diff --git a/.vsts-dotnet-ci.yml b/.vsts-dotnet-ci.yml index e62696fc6b..8f507d7a09 100644 --- a/.vsts-dotnet-ci.yml +++ b/.vsts-dotnet-ci.yml @@ -52,7 +52,7 @@ jobs: - template: /build/ci/job-template.yml parameters: architecture: arm - name: Ubuntu_x64_cross_arm_Net60 + name: Ubuntu_x64_cross_arm buildScript: ./build.sh container: UbuntuCrossArmContainer customMatrixes: @@ -60,12 +60,12 @@ jobs: _configuration: Debug _config_short: DI _includeBenchmarkData: false - _targetFramework: net6.0 + _targetFramework: net8.0 Release_Build: _configuration: Release _config_short: RI _includeBenchmarkData: true - _targetFramework: net6.0 + _targetFramework: net8.0 innerLoop: true pool: name: NetCore-Public @@ -75,7 +75,7 @@ jobs: - template: /build/ci/job-template.yml parameters: architecture: arm64 - name: Ubuntu_x64_cross_arm64_Net60 + name: Ubuntu_x64_cross_arm64 buildScript: ./build.sh container: UbuntuCrossArm64Container customMatrixes: @@ -83,12 +83,12 @@ jobs: _configuration: Debug _config_short: DI _includeBenchmarkData: false - _targetFramework: net6.0 + _targetFramework: net8.0 Release_Build: _configuration: Release _config_short: RI _includeBenchmarkData: true - _targetFramework: net6.0 + _targetFramework: net8.0 innerLoop: true pool: name: NetCore-Public @@ -97,7 +97,7 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Centos_x64_Net60 + name: Centos_x64 buildScript: ./build.sh container: CentosContainer innerLoop: true @@ -108,7 +108,7 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Ubuntu_x64_Net60 + name: Ubuntu_x64 buildScript: ./build.sh container: UbuntuContainer innerLoop: true @@ -119,7 +119,7 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: MacOS_x64_Net60 + name: MacOS_x64 buildScript: ./build.sh innerLoop: true pool: @@ -129,19 +129,19 @@ jobs: - template: /build/ci/job-template.yml parameters: architecture: arm64 - name: MacOS_cross_arm64_Net6 + name: MacOS_cross_arm64 buildScript: ./build.sh customMatrixes: Debug_Build: _configuration: Debug _config_short: DI _includeBenchmarkData: false - _targetFramework: net6.0 + _targetFramework: net8.0 Release_Build: _configuration: Release _config_short: RI _includeBenchmarkData: true - _targetFramework: net6.0 + _targetFramework: net8.0 innerLoop: true pool: vmImage: macOS-13 @@ -150,19 +150,19 @@ jobs: - template: /build/ci/job-template.yml parameters: architecture: arm64 - name: Windows_cross_arm64_Net6 + name: Windows_cross_arm64 buildScript: build.cmd customMatrixes: Debug_Build: _configuration: Debug _config_short: DI _includeBenchmarkData: false - _targetFramework: net6.0 + _targetFramework: net8.0 Release_Build: _configuration: Release _config_short: RI _includeBenchmarkData: false - _targetFramework: net6.0 + _targetFramework: net8.0 innerLoop: true vsTestConfiguration: "/Framework:.NETCoreApp,Version=v6.0" pool: @@ -172,7 +172,7 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Windows_x64_Net6 + name: Windows_x64 buildScript: build.cmd innerLoop: true vsTestConfiguration: "/Framework:.NETCoreApp,Version=v3.1" @@ -205,7 +205,7 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Windows_x86_Net6 + name: Windows_x86 architecture: x86 buildScript: build.cmd innerLoop: true diff --git a/build/Codecoverage.proj b/build/Codecoverage.proj index 495323f85e..1ca3a078f2 100644 --- a/build/Codecoverage.proj +++ b/build/Codecoverage.proj @@ -2,7 +2,7 @@ - net6.0 + net8.0 diff --git a/build/ci/job-template.yml b/build/ci/job-template.yml index b218e0622f..6d0f8f3bd6 100644 --- a/build/ci/job-template.yml +++ b/build/ci/job-template.yml @@ -51,12 +51,12 @@ jobs: _configuration: Debug _config_short: D _includeBenchmarkData: false - _targetFramework: net6.0 + _targetFramework: net8.0 Release_Build: _configuration: Release _config_short: R _includeBenchmarkData: true - _targetFramework: net6.0 + _targetFramework: net8.0 ${{ if ne(parameters.customMatrixes, '') }}: ${{ insert }}: ${{ parameters.customMatrixes }} @@ -76,7 +76,7 @@ jobs: - ${{ if and( eq(parameters.nightlyBuild, 'true'), eq(parameters.pool.vmImage, 'ubuntu-18.04')) }}: - bash: echo "##vso[task.setvariable variable=LD_LIBRARY_PATH]$(nightlyBuildRunPath):$LD_LIBRARY_PATH" displayName: Set LD_LIBRARY_PATH for Ubuntu and CentOS to locate Native shared library in current running path - - script: ${{ parameters.buildScript }} -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} /p:TestArchitectures=${{ parameters.architecture }} /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) /p:CreateHardLinksForCopyFilesToOutputDirectoryIfPossible=True $(hardLink) + - script: ${{ parameters.buildScript }} -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) /p:CreateHardLinksForCopyFilesToOutputDirectoryIfPossible=True $(hardLink) displayName: Build - ${{ if eq(parameters.pool.vmImage, 'macOS-10.15') }}: - task: Bash@3 @@ -116,15 +116,15 @@ jobs: displayName: Clean up useless project - script: $(dotnetPath) msbuild -restore $(nightlyBuildProjPath) /p:ReferenceTypeForTestFramework="Nuget" /p:Configuration=$(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} $(testTargetFramework) displayName: Build Nightly-Build Project with latest package versions - - script: $(dotnetPath) msbuild $(runNightlyBuildProj) /t:RunNightlyBuildTests /p:Configuration=$(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} /p:TestArchitectures=${{ parameters.architecture }} $(testTargetFramework) + - script: $(dotnetPath) msbuild $(runNightlyBuildProj) /t:RunNightlyBuildTests /p:Configuration=$(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} $(testTargetFramework) displayName: Run Nightly Build Tests - ${{ if eq(parameters.nightlyBuild, 'false') }}: - ${{ if eq(parameters.innerLoop, 'false') }}: - ${{ if and(eq(parameters.runSpecific, 'false'), eq(parameters.useVSTestTask, 'false')) }}: - - script: set PATH=%PATH%;%USERPROFILE%\.nuget\packages\libtorch-cpu-win-x64\2.2.1.1\runtimes\win-x64\native;%USERPROFILE%\.nuget\packages\torchsharp\0.102.7\runtimes\win-x64\native & ${{ parameters.buildScript }} /p:Build=false -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} /p:TestArchitectures=${{ parameters.architecture }} -test -integrationTest /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) + - script: set PATH=%PATH%;%USERPROFILE%\.nuget\packages\libtorch-cpu-win-x64\2.2.1.1\runtimes\win-x64\native;%USERPROFILE%\.nuget\packages\torchsharp\0.102.7\runtimes\win-x64\native & ${{ parameters.buildScript }} /p:Build=false -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} -test -integrationTest /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) displayName: Run All Tests. - ${{ if and(eq(parameters.runSpecific, 'true'), eq(parameters.useVSTestTask, 'false')) }}: - - script: ${{ parameters.buildScript }} /p:Build=false -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} /p:TestArchitectures=${{ parameters.architecture }} -test -integrationTest /p:TestRunnerAdditionalArguments='-trait$(spaceValue)Category=RunSpecificTest' /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) + - script: ${{ parameters.buildScript }} /p:Build=false -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} -test -integrationTest /p:TestRunnerAdditionalArguments='-trait$(spaceValue)Category=RunSpecificTest' /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) displayName: Run Specific Tests. - ${{ if and(eq(parameters.buildScript, 'build.cmd'), eq(parameters.useVSTestTask, 'true')) }}: - task: VSTest@2 @@ -149,7 +149,7 @@ jobs: publishRunAttachments: true - ${{ if eq(parameters.innerLoop, 'true') }}: - ${{ if eq(parameters.codeCoverage, True) }}: - - script: ${{ parameters.buildScript }} /p:Build=false -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} /p:TestArchitectures=${{ parameters.architecture }} -test -integrationTest /p:TestRunnerAdditionalArguments='-notrait$(spaceValue)Category=SkipInCI' /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) + - script: ${{ parameters.buildScript }} /p:Build=false -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} -test -integrationTest /p:TestRunnerAdditionalArguments='-notrait$(spaceValue)Category=SkipInCI' /p:Coverage=${{ parameters.codeCoverage }} $(testTargetFramework) displayName: Run CI Tests. - ${{ if eq(parameters.codeCoverage, False) }}: - template: /build/ci/send-to-helix.yml @@ -236,5 +236,5 @@ jobs: script: Get-ChildItem -Path '.\artifacts\bin' -Recurse | Where-Object {$_.FullName -like "*runtimes*"} | Remove-Item -Recurse -Confirm:$false -Force displayName: Clean up runtime folder for package (Unix) - ${{ if eq(parameters.nightlyBuild, 'false') }}: - - script: ${{ parameters.buildScript }} /p:Build=false -pack -ci -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} /p:TestArchitectures=${{ parameters.architecture }} $(testTargetFramework) + - script: ${{ parameters.buildScript }} /p:Build=false -pack -ci -configuration $(_configuration) /p:TargetArchitecture=${{ parameters.architecture }} $(testTargetFramework) displayName: Build Packages diff --git a/build/ci/send-to-helix.yml b/build/ci/send-to-helix.yml index 9042bc62e7..04f8d5afdf 100644 --- a/build/ci/send-to-helix.yml +++ b/build/ci/send-to-helix.yml @@ -20,6 +20,31 @@ parameters: condition: succeeded() # optional -- condition for step to execute; defaults to succeeded() steps: + - script: ${{ parameters.MsBuildScript}} + $(Build.SourcesDirectory)/eng/helix.proj + /t:Restore + /bl:$(Build.SourcesDirectory)/artifacts/log/${{ parameters.Configuration }}/SendToHelix.binlog + /p:Configuration=${{ parameters.Configuration }} + /p:TargetArchitecture=${{ parameters.Architecture }} + /p:BuildConfig=${{ parameters.Configuration }} + /p:BuildArchitecture=${{ parameters.Architecture }} + /p:HelixSource=${{ parameters.HelixSource }} + /p:HelixType=${{ parameters.HelixType }} + /p:HelixBuild=${{ parameters.HelixBuild }} + /p:HelixConfiguration="${{ parameters.HelixConfiguration }}" + /p:HelixAccessToken="${{ parameters.HelixAccessToken }}" + /p:EnableXUnitReporter=${{ parameters.EnableXUnitReporter }} + /p:WaitForWorkItemCompletion=${{ parameters.WaitForWorkItemCompletion }} + /p:HelixBaseUri=${{ parameters.HelixBaseUri }} + ${{ parameters.WarnAsError }} + ${{ parameters.TestTargetFramework }} + displayName: "Helix Restore" + env: + SYSTEM_ACCESSTOKEN: $(System.AccessToken) + Creator: ${{ parameters.Creator }} + HelixTargetQueues: ${{ parameters.HelixTargetQueues }} + continueOnError: ${{ parameters.continueOnError }} + - script: ${{ parameters.MsBuildScript}} $(Build.SourcesDirectory)/eng/helix.proj /t:Test diff --git a/build/codecoverage-ci.yml b/build/codecoverage-ci.yml index 20f8693b44..b74792346a 100644 --- a/build/codecoverage-ci.yml +++ b/build/codecoverage-ci.yml @@ -44,7 +44,7 @@ jobs: _configuration: Debug _config_short: D _includeBenchmarkData: false - _targetFramework: net6.0 + _targetFramework: net8.0 codeCoverage: true pool: name: NetCore-Public diff --git a/docs/samples/Microsoft.ML.AutoML.Samples/Microsoft.ML.AutoML.Samples.csproj b/docs/samples/Microsoft.ML.AutoML.Samples/Microsoft.ML.AutoML.Samples.csproj index 464a2cedd7..3619f93d8e 100644 --- a/docs/samples/Microsoft.ML.AutoML.Samples/Microsoft.ML.AutoML.Samples.csproj +++ b/docs/samples/Microsoft.ML.AutoML.Samples/Microsoft.ML.AutoML.Samples.csproj @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 false $(NoWarn) diff --git a/docs/samples/Microsoft.ML.Samples.GPU/Microsoft.ML.Samples.GPU.csproj b/docs/samples/Microsoft.ML.Samples.GPU/Microsoft.ML.Samples.GPU.csproj index 4c1cb229e4..0483f0bfbf 100644 --- a/docs/samples/Microsoft.ML.Samples.GPU/Microsoft.ML.Samples.GPU.csproj +++ b/docs/samples/Microsoft.ML.Samples.GPU/Microsoft.ML.Samples.GPU.csproj @@ -1,7 +1,7 @@  - net6.0 + net8.0 Exe false diff --git a/docs/samples/Microsoft.ML.Samples.OneDal/Microsoft.ML.Samples.OneDal.csproj b/docs/samples/Microsoft.ML.Samples.OneDal/Microsoft.ML.Samples.OneDal.csproj index feeb4e6f23..36a5d25034 100755 --- a/docs/samples/Microsoft.ML.Samples.OneDal/Microsoft.ML.Samples.OneDal.csproj +++ b/docs/samples/Microsoft.ML.Samples.OneDal/Microsoft.ML.Samples.OneDal.csproj @@ -2,7 +2,7 @@ Exe - net7.0 + net8.0 false diff --git a/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj b/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj index 8e168ad456..7e7442a029 100644 --- a/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj +++ b/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj @@ -1,7 +1,7 @@  - net6.0 + net8.0 Exe false diff --git a/eng/Versions.props b/eng/Versions.props index 81595bd15c..734f1f10a2 100644 --- a/eng/Versions.props +++ b/eng/Versions.props @@ -91,6 +91,7 @@ 8.0.1 6.12.0 1.1.2-beta1.23431.1 + 9.0.0-beta.24212.4 5.0.0-preview.5.20278.1 8.0.0-beta.24525.2 10.0.0-beta.24504.4 diff --git a/eng/helix.proj b/eng/helix.proj index f7fb259148..05fa4d6f17 100644 --- a/eng/helix.proj +++ b/eng/helix.proj @@ -1,4 +1,4 @@ - + @@ -39,10 +39,11 @@ - + runtime win-x86 linux-arm + osx-arm64 @@ -89,7 +90,11 @@ - + + + + + $(HelixPreCommands);export PATH=$HELIX_CORRELATION_PAYLOAD/$(DotNetCliDestination):$PATH $(HelixPreCommands);set PATH=%HELIX_CORRELATION_PAYLOAD%\$(DotNetCliDestination)%3B%PATH% - $(HelixPreCommands);export LD_LIBRARY_PATH=/opt/homebrew/opt/mono-libgdiplus/lib;ls /usr/lib;ls $HELIX_WORKITEM_ROOT;export KMP_DUPLICATE_LIB_OK=TRUE;export DYLD_PRINT_LIBRARIES=1;otool -L $HELIX_WORKITEM_ROOT/runtimes/osx-x64/native/lib_lightgbm.dylib + $(HelixPreCommands);export LD_LIBRARY_PATH=/opt/homebrew/opt/mono-libgdiplus/lib;ls /usr/lib;ls $HELIX_WORKITEM_ROOT;export KMP_DUPLICATE_LIB_OK=TRUE;otool -L $HELIX_WORKITEM_ROOT/runtimes/osx-x64/native/lib_lightgbm.dylib $(HelixPreCommands);sudo apt update;sudo apt-get install libomp-dev libomp5 -y $HELIX_CORRELATION_PAYLOAD %HELIX_CORRELATION_PAYLOAD% + runTests.sh + .\runTests.cmd + /usr/local/opt/libomp/lib/libiomp5.dylib;/usr/local/opt/libomp/lib/libomp.dylib; /usr/local/opt/libomp/lib/libomp.dylib; @@ -150,15 +158,37 @@ Files="@(WindowsFiles);@(OsxFiles)" /> - + + dotnet exec --roll-forward Major --runtimeconfig %(ProjectsWithTargetFramework.Filename).runtimeconfig.json --depsfile %(ProjectsWithTargetFramework.Filename).deps.json $(HelixCorrelationPayloadPath)/xunit-runner/tools/netcoreapp2.0/xunit.console.dll %(ProjectsWithTargetFramework.Filename).dll -notrait Category=SkipInCI -xml testResults.xml + + %(ProjectsWithTargetFramework.Filename) + %(ProjectsWithTargetFramework.TargetFrameworks) + $(BUILD_SOURCESDIRECTORY)\artifacts\bin\%(ProjectsWithTargetFramework.Filename)\$(BuildConfig)\%(ProjectsWithTargetFramework.TargetFrameworks)\$(RunTestScript) + + + $(HelixCorrelationPayloadPath)/xunit-runner/tools/net462/xunit.console.exe %(ProjectsWithTargetFramework.Filename).dll -notrait Category=SkipInCI -xml testResults.xml + + %(ProjectsWithTargetFramework.Filename) + %(ProjectsWithTargetFramework.TargetFrameworks) + $(BUILD_SOURCESDIRECTORY)\artifacts\bin\%(ProjectsWithTargetFramework.Filename)\$(BuildConfig)\%(ProjectsWithTargetFramework.TargetFrameworks)\win-x64\$(RunTestScript) + + + + + + + $(BUILD_SOURCESDIRECTORY)\artifacts\bin\%(ProjectsWithTargetFramework.Filename)\$(BuildConfig)\%(ProjectsWithTargetFramework.TargetFrameworks)\ $(BUILD_SOURCESDIRECTORY)\artifacts\bin\%(ProjectsWithTargetFramework.Filename)\$(BuildConfig)\%(ProjectsWithTargetFramework.TargetFrameworks)\win-x64 - dotnet exec --roll-forward Major --runtimeconfig %(ProjectsWithTargetFramework.Filename).runtimeconfig.json --depsfile %(ProjectsWithTargetFramework.Filename).deps.json $(HelixCorrelationPayloadPath)/xunit-runner/tools/netcoreapp2.0/xunit.console.dll %(ProjectsWithTargetFramework.Filename).dll -notrait Category=SkipInCI -xml testResults.xml - $(HelixCorrelationPayloadPath)/xunit-runner/tools/net462/xunit.console.exe %(ProjectsWithTargetFramework.Filename).dll -notrait Category=SkipInCI -xml testResults.xml + ./$(RunTestScript) + call $(RunTestScript) 01:00:00 00:30:00 $(WorkItemTimeout) + \ No newline at end of file diff --git a/eng/testing/runTests.cmd b/eng/testing/runTests.cmd new file mode 100644 index 0000000000..818a6ff6e7 --- /dev/null +++ b/eng/testing/runTests.cmd @@ -0,0 +1,42 @@ +@echo off +setlocal ENABLEEXTENSIONS ENABLEDELAYEDEXPANSION + +set EXECUTION_DIR=%~dp0 + + +:: ========================= BEGIN Test Execution ============================= +echo ----- start %DATE% %TIME% =============== To repro directly: ===================================================== +echo pushd %EXECUTION_DIR% +echo ${runCommand} +echo popd +echo =========================================================================================================== +pushd %EXECUTION_DIR% +@echo on +${runCommand} +@set _exit_code=%ERRORLEVEL% +@echo off +if exist testResults.xml ( + set HAS_TEST_RESULTS=1 +) +popd +echo ----- end %DATE% %TIME% ----- exit code %_exit_code% ---------------------------------------------------------- +:: ========================= END Test Execution =============================== + +:: The tests either failed or crashed, copy output files +if not %_exit_code%==0 ( + if not "%HELIX_WORKITEM_UPLOAD_ROOT%" == "" ( + powershell Compress-Archive %EXECUTION_DIR%\TestOutput %HELIX_WORKITEM_UPLOAD_ROOT%\TestOutput.zip + ) +) + +:: The helix work item should not exit with non-zero if tests ran and produced results +:: The xunit console runner returns 1 when tests fail +if %_exit_code%==1 ( + if %HAS_TEST_RESULTS%==1 ( + if not "%HELIX_WORKITEM_PAYLOAD%"=="" ( + exit /b 0 + ) + ) +) + +exit /b %_exit_code% \ No newline at end of file diff --git a/eng/testing/runTests.sh b/eng/testing/runTests.sh new file mode 100644 index 0000000000..dcf3cdf9a9 --- /dev/null +++ b/eng/testing/runTests.sh @@ -0,0 +1,72 @@ +#!/usr/bin/env bash + + + + +EXECUTION_DIR=$(dirname "$0") + + + + +# ========================= BEGIN Test Execution ============================= + +echo ----- start $(date) =============== To repro directly: ===================================================== + +echo pushd $EXECUTION_DIR + +echo ${runCommand} + +echo popd + +echo =========================================================================================================== + +pushd $EXECUTION_DIR + +${runCommand} + +test_exitcode=$? + +if [[ -s testResults.xml ]]; then + + has_test_results=1; + +fi; + +popd + +echo ----- end $(date) ----- exit code $test_exitcode ---------------------------------------------------------- + +# ========================= END Test Execution =============================== + + + + +# The tests either failed or crashed, copy output files + +if [[ "$test_exitcode" != "0" && "$HELIX_WORKITEM_UPLOAD_ROOT" != "" ]]; then + + tar -czf $HELIX_WORKITEM_UPLOAD_ROOT/TestOutput.tar.gz $EXECUTION_DIR/TestOutput/ + +fi + + + + +# The helix work item should not exit with non-zero if tests ran and produced results + +# The xunit console runner returns 1 when tests fail + +if [[ "$test_exitcode" == "1" && "$has_test_results" == "1" ]]; then + + if [ -n "$HELIX_WORKITEM_PAYLOAD" ]; then + + exit 0 + + fi + +fi + + + + +exit $test_exitcode \ No newline at end of file diff --git a/global.json b/global.json index 1c5b62f839..628da527bf 100644 --- a/global.json +++ b/global.json @@ -1,6 +1,6 @@ { "tools": { - "dotnet": "9.0.100-rc.1.24452.12", + "dotnet": "9.0.100", "runtimes": { "dotnet": [ "$(DotNetRuntime60Version)", diff --git a/src/Microsoft.Data.Analysis.Interactive/Microsoft.Data.Analysis.Interactive.csproj b/src/Microsoft.Data.Analysis.Interactive/Microsoft.Data.Analysis.Interactive.csproj index a13c2e9dd3..37d7c36951 100644 --- a/src/Microsoft.Data.Analysis.Interactive/Microsoft.Data.Analysis.Interactive.csproj +++ b/src/Microsoft.Data.Analysis.Interactive/Microsoft.Data.Analysis.Interactive.csproj @@ -1,7 +1,7 @@  - net6.0 + net8.0 false diff --git a/src/Microsoft.Data.Analysis/Microsoft.Data.Analysis.csproj b/src/Microsoft.Data.Analysis/Microsoft.Data.Analysis.csproj index 2be1a20dcb..86636b2b8b 100644 --- a/src/Microsoft.Data.Analysis/Microsoft.Data.Analysis.csproj +++ b/src/Microsoft.Data.Analysis/Microsoft.Data.Analysis.csproj @@ -21,7 +21,7 @@ We cannot include it with a ProjectReference, since that would create a circular dependency. If M.D.A.I undergoes TFM or dependency changes, we need to update the TargetFramework passed in below. --> - + diff --git a/src/Microsoft.ML.AutoML.Interactive/Microsoft.ML.AutoML.Interactive.csproj b/src/Microsoft.ML.AutoML.Interactive/Microsoft.ML.AutoML.Interactive.csproj index 2ae1ca8467..7144833fa2 100644 --- a/src/Microsoft.ML.AutoML.Interactive/Microsoft.ML.AutoML.Interactive.csproj +++ b/src/Microsoft.ML.AutoML.Interactive/Microsoft.ML.AutoML.Interactive.csproj @@ -1,7 +1,7 @@  - net6.0 + net8.0 false $(NoWarn) diff --git a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj index 909139b33b..d79ca0e5cf 100644 --- a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj +++ b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj @@ -17,8 +17,8 @@ - - + + diff --git a/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.cs b/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.cs index 8acec761a7..86fca91345 100644 --- a/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.cs +++ b/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.cs @@ -27,7 +27,7 @@ internal partial class PredictProject : PredictProjectBase public virtual string TransformText() { this.Write("\r\n\r\n \r\n Exe\r\n net6.0\r\n \r\n net8.0\r\n \r\n \r\n \r\n"); diff --git a/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.tt b/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.tt index a23b575ccf..b12ca819be 100644 --- a/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.tt +++ b/src/Microsoft.ML.CodeGenerator/Templates/Console/PredictProject.tt @@ -8,7 +8,7 @@ Exe - net6.0 + net8.0 diff --git a/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj b/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj index 78a1e768af..959ea8e395 100644 --- a/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj +++ b/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj @@ -1,7 +1,7 @@  - net6.0 + net8.0 Exe MML Microsoft.ML.Tools.Console.Console diff --git a/src/Microsoft.ML.Core/CommandLine/CmdParser.cs b/src/Microsoft.ML.Core/CommandLine/CmdParser.cs index fc97810663..96b8857e91 100644 --- a/src/Microsoft.ML.Core/CommandLine/CmdParser.cs +++ b/src/Microsoft.ML.Core/CommandLine/CmdParser.cs @@ -10,6 +10,7 @@ using System.Linq; using System.Reflection; using System.Text; +using Microsoft.ML.Data; using Microsoft.ML.Internal.Utilities; using Microsoft.ML.Runtime; @@ -2015,6 +2016,10 @@ private void AppendSettingCore(IHostEnvironment env, StringBuilder sb, object va var buffer = new StringBuilder(); sb.Append(GetString(env, value, buffer)); } + else if (value is Data.InternalDataKind kind) + { + sb.Append(kind.GetString()); + } else sb.Append(value.ToString()); } diff --git a/src/Microsoft.ML.Core/Properties/AssemblyInfo.cs b/src/Microsoft.ML.Core/Properties/AssemblyInfo.cs index 68b9250eeb..7b8699ceb0 100644 --- a/src/Microsoft.ML.Core/Properties/AssemblyInfo.cs +++ b/src/Microsoft.ML.Core/Properties/AssemblyInfo.cs @@ -5,7 +5,9 @@ using System.Runtime.CompilerServices; using Microsoft.ML; +[assembly: InternalsVisibleTo(assemblyName: "Microsoft.Data.Analysis.Tests" + PublicKey.Value)] [assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.TestFramework" + PublicKey.Value)] +[assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.TestFrameworkCommon" + PublicKey.Value)] [assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.Tests" + PublicKey.TestValue)] [assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.Core.Tests" + PublicKey.TestValue)] [assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.Predictor.Tests" + PublicKey.Value)] diff --git a/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj b/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj index f6f7abb7da..8e06f2b846 100644 --- a/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj +++ b/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj @@ -1,7 +1,7 @@  - netstandard2.0;net6.0 + netstandard2.0;net8.0 true Microsoft.ML.CpuMath contains optimized math routines for ML.NET. true @@ -14,7 +14,7 @@ - + diff --git a/src/Microsoft.ML.Data/Utilities/TimerScope.cs b/src/Microsoft.ML.Data/Utilities/TimerScope.cs index 4b83e9c81d..a99c59612f 100644 --- a/src/Microsoft.ML.Data/Utilities/TimerScope.cs +++ b/src/Microsoft.ML.Data/Utilities/TimerScope.cs @@ -46,7 +46,7 @@ public void Dispose() // REVIEW: This is \n\n is to prevent changes across bunch of baseline files. // Ideally we should change our comparison method to ignore empty lines. - _ch.Info("{0}\t Time elapsed(s): {1}\n\n", DateTime.UtcNow, elapsedSeconds); + _ch.Info("{0}\t Time elapsed(s): {1}\n\n", DateTime.UtcNow.ToString("MM/dd/yyyy H:mm:ss tt"), elapsedSeconds); using (var pipe = _host.StartPipe("TelemetryPipe")) { 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 index 252cce0312..456d1747ca 100644 --- a/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer.csproj +++ b/src/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer/Microsoft.ML.DnnAnalyzer.csproj @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 DnnAnalyzer diff --git a/src/Microsoft.ML.EntryPoints/JsonUtils/JsonManifestUtils.cs b/src/Microsoft.ML.EntryPoints/JsonUtils/JsonManifestUtils.cs index 09b062b0bc..9264b7a9bf 100644 --- a/src/Microsoft.ML.EntryPoints/JsonUtils/JsonManifestUtils.cs +++ b/src/Microsoft.ML.EntryPoints/JsonUtils/JsonManifestUtils.cs @@ -310,7 +310,7 @@ private static JToken BuildTypeToken(IExceptionContext ectx, FieldInfo fieldInfo ectx.AssertValue(type); ectx.AssertValue(catalog); - // REVIEW: Allows newly introduced types to not break the manifest bulding process. + // REVIEW: Allows newly introduced types to not break the manifest building process. // Where possible, these types should be replaced by component kinds. if (type == typeof(CommonInputs.IEvaluatorInput) || type == typeof(CommonOutputs.IEvaluatorOutput)) diff --git a/src/Microsoft.ML.FastTree/TreeEnsemble/InternalTreeEnsemble.cs b/src/Microsoft.ML.FastTree/TreeEnsemble/InternalTreeEnsemble.cs index ce3b9328c4..d4a72cc126 100644 --- a/src/Microsoft.ML.FastTree/TreeEnsemble/InternalTreeEnsemble.cs +++ b/src/Microsoft.ML.FastTree/TreeEnsemble/InternalTreeEnsemble.cs @@ -198,7 +198,7 @@ internal string ToTreeEnsembleIni(FeaturesToContentMap fmap, protected int AppendComments(StringBuilder sb, string trainingParams) { - sb.AppendFormat("\n\n[Comments]\nC:0=Regression Tree Ensemble\nC:1=Generated using FastTree\nC:2=Created on {0}\n", DateTime.UtcNow); + sb.AppendFormat("\n\n[Comments]\nC:0=Regression Tree Ensemble\nC:1=Generated using FastTree\nC:2=Created on {0}\n", DateTime.UtcNow.ToString("MM/dd/yyyy H:mm:ss tt")); string[] trainingParamsList = trainingParams.Split(new char[] { '\n' }); int i = 0; diff --git a/src/Microsoft.ML.GenAI.Core/Microsoft.ML.GenAI.Core.csproj b/src/Microsoft.ML.GenAI.Core/Microsoft.ML.GenAI.Core.csproj index 0efeeb0936..9ed5a2702e 100644 --- a/src/Microsoft.ML.GenAI.Core/Microsoft.ML.GenAI.Core.csproj +++ b/src/Microsoft.ML.GenAI.Core/Microsoft.ML.GenAI.Core.csproj @@ -1,13 +1,13 @@  - net6.0;net8.0 + net8.0 true enable preview - + true diff --git a/src/Microsoft.ML.GenAI.LLaMA/Microsoft.ML.GenAI.LLaMA.csproj b/src/Microsoft.ML.GenAI.LLaMA/Microsoft.ML.GenAI.LLaMA.csproj index 81b334564e..0e1207c3df 100644 --- a/src/Microsoft.ML.GenAI.LLaMA/Microsoft.ML.GenAI.LLaMA.csproj +++ b/src/Microsoft.ML.GenAI.LLaMA/Microsoft.ML.GenAI.LLaMA.csproj @@ -1,13 +1,13 @@  - net6.0;net8.0 + net8.0 enable enable true - + true diff --git a/src/Microsoft.ML.GenAI.Mistral/Microsoft.ML.GenAI.Mistral.csproj b/src/Microsoft.ML.GenAI.Mistral/Microsoft.ML.GenAI.Mistral.csproj index 4d0a2fb4b1..f20b587631 100644 --- a/src/Microsoft.ML.GenAI.Mistral/Microsoft.ML.GenAI.Mistral.csproj +++ b/src/Microsoft.ML.GenAI.Mistral/Microsoft.ML.GenAI.Mistral.csproj @@ -1,13 +1,13 @@  - net6.0;net8.0 + net8.0 enable enable true - + true diff --git a/src/Microsoft.ML.GenAI.Phi/Microsoft.ML.GenAI.Phi.csproj b/src/Microsoft.ML.GenAI.Phi/Microsoft.ML.GenAI.Phi.csproj index 0e2f8021a2..d19dbe58fa 100644 --- a/src/Microsoft.ML.GenAI.Phi/Microsoft.ML.GenAI.Phi.csproj +++ b/src/Microsoft.ML.GenAI.Phi/Microsoft.ML.GenAI.Phi.csproj @@ -1,13 +1,13 @@  - net6.0;net8.0 + net8.0 enable enable true - + true diff --git a/src/Native/Native.proj b/src/Native/Native.proj index 25892958cf..232e1047af 100644 --- a/src/Native/Native.proj +++ b/src/Native/Native.proj @@ -185,7 +185,7 @@ Include="$(NativeAssetsBuiltPath)\$(NativeLibPrefix)CpuMathNative$(NativeLibExtension)" RelativePath="Microsoft.ML.CpuMath\runtimes\$(PackageRid)\nativeassets\netstandard2.0" /> + RelativePath="Microsoft.ML.CpuMath\runtimes\$(PackageRid)\nativeassets\net8.0" /> diff --git a/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt index b26b82808a..c5865349b0 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned 5 1 11.925824 0.99988866 0.00016064073906035849 1 6 0 -0.4527979 0.28799012 0.49003084056125074 0 -8 0 -3.796255 0.026382547 0.038573064761285226 0 -9 0 -3.8130417 0.026036168 0.038059895132779242 0 -10 0 -4.7285223 0.0125917215 0.018281354087045291 0 -11 0 -4.606969 0.013873976 0.02015606392328537 0 -18 1 7.3475924 0.99550986 0.006492485535140746 1 +8 0 -3.7962546 0.02638256 0.03857308408160709 0 +9 0 -3.8130417 0.02603617 0.038059897891843629 0 +10 0 -4.7285223 0.012591722 0.018281355447793919 0 +11 0 -4.606969 0.013873977 0.020156065285803371 0 +18 1 7.3475914 0.99550986 0.006492485535140746 1 20 1 6.1389017 0.9881576 0.017186984398807038 1 21 1 7.1486177 0.9947303 0.0076226814052998944 1 25 1 1.6632223 0.6911742 0.53287870995530984 1 -28 0 -4.606969 0.013873976 0.02015606392328537 0 -31 0 -4.2645645 0.01821836 0.026525908568759592 0 +28 0 -4.606969 0.013873977 0.020156065285803371 0 +31 0 -4.2645645 0.018218363 0.026525911305853887 0 32 1 7.198575 0.99493784 0.0073217047835288053 1 -35 0 -4.606969 0.013873976 0.02015606392328537 0 -37 0 -1.714282 0.12729812 0.19643918434677948 0 +35 0 -4.606969 0.013873977 0.020156065285803371 0 +37 0 -1.7142825 0.12729807 0.19643911044583612 0 40 0 ? ? ? 