMetadata(MetadataUtils.Kinds.CategoricalSlotRanges, colE, ref slotRanges); Assert.True(slotRanges.Length == 4); - Assert.Equal(slotRanges.Items().Select(x => x.Value.RawValue), new int[4] { 0, 5, 6, 11 }); + Assert.Equal(slotRanges.Items().Select(x => x.Value), new int[4] { 0, 5, 6, 11 }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colE, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colF); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.IsNormalized }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colF, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colG); Assert.Equal(types.Select(x => x.Key), new string[2] { MetadataUtils.Kinds.CategoricalSlotRanges, MetadataUtils.Kinds.IsNormalized }); result.Schema.GetMetadata(MetadataUtils.Kinds.CategoricalSlotRanges, colG, ref slotRanges); Assert.True(slotRanges.Length == 4); - Assert.Equal(slotRanges.Items().Select(x => x.Value.RawValue), new int[4] { 0, 5, 6, 11 }); + Assert.Equal(slotRanges.Items().Select(x => x.Value), new int[4] { 0, 5, 6, 11 }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colF, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); types = result.Schema.GetMetadataTypes(colH); Assert.Equal(types.Select(x => x.Key), new string[1] { MetadataUtils.Kinds.IsNormalized }); result.Schema.GetMetadata(MetadataUtils.Kinds.IsNormalized, colF, ref normalized); - Assert.True(normalized.IsTrue); + Assert.True(normalized); } [Fact] public void TestCommandLine() { - using (var env = new ConsoleEnvironment()) - { - Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} xf=KeyToVector{col=C:B col={name=D source=B bag+}} in=f:\2.txt" }), (int)0); - } + Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} xf=KeyToVector{col=C:B col={name=D source=B bag+}} in=f:\2.txt" })); } [Fact] diff --git a/test/Microsoft.ML.Tests/Transformers/NAReplaceTests.cs b/test/Microsoft.ML.Tests/Transformers/NAReplaceTests.cs new file mode 100644 index 0000000000..82dd6cf54a --- /dev/null +++ b/test/Microsoft.ML.Tests/Transformers/NAReplaceTests.cs @@ -0,0 +1,126 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.Runtime.Tools; +using System.IO; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests.Transformers +{ + public class NAReplaceTests : TestDataPipeBase + { + private class TestClass + { + public float A; + public double B; + [VectorType(2)] + public float[] C; + [VectorType(2)] + public double[] D; + } + + public NAReplaceTests(ITestOutputHelper output) : base(output) + { + } + + [Fact] + public void NAReplaceWorkout() + { + var data = new[] { + new TestClass() { A = 1, B = 3, C= new float[2]{ 1, 2 } , D = new double[2]{ 3,4} }, + new TestClass() { A = float.NaN, B = double.NaN, C= new float[2]{ float.NaN, float.NaN } , D = new double[2]{ double.NaN,double.NaN}}, + new TestClass() { A = float.NegativeInfinity, B = double.NegativeInfinity,C= new float[2]{ float.NegativeInfinity, float.NegativeInfinity } , D = new double[2]{ double.NegativeInfinity, double.NegativeInfinity}}, + new TestClass() { A = float.PositiveInfinity, B = double.PositiveInfinity,C= new float[2]{ float.PositiveInfinity, float.PositiveInfinity, } , D = new double[2]{ double.PositiveInfinity, double.PositiveInfinity}}, + new TestClass() { A = 2, B = 1 ,C= new float[2]{ 3, 4 } , D = new double[2]{ 5,6}}, + }; + + var dataView = ComponentCreation.CreateDataView(Env, data); + var pipe = new NAReplaceEstimator(Env, + new NAReplaceTransform.ColumnInfo("A", "NAA", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + new NAReplaceTransform.ColumnInfo("B", "NAB", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + new NAReplaceTransform.ColumnInfo("C", "NAC", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + new NAReplaceTransform.ColumnInfo("D", "NAD", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean)); + TestEstimatorCore(pipe, dataView); + Done(); + } + + [Fact] + public void NAReplaceStatic() + { + string dataPath = GetDataPath("breast-cancer.txt"); + var reader = TextLoader.CreateReader(Env, ctx => ( + ScalarFloat: ctx.LoadFloat(1), + ScalarDouble: ctx.LoadDouble(1), + VectorFloat: ctx.LoadFloat(1, 4), + VectorDoulbe: ctx.LoadDouble(1, 4) + )); + + var