0 -41 1 2.5451746 0.8203416 0.28570332741079429 1 +41 1 2.5451746 0.82034165 0.28570322258699443 1 44 1 8.165841 0.9976778 0.0033541180091675089 1 -45 0 -4.602255 0.0139262155 0.020232492307431734 0 +45 0 -4.602255 0.013926216 0.020232493670021917 0 46 1 5.6216097 0.9821182 0.026031444169057363 1 -48 0 -3.379683 0.03656119 0.053735055635659848 0 +48 0 -3.379683 0.036561195 0.053735061214071053 0 50 1 2.8003244 0.8487653 0.2365623958486662 1 -51 1 0.14775276 0.39660606 1.3342213809867165 1 +51 1 0.14775372 0.39660627 1.3342206221239763 1 52 1 4.696246 0.9629518 0.054464539198696107 1 54 1 6.743868 0.9927051 0.01056288089528439 1 56 1 6.5947094 0.9917779 0.011911021598241851 1 60 1 2.2064123 0.7763946 0.36513800059720591 1 63 1 0.8789625 0.5427823 0.88155440156950038 1 -64 0 -4.8905344 0.011062949 0.016049403073119514 0 -66 0 -3.697434 0.028514326 0.041735373036084926 0 +64 0 -4.8905344 0.01106295 0.016049404431764596 0 +66 0 -3.697434 0.028514327 0.041735375802187391 0 68 1 9.899808 0.99942744 0.00082626923994757486 1 69 0 -4.3595524 0.016894378 0.024581670707795066 0 -70 0 -3.0557137 0.046994247 0.069443170930934078 0 +70 0 -3.0557137 0.04699425 0.069443176570415086 0 71 1 7.555621 0.9962023 0.0054893674226172791 1 -72 0 -1.6769085 0.13069288 0.20206214111088219 0 -73 1 7.7111273 0.9966495 0.0048418602396339801 1 +72 0 -1.6769085 0.1306929 0.20206216584072867 0 +73 1 7.7111263 0.99664944 0.0048419465200441776 1 74 1 2.4994192 0.8148248 0.29543816942188406 1 -76 0 -3.8396955 0.02549526 0.037258890759872848 0 -77 0 -3.1438046 0.043905534 0.064774925308085776 0 -79 0 -4.4265766 0.016017534 0.023295486850883066 0 -82 0 -3.1870723 0.042460293 0.062595780681733368 0 -88 0 -3.697434 0.028514326 0.041735373036084926 0 -90 0 -4.54813 0.01454009 0.021130913771998817 0 -91 0 -4.5069323 0.015025148 0.021841204430750202 0 -92 0 -3.697434 0.028514326 0.041735373036084926 0 -93 0 -4.8905344 0.011062949 0.016049403073119514 0 -95 0 -4.54813 0.01454009 0.021130913771998817 0 -96 0 -4.790498 0.011983715 0.017393273625651583 0 -97 0 -3.413869 0.0356 0.052296442645105176 0 +76 0 -3.8396955 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-5.1684093 0.011276632 0.016361164283329451 0 -663 0 -5.1684093 0.011276632 0.016361164283329451 0 -664 0 -4.2574205 0.02270922 0.033140214869637266 0 -666 0 -3.0702114 0.055484954 0.08235431655806906 0 -667 0 -4.4382315 0.01977694 0.028818008476875413 0 -669 1 6.9817867 0.99342644 0.0095149472206866505 1 -671 0 -3.9565368 0.028554758 0.041795418071838834 0 -672 0 -4.921291 0.013645153 0.019821336171893227 0 +651 0 -4.965017 0.013192964 0.019160092388520032 0 +655 0 -4.7037654 0.016131792 0.023463019884524334 0 +658 1 7.546403 0.99576104 0.006128529583862446 1 +659 0 -5.935418 0.0062253983 0.0090094240348259531 0 +662 0 -5.1684093 0.011276631 0.016361162924390743 0 +663 0 -5.1684093 0.011276631 0.016361162924390743 0 +664 0 -4.2574205 0.022709219 0.033140212119965443 0 +666 0 -3.0702114 0.05548495 0.082354310867892036 0 +667 0 -4.4382315 0.019776938 0.028818005735429076 0 +669 1 6.9817877 0.9934265 0.0095148606603544482 1 +671 0 -3.9565368 0.028554756 0.041795415305621239 0 +672 0 -4.921291 0.013645152 0.019821334809691313 0 673 0 -3.289723 0.047153607 0.069684436821807083 0 -674 0 -5.669884 0.0076494967 0.011078316903702529 0 -675 0 -3.7340279 0.03379261 0.049595209486744243 0 -676 0 -5.419147 0.009289299 0.013464259841054335 0 -677 0 -4.0031805 0.02756116 0.040320577663836212 0 -684 0 -5.935418 0.006225399 0.0090094253868573462 0 -686 0 -5.935418 0.006225399 0.0090094253868573462 0 -687 0 -4.295347 0.022060877 0.032183434683621268 0 -690 0 -5.4890733 0.008799812 0.012751633519735472 0 +674 0 -5.669884 0.007649496 0.011078316226716699 0 +675 0 -3.7340279 0.033792607 0.049595203924317495 0 +676 0 -5.419147 0.009289298 0.013464258484841609 0 +677 0 -4.0031805 0.027561158 0.040320574900445029 0 +684 0 -5.935418 0.0062253983 0.0090094240348259531 0 +686 0 -5.935418 0.0062253983 0.0090094240348259531 0 +687 0 -4.295347 0.022060875 0.032183431935772396 0 +690 0 -5.4890733 0.008799811 0.012751632164192488 0 695 0 -5.4523587 0.00905354 0.013120983569838577 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt index 48a92460de..5b355d6992 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/linux-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt @@ -1,156 +1,156 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.5726995 0.047724232 0.070548673242470078 0 -1 0 3.6101446 0.92095214 3.6611298070520353 1 -2 0 -4.0709443 0.03320216 0.048713845243635014 0 -3 0 2.470542 0.83073896 2.5626781731328263 1 -4 0 -3.4358397 0.05267027 0.078061433982946932 0 -5 1 12.382593 0.99988943 0.0001595227277818751 1 -6 0 -1.4209604 0.20405398 0.32925750806385767 0 +0 0 -3.5726995 0.04772423 0.070548667598666043 0 +1 0 3.6101456 0.92095214 3.6611298070520353 1 +2 0 -4.070944 0.03320217 0.048713861920724008 0 +3 0 2.470542 0.8307389 2.5626776650932253 1 +4 0 -3.4358397 0.052670266 0.078061428309676412 0 +5 1 12.382595 0.99988943 0.0001595227277818751 1 +6 0 -1.4209604 0.20405397 0.32925748105470037 0 7 0 -4.7010875 0.02084881 0.030396453391412736 0 -8 0 -4.6745405 0.021263903 0.031008185910590607 0 -9 0 -4.406417 0.025935683 0.037911059564125327 0 +8 0 -4.6745405 0.021263901 0.031008183164979278 0 +9 0 -4.406417 0.025935682 0.037911056805345573 0 10 0 -5.5593443 0.010982092 0.01593145032913975 0 11 0 -5.4818344 0.011639431 0.016890640083211839 0 -12 1 -0.14206886 0.40347567 1.3094464075888503 0 -13 0 -4.5691886 0.022992346 0.033558231039772665 0 +12 1 -0.14206791 0.40347582 1.3094458747728994 0 +13 0 -4.569188 0.022992352 0.033558239291178633 0 14 1 9.321613 0.998874 0.0016253773809814112 1 -15 1 1.3856993 0.6830734 0.54988747944718552 1 +15 1 1.3856983 0.6830732 0.54988785711378696 1 16 0 -4.533843 0.023602419 0.034459373415090033 0 17 0 -4.046695 0.03379773 0.049602852281362811 0 18 1 7.8903713 0.9966727 0.0048082975514731332 1 -19 0 -3.0987039 0.06699134 0.10003761775121778 0 +19 0 -3.0987039 0.06699133 0.10003760623051484 0 20 1 7.528511 0.9956263 0.0063237968594455725 1 21 1 7.875206 0.9966343 0.0048638619113426269 1 22 0 -5.0078387 0.016592303 0.024138448331127087 0 23 1 ? ? ? 0 -24 0 -5.4686823 0.011754767 0.017059004011272673 0 +24 0 -5.4686823 0.011754766 0.01705900265167648 0 25 1 1.741828 0.73848027 0.43736871808798289 1 -26 0 -4.9710746 0.01705355 0.024815273012732428 0 -27 0 -4.059848 0.03347343 0.049118699463849731 0 +26 0 -4.971074 0.017053556 0.024815281214284793 0 +27 0 -4.0598474 0.03347344 0.049118716145619364 0 28 0 -5.4818344 0.011639431 0.016890640083211839 0 29 0 -5.8557806 0.008789949 0.012737278390963667 0 -30 0 -5.0985007 0.015506633 0.022546608302385175 0 +30 0 -5.0985003 0.015506638 0.022546616491050588 0 31 0 -4.9946866 0.016755885 0.024378447925635292 0 32 1 7.46414 0.9954084 0.0066395103905325147 1 -33 0 -4.6892586 0.02103278 0.030667542438444956 0 -34 0 -4.71424 0.020646105 0.030097814327393193 0 +33 0 -4.689259 0.021032773 0.030667531458592488 0 +34 0 -4.71424 0.020646103 0.030097811583513857 0 35 0 -5.4818344 0.011639431 0.016890640083211839 0 36 1 9.09911 0.99866724 0.0019240484689165348 1 -37 0 -1.113348 0.24456774 0.40462569514389818 0 +37 0 -1.113348 0.24456772 0.40462566668624267 0 38 1 6.140955 0.9875705 0.018044317542393044 1 -39 1 2.5109034 0.83500063 0.26015080155533699 1 +39 1 2.5109034 0.8350006 0.26015090453888573 1 40 0 ? ? ? 0 -41 1 3.3300762 0.90403634 0.14554732340521623 1 +41 1 3.3300762 0.9040363 0.14554741852454292 1 42 1 8.577511 0.99802166 0.0028569650360322531 1 43 1 0.49126053 0.5223421 0.93693314652059867 1 -44 1 8.255751 0.9974761 0.0036458194620463992 1 +44 1 8.255749 0.9974761 0.0036458194620463992 1 45 0 -5.63222 0.010397597 0.015079091662558042 0 -46 1 4.5673847 0.96013206 0.058695238307073898 1 +46 1 4.5673857 0.9601321 0.05869514874509995 1 47 0 -5.95583 0.008152755 0.011810147478593156 0 -48 0 -3.4358397 0.05267027 0.078061433982946932 0 -49 1 5.3666534 0.9778563 0.032305655935016012 1 -50 1 2.5949688 0.8436 0.24536904232329945 1 -51 1 0.12595844 0.4532164 1.1417280158533796 1 +48 0 -3.4358397 0.052670266 0.078061428309676412 0 +49 1 5.3666544 0.9778563 0.032305655935016012 1 +50 1 2.5949688 0.8435999 0.2453691442570726 1 +51 1 0.12595844 0.45321637 1.1417281107212258 1 52 1 5.2992115 0.976721 0.033981595778575298 1 53 1 8.407227 0.99774945 0.0032505195711204126 1 -54 1 7.649309 0.9960077 0.0057712269479890379 1 +54 1 7.6493073 0.9960077 0.0057712269479890379 1 55 1 4.478711 0.957476 0.062691739926295564 1 56 1 5.5541334 0.9807353 0.02806428572372743 1 57 1 1.6657066 0.7271758 0.45962396180677062 1 -58 1 2.5265894 0.8366335 0.25733232225246744 1 -59 1 1.7368536 0.7377509 0.43879434296981179 1 +58 1 2.5265903 0.83663356 0.25733221946992052 1 +59 1 1.7368546 0.737751 0.43879410985231937 1 60 1 2.3288136 0.8150797 0.29498697868205404 1 -61 0 -5.5060835 0.0114297075 0.016584542177967555 0 +61 0 -5.5060835 0.011429707 0.016584540818818418 0 62 1 6.380089 0.98961115 0.015066341261387473 1 -63 1 0.33490086 0.49270463 1.0212050663068744 1 +63 1 0.33490086 0.4927046 1.0212051535714572 1 64 0 -5.95583 0.008152755 0.011810147478593156 0 -65 1 3.8072634 0.9311753 0.1028753176207461 1 +65 1 3.8072643 0.93117535 0.1028752252736623 1 66 0 -4.046695 0.03379773 0.049602852281362811 0 -67 1 4.218014 0.9486548 0.076044929659653121 1 +67 1 4.218014 0.9486547 0.076045020305197497 1 68 1 10.826725 0.9996402 0.00051913703181331893 1 -69 0 -5.271654 0.013623926 0.019790289199966723 0 -70 0 -3.4726496 0.051294282 0.075967452463448076 0 +69 0 -5.2716546 0.01362392 0.019790281026931159 0 +70 0 -3.4726496 0.05129428 0.075967446798405988 0 71 1 7.895046 0.99668443 0.0047913008067115544 1 -72 0 -2.1755848 0.1263537 0.1948787725155719 0 +72 0 -2.1755848 0.12635368 0.19487874790855292 0 73 1 8.9055195 0.9984568 0.0022281210940979582 1 -74 1 2.5993576 0.8440387 0.2446189028997863 1 -75 0 -4.0411606 0.033935104 0.049807988000729261 0 +74 1 2.5993576 0.84403867 0.244619004780572 1 +75 0 -4.0411606 0.0339351 0.049807982437482065 0 76 0 -5.0750337 0.015780753 0.022948364676579087 0 -77 0 -3.499567 0.05030968 0.074470946934380028 0 -78 0 -3.6211972 0.046079826 0.06805955070059394 0 -79 0 -5.391173 0.012457768 0.018085649525652777 0 -80 0 -2.7157316 0.087596945 0.13225681838671866 0 -81 0 -4.2284155 0.029574301 0.043310338334976231 0 -82 0 -3.4452734 0.05231434 0.077519488826293739 0 +77 0 -3.499567 0.050309677 0.074470941275211228 0 +78 0 -3.6211972 0.046079822 0.068059545066518914 0 +79 0 -5.3911724 0.012457772 0.018085654967908992 0 +80 0 -2.7157316 0.08759694 0.13225680660583347 0 +81 0 -4.2284155 0.0295743 0.043310335565852412 0 +82 0 -3.4452734 0.052314337 0.077519483155153973 0 83 0 -2.1223526 0.13087894 0.20237095101991351 0 84 1 9.694054 0.99915093 0.0012254667568283894 1 85 1 6.2895603 0.9888809 0.016131360230936344 1 86 1 2.6168842 0.84578085 0.24164420028856959 1 -87 1 6.919142 0.9930738 0.010027129067340199 1 +87 1 6.91914 0.9930738 0.010027129067340199 1 88 0 -4.046695 0.03379773 0.049602852281362811 0 89 0 -5.085745 0.015655048 0.022764115650391988 0 -90 0 -5.4686823 0.011754767 0.017059004011272673 0 -91 0 -5.189559 0.014486661 0.021052696761167319 0 +90 0 -5.4686823 0.011754766 0.01705900265167648 0 +91 0 -5.189559 0.01448666 0.021052695397802253 0 92 0 -4.046695 0.03379773 0.049602852281362811 0 93 0 -5.95583 0.008152755 0.011810147478593156 0 94 0 -4.9946866 0.016755885 0.024378447925635292 0 -95 0 -5.4686823 0.011754767 0.017059004011272673 0 +95 0 -5.4686823 0.011754766 0.01705900265167648 0 96 0 -5.663555 0.010155837 0.014726683875270435 0 -97 0 -3.5726995 0.047724232 0.070548673242470078 0 +97 0 -3.5726995 0.04772423 0.070548667598666043 0 98 1 8.590233 0.9980406 0.0028295658493644239 1 99 1 10.917194 0.99966407 0.00048472853254951715 1 100 1 4.8476696 0.9675202 0.047636346312542086 1 101 1 -0.84280396 0.28443488 1.8138296783750301 0 -102 0 -3.7530966 0.041876126 0.061715903711063051 0 -103 1 1.7746439 0.7432593 0.42806246416940569 1 +102 0 -3.753096 0.04187614 0.061715926148486977 0 +103 1 1.7746449 0.7432594 0.42806234847449176 1 104 1 12.140858 0.9998672 0.00019160139559387039 1 -105 1 2.5560713 0.83966726 0.25211035808697119 1 +105 1 2.5560703 0.83966714 0.25211056290937062 1 106 1 9.259369 0.99881965 0.0017038920105930569 1 107 1 6.720646 0.99195737 0.011649978820645172 1 108 0 -5.5617743 0.010962089 0.015902272030853013 0 109 1 6.871727 0.992822 0.010393022735684912 1 -110 0 -2.766693 0.08455608 0.12745658613888911 0 -111 1 3.848031 0.93313104 0.099848402613633994 1 +110 0 -2.766693 0.08455607 0.12745657439713692 0 +111 1 3.848032 0.93313104 0.099848402613633994 1 112 1 9.425768 0.9989595 0.0015019320975399703 1 -113 1 9.506624 0.9990213 0.0014126689718023965 1 -114 0 -3.0727458 0.068232656 0.10195832549468188 0 -115 0 -4.6439905 0.021751598 0.03172724574533177 0 -116 0 -0.66188717 0.3131727 0.54198070603227311 0 +113 1 9.506622 0.9990213 0.0014126689718023965 1 +114 0 -3.0727453 0.06823267 0.10195834856678422 0 +115 0 -4.6439905 0.021751596 0.031727242998351647 0 +116 0 -0.66188717 0.31317267 0.54198064343187602 0 117 1 9.617275 0.9991 0.0012989676266954972 1 -118 0 -5.3621607 0.012731451 0.018485525732496611 0 -119 0 -3.9435177 0.036449015 0.053567088295960068 0 -120 0 -4.8696556 0.018392263 0.026781474537826103 0 -121 0 -3.469522 0.05140986 0.076143221101649672 0 +118 0 -5.3621607 0.01273145 0.018485524371555397 0 +119 0 -3.9435177 0.03644901 0.053567082718198315 0 +120 0 -4.8696556 0.018392261 0.026781471800246908 0 +121 0 -3.469522 0.051409855 0.076143215435917344 0 122 1 9.680523 0.99914217 0.0012381182790972595 1 123 1 3.8165932 0.9316275 0.10217485034891999 1 124 1 7.6522446 0.99601656 0.0057583629403995997 1 125 0 -5.95583 0.008152755 0.011810147478593156 0 -126 1 8.564951 0.9980027 0.0028843647430662487 1 +126 1 8.564953 0.9980027 0.0028843647430662487 1 127 0 -4.520691 0.023833437 0.034800759324198648 0 128 1 4.84898 0.9675514 0.04758977495588574 1 -129 0 -5.717684 0.009751258 0.014137132147530099 0 -130 0 -3.4726496 0.051294282 0.075967452463448076 0 +129 0 -5.717684 0.0097512575 0.014137130790684689 0 +130 0 -3.4726496 0.05129428 0.075967446798405988 0 131 0 -4.9946866 0.016755885 0.024378447925635292 0 132 1 8.602232 0.9980583 0.0028039765128859018 1 133 0 -4.810811 0.01921573 0.027992252541605728 0 -134 0 -4.9171767 0.017752616 0.025841674110087472 0 -135 0 -2.7288966 0.08680205 0.13100047471163517 0 +134 0 -4.9171767 0.017752614 0.025841671374291015 0 +135 0 -2.7288966 0.08680204 0.13100046294100468 0 136 0 -4.533843 0.023602419 0.034459373415090033 0 137 0 -5.494987 0.01152521 0.016723922858878242 0 -138 0 -4.2402444 0.029317858 0.04292914403934768 0 +138 0 -4.2402444 0.029317856 0.042929141270955429 0 139 0 ? ? ? 0 140 0 -5.494987 0.01152521 0.016723922858878242 0 141 0 -5.9689827 0.00807247 0.011693373854840414 0 -142 1 4.4324036 0.95602256 0.064883430994521443 1 -143 0 -4.6439905 0.021751598 0.03172724574533177 0 +142 1 4.4324026 0.9560225 0.064883520941486764 1 +143 0 -4.6439905 0.021751596 0.031727242998351647 0 144 0 -5.4818344 0.011639431 0.016890640083211839 0 145 0 ? ? ? 0 146 1 1.3394346 0.67542744 0.56612731001545713 1 -147 0 -5.4154215 0.012233486 0.017758034331297672 0 -148 0 -1.0123739 0.25899458 0.4324439985553144 0 +147 0 -5.4154215 0.0122334855 0.017758032971042548 0 +148 0 -1.012373 0.2589947 0.43244423064898052 0 149 1 11.461615 0.99977773 0.00032069729386163213 1 150 0 -5.5593443 0.010982092 0.01593145032913975 0 -151 1 5.006485 0.9711001 0.042308092038938809 1 +151 1 5.006484 0.9711001 0.042308092038938809 1 152 1 9.715748 0.99916476 0.0012054999542047689 1 153 0 -4.121497 0.03199298 0.046910583933444494 0 154 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -160,17 +160,17 @@ Instance Label Score Probability Log-loss Assigned 158 0 ? ? ? 0 159 1 12.346203 0.99988633 0.0001639947780942108 1 160 1 9.039494 0.99860567 0.002012998795947356 1 -161 0 -3.8496675 0.039033547 0.057442027050147004 0 +161 0 -3.8496675 0.039033543 0.057442021457383786 0 162 0 -4.520691 0.023833437 0.034800759324198648 0 -163 0 -3.3870554 0.05454763 0.080923315355362818 0 +163 0 -3.3870554 0.054547627 0.080923309670827032 0 164 0 ? ? ? 0 -165 0 -3.3999205 0.05404651 0.080158845547395108 0 +165 0 -3.3999205 0.054046508 0.080158839865870704 0 166 1 7.976185 0.9968817 0.004505750926832852 1 167 1 8.355644 0.99765986 0.0033800618776249807 1 168 0 -4.520691 0.023833437 0.034800759324198648 0 169 0 -6.2282124 0.0066409498 0.009612819847748871 0 170 0 -5.494987 0.01152521 0.016723922858878242 0 -171 0 -5.4686823 0.011754767 0.017059004011272673 0 +171 0 -5.4686823 0.011754766 0.01705900265167648 0 172 0 -5.95583 0.008152755 0.011810147478593156 0 173 1 15.1560135 0.9999865 1.9434170443242565E-05 1 174 1 6.1769257 0.9879011 0.017561488134486943 1 @@ -181,72 +181,72 @@ Instance Label Score Probability Log-loss Assigned 179 1 2.290575 0.8106676 0.30281765649168213 1 180 0 -5.5593443 0.010982092 0.01593145032913975 0 181 0 -6.4429784 0.005648197 0.0081717255492616877 0 -182 0 -3.0987039 0.06699134 0.10003761775121778 0 +182 0 -3.0987039 0.06699133 0.10003760623051484 0 183 1 9.1599655 0.9987273 0.0018372561468384253 1 184 1 6.2014637 0.98812157 0.017239546569330137 1 -185 0 -5.0853486 0.01565968 0.022770905093500125 0 +185 0 -5.085348 0.015659686 0.022770913283438728 0 186 1 5.7654095 0.9835412 0.023942621168576837 1 187 1 13.977451 0.99996704 4.7553986690113E-05 1 188 1 9.065283 0.99863267 0.0019739908621602127 1 -189 0 -4.7540584 0.020044118 0.029211295141143595 0 -190 1 11.957216 0.99984735 0.0002202405944450654 1 -191 1 10.956871 0.999674 0.00047036322693564959 1 -192 0 -4.059848 0.03347343 0.049118699463849731 0 -193 0 -5.4686823 0.011754767 0.017059004011272673 0 +189 0 -4.7540584 0.020044116 0.029211292398949824 0 +190 1 11.957218 0.99984735 0.0002202405944450654 1 +191 1 10.956873 0.999674 0.00047036322693564959 1 +192 0 -4.0598474 0.03347344 0.049118716145619364 0 +193 0 -5.4686823 0.011754766 0.01705900265167648 0 194 0 -4.520691 0.023833437 0.034800759324198648 0 195 0 -4.046695 0.03379773 0.049602852281362811 0 196 0 6.8652763 0.992787 7.1151855643261381 1 -197 0 -2.6564164 0.09126051 0.13806131755901335 0 +197 0 -2.6564164 0.0912605 0.13806130573063377 0 198 0 -6.4429784 0.005648197 0.0081717255492616877 0 199 0 -5.0078387 0.016592303 0.024138448331127087 0 200 1 10.36586 0.9994898 0.00073627359086373204 1 201 1 9.869495 0.9992566 0.0010728826448553252 1 -202 0 -5.4686823 0.011754767 0.017059004011272673 0 -203 0 -3.5726995 0.047724232 0.070548673242470078 0 -204 0 -5.4686823 0.011754767 0.017059004011272673 0 -205 1 12.086603 0.99986166 0.00019959967319162043 1 +202 0 -5.4686823 0.011754766 0.01705900265167648 0 +203 0 -3.5726995 0.04772423 0.070548667598666043 0 +204 0 -5.4686823 0.011754766 0.01705900265167648 0 +205 1 12.086601 0.99986166 0.00019959967319162043 1 206 1 5.94417 0.98559827 0.020928376857247459 1 207 0 -5.5593443 0.010982092 0.01593145032913975 0 208 0 -5.5593443 0.010982092 0.01593145032913975 0 -209 0 -3.6633615 0.04469411 0.065965336354842946 0 +209 0 -3.6633615 0.044694107 0.065965330728940397 0 210 1 14.534115 0.99997836 3.1215188826316377E-05 1 211 1 9.64962 0.99912184 0.0012674667953664828 1 -212 0 -5.4686823 0.011754767 0.017059004011272673 0 -213 1 14.52906 0.9999783 3.1301182014901005E-05 1 +212 0 -5.4686823 0.011754766 0.01705900265167648 0 +213 1 14.529058 0.9999783 3.1301182014901005E-05 1 214 1 13.868914 0.9999642 5.1681712271066226E-05 1 215 1 7.643734 0.9959909 0.0057955739072098177 1 216 0 -5.95583 0.008152755 0.011810147478593156 0 -217 0 -5.4686823 0.011754767 0.017059004011272673 0 +217 0 -5.4686823 0.011754766 0.01705900265167648 0 218 1 7.88678 0.99666363 0.0048214119279433977 1 -219 0 -2.511506 0.100795746 0.15327923389796996 0 -220 0 -5.1632547 0.014774316 0.021473856707274121 0 +219 0 -2.511506 0.10079574 0.15327922194416127 0 +220 0 -5.1632547 0.014774315 0.021473855343510994 0 221 1 10.395218 0.999501 0.00072009905980506843 1 -222 1 -2.214662 0.1231175 3.0218922639873447 0 +222 1 -2.214662 0.12311749 3.0218923512935034 0 223 1 5.7424126 0.9832564 0.024360423750620218 1 224 1 9.995327 0.99932426 0.00097521318777220637 1 225 0 -5.95583 0.008152755 0.011810147478593156 0 226 1 10.225868 0.9994326 0.00081878372812222485 1 -227 1 7.459608 0.9953927 0.0066623169983452231 1 +227 1 7.459608 0.9953926 0.0066624033876966004 1 228 0 -5.5593443 0.010982092 0.01593145032913975 0 -229 1 12.666517 0.9999109 0.00012856275965089447 1 +229 1 12.666515 0.9999109 0.00012856275965089447 1 230 1 6.1583214 0.9877312 0.017809586200967932 1 -231 1 8.623034 0.99808866 0.0027601224428665879 1 -232 0 1.2822819 0.66585165 1.5814393559263284 1 +231 1 8.623032 0.99808866 0.0027601224428665879 1 +232 0 1.2822819 0.6658516 1.5814390985815938 1 233 1 6.382519 0.9896301 0.015038709216571163 1 -234 0 -2.8964381 0.07724253 0.115976584697302 0 +234 0 -2.8964386 0.0772425 0.11597653810254285 0 235 0 ? ? ? 0 236 1 11.420414 0.99977064 0.00033093257283884215 1 -237 1 6.5357933 0.9907579 0.01339555438417007 1 +237 1 6.535795 0.9907579 0.01339555438417007 1 238 1 12.422876 0.9998928 0.00015470668910224405 1 239 1 5.902529 0.98514295 0.021595017147241997 1 -240 0 -2.017991 0.14015022 0.21784345766297453 0 +240 0 -2.0179915 0.14015016 0.21784335765559609 0 241 0 -4.0004973 0.034961022 0.051340881416726061 0 242 0 -4.9946866 0.016755885 0.024378447925635292 0 -243 0 -2.6953988 0.088837564 0.13421982380863931 0 -244 0 -5.4686823 0.011754767 0.017059004011272673 0 +243 0 -2.6953983 0.08883759 0.13421985919941723 0 +244 0 -5.4686823 0.011754766 0.01705900265167648 0 245 0 -2.817525 0.081618704 0.12283483473472002 0 -246 1 11.424004 0.9997713 0.00032998645156814019 1 -247 1 3.104393 0.8881221 0.17117009101578401 1 +246 1 11.424002 0.9997713 0.00032998645156814019 1 +247 1 3.104394 0.88812214 0.17116999419202034 1 248 0 -3.0615559 0.068774305 0.10279722872890984 0 249 0 ? ? ? 0 250 0 -6.021953 0.0077569964 0.011234610256342446 0 @@ -258,64 +258,64 @@ Instance Label Score Probability Log-loss Assigned 256 0 -5.494987 0.01152521 0.016723922858878242 0 257 0 -5.0078387 0.016592303 0.024138448331127087 0 258 0 -4.520691 0.023833437 0.034800759324198648 0 -259 0 2.9647493 0.877156 3.025100925818462 1 +259 0 2.9647484 0.8771559 3.0250995258103677 1 260 1 9.870926 0.99925745 0.0010716778711863072 1 261 1 12.206299 0.99987364 0.00018231312886902128 1 -262 1 9.653841 0.99912465 0.0012634216564773898 1 +262 1 9.653839 0.99912465 0.0012634216564773898 1 263 1 8.981979 0.9985436 0.002102643541396716 1 264 1 5.664709 0.9822578 0.025826400239286989 1 -265 0 -2.494875 0.10194498 0.15512426427086881 0 -266 1 7.3661613 0.99505585 0.0071505858244227481 1 -267 1 3.3009596 0.90210295 0.14863601285829425 1 +265 0 -2.494875 0.101944976 0.15512425230176288 0 +266 1 7.3661633 0.99505585 0.0071505858244227481 1 +267 1 3.3009605 0.902103 0.14863591753511315 1 268 1 9.372967 0.998917 0.0015633090806578933 1 -269 0 -5.4686823 0.011754767 0.017059004011272673 0 -270 1 6.031377 0.9865078 0.01959767586907752 1 -271 0 -3.5726995 0.047724232 0.070548673242470078 0 -272 1 3.3009596 0.90210295 0.14863601285829425 1 -273 1 0.21747208 0.4704687 1.0878293505835543 1 -274 0 -4.3236628 0.027569678 0.040333214707074766 0 +269 0 -5.4686823 0.011754766 0.01705900265167648 0 +270 1 6.031378 0.9865078 0.01959767586907752 1 +271 0 -3.5726995 0.04772423 0.070548667598666043 0 +272 1 3.3009605 0.902103 0.14863591753511315 1 +273 1 0.21747303 0.47046885 1.0878288936386407 1 +274 0 -4.323663 0.027569667 0.040333198126582509 0 275 0 ? ? ? 0 276 0 -5.0078387 0.016592303 0.024138448331127087 0 277 0 -5.95583 0.008152755 0.011810147478593156 0 -278 0 -5.4686823 0.011754767 0.017059004011272673 0 -279 1 7.127907 0.9940822 0.0085629246022787941 1 +278 0 -5.4686823 0.011754766 0.01705900265167648 0 +279 1 7.127905 0.9940822 0.0085629246022787941 1 280 0 -4.520691 0.023833437 0.034800759324198648 0 -281 0 -4.6892586 0.02103278 0.030667542438444956 0 +281 0 -4.689259 0.021032773 0.030667531458592488 0 282 1 4.4381247 0.95620465 0.064608669189911136 1 283 1 6.0636253 0.9868295 0.019127222915579972 1 284 1 7.431343 0.99529326 0.006806421628886848 1 285 1 14.218479 0.9999725 3.9642545670177728E-05 1 286 1 15.281265 0.9999877 1.7714321792245208E-05 1 -287 0 -4.9171767 0.017752616 0.025841674110087472 0 +287 0 -4.9171767 0.017752614 0.025841671374291015 0 288 1 2.2163515 0.80187416 0.31855224459431192 1 289 1 8.312019 0.9975813 0.0034936687628036997 1 290 0 -6.4429784 0.005648197 0.0081717255492616877 0 -291 0 -5.4686823 0.011754767 0.017059004011272673 0 +291 0 -5.4686823 0.011754766 0.01705900265167648 0 292 1 ? ? ? 0 -293 1 5.542121 0.9805624 0.028318669172894068 1 +293 1 5.542122 0.9805624 0.028318669172894068 1 294 0 ? ? ? 0 295 1 7.7866364 0.99640125 0.0052012629006476284 1 -296 0 1.823431 0.75025773 2.0014880731401443 1 +296 0 1.823431 0.7502577 2.0014877288199164 1 297 0 ? ? ? 0 298 0 -2.725597 0.08700067 0.13131429030889247 0 299 1 7.8274345 0.9965105 0.0050430801821242759 1 300 1 7.348074 0.9949879 0.0072491063826461647 1 -301 0 -5.4686823 0.011754767 0.017059004011272673 0 +301 0 -5.4686823 0.011754766 0.01705900265167648 0 302 1 15.735764 0.9999913 1.2554788140693439E-05 1 -303 0 -5.4686823 0.011754767 0.017059004011272673 0 +303 0 -5.4686823 0.011754766 0.01705900265167648 0 304 1 5.9607983 0.98577625 0.020667878309752356 1 305 1 8.459471 0.9978367 0.003124349823870973 1 -306 0 -5.4686823 0.011754767 0.017059004011272673 0 -307 0 -5.4686823 0.011754767 0.017059004011272673 0 +306 0 -5.4686823 0.011754766 0.01705900265167648 0 +307 0 -5.4686823 0.011754766 0.01705900265167648 0 308 1 7.422592 0.9952621 0.0068516084791740307 1 -309 0 -1.7474079 0.16675755 0.26319176134218036 0 -310 0 -5.391173 0.012457768 0.018085649525652777 0 +309 0 -1.7474074 0.1667576 0.26319183874281699 0 +310 0 -5.3911724 0.012457772 0.018085654967908992 0 311 0 -6.4429784 0.005648197 0.0081717255492616877 0 312 1 3.6294708 0.9220128 0.11714130634122567 1 313 0 -6.4429784 0.005648197 0.0081717255492616877 0 -314 0 -6.046492 0.0076150266 0.011028204574905828 0 +314 0 -6.0464916 0.007615029 0.011028207959717411 0 315 0 ? ? ? 0 -316 1 3.6177406 0.92137057 0.11814658455212051 1 +316 1 3.6177397 0.9213705 0.11814667788191786 1 317 1 9.215706 0.99877995 0.0017612310516048875 1 318 0 -5.1966968 0.014409561 0.02093983363547448 0 319 0 2.6369457 0.84775543 2.7155373416013662 1 @@ -323,77 +323,77 @@ Instance Label Score Probability Log-loss Assigned 321 0 ? ? ? 0 322 0 -4.520691 0.023833437 0.034800759324198648 0 323 1 5.5612926 0.98083764 0.027913746222999549 1 -324 0 -5.4686823 0.011754767 0.017059004011272673 0 -325 0 -4.192758 0.030360555 0.044479706359279253 0 +324 0 -5.4686823 0.011754766 0.01705900265167648 0 +325 0 -4.192758 0.030360553 0.044479703587910022 0 326 1 4.4103794 0.95531476 0.065951946768905437 1 327 0 -5.95583 0.008152755 0.011810147478593156 0 -328 1 3.373887 0.9068811 0.141014691181642 1 +328 1 3.373888 0.90688115 0.14101459636069669 1 329 1 7.8321342 0.9965229 0.0050251314657524084 1 330 1 5.856251 0.98462033 0.022360562903046506 1 -331 0 -3.2490888 0.06020364 0.08957991749201398 0 +331 0 -3.2490892 0.06020362 0.089579883179530889 0 332 0 -3.1363668 0.06522752 0.097312838862202627 0 333 1 4.914962 0.9690866 0.04530251936714582 1 334 1 5.9119453 0.98524714 0.021442445493118613 1 335 0 -6.4429784 0.005648197 0.0081717255492616877 0 336 1 5.543519 0.9805826 0.028288940560717461 1 -337 0 -5.4686823 0.011754767 0.017059004011272673 0 -338 0 -6.046492 0.0076150266 0.011028204574905828 0 +337 0 -5.4686823 0.011754766 0.01705900265167648 0 +338 0 -6.0464916 0.007615029 0.011028207959717411 0 339 1 5.684025 0.98251134 0.025454033740074194 1 340 1 6.620781 0.9913299 0.012562837713558307 1 -341 0 -5.4686823 0.011754767 0.017059004011272673 0 +341 0 -5.4686823 0.011754766 0.01705900265167648 0 342 0 -5.9689827 0.00807247 0.011693373854840414 0 343 0 -6.4429784 0.005648197 0.0081717255492616877 0 344 1 10.162451 0.99940467 0.00085913711935366719 1 345 0 -6.4429784 0.005648197 0.0081717255492616877 0 -346 0 -3.3358254 0.056586877 0.084038425056544686 0 -347 0 -6.1395845 0.0070995023 0.010278947729356253 0 -348 1 0.15727425 0.45910957 1.1230895756645052 1 +346 0 -3.335825 0.056586895 0.084038453540661334 0 +347 0 -6.1395845 0.007099502 0.010278947052745423 0 +348 1 0.15727425 0.45910954 1.1230896693146193 1 349 1 4.0622606 0.94258505 0.085305293794040227 1 350 0 -3.93614 0.036646076 0.053862170913072119 0 351 0 -4.9946866 0.016755885 0.024378447925635292 0 352 0 0.47192955 0.51868224 1.0549384413455107 1 353 1 8.696342 0.99819183 0.0026109941163622473 1 354 0 -5.95583 0.008152755 0.011810147478593156 0 -355 0 -4.246153 0.029190574 0.042739978357172199 0 -356 1 -0.69921684 0.30711415 1.7031530909424435 0 +355 0 -4.2461534 0.029190563 0.042739961748996601 0 +356 1 -0.69921684 0.30711412 1.7031532309414121 0 357 1 12.852016 0.9999226 0.00011170705633068974 1 358 1 5.582206 0.9811335 0.027478611445726227 1 359 1 5.3672857 0.97786665 0.032290354697391166 1 360 1 15.333874 0.9999882 1.7026382905914664E-05 1 361 1 6.317689 0.9891131 0.0157926107591757 1 362 0 -3.5059962 0.05007717 0.074117778802898118 0 -363 0 -2.0658464 0.13583238 0.2106169271876272 0 +363 0 -2.0658464 0.13583237 0.21061690231070429 0 364 0 -4.9946866 0.016755885 0.024378447925635292 0 365 0 -5.4818344 0.011639431 0.016890640083211839 0 366 1 13.694571 0.9999591 5.899125529457858E-05 1 367 1 11.299244 0.9997486 0.00036275701338483524 1 368 0 -5.8557806 0.008789949 0.012737278390963667 0 -369 0 -5.4592943 0.011837783 0.017180200787587779 0 +369 0 -5.4592943 0.011837782 0.017180199427877362 0 370 0 -3.89473 0.037771333 0.055548313644665219 0 371 0 -5.8557806 0.008789949 0.012737278390963667 0 -372 0 -4.2402444 0.029317858 0.04292914403934768 0 -373 0 -3.7544203 0.04183586 0.061655273456784274 0 -374 0 -4.71424 0.020646105 0.030097814327393193 0 +372 0 -4.2402444 0.029317856 0.042929141270955429 0 +373 0 -3.7544198 0.04183587 0.061655290284144987 0 +374 0 -4.71424 0.020646103 0.030097811583513857 0 375 0 -6.4429784 0.005648197 0.0081717255492616877 0 376 0 -5.95583 0.008152755 0.011810147478593156 0 -377 0 -6.046492 0.0076150266 0.011028204574905828 0 +377 0 -6.0464916 0.007615029 0.011028207959717411 0 378 0 -3.803866 0.040357877 0.059431608857550028 0 -379 0 -2.2557268 0.11979375 0.18408647967245978 0 +379 0 -2.2557268 0.11979374 0.18408646746064505 0 380 0 -6.4429784 0.005648197 0.0081717255492616877 0 381 1 10.07641 0.99936455 0.00091704491714961699 1 -382 0 -3.59721 0.04688631 0.069279781860915407 0 +382 0 -3.5972104 0.04688629 0.069279753666703942 0 383 0 -5.9689827 0.00807247 0.011693373854840414 0 384 0 -5.9689827 0.00807247 0.011693373854840414 0 -385 0 -3.7061968 0.043327074 0.063902325254427392 0 +385 0 -3.7061968 0.04332707 0.063902319636563967 0 386 1 6.0875864 0.98706365 0.018784981568614377 1 -387 0 -2.3345594 0.11363012 0.17401924291232526 0 -388 0 -5.2848067 0.013490496 0.019595145292487764 0 -389 0 -3.3224106 0.057132594 0.084873194157620097 0 +387 0 -2.3345594 0.113630116 0.17401923078542889 0 +388 0 -5.2848067 0.013490495 0.019595143930499406 0 +389 0 -3.322411 0.057132572 0.084873159956897426 0 390 0 -5.6504025 0.010256628 0.014873593254337859 0 391 1 10.030338 0.99934196 0.00094965672085389622 1 392 0 -5.0078387 0.016592303 0.024138448331127087 0 393 0 -6.53364 0.0052747843 0.0076300462926347243 0 -394 0 -5.241206 0.013937826 0.020249479819272553 0 +394 0 -5.241206 0.013937825 0.020249478456666324 0 395 0 -5.0078387 0.016592303 0.024138448331127087 0 396 0 -4.520691 0.023833437 0.034800759324198648 0 397 0 -5.0209913 0.016430287 0.023900783632758753 0 @@ -401,38 +401,38 @@ Instance Label Score Probability Log-loss Assigned 399 0 -5.7283545 0.0096734045 0.014023711114027561 0 400 1 10.056744 0.999355 0.00093081234331069047 1 401 0 -5.494987 0.01152521 0.016723922858878242 0 -402 0 -3.2177973 0.061560772 0.091664774253514145 0 +402 0 -3.2177973 0.06156077 0.091664768526496687 0 403 0 -4.145746 0.031428177 0.046069061057006429 0 -404 0 -5.507669 0.011416126 0.016564721842253591 0 +404 0 -5.507669 0.011416125 0.016564720483123126 0 405 0 -5.95583 0.008152755 0.011810147478593156 0 -406 0 -4.1128182 0.0321975 0.047215431190392593 0 +406 0 -4.1128182 0.032197498 0.047215425637133698 0 407 0 -5.95583 0.008152755 0.011810147478593156 0 -408 0 -3.691061 0.04380549 0.064623974204670379 0 -409 0 -4.71424 0.020646105 0.030097814327393193 0 +408 0 -3.6910605 0.043805506 0.064623996687367499 0 +409 0 -4.71424 0.020646103 0.030097811583513857 0 410 0 -5.95583 0.008152755 0.011810147478593156 0 411 0 ? ? ? 0 412 1 9.230705 0.9987937 0.0017413429278913565 1 -413 0 -3.2791004 0.05892837 0.087623557240190011 0 +413 0 -3.2791004 0.058928367 0.087623551529192406 0 414 1 6.7173824 0.9919376 0.011678759699519599 1 415 0 -0.6668339 0.3123661 0.54028742034074773 0 416 1 8.809383 0.9983402 0.0023965899325754819 1 417 0 -5.95583 0.008152755 0.011810147478593156 0 418 0 -1.8758612 0.15365387 0.2406803006815022 0 419 0 -5.442145 0.011990931 0.017403810256483163 0 -420 0 -2.5893164 0.09557056 0.14492013592578398 0 +420 0 -2.5893164 0.09557055 0.14492012404103641 0 421 1 11.824856 0.99983126 0.00024346198385283565 1 -422 0 -2.8105893 0.08201394 0.12345585262262933 0 -423 0 -3.4726496 0.051294282 0.075967452463448076 0 +422 0 -2.8105888 0.082013965 0.12345588775033869 0 +423 0 -3.4726496 0.05129428 0.075967446798405988 0 424 0 -5.494987 0.01152521 0.016723922858878242 0 425 1 14.817663 0.9999826 2.5109685538071405E-05 1 426 0 -2.8241482 0.08124289 0.12224458409135237 0 -427 1 4.3530817 0.9534221 0.068812983796401356 1 +427 1 4.3530827 0.9534222 0.068812893604113898 1 428 0 -5.95583 0.008152755 0.011810147478593156 0 429 0 -5.4818344 0.011639431 0.016890640083211839 0 430 0 -5.50395 0.011448007 0.016611248346541513 0 -431 0 -2.5834928 0.09595309 0.14553046360362973 0 -432 0 -3.8628197 0.03866103 0.056882875876087251 0 -433 0 -4.4631066 0.024871144 0.036335222703172541 0 +431 0 -2.5834928 0.095953085 0.14553045171385326 0 +432 0 -3.8628197 0.038661025 0.056882870285491223 0 +433 0 -4.4631066 0.024871143 0.036335219947404518 0 434 0 5.0084 0.97114086 5.1148279570464927 1 435 1 7.444332 0.9953392 0.0067398103254229808 1 436 1 3.841199 0.9328069 0.10034962060198541 1 @@ -440,52 +440,52 @@ Instance Label Score Probability Log-loss Assigned 438 0 -3.822938 0.039801266 0.058595061116589527 0 439 0 -4.5469956 0.023373578 0.034121284818622755 0 440 1 10.154686 0.99940115 0.00086421363869066531 1 -441 0 -1.8604465 0.1551807 0.24328528875750627 0 -442 0 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0.00067149052326743921 1 +455 1 0.81635284 0.58322746 0.77786945830087184 1 +456 1 10.487385 0.99953467 0.00067149052326743921 1 457 1 9.06263 0.9986299 0.0019779518845872311 1 -458 0 -4.253397 0.02903525 0.042509173626544897 0 -459 0 -3.9597979 0.036017757 0.052921523163131159 0 +458 0 -4.2533965 0.029035257 0.042509184696890877 0 +459 0 -3.9597979 0.036017753 0.052921517587864737 0 460 0 -3.93614 0.036646076 0.053862170913072119 0 -461 0 -3.6959996 0.04364884 0.064387638591170013 0 -462 0 -3.4621444 0.05168347 0.076559412170907212 0 -463 0 -4.823963 0.019028598 0.027717016988036743 0 +461 0 -3.6959996 0.043648835 0.064387632971416475 0 +462 0 -3.4621444 0.051683467 0.076559406503540178 0 +463 0 -4.823963 0.019028597 0.027717014248681737 0 464 0 -5.0209913 0.016430287 0.023900783632758753 0 465 1 9.7853985 0.99920774 0.0011434497150273235 1 -466 1 9.541933 0.99904716 0.0013753126588050381 1 +466 1 9.541931 0.99904716 0.0013753126588050381 1 467 1 7.7145195 0.99619967 0.0054931654697981886 1 468 0 -5.0209913 0.016430287 0.023900783632758753 0 469 0 -5.6622314 0.010165936 0.014741403595886524 0 -470 0 -5.0985007 0.015506633 0.022546608302385175 0 -471 0 -3.4621444 0.05168347 0.076559412170907212 0 -472 0 -4.163662 0.0310171 0.04545688944873634 0 +470 0 -5.0985003 0.015506638 0.022546616491050588 0 +471 0 -3.4621444 0.051683467 0.076559406503540178 0 +472 0 -4.163662 0.031017099 0.045456886675489347 0 473 0 -5.0209913 0.016430287 0.023900783632758753 0 474 0 -4.5469956 0.023373578 0.034121284818622755 0 475 0 -5.494987 0.01152521 0.016723922858878242 0 -476 0 -4.727392 0.020445334 0.02980208737260864 0 +476 0 -4.727392 0.020445332 0.029802084629291691 0 477 0 -5.0209913 0.016430287 0.023900783632758753 0 -478 0 -4.4195695 0.025684815 0.03753954391072279 0 +478 0 -4.4195695 0.025684813 0.037539541152653365 0 479 1 8.32148 0.9975986 0.0034686710325429922 1 -480 0 -4.6376576 0.021854054 0.031878354047421072 0 +480 0 -4.6376576 0.021854052 0.031878351300153221 0 481 0 -3.082261 0.067775175 0.10125016271957035 0 482 1 15.481422 0.99998945 1.5220544890042414E-05 1 -483 1 10.906595 0.9996614 0.0004885994477486597 1 -484 0 -4.253397 0.02903525 0.042509173626544897 0 -485 0 -5.023291 0.016402118 0.023859466397713937 0 -486 0 -5.0985007 0.015506633 0.022546608302385175 0 +483 1 10.906593 0.9996614 0.0004885994477486597 1 +484 0 -4.2533965 0.029035257 0.042509184696890877 0 +485 0 -5.0232906 0.016402123 0.023859474593834452 0 +486 0 -5.0985003 0.015506638 0.022546616491050588 0 487 1 13.475906 0.9999518 6.9568659593352019E-05 1 488 1 1.3273258 0.67341053 0.57044180397199062 1 489 1 -0.6232014 0.31951928 1.646025101672679 0 @@ -493,153 +493,153 @@ Instance Label Score Probability Log-loss Assigned 491 1 6.6713333 0.9916534 0.012092158077135179 1 492 0 -4.624505 0.022068353 0.032194464591169099 0 493 1 9.94891 0.99930006 0.0010101496965434329 1 -494 0 0.9629116 0.60997653 1.3583671531547612 1 -495 0 -5.0985007 0.015506633 0.022546608302385175 0 +494 0 0.9629116 0.6099765 1.3583669326774681 1 +495 0 -5.0985003 0.015506638 0.022546616491050588 0 496 0 -6.53364 0.0052747843 0.0076300462926347243 0 -497 0 -4.893565 0.018067656 0.02630447000187312 0 +497 0 -4.893565 0.018067654 0.026304467265198916 0 498 0 -4.533843 0.023602419 0.034459373415090033 0 499 0 -4.533843 0.023602419 0.034459373415090033 0 -500 0 -3.0987039 0.06699134 0.10003761775121778 0 +500 0 -3.0987039 0.06699133 0.10003760623051484 0 501 0 -4.533843 0.023602419 0.034459373415090033 0 -502 0 -4.2284155 0.029574301 0.043310338334976231 0 +502 0 -4.2284155 0.0295743 0.043310335565852412 0 503 0 -4.046695 0.03379773 0.049602852281362811 0 504 0 -6.4429784 0.005648197 0.0081717255492616877 0 -505 0 -5.2401347 0.013949001 0.020265829824057998 0 +505 0 -5.2401347 0.013949 0.020265828461436326 0 506 1 10.447666 0.9995204 0.00069205216452113084 1 -507 0 -5.0937376 0.01556189 0.022627585557372452 0 +507 0 -5.0937376 0.015561889 0.022627584192518285 0 508 0 -4.5469956 0.023373578 0.034121284818622755 0 509 0 -5.9689827 0.00807247 0.011693373854840414 0 510 0 -6.4429784 0.005648197 0.0081717255492616877 0 -511 0 -4.059848 0.03347343 0.049118699463849731 0 +511 0 -4.0598474 0.03347344 0.049118716145619364 0 512 0 -4.5469956 0.023373578 0.034121284818622755 0 -513 0 -5.0985007 0.015506633 0.022546608302385175 0 +513 0 -5.0985003 0.015506638 0.022546616491050588 0 514 1 10.719854 0.9996099 0.00056292303371696174 1 515 1 8.6480255 0.9981245 0.0027083436166447333 1 516 0 -6.53364 0.0052747843 0.0076300462926347243 0 -517 0 -6.046492 0.0076150266 0.011028204574905828 0 +517 0 -6.0464916 0.007615029 0.011028207959717411 0 518 0 -4.8959603 0.01803545 0.026257150944159223 0 519 1 6.535843 0.9907582 0.013395120416830476 1 520 0 -6.3523164 0.0060478863 0.0087517471600555233 0 -521 0 -4.9303293 0.017579472 0.02558738782535638 0 -522 1 5.502534 0.97998166 0.029173343678465424 1 +521 0 -4.9303293 0.01757947 0.025587385090042082 0 +522 1 5.502533 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0.03347343 0.049118699463849731 0 +574 1 5.950966 0.9856713 0.020821502205009505 1 +575 0 -3.2791004 0.058928367 0.087623551529192406 0 +576 0 -4.0598474 0.03347344 0.049118716145619364 0 577 0 -5.95583 0.008152755 0.011810147478593156 0 578 0 -5.95583 0.008152755 0.011810147478593156 0 -579 0 -5.4686823 0.011754767 0.017059004011272673 0 +579 0 -5.4686823 0.011754766 0.01705900265167648 0 580 0 -3.7662487 0.04147765 0.061116023687271794 0 581 1 8.417797 0.9977674 0.0032245780320862543 1 582 1 7.907978 0.9967167 0.0047446254970359954 1 583 0 -5.494987 0.01152521 0.016723922858878242 0 -584 0 -2.9291954 0.075489886 0.11323899247123828 0 +584 0 -2.9291954 0.07548988 0.11323898084463141 0 585 0 -6.4429784 0.005648197 0.0081717255492616877 0 586 1 13.98102 0.9999671 4.7467992532759307E-05 1 -587 0 -3.8628197 0.03866103 0.056882875876087251 0 +587 0 -3.8628197 0.038661025 0.056882870285491223 0 588 1 5.463169 0.9793874 0.030048455526317758 1 589 0 -4.5469956 0.023373578 0.034121284818622755 0 590 1 3.9684029 0.9386082 0.09140498577121392 1 -591 1 5.966527 0.98583704 0.020578904318245012 1 +591 1 5.966526 0.98583704 0.020578904318245012 1 592 1 5.7801704 0.98372144 0.023678256090487241 1 -593 0 -4.253397 0.02903525 0.042509173626544897 0 -594 1 5.101775 0.97306114 0.039397632242971693 1 -595 0 -4.059848 0.03347343 0.049118699463849731 0 -596 0 -4.2402444 0.029317858 0.04292914403934768 0 -597 0 -2.9855018 0.07256243 0.10867791847551245 0 +593 0 -4.2533965 0.029035257 0.042509184696890877 0 +594 1 5.101776 0.97306114 0.039397632242971693 1 +595 0 -4.0598474 0.03347344 0.049118716145619364 0 +596 0 -4.2402444 0.029317856 0.042929141270955429 0 +597 0 -2.9855018 0.07256242 0.10867790688560501 0 598 0 -5.0078387 0.016592303 0.024138448331127087 0 -599 0 -3.6294346 0.04580593 0.067645376058973333 0 +599 0 -3.6294346 0.045805927 0.067645370426515528 0 600 0 -5.0078387 0.016592303 0.024138448331127087 0 -601 0 -6.046492 0.0076150266 0.011028204574905828 0 +601 0 -6.0464916 0.007615029 0.011028207959717411 0 602 0 -4.533843 0.023602419 0.034459373415090033 0 -603 1 4.8058815 0.9665091 0.049144773305248697 1 +603 1 4.8058825 0.96650916 0.049144684334205956 1 604 1 6.1928043 0.9880442 0.01735250950197232 1 -605 1 9.955452 0.9993035 0.0010051587149210726 1 -606 0 -4.715564 0.020625811 0.030067920071712086 0 +605 1 9.95545 0.9993035 0.0010051587149210726 1 +606 0 -4.715564 0.02062581 0.030067917327889607 0 607 0 -6.4429784 0.005648197 0.0081717255492616877 0 608 1 11.148428 0.9997181 0.00040671030044872971 1 609 0 -4.5469956 0.023373578 0.034121284818622755 0 610 1 8.926189 0.99848074 0.0021934995672910755 1 -611 1 6.910961 0.993031 0.010089302795775161 1 -612 1 16.893517 0.99999636 5.2454803872987944E-06 1 -613 0 -5.226982 0.014086901 0.020467605961895518 0 -614 0 -5.5724964 0.010874255 0.015774155610380477 0 -615 0 -3.9466457 0.03636577 0.053442453015430637 0 +611 1 6.9109592 0.993031 0.010089302795775161 1 +612 1 16.893515 0.99999636 5.2454803872987944E-06 1 +613 0 -5.226982 0.0140869 0.020467604599083256 0 +614 0 -5.5724964 0.010874254 0.015774154251994583 0 +615 0 -3.9466453 0.03636578 0.053442469747270488 0 616 0 -5.0078387 0.016592303 0.024138448331127087 0 617 0 ? ? ? 0 618 0 -4.533843 0.023602419 0.034459373415090033 0 -619 0 -4.059848 0.03347343 0.049118699463849731 0 +619 0 -4.0598474 0.03347344 0.049118716145619364 0 620 0 -5.0078387 0.016592303 0.024138448331127087 0 -621 0 0.35605335 0.4967156 0.99055422977402885 1 -622 0 -2.2074018 0.12371333 0.19052518105806376 0 +621 0 0.3560543 0.49671578 0.99055474235503638 1 +622 0 -2.2074018 0.12371332 0.19052516879162634 0 623 0 -6.4429784 0.005648197 0.0081717255492616877 0 -624 0 -3.8450823 0.039164227 0.057638228932157462 0 -625 0 -3.4678864 0.0514704 0.076235297856163745 0 -626 1 5.7601643 0.98347664 0.024037311317372478 1 -627 0 -4.1699953 0.03087303 0.045242403057694114 0 +624 0 -3.8450818 0.039164238 0.057638245712729186 0 +625 0 -3.4678864 0.051470395 0.076235292190069803 0 +626 1 5.760165 0.98347664 0.024037311317372478 1 +627 0 -4.1699953 0.030873029 0.045242400284859388 0 628 0 -5.9689827 0.00807247 0.011693373854840414 0 629 0 -5.0209913 0.016430287 0.023900783632758753 0 -630 0 -3.358376 0.05568055 0.082653110243383579 0 -631 0 -4.059848 0.03347343 0.049118699463849731 0 +630 0 -3.3583755 0.055680566 0.08265313300880639 0 +631 0 -4.0598474 0.03347344 0.049118716145619364 0 632 0 -6.4429784 0.005648197 0.0081717255492616877 0 633 1 4.3299503 0.95263666 0.070002027657626642 1 634 0 -5.494987 0.01152521 0.016723922858878242 0 635 0 -4.61419 0.022237854 0.032444542423638763 0 -636 1 10.127518 0.9993887 0.00088219670683892097 1 +636 1 10.12752 0.9993887 0.00088219670683892097 1 637 0 -2.4650178 0.10403733 0.1584894700655759 0 638 0 -5.0209913 0.016430287 0.023900783632758753 0 639 0 -3.93614 0.036646076 0.053862170913072119 0 -640 0 -4.4101357 0.02586451 0.037805647681891714 0 +640 0 -4.4101357 0.025864508 0.037805644923313529 0 641 0 -5.0078387 0.016592303 0.024138448331127087 0 642 0 -5.0078387 0.016592303 0.024138448331127087 0 643 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -649,13 +649,13 @@ Instance Label Score Probability Log-loss Assigned 647 0 -5.832123 0.008947697 0.012966896248755017 0 648 1 12.236198 0.9998765 0.00017818502951934642 1 649 0 -5.0078387 0.016592303 0.024138448331127087 0 -650 0 -3.7496567 0.041980952 0.061873754012903771 0 -651 0 -5.217549 0.014186632 0.020613550094586007 0 -652 0 -3.8628197 0.03866103 0.056882875876087251 0 +650 0 -3.7496567 0.04198095 0.061873748402934067 0 +651 0 -5.217549 0.014186631 0.020613548731635876 0 +652 0 -3.8628197 0.038661025 0.056882870285491223 0 653 0 -4.533843 0.023602419 0.034459373415090033 0 654 0 -4.520691 0.023833437 0.034800759324198648 0 655 0 -5.0078387 0.016592303 0.024138448331127087 0 -656 0 -4.059848 0.03347343 0.049118699463849731 0 +656 0 -4.0598474 0.03347344 0.049118716145619364 0 657 0 -0.4869156 0.3424043 0.6047272426002388 0 658 1 9.086258 0.9986542 0.0019429058245893991 1 659 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -663,36 +663,36 @@ Instance Label Score Probability Log-loss Assigned 661 0 -4.046695 0.03379773 0.049602852281362811 0 662 0 -5.3686323 0.012669891 0.018395571683516153 0 663 0 -5.3686323 0.012669891 0.018395571683516153 0 -664 0 -4.3969836 0.026117077 0.038179748349732472 0 +664 0 -4.3969836 0.026117075 0.038179745590438879 0 665 0 -6.4429784 0.005648197 0.0081717255492616877 0 -666 0 -3.4969325 0.050405253 0.074616140216202456 0 +666 0 -3.496932 0.050405268 0.074616162855156168 0 667 0 -4.520691 0.023833437 0.034800759324198648 0 -668 1 2.8043652 0.86343354 0.21184295985895515 1 +668 1 2.8043652 0.8634335 0.21184305945125079 1 669 1 8.147335 0.99726033 0.0039579294672795707 1 670 1 6.4856205 0.99040276 0.013912762642680866 1 671 0 -4.087837 0.032793265 0.048103804028856063 0 672 0 -4.9946866 0.016755885 0.024378447925635292 0 -673 0 -3.9078827 0.03741038 0.055007226361106963 0 +673 0 -3.9078822 0.03741039 0.055007243111104311 0 674 0 -5.95583 0.008152755 0.011810147478593156 0 -675 0 -4.1401944 0.031556632 0.046260409123226896 0 +675 0 -4.1401944 0.03155663 0.046260403573642889 0 676 0 -5.6622314 0.010165936 0.014741403595886524 0 677 0 -4.5469956 0.023373578 0.034121284818622755 0 678 0 -6.4429784 0.005648197 0.0081717255492616877 0 679 0 -5.9689827 0.00807247 0.011693373854840414 0 680 1 16.78001 0.99999607 5.6754386418026423E-06 1 681 1 9.801077 0.9992171 0.0011299384356403619 1 -682 0 -3.3756714 0.05499471 0.08160568921637594 0 +682 0 -3.3756719 0.054994687 0.081605655093025531 0 683 0 -6.4429784 0.005648197 0.0081717255492616877 0 684 0 -6.4429784 0.005648197 0.0081717255492616877 0 685 0 -6.4429784 0.005648197 0.0081717255492616877 0 686 0 -6.4429784 0.005648197 0.0081717255492616877 0 687 0 -4.613783 0.022244573 0.032454455742594257 0 688 0 -5.0209913 0.016430287 0.023900783632758753 0 -689 0 -4.1777043 0.03069854 0.044982670054638463 0 +689 0 -4.1777043 0.030698538 0.044982667282302893 0 690 0 -5.832123 0.008947697 0.012966896248755017 0 -691 1 4.4967804 0.95803064 0.061856295892801602 1 +691 1 4.4967794 0.9580306 0.061856385651233553 1 692 0 -5.494987 0.01152521 0.016723922858878242 0 -693 0 -4.6842284 0.021111494 0.030783546498899184 0 +693 0 -4.6842284 0.021111492 0.030783543753715333 0 694 0 -4.9545784 0.017264586 0.02512504882755813 0 695 0 -5.9689827 0.00807247 0.011693373854840414 0 696 1 6.7127857 0.9919096 0.011719418003861082 1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt index 6da3dc0dcf..e598352377 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt @@ -13,15 +13,15 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 131 | 3 | 0.9776 - negative || 8 | 212 | 0.9636 + positive || 133 | 1 | 0.9925 + negative || 9 | 211 | 0.9591 ||====================== -Precision || 0.9424 | 0.9860 | -OVERALL 0/1 ACCURACY: 0.968927 +Precision || 0.9366 | 0.9953 | +OVERALL 0/1 ACCURACY: 0.971751 LOG LOSS/instance: Infinity Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): -Infinity -AUC: 0.994437 +AUC: 0.994403 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table @@ -40,16 +40,16 @@ AUC: 0.997619 OVERALL RESULTS --------------------------------------- -AUC: 0.996028 (0.0016) -Accuracy: 0.972305 (0.0034) -Positive precision: 0.956660 (0.0142) -Positive recall: 0.964996 (0.0126) -Negative precision: 0.981961 (0.0041) -Negative recall: 0.975122 (0.0115) +AUC: 0.996011 (0.0016) +Accuracy: 0.973718 (0.0020) +Positive precision: 0.953747 (0.0171) +Positive recall: 0.972459 (0.0201) +Negative precision: 0.986580 (0.0087) +Negative recall: 0.972849 (0.0138) Log-loss: Infinity (NaN) Log-loss reduction: -Infinity (NaN) -F1 Score: 0.960623 (0.0009) -AUPRC: 0.992280 (0.0025) +F1 Score: 0.962653 (0.0011) +AUPRC: 0.992269 (0.0025) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt index 8dd72b1661..95bd831962 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt @@ -1,4 +1,4 @@ AveragedPerceptron 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 -0.996028 0.972305 0.95666 0.964996 0.981961 0.975122 Infinity -Infinity 0.960623 0.99228 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1 +0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 Infinity -Infinity 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt index 1cef38aa8a..efa236c250 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt @@ -1,379 +1,379 @@ Instance Label Score Probability Log-loss Assigned -5 1 11.285979 1 -0 1 -6 0 -0.93471193 0.09090909 0.13750352804950119 0 -8 0 -3.7769966 1E-15 1.4415419267167138E-15 0 -9 0 -3.7947202 1E-15 1.4415419267167138E-15 0 -10 0 -4.7855167 1E-15 1.4415419267167138E-15 0 -11 0 -4.6251884 1E-15 1.4415419267167138E-15 0 -18 1 6.8803635 1 -0 1 -20 1 5.573552 1 -0 1 -21 1 6.7444105 1 -0 1 -25 1 1.2789736 0.875 0.19264507794239591 1 -28 0 -4.6251884 1E-15 1.4415419267167138E-15 0 -31 0 -4.3083706 1E-15 1.4415419267167138E-15 0 -32 1 6.9428844 1 -0 1 -35 0 -4.6251884 1E-15 1.4415419267167138E-15 0 -37 0 -1.8143315 0.09090909 0.13750352804950119 0 +5 1 11.925824 1 -0 1 +6 0 -0.4527979 0.2937258 0.50169967537103388 0 +8 0 -3.7962546 1E-15 1.4415419267167138E-15 0 +9 0 -3.8130417 1E-15 1.4415419267167138E-15 0 +10 0 -4.7285223 1E-15 1.4415419267167138E-15 0 +11 0 -4.606969 1E-15 1.4415419267167138E-15 0 +18 1 7.3475924 1 -0 1 +20 1 6.1389017 1 -0 1 +21 1 7.1486177 1 -0 1 +25 1 1.6632223 0.88235295 0.18057223417631088 1 +28 0 -4.606969 1E-15 1.4415419267167138E-15 0 +31 0 -4.2645645 1E-15 1.4415419267167138E-15 0 +32 1 7.198575 1 -0 1 +35 0 -4.606969 1E-15 1.4415419267167138E-15 0 +37 0 -1.714282 0.1 0.15200309583369792 0 40 0 ? ? ? 