data = reader.Read(new MultiFileSource(dataPath)); + var wrongCollection = new[] { new TestClass() { A = 1, B = 3, C = new float[2] { 1, 2 }, D = new double[2] { 3, 4 } } }; + var invalidData = ComponentCreation.CreateDataView(Env, wrongCollection); + + var est = data.MakeNewEstimator(). + Append(row => ( + A: row.ScalarFloat.ReplaceWithMissingValues(NAReplaceTransform.ColumnInfo.ReplacementMode.Maximum), + B: row.ScalarDouble.ReplaceWithMissingValues(NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + C: row.VectorFloat.ReplaceWithMissingValues(NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + D: row.VectorDoulbe.ReplaceWithMissingValues(NAReplaceTransform.ColumnInfo.ReplacementMode.Minimum) + )); + + TestEstimatorCore(est.AsDynamic, data.AsDynamic, invalidInput: invalidData); + var outputPath = GetOutputPath("NAReplace", "featurized.tsv"); + using (var ch = Env.Start("save")) + { + var saver = new TextSaver(Env, new TextSaver.Arguments { Silent = true }); + IDataView savedData = TakeFilter.Create(Env, est.Fit(data).Transform(data).AsDynamic, 4); + savedData = new ChooseColumnsTransform(Env, savedData, "A", "B", "C", "D"); + using (var fs = File.Create(outputPath)) + DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true); + } + + CheckEquality("NAReplace", "featurized.tsv"); + Done(); + } + + [Fact] + public void TestCommandLine() + { + Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=NAReplace{col=C:A} in=f:\2.txt" }), (int)0); + } + + [Fact] + public void TestOldSavingAndLoading() + { + var data = new[] { + new TestClass() { A = 1, B = 3, C= new float[2]{ 1, 2 } , D = new double[2]{ 3,4} }, + new TestClass() { A = float.NaN, B = double.NaN, C= new float[2]{ float.NaN, float.NaN } , D = new double[2]{ double.NaN,double.NaN}}, + new TestClass() { A = float.NegativeInfinity, B = double.NegativeInfinity,C= new float[2]{ float.NegativeInfinity, float.NegativeInfinity } , D = new double[2]{ double.NegativeInfinity, double.NegativeInfinity}}, + new TestClass() { A = float.PositiveInfinity, B = double.PositiveInfinity,C= new float[2]{ float.PositiveInfinity, float.PositiveInfinity, } , D = new double[2]{ double.PositiveInfinity, double.PositiveInfinity}}, + new TestClass() { A = 2, B = 1 ,C= new float[2]{ 3, 4 } , D = new double[2]{ 5,6}}, + }; + + var dataView = ComponentCreation.CreateDataView(Env, data); + var pipe = new NAReplaceEstimator(Env, + new NAReplaceTransform.ColumnInfo("A", "NAA", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + new NAReplaceTransform.ColumnInfo("B", "NAB", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + new NAReplaceTransform.ColumnInfo("C", "NAC", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean), + new NAReplaceTransform.ColumnInfo("D", "NAD", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean)); + + var result = pipe.Fit(dataView).Transform(dataView); + var resultRoles = new RoleMappedData(result); + using (var ms = new MemoryStream()) + { + TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles); + ms.Position = 0; + var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms); + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs index 74e0fe39ec..9946996bb3 100644 --- a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs +++ b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs @@ -6,6 +6,7 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.Transforms; using System.IO; using Xunit; using Xunit.Abstractions; @@ -53,5 +54,39 @@ public void TextFeaturizerWorkout() CheckEquality("Text", "featurized.tsv"); Done(); } + + [Fact] + public void TextTokenizationWorkout() + { + string sentimentDataPath = GetDataPath("wikipedia-detox-250-line-data.tsv"); + var data = TextLoader.CreateReader(Env, ctx => ( + label: ctx.LoadBool(0), + text: ctx.LoadText(1)), hasHeader: true) + .Read(new MultiFileSource(sentimentDataPath)); + + var invalidData = TextLoader.CreateReader(Env, ctx => ( + label: ctx.LoadBool(0), + text: ctx.LoadFloat(1)), hasHeader: true) + .Read(new MultiFileSource(sentimentDataPath)); + + var est = new WordTokenizer(Env, "text", "words") + .Append(new CharacterTokenizer(Env, "text", "chars")) + .Append(new