0 -41 1 2.4075565 0.875 0.19264507794239591 1 -44 1 8.039285 1 -0 1 -45 0 -4.625085 1E-15 1.4415419267167138E-15 0 -46 1 5.138131 1 -0 1 -48 0 -3.4336777 1E-15 1.4415419267167138E-15 0 -50 1 2.7120514 0.875 0.19264507794239591 1 -51 1 -0.062075615 0.6666667 0.58496245772549416 0 -52 1 4.4027233 1 -0 1 -54 1 6.3079214 1 -0 1 -56 1 6.356518 1 -0 1 -60 1 1.9474735 0.875 0.19264507794239591 1 -63 1 0.78555584 0.6666667 0.58496245772549416 1 -64 0 -4.916337 1E-15 1.4415419267167138E-15 0 -66 0 -3.7260728 1E-15 1.4415419267167138E-15 0 -68 1 9.2772875 1 -0 1 -69 0 -4.4157114 1E-15 1.4415419267167138E-15 0 -70 0 -3.0868545 1E-15 1.4415419267167138E-15 0 -71 1 7.5159607 1 -0 1 -72 0 -1.8410158 0.09090909 0.13750352804950119 0 -73 1 7.1320066 1 -0 1 -74 1 2.4329157 0.875 0.19264507794239591 1 -76 0 -3.9190063 1E-15 1.4415419267167138E-15 0 -77 0 -3.1092033 1E-15 1.4415419267167138E-15 0 -79 0 -4.4391913 1E-15 1.4415419267167138E-15 0 -82 0 -3.1867537 1E-15 1.4415419267167138E-15 0 -88 0 -3.7260728 1E-15 1.4415419267167138E-15 0 -90 0 -4.5995197 1E-15 1.4415419267167138E-15 0 -91 0 -4.5046597 1E-15 1.4415419267167138E-15 0 -92 0 -3.7260728 1E-15 1.4415419267167138E-15 0 -93 0 -4.916337 1E-15 1.4415419267167138E-15 0 -95 0 -4.5995197 1E-15 1.4415419267167138E-15 0 -96 0 -4.7958083 1E-15 1.4415419267167138E-15 0 -97 0 -3.4349236 1E-15 1.4415419267167138E-15 0 -98 1 9.07517 1 -0 1 -99 1 8.952344 1 -0 1 -100 1 4.9092436 1 -0 1 -102 0 -3.393629 1E-15 1.4415419267167138E-15 0 -104 1 10.959611 1 -0 1 -105 1 2.0113592 0.875 0.19264507794239591 1 -106 1 8.251353 1 -0 1 -108 0 -4.5487204 1E-15 1.4415419267167138E-15 0 -109 1 5.864868 1 -0 1 -111 1 3.7846975 1 -0 1 -112 1 6.380026 1 -0 1 -113 1 9.461209 1 -0 1 -115 0 -3.6043515 1E-15 1.4415419267167138E-15 0 -117 1 7.9902315 1 -0 1 -120 0 -4.120878 1E-15 1.4415419267167138E-15 0 -121 0 -3.0707016 1E-15 1.4415419267167138E-15 0 -122 1 10.129083 1 -0 1 -123 1 4.173232 1 -0 1 -125 0 -4.916337 1E-15 1.4415419267167138E-15 0 -128 1 4.5026884 1 -0 1 -129 0 -3.7451797 1E-15 1.4415419267167138E-15 0 -131 0 -4.3083706 1E-15 1.4415419267167138E-15 0 -132 1 8.723828 1 -0 1 -133 0 -4.150231 1E-15 1.4415419267167138E-15 0 -137 0 -4.650857 1E-15 1.4415419267167138E-15 0 -138 0 -3.7104468 1E-15 1.4415419267167138E-15 0 -141 0 -4.942006 1E-15 1.4415419267167138E-15 0 -144 0 -4.6251884 1E-15 1.4415419267167138E-15 0 +41 1 2.5451756 0.88235295 0.18057223417631088 1 +44 1 8.165841 1 -0 1 +45 0 -4.602255 1E-15 1.4415419267167138E-15 0 +46 1 5.6216097 1 -0 1 +48 0 -3.379683 1E-15 1.4415419267167138E-15 0 +50 1 2.8003244 0.88235295 0.18057223417631088 1 +51 1 0.14775372 0.6666667 0.58496245772549416 1 +52 1 4.696246 1 -0 1 +54 1 6.743867 1 -0 1 +56 1 6.5947084 1 -0 1 +60 1 2.2064123 0.88235295 0.18057223417631088 1 +63 1 0.8789625 0.6666667 0.58496245772549416 1 +64 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +66 0 -3.697434 1E-15 1.4415419267167138E-15 0 +68 1 9.899808 1 -0 1 +69 0 -4.3595524 1E-15 1.4415419267167138E-15 0 +70 0 -3.0557137 1E-15 1.4415419267167138E-15 0 +71 1 7.555622 1 -0 1 +72 0 -1.676908 0.1 0.15200309583369792 0 +73 1 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7.360214 1 -0 1 -15 1 0.6494303 0.6 0.73696553683865695 1 +15 1 0.6494312 0.6 0.73696553683865695 1 16 0 -4.220706 1E-15 1.4415419267167138E-15 0 -17 0 -3.9551725 1E-15 1.4415419267167138E-15 0 -19 0 -2.9890537 1E-15 1.4415419267167138E-15 0 +17 0 -3.955172 1E-15 1.4415419267167138E-15 0 +19 0 -2.9890532 1E-15 1.4415419267167138E-15 0 22 0 -4.7037654 1E-15 1.4415419267167138E-15 0 23 1 ? ? ? 0 24 0 -5.4043503 1E-15 1.4415419267167138E-15 0 @@ -381,47 +381,47 @@ Instance Label Score Probability Log-loss Assigned 27 0 -3.7376466 1E-15 1.4415419267167138E-15 0 29 0 -5.4339433 1E-15 1.4415419267167138E-15 0 30 0 -4.649441 1E-15 1.4415419267167138E-15 0 -33 0 -4.698118 1E-15 1.4415419267167138E-15 0 +33 0 -4.6981187 1E-15 1.4415419267167138E-15 0 34 0 -4.4530277 1E-15 1.4415419267167138E-15 0 36 1 7.832773 1 -0 1 38 1 4.92861 0.93333334 0.09953566740867692 1 39 1 1.079258 0.6 0.73696553683865695 1 42 1 6.8985863 1 -0 1 -43 1 -0.49528694 0.5 1 0 +43 1 -0.49528742 0.5 1 0 47 0 -5.669884 1E-15 1.4415419267167138E-15 0 -49 1 5.3024044 0.93333334 0.09953566740867692 1 +49 1 5.3024054 0.93333334 0.09953566740867692 1 53 1 5.116103 0.93333334 0.09953566740867692 1 55 1 4.4195347 0.93333334 0.09953566740867692 1 -57 1 0.5701313 0.6 0.73696553683865695 1 -58 1 1.1371031 0.6 0.73696553683865695 1 +57 1 0.57013035 0.6 0.73696553683865695 1 +58 1 1.137104 0.6 0.73696553683865695 1 59 1 1.6442327 0.9047619 0.14438990028345636 1 61 0 -5.2770567 1E-15 1.4415419267167138E-15 0 -62 1 5.7670774 1 -0 1 -65 1 2.7867746 0.9047619 0.14438990028345636 1 +62 1 5.7670784 1 -0 1 +65 1 2.7867756 0.9047619 0.14438990028345636 1 67 1 2.9058514 0.9047619 0.14438990028345636 1 -75 0 -4.291042 1E-15 1.4415419267167138E-15 0 +75 0 -4.2910423 1E-15 1.4415419267167138E-15 0 78 0 -3.652576 1E-15 1.4415419267167138E-15 0 80 0 -3.2955709 1E-15 1.4415419267167138E-15 0 81 0 -3.9975338 1E-15 1.4415419267167138E-15 0 -83 0 -2.9832687 1E-15 1.4415419267167138E-15 0 -84 1 6.5824003 1 -0 1 +83 0 -2.9832683 1E-15 1.4415419267167138E-15 0 +84 1 6.5823994 1 -0 1 85 1 4.7604074 0.93333334 0.09953566740867692 1 86 1 1.466999 0.9047619 0.14438990028345636 1 -87 1 5.223543 0.93333334 0.09953566740867692 1 +87 1 5.223544 0.93333334 0.09953566740867692 1 89 0 -5.017977 1E-15 1.4415419267167138E-15 0 -94 0 -4.9212914 1E-15 1.4415419267167138E-15 0 -101 1 -0.85990286 0.41515666 1.2682722439451406 0 +94 0 -4.921291 1E-15 1.4415419267167138E-15 0 +101 1 -0.85990334 0.4151543 1.2682804255962932 0 103 1 0.36755466 0.6 0.73696553683865695 1 107 1 4.617798 0.93333334 0.09953566740867692 1 110 0 -3.1312823 1E-15 1.4415419267167138E-15 0 114 0 -2.8847933 1E-15 1.4415419267167138E-15 0 -116 0 -0.40737772 0.5 1 0 +116 0 -0.40737724 0.5 1 0 118 0 -5.2584124 1E-15 1.4415419267167138E-15 0 -119 0 -3.786881 1E-15 1.4415419267167138E-15 0 +119 0 -3.7868814 1E-15 1.4415419267167138E-15 0 124 1 5.0384197 0.93333334 0.09953566740867692 1 126 1 6.4765244 1 -0 1 127 0 -4.4382315 1E-15 1.4415419267167138E-15 0 -130 0 -3.2361722 1E-15 1.4415419267167138E-15 0 +130 0 -3.2361717 1E-15 1.4415419267167138E-15 0 134 0 -4.75809 1E-15 1.4415419267167138E-15 0 135 0 -2.670867 1E-15 1.4415419267167138E-15 0 136 0 -4.220706 1E-15 1.4415419267167138E-15 0 @@ -429,12 +429,12 @@ Instance Label Score Probability Log-loss Assigned 140 0 -4.9692993 1E-15 1.4415419267167138E-15 0 142 1 3.7043686 0.93333334 0.09953566740867692 1 143 0 -4.364107 1E-15 1.4415419267167138E-15 0 -146 1 0.36148834 0.6 0.73696553683865695 1 -148 0 -2.357305 1E-15 1.4415419267167138E-15 0 -149 1 8.756336 1 -0 1 +146 1 0.3614874 0.6 0.73696553683865695 1 +148 0 -2.3573046 1E-15 1.4415419267167138E-15 0 +149 1 8.756334 1 -0 1 153 0 -3.7005844 1E-15 1.4415419267167138E-15 0 155 1 3.1619148 0.9047619 0.14438990028345636 1 -157 0 -4.9212914 1E-15 1.4415419267167138E-15 0 +157 0 -4.921291 1E-15 1.4415419267167138E-15 0 158 0 ? ? ? 0 159 1 10.452139 1 -0 1 160 1 7.997595 1 -0 1 @@ -445,62 +445,62 @@ Instance Label Score Probability Log-loss Assigned 168 0 -4.4382315 1E-15 1.4415419267167138E-15 0 170 0 -4.9692993 1E-15 1.4415419267167138E-15 0 172 0 -5.669884 1E-15 1.4415419267167138E-15 0 -175 1 6.0381765 1 -0 1 -178 0 -3.9551725 1E-15 1.4415419267167138E-15 0 -182 0 -2.9890537 1E-15 1.4415419267167138E-15 0 +175 1 6.0381775 1 -0 1 +178 0 -3.955172 1E-15 1.4415419267167138E-15 0 +182 0 -2.9890532 1E-15 1.4415419267167138E-15 0 184 1 5.409273 1 -0 1 185 0 -4.8669662 1E-15 1.4415419267167138E-15 0 186 1 3.9876003 0.93333334 0.09953566740867692 1 190 1 10.521242 1 -0 1 193 0 -5.4043503 1E-15 1.4415419267167138E-15 0 194 0 -4.4382315 1E-15 1.4415419267167138E-15 0 -195 0 -3.9551725 1E-15 1.4415419267167138E-15 0 +195 0 -3.955172 1E-15 1.4415419267167138E-15 0 197 0 -2.8025956 1E-15 1.4415419267167138E-15 0 200 1 8.673521 1 -0 1 -203 0 -3.4721131 1E-15 1.4415419267167138E-15 0 -208 0 -5.350025 1E-15 1.4415419267167138E-15 0 +203 0 -3.4721127 1E-15 1.4415419267167138E-15 0 +208 0 -5.3500257 1E-15 1.4415419267167138E-15 0 213 1 12.300528 1 -0 1 214 1 11.895983 1 -0 1 -215 1 6.600219 1 -0 1 +215 1 6.6002197 1 -0 1 217 0 -5.4043503 1E-15 1.4415419267167138E-15 0 220 0 -5.181178 1E-15 1.4415419267167138E-15 0 221 1 7.9662914 1 -0 1 222 1 -2.1487255 1E-15 49.828921418077073 0 -224 1 8.4735565 1 -0 1 +224 1 8.473555 1 -0 1 225 0 -5.669884 1E-15 1.4415419267167138E-15 0 227 1 6.748211 1 -0 1 229 1 10.504805 1 -0 1 -230 1 4.829337 0.93333334 0.09953566740867692 1 +230 1 4.829338 0.93333334 0.09953566740867692 1 231 1 6.912092 1 -0 1 232 0 1.0722923 0.6 1.3219281808786905 1 234 0 -2.7037287 1E-15 1.4415419267167138E-15 0 235 0 ? ? ? 0 236 1 9.440506 1 -0 1 238 1 10.690645 1 -0 1 -243 0 -3.301972 1E-15 1.4415419267167138E-15 0 -245 0 -2.856113 1E-15 1.4415419267167138E-15 0 +243 0 -3.3019714 1E-15 1.4415419267167138E-15 0 +245 0 -2.8561125 1E-15 1.4415419267167138E-15 0 251 1 7.355525 1 -0 1 253 1 6.8985863 1 -0 1 -255 1 3.745204 0.93333334 0.09953566740867692 1 +255 1 3.745205 0.93333334 0.09953566740867692 1 256 0 -4.9692993 1E-15 1.4415419267167138E-15 0 261 1 9.009869 1 -0 1 263 1 7.1455708 1 -0 1 264 1 4.0145664 0.93333334 0.09953566740867692 1 -265 0 -2.5156913 1E-15 1.4415419267167138E-15 0 +265 0 -2.5156918 1E-15 1.4415419267167138E-15 0 266 1 7.325534 1 -0 1 270 1 5.5723915 1 -0 1 -273 1 0.03731823 0.6 0.73696553683865695 1 +273 1 0.037317276 0.6 0.73696553683865695 1 274 0 -4.2340226 1E-15 1.4415419267167138E-15 0 -281 0 -4.698118 1E-15 1.4415419267167138E-15 0 -282 1 2.860156 0.9047619 0.14438990028345636 1 +281 0 -4.6981187 1E-15 1.4415419267167138E-15 0 +282 1 2.860157 0.9047619 0.14438990028345636 1 286 1 12.544172 1 -0 1 287 0 -4.75809 1E-15 1.4415419267167138E-15 0 -289 1 6.6595697 1 -0 1 +289 1 6.6595707 1 -0 1 292 1 ? ? ? 0 294 0 ? ? ? 0 295 1 5.621522 1 -0 1 -298 0 -2.4584546 1E-15 1.4415419267167138E-15 0 -302 1 12.725584 1 -0 1 +298 0 -2.4584541 1E-15 1.4415419267167138E-15 0 +302 1 12.72558 1 -0 1 305 1 8.040865 1 -0 1 306 0 -5.4043503 1E-15 1.4415419267167138E-15 0 307 0 -5.4043503 1E-15 1.4415419267167138E-15 0 @@ -512,34 +512,34 @@ Instance Label Score Probability Log-loss Assigned 322 0 -4.4382315 1E-15 1.4415419267167138E-15 0 324 0 -5.4043503 1E-15 1.4415419267167138E-15 0 325 0 -3.7860875 1E-15 1.4415419267167138E-15 0 -326 1 3.6223288 0.90666085 0.14136509713640041 1 -330 1 4.9927454 0.93333334 0.09953566740867692 1 +326 1 3.6223297 0.9066624 0.14136263119498629 1 +330 1 4.9927444 0.93333334 0.09953566740867692 1 334 1 5.514736 1 -0 1 335 0 -5.935418 1E-15 1.4415419267167138E-15 0 337 0 -5.4043503 1E-15 1.4415419267167138E-15 0 339 1 5.1161976 0.93333334 0.09953566740867692 1 -340 1 5.5803347 1 -0 1 +340 1 5.5803356 1 -0 1 341 0 -5.4043503 1E-15 1.4415419267167138E-15 0 342 0 -5.4523587 1E-15 1.4415419267167138E-15 0 345 0 -5.935418 1E-15 1.4415419267167138E-15 0 -351 0 -4.9212914 1E-15 1.4415419267167138E-15 0 -356 1 -0.9127703 0.25679532 1.9613092019943366 0 +351 0 -4.921291 1E-15 1.4415419267167138E-15 0 +356 1 -0.9127703 0.2567953 1.9613093694259927 0 357 1 9.874601 1 -0 1 359 1 4.653248 0.93333334 0.09953566740867692 1 362 0 -3.5174994 1E-15 1.4415419267167138E-15 0 363 0 -1.6840982 1E-15 1.4415419267167138E-15 0 -364 0 -4.9212914 1E-15 1.4415419267167138E-15 0 +364 0 -4.921291 1E-15 1.4415419267167138E-15 0 365 0 -5.186825 1E-15 1.4415419267167138E-15 0 367 1 9.040358 1 -0 1 -369 0 -5.1140842 1E-15 1.4415419267167138E-15 0 +369 0 -5.1140847 1E-15 1.4415419267167138E-15 0 372 0 -3.9699688 1E-15 1.4415419267167138E-15 0 374 0 -4.4530277 1E-15 1.4415419267167138E-15 0 375 0 -5.935418 1E-15 1.4415419267167138E-15 0 380 0 -5.935418 1E-15 1.4415419267167138E-15 0 382 0 -3.5015903 1E-15 1.4415419267167138E-15 0 -385 0 -3.4273872 1E-15 1.4415419267167138E-15 0 +385 0 -3.4273868 1E-15 1.4415419267167138E-15 0 386 1 5.084236 0.93333334 0.09953566740867692 1 -390 0 -5.4467115 1E-15 1.4415419267167138E-15 0 +390 0 -5.446712 1E-15 1.4415419267167138E-15 0 393 0 -5.881093 1E-15 1.4415419267167138E-15 0 394 0 -4.909887 1E-15 1.4415419267167138E-15 0 397 0 -4.48624 1E-15 1.4415419267167138E-15 0 @@ -549,12 +549,12 @@ Instance Label Score Probability Log-loss Assigned 403 0 -3.7908158 1E-15 1.4415419267167138E-15 0 405 0 -5.669884 1E-15 1.4415419267167138E-15 0 407 0 -5.669884 1E-15 1.4415419267167138E-15 0 -408 0 -3.5375085 1E-15 1.4415419267167138E-15 0 +408 0 -3.537508 1E-15 1.4415419267167138E-15 0 410 0 -5.669884 1E-15 1.4415419267167138E-15 0 411 0 ? ? ? 0 -412 1 7.6394253 1 -0 1 +412 1 7.639426 1 -0 1 417 0 -5.669884 1E-15 1.4415419267167138E-15 0 -420 0 -2.6969714 1E-15 1.4415419267167138E-15 0 +420 0 -2.696971 1E-15 1.4415419267167138E-15 0 421 1 9.498289 1 -0 1 424 0 -4.9692993 1E-15 1.4415419267167138E-15 0 425 1 11.849485 1 -0 1 @@ -565,9 +565,9 @@ Instance Label Score Probability Log-loss Assigned 433 0 -4.013695 1E-15 1.4415419267167138E-15 0 435 1 7.0216722 1 -0 1 437 0 -4.48624 1E-15 1.4415419267167138E-15 0 -438 0 -3.5384212 1E-15 1.4415419267167138E-15 0 +438 0 -3.5384207 1E-15 1.4415419267167138E-15 0 443 0 -5.54259 1E-15 1.4415419267167138E-15 0 -444 0 -2.6761093 1E-15 1.4415419267167138E-15 0 +444 0 -2.6761103 1E-15 1.4415419267167138E-15 0 445 0 -5.4523587 1E-15 1.4415419267167138E-15 0 446 0 -5.935418 1E-15 1.4415419267167138E-15 0 447 0 -4.0031805 1E-15 1.4415419267167138E-15 0 @@ -576,31 +576,31 @@ Instance Label Score Probability Log-loss Assigned 459 0 -3.5017061 1E-15 1.4415419267167138E-15 0 460 0 -3.5568361 1E-15 1.4415419267167138E-15 0 461 0 -3.3979883 1E-15 1.4415419267167138E-15 0 -462 0 -3.0737762 1E-15 1.4415419267167138E-15 0 +462 0 -3.0737767 1E-15 1.4415419267167138E-15 0 463 0 -4.282031 1E-15 1.4415419267167138E-15 0 468 0 -4.48624 1E-15 1.4415419267167138E-15 0 469 0 -5.4191465 1E-15 1.4415419267167138E-15 0 470 0 -4.649441 1E-15 1.4415419267167138E-15 0 -471 0 -3.0737762 1E-15 1.4415419267167138E-15 0 +471 0 -3.0737767 1E-15 1.4415419267167138E-15 0 472 0 -3.4657965 1E-15 1.4415419267167138E-15 0 473 0 -4.48624 1E-15 1.4415419267167138E-15 0 475 0 -4.9692993 1E-15 1.4415419267167138E-15 0 -476 0 -4.2355022 1E-15 1.4415419267167138E-15 0 +476 0 -4.2355027 1E-15 1.4415419267167138E-15 0 477 0 -4.48624 1E-15 1.4415419267167138E-15 0 478 0 -3.744658 1E-15 1.4415419267167138E-15 0 479 1 6.673233 1 -0 1 481 0 -2.5658813 1E-15 1.4415419267167138E-15 0 485 0 -4.6490927 1E-15 1.4415419267167138E-15 0 486 0 -4.649441 1E-15 1.4415419267167138E-15 0 -488 1 0.9591036 0.6 0.73696553683865695 1 +488 1 0.95910263 0.6 0.73696553683865695 1 490 0 -5.935418 1E-15 1.4415419267167138E-15 0 -491 1 5.556223 1 -0 1 +491 1 5.556222 1 -0 1 494 0 -0.015696526 0.59426165 1.3013784104855217 0 496 0 -5.881093 1E-15 1.4415419267167138E-15 0 498 0 -4.220706 1E-15 1.4415419267167138E-15 0 499 0 -4.220706 1E-15 1.4415419267167138E-15 0 -500 0 -2.9890537 1E-15 1.4415419267167138E-15 0 -503 0 -3.9551725 1E-15 1.4415419267167138E-15 0 +500 0 -2.9890532 1E-15 1.4415419267167138E-15 0 +503 0 -3.955172 1E-15 1.4415419267167138E-15 0 505 0 -4.4522543 1E-15 1.4415419267167138E-15 0 506 1 8.766861 1 -0 1 508 0 -4.0031805 1E-15 1.4415419267167138E-15 0 @@ -609,12 +609,12 @@ Instance Label Score Probability Log-loss Assigned 512 0 -4.0031805 1E-15 1.4415419267167138E-15 0 515 1 7.3749876 1 -0 1 516 0 -5.881093 1E-15 1.4415419267167138E-15 0 -518 0 -4.49539 1E-15 1.4415419267167138E-15 0 +518 0 -4.4953895 1E-15 1.4415419267167138E-15 0 524 0 -4.7037654 1E-15 1.4415419267167138E-15 0 525 0 -4.746127 1E-15 1.4415419267167138E-15 0 526 0 -4.48624 1E-15 1.4415419267167138E-15 0 -530 1 5.325206 0.93333334 0.09953566740867692 1 -536 0 -3.4721131 1E-15 1.4415419267167138E-15 0 +530 1 5.325205 0.93333334 0.09953566740867692 1 +536 0 -3.4721127 1E-15 1.4415419267167138E-15 0 537 0 -3.2213755 1E-15 1.4415419267167138E-15 0 542 0 -3.4861355 1E-15 1.4415419267167138E-15 0 543 0 -4.220706 1E-15 1.4415419267167138E-15 0 @@ -622,7 +622,7 @@ Instance Label Score Probability Log-loss Assigned 550 0 -4.7037654 1E-15 1.4415419267167138E-15 0 551 0 -5.4043503 1E-15 1.4415419267167138E-15 0 552 0 -3.2360563 1E-15 1.4415419267167138E-15 0 -553 0 -1.4225526 1E-15 1.4415419267167138E-15 0 +553 0 -1.4225531 1E-15 1.4415419267167138E-15 0 554 0 -4.9692993 1E-15 1.4415419267167138E-15 0 555 0 -1.7431297 1E-15 1.4415419267167138E-15 0 556 0 -2.9508896 1E-15 1.4415419267167138E-15 0 @@ -631,7 +631,7 @@ Instance Label Score Probability Log-loss Assigned 567 0 -3.4350505 1E-15 1.4415419267167138E-15 0 568 1 3.5748348 0.9047619 0.14438990028345636 1 570 1 6.466878 1 -0 1 -571 1 9.048693 1 -0 1 +571 1 9.048691 1 -0 1 572 0 -4.7037654 1E-15 1.4415419267167138E-15 0 573 0 -5.669884 1E-15 1.4415419267167138E-15 0 574 1 5.533702 1 -0 1 @@ -644,15 +644,15 @@ Instance Label Score Probability Log-loss Assigned 587 0 -3.5334377 1E-15 1.4415419267167138E-15 0 588 1 4.6442137 0.93333334 0.09953566740867692 1 589 0 -4.0031805 1E-15 1.4415419267167138E-15 0 -591 1 4.243066 0.93333334 0.09953566740867692 1 -592 1 4.8517914 0.93333334 0.09953566740867692 1 +591 1 4.243067 0.93333334 0.09953566740867692 1 +592 1 4.8517895 0.93333334 0.09953566740867692 1 595 0 -3.7376466 1E-15 1.4415419267167138E-15 0 596 0 -3.9699688 1E-15 1.4415419267167138E-15 0 597 0 -2.9706383 1E-15 1.4415419267167138E-15 0 598 0 -4.7037654 1E-15 1.4415419267167138E-15 0 599 0 -2.9381208 1E-15 1.4415419267167138E-15 0 601 0 -5.6155596 1E-15 1.4415419267167138E-15 0 -603 1 3.1762495 0.9047619 0.14438990028345636 1 +603 1 3.1762505 0.9047619 0.14438990028345636 1 605 1 8.159748 1 -0 1 608 1 8.079367 1 -0 1 610 1 6.972576 1 -0 1 @@ -667,15 +667,15 @@ Instance Label Score Probability Log-loss Assigned 630 0 -2.7601237 1E-15 1.4415419267167138E-15 0 631 0 -3.7376466 1E-15 1.4415419267167138E-15 0 632 0 -5.935418 1E-15 1.4415419267167138E-15 0 -635 0 -4.217087 1E-15 1.4415419267167138E-15 0 -636 1 8.162584 1 -0 1 +635 0 -4.2170873 1E-15 1.4415419267167138E-15 0 +636 1 8.162586 1 -0 1 637 0 -2.370799 1E-15 1.4415419267167138E-15 0 -640 0 -4.0398955 1E-15 1.4415419267167138E-15 0 +640 0 -4.039895 1E-15 1.4415419267167138E-15 0 643 0 -5.935418 1E-15 1.4415419267167138E-15 0 646 0 -5.426158 1E-15 1.4415419267167138E-15 0 647 0 -5.4890733 1E-15 1.4415419267167138E-15 0 -648 1 8.579458 1 -0 1 -650 0 -3.6219687 1E-15 1.4415419267167138E-15 0 +648 1 8.579456 1 -0 1 +650 0 -3.6219683 1E-15 1.4415419267167138E-15 0 651 0 -4.965017 1E-15 1.4415419267167138E-15 0 655 0 -4.7037654 1E-15 1.4415419267167138E-15 0 658 1 7.546403 1 -0 1 @@ -683,11 +683,11 @@ Instance Label Score Probability Log-loss Assigned 662 0 -5.1684093 1E-15 1.4415419267167138E-15 0 663 0 -5.1684093 1E-15 1.4415419267167138E-15 0 664 0 -4.2574205 1E-15 1.4415419267167138E-15 0 -666 0 -3.070212 1E-15 1.4415419267167138E-15 0 +666 0 -3.0702114 1E-15 1.4415419267167138E-15 0 667 0 -4.4382315 1E-15 1.4415419267167138E-15 0 -669 1 6.981786 1 -0 1 +669 1 6.9817867 1 -0 1 671 0 -3.9565368 1E-15 1.4415419267167138E-15 0 -672 0 -4.9212914 1E-15 1.4415419267167138E-15 0 +672 0 -4.921291 1E-15 1.4415419267167138E-15 0 673 0 -3.289723 1E-15 1.4415419267167138E-15 0 674 0 -5.669884 1E-15 1.4415419267167138E-15 0 675 0 -3.7340279 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt index bd0288a2bb..7d3ac2552a 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt @@ -11,15 +11,15 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 131 | 3 | 0.9776 - negative || 8 | 212 | 0.9636 + positive || 133 | 1 | 0.9925 + negative || 9 | 211 | 0.9591 ||====================== -Precision || 0.9424 | 0.9860 | -OVERALL 0/1 ACCURACY: 0.968927 -LOG LOSS/instance: 0.138699 +Precision || 0.9366 | 0.9953 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: 0.139629 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.855069 -AUC: 0.994437 +LOG-LOSS REDUCTION (RIG): 0.854097 +AUC: 0.994403 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table @@ -38,16 +38,16 @@ AUC: 0.997619 OVERALL RESULTS --------------------------------------- -AUC: 0.996028 (0.0016) -Accuracy: 0.972305 (0.0034) -Positive precision: 0.956660 (0.0142) -Positive recall: 0.964996 (0.0126) -Negative precision: 0.981961 (0.0041) -Negative recall: 0.975122 (0.0115) -Log-loss: 0.129850 (0.0088) -Log-loss reduction: 0.860569 (0.0055) -F1 Score: 0.960623 (0.0009) -AUPRC: 0.992280 (0.0025) +AUC: 0.996011 (0.0016) +Accuracy: 0.973718 (0.0020) +Positive precision: 0.953747 (0.0171) +Positive recall: 0.972459 (0.0201) +Negative precision: 0.986580 (0.0087) +Negative recall: 0.972849 (0.0138) +Log-loss: 0.130315 (0.0093) +Log-loss reduction: 0.860083 (0.0060) +F1 Score: 0.962653 (0.0011) +AUPRC: 0.992269 (0.0025) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt index e59aeb877d..ae8060865c 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt @@ -1,4 +1,4 @@ AveragedPerceptron 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 -0.996028 0.972305 0.95666 0.964996 0.981961 0.975122 0.12985 0.860569 0.960623 0.99228 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1 +0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 0.130315 0.860083 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt index 2a3a57c76c..5dfec709ea 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt @@ -1,506 +1,506 @@ Instance Label Score Probability Log-loss Assigned -5 1 11.285979 0.999881 0.00017164889637290732 1 -6 0 -0.93471193 0.23396653 0.38452066506363075 0 -8 0 -3.7769966 0.027554922 0.040311323096438006 0 -9 0 -3.7947202 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0.00020536190066155745 1 -215 1 6.600219 0.99116385 0.012804525065380799 1 +215 1 6.6002197 0.99116385 0.012804525065380799 1 217 0 -5.4043503 0.009396286 0.013620064546924614 0 220 0 -5.181178 0.011165957 0.016199682480376523 0 221 1 7.9662914 0.9969428 0.0044173368194155311 1 -222 1 -2.1487255 0.10767716 3.2152158079150412 0 -224 1 8.4735565 0.997941 0.0029735466380567798 1 -225 0 -5.669884 0.0076494967 0.011078316903702529 0 +222 1 -2.1487255 0.107677154 3.2152159077404439 0 +224 1 8.473555 0.997941 0.0029735466380567798 1 +225 0 -5.669884 0.007649497 0.011078317580688359 0 227 1 6.748211 0.992121 0.011412038429712602 1 229 1 10.504805 0.9995781 0.00060877502594878564 1 -230 1 4.829337 0.9656635 0.050407558021295686 1 +230 1 4.829338 0.9656635 0.050407558021295686 1 231 1 6.912092 0.9930612 0.010045486491117618 1 232 0 1.0722923 0.59906816 1.3185711185935187 1 -234 0 -2.7037287 0.07254336 0.10864826020710561 0 +234 0 -2.7037287 0.07254335 0.10864824861743642 0 235 0 ? ? ? 0 236 1 9.440506 0.9990316 0.0013977779753363091 1 238 1 10.690645 0.9996351 0.00052653496634314379 1 -243 0 -3.301972 0.046725534 0.069036441226174985 0 -245 0 -2.856113 0.06493041 0.096854356008988451 0 +243 0 -3.3019714 0.04672555 0.069036463777740448 0 +245 0 -2.8561125 0.064930424 0.096854378999610469 0 251 1 7.355525 0.99508274 0.0071116115660869242 1 253 1 6.8985863 0.9929881 0.010151652400815765 1 -255 1 3.745204 0.9234148 0.11494919833274581 1 +255 1 3.745205 0.9234149 0.11494910520956844 1 256 0 -4.9692993 0.013149481 0.019096523087584735 0 261 1 9.009869 0.9986448 0.0019564247198453513 1 263 1 7.1455708 0.9942114 0.0083754842729617477 1 264 1 4.0145664 0.9370327 0.093828700465107676 1 -265 0 -2.5156913 0.0830686 0.12511429472057489 0 +265 0 -2.5156918 0.083068565 0.12511423610705438 0 266 1 7.325534 0.99496675 0.0072797874039680641 1 270 1 5.5723915 0.9804892 0.028426363787945077 1 -273 1 0.03731823 0.39964545 1.3232074389645836 1 -274 0 -4.2340226 0.023118448 0.033744449784985975 0 -281 0 -4.698118 0.016201962 0.023565916692469265 0 -282 1 2.860156 0.8579384 0.2210540141016637 1 +273 1 0.037317276 0.39964524 1.3232081920564036 1 +274 0 -4.2340226 0.023118446 0.033744447034162281 0 +281 0 -4.6981187 0.016201956 0.023565908498016312 0 +282 1 2.860157 0.85793847 0.22105391387148221 1 286 1 12.544172 0.9999142 0.00012374682432127928 1 -287 0 -4.75809 0.015471877 0.022495677067487178 0 -289 1 6.6595697 0.9915608 0.012226832918130892 1 +287 0 -4.75809 0.015471876 0.022495675702757793 0 +289 1 6.6595707 0.9915608 0.012226832918130892 1 292 1 ? ? ? 0 294 0 ? ? ? 0 295 1 5.621522 0.98121005 0.027366079814265764 1 -298 0 -2.4584546 0.086538464 0.13058411343083171 0 -302 1 12.725584 0.99992555 0.0001074071635410925 1 +298 0 -2.4584541 0.086538486 0.13058414873253427 0 +302 1 12.72558 0.99992555 0.0001074071635410925 1 305 1 8.040865 0.9971154 0.0041676501364772073 1 306 0 -5.4043503 0.009396286 0.013620064546924614 0 307 0 -5.4043503 0.009396286 0.013620064546924614 0 @@ -511,35 +511,35 @@ Instance Label Score Probability Log-loss Assigned 320 1 5.5611877 0.98032105 0.028673793676042177 1 322 0 -4.4382315 0.01977694 0.028818008476875413 0 324 0 -5.4043503 0.009396286 0.013620064546924614 0 -325 0 -3.7860875 0.03248962 0.047650955719163814 0 -326 1 3.6223288 0.91634446 0.12603806988928137 1 -330 1 4.9927454 0.9696536 0.044458635792904468 1 +325 0 -3.7860875 0.032489616 0.04765095016422824 0 +326 1 3.6223297 0.9163445 0.12603797604758099 1 +330 1 4.9927444 0.96965355 0.044458724475427286 1 334 1 5.514736 0.97960865 0.029722574521978982 1 335 0 -5.935418 0.006225399 0.0090094253868573462 0 337 0 -5.4043503 0.009396286 0.013620064546924614 0 339 1 5.1161976 0.97236645 0.040427975332860647 1 -340 1 5.5803347 0.98060757 0.028252197193330195 1 +340 1 5.5803356 0.98060757 0.028252197193330195 1 341 0 -5.4043503 0.009396286 0.013620064546924614 0 342 0 -5.4523587 0.009053541 0.013120984925728642 0 345 0 -5.935418 0.006225399 0.0090094253868573462 0 -351 0 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0.0090094253868573462 0 382 0 -3.5015903 0.040250305 0.059269898420679812 0 -385 0 -3.4273872 0.042550266 0.062731346866081117 0 +385 0 -3.4273868 0.042550277 0.062731363705997648 0 386 1 5.084236 0.97168756 0.041435602932387204 1 -390 0 -5.4467115 0.009093205 0.013178732083492451 0 +390 0 -5.446712 0.009093203 0.013178728015659433 0 393 0 -5.881093 0.006493533 0.0093987365367615445 0 394 0 -4.909887 0.013765578 0.019997487805960071 0 397 0 -4.48624 0.019062832 0.027767364472202984 0 @@ -547,112 +547,112 @@ Instance Label Score Probability Log-loss Assigned 401 0 -4.9692993 0.013149481 0.019096523087584735 0 402 0 -2.7191267 0.07173818 0.10739631960424616 0 403 0 -3.7908158 0.032373708 0.047478124251200779 0 -405 0 -5.669884 0.0076494967 0.011078316903702529 0 -407 0 -5.669884 0.0076494967 0.011078316903702529 0 -408 0 -3.5375085 0.039180238 0.057662270098084903 0 -410 0 -5.669884 0.0076494967 0.011078316903702529 0 +405 0 -5.669884 0.007649497 0.011078317580688359 0 +407 0 -5.669884 0.007649497 0.011078317580688359 0 +408 0 -3.537508 0.03918025 0.057662286878936264 0 +410 0 -5.669884 0.007649497 0.011078317580688359 0 411 0 ? ? ? 0 -412 1 7.6394253 0.996057 0.0056998288375171091 1 -417 0 -5.669884 0.0076494967 0.011078316903702529 0 -420 0 -2.6969714 0.07289933 0.10920208612242448 0 +412 1 7.639426 0.996057 0.0056998288375171091 1 +417 0 -5.669884 0.007649497 0.011078317580688359 0 +420 0 -2.696971 0.07289934 0.109202109310663 0 421 1 9.498289 0.9990743 0.0013361498208689703 1 424 0 -4.9692993 0.013149481 0.019096523087584735 0 425 1 11.849485 0.99985236 0.00021301623841437668 1 -426 0 -2.2324486 0.101552226 0.15449345077948776 0 +426 0 -2.2324486 0.10155222 0.15449343881561414 0 427 1 4.1596622 0.9433984 0.084060916290148127 1 -431 0 -2.9302087 0.06150328 0.091576391900387383 0 -432 0 -3.5334377 0.03930013 0.057842300709135597 0 +431 0 -2.9302087 0.061503276 0.09157638617372077 0 +432 0 -3.5334377 0.039300125 0.057842295114820461 0 433 0 -4.013695 0.02734187 0.039995280296142854 0 435 1 7.0216722 0.99362683 0.009223960722536808 1 437 0 -4.48624 0.019062832 0.027767364472202984 0 -438 0 -3.5384212 0.039153405 0.057621979836806482 0 +438 0 -3.5384207 0.039153416 0.057621996617189197 0 443 0 -5.54259 0.00844254 0.012231716526506063 0 -444 0 -2.6761093 0.074008495 0.11092913595470864 0 +444 0 -2.6761103 0.074008435 0.11092904309065733 0 445 0 -5.4523587 0.009053541 0.013120984925728642 0 446 0 -5.935418 0.006225399 0.0090094253868573462 0 -447 0 -4.0031805 0.027561162 0.040320580427227401 0 +447 0 -4.0031805 0.02756116 0.040320577663836212 0 448 0 -5.881093 0.006493533 0.0093987365367615445 0 458 0 -3.7524433 0.03332603 0.048898700380320723 0 -459 0 -3.5017061 0.04024681 0.059264645767511073 0 -460 0 -3.5568361 0.038615778 0.056814968504256366 0 -461 0 -3.3979883 0.04349581 0.064156808821778086 0 -462 0 -3.0737762 0.055339195 0.082131694177837 0 -463 0 -4.282031 0.022286426 0.032516212828799247 0 +459 0 -3.5017061 0.040246807 0.05926464016767781 0 +460 0 -3.5568361 0.038615774 0.056814962913923475 0 +461 0 -3.3979883 0.043495808 0.064156803202923621 0 +462 0 -3.0737767 0.05533917 0.082131654352744068 0 +463 0 -4.282031 0.022286424 0.032516210080316472 0 468 0 -4.48624 0.019062832 0.027767364472202984 0 469 0 -5.4191465 0.009289303 0.013464265265905247 0 -470 0 -4.649441 0.016819378 0.024471614226605987 0 -471 0 -3.0737762 0.055339195 0.082131694177837 0 +470 0 -4.649441 0.016819376 0.024471611493406355 0 +471 0 -3.0737767 0.05533917 0.082131654352744068 0 472 0 -3.4657965 0.04134449 0.060915614533985189 0 473 0 -4.48624 0.019062832 0.027767364472202984 0 475 0 -4.9692993 0.013149481 0.019096523087584735 0 -476 0 -4.2355022 0.023092356 0.033705916761290887 0 +476 0 -4.2355027 0.023092348 0.03370590575829005 0 477 0 -4.48624 0.019062832 0.027767364472202984 0 -478 0 -3.744658 0.03352252 0.049191978777848748 0 +478 0 -3.744658 0.033522516 0.049191973216976458 0 479 1 6.673233 0.9916496 0.012097621138980599 1 -481 0 -2.5658813 0.080130555 0.120498977234164 0 +481 0 -2.5658813 0.08013055 0.12049896554890183 0 485 0 -4.6490927 0.016823875 0.024478212185620931 0 -486 0 -4.649441 0.016819378 0.024471614226605987 0 -488 1 0.9591036 0.5776565 0.79171621850183493 1 +486 0 -4.649441 0.016819376 0.024471611493406355 0 +488 1 0.95910263 0.57765627 0.79171681395149796 1 490 0 -5.935418 0.006225399 0.0090094253868573462 0 -491 1 5.556223 0.98024607 0.028784146529073261 1 -494 0 -0.015696526 0.38975123 0.71253060259071122 0 +491 1 5.556222 0.98024607 0.028784146529073261 1 +494 0 -0.015696526 0.3897512 0.7125305321347537 0 496 0 -5.881093 0.006493533 0.0093987365367615445 0 -498 0 -4.220706 0.023354558 0.03409318909369452 0 -499 0 -4.220706 0.023354558 0.03409318909369452 0 -500 0 -2.9890537 0.05890274 0.087584266111585152 0 -503 0 -3.9551725 0.028584337 0.04183934627600109 0 +498 0 -4.220706 0.023354556 0.034093186342205803 0 +499 0 -4.220706 0.023354556 0.034093186342205803 0 +500 0 -2.9890532 0.058902755 0.087584288954953682 0 +503 0 -3.955172 0.028584346 0.041839360107510271 0 505 0 -4.4522543 0.019565688 0.028507120839304224 0 506 1 8.766861 0.99836195 0.0023651512585342507 1 -508 0 -4.0031805 0.027561162 0.040320580427227401 0 +508 0 -4.0031805 0.02756116 0.040320577663836212 0 509 0 -5.4523587 0.009053541 0.013120984925728642 0 511 0 -3.7376466 0.033700433 0.049457579363906538 0 -512 0 -4.0031805 0.027561162 0.040320580427227401 0 +512 0 -4.0031805 0.02756116 0.040320577663836212 0 515 1 7.3749876 0.9951566 0.0070045457965879953 1 516 0 -5.881093 0.006493533 0.0093987365367615445 0 -518 0 -4.49539 0.018929627 0.02757146870195281 0 +518 0 -4.4953895 0.018929632 0.027571476919188806 0 524 0 -4.7037654 0.016131794 0.023463022615813851 0 525 0 -4.746127 0.015614877 0.022705241025312198 0 526 0 -4.48624 0.019062832 0.027767364472202984 0 -530 1 5.325206 0.9764312 0.034409713804049308 1 -536 0 -3.4721131 0.041149348 0.060621972477918451 0 -537 0 -3.2213755 0.049611427 0.073410604725632031 0 -542 0 -3.4861355 0.0407193 0.059975064305642013 0 -543 0 -4.220706 0.023354558 0.03409318909369452 0 +530 1 5.325205 0.9764312 0.034409713804049308 1 +536 0 -3.4721127 0.041149363 0.060621994898335516 0 +537 0 -3.2213755 0.049611423 0.073410599070621044 0 +542 0 -3.4861355 0.040719297 0.059975058703050588 0 +543 0 -4.220706 0.023354556 0.034093186342205803 0 545 0 -3.7376466 0.033700433 0.049457579363906538 0 550 0 -4.7037654 0.016131794 0.023463022615813851 0 551 0 -5.4043503 0.009396286 0.013620064546924614 0 -552 0 -3.2360563 0.049073458 0.072594195629606423 0 -553 0 -1.4225526 0.17546201 0.27834212421314986 0 +552 0 -3.2360563 0.049073454 0.07259418997779464 0 +553 0 -1.4225531 0.17546193 0.27834199385026431 0 554 0 -4.9692993 0.013149481 0.019096523087584735 0 555 0 -1.7431297 0.14211455 0.22114307136971645 0 -556 0 -2.9508896 0.06057732 0.090153673385328204 0 +556 0 -2.9508896 0.060577318 0.090153667664306186 0 562 0 -5.4043503 0.009396286 0.013620064546924614 0 -564 0 -3.7509632 0.0333633 0.048954326671071656 0 -567 0 -3.4350505 0.04230704 0.062364900694131351 0 -568 1 3.5748348 0.913456 0.13059282121029733 1 +564 0 -3.7509632 0.033363298 0.04895432111111532 0 +567 0 -3.4350505 0.042307038 0.062364895082251487 0 +568 1 3.5748348 0.91345596 0.1305929153487409 1 570 1 6.466878 0.99020326 0.014203393857934222 1 -571 1 9.048693 0.99868524 0.0018980446658527542 1 +571 1 9.048691 0.99868524 0.0018980446658527542 1 572 0 -4.7037654 0.016131794 0.023463022615813851 0 -573 0 -5.669884 0.0076494967 0.011078316903702529 0 +573 0 -5.669884 0.007649497 0.011078317580688359 0 574 1 5.533702 0.9799025 0.029289877586105746 1 -575 0 -3.2213755 0.049611427 0.073410604725632031 0 +575 0 -3.2213755 0.049611423 0.073410599070621044 0 576 0 -3.7376466 0.033700433 0.049457579363906538 0 579 0 -5.4043503 0.009396286 0.013620064546924614 0 580 0 -3.4869094 0.04069569 0.059939555518058511 0 583 0 -4.9692993 0.013149481 0.019096523087584735 0 585 0 -5.935418 0.006225399 0.0090094253868573462 0 -587 0 -3.5334377 0.03930013 0.057842300709135597 0 +587 0 -3.5334377 0.039300125 0.057842295114820461 0 588 1 4.6442137 0.9605316 0.058095029247836778 1 -589 0 -4.0031805 0.027561162 0.040320580427227401 0 -591 1 4.243066 0.9467787 0.078900822913083493 1 -592 1 4.8517914 0.96624035 0.049545999536437443 1 +589 0 -4.0031805 0.02756116 0.040320577663836212 0 +591 1 4.243067 0.9467788 0.078900732087929318 1 +592 1 4.8517895 0.9662403 0.049546088532232753 1 595 0 -3.7376466 0.033700433 0.049457579363906538 0 596 0 -3.9699688 0.02826512 0.041365340879266133 0 -597 0 -2.9706383 0.059705295 0.08881510055974505 0 +597 0 -2.9706383 0.05970529 0.088815094844028691 0 598 0 -4.7037654 0.016131794 0.023463022615813851 0 -599 0 -2.9381208 0.061147474 0.091029535901308245 0 +599 0 -2.9381208 0.06114747 0.091029530176811924 0 601 0 -5.6155596 0.007978473 0.011556667054376319 0 -603 1 3.1762495 0.8854623 0.17549723761975466 1 +603 1 3.1762505 0.8854623 0.17549723761975466 1 605 1 8.159748 0.99737054 0.0037985035173251067 1 608 1 8.079367 0.9972006 0.0040443320699722306 1 610 1 6.972576 0.9933793 0.0095834180703139316 1 @@ -661,38 +661,38 @@ Instance Label Score Probability Log-loss Assigned 616 0 -4.7037654 0.016131794 0.023463022615813851 0 620 0 -4.7037654 0.016131794 0.023463022615813851 0 623 0 -5.935418 0.006225399 0.0090094253868573462 0 -625 0 -3.343666 0.04529595 0.066874515461359224 0 -626 1 3.8647957 0.92976606 0.10506033360236412 1 +625 0 -3.343666 0.045295946 0.066874509831910151 0 +626 1 3.8647957 0.929766 0.10506042608942295 1 628 0 -5.4523587 0.009053541 0.013120984925728642 0 -630 0 -2.7601237 0.06963448 0.10413046788911323 0 +630 0 -2.7601237 0.069634475 0.10413045633568027 0 631 0 -3.7376466 0.033700433 0.049457579363906538 0 632 0 -5.935418 0.006225399 0.0090094253868573462 0 -635 0 -4.217087 0.023419132 0.034188580858367221 0 -636 1 8.162584 0.9973763 0.0037901403924413767 1 -637 0 -2.370799 0.09210701 0.13940583705428458 0 -640 0 -4.0398955 0.026802769 0.039195879346332799 0 +635 0 -4.2170873 0.023419123 0.034188567100013993 0 +636 1 8.162586 0.9973763 0.0037901403924413767 1 +637 0 -2.370799 0.092107005 0.13940582521487643 0 +640 0 -4.039895 0.026802778 0.039195893152521553 0 643 0 -5.935418 0.006225399 0.0090094253868573462 0 646 0 -5.426158 0.00923903 0.013391058760089801 0 647 0 -5.4890733 0.008799812 0.012751633519735472 0 -648 1 8.579458 0.9981042 0.0027376359023654488 1 -650 0 -3.6219687 0.036770523 0.054048551776378198 0 +648 1 8.579456 0.9981042 0.0027376359023654488 1 +650 0 -3.6219683 0.036770534 0.054048568515248828 0 651 0 -4.965017 0.013192966 0.019160095111675445 0 655 0 -4.7037654 0.016131794 0.023463022615813851 0 658 1 7.546403 0.99576104 0.006128529583862446 1 659 0 -5.935418 0.006225399 0.0090094253868573462 0 662 0 -5.1684093 0.011276633 0.016361165642268163 0 663 0 -5.1684093 0.011276633 0.016361165642268163 0 -664 0 -4.2574205 0.022709223 0.03314021761930909 0 -666 0 -3.070212 0.05548494 0.082354293797361033 0 +664 0 -4.2574205 0.02270922 0.033140214869637266 0 +666 0 -3.0702114 0.055484954 0.08235431655806906 0 667 0 -4.4382315 0.01977694 0.028818008476875413 0 -669 1 6.981786 0.99342644 0.0095149472206866505 1 -671 0 -3.9565368 0.02855476 0.04179542083805643 0 -672 0 -4.9212914 0.013645148 0.019821329360883679 0 -673 0 -3.289723 0.04715361 0.06968444246223128 0 -674 0 -5.669884 0.0076494967 0.011078316903702529 0 -675 0 -3.7340279 0.033792615 0.049595215049171026 0 +669 1 6.9817867 0.99342644 0.0095149472206866505 1 +671 0 -3.9565368 0.028554758 0.041795418071838834 0 +672 0 -4.921291 0.013645153 0.019821336171893227 0 +673 0 -3.289723 0.047153607 0.069684436821807083 0 +674 0 -5.669884 0.007649497 0.011078317580688359 0 +675 0 -3.7340279 0.03379261 0.049595209486744243 0 676 0 -5.4191465 0.009289303 0.013464265265905247 0 -677 0 -4.0031805 0.027561162 0.040320580427227401 0 +677 0 -4.0031805 0.02756116 0.040320577663836212 0 684 0 -5.935418 0.006225399 0.0090094253868573462 0 686 0 -5.935418 0.006225399 0.0090094253868573462 0 687 0 -4.295347 0.022060877 0.032183434683621268 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt index 9206366946..45cc4803b0 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt @@ -13,15 +13,15 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 131 | 3 | 0.9776 - negative || 8 | 212 | 0.9636 + positive || 133 | 1 | 0.9925 + negative || 9 | 211 | 0.9591 ||====================== -Precision || 0.9424 | 0.9860 | -OVERALL 0/1 ACCURACY: 0.968927 +Precision || 0.9366 | 0.9953 | +OVERALL 0/1 ACCURACY: 0.971751 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.000000 -AUC: 0.994437 +AUC: 0.994403 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table @@ -40,16 +40,16 @@ AUC: 0.997619 OVERALL RESULTS --------------------------------------- -AUC: 0.996028 (0.0016) -Accuracy: 0.972305 (0.0034) -Positive precision: 0.956660 (0.0142) -Positive recall: 0.964996 (0.0126) -Negative precision: 0.981961 (0.0041) -Negative recall: 0.975122 (0.0115) +AUC: 0.996011 (0.0016) +Accuracy: 0.973718 (0.0020) +Positive precision: 0.953747 (0.0171) +Positive recall: 0.972459 (0.0201) +Negative precision: 0.986580 (0.0087) +Negative recall: 0.972849 (0.0138) Log-loss: NaN (NaN) Log-loss reduction: 0.000000 (0.0000) -F1 Score: 0.960623 (0.0009) -AUPRC: 0.992280 (0.0025) +F1 Score: 0.962653 (0.0011) +AUPRC: 0.992269 (0.0025) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt index d0fd8a6afd..9e9d966393 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt @@ -1,4 +1,4 @@ AveragedPerceptron 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 -0.996028 0.972305 0.95666 0.964996 0.981961 0.975122 NaN 0 0.960623 0.99228 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1 +0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 NaN 0 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration.txt index d5dee0b8f6..8ee0e5ef80 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration.txt @@ -1,379 +1,379 @@ Instance Label Score Assigned -5 1 11.285979 1 -6 0 -0.93471193 0 -8 0 -3.7769966 0 -9 0 -3.7947202 0 -10 0 -4.7855167 0 -11 0 -4.6251884 0 -18 1 6.8803635 1 -20 1 5.573552 1 -21 1 6.7444105 1 -25 1 1.2789736 1 -28 0 -4.6251884 0 -31 0 -4.3083706 0 -32 1 6.9428844 1 -35 0 -4.6251884 0 -37 0 -1.8143315 0 +5 1 11.925824 1 +6 0 -0.4527979 0 +8 0 -3.7962546 0 +9 0 -3.8130417 0 +10 0 -4.7285223 0 +11 0 -4.606969 0 +18 1 7.3475924 1 +20 1 6.1389017 1 +21 1 7.1486177 1 +25 1 1.6632223 1 +28 0 -4.606969 0 +31 0 -4.2645645 0 +32 1 7.198575 1 +35 0 -4.606969 0 +37 0 -1.714282 0 40 0 ? 0 -41 1 2.4075565 1 -44 1 8.039285 1 -45 0 -4.625085 0 -46 1 5.138131 1 -48 0 -3.4336777 0 -50 1 2.7120514 1 -51 1 -0.062075615 0 -52 1 4.4027233 1 -54 1 6.3079214 1 -56 1 6.356518 1 -60 1 1.9474735 1 -63 1 0.78555584 1 -64 0 -4.916337 0 -66 0 -3.7260728 0 -68 1 9.2772875 1 -69 0 -4.4157114 0 -70 0 -3.0868545 0 -71 1 7.5159607 1 -72 0 -1.8410158 0 -73 1 7.1320066 1 -74 1 2.4329157 1 -76 0 -3.9190063 0 -77 0 -3.1092033 0 -79 0 -4.4391913 0 -82 0 -3.1867537 0 -88 0 -3.7260728 0 -90 0 -4.5995197 0 -91 0 -4.5046597 0 -92 0 -3.7260728 0 -93 0 -4.916337 0 -95 0 -4.5995197 0 -96 0 -4.7958083 0 -97 0 -3.4349236 0 -98 1 9.07517 1 -99 1 8.952344 1 -100 1 4.9092436 1 -102 0 -3.393629 0 -104 1 10.959611 1 -105 1 2.0113592 1 -106 1 8.251353 1 -108 0 -4.5487204 0 -109 1 5.864868 1 -111 1 3.7846975 1 -112 1 6.380026 1 -113 1 9.461209 1 -115 0 -3.6043515 0 -117 1 7.9902315 1 -120 0 -4.120878 0 -121 0 -3.0707016 0 -122 1 10.129083 1 -123 1 4.173232 1 -125 0 -4.916337 0 -128 1 4.5026884 1 -129 0 -3.7451797 0 -131 0 -4.3083706 0 -132 1 8.723828 1 -133 0 -4.150231 0 -137 0 -4.650857 0 -138 0 -3.7104468 0 -141 0 -4.942006 0 -144 0 -4.6251884 0 +41 1 2.5451756 1 +44 1 8.165841 1 +45 0 -4.602255 0 +46 1 5.6216097 1 +48 0 -3.379683 0 +50 1 2.8003244 1 +51 1 0.14775372 1 +52 1 4.696246 1 +54 1 6.743867 1 +56 1 6.5947084 1 +60 1 2.2064123 1 +63 1 0.8789625 1 +64 0 -4.8905344 0 +66 0 -3.697434 0 +68 1 9.899808 1 +69 0 -4.3595524 0 +70 0 -3.0557137 0 +71 1 7.555622 1 +72 0 -1.676908 0 +73 1 7.7111263 1 +74 1 2.4994192 1 +76 0 -3.839695 0 +77 0 -3.1438046 0 +79 0 -4.4265766 0 +82 0 -3.1870723 0 +88 0 -3.697434 0 +90 0 -4.54813 0 +91 0 -4.5069323 0 +92 0 -3.697434 0 +93 0 -4.8905344 0 +95 0 -4.54813 0 +96 0 -4.790498 0 +97 0 -3.413869 0 +98 1 9.294541 1 +99 1 9.62131 1 +100 1 5.1074305 1 +102 0 -3.3471546 0 +104 1 11.120679 1 +105 1 2.1430416 1 +106 1 8.747506 1 +108 0 -4.513379 0 +109 1 6.3912535 1 +111 1 4.1208715 1 +112 1 7.006652 1 +113 1 9.855809 1 +115 0 -3.4127908 0 +117 1 8.638749 1 +120 0 -4.038975 0 +121 0 -3.0156002 0 +122 1 10.533511 1 +123 1 4.6804914 1 +125 0 -4.8905344 0 +128 1 4.9510326 1 +129 0 -3.7898016 0 +131 0 -4.2645645 0 +132 1 9.206267 1 +133 0 -4.134826 0 +137 0 -4.6658077 0 +138 0 -3.6895585 0 +141 0 -4.9493732 0 +144 0 -4.606969 0 145 0 ? 0 -147 0 -4.3231397 0 -150 0 -4.7855167 0 -151 1 4.569235 1 -152 1 8.551608 1 -154 0 -5.233155 0 -156 0 -4.3357234 0 -161 0 -3.5422645 0 +147 0 -4.255105 0 +150 0 -4.7285223 0 +151 1 5.0748987 1 +152 1 8.881612 1 +154 0 -5.232939 0 +156 0 -4.3134584 0 +161 0 -3.5088563 0 164 0 ? 0 -167 1 7.4310427 1 -169 0 -5.2729545 0 -171 0 -4.5995197 0 -173 1 13.79783 1 -174 1 5.2489986 1 -176 0 -4.3083706 0 -177 1 6.069394 1 -179 1 2.427494 1 -180 0 -4.7855167 0 -181 0 -5.233155 0 -183 1 8.429484 1 -187 1 11.391686 1 -188 1 7.5789557 1 -189 0 -3.686462 0 -191 1 10.154208 1 -192 0 -3.7517414 0 -196 0 5.425536 1 -198 0 -5.233155 0 -199 0 -4.334039 0 -201 1 9.260581 1 -202 0 -4.5995197 0 -204 0 -4.5995197 0 -205 1 11.455533 1 -206 1 6.5003185 1 -207 0 -4.7855167 0 -209 0 -3.6209207 0 -210 1 12.897248 1 -211 1 8.615066 1 -212 0 -4.5995197 0 -216 0 -4.916337 0 -218 1 7.456811 1 -219 0 -2.4788876 0 -223 1 4.949767 1 -226 1 8.538112 1 -228 0 -4.7855167 0 -233 1 5.219512 1 -237 1 6.374799 1 -239 1 4.7197256 1 -240 0 -2.0196419 0 -241 0 -3.9108233 0 -242 0 -4.3083706 0 -244 0 -4.5995197 0 -246 1 10.46416 1 -247 1 2.8055887 1 -248 0 -2.8070307 0 +167 1 7.490013 1 +169 0 -5.254455 0 +171 0 -4.54813 0 +173 1 14.494651 1 +174 1 5.7514915 1 +176 0 -4.2645645 0 +177 1 6.138485 1 +179 1 2.8749352 1 +180 0 -4.7285223 0 +181 0 -5.232939 0 +183 1 9.06396 1 +187 1 12.00492 1 +188 1 8.132482 1 +189 0 -3.6207743 0 +191 1 10.797475 1 +192 0 -3.7562728 0 +196 0 5.8725023 1 +198 0 -5.232939 0 +199 0 -4.3234034 0 +201 1 9.770606 1 +202 0 -4.54813 0 +204 0 -4.54813 0 +205 1 12.087651 1 +206 1 6.641532 1 +207 0 -4.7285223 0 +209 0 -3.5942607 0 +210 1 13.547517 1 +211 1 9.089206 1 +212 0 -4.54813 0 +216 0 -4.8905344 0 +218 1 7.7499723 1 +219 0 -2.4297438 0 +223 1 5.4305964 1 +226 1 9.166205 1 +228 0 -4.7285223 0 +233 1 5.6998796 1 +237 1 6.476473 1 +239 1 5.1697493 1 +240 0 -1.9057703 0 +241 0 -3.8436403 0 +242 0 -4.2645645 0 +244 0 -4.54813 0 +246 1 11.068807 1 +247 1 3.1855059 1 +248 0 -2.7545462 0 249 0 ? 0 -250 0 -4.652541 0 -252 0 3.7707853 1 -254 1 7.661195 1 -257 0 -4.334039 0 -258 0 -4.0172215 0 -259 0 4.008581 1 -260 1 7.7233286 1 -262 1 9.050584 1 -267 1 3.073165 1 -268 1 8.364619 1 -269 0 -4.5995197 0 -271 0 -3.4349236 0 -272 1 3.073165 1 +250 0 -4.655863 0 +252 0 4.2659445 1 +254 1 7.919627 1 +257 0 -4.3234034 0 +258 0 -3.9809995 0 +259 0 4.3407555 1 +260 1 8.330071 1 +262 1 9.629434 1 +267 1 3.677929 1 +268 1 8.609313 1 +269 0 -4.54813 0 +271 0 -3.413869 0 +272 1 3.677929 1 275 0 ? 0 -276 0 -4.334039 0 -277 0 -4.916337 0 -278 0 -4.5995197 0 -279 1 6.3361444 1 -280 0 -4.0172215 0 -283 1 5.09361 1 -284 1 5.780157 1 -285 1 12.663693 1 -288 1 1.8098211 1 -290 0 -5.233155 0 -291 0 -4.5995197 0 -293 1 4.209258 1 -296 0 1.7716074 1 +276 0 -4.3234034 0 +277 0 -4.8905344 0 +278 0 -4.54813 0 +279 1 6.636298 1 +280 0 -3.9809995 0 +283 1 5.5930414 1 +284 1 6.3683043 1 +285 1 13.342931 1 +288 1 2.133194 1 +290 0 -5.232939 0 +291 0 -4.54813 0 +293 1 4.659646 1 +296 0 2.0631151 1 297 0 ? 0 -299 1 5.4912786 1 -300 1 6.2749596 1 -301 0 -4.5995197 0 -303 0 -4.5995197 0 -304 1 4.320197 1 -308 1 6.5411158 1 -309 0 -1.7547832 0 -311 0 -5.233155 0 -312 1 3.2156925 1 -314 0 -5.102334 0 -316 1 3.9409618 1 -317 1 8.260409 1 -319 0 2.46204 1 +299 1 6.110527 1 +300 1 6.6979456 1 +301 0 -4.54813 0 +303 0 -4.54813 0 +304 1 4.865142 1 +308 1 6.804652 1 +309 0 -1.4760432 0 +311 0 -5.232939 0 +312 1 3.2489738 1 +314 0 -5.070926 0 +316 1 4.409379 1 +317 1 8.798545 1 +319 0 2.7989092 1 321 0 ? 0 -323 1 4.269208 1 -327 0 -4.916337 0 -328 1 4.1030073 1 -329 1 6.3562107 1 -331 0 -3.1436715 0 -332 0 -2.8411217 0 -333 1 4.4240284 1 -336 1 4.790291 1 -338 0 -5.102334 0 -343 0 -5.233155 0 -344 1 8.780366 1 -346 0 -2.788134 0 -347 0 -5.0515347 0 -348 1 -0.033994675 0 -349 1 2.9944906 1 -350 0 -3.776164 0 -352 0 1.3297043 1 -353 1 8.744256 1 -354 0 -4.916337 0 -355 0 -3.6730852 0 -358 1 6.1854076 1 -360 1 14.4099455 1 -361 1 6.113164 1 -366 1 12.847375 1 -368 0 -4.568268 0 -370 0 -3.0281096 0 -371 0 -4.568268 0 -373 0 -3.605544 0 -376 0 -4.916337 0 -377 0 -5.102334 0 -378 0 -3.5256414 0 -379 0 -1.5692582 0 -381 1 8.122036 1 -383 0 -4.942006 0 -384 0 -4.942006 0 -387 0 -1.9415016 0 -388 0 -4.44138 0 -389 0 -2.9747353 0 -391 1 8.779809 1 -392 0 -4.334039 0 -395 0 -4.334039 0 -396 0 -4.0172215 0 -398 0 -3.902061 0 -399 0 -4.3200974 0 -404 0 -4.508781 0 -406 0 -3.4622765 0 -409 0 -4.0015955 0 -413 0 -3.1024804 0 -414 1 5.959919 1 -415 0 -0.721817 0 -416 1 8.443301 1 -418 0 -1.8258505 0 -419 0 -3.8876748 0 -422 0 -2.1972284 0 -423 0 -3.0868545 0 -428 0 -4.916337 0 -429 0 -4.6251884 0 -430 0 -4.2361884 0 -434 0 5.330062 1 -436 1 4.9601755 1 -439 0 -4.068559 0 -440 1 7.0005217 1 -441 0 -1.8277493 0 -442 0 -4.126358 0 -449 1 9.384189 1 -450 0 -3.936492 0 -451 0 -4.068559 0 -452 0 -4.358462 0 -453 1 7.3491344 1 -454 0 -4.259669 0 -455 1 0.2950573 1 -456 1 8.340758 1 -457 1 7.996641 1 -464 0 -4.3597083 0 -465 1 8.680116 1 -466 1 8.110646 1 -467 1 6.858451 1 -474 0 -4.068559 0 -480 0 -4.254556 0 -482 1 13.881022 1 -483 1 9.617421 1 -484 0 -3.736116 0 -487 1 11.720016 1 -489 1 -0.5905738 0 -492 0 -4.228887 0 -493 1 9.492114 1 -495 0 -4.520036 0 -497 0 -4.111538 0 -501 0 -4.0428905 0 -502 0 -3.8966928 0 -504 0 -5.233155 0 -507 0 -4.0115523 0 -510 0 -5.233155 0 -513 0 -4.520036 0 -514 1 8.787938 1 -517 0 -5.102334 0 -519 1 6.320156 1 -520 0 -5.0471582 0 -521 0 -4.1737113 0 -522 1 3.983387 1 -523 1 6.156104 1 -527 0 -3.7260728 0 -528 0 -2.9663248 0 -529 0 -4.228887 0 -531 0 -3.4622765 0 -532 0 -4.7855167 0 -533 0 -4.334039 0 -534 0 -4.6251884 0 -535 0 -3.7884345 0 -538 0 -4.0428905 0 -539 0 -3.4605927 0 -540 0 -3.3445406 0 -541 0 -4.650857 0 -544 0 -3.8141036 0 -546 1 10.355874 1 -547 0 -5.128003 0 -548 0 -4.836854 0 -549 1 5.2726183 1 -557 0 -3.776164 0 -558 0 -4.6251884 0 -559 0 -3.7517414 0 -560 0 -3.4349236 0 -561 0 -3.4349236 0 -563 0 -4.334039 0 -565 1 10.456666 1 -566 0 -3.6847782 0 -569 1 8.855367 1 -577 0 -4.916337 0 -578 0 -4.916337 0 -581 1 8.00238 1 -582 1 7.645852 1 -584 0 -3.1515741 0 -586 1 12.260621 1 -590 1 4.0090714 1 -593 0 -3.736116 0 -594 1 5.269803 1 -600 0 -4.334039 0 -602 0 -4.0428905 0 -604 1 4.515382 1 -606 0 -4.2135105 0 -607 0 -5.233155 0 -609 0 -4.068559 0 -612 1 14.881892 1 -613 0 -4.128848 0 -614 0 -4.8111854 0 +323 1 4.8855343 1 +327 0 -4.8905344 0 +328 1 4.1213064 1 +329 1 6.918497 1 +331 0 -3.125589 0 +332 0 -2.8317442 0 +333 1 4.9879713 1 +336 1 5.3119774 1 +338 0 -5.070926 0 +343 0 -5.232939 0 +344 1 9.373975 1 +346 0 -2.8051786 0 +347 0 -5.0361757 0 +348 1 0.09843922 1 +349 1 3.543579 1 +350 0 -3.7809267 0 +352 0 1.5155859 1 +353 1 9.391765 1 +354 0 -4.8905344 0 +355 0 -3.6708684 0 +358 1 6.2604885 1 +360 1 15.035466 1 +361 1 6.5571547 1 +366 1 13.467439 1 +368 0 -4.532379 0 +370 0 -3.0555735 0 +371 0 -4.532379 0 +373 0 -3.5973978 0 +376 0 -4.8905344 0 +377 0 -5.070926 0 +378 0 -3.4778075 0 +379 0 -1.5486526 0 +381 1 8.643491 1 +383 0 -4.9493732 0 +384 0 -4.9493732 0 +387 0 -1.9204488 0 +388 0 -4.418391 0 +389 0 -2.9322662 0 +391 1 9.323798 1 +392 0 -4.3234034 0 +395 0 -4.3234034 0 +396 0 -3.9809995 0 +398 0 -3.9080298 0 +399 0 -4.305583 0 +404 0 -4.4998136 0 +406 0 -3.4627619 0 +409 0 -3.973124 0 +413 0 -3.063589 0 +414 1 6.5001116 1 +415 0 -0.5379734 0 +416 1 8.981729 1 +418 0 -1.7460232 0 +419 0 -3.9546041 0 +422 0 -2.1661267 0 +423 0 -3.0557137 0 +428 0 -4.8905344 0 +429 0 -4.606969 0 +430 0 -4.248364 0 +434 0 5.678664 1 +436 1 4.8968496 1 +439 0 -4.098677 0 +440 1 7.6677313 1 +441 0 -1.6798639 0 +442 0 -4.0051136 0 +449 1 10.087396 1 +450 0 -3.9024792 0 +451 0 -4.098677 0 +452 0 -4.348057 0 +453 1 8.068194 1 +454 0 -4.315388 0 +455 1 0.4596901 1 +456 1 8.942605 1 +457 1 8.346686 1 +464 0 -4.382242 0 +465 1 8.953591 1 +466 1 8.690221 1 +467 1 7.4474783 1 +474 0 -4.098677 0 +480 0 -4.279069 0 +482 1 14.295785 1 +483 1 10.273198 1 +484 0 -3.7483978 0 +487 1 12.309564 1 +489 1 -0.43852377 0 +492 0 -4.22023 0 +493 1 10.096398 1 +495 0 -4.5037956 0 +497 0 -4.155446 0 +501 0 -4.039838 0 +502 0 -3.8809624 0 +504 0 -5.232939 0 +507 0 -3.8678274 0 +510 0 -5.232939 0 +513 0 -4.5037956 0 +514 1 9.508458 1 +517 0 -5.070926 0 +519 1 6.943595 1 +520 0 -5.0525465 0 +521 0 -4.2018504 0 +522 1 4.3094034 1 +523 1 6.7598047 1 +527 0 -3.697434 0 +528 0 -2.955677 0 +529 0 -4.22023 0 +531 0 -3.4627619 0 +532 0 -4.7285223 0 +533 0 -4.3234034 0 +534 0 -4.606969 0 +535 0 -3.6886954 0 +538 0 -4.039838 0 +539 0 -3.4727077 0 +540 0 -3.3012362 0 +541 0 -4.6658077 0 +544 0 -3.7475338 0 +546 1 11.016191 1 +547 0 -5.1297655 0 +548 0 -4.8462 0 +549 1 5.5535192 1 +557 0 -3.7809267 0 +558 0 -4.606969 0 +559 0 -3.7562728 0 +560 0 -3.413869 0 +561 0 -3.413869 0 +563 0 -4.3234034 0 +565 1 11.259841 1 +566 0 -3.6307197 0 +569 1 9.242663 1 +577 0 -4.8905344 0 +578 0 -4.8905344 0 +581 1 8.654239 1 +582 1 8.128011 1 +584 0 -3.123908 0 +586 1 12.971318 1 +590 1 4.1888075 1 +593 0 -3.7483978 0 +594 1 5.7850237 1 +600 0 -4.3234034 0 +602 0 -4.039838 0 +604 1 5.138941 1 +606 0 -4.2233667 0 +607 0 -5.232939 0 +609 0 -4.098677 0 +612 1 15.565835 1 +613 0 -4.153376 0 +614 0 -4.787361 0 617 0 ? 0 -618 0 -4.0428905 0 -619 0 -3.7517414 0 -621 0 -0.14650154 0 -622 0 -2.5446053 0 -624 0 -3.7915406 0 -627 0 -3.3132024 0 -629 0 -4.3597083 0 -633 1 4.0237722 1 -634 0 -4.650857 0 -638 0 -4.3597083 0 -639 0 -3.776164 0 -641 0 -4.334039 0 -642 0 -4.334039 0 -644 0 -4.942006 0 -645 0 -4.334039 0 -649 0 -4.334039 0 -652 0 -3.567933 0 -653 0 -4.0428905 0 -654 0 -4.0172215 0 -656 0 -3.7517414 0 -657 0 0.674386 1 -660 0 -4.916337 0 -661 0 -3.7260728 0 -665 0 -5.233155 0 -668 1 3.2994661 1 -670 1 6.4614477 1 -678 0 -5.233155 0 -679 0 -4.942006 0 -680 1 14.404435 1 -681 1 9.278363 1 -682 0 -3.2511153 0 -683 0 -5.233155 0 -685 0 -5.233155 0 -688 0 -4.3597083 0 -689 0 -3.1943884 0 -691 1 5.2444315 1 -692 0 -4.650857 0 -693 0 -4.042787 0 -694 0 -4.057659 0 -696 1 7.3569994 1 -697 1 4.656295 1 -698 1 5.6929607 1 -0 0 -3.4721131 0 +618 0 -4.039838 0 +619 0 -3.7562728 0 +621 0 0.0042414665 1 +622 0 -2.5211115 0 +624 0 -3.7777896 0 +627 0 -3.1292257 0 +629 0 -4.382242 0 +633 1 4.158328 1 +634 0 -4.6658077 0 +638 0 -4.382242 0 +639 0 -3.7809267 0 +641 0 -4.3234034 0 +642 0 -4.3234034 0 +644 0 -4.9493732 0 +645 0 -4.3234034 0 +649 0 -4.3234034 0 +652 0 -3.5676956 0 +653 0 -4.039838 0 +654 0 -3.9809995 0 +656 0 -3.7562728 0 +657 0 0.6928787 1 +660 0 -4.8905344 0 +661 0 -3.697434 0 +665 0 -5.232939 0 +668 1 3.362691 1 +670 1 6.9357576 1 +678 0 -5.232939 0 +679 0 -4.9493732 0 +680 1 15.087735 1 +681 1 9.870067 1 +682 0 -3.2252913 0 +683 0 -5.232939 0 +685 0 -5.232939 0 +688 0 -4.382242 0 +689 0 -3.214981 0 +691 1 5.504386 1 +692 0 -4.6658077 0 +693 0 -4.035124 0 +694 0 -4.030379 0 +696 1 7.6070995 1 +697 1 5.0033865 1 +698 1 6.1210947 1 +0 0 -3.4721127 0 1 0 2.4163914 1 -2 0 -4.045404 0 -3 0 2.9251795 1 +2 0 -4.0454035 0 +3 0 2.9251804 1 4 0 -3.5088277 0 -7 0 -4.670553 0 +7 0 -4.6705537 0 12 1 -0.34343147 0 -13 0 -4.6186943 0 +13 0 -4.618695 0 14 1 7.360214 1 -15 1 0.6494303 1 +15 1 0.6494312 1 16 0 -4.220706 0 -17 0 -3.9551725 0 -19 0 -2.9890537 0 +17 0 -3.955172 0 +19 0 -2.9890532 0 22 0 -4.7037654 0 23 1 ? 0 24 0 -5.4043503 0 @@ -381,47 +381,47 @@ Instance Label Score Assigned 27 0 -3.7376466 0 29 0 -5.4339433 0 30 0 -4.649441 0 -33 0 -4.698118 0 +33 0 -4.6981187 0 34 0 -4.4530277 0 36 1 7.832773 1 38 1 4.92861 1 39 1 1.079258 1 42 1 6.8985863 1 -43 1 -0.49528694 0 +43 1 -0.49528742 0 47 0 -5.669884 0 -49 1 5.3024044 1 +49 1 5.3024054 1 53 1 5.116103 1 55 1 4.4195347 1 -57 1 0.5701313 1 -58 1 1.1371031 1 +57 1 0.57013035 1 +58 1 1.137104 1 59 1 1.6442327 1 61 0 -5.2770567 0 -62 1 5.7670774 1 -65 1 2.7867746 1 +62 1 5.7670784 1 +65 1 2.7867756 1 67 1 2.9058514 1 -75 0 -4.291042 0 +75 0 -4.2910423 0 78 0 -3.652576 0 80 0 -3.2955709 0 81 0 -3.9975338 0 -83 0 -2.9832687 0 -84 1 6.5824003 1 +83 0 -2.9832683 0 +84 1 6.5823994 1 85 1 4.7604074 1 86 1 1.466999 1 -87 1 5.223543 1 +87 1 5.223544 1 89 0 -5.017977 0 -94 0 -4.9212914 0 -101 1 -0.85990286 0 +94 0 -4.921291 0 +101 1 -0.85990334 0 103 1 0.36755466 1 107 1 4.617798 1 110 0 -3.1312823 0 114 0 -2.8847933 0 -116 0 -0.40737772 0 +116 0 -0.40737724 0 118 0 -5.2584124 0 -119 0 -3.786881 0 +119 0 -3.7868814 0 124 1 5.0384197 1 126 1 6.4765244 1 127 0 -4.4382315 0 -130 0 -3.2361722 0 +130 0 -3.2361717 0 134 0 -4.75809 0 135 0 -2.670867 0 136 0 -4.220706 0 @@ -429,12 +429,12 @@ Instance Label Score Assigned 140 0 -4.9692993 0 142 1 3.7043686 1 143 0 -4.364107 0 -146 1 0.36148834 1 -148 0 -2.357305 0 -149 1 8.756336 1 +146 1 0.3614874 1 +148 0 -2.3573046 0 +149 1 8.756334 1 153 0 -3.7005844 0 155 1 3.1619148 1 -157 0 -4.9212914 0 +157 0 -4.921291 0 158 0 ? 0 159 1 10.452139 1 160 1 7.997595 1 @@ -445,62 +445,62 @@ Instance Label Score Assigned 168 0 -4.4382315 0 170 0 -4.9692993 0 172 0 -5.669884 0 -175 1 6.0381765 1 -178 0 -3.9551725 0 -182 0 -2.9890537 0 +175 1 6.0381775 1 +178 0 -3.955172 0 +182 0 -2.9890532 0 184 1 5.409273 1 185 0 -4.8669662 0 186 1 3.9876003 1 190 1 10.521242 1 193 0 -5.4043503 0 194 0 -4.4382315 0 -195 0 -3.9551725 0 +195 0 -3.955172 0 197 0 -2.8025956 0 200 1 8.673521 1 -203 0 -3.4721131 0 -208 0 -5.350025 0 +203 0 -3.4721127 0 +208 0 -5.3500257 0 213 1 12.300528 1 214 1 11.895983 1 -215 1 6.600219 1 +215 1 6.6002197 1 217 0 -5.4043503 0 220 0 -5.181178 0 221 1 7.9662914 1 222 1 -2.1487255 0 -224 1 8.4735565 1 +224 1 8.473555 1 225 0 -5.669884 0 227 1 6.748211 1 229 1 10.504805 1 -230 1 4.829337 1 +230 1 4.829338 1 231 1 6.912092 1 232 0 1.0722923 1 234 0 -2.7037287 0 235 0 ? 0 236 1 9.440506 1 238 1 10.690645 1 -243 0 -3.301972 0 -245 0 -2.856113 0 +243 0 -3.3019714 0 +245 0 -2.8561125 0 251 1 7.355525 1 253 1 6.8985863 1 -255 1 3.745204 1 +255 1 3.745205 1 256 0 -4.9692993 0 261 1 9.009869 1 263 1 7.1455708 1 264 1 4.0145664 1 -265 0 -2.5156913 0 +265 0 -2.5156918 0 266 1 7.325534 1 270 1 5.5723915 1 -273 1 0.03731823 1 +273 1 0.037317276 1 274 0 -4.2340226 0 -281 0 -4.698118 0 -282 1 2.860156 1 +281 0 -4.6981187 0 +282 1 2.860157 1 286 1 12.544172 1 287 0 -4.75809 0 -289 1 6.6595697 1 +289 1 6.6595707 1 292 1 ? 0 294 0 ? 