KeyToValueEstimator(Env, "chars")); + TestEstimatorCore(est, data.AsDynamic, invalidInput: invalidData.AsDynamic); + + var outputPath = GetOutputPath("Text", "tokenized.tsv"); + using (var ch = Env.Start("save")) + { + var saver = new TextSaver(Env, new TextSaver.Arguments { Silent = true }); + IDataView savedData = TakeFilter.Create(Env, est.Fit(data.AsDynamic).Transform(data.AsDynamic), 4); + savedData = new ChooseColumnsTransform(Env, savedData, "text", "words", "chars"); + + using (var fs = File.Create(outputPath)) + DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true); + } + + CheckEquality("Text", "tokenized.tsv"); + Done(); + } } } diff --git a/test/Microsoft.ML.Tests/Transformers/WordEmbeddingsTests.cs b/test/Microsoft.ML.Tests/Transformers/WordEmbeddingsTests.cs new file mode 100644 index 0000000000..5061cdbc64 --- /dev/null +++ b/test/Microsoft.ML.Tests/Transformers/WordEmbeddingsTests.cs @@ -0,0 +1,62 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Data.StaticPipe; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.Scenarios; +using System.IO; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests.Transformers +{ + public sealed class WordEmbeddingsTests : TestDataPipeBase + { + public WordEmbeddingsTests(ITestOutputHelper helper) + : base(helper) + { + } + + [Fact] + public void TestWordEmbeddings() + { + var dataPath = GetDataPath(ScenariosTests.SentimentDataPath); + var testDataPath = GetDataPath(ScenariosTests.SentimentTestPath); + + var data = TextLoader.CreateReader(Env, ctx => ( + label: ctx.LoadBool(0), + SentimentText: ctx.LoadText(1)), hasHeader: true) + .Read(new MultiFileSource(dataPath)); + + var dynamicData = TextTransform.Create(Env, new TextTransform.Arguments() + { + Column = new TextTransform.Column + { + Name = "SentimentText_Features", + Source = new[] { "SentimentText" } + }, + KeepDiacritics = false, + KeepPunctuations = false, + TextCase = Runtime.TextAnalytics.TextNormalizerTransform.CaseNormalizationMode.Lower, + OutputTokens = true, + StopWordsRemover = new Runtime.TextAnalytics.PredefinedStopWordsRemoverFactory(), + VectorNormalizer = TextTransform.TextNormKind.None, + CharFeatureExtractor = null, + WordFeatureExtractor = null, + }, data.AsDynamic); + + var data2 = dynamicData.AssertStatic(Env, ctx => ( + SentimentText_Features_TransformedText: ctx.Text.VarVector, + SentimentText: ctx.Text.Scalar, + label: ctx.Bool.Scalar)); + + var est = data2.MakeNewEstimator() + .Append(row => row.SentimentText_Features_TransformedText.WordEmbeddings()); + TestEstimatorCore(est.AsDynamic, data2.AsDynamic, invalidInput: data.AsDynamic); + Done(); + } + } +} diff --git a/test/data/README.md b/test/data/README.md index ea9133e33e..165c928ba7 100644 --- a/test/data/README.md +++ b/test/data/README.md @@ -46,6 +46,25 @@ Redistributing the dataset "taxi-fare-test.csv", "taxi-fare-train.csv" with attr > > The dataset is provided under terms provided by City of New York: https://opendata.cityofnewyork.us/overview/#termsofuse. +### MSLR-WEB10K, MSLR-WEB30K + +This dataset is originally from [Introducing LETOR 4.0 Datasets](https://arxiv.org/abs/1306.2597). +The dataset is under a CC-by 4.0 license. +``` +@article{DBLP:journals/corr/QinL13, + author = {Tao Qin and + Tie{-}Yan Liu}, + title = {Introducing {LETOR} 4.0 Datasets}, + journal = {CoRR}, + volume = {abs/1306.2597}, + year = {2013}, + url = {https://arxiv.org/abs/1306.2597}, + timestamp = {Mon, 01 Jul 2013 20:31:25 +0200}, + biburl = {https://dblp.uni-trier.de/rec/bib/journals/corr/QinL13}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + # Images ### Located in `images` folder diff --git a/test/data/generated_regression_dataset.csv b/test/data/generated_regression_dataset.csv new file mode 100644 index 0000000000..e7003a90b6 --- /dev/null +++ b/test/data/generated_regression_dataset.csv @@ -0,0 +1,501 @@ +feature_0;feature_1;feature_2;feature_3;feature_4;feature_5;feature_6;feature_7;feature_8;feature_9;feature_10;target +-2.75;0.77;-0.61;0.14;1.39;0.38;-0.53;-0.50;-2.13;-0.39;0.46;140.66 +-0.61;-0.37;-0.12;0.55;-1.00;0.84;-0.02;1.30;-0.24;-0.50;-2.12;148.12 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