0 295 1 5.621522 1 -298 0 -2.4584546 0 -302 1 12.725584 1 +298 0 -2.4584541 0 +302 1 12.72558 1 305 1 8.040865 1 306 0 -5.4043503 0 307 0 -5.4043503 0 @@ -512,34 +512,34 @@ Instance Label Score Assigned 322 0 -4.4382315 0 324 0 -5.4043503 0 325 0 -3.7860875 0 -326 1 3.6223288 1 -330 1 4.9927454 1 +326 1 3.6223297 1 +330 1 4.9927444 1 334 1 5.514736 1 335 0 -5.935418 0 337 0 -5.4043503 0 339 1 5.1161976 1 -340 1 5.5803347 1 +340 1 5.5803356 1 341 0 -5.4043503 0 342 0 -5.4523587 0 345 0 -5.935418 0 -351 0 -4.9212914 0 +351 0 -4.921291 0 356 1 -0.9127703 0 357 1 9.874601 1 359 1 4.653248 1 362 0 -3.5174994 0 363 0 -1.6840982 0 -364 0 -4.9212914 0 +364 0 -4.921291 0 365 0 -5.186825 0 367 1 9.040358 1 -369 0 -5.1140842 0 +369 0 -5.1140847 0 372 0 -3.9699688 0 374 0 -4.4530277 0 375 0 -5.935418 0 380 0 -5.935418 0 382 0 -3.5015903 0 -385 0 -3.4273872 0 +385 0 -3.4273868 0 386 1 5.084236 1 -390 0 -5.4467115 0 +390 0 -5.446712 0 393 0 -5.881093 0 394 0 -4.909887 0 397 0 -4.48624 0 @@ -549,12 +549,12 @@ Instance Label Score Assigned 403 0 -3.7908158 0 405 0 -5.669884 0 407 0 -5.669884 0 -408 0 -3.5375085 0 +408 0 -3.537508 0 410 0 -5.669884 0 411 0 ? 0 -412 1 7.6394253 1 +412 1 7.639426 1 417 0 -5.669884 0 -420 0 -2.6969714 0 +420 0 -2.696971 0 421 1 9.498289 1 424 0 -4.9692993 0 425 1 11.849485 1 @@ -565,9 +565,9 @@ Instance Label Score Assigned 433 0 -4.013695 0 435 1 7.0216722 1 437 0 -4.48624 0 -438 0 -3.5384212 0 +438 0 -3.5384207 0 443 0 -5.54259 0 -444 0 -2.6761093 0 +444 0 -2.6761103 0 445 0 -5.4523587 0 446 0 -5.935418 0 447 0 -4.0031805 0 @@ -576,31 +576,31 @@ Instance Label Score Assigned 459 0 -3.5017061 0 460 0 -3.5568361 0 461 0 -3.3979883 0 -462 0 -3.0737762 0 +462 0 -3.0737767 0 463 0 -4.282031 0 468 0 -4.48624 0 469 0 -5.4191465 0 470 0 -4.649441 0 -471 0 -3.0737762 0 +471 0 -3.0737767 0 472 0 -3.4657965 0 473 0 -4.48624 0 475 0 -4.9692993 0 -476 0 -4.2355022 0 +476 0 -4.2355027 0 477 0 -4.48624 0 478 0 -3.744658 0 479 1 6.673233 1 481 0 -2.5658813 0 485 0 -4.6490927 0 486 0 -4.649441 0 -488 1 0.9591036 1 +488 1 0.95910263 1 490 0 -5.935418 0 -491 1 5.556223 1 +491 1 5.556222 1 494 0 -0.015696526 0 496 0 -5.881093 0 498 0 -4.220706 0 499 0 -4.220706 0 -500 0 -2.9890537 0 -503 0 -3.9551725 0 +500 0 -2.9890532 0 +503 0 -3.955172 0 505 0 -4.4522543 0 506 1 8.766861 1 508 0 -4.0031805 0 @@ -609,12 +609,12 @@ Instance Label Score Assigned 512 0 -4.0031805 0 515 1 7.3749876 1 516 0 -5.881093 0 -518 0 -4.49539 0 +518 0 -4.4953895 0 524 0 -4.7037654 0 525 0 -4.746127 0 526 0 -4.48624 0 -530 1 5.325206 1 -536 0 -3.4721131 0 +530 1 5.325205 1 +536 0 -3.4721127 0 537 0 -3.2213755 0 542 0 -3.4861355 0 543 0 -4.220706 0 @@ -622,7 +622,7 @@ Instance Label Score Assigned 550 0 -4.7037654 0 551 0 -5.4043503 0 552 0 -3.2360563 0 -553 0 -1.4225526 0 +553 0 -1.4225531 0 554 0 -4.9692993 0 555 0 -1.7431297 0 556 0 -2.9508896 0 @@ -631,7 +631,7 @@ Instance Label Score Assigned 567 0 -3.4350505 0 568 1 3.5748348 1 570 1 6.466878 1 -571 1 9.048693 1 +571 1 9.048691 1 572 0 -4.7037654 0 573 0 -5.669884 0 574 1 5.533702 1 @@ -644,15 +644,15 @@ Instance Label Score Assigned 587 0 -3.5334377 0 588 1 4.6442137 1 589 0 -4.0031805 0 -591 1 4.243066 1 -592 1 4.8517914 1 +591 1 4.243067 1 +592 1 4.8517895 1 595 0 -3.7376466 0 596 0 -3.9699688 0 597 0 -2.9706383 0 598 0 -4.7037654 0 599 0 -2.9381208 0 601 0 -5.6155596 0 -603 1 3.1762495 1 +603 1 3.1762505 1 605 1 8.159748 1 608 1 8.079367 1 610 1 6.972576 1 @@ -667,15 +667,15 @@ Instance Label Score Assigned 630 0 -2.7601237 0 631 0 -3.7376466 0 632 0 -5.935418 0 -635 0 -4.217087 0 -636 1 8.162584 1 +635 0 -4.2170873 0 +636 1 8.162586 1 637 0 -2.370799 0 -640 0 -4.0398955 0 +640 0 -4.039895 0 643 0 -5.935418 0 646 0 -5.426158 0 647 0 -5.4890733 0 -648 1 8.579458 1 -650 0 -3.6219687 0 +648 1 8.579456 1 +650 0 -3.6219683 0 651 0 -4.965017 0 655 0 -4.7037654 0 658 1 7.546403 1 @@ -683,11 +683,11 @@ Instance Label Score Assigned 662 0 -5.1684093 0 663 0 -5.1684093 0 664 0 -4.2574205 0 -666 0 -3.070212 0 +666 0 -3.0702114 0 667 0 -4.4382315 0 -669 1 6.981786 1 +669 1 6.9817867 1 671 0 -3.9565368 0 -672 0 -4.9212914 0 +672 0 -4.921291 0 673 0 -3.289723 0 674 0 -5.669884 0 675 0 -3.7340279 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt index 08ac6f6825..0505f9c408 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt @@ -9,28 +9,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 234 | 5 | 0.9791 + positive || 233 | 6 | 0.9749 negative || 12 | 432 | 0.9730 ||====================== -Precision || 0.9512 | 0.9886 | -OVERALL 0/1 ACCURACY: 0.975110 -LOG LOSS/instance: 0.084507 +Precision || 0.9510 | 0.9863 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.083944 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.909522 +LOG-LOSS REDUCTION (RIG): 0.910125 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) -Accuracy: 0.975110 (0.0000) -Positive precision: 0.951220 (0.0000) -Positive recall: 0.979079 (0.0000) -Negative precision: 0.988558 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.951020 (0.0000) +Positive recall: 0.974895 (0.0000) +Negative precision: 0.986301 (0.0000) Negative recall: 0.972973 (0.0000) -Log-loss: 0.084507 (0.0000) -Log-loss reduction: 0.909522 (0.0000) -F1 Score: 0.964948 (0.0000) -AUPRC: 0.992065 (0.0000) +Log-loss: 0.083944 (0.0000) +Log-loss reduction: 0.910125 (0.0000) +F1 Score: 0.962810 (0.0000) +AUPRC: 0.992010 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt index e852d92116..1b1e71c268 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt @@ -1,4 +1,4 @@ AveragedPerceptron 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 -0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 0.084507 0.909522 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali=PAV dout=%Output% data=%Data% out=%Output% seed=1 +0.996146 0.973646 0.95102 0.974895 0.986301 0.972973 0.083944 0.910125 0.96281 0.99201 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali=PAV dout=%Output% data=%Data% out=%Output% seed=1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt index 67338259ca..651d20da42 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.5726995 1E-15 1.4415419267167138E-15 0 -1 0 3.6101465 0.8333333 2.5849623287385155 1 -2 0 -4.070944 1E-15 1.4415419267167138E-15 0 -3 0 2.470542 0.8095238 2.3923175087700885 1 -4 0 -3.4358397 1E-15 1.4415419267167138E-15 0 -5 1 12.382595 1 -0 1 -6 0 -1.4209604 0.071428575 0.10691520887754996 0 -7 0 -4.701088 1E-15 1.4415419267167138E-15 0 -8 0 -4.6745405 1E-15 1.4415419267167138E-15 0 -9 0 -4.406417 1E-15 1.4415419267167138E-15 0 -10 0 -5.559344 1E-15 1.4415419267167138E-15 0 -11 0 -5.4818344 1E-15 1.4415419267167138E-15 0 -12 1 -0.14206886 0.6363636 0.65207672114864346 0 -13 0 -4.5691886 1E-15 1.4415419267167138E-15 0 -14 1 9.321611 1 -0 1 -15 1 1.3856993 0.8095238 0.30485456129516797 1 -16 0 -4.533843 1E-15 1.4415419267167138E-15 0 -17 0 -4.046695 1E-15 1.4415419267167138E-15 0 -18 1 7.8903713 1 -0 1 -19 0 -3.0987039 1E-15 1.4415419267167138E-15 0 -20 1 7.528511 1 -0 1 -21 1 7.875206 1 -0 1 -22 0 -5.0078387 1E-15 1.4415419267167138E-15 0 +0 0 -3.7050557 1E-15 1.4415419267167138E-15 0 +1 0 3.4661026 0.85714287 2.807355008048932 1 +2 0 -4.24041 1E-15 1.4415419267167138E-15 0 +3 0 2.1493654 0.8 2.3219281808786905 1 +4 0 -3.5576057 1E-15 1.4415419267167138E-15 0 +5 1 12.155442 1 -0 1 +6 0 -1.573504 0.083333336 0.12553088599255555 0 +7 0 -4.9137383 1E-15 1.4415419267167138E-15 0 +8 0 -4.903472 1E-15 1.4415419267167138E-15 0 +9 0 -4.57476 1E-15 1.4415419267167138E-15 0 +10 0 -5.7696943 1E-15 1.4415419267167138E-15 0 +11 0 -5.679842 1E-15 1.4415419267167138E-15 0 +12 1 -0.2966156 0.6923077 0.53051467848041345 0 +13 0 -4.7757645 1E-15 1.4415419267167138E-15 0 +14 1 9.220881 1 -0 1 +15 1 1.1802778 0.8 0.32192807338953117 1 +16 0 -4.687022 1E-15 1.4415419267167138E-15 0 +17 0 -4.2014656 1E-15 1.4415419267167138E-15 0 +18 1 7.81973 1 -0 1 +19 0 -3.2086458 1E-15 1.4415419267167138E-15 0 +20 1 7.5404253 1 -0 1 +21 1 7.730527 1 -0 1 +22 0 -5.183432 1E-15 1.4415419267167138E-15 0 23 1 ? ? ? 0 -24 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -25 1 1.741828 0.8095238 0.30485456129516797 1 -26 0 -4.9710746 1E-15 1.4415419267167138E-15 0 -27 0 -4.0598474 1E-15 1.4415419267167138E-15 0 -28 0 -5.4818344 1E-15 1.4415419267167138E-15 0 -29 0 -5.8557806 1E-15 1.4415419267167138E-15 0 -30 0 -5.0985007 1E-15 1.4415419267167138E-15 0 -31 0 -4.9946866 1E-15 1.4415419267167138E-15 0 -32 1 7.46414 1 -0 1 -33 0 -4.689259 1E-15 1.4415419267167138E-15 0 -34 0 -4.71424 1E-15 1.4415419267167138E-15 0 -35 0 -5.4818344 1E-15 1.4415419267167138E-15 0 -36 1 9.099108 1 -0 1 -37 0 -1.113348 0.071428575 0.10691520887754996 0 -38 1 6.140953 0.98 0.029146317580716615 1 -39 1 2.5109034 0.8095238 0.30485456129516797 1 +24 0 -5.690696 1E-15 1.4415419267167138E-15 0 +25 1 1.6430082 0.8 0.32192807338953117 1 +26 0 -5.1501684 1E-15 1.4415419267167138E-15 0 +27 0 -4.190612 1E-15 1.4415419267167138E-15 0 +28 0 -5.679842 1E-15 1.4415419267167138E-15 0 +29 0 -6.1007133 1E-15 1.4415419267167138E-15 0 +30 0 -5.2624307 1E-15 1.4415419267167138E-15 0 +31 0 -5.194286 1E-15 1.4415419267167138E-15 0 +32 1 7.2332277 1 -0 1 +33 0 -4.872356 1E-15 1.4415419267167138E-15 0 +34 0 -4.9028845 1E-15 1.4415419267167138E-15 0 +35 0 -5.679842 1E-15 1.4415419267167138E-15 0 +36 1 8.852162 1 -0 1 +37 0 -1.2331572 0.083333336 0.12553088599255555 0 +38 1 6.101239 0.9814815 0.026967031375491075 1 +39 1 2.2340298 0.8 0.32192807338953117 1 40 0 ? ? ? 0 -41 1 3.3300762 0.8333333 0.26303444023032446 1 -42 1 8.577511 1 -0 1 -43 1 0.49126053 0.6363636 0.65207672114864346 1 -44 1 8.255751 1 -0 1 -45 0 -5.6322193 1E-15 1.4415419267167138E-15 0 -46 1 4.5673857 0.9285714 0.10691524360481655 1 -47 0 -5.95583 1E-15 1.4415419267167138E-15 0 -48 0 -3.4358397 1E-15 1.4415419267167138E-15 0 -49 1 5.3666544 0.98 0.029146317580716615 1 -50 1 2.5949678 0.8095238 0.30485456129516797 1 -51 1 0.12595749 0.6363636 0.65207672114864346 1 -52 1 5.2992115 0.98 0.029146317580716615 1 -53 1 8.407228 1 -0 1 -54 1 7.649311 1 -0 1 -55 1 4.478709 0.9285714 0.10691524360481655 1 -56 1 5.5541325 0.98 0.029146317580716615 1 -57 1 1.6657066 0.8095238 0.30485456129516797 1 -58 1 2.5265894 0.8095238 0.30485456129516797 1 -59 1 1.7368536 0.8095238 0.30485456129516797 1 -60 1 2.3288136 0.8095238 0.30485456129516797 1 -61 0 -5.5060835 1E-15 1.4415419267167138E-15 0 -62 1 6.380088 0.98 0.029146317580716615 1 -63 1 0.3348999 0.6363636 0.65207672114864346 1 -64 0 -5.95583 1E-15 1.4415419267167138E-15 0 -65 1 3.8072634 0.9285714 0.10691524360481655 1 -66 0 -4.046695 1E-15 1.4415419267167138E-15 0 -67 1 4.218014 0.9285714 0.10691524360481655 1 -68 1 10.826723 1 -0 1 -69 0 -5.2716546 1E-15 1.4415419267167138E-15 0 -70 0 -3.4726496 1E-15 1.4415419267167138E-15 0 -71 1 7.895048 1 -0 1 -72 0 -2.1755848 0.071428575 0.10691520887754996 0 -73 1 8.9055195 1 -0 1 -74 1 2.5993576 0.8095238 0.30485456129516797 1 -75 0 -4.0411606 1E-15 1.4415419267167138E-15 0 -76 0 -5.075033 1E-15 1.4415419267167138E-15 0 -77 0 -3.4995675 1E-15 1.4415419267167138E-15 0 -78 0 -3.6211967 1E-15 1.4415419267167138E-15 0 -79 0 -5.3911724 1E-15 1.4415419267167138E-15 0 -80 0 -2.7157316 1E-15 1.4415419267167138E-15 0 -81 0 -4.2284155 1E-15 1.4415419267167138E-15 0 -82 0 -3.4452734 1E-15 1.4415419267167138E-15 0 -83 0 -2.1223516 0.071428575 0.10691520887754996 0 -84 1 9.694054 1 -0 1 -85 1 6.2895603 0.98 0.029146317580716615 1 -86 1 2.6168842 0.8095238 0.30485456129516797 1 -87 1 6.91914 1 -0 1 -88 0 -4.046695 1E-15 1.4415419267167138E-15 0 -89 0 -5.085745 1E-15 1.4415419267167138E-15 0 -90 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -91 0 -5.189559 1E-15 1.4415419267167138E-15 0 -92 0 -4.046695 1E-15 1.4415419267167138E-15 0 -93 0 -5.95583 1E-15 1.4415419267167138E-15 0 -94 0 -4.9946866 1E-15 1.4415419267167138E-15 0 -95 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -96 0 -5.663555 1E-15 1.4415419267167138E-15 0 -97 0 -3.5726995 1E-15 1.4415419267167138E-15 0 -98 1 8.590233 1 -0 1 -99 1 10.917194 1 -0 1 -100 1 4.8476696 0.9285714 0.10691524360481655 1 -101 1 -0.842803 0.5 1 0 -102 0 -3.7530966 1E-15 1.4415419267167138E-15 0 -103 1 1.7746449 0.8095238 0.30485456129516797 1 -104 1 12.140858 1 -0 1 -105 1 2.5560703 0.8095238 0.30485456129516797 1 -106 1 9.259369 1 -0 1 -107 1 6.720646 0.98 0.029146317580716615 1 -108 0 -5.5617743 1E-15 1.4415419267167138E-15 0 -109 1 6.871727 1 -0 1 -110 0 -2.766693 1E-15 1.4415419267167138E-15 0 -111 1 3.848031 0.9285714 0.10691524360481655 1 -112 1 9.42577 1 -0 1 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0.026967031375491075 1 +63 1 0.09457874 0.6923077 0.53051467848041345 1 +64 0 -6.1762524 1E-15 1.4415419267167138E-15 0 +65 1 3.6413336 0.9230769 0.11547721025399223 1 +66 0 -4.2014656 1E-15 1.4415419267167138E-15 0 +67 1 3.980445 0.9230769 0.11547721025399223 1 +68 1 10.612863 1 -0 1 +69 0 -5.4978714 1E-15 1.4415419267167138E-15 0 +70 0 -3.6295166 1E-15 1.4415419267167138E-15 0 +71 1 7.5565577 1 -0 1 +72 0 -2.4480972 1E-15 1.4415419267167138E-15 0 +73 1 8.905442 1 -0 1 +74 1 2.4551315 0.8 0.32192807338953117 1 +75 0 -4.274302 1E-15 1.4415419267167138E-15 0 +76 0 -5.334609 1E-15 1.4415419267167138E-15 0 +77 0 -3.644158 1E-15 1.4415419267167138E-15 0 +78 0 -3.7829442 1E-15 1.4415419267167138E-15 0 +79 0 -5.6008434 1E-15 1.4415419267167138E-15 0 +80 0 -2.9382496 1E-15 1.4415419267167138E-15 0 +81 0 -4.3650923 1E-15 1.4415419267167138E-15 0 +82 0 -3.5927935 1E-15 1.4415419267167138E-15 0 +83 0 -2.2981849 1E-15 1.4415419267167138E-15 0 +84 1 9.3922205 1 -0 1 +85 1 5.7727385 0.9814815 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1.4415419267167138E-15 0 +109 1 6.7095194 0.9814815 0.026967031375491075 1 +110 0 -3.014533 1E-15 1.4415419267167138E-15 0 +111 1 3.7277098 0.9230769 0.11547721025399223 1 +112 1 9.536014 1 -0 1 +113 1 9.218173 1 -0 1 +114 0 -3.277872 1E-15 1.4415419267167138E-15 0 +115 0 -4.8319454 1E-15 1.4415419267167138E-15 0 +116 0 -0.7799158 0.4 0.73696560849809378 0 +117 1 9.464227 1 -0 1 +118 0 -5.5888796 1E-15 1.4415419267167138E-15 0 +119 0 -4.1281476 1E-15 1.4415419267167138E-15 0 +120 0 -5.088218 1E-15 1.4415419267167138E-15 0 +121 0 -3.6317377 1E-15 1.4415419267167138E-15 0 +122 1 9.371118 1 -0 1 +123 1 3.5896034 0.9230769 0.11547721025399223 1 +124 1 7.4270334 1 -0 1 +125 0 -6.1762524 1E-15 1.4415419267167138E-15 0 +126 1 8.480126 1 -0 1 +127 0 -4.697876 1E-15 1.4415419267167138E-15 0 +128 1 4.7593575 0.9230769 0.11547721025399223 1 +129 0 -6.02982 1E-15 1.4415419267167138E-15 0 +130 0 -3.6295166 1E-15 1.4415419267167138E-15 0 +131 0 -5.194286 1E-15 1.4415419267167138E-15 0 +132 1 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0 -5.559344 1E-15 1.4415419267167138E-15 0 -151 1 5.006485 0.9285714 0.10691524360481655 1 -152 1 9.715748 1 -0 1 -153 0 -4.1214976 1E-15 1.4415419267167138E-15 0 -154 0 -6.442978 1E-15 1.4415419267167138E-15 0 -155 1 3.7769232 0.9285714 0.10691524360481655 1 -156 0 -5.5348053 1E-15 1.4415419267167138E-15 0 -157 0 -4.9946866 1E-15 1.4415419267167138E-15 0 +146 1 1.0868425 0.6923077 0.53051467848041345 1 +147 0 -5.639788 1E-15 1.4415419267167138E-15 0 +148 0 -1.259141 0.083333336 0.12553088599255555 0 +149 1 11.153153 1 -0 1 +150 0 -5.7696943 1E-15 1.4415419267167138E-15 0 +151 1 4.855405 0.9814815 0.026967031375491075 1 +152 1 9.511897 1 -0 1 +153 0 -4.257647 1E-15 1.4415419267167138E-15 0 +154 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +155 1 3.6720238 0.9230769 0.11547721025399223 1 +156 0 -5.7834425 1E-15 1.4415419267167138E-15 0 +157 0 -5.194286 1E-15 1.4415419267167138E-15 0 158 0 ? ? ? 0 -159 1 12.346203 1 -0 1 -160 1 9.039494 1 -0 1 -161 0 -3.8496675 1E-15 1.4415419267167138E-15 0 -162 0 -4.520691 1E-15 1.4415419267167138E-15 0 -163 0 -3.387055 1E-15 1.4415419267167138E-15 0 +159 1 12.03571 1 -0 1 +160 1 8.89737 1 -0 1 +161 0 -4.0086412 1E-15 1.4415419267167138E-15 0 +162 0 -4.697876 1E-15 1.4415419267167138E-15 0 +163 0 -3.6110554 1E-15 1.4415419267167138E-15 0 164 0 ? ? ? 0 -165 0 -3.3999205 1E-15 1.4415419267167138E-15 0 -166 1 7.976183 1 -0 1 -167 1 8.355644 1 -0 1 -168 0 -4.520691 1E-15 1.4415419267167138E-15 0 -169 0 -6.2282124 1E-15 1.4415419267167138E-15 0 -170 0 -5.4949865 1E-15 1.4415419267167138E-15 0 -171 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -172 0 -5.95583 1E-15 1.4415419267167138E-15 0 -173 1 15.1560135 1 -0 1 -174 1 6.1769247 0.98 0.029146317580716615 1 -175 1 7.842922 1 -0 1 -176 0 -4.9946866 1E-15 1.4415419267167138E-15 0 -177 1 4.766121 0.9285714 0.10691524360481655 1 -178 0 -4.046695 1E-15 1.4415419267167138E-15 0 -179 1 2.290575 0.8095238 0.30485456129516797 1 -180 0 -5.559344 1E-15 1.4415419267167138E-15 0 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0.53051467848041345 1 +274 0 -4.5050516 1E-15 1.4415419267167138E-15 0 275 0 ? ? ? 0 -276 0 -5.0078387 1E-15 1.4415419267167138E-15 0 -277 0 -5.95583 1E-15 1.4415419267167138E-15 0 -278 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -279 1 7.127905 1 -0 1 -280 0 -4.520691 1E-15 1.4415419267167138E-15 0 -281 0 -4.689259 1E-15 1.4415419267167138E-15 0 -282 1 4.4381237 0.9285714 0.10691524360481655 1 -283 1 6.0636253 0.98 0.029146317580716615 1 -284 1 7.431343 1 -0 1 -285 1 14.218481 1 -0 1 -286 1 15.281263 1 -0 1 -287 0 -4.917177 1E-15 1.4415419267167138E-15 0 -288 1 2.2163515 0.8095238 0.30485456129516797 1 -289 1 8.312019 1 -0 1 -290 0 -6.442978 1E-15 1.4415419267167138E-15 0 -291 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +276 0 -5.183432 1E-15 1.4415419267167138E-15 0 +277 0 -6.1762524 1E-15 1.4415419267167138E-15 0 +278 0 -5.690696 1E-15 1.4415419267167138E-15 0 +279 1 6.9710016 1 -0 1 +280 0 -4.697876 1E-15 1.4415419267167138E-15 0 +281 0 -4.872356 1E-15 1.4415419267167138E-15 0 +282 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1.4415419267167138E-15 0 +314 0 -6.255251 1E-15 1.4415419267167138E-15 0 315 0 ? ? ? 0 -316 1 3.6177397 0.9285714 0.10691524360481655 1 -317 1 9.215706 1 -0 1 -318 0 -5.1966968 1E-15 1.4415419267167138E-15 0 -319 0 2.6369457 0.8095238 2.3923175087700885 1 -320 1 7.3824844 1 -0 1 +316 1 3.4135065 0.85714287 0.22239240700456045 1 +317 1 9.002616 1 -0 1 +318 0 -5.4537 1E-15 1.4415419267167138E-15 0 +319 0 2.4164848 0.8 2.3219281808786905 1 +320 1 7.184394 1 -0 1 321 0 ? ? ? 0 -322 0 -4.520691 1E-15 1.4415419267167138E-15 0 -323 1 5.5612926 0.98 0.029146317580716615 1 -324 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -325 0 -4.1927576 1E-15 1.4415419267167138E-15 0 -326 1 4.4103804 0.9285714 0.10691524360481655 1 -327 0 -5.95583 1E-15 1.4415419267167138E-15 0 -328 1 3.373887 0.8333333 0.26303444023032446 1 -329 1 7.8321342 1 -0 1 -330 1 5.8562517 0.98 0.029146317580716615 1 -331 0 -3.2490892 1E-15 1.4415419267167138E-15 0 -332 0 -3.1363668 1E-15 1.4415419267167138E-15 0 -333 1 4.914962 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1E-15 1.4415419267167138E-15 0 +647 0 -6.0179486 1E-15 1.4415419267167138E-15 0 +648 1 11.790706 1 -0 1 +649 0 -5.183432 1E-15 1.4415419267167138E-15 0 +650 0 -3.8956623 1E-15 1.4415419267167138E-15 0 +651 0 -5.4201303 1E-15 1.4415419267167138E-15 0 +652 0 -3.9977875 1E-15 1.4415419267167138E-15 0 +653 0 -4.687022 1E-15 1.4415419267167138E-15 0 +654 0 -4.697876 1E-15 1.4415419267167138E-15 0 +655 0 -5.183432 1E-15 1.4415419267167138E-15 0 +656 0 -4.190612 1E-15 1.4415419267167138E-15 0 +657 0 -0.8079653 0.4 0.73696560849809378 0 +658 1 8.931214 1 -0 1 +659 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +660 0 -6.1762524 1E-15 1.4415419267167138E-15 0 +661 0 -4.2014656 1E-15 1.4415419267167138E-15 0 +662 0 -5.615157 1E-15 1.4415419267167138E-15 0 +663 0 -5.615157 1E-15 1.4415419267167138E-15 0 +664 0 -4.5395722 1E-15 1.4415419267167138E-15 0 +665 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +666 0 -3.645947 1E-15 1.4415419267167138E-15 0 +667 0 -4.697876 1E-15 1.4415419267167138E-15 0 +668 1 2.434762 0.8 0.32192807338953117 1 +669 1 7.828244 1 -0 1 +670 1 6.2092333 0.9814815 0.026967031375491075 1 +671 0 -4.2636833 1E-15 1.4415419267167138E-15 0 +672 0 -5.194286 1E-15 1.4415419267167138E-15 0 +673 0 -4.0920978 1E-15 1.4415419267167138E-15 0 +674 0 -6.1762524 1E-15 1.4415419267167138E-15 0 +675 0 -4.3309355 1E-15 1.4415419267167138E-15 0 +676 0 -5.8957047 1E-15 1.4415419267167138E-15 0 +677 0 -4.6761684 1E-15 1.4415419267167138E-15 0 +678 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +679 0 -6.1653986 1E-15 1.4415419267167138E-15 0 +680 1 16.591042 1 -0 1 +681 1 9.56899 1 -0 1 +682 0 -3.5122313 1E-15 1.4415419267167138E-15 0 +683 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +684 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +685 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +686 0 -6.6618085 1E-15 1.4415419267167138E-15 0 +687 0 -4.7977834 1E-15 1.4415419267167138E-15 0 +688 0 -5.172579 1E-15 1.4415419267167138E-15 0 +689 0 -4.4534364 1E-15 1.4415419267167138E-15 0 +690 0 -6.0179486 1E-15 1.4415419267167138E-15 0 +691 1 4.169842 0.9230769 0.11547721025399223 1 +692 0 -5.668988 1E-15 1.4415419267167138E-15 0 +693 0 -4.8679504 1E-15 1.4415419267167138E-15 0 +694 0 -5.132524 1E-15 1.4415419267167138E-15 0 +695 0 -6.1653986 1E-15 1.4415419267167138E-15 0 +696 1 6.262699 0.9814815 0.026967031375491075 1 +697 1 4.767309 0.9230769 0.11547721025399223 1 +698 1 5.8011494 0.9814815 0.026967031375491075 1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt index d12e4da2b0..d3a552f3aa 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt @@ -8,28 +8,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 234 | 5 | 0.9791 + positive || 233 | 6 | 0.9749 negative || 12 | 432 | 0.9730 ||====================== -Precision || 0.9512 | 0.9886 | -OVERALL 0/1 ACCURACY: 0.975110 -LOG LOSS/instance: 0.120617 +Precision || 0.9510 | 0.9863 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.120727 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.870860 +LOG-LOSS REDUCTION (RIG): 0.870742 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) -Accuracy: 0.975110 (0.0000) -Positive precision: 0.951220 (0.0000) -Positive recall: 0.979079 (0.0000) -Negative precision: 0.988558 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.951020 (0.0000) +Positive recall: 0.974895 (0.0000) +Negative precision: 0.986301 (0.0000) Negative recall: 0.972973 (0.0000) -Log-loss: 0.120617 (0.0000) -Log-loss reduction: 0.870860 (0.0000) -F1 Score: 0.964948 (0.0000) -AUPRC: 0.992065 (0.0000) +Log-loss: 0.120727 (0.0000) +Log-loss reduction: 0.870742 (0.0000) +F1 Score: 0.962810 (0.0000) +AUPRC: 0.992010 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt index 0860eb7030..b59497818f 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt @@ -1,4 +1,4 @@ AveragedPerceptron 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 -0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 0.120617 0.87086 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron numcali=200 dout=%Output% data=%Data% out=%Output% seed=1 +0.996146 0.973646 0.95102 0.974895 0.986301 0.972973 0.120727 0.870742 0.96281 0.99201 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron numcali=200 dout=%Output% data=%Data% out=%Output% seed=1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt index 55a840eda7..bd34063997 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.5726995 0.047724236 0.070548678886274141 0 -1 0 3.6101465 0.9209522 3.6611308948912158 1 -2 0 -4.070944 0.033202175 0.04871386747975371 0 -3 0 2.470542 0.83073896 2.5626781731328263 1 -4 0 -3.4358397 0.052670274 0.078061439656217479 0 -5 1 12.382595 0.99988943 0.0001595227277818751 1 -6 0 -1.4209604 0.20405398 0.32925750806385767 0 -7 0 -4.701088 0.020848805 0.03039644515807061 0 -8 0 -4.6745405 0.021263905 0.031008188656201943 0 -9 0 -4.406417 0.025935685 0.037911062322905087 0 -10 0 -5.559344 0.010982096 0.015931457121809788 0 -11 0 -5.4818344 0.011639432 0.016890641442649377 0 -12 1 -0.14206886 0.40347567 1.3094464075888503 0 -13 0 -4.5691886 0.022992346 0.033558231039772665 0 -14 1 9.321611 0.998874 0.0016253773809814112 1 -15 1 1.3856993 0.6830734 0.54988747944718552 1 -16 0 -4.533843 0.02360242 0.034459376167277234 0 -17 0 -4.046695 0.033797733 0.049602857843819056 0 -18 1 7.8903713 0.9966727 0.0048082975514731332 1 -19 0 -3.0987039 0.06699134 0.10003761775121778 0 -20 1 7.528511 0.9956263 0.0063237968594455725 1 -21 1 7.875206 0.9966343 0.0048638619113426269 1 -22 0 -5.0078387 0.016592305 0.02413845106369562 0 +0 0 -3.7050557 0.049417876 0.073116822887907204 0 +1 0 3.4661026 0.92318994 3.7025608769012859 1 +2 0 -4.24041 0.03346807 0.04911070335781155 0 +3 0 2.1493654 0.8156323 2.4393420399528236 1 +4 0 -3.5576057 0.054948628 0.081535339956679373 0 +5 1 12.155442 0.99988633 0.0001639947780942108 1 +6 0 -1.573504 0.20770586 0.33589195471326805 0 +7 0 -4.9137383 0.020348059 0.029658827501287101 0 +8 0 -4.903472 0.02050398 0.029888463292459169 0 +9 0 -4.57476 0.02616267 0.038247291919649574 0 +10 0 -5.7696943 0.010729991 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5.6754386418026423E-06 1 +681 1 9.56899 0.999191 0.0011676326392758554 1 +682 0 -3.5122313 0.056764804 0.084310542391200338 0 +683 0 -6.6618085 0.0054803924 0.0079282798352131763 0 +684 0 -6.6618085 0.0054803924 0.0079282798352131763 0 +685 0 -6.6618085 0.0054803924 0.0079282798352131763 0 +686 0 -6.6618085 0.0054803924 0.0079282798352131763 0 +687 0 -4.7977834 0.022178661 0.032357205372034376 0 +688 0 -5.172579 0.01677935 0.024412878962009357 0 +689 0 -4.4534364 0.028614214 0.041883718439598289 0 +690 0 -6.0179486 0.008903549 0.012902631287702191 0 +691 1 4.169842 0.9534993 0.068696189508194894 1 +692 0 -5.668988 0.0115725 0.016792944693470865 0 +693 0 -4.8679504 0.021052541 0.030696664046168932 0 +694 0 -5.132524 0.017288381 0.025159981694104945 0 +695 0 -6.1653986 0.007968302 0.011541875502083741 0 +696 1 6.262699 0.99013895 0.01429709952126406 1 +697 1 4.767309 0.9699441 0.044026461927471366 1 +698 1 5.8011494 0.9860594 0.020253498150637261 1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt index 5d98ed80f7..386483cdd6 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt @@ -8,11 +8,11 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 234 | 5 | 0.9791 + positive || 233 | 6 | 0.9749 negative || 12 | 432 | 0.9730 ||====================== -Precision || 0.9512 | 0.9886 | -OVERALL 0/1 ACCURACY: 0.975110 +Precision || 0.9510 | 0.9863 | +OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.000000 @@ -21,15 +21,15 @@ AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) -Accuracy: 0.975110 (0.0000) -Positive precision: 0.951220 (0.0000) -Positive recall: 0.979079 (0.0000) -Negative precision: 0.988558 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.951020 (0.0000) +Positive recall: 0.974895 (0.0000) +Negative precision: 0.986301 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: NaN (0.0000) Log-loss reduction: 0.000000 (0.0000) -F1 Score: 0.964948 (0.0000) -AUPRC: 0.992065 (0.0000) +F1 Score: 0.962810 (0.0000) +AUPRC: 0.992010 (0.0000) --------------------------------------- Warning: Data does not contain a probability column. Will not output the Log-loss column diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt index ac4bbeaf12..e3e77cb0a8 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt @@ -1,4 +1,4 @@ AveragedPerceptron 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 -0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 NaN 0 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali={} dout=%Output% data=%Data% out=%Output% seed=1 +0.996146 0.973646 0.95102 0.974895 0.986301 0.972973 NaN 0 0.96281 0.99201 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali={} dout=%Output% data=%Data% out=%Output% seed=1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt index 3bf1ca0969..750c3c6a9d 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt @@ -1,700 +1,700 @@ Instance Label Score Assigned -0 0 -3.5726995 0 -1 0 3.6101465 1 -2 0 -4.070944 0 -3 0 2.470542 1 -4 0 -3.4358397 0 -5 1 12.382595 1 -6 0 -1.4209604 0 -7 0 -4.701088 0 -8 0 -4.6745405 0 -9 0 -4.406417 0 -10 0 -5.559344 0 -11 0 -5.4818344 0 -12 1 -0.14206886 0 -13 0 -4.5691886 0 -14 1 9.321611 1 -15 1 1.3856993 1 -16 0 -4.533843 0 -17 0 -4.046695 0 -18 1 7.8903713 1 -19 0 -3.0987039 0 -20 1 7.528511 1 -21 1 7.875206 1 -22 0 -5.0078387 0 +0 0 -3.7050557 0 +1 0 3.4661026 1 +2 0 -4.24041 0 +3 0 2.1493654 1 +4 0 -3.5576057 0 +5 1 12.155442 1 +6 0 -1.573504 0 +7 0 -4.9137383 0 +8 0 -4.903472 0 +9 0 -4.57476 0 +10 0 -5.7696943 0 +11 0 -5.679842 0 +12 1 -0.2966156 0 +13 0 -4.7757645 0 +14 1 9.220881 1 +15 1 1.1802778 1 +16 0 -4.687022 0 +17 0 -4.2014656 0 +18 1 7.81973 1 +19 0 -3.2086458 0 +20 1 7.5404253 1 +21 1 7.730527 1 +22 0 -5.183432 0 23 1 ? 0 -24 0 -5.4686823 0 -25 1 1.741828 1 -26 0 -4.9710746 0 -27 0 -4.0598474 0 -28 0 -5.4818344 0 -29 0 -5.8557806 0 -30 0 -5.0985007 0 -31 0 -4.9946866 0 -32 1 7.46414 1 -33 0 -4.689259 0 -34 0 -4.71424 0 -35 0 -5.4818344 0 -36 1 9.099108 1 -37 0 -1.113348 0 -38 1 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0 -4.0598474 0 -620 0 -5.0078387 0 -621 0 0.3560543 1 -622 0 -2.2074018 0 -623 0 -6.442978 0 -624 0 -3.8450818 0 -625 0 -3.4678864 0 -626 1 5.760166 1 -627 0 -4.1699953 0 -628 0 -5.9689827 0 -629 0 -5.0209913 0 -630 0 -3.358376 0 -631 0 -4.0598474 0 -632 0 -6.442978 0 -633 1 4.3299494 1 -634 0 -5.4949865 0 -635 0 -4.6141906 0 -636 1 10.12752 1 -637 0 -2.4650183 0 -638 0 -5.0209913 0 -639 0 -3.93614 0 -640 0 -4.4101357 0 -641 0 -5.0078387 0 -642 0 -5.0078387 0 -643 0 -6.442978 0 -644 0 -5.9689827 0 -645 0 -5.0078387 0 -646 0 -6.021953 0 -647 0 -5.832123 0 -648 1 12.2362 1 -649 0 -5.0078387 0 -650 0 -3.7496572 0 -651 0 -5.2175484 0 -652 0 -3.8628197 0 -653 0 -4.533843 0 -654 0 -4.520691 0 -655 0 -5.0078387 0 -656 0 -4.0598474 0 -657 0 -0.4869156 0 -658 1 9.086258 1 -659 0 -6.442978 0 -660 0 -5.95583 0 -661 0 -4.046695 0 -662 0 -5.3686323 0 -663 0 -5.3686323 0 -664 0 -4.3969836 0 -665 0 -6.442978 0 -666 0 -3.496932 0 -667 0 -4.520691 0 -668 1 2.804366 1 -669 1 8.147337 1 -670 1 6.4856215 1 -671 0 -4.087837 0 -672 0 -4.9946866 0 -673 0 -3.9078827 0 -674 0 -5.95583 0 -675 0 -4.140195 0 -676 0 -5.6622314 0 -677 0 -4.5469956 0 -678 0 -6.442978 0 -679 0 -5.9689827 0 -680 1 16.78001 1 -681 1 9.801079 1 -682 0 -3.3756719 0 -683 0 -6.442978 0 -684 0 -6.442978 0 -685 0 -6.442978 0 -686 0 -6.442978 0 -687 0 -4.613783 0 -688 0 -5.0209913 0 -689 0 -4.177704 0 -690 0 -5.832123 0 -691 1 4.4967804 1 -692 0 -5.4949865 0 -693 0 -4.684228 0 -694 0 -4.9545784 0 -695 0 -5.9689827 0 -696 1 6.7127876 1 -697 1 5.064643 1 -698 1 6.1036224 1 +618 0 -4.687022 0 +619 0 -4.190612 0 +620 0 -5.183432 0 +621 0 0.21396732 1 +622 0 -2.2733846 0 +623 0 -6.6618085 0 +624 0 -3.9476805 0 +625 0 -3.656497 0 +626 1 5.46276 1 +627 0 -4.335535 0 +628 0 -6.1653986 0 +629 0 -5.172579 0 +630 0 -3.506401 0 +631 0 -4.190612 0 +632 0 -6.6618085 0 +633 1 4.123639 1 +634 0 -5.668988 0 +635 0 -4.827346 0 +636 1 9.884419 1 +637 0 -2.6317916 0 +638 0 -5.172579 0 +639 0 -4.0323086 0 +640 0 -4.528719 0 +641 0 -5.183432 0 +642 0 -5.183432 0 +643 0 -6.6618085 0 +644 0 -6.1653986 0 +645 0 -5.183432 0 +646 0 -6.268999 0 +647 0 -6.0179486 0 +648 1 11.790706 1 +649 0 -5.183432 0 +650 0 -3.8956623 0 +651 0 -5.4201303 0 +652 0 -3.9977875 0 +653 0 -4.687022 0 +654 0 -4.697876 0 +655 0 -5.183432 0 +656 0 -4.190612 0 +657 0 -0.8079653 0 +658 1 8.931214 1 +659 0 -6.6618085 0 +660 0 -6.1762524 0 +661 0 -4.2014656 0 +662 0 -5.615157 0 +663 0 -5.615157 0 +664 0 -4.5395722 0 +665 0 -6.6618085 0 +666 0 -3.645947 0 +667 0 -4.697876 0 +668 1 2.434762 1 +669 1 7.828244 1 +670 1 6.2092333 1 +671 0 -4.2636833 0 +672 0 -5.194286 0 +673 0 -4.0920978 0 +674 0 -6.1762524 0 +675 0 -4.3309355 0 +676 0 -5.8957047 0 +677 0 -4.6761684 0 +678 0 -6.6618085 0 +679 0 -6.1653986 0 +680 1 16.591042 1 +681 1 9.56899 1 +682 0 -3.5122313 0 +683 0 -6.6618085 0 +684 0 -6.6618085 0 +685 0 -6.6618085 0 +686 0 -6.6618085 0 +687 0 -4.7977834 0 +688 0 -5.172579 0 +689 0 -4.4534364 0 +690 0 -6.0179486 0 +691 1 4.169842 1 +692 0 -5.668988 0 +693 0 -4.8679504 0 +694 0 -5.132524 0 +695 0 -6.1653986 0 +696 1 6.262699 1 +697 1 4.767309 1 +698 1 5.8011494 1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt index 88cbb23bee..a7efac656f 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt @@ -1,11 +1,11 @@ Instance Label Score Probability Log-loss Assigned 5 1 11.925824 1 -0 1 6 0 -0.4527979 0.29372627 0.50170064939900283 0 -8 0 -3.796255 1E-15 1.4415419267167138E-15 0 +8 0 -3.7962546 1E-15 1.4415419267167138E-15 0 9 0 -3.8130417 1E-15 1.4415419267167138E-15 0 10 0 -4.7285223 1E-15 1.4415419267167138E-15 0 11 0 -4.606969 1E-15 1.4415419267167138E-15 0 -18 1 7.3475924 1 -0 1 +18 1 7.3475914 1 -0 1 20 1 6.1389017 1 -0 1 21 1 7.1486177 1 -0 1 25 1 1.6632223 0.88235295 0.18057223417631088 1 @@ -13,7 +13,7 @@ Instance Label Score Probability Log-loss Assigned 31 0 -4.2645645 1E-15 1.4415419267167138E-15 0 32 1 7.198575 1 -0 1 35 0 -4.606969 1E-15 1.4415419267167138E-15 0 -37 0 -1.714282 0.1 0.15200309583369792 0 +37 0 -1.7142825 0.1 0.15200309583369792 0 40 0 ? ? ? 0 41 1 2.5451746 0.88235295 0.18057223417631088 1 44 1 8.165841 1 -0 1 @@ -21,7 +21,7 @@ Instance Label Score Probability Log-loss Assigned 46 1 5.6216097 1 -0 1 48 0 -3.379683 1E-15 1.4415419267167138E-15 0 50 1 2.8003244 0.88235295 0.18057223417631088 1 -51 1 0.14775276 0.6666667 0.58496245772549416 1 +51 1 0.14775372 0.6666667 0.58496245772549416 1 52 1 4.696246 1 -0 1 54 1 6.743868 1 -0 1 56 1 6.5947094 1 -0 1 @@ -34,7 +34,7 @@ Instance Label Score Probability Log-loss Assigned 70 0 -3.0557137 1E-15 1.4415419267167138E-15 0 71 1 7.555621 1 -0 1 72 0 -1.6769085 0.1 0.15200309583369792 0 -73 1 7.7111273 1 -0 1 +73 1 7.7111263 1 -0 1 74 1 2.4994192 0.88235295 0.18057223417631088 1 76 0 -3.8396955 1E-15 1.4415419267167138E-15 0 77 0 -3.1438046 1E-15 1.4415419267167138E-15 0 @@ -53,12 +53,12 @@ Instance Label Score Probability Log-loss Assigned 100 1 5.1074314 1 -0 1 102 0 -3.3471546 1E-15 1.4415419267167138E-15 0 104 1 11.120679 1 -0 1 -105 1 2.1430416 0.88235295 0.18057223417631088 1 +105 1 2.1430407 0.88235295 0.18057223417631088 1 106 1 8.747506 1 -0 1 -108 0 -4.5133796 1E-15 1.4415419267167138E-15 0 +108 0 -4.513379 1E-15 1.4415419267167138E-15 0 109 1 6.3912544 1 -0 1 111 1 4.1208715 0.88235295 0.18057223417631088 1 -112 1 7.006652 1 -0 1 +112 1 7.006653 1 -0 1 113 1 9.855811 1 -0 1 115 0 -3.4127908 1E-15 1.4415419267167138E-15 0 117 1 8.638749 1 -0 1 @@ -71,7 +71,7 @@ Instance Label Score Probability Log-loss Assigned 129 0 -3.789802 1E-15 1.4415419267167138E-15 0 131 0 -4.2645645 1E-15 1.4415419267167138E-15 0 132 1 9.206267 1 -0 1 -133 0 -4.1348257 1E-15 1.4415419267167138E-15 0 +133 0 -4.134826 1E-15 1.4415419267167138E-15 0 137 0 -4.6658077 1E-15 1.4415419267167138E-15 0 138 0 -3.6895585 1E-15 1.4415419267167138E-15 0 141 0 -4.9493732 1E-15 1.4415419267167138E-15 0 @@ -88,27 +88,27 @@ Instance Label Score Probability Log-loss Assigned 167 1 7.490014 1 -0 1 169 0 -5.254455 1E-15 1.4415419267167138E-15 0 171 0 -4.54813 1E-15 1.4415419267167138E-15 0 -173 1 14.494651 1 -0 1 +173 1 14.494649 1 -0 1 174 1 5.7514915 1 -0 1 176 0 -4.2645645 1E-15 1.4415419267167138E-15 0 177 1 6.138485 1 -0 1 179 1 2.8749352 0.88235295 0.18057223417631088 1 180 0 -4.7285223 1E-15 1.4415419267167138E-15 0 181 0 -5.232939 1E-15 1.4415419267167138E-15 0 -183 1 9.06396 1 -0 1 -187 1 12.00492 1 -0 1 -188 1 8.1324835 1 -0 1 +183 1 9.063958 1 -0 1 +187 1 12.004918 1 -0 1 +188 1 8.132482 1 -0 1 189 0 -3.6207743 1E-15 1.4415419267167138E-15 0 -191 1 10.797473 1 -0 1 -192 0 -3.7562728 1E-15 1.4415419267167138E-15 0 +191 1 10.797475 1 -0 1 +192 0 -3.7562733 1E-15 1.4415419267167138E-15 0 196 0 5.8725023 1 Infinity 1 198 0 -5.232939 1E-15 1.4415419267167138E-15 0 199 0 -4.3234034 1E-15 1.4415419267167138E-15 0 -201 1 9.770606 1 -0 1 +201 1 9.770608 1 -0 1 202 0 -4.54813 1E-15 1.4415419267167138E-15 0 204 0 -4.54813 1E-15 1.4415419267167138E-15 0 -205 1 12.087651 1 -0 1 -206 1 6.641531 1 -0 1 +205 1 12.087653 1 -0 1 +206 1 6.641532 1 -0 1 207 0 -4.7285223 1E-15 1.4415419267167138E-15 0 209 0 -3.5942607 1E-15 1.4415419267167138E-15 0 210 1 13.547517 1 -0 1 @@ -116,26 +116,26 @@ Instance Label Score Probability Log-loss Assigned 212 0 -4.54813 1E-15 1.4415419267167138E-15 0 216 0 -4.8905344 1E-15 1.4415419267167138E-15 0 218 1 7.7499733 1 -0 1 -219 0 -2.4297438 1E-15 1.4415419267167138E-15 0 +219 0 -2.4297442 1E-15 1.4415419267167138E-15 0 223 1 5.4305964 1 -0 1 -226 1 9.166205 1 -0 1 +226 1 9.166207 1 -0 1 228 0 -4.7285223 1E-15 1.4415419267167138E-15 0 -233 1 5.6998806 1 -0 1 +233 1 5.6998796 1 -0 1 237 1 6.476473 1 -0 1 239 1 5.16975 1 -0 1 240 0 -1.9057708 0.1 0.15200309583369792 0 -241 0 -3.8436408 1E-15 1.4415419267167138E-15 0 +241 0 -3.8436403 1E-15 1.4415419267167138E-15 0 242 0 -4.2645645 1E-15 1.4415419267167138E-15 0 244 0 -4.54813 1E-15 1.4415419267167138E-15 0 -246 1 11.068809 1 -0 1 -247 1 3.1855068 0.88235295 0.18057223417631088 1 +246 1 11.068807 1 -0 1 +247 1 3.1855059 0.88235295 0.18057223417631088 1 248 0 -2.7545462 1E-15 1.4415419267167138E-15 0 249 0 ? ? ? 0 250 0 -4.655863 1E-15 1.4415419267167138E-15 0 252 0 4.2659445 0.96023005 4.6521775310747371 1 -254 1 7.919628 1 -0 1 +254 1 7.919627 1 -0 1 257 0 -4.3234034 1E-15 1.4415419267167138E-15 0 -258 0 -3.9809995 1E-15 1.4415419267167138E-15 0 +258 0 -3.9809992 1E-15 1.4415419267167138E-15 0 259 0 4.3407545 1 Infinity 1 260 1 8.330071 1 -0 1 262 1 9.629434 1 -0 1 @@ -149,60 +149,60 @@ Instance Label Score Probability Log-loss Assigned 277 0 -4.8905344 1E-15 1.4415419267167138E-15 0 278 0 -4.54813 1E-15 1.4415419267167138E-15 0 279 1 6.636299 1 -0 1 -280 0 -3.9809995 1E-15 1.4415419267167138E-15 0 +280 0 -3.9809992 1E-15 1.4415419267167138E-15 0 283 1 5.5930414 1 -0 1 -284 1 6.3683033 1 -0 1 +284 1 6.3683043 1 -0 1 285 1 13.342931 1 -0 1 -288 1 2.133194 0.88235295 0.18057223417631088 1 +288 1 2.133195 0.88235295 0.18057223417631088 1 290 0 -5.232939 1E-15 1.4415419267167138E-15 0 291 0 -4.54813 1E-15 1.4415419267167138E-15 0 293 1 4.659646 1 -0 1 296 0 2.0631151 0.88235295 3.0874629272416674 1 297 0 ? ? ? 0 299 1 6.110528 1 -0 1 -300 1 6.6979437 1 -0 1 +300 1 6.6979446 1 -0 1 301 0 -4.54813 1E-15 1.4415419267167138E-15 0 303 0 -4.54813 1E-15 1.4415419267167138E-15 0 304 1 4.865142 1 -0 1 -308 1 6.804653 1 -0 1 -309 0 -1.4760427 0.1 0.15200309583369792 0 +308 1 6.804652 1 -0 1 +309 0 -1.4760432 0.1 0.15200309583369792 0 311 0 -5.232939 1E-15 1.4415419267167138E-15 0 312 1 3.2489738 0.88235295 0.18057223417631088 1 -314 0 -5.070926 1E-15 1.4415419267167138E-15 0 +314 0 -5.0709267 1E-15 1.4415419267167138E-15 0 316 1 4.409379 1 -0 1 317 1 8.798545 1 -0 1 319 0 2.7989082 0.88235295 3.0874629272416674 1 321 0 ? ? ? 0 -323 1 4.8855352 1 -0 1 +323 1 4.8855343 1 -0 1 327 0 -4.8905344 1E-15 1.4415419267167138E-15 0 -328 1 4.1213074 0.88235295 0.18057223417631088 1 +328 1 4.1213064 0.88235295 0.18057223417631088 1 329 1 6.918497 1 -0 1 331 0 -3.1255894 1E-15 1.4415419267167138E-15 0 332 0 -2.8317442 1E-15 1.4415419267167138E-15 0 333 1 4.9879713 1 -0 1 336 1 5.3119774 1 -0 1 -338 0 -5.070926 1E-15 1.4415419267167138E-15 0 +338 0 -5.0709267 1E-15 1.4415419267167138E-15 0 343 0 -5.232939 1E-15 1.4415419267167138E-15 0 344 1 9.373975 1 -0 1 346 0 -2.8051786 1E-15 1.4415419267167138E-15 0 347 0 -5.0361757 1E-15 1.4415419267167138E-15 0 348 1 0.09843826 0.6666667 0.58496245772549416 1 -349 1 3.543579 0.88235295 0.18057223417631088 1 +349 1 3.5435781 0.88235295 0.18057223417631088 1 350 0 -3.7809262 1E-15 1.4415419267167138E-15 0 -352 0 1.515585 0.88235295 3.0874629272416674 1 +352 0 1.5155859 0.88235295 3.0874629272416674 1 353 1 9.391767 1 -0 1 354 0 -4.8905344 1E-15 1.4415419267167138E-15 0 355 0 -3.6708689 1E-15 1.4415419267167138E-15 0 358 1 6.2604895 1 -0 1 -360 1 15.035466 1 -0 1 +360 1 15.035464 1 -0 1 361 1 6.5571547 1 -0 1 366 1 13.467437 1 -0 1 368 0 -4.532379 1E-15 1.4415419267167138E-15 0 370 0 -3.0555735 1E-15 1.4415419267167138E-15 0 371 0 -4.532379 1E-15 1.4415419267167138E-15 0 -373 0 -3.5973973 1E-15 1.4415419267167138E-15 0 +373 0 -3.5973978 1E-15 1.4415419267167138E-15 0 376 0 -4.8905344 1E-15 1.4415419267167138E-15 0 -377 0 -5.070926 1E-15 1.4415419267167138E-15 0 +377 0 -5.0709267 1E-15 1.4415419267167138E-15 0 378 0 -3.477808 1E-15 1.4415419267167138E-15 0 379 0 -1.5486526 0.1 0.15200309583369792 0 381 1 8.643491 1 -0 1 @@ -211,15 +211,15 @@ Instance Label Score Probability Log-loss Assigned 387 0 -1.9204493 0.1 0.15200309583369792 0 388 0 -4.418391 1E-15 1.4415419267167138E-15 0 389 0 -2.9322662 1E-15 1.4415419267167138E-15 0 -391 1 9.323798 1 -0 1 +391 1 9.3238 1 -0 1 392 0 -4.3234034 1E-15 1.4415419267167138E-15 0 395 0 -4.3234034 1E-15 1.4415419267167138E-15 0 -396 0 -3.9809995 1E-15 1.4415419267167138E-15 0 -398 0 -3.9080298 1E-15 1.4415419267167138E-15 0 +396 0 -3.9809992 1E-15 1.4415419267167138E-15 0 +398 0 -3.9080296 1E-15 1.4415419267167138E-15 0 399 0 -4.305583 1E-15 1.4415419267167138E-15 0 404 0 -4.499814 1E-15 1.4415419267167138E-15 0 -406 0 -3.4627624 1E-15 1.4415419267167138E-15 0 -409 0 -3.973124 1E-15 1.4415419267167138E-15 0 +406 0 -3.4627619 1E-15 1.4415419267167138E-15 0 +409 0 -3.9731238 1E-15 1.4415419267167138E-15 0 413 0 -3.063589 1E-15 1.4415419267167138E-15 0 414 1 6.5001116 1 -0 1 415 0 -0.5379734 0.15984297 0.25126909064706171 0 @@ -230,7 +230,7 @@ Instance Label Score Probability Log-loss Assigned 423 0 -3.0557137 1E-15 1.4415419267167138E-15 0 428 0 -4.8905344 1E-15 1.4415419267167138E-15 0 429 0 -4.606969 1E-15 1.4415419267167138E-15 0 -430 0 -4.2483644 1E-15 1.4415419267167138E-15 0 +430 0 -4.248364 1E-15 1.4415419267167138E-15 0 434 0 5.678664 1 Infinity 1 436 1 4.8968487 1 -0 1 439 0 -4.098677 1E-15 1.4415419267167138E-15 0 @@ -238,7 +238,7 @@ Instance Label Score Probability Log-loss Assigned 441 0 -1.6798639 0.1 0.15200309583369792 0 442 0 -4.0051136 1E-15 1.4415419267167138E-15 0 449 1 10.087396 1 -0 1 -450 0 -3.9024792 1E-15 1.4415419267167138E-15 0 +450 0 -3.9024794 1E-15 1.4415419267167138E-15 0 451 0 -4.098677 1E-15 1.4415419267167138E-15 0 452 0 -4.348057 1E-15 1.4415419267167138E-15 0 453 1 8.068193 1 -0 1 @@ -247,7 +247,7 @@ Instance Label Score Probability Log-loss Assigned 456 1 8.942605 1 -0 1 457 1 8.346686 1 -0 1 464 0 -4.3822427 1E-15 1.4415419267167138E-15 0 -465 1 8.953591 1 -0 1 +465 1 8.953593 1 -0 1 466 1 8.690221 1 -0 1 467 1 7.4474792 1 -0 1 474 0 -4.098677 1E-15 1.4415419267167138E-15 0 @@ -255,8 +255,8 @@ Instance Label Score Probability Log-loss Assigned 482 1 14.295785 1 -0 1 483 1 10.273198 1 -0 1 484 0 -3.7483978 1E-15 1.4415419267167138E-15 0 -487 1 12.309564 1 -0 1 -489 1 -0.4385233 0.31616384 1.6612557298850197 0 +487 1 12.309562 1 -0 1 +489 1 -0.43852377 0.31616306 1.6612592656739571 0 492 0 -4.22023 1E-15 1.4415419267167138E-15 0 493 1 10.096398 1 -0 1 495 0 -4.5037956 1E-15 1.4415419267167138E-15 0 @@ -268,16 +268,16 @@ Instance Label Score Probability Log-loss Assigned 510 0 -5.232939 1E-15 1.4415419267167138E-15 0 513 0 -4.5037956 1E-15 1.4415419267167138E-15 0 514 1 9.508458 1 -0 1 -517 0 -5.070926 1E-15 1.4415419267167138E-15 0 +517 0 -5.0709267 1E-15 1.4415419267167138E-15 0 519 1 6.943595 1 -0 1 520 0 -5.0525465 1E-15 1.4415419267167138E-15 0 521 0 -4.2018504 1E-15 1.4415419267167138E-15 0 -522 1 4.3094044 0.9868354 0.019118596181580929 1 +522 1 4.3094034 0.9868348 0.019119467566511453 1 523 1 6.7598047 1 -0 1 527 0 -3.697434 1E-15 1.4415419267167138E-15 0 528 0 -2.955677 1E-15 1.4415419267167138E-15 0 529 0 -4.22023 1E-15 1.4415419267167138E-15 0 -531 0 -3.4627624 1E-15 1.4415419267167138E-15 0 +531 0 -3.4627619 1E-15 1.4415419267167138E-15 0 532 0 -4.7285223 1E-15 1.4415419267167138E-15 0 533 0 -4.3234034 1E-15 1.4415419267167138E-15 0 534 0 -4.606969 1E-15 1.4415419267167138E-15 0 @@ -287,26 +287,26 @@ Instance Label Score Probability Log-loss Assigned 540 0 -3.3012362 1E-15 1.4415419267167138E-15 0 541 0 -4.6658077 1E-15 1.4415419267167138E-15 0 544 0 -3.7475343 1E-15 1.4415419267167138E-15 0 -546 1 11.016192 1 -0 1 +546 1 11.016191 1 -0 1 547 0 -5.1297655 1E-15 1.4415419267167138E-15 0 548 0 -4.8462 1E-15 1.4415419267167138E-15 0 549 1 5.5535192 1 -0 1 557 0 -3.7809262 1E-15 1.4415419267167138E-15 0 558 0 -4.606969 1E-15 1.4415419267167138E-15 0 -559 0 -3.7562728 1E-15 1.4415419267167138E-15 0 +559 0 -3.7562733 1E-15 1.4415419267167138E-15 0 560 0 -3.413869 1E-15 1.4415419267167138E-15 0 561 0 -3.413869 1E-15 1.4415419267167138E-15 0 563 0 -4.3234034 1E-15 1.4415419267167138E-15 0 565 1 11.259841 1 -0 1 -566 0 -3.6307197 1E-15 1.4415419267167138E-15 0 +566 0 -3.6307201 1E-15 1.4415419267167138E-15 0 569 1 9.242663 1 -0 1 577 0 -4.8905344 1E-15 1.4415419267167138E-15 0 578 0 -4.8905344 1E-15 1.4415419267167138E-15 0 -581 1 8.654239 1 -0 1 +581 1 8.654241 1 -0 1 582 1 8.128011 1 -0 1 584 0 -3.123908 1E-15 1.4415419267167138E-15 0 586 1 12.971318 1 -0 1 -590 1 4.1888075 0.9130083 0.13130016254865498 1 +590 1 4.1888065 0.9130077 0.13130110439503334 1 593 0 -3.7483978 1E-15 1.4415419267167138E-15 0 594 1 5.7850237 1 -0 1 600 0 -4.3234034 1E-15 1.4415419267167138E-15 0 @@ -316,17 +316,17 @@ Instance Label Score Probability Log-loss Assigned 607 0 -5.232939 1E-15 1.4415419267167138E-15 0 609 0 -4.098677 1E-15 1.4415419267167138E-15 0 612 1 15.565835 1 -0 1 -613 0 -4.153376 1E-15 1.4415419267167138E-15 0 +613 0 -4.1533766 1E-15 1.4415419267167138E-15 0 614 0 -4.787361 1E-15 1.4415419267167138E-15 0 617 0 ? ? ? 0 618 0 -4.0398383 1E-15 1.4415419267167138E-15 0 -619 0 -3.7562728 1E-15 1.4415419267167138E-15 0 +619 0 -3.7562733 1E-15 1.4415419267167138E-15 0 621 0 0.0042419434 0.6666667 1.5849625867124844 1 622 0 -2.5211115 1E-15 1.4415419267167138E-15 0 624 0 -3.7777896 1E-15 1.4415419267167138E-15 0 627 0 -3.1292257 1E-15 1.4415419267167138E-15 0 629 0 -4.3822427 1E-15 1.4415419267167138E-15 0 -633 1 4.158328 0.89434934 0.16108963062321399 1 +633 1 4.158327 0.89434874 0.16109059211947238 1 634 0 -4.6658077 1E-15 1.4415419267167138E-15 0 638 0 -4.3822427 1E-15 1.4415419267167138E-15 0 639 0 -3.7809262 1E-15 1.4415419267167138E-15 0 @@ -337,9 +337,9 @@ Instance Label Score Probability Log-loss Assigned 649 0 -4.3234034 1E-15 1.4415419267167138E-15 0 652 0 -3.5676956 1E-15 1.4415419267167138E-15 0 653 0 -4.0398383 1E-15 1.4415419267167138E-15 0 -654 0 -3.9809995 1E-15 1.4415419267167138E-15 0 -656 0 -3.7562728 1E-15 1.4415419267167138E-15 0 -657 0 0.6928787 0.6666667 1.5849625867124844 1 +654 0 -3.9809992 1E-15 1.4415419267167138E-15 0 +656 0 -3.7562733 1E-15 1.4415419267167138E-15 0 +657 0 0.6928797 0.6666667 1.5849625867124844 1 660 0 -4.8905344 1E-15 1.4415419267167138E-15 0 661 0 -3.697434 1E-15 1.4415419267167138E-15 0 665 0 -5.232939 1E-15 1.4415419267167138E-15 0 @@ -358,19 +358,19 @@ Instance Label Score Probability Log-loss Assigned 692 0 -4.6658077 1E-15 1.4415419267167138E-15 0 693 0 -4.035124 1E-15 1.4415419267167138E-15 0 694 0 -4.030379 1E-15 1.4415419267167138E-15 0 -696 1 7.6071005 1 -0 1 +696 1 7.6070995 1 -0 1 697 1 5.0033855 1 -0 1 -698 1 6.1210938 1 -0 1 +698 1 6.1210947 1 -0 1 0 0 -3.4721127 1E-15 1.4415419267167138E-15 0 1 0 2.4163914 0.9047619 3.3923175087700881 1 2 0 -4.045404 1E-15 1.4415419267167138E-15 0 3 0 2.9251795 0.9047619 3.3923175087700881 1 4 0 -3.5088272 1E-15 1.4415419267167138E-15 0 7 0 -4.6705537 1E-15 1.4415419267167138E-15 0 -12 1 -0.34343147 0.51152515 0.96712290902641618 0 +12 1 -0.34343147 0.51152503 0.96712324524188775 0 13 0 -4.618695 1E-15 1.4415419267167138E-15 0 14 1 7.3602133 1 -0 1 -15 1 0.6494303 0.6 0.73696553683865695 1 +15 1 0.6494312 0.6 0.73696553683865695 1 16 0 -4.220706 1E-15 1.4415419267167138E-15 0 17 0 -3.955172 1E-15 1.4415419267167138E-15 0 19 0 -2.9890532 1E-15 1.4415419267167138E-15 0 @@ -383,31 +383,31 @@ Instance Label Score Probability Log-loss Assigned 30 0 -4.649441 1E-15 1.4415419267167138E-15 0 33 0 -4.6981187 1E-15 1.4415419267167138E-15 0 34 0 -4.453028 1E-15 1.4415419267167138E-15 0 -36 1 7.832773 1 -0 1 -38 1 4.928609 0.93333334 0.09953566740867692 1 +36 1 7.8327723 1 -0 1 +38 1 4.92861 0.93333334 0.09953566740867692 1 39 1 1.079258 0.6 0.73696553683865695 1 42 1 6.8985863 1 -0 1 -43 1 -0.49528694 0.5 1 0 +43 1 -0.49528742 0.5 1 0 47 0 -5.669884 1E-15 1.4415419267167138E-15 0 49 1 5.3024063 0.93333334 0.09953566740867692 1 53 1 5.116103 0.93333334 0.09953566740867692 1 55 1 4.4195347 0.93333334 0.09953566740867692 1 57 1 0.5701313 0.6 0.73696553683865695 1 -58 1 1.1371031 0.6 0.73696553683865695 1 -59 1 1.6442327 0.9047619 0.14438990028345636 1 -61 0 -5.277056 1E-15 1.4415419267167138E-15 0 +58 1 1.1371021 0.6 0.73696553683865695 1 +59 1 1.6442318 0.9047619 0.14438990028345636 1 +61 0 -5.2770567 1E-15 1.4415419267167138E-15 0 62 1 5.7670774 1 -0 1 65 1 2.7867746 0.9047619 0.14438990028345636 1 -67 1 2.9058533 0.9047619 0.14438990028345636 1 +67 1 2.9058523 0.9047619 0.14438990028345636 1 75 0 -4.2910423 1E-15 1.4415419267167138E-15 0 78 0 -3.652576 1E-15 1.4415419267167138E-15 0 80 0 -3.2955704 1E-15 1.4415419267167138E-15 0 81 0 -3.9975338 1E-15 1.4415419267167138E-15 0 -83 0 -2.9832683 1E-15 1.4415419267167138E-15 0 -84 1 6.5823994 1 -0 1 +83 0 -2.9832687 1E-15 1.4415419267167138E-15 0 +84 1 6.5824003 1 -0 1 85 1 4.7604074 0.93333334 0.09953566740867692 1 86 1 1.466999 0.9047619 0.14438990028345636 1 -87 1 5.223544 0.93333334 0.09953566740867692 1 +87 1 5.223545 0.93333334 0.09953566740867692 1 89 0 -5.017977 1E-15 1.4415419267167138E-15 0 94 0 -4.921291 1E-15 1.4415419267167138E-15 0 101 1 -0.85990334 0.4151543 1.2682804255962932 0 @@ -416,7 +416,7 @@ Instance Label Score Probability Log-loss Assigned 110 0 -3.1312823 1E-15 1.4415419267167138E-15 0 114 0 -2.8847933 1E-15 1.4415419267167138E-15 0 116 0 -0.40737772 0.5 1 0 -118 0 -5.258412 1E-15 1.4415419267167138E-15 0 +118 0 -5.2584124 1E-15 1.4415419267167138E-15 0 119 0 -3.7868814 1E-15 1.4415419267167138E-15 0 124 1 5.0384197 0.93333334 0.09953566740867692 1 126 1 6.4765244 1 -0 1 @@ -436,19 +436,19 @@ Instance Label Score Probability Log-loss Assigned 155 1 3.1619139 0.9047619 0.14438990028345636 1 157 0 -4.921291 1E-15 1.4415419267167138E-15 0 158 0 ? ? ? 0 -159 1 10.452137 1 -0 1 +159 1 10.452139 1 -0 1 160 1 7.997595 1 -0 1 162 0 -4.4382315 1E-15 1.4415419267167138E-15 0 -163 0 -3.6690454 1E-15 1.4415419267167138E-15 0 +163 0 -3.669045 1E-15 1.4415419267167138E-15 0 165 0 -3.3581352 1E-15 1.4415419267167138E-15 0 166 1 6.3355865 1 -0 1 168 0 -4.4382315 1E-15 1.4415419267167138E-15 0 170 0 -4.9692993 1E-15 1.4415419267167138E-15 0 172 0 -5.669884 1E-15 1.4415419267167138E-15 0 -175 1 6.0381765 1 -0 1 +175 1 6.0381775 1 -0 1 178 0 -3.955172 1E-15 1.4415419267167138E-15 0 182 0 -2.9890532 1E-15 1.4415419267167138E-15 0 -184 1 5.409272 1 -0 1 +184 1 5.409273 1 -0 1 185 0 -4.8669662 1E-15 1.4415419267167138E-15 0 186 1 3.9876003 0.93333334 0.09953566740867692 1 190 1 10.521242 1 -0 1 @@ -456,21 +456,21 @@ Instance Label Score Probability Log-loss Assigned 194 0 -4.4382315 1E-15 1.4415419267167138E-15 0 195 0 -3.955172 1E-15 1.4415419267167138E-15 0 197 0 -2.8025956 1E-15 1.4415419267167138E-15 0 -200 1 8.673523 1 -0 1 +200 1 8.673521 1 -0 1 203 0 -3.4721127 1E-15 1.4415419267167138E-15 0 208 0 -5.3500257 1E-15 1.4415419267167138E-15 0 213 1 12.300528 1 -0 1 214 1 11.895983 1 -0 1 -215 1 6.600219 1 -0 1 +215 1 6.6002197 1 -0 1 217 0 -5.4043503 1E-15 1.4415419267167138E-15 0 220 0 -5.181178 1E-15 1.4415419267167138E-15 0 221 1 7.9662914 1 -0 1 -222 1 -2.1487255 1E-15 49.828921418077073 0 +222 1 -2.148725 1E-15 49.828921418077073 0 224 1 8.4735565 1 -0 1 225 0 -5.669884 1E-15 1.4415419267167138E-15 0 227 1 6.748211 1 -0 1 229 1 10.504805 1 -0 1 -230 1 4.829337 0.93333334 0.09953566740867692 1 +230 1 4.829338 0.93333334 0.09953566740867692 1 231 1 6.912092 1 -0 1 232 0 1.0722923 0.6 1.3219281808786905 1 234 0 -2.7037287 1E-15 1.4415419267167138E-15 0 @@ -481,9 +481,9 @@ Instance Label Score Probability Log-loss Assigned 245 0 -2.8561125 1E-15 1.4415419267167138E-15 0 251 1 7.355525 1 -0 1 253 1 6.8985863 1 -0 1 -255 1 3.7452059 0.93333334 0.09953566740867692 1 +255 1 3.745205 0.93333334 0.09953566740867692 1 256 0 -4.9692993 1E-15 1.4415419267167138E-15 0 -261 1 9.009869 1 -0 1 +261 1 9.009871 1 -0 1 263 1 7.14557 1 -0 1 264 1 4.0145664 0.93333334 0.09953566740867692 1 265 0 -2.5156918 1E-15 1.4415419267167138E-15 0 @@ -498,13 +498,13 @@ Instance Label Score Probability Log-loss Assigned 289 1 6.6595707 1 -0 1 292 1 ? ? ? 0 294 0 ? ? ? 0 -295 1 5.621521 1 -0 1 +295 1 5.621522 1 -0 1 298 0 -2.4584541 1E-15 1.4415419267167138E-15 0 302 1 12.725582 1 -0 1 -305 1 8.040863 1 -0 1 +305 1 8.040865 1 -0 1 306 0 -5.4043503 1E-15 1.4415419267167138E-15 0 307 0 -5.4043503 1E-15 1.4415419267167138E-15 0 -310 0 -5.2411494 1E-15 1.4415419267167138E-15 0 +310 0 -5.24115 1E-15 1.4415419267167138E-15 0 313 0 -5.935418 1E-15 1.4415419267167138E-15 0 315 0 ? ? ? 0 318 0 -5.567325 1E-15 1.4415419267167138E-15 0 @@ -525,7 +525,7 @@ Instance Label Score Probability Log-loss Assigned 351 0 -4.921291 1E-15 1.4415419267167138E-15 0 356 1 -0.9127703 0.2567953 1.9613093694259927 0 357 1 9.874601 1 -0 1 -359 1 4.653247 0.93333334 0.09953566740867692 1 +359 1 4.653248 0.93333334 0.09953566740867692 1 362 0 -3.5174994 1E-15 1.4415419267167138E-15 0 363 0 -1.6840982 1E-15 1.4415419267167138E-15 0 364 0 -4.921291 1E-15 1.4415419267167138E-15 0 @@ -537,13 +537,13 @@ Instance Label Score Probability Log-loss Assigned 375 0 -5.935418 1E-15 1.4415419267167138E-15 0 380 0 -5.935418 1E-15 1.4415419267167138E-15 0 382 0 -3.5015903 1E-15 1.4415419267167138E-15 0 -385 0 -3.4273863 1E-15 1.4415419267167138E-15 0 +385 0 -3.4273868 1E-15 1.4415419267167138E-15 0 386 1 5.084236 0.93333334 0.09953566740867692 1 390 0 -5.4467115 1E-15 1.4415419267167138E-15 0 393 0 -5.8810935 1E-15 1.4415419267167138E-15 0 394 0 -4.909887 1E-15 1.4415419267167138E-15 0 397 0 -4.48624 1E-15 1.4415419267167138E-15 0 -400 1 7.3133698 1 -0 1 +400 1 7.313369 1 -0 1 401 0 -4.9692993 1E-15 1.4415419267167138E-15 0 402 0 -2.7191267 1E-15 1.4415419267167138E-15 0 403 0 -3.7908158 1E-15 1.4415419267167138E-15 0 @@ -559,7 +559,7 @@ Instance Label Score Probability Log-loss Assigned 424 0 -4.9692993 1E-15 1.4415419267167138E-15 0 425 1 11.849485 1 -0 1 426 0 -2.2324486 1E-15 1.4415419267167138E-15 0 -427 1 4.1596613 0.93333334 0.09953566740867692 1 +427 1 4.1596622 0.93333334 0.09953566740867692 1 431 0 -2.9302087 1E-15 1.4415419267167138E-15 0 432 0 -3.5334377 1E-15 1.4415419267167138E-15 0 433 0 -4.013695 1E-15 1.4415419267167138E-15 0 @@ -572,9 +572,9 @@ Instance Label Score Probability Log-loss Assigned 446 0 -5.935418 1E-15 1.4415419267167138E-15 0 447 0 -4.0031805 1E-15 1.4415419267167138E-15 0 448 0 -5.8810935 1E-15 1.4415419267167138E-15 0 -458 0 -3.7524428 1E-15 1.4415419267167138E-15 0 +458 0 -3.7524433 1E-15 1.4415419267167138E-15 0 459 0 -3.5017056 1E-15 1.4415419267167138E-15 0 -460 0 -3.5568361 1E-15 1.4415419267167138E-15 0 +460 0 -3.5568357 1E-15 1.4415419267167138E-15 0 461 0 -3.3979883 1E-15 1.4415419267167138E-15 0 462 0 -3.0737762 1E-15 1.4415419267167138E-15 0 463 0 -4.282031 1E-15 1.4415419267167138E-15 0 @@ -594,8 +594,8 @@ Instance Label Score Probability Log-loss Assigned 486 0 -4.649441 1E-15 1.4415419267167138E-15 0 488 1 0.95910263 0.6 0.73696553683865695 1 490 0 -5.935418 1E-15 1.4415419267167138E-15 0 -491 1 5.556222 1 -0 1 -494 0 -0.01569748 0.5942614 1.3013775627342616 0 +491 1 5.556223 1 -0 1 +494 0 -0.015696526 0.59426165 1.3013784104855217 0 496 0 -5.8810935 1E-15 1.4415419267167138E-15 0 498 0 -4.220706 1E-15 1.4415419267167138E-15 0 499 0 -4.220706 1E-15 1.4415419267167138E-15 0 @@ -609,7 +609,7 @@ Instance Label Score Probability Log-loss Assigned 512 0 -4.0031805 1E-15 1.4415419267167138E-15 0 515 1 7.3749876 1 -0 1 516 0 -5.8810935 1E-15 1.4415419267167138E-15 0 -518 0 -4.49539 1E-15 1.4415419267167138E-15 0 +518 0 -4.4953895 1E-15 1.4415419267167138E-15 0 524 0 -4.7037654 1E-15 1.4415419267167138E-15 0 525 0 -4.746127 1E-15 1.4415419267167138E-15 0 526 0 -4.48624 1E-15 1.4415419267167138E-15 0 @@ -629,12 +629,12 @@ Instance Label Score Probability Log-loss Assigned 562 0 -5.4043503 1E-15 1.4415419267167138E-15 0 564 0 -3.7509632 1E-15 1.4415419267167138E-15 0 567 0 -3.4350505 1E-15 1.4415419267167138E-15 0 -568 1 3.5748358 0.9047619 0.14438990028345636 1 +568 1 3.5748348 0.9047619 0.14438990028345636 1 570 1 6.466877 1 -0 1 571 1 9.048693 1 -0 1 572 0 -4.7037654 1E-15 1.4415419267167138E-15 0 573 0 -5.669884 1E-15 1.4415419267167138E-15 0 -574 1 5.533701 1 -0 1 +574 1 5.533702 1 -0 1 575 0 -3.2213755 1E-15 1.4415419267167138E-15 0 576 0 -3.7376466 1E-15 1.4415419267167138E-15 0 579 0 -5.4043503 1E-15 1.4415419267167138E-15 0 @@ -642,7 +642,7 @@ Instance Label Score Probability Log-loss Assigned 583 0 -4.9692993 1E-15 1.4415419267167138E-15 0 585 0 -5.935418 1E-15 1.4415419267167138E-15 0 587 0 -3.5334377 1E-15 1.4415419267167138E-15 0 -588 1 4.6442146 0.93333334 0.09953566740867692 1 +588 1 4.6442137 0.93333334 0.09953566740867692 1 589 0 -4.0031805 1E-15 1.4415419267167138E-15 0 591 1 4.243067 0.93333334 0.09953566740867692 1 592 1 4.8517904 0.93333334 0.09953566740867692 1 @@ -652,10 +652,10 @@ Instance Label Score Probability Log-loss Assigned 598 0 -4.7037654 1E-15 1.4415419267167138E-15 0 599 0 -2.9381208 1E-15 1.4415419267167138E-15 0 601 0 -5.6155596 1E-15 1.4415419267167138E-15 0 -603 1 3.1762505 0.9047619 0.14438990028345636 1 +603 1 3.1762495 0.9047619 0.14438990028345636 1 605 1 8.159748 1 -0 1 608 1 8.079367 1 -0 1 -610 1 6.972576 1 -0 1 +610 1 6.972575 1 -0 1 611 1 5.494137 1 -0 1 615 0 -3.7192311 1E-15 1.4415419267167138E-15 0 616 0 -4.7037654 1E-15 1.4415419267167138E-15 0 @@ -676,16 +676,16 @@ Instance Label Score Probability Log-loss Assigned 647 0 -5.4890733 1E-15 1.4415419267167138E-15 0 648 1 8.579456 1 -0 1 650 0 -3.6219683 1E-15 1.4415419267167138E-15 0 -651 0 -4.9650173 1E-15 1.4415419267167138E-15 0 +651 0 -4.965017 1E-15 1.4415419267167138E-15 0 655 0 -4.7037654 1E-15 1.4415419267167138E-15 0 -658 1 7.546404 1 -0 1 +658 1 7.546403 1 -0 1 659 0 -5.935418 1E-15 1.4415419267167138E-15 0 662 0 -5.1684093 1E-15 1.4415419267167138E-15 0 663 0 -5.1684093 1E-15 1.4415419267167138E-15 0 664 0 -4.2574205 1E-15 1.4415419267167138E-15 0 666 0 -3.0702114 1E-15 1.4415419267167138E-15 0 667 0 -4.4382315 1E-15 1.4415419267167138E-15 0 -669 1 6.9817867 1 -0 1 +669 1 6.9817877 1 -0 1 671 0 -3.9565368 1E-15 1.4415419267167138E-15 0 672 0 -4.921291 1E-15 1.4415419267167138E-15 0 673 0 -3.289723 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt index d1416a6dd9..c5865349b0 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned 5 1 11.925824 0.99988866 0.00016064073906035849 1 6 0 -0.4527979 0.28799012 0.49003084056125074 0 -8 0 -3.796255 0.026382547 0.038573064761285226 0 -9 0 -3.8130417 0.026036168 0.038059895132779242 0 -10 0 -4.7285223 0.0125917215 0.018281354087045291 0 -11 0 -4.606969 0.013873976 0.02015606392328537 0 -18 1 7.3475924 0.99550986 0.006492485535140746 1 +8 0 -3.7962546 0.02638256 0.03857308408160709 0 +9 0 -3.8130417 0.02603617 0.038059897891843629 0 +10 0 -4.7285223 0.012591722 0.018281355447793919 0 +11 0 -4.606969 0.013873977 0.020156065285803371 0 +18 1 7.3475914 0.99550986 0.006492485535140746 1 20 1 6.1389017 0.9881576 0.017186984398807038 1 21 1 7.1486177 0.9947303 0.0076226814052998944 1 25 1 1.6632223 0.6911742 0.53287870995530984 1 -28 0 -4.606969 0.013873976 0.02015606392328537 0 -31 0 -4.2645645 0.01821836 0.026525908568759592 0 +28 0 -4.606969 0.013873977 0.020156065285803371 0 +31 0 -4.2645645 0.018218363 0.026525911305853887 0 32 1 7.198575 0.99493784 0.0073217047835288053 1 -35 0 -4.606969 0.013873976 0.02015606392328537 0 -37 0 -1.714282 0.12729812 0.19643918434677948 0 +35 0 -4.606969 0.013873977 0.020156065285803371 0 +37 0 -1.7142825 0.12729807 0.19643911044583612 0 40 0 ? ? ? 0 -41 1 2.5451746 0.8203416 0.28570332741079429 1 +41 1 2.5451746 0.82034165 0.28570322258699443 1 44 1 8.165841 0.9976778 0.0033541180091675089 1 -45 0 -4.602255 0.0139262155 0.020232492307431734 0 +45 0 -4.602255 0.013926216 0.020232493670021917 0 46 1 5.6216097 0.9821182 0.026031444169057363 1 -48 0 -3.379683 0.03656119 0.053735055635659848 0 +48 0 -3.379683 0.036561195 0.053735061214071053 0 50 1 2.8003244 0.8487653 0.2365623958486662 1 -51 1 0.14775276 0.39660606 1.3342213809867165 1 +51 1 0.14775372 0.39660627 1.3342206221239763 1 52 1 4.696246 0.9629518 0.054464539198696107 1 54 1 6.743868 0.9927051 0.01056288089528439 1 56 1 6.5947094 0.9917779 0.011911021598241851 1 60 1 2.2064123 0.7763946 0.36513800059720591 1 63 1 0.8789625 0.5427823 0.88155440156950038 1 -64 0 -4.8905344 0.011062949 0.016049403073119514 0 -66 0 -3.697434 0.028514326 0.041735373036084926 0 +64 0 -4.8905344 0.01106295 0.016049404431764596 0 +66 0 -3.697434 0.028514327 0.041735375802187391 0 68 1 9.899808 0.99942744 0.00082626923994757486 1 69 0 -4.3595524 0.016894378 0.024581670707795066 0 -70 0 -3.0557137 0.046994247 0.069443170930934078 0 +70 0 -3.0557137 0.04699425 0.069443176570415086 0 71 1 7.555621 0.9962023 0.0054893674226172791 1 -72 0 -1.6769085 0.13069288 0.20206214111088219 0 -73 1 7.7111273 0.9966495 0.0048418602396339801 1 +72 0 -1.6769085 0.1306929 0.20206216584072867 0 +73 1 7.7111263 0.99664944 0.0048419465200441776 1 74 1 2.4994192 0.8148248 0.29543816942188406 1 -76 0 -3.8396955 0.02549526 0.037258890759872848 0 -77 0 -3.1438046 0.043905534 0.064774925308085776 0 -79 0 -4.4265766 0.016017534 0.023295486850883066 0 -82 0 -3.1870723 0.042460293 0.062595780681733368 0 -88 0 -3.697434 0.028514326 0.041735373036084926 0 -90 0 -4.54813 0.01454009 0.021130913771998817 0 -91 0 -4.5069323 0.015025148 0.021841204430750202 0 -92 0 -3.697434 0.028514326 0.041735373036084926 0 -93 0 -4.8905344 0.011062949 0.016049403073119514 0 -95 0 -4.54813 0.01454009 0.021130913771998817 0 -96 0 -4.790498 0.011983715 0.017393273625651583 0 -97 0 -3.413869 0.0356 0.052296442645105176 0 +76 0 -3.8396955 0.025495263 0.037258896274938731 0 +77 0 -3.1438046 0.043905538 0.064774930929348148 0 +79 0 -4.4265766 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0 +108 0 -4.513379 0.014948206 0.021728511485699098 0 109 1 6.3912544 0.99032205 0.014030327944048709 1 111 1 4.1208715 0.9422745 0.085780676489496735 1 -112 1 7.006652 0.9940932 0.0085470080951718218 1 +112 1 7.006653 0.9940932 0.0085470080951718218 1 113 1 9.855811 0.9994067 0.00085621167564539895 1 115 0 -3.4127908 0.03562994 0.05234123234660417 0 117 1 8.638749 0.99841446 0.0022892705964363013 1 -120 0 -4.0389752 0.021784049 0.031775104426885928 0 -121 0 -3.0156007 0.048468217 0.071676249166093706 0 +120 0 -4.0389752 0.02178405 0.031775107173957186 0 +121 0 -3.0156007 0.04846822 0.071676254814310558 0 122 1 10.533509 0.9996569 0.00049505099616089013 1 123 1 4.6804914 0.9624947 0.055149541356901512 1 -125 0 -4.8905344 0.011062949 0.016049403073119514 0 +125 0 -4.8905344 0.01106295 0.016049404431764596 0 128 1 4.9510326 0.9696393 0.044479919754725615 1 -129 0 -3.789802 0.02651689 0.038772146811620695 0 -131 0 -4.2645645 0.01821836 0.026525908568759592 0 +129 0 -3.789802 0.026516892 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b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt index b45c3a1d1d..ae9abc03fe 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt @@ -1,20 +1,20 @@ Instance Label Score Probability Log-loss Assigned 0 0 -3.5726995 1E-15 1.4415419267167138E-15 0 -1 0 3.6101446 0.8333333 2.5849623287385155 1 -2 0 -4.0709443 1E-15 1.4415419267167138E-15 0 +1 0 3.6101456 0.8333333 2.5849623287385155 1 +2 0 -4.070944 1E-15 1.4415419267167138E-15 0 3 0 2.470542 0.8095238 2.3923175087700885 1 4 0 -3.4358397 1E-15 1.4415419267167138E-15 0 -5 1 12.382593 1 -0 1 +5 1 12.382595 1 -0 1 6 0 -1.4209604 0.071428575 0.10691520887754996 0 7 0 -4.7010875 1E-15 1.4415419267167138E-15 0 8 0 -4.6745405 1E-15 1.4415419267167138E-15 0 9 0 -4.406417 1E-15 1.4415419267167138E-15 0 10 0 -5.5593443 1E-15 1.4415419267167138E-15 0 11 0 -5.4818344 1E-15 1.4415419267167138E-15 0 -12 1 -0.14206886 0.6363636 0.65207672114864346 0 -13 0 -4.5691886 1E-15 1.4415419267167138E-15 0 +12 1 -0.14206791 0.6363636 0.65207672114864346 0 +13 0 -4.569188 1E-15 1.4415419267167138E-15 0 14 1 9.321613 1 -0 1 -15 1 1.3856993 0.8095238 0.30485456129516797 1 +15 1 1.3856983 0.8095238 0.30485456129516797 1 16 0 -4.533843 1E-15 1.4415419267167138E-15 0 17 0 -4.046695 1E-15 1.4415419267167138E-15 0 18 1 7.8903713 1 -0 1 @@ -25,14 +25,14 @@ Instance Label Score Probability Log-loss Assigned 23 1 ? ? ? 0 24 0 -5.4686823 1E-15 1.4415419267167138E-15 0 25 1 1.741828 0.8095238 0.30485456129516797 1 -26 0 -4.9710746 1E-15 1.4415419267167138E-15 0 -27 0 -4.059848 1E-15 1.4415419267167138E-15 0 +26 0 -4.971074 1E-15 1.4415419267167138E-15 0 +27 0 -4.0598474 1E-15 1.4415419267167138E-15 0 28 0 -5.4818344 1E-15 1.4415419267167138E-15 0 29 0 -5.8557806 1E-15 1.4415419267167138E-15 0 -30 0 -5.0985007 1E-15 1.4415419267167138E-15 0 +30 0 -5.0985003 1E-15 1.4415419267167138E-15 0 31 0 -4.9946866 1E-15 1.4415419267167138E-15 0 32 1 7.46414 1 -0 1 -33 0 -4.6892586 1E-15 1.4415419267167138E-15 0 +33 0 -4.689259 1E-15 1.4415419267167138E-15 0 34 0 -4.71424 1E-15 1.4415419267167138E-15 0 35 0 -5.4818344 1E-15 1.4415419267167138E-15 0 36 1 9.09911 1 -0 1 @@ -43,32 +43,32 @@ Instance Label Score Probability Log-loss Assigned 41 1 3.3300762 0.8333333 0.26303444023032446 1 42 1 8.577511 1 -0 1 43 1 0.49126053 0.6363636 0.65207672114864346 1 -44 1 8.255751 1 -0 1 +44 1 8.255749 1 -0 1 45 0 -5.63222 1E-15 1.4415419267167138E-15 0 -46 1 4.5673847 0.9285714 0.10691524360481655 1 +46 1 4.5673857 0.9285714 0.10691524360481655 1 47 0 -5.95583 1E-15 1.4415419267167138E-15 0 48 0 -3.4358397 1E-15 1.4415419267167138E-15 0 -49 1 5.3666534 0.98 0.029146317580716615 1 +49 1 5.3666544 0.98 0.029146317580716615 1 50 1 2.5949688 0.8095238 0.30485456129516797 1 51 1 0.12595844 0.6363636 0.65207672114864346 1 52 1 5.2992115 0.98 0.029146317580716615 1 53 1 8.407227 1 -0 1 -54 1 7.649309 1 -0 1 +54 1 7.6493073 1 -0 1 55 1 4.478711 0.9285714 0.10691524360481655 1 56 1 5.5541334 0.98 0.029146317580716615 1 57 1 1.6657066 0.8095238 0.30485456129516797 1 -58 1 2.5265894 0.8095238 0.30485456129516797 1 -59 1 1.7368536 0.8095238 0.30485456129516797 1 +58 1 2.5265903 0.8095238 0.30485456129516797 1 +59 1 1.7368546 0.8095238 0.30485456129516797 1 60 1 2.3288136 0.8095238 0.30485456129516797 1 61 0 -5.5060835 1E-15 1.4415419267167138E-15 0 62 1 6.380089 0.98 0.029146317580716615 1 63 1 0.33490086 0.6363636 0.65207672114864346 1 64 0 -5.95583 1E-15 1.4415419267167138E-15 0 -65 1 3.8072634 0.9285714 0.10691524360481655 1 +65 1 3.8072643 0.9285714 0.10691524360481655 1 66 0 -4.046695 1E-15 1.4415419267167138E-15 0 67 1 4.218014 0.9285714 0.10691524360481655 1 68 1 10.826725 1 -0 1 -69 0 -5.271654 1E-15 1.4415419267167138E-15 0 +69 0 -5.2716546 1E-15 1.4415419267167138E-15 0 70 0 -3.4726496 1E-15 1.4415419267167138E-15 0 71 1 7.895046 1 -0 1 72 0 -2.1755848 0.071428575 0.10691520887754996 0 @@ -78,7 +78,7 @@ Instance Label Score Probability Log-loss Assigned 76 0 -5.0750337 1E-15 1.4415419267167138E-15 0 77 0 -3.499567 1E-15 1.4415419267167138E-15 0 78 0 -3.6211972 1E-15 1.4415419267167138E-15 0 -79 0 -5.391173 1E-15 1.4415419267167138E-15 0 +79 0 -5.3911724 1E-15 1.4415419267167138E-15 0 80 0 -2.7157316 1E-15 1.4415419267167138E-15 0 81 0 -4.2284155 1E-15 1.4415419267167138E-15 0 82 0 -3.4452734 1E-15 1.4415419267167138E-15 0 @@ -86,7 +86,7 @@ Instance Label Score Probability Log-loss Assigned 84 1 9.694054 1 -0 1 85 1 6.2895603 0.98 0.029146317580716615 1 86 1 2.6168842 0.8095238 0.30485456129516797 1 -87 1 6.919142 1 -0 1 +87 1 6.91914 1 -0 1 88 0 -4.046695 1E-15 1.4415419267167138E-15 0 89 0 -5.085745 1E-15 1.4415419267167138E-15 0 90 0 -5.4686823 1E-15 1.4415419267167138E-15 0 @@ -101,19 +101,19 @@ Instance Label Score Probability Log-loss Assigned 99 1 10.917194 1 -0 1 100 1 4.8476696 0.9285714 0.10691524360481655 1 101 1 -0.84280396 0.5 1 0 -102 0 -3.7530966 1E-15 1.4415419267167138E-15 0 -103 1 1.7746439 0.8095238 0.30485456129516797 1 +102 0 -3.753096 1E-15 1.4415419267167138E-15 0 +103 1 1.7746449 0.8095238 0.30485456129516797 1 104 1 12.140858 1 -0 1 -105 1 2.5560713 0.8095238 0.30485456129516797 1 +105 1 2.5560703 0.8095238 0.30485456129516797 1 106 1 9.259369 1 -0 1 107 1 6.720646 0.98 0.029146317580716615 1 108 0 -5.5617743 1E-15 1.4415419267167138E-15 0 109 1 6.871727 1 -0 1 110 0 -2.766693 1E-15 1.4415419267167138E-15 0 -111 1 3.848031 0.9285714 0.10691524360481655 1 +111 1 3.848032 0.9285714 0.10691524360481655 1 112 1 9.425768 1 -0 1 -113 1 9.506624 1 -0 1 -114 0 -3.0727458 1E-15 1.4415419267167138E-15 0 +113 1 9.506622 1 -0 1 +114 0 -3.0727453 1E-15 1.4415419267167138E-15 0 115 0 -4.6439905 1E-15 1.4415419267167138E-15 0 116 0 -0.66188717 0.5 1 0 117 1 9.617275 1 -0 1 @@ -125,7 +125,7 @@ Instance Label Score Probability Log-loss Assigned 123 1 3.8165932 0.9285714 0.10691524360481655 1 124 1 7.6522446 1 -0 1 125 0 -5.95583 1E-15 1.4415419267167138E-15 0 -126 1 8.564951 1 -0 1 +126 1 8.564953 1 -0 1 127 0 -4.520691 1E-15 1.4415419267167138E-15 0 128 1 4.84898 0.9285714 0.10691524360481655 1 129 0 -5.717684 1E-15 1.4415419267167138E-15 0 @@ -141,16 +141,16 @@ Instance Label Score Probability Log-loss Assigned 139 0 ? ? ? 0 140 0 -5.494987 1E-15 1.4415419267167138E-15 0 141 0 -5.9689827 1E-15 1.4415419267167138E-15 0 -142 1 4.4324036 0.9285714 0.10691524360481655 1 +142 1 4.4324026 0.9285714 0.10691524360481655 1 143 0 -4.6439905 1E-15 1.4415419267167138E-15 0 144 0 -5.4818344 1E-15 1.4415419267167138E-15 0 145 0 ? ? ? 0 146 1 1.3394346 0.8095238 0.30485456129516797 1 147 0 -5.4154215 1E-15 1.4415419267167138E-15 0 -148 0 -1.0123739 0.071428575 0.10691520887754996 0 +148 0 -1.012373 0.071428575 0.10691520887754996 0 149 1 11.461615 1 -0 1 150 0 -5.5593443 1E-15 1.4415419267167138E-15 0 -151 1 5.006485 0.9285714 0.10691524360481655 1 +151 1 5.006484 0.9285714 0.10691524360481655 1 152 1 9.715748 1 -0 1 153 0 -4.121497 1E-15 1.4415419267167138E-15 0 154 0 -6.4429784 1E-15 1.4415419267167138E-15 0 @@ -184,14 +184,14 @@ Instance Label Score Probability Log-loss Assigned 182 0 -3.0987039 1E-15 1.4415419267167138E-15 0 183 1 9.1599655 1 -0 1 184 1 6.2014637 0.98 0.029146317580716615 1 -185 0 -5.0853486 1E-15 1.4415419267167138E-15 0 +185 0 -5.085348 1E-15 1.4415419267167138E-15 0 186 1 5.7654095 0.98 0.029146317580716615 1 187 1 13.977451 1 -0 1 188 1 9.065283 1 -0 1 189 0 -4.7540584 1E-15 1.4415419267167138E-15 0 -190 1 11.957216 1 -0 1 -191 1 10.956871 1 -0 1 -192 0 -4.059848 1E-15 1.4415419267167138E-15 0 +190 1 11.957218 1 -0 1 +191 1 10.956873 1 -0 1 +192 0 -4.0598474 1E-15 1.4415419267167138E-15 0 193 0 -5.4686823 1E-15 1.4415419267167138E-15 0 194 0 -4.520691 1E-15 1.4415419267167138E-15 0 195 0 -4.046695 1E-15 1.4415419267167138E-15 0 @@ -204,7 +204,7 @@ Instance Label Score Probability Log-loss Assigned 202 0 -5.4686823 1E-15 1.4415419267167138E-15 0 203 0 -3.5726995 1E-15 1.4415419267167138E-15 0 204 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -205 1 12.086603 1 -0 1 +205 1 12.086601 1 -0 1 206 1 5.94417 0.98 0.029146317580716615 1 207 0 -5.5593443 1E-15 1.4415419267167138E-15 0 208 0 -5.5593443 1E-15 1.4415419267167138E-15 0 @@ -212,7 +212,7 @@ Instance Label Score Probability Log-loss Assigned 210 1 14.534115 1 -0 1 211 1 9.64962 1 -0 1 212 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -213 1 14.52906 1 -0 1 +213 1 14.529058 1 -0 1 214 1 13.868914 1 -0 1 215 1 7.643734 1 -0 1 216 0 -5.95583 1E-15 1.4415419267167138E-15 0 @@ -228,25 +228,25 @@ Instance Label Score Probability Log-loss Assigned 226 1 10.225868 1 -0 1 227 1 7.459608 1 -0 1 228 0 -5.5593443 1E-15 1.4415419267167138E-15 0 -229 1 12.666517 1 -0 1 +229 1 12.666515 1 -0 1 230 1 6.1583214 0.98 0.029146317580716615 1 -231 1 8.623034 1 -0 1 +231 1 8.623032 1 -0 1 232 0 1.2822819 0.6363636 1.4594315756416352 1 233 1 6.382519 0.98 0.029146317580716615 1 -234 0 -2.8964381 1E-15 1.4415419267167138E-15 0 +234 0 -2.8964386 1E-15 1.4415419267167138E-15 0 235 0 ? ? ? 0 236 1 11.420414 1 -0 1 -237 1 6.5357933 0.98 0.029146317580716615 1 +237 1 6.535795 0.98 0.029146317580716615 1 238 1 12.422876 1 -0 1 239 1 5.902529 0.98 0.029146317580716615 1 -240 0 -2.017991 0.071428575 0.10691520887754996 0 +240 0 -2.0179915 0.071428575 0.10691520887754996 0 241 0 -4.0004973 1E-15 1.4415419267167138E-15 0 242 0 -4.9946866 1E-15 1.4415419267167138E-15 0 -243 0 -2.6953988 1E-15 1.4415419267167138E-15 0 +243 0 -2.6953983 1E-15 1.4415419267167138E-15 0 244 0 -5.4686823 1E-15 1.4415419267167138E-15 0 245 0 -2.817525 1E-15 1.4415419267167138E-15 0 -246 1 11.424004 1 -0 1 -247 1 3.104393 0.8333333 0.26303444023032446 1 +246 1 11.424002 1 -0 1 +247 1 3.104394 0.8333333 0.26303444023032446 1 248 0 -3.0615559 1E-15 1.4415419267167138E-15 0 249 0 ? ? ? 0 250 0 -6.021953 1E-15 1.4415419267167138E-15 0 @@ -258,29 +258,29 @@ Instance Label Score Probability Log-loss Assigned 256 0 -5.494987 1E-15 1.4415419267167138E-15 0 257 0 -5.0078387 1E-15 1.4415419267167138E-15 0 258 0 -4.520691 1E-15 1.4415419267167138E-15 0 -259 0 2.9647493 0.8095238 2.3923175087700885 1 +259 0 2.9647484 0.8095238 2.3923175087700885 1 260 1 9.870926 1 -0 1 261 1 12.206299 1 -0 1 -262 1 9.653841 1 -0 1 +262 1 9.653839 1 -0 1 263 1 8.981979 1 -0 1 264 1 5.664709 0.98 0.029146317580716615 1 265 0 -2.494875 1E-15 1.4415419267167138E-15 0 -266 1 7.3661613 1 -0 1 -267 1 3.3009596 0.8333333 0.26303444023032446 1 +266 1 7.3661633 1 -0 1 +267 1 3.3009605 0.8333333 0.26303444023032446 1 268 1 9.372967 1 -0 1 269 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -270 1 6.031377 0.98 0.029146317580716615 1 +270 1 6.031378 0.98 0.029146317580716615 1 271 0 -3.5726995 1E-15 1.4415419267167138E-15 0 -272 1 3.3009596 0.8333333 0.26303444023032446 1 -273 1 0.21747208 0.6363636 0.65207672114864346 1 -274 0 -4.3236628 1E-15 1.4415419267167138E-15 0 +272 1 3.3009605 0.8333333 0.26303444023032446 1 +273 1 0.21747303 0.6363636 0.65207672114864346 1 +274 0 -4.323663 1E-15 1.4415419267167138E-15 0 275 0 ? ? ? 0 276 0 -5.0078387 1E-15 1.4415419267167138E-15 0 277 0 -5.95583 1E-15 1.4415419267167138E-15 0 278 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -279 1 7.127907 1 -0 1 +279 1 7.127905 1 -0 1 280 0 -4.520691 1E-15 1.4415419267167138E-15 0 -281 0 -4.6892586 1E-15 1.4415419267167138E-15 0 +281 0 -4.689259 1E-15 1.4415419267167138E-15 0 282 1 4.4381247 0.9285714 0.10691524360481655 1 283 1 6.0636253 0.98 0.029146317580716615 1 284 1 7.431343 1 -0 1 @@ -292,7 +292,7 @@ Instance Label Score Probability Log-loss Assigned 290 0 -6.4429784 1E-15 1.4415419267167138E-15 0 291 0 -5.4686823 1E-15 1.4415419267167138E-15 0 292 1 ? ? ? 0 -293 1 5.542121 0.98 0.029146317580716615 1 +293 1 5.542122 0.98 0.029146317580716615 1 294 0 ? ? ? 0 295 1 7.7866364 1 -0 1 296 0 1.823431 0.8095238 2.3923175087700885 1 @@ -308,14 +308,14 @@ Instance Label Score Probability Log-loss Assigned 306 0 -5.4686823 1E-15 1.4415419267167138E-15 0 307 0 -5.4686823 1E-15 1.4415419267167138E-15 0 308 1 7.422592 1 -0 1 -309 0 -1.7474079 0.071428575 0.10691520887754996 0 -310 0 -5.391173 1E-15 1.4415419267167138E-15 0 +309 0 -1.7474074 0.071428575 0.10691520887754996 0 +310 0 -5.3911724 1E-15 1.4415419267167138E-15 0 311 0 -6.4429784 1E-15 1.4415419267167138E-15 0 312 1 3.6294708 0.9285714 0.10691524360481655 1 313 0 -6.4429784 1E-15 1.4415419267167138E-15 0 -314 0 -6.046492 1E-15 1.4415419267167138E-15 0 +314 0 -6.0464916 1E-15 1.4415419267167138E-15 0 315 0 ? ? ? 0 -316 1 3.6177406 0.9285714 0.10691524360481655 1 +316 1 3.6177397 0.9285714 0.10691524360481655 1 317 1 9.215706 1 -0 1 318 0 -5.1966968 1E-15 1.4415419267167138E-15 0 319 0 2.6369457 0.8095238 2.3923175087700885 1 @@ -327,17 +327,17 @@ Instance Label Score Probability Log-loss Assigned 325 0 -4.192758 1E-15 1.4415419267167138E-15 0 326 1 4.4103794 0.9285714 0.10691524360481655 1 327 0 -5.95583 1E-15 1.4415419267167138E-15 0 -328 1 3.373887 0.8333333 0.26303444023032446 1 +328 1 3.373888 0.8333333 0.26303444023032446 1 329 1 7.8321342 1 -0 1 330 1 5.856251 0.98 0.029146317580716615 1 -331 0 -3.2490888 1E-15 1.4415419267167138E-15 0 +331 0 -3.2490892 1E-15 1.4415419267167138E-15 0 332 0 -3.1363668 1E-15 1.4415419267167138E-15 0 333 1 4.914962 0.9285714 0.10691524360481655 1 334 1 5.9119453 0.98 0.029146317580716615 1 335 0 -6.4429784 1E-15 1.4415419267167138E-15 0 336 1 5.543519 0.98 0.029146317580716615 1 337 0 -5.4686823 1E-15 1.4415419267167138E-15 0 -338 0 -6.046492 1E-15 1.4415419267167138E-15 0 +338 0 -6.0464916 1E-15 1.4415419267167138E-15 0 339 1 5.684025 0.98 0.029146317580716615 1 340 1 6.620781 0.98 0.029146317580716615 1 341 0 -5.4686823 1E-15 1.4415419267167138E-15 0 @@ -345,7 +345,7 @@ Instance Label Score Probability Log-loss Assigned 343 0 -6.4429784 1E-15 1.4415419267167138E-15 0 344 1 10.162451 1 -0 1 345 0 -6.4429784 1E-15 1.4415419267167138E-15 0 -346 0 -3.3358254 1E-15 1.4415419267167138E-15 0 +346 0 -3.335825 1E-15 1.4415419267167138E-15 0 347 0 -6.1395845 1E-15 1.4415419267167138E-15 0 348 1 0.15727425 0.6363636 0.65207672114864346 1 349 1 4.0622606 0.9285714 0.10691524360481655 1 @@ -354,7 +354,7 @@ Instance Label Score Probability Log-loss Assigned 352 0 0.47192955 0.6363636 1.4594315756416352 1 353 1 8.696342 1 -0 1 354 0 -5.95583 1E-15 1.4415419267167138E-15 0 -355 0 -4.246153 1E-15 1.4415419267167138E-15 0 +355 0 -4.2461534 1E-15 1.4415419267167138E-15 0 356 1 -0.69921684 0.5 1 0 357 1 12.852016 1 -0 1 358 1 5.582206 0.98 0.029146317580716615 1 @@ -372,23 +372,23 @@ Instance Label Score Probability Log-loss Assigned 370 0 -3.89473 1E-15 1.4415419267167138E-15 0 371 0 -5.8557806 1E-15 1.4415419267167138E-15 0 372 0 -4.2402444 1E-15 1.4415419267167138E-15 0 -373 0 -3.7544203 1E-15 1.4415419267167138E-15 0 +373 0 -3.7544198 1E-15 1.4415419267167138E-15 0 374 0 -4.71424 1E-15 1.4415419267167138E-15 0 375 0 -6.4429784 1E-15 1.4415419267167138E-15 0 376 0 -5.95583 1E-15 1.4415419267167138E-15 0 -377 0 -6.046492 1E-15 1.4415419267167138E-15 0 +377 0 -6.0464916 1E-15 1.4415419267167138E-15 0 378 0 -3.803866 1E-15 1.4415419267167138E-15 0 379 0 -2.2557268 1E-15 1.4415419267167138E-15 0 380 0 -6.4429784 1E-15 1.4415419267167138E-15 0 381 1 10.07641 1 -0 1 -382 0 -3.59721 1E-15 1.4415419267167138E-15 0 +382 0 -3.5972104 1E-15 1.4415419267167138E-15 0 383 0 -5.9689827 1E-15 1.4415419267167138E-15 0 384 0 -5.9689827 1E-15 1.4415419267167138E-15 0 385 0 -3.7061968 1E-15 1.4415419267167138E-15 0 386 1 6.0875864 0.98 0.029146317580716615 1 387 0 -2.3345594 1E-15 1.4415419267167138E-15 0 388 0 -5.2848067 1E-15 1.4415419267167138E-15 0 -389 0 -3.3224106 1E-15 1.4415419267167138E-15 0 +389 0 -3.322411 1E-15 1.4415419267167138E-15 0 390 0 -5.6504025 1E-15 1.4415419267167138E-15 0 391 1 10.030338 1 -0 1 392 0 -5.0078387 1E-15 1.4415419267167138E-15 0 @@ -407,7 +407,7 @@ Instance Label Score Probability Log-loss Assigned 405 0 -5.95583 1E-15 1.4415419267167138E-15 0 406 0 -4.1128182 1E-15 1.4415419267167138E-15 0 407 0 -5.95583 1E-15 1.4415419267167138E-15 0 -408 0 -3.691061 1E-15 1.4415419267167138E-15 0 +408 0 -3.6910605 1E-15 1.4415419267167138E-15 0 409 0 -4.71424 1E-15 1.4415419267167138E-15 0 410 0 -5.95583 1E-15 1.4415419267167138E-15 0 411 0 ? ? ? 0 @@ -421,12 +421,12 @@ Instance Label Score Probability Log-loss Assigned 419 0 -5.442145 1E-15 1.4415419267167138E-15 0 420 0 -2.5893164 1E-15 1.4415419267167138E-15 0 421 1 11.824856 1 -0 1 -422 0 -2.8105893 1E-15 1.4415419267167138E-15 0 +422 0 -2.8105888 1E-15 1.4415419267167138E-15 0 423 0 -3.4726496 1E-15 1.4415419267167138E-15 0 424 0 -5.494987 1E-15 1.4415419267167138E-15 0 425 1 14.817663 1 -0 1 426 0 -2.8241482 1E-15 1.4415419267167138E-15 0 -427 1 4.3530817 0.9285714 0.10691524360481655 1 +427 1 4.3530827 0.9285714 0.10691524360481655 1 428 0 -5.95583 1E-15 1.4415419267167138E-15 0 429 0 -5.4818344 1E-15 1.4415419267167138E-15 0 430 0 -5.50395 1E-15 1.4415419267167138E-15 0 @@ -440,8 +440,8 @@ Instance Label Score Probability Log-loss Assigned 438 0 -3.822938 1E-15 1.4415419267167138E-15 0 439 0 -4.5469956 1E-15 1.4415419267167138E-15 0 440 1 10.154686 1 -0 1 -441 0 -1.8604465 0.071428575 0.10691520887754996 0 -442 0 -4.932629 1E-15 1.4415419267167138E-15 0 +441 0 -1.8604469 0.071428575 0.10691520887754996 0 +442 0 -4.9326286 1E-15 1.4415419267167138E-15 0 443 0 -5.9932313 1E-15 1.4415419267167138E-15 0 444 0 -2.442047 1E-15 1.4415419267167138E-15 0 445 0 -5.9689827 1E-15 1.4415419267167138E-15 0 @@ -449,15 +449,15 @@ Instance Label Score Probability Log-loss Assigned 447 0 -4.5469956 1E-15 1.4415419267167138E-15 0 448 0 -6.53364 1E-15 1.4415419267167138E-15 0 449 1 10.298004 1 -0 1 -450 0 -4.0136495 1E-15 1.4415419267167138E-15 0 +450 0 -4.01365 1E-15 1.4415419267167138E-15 0 451 0 -4.5469956 1E-15 1.4415419267167138E-15 0 452 0 -4.8841314 1E-15 1.4415419267167138E-15 0 453 1 8.777971 1 -0 1 454 0 -5.6234684 1E-15 1.4415419267167138E-15 0 455 1 0.81635284 0.6363636 0.65207672114864346 1 -456 1 10.487387 1 -0 1 +456 1 10.487385 1 -0 1 457 1 9.06263 1 -0 1 -458 0 -4.253397 1E-15 1.4415419267167138E-15 0 +458 0 -4.2533965 1E-15 1.4415419267167138E-15 0 459 0 -3.9597979 1E-15 1.4415419267167138E-15 0 460 0 -3.93614 1E-15 1.4415419267167138E-15 0 461 0 -3.6959996 1E-15 1.4415419267167138E-15 0 @@ -465,11 +465,11 @@ Instance Label Score Probability Log-loss Assigned 463 0 -4.823963 1E-15 1.4415419267167138E-15 0 464 0 -5.0209913 1E-15 1.4415419267167138E-15 0 465 1 9.7853985 1 -0 1 -466 1 9.541933 1 -0 1 +466 1 9.541931 1 -0 1 467 1 7.7145195 1 -0 1 468 0 -5.0209913 1E-15 1.4415419267167138E-15 0 469 0 -5.6622314 1E-15 1.4415419267167138E-15 0 -470 0 -5.0985007 1E-15 1.4415419267167138E-15 0 +470 0 -5.0985003 1E-15 1.4415419267167138E-15 0 471 0 -3.4621444 1E-15 1.4415419267167138E-15 0 472 0 -4.163662 1E-15 1.4415419267167138E-15 0 473 0 -5.0209913 1E-15 1.4415419267167138E-15 0 @@ -482,10 +482,10 @@ Instance Label Score Probability Log-loss Assigned 480 0 -4.6376576 1E-15 1.4415419267167138E-15 0 481 0 -3.082261 1E-15 1.4415419267167138E-15 0 482 1 15.481422 1 -0 1 -483 1 10.906595 1 -0 1 -484 0 -4.253397 1E-15 1.4415419267167138E-15 0 -485 0 -5.023291 1E-15 1.4415419267167138E-15 0 -486 0 -5.0985007 1E-15 1.4415419267167138E-15 0 +483 1 10.906593 1 -0 1 +484 0 -4.2533965 1E-15 1.4415419267167138E-15 0 +485 0 -5.0232906 1E-15 1.4415419267167138E-15 0 +486 0 -5.0985003 1E-15 1.4415419267167138E-15 0 487 1 13.475906 1 -0 1 488 1 1.3273258 0.8095238 0.30485456129516797 1 489 1 -0.6232014 0.5 1 0 @@ -494,7 +494,7 @@ Instance Label Score Probability Log-loss Assigned 492 0 -4.624505 1E-15 1.4415419267167138E-15 0 493 1 9.94891 1 -0 1 494 0 0.9629116 0.6363636 1.4594315756416352 1 -495 0 -5.0985007 1E-15 1.4415419267167138E-15 0 +495 0 -5.0985003 1E-15 1.4415419267167138E-15 0 496 0 -6.53364 1E-15 1.4415419267167138E-15 0 497 0 -4.893565 1E-15 1.4415419267167138E-15 0 498 0 -4.533843 1E-15 1.4415419267167138E-15 0 @@ -510,18 +510,18 @@ Instance Label Score Probability Log-loss Assigned 508 0 -4.5469956 1E-15 1.4415419267167138E-15 0 509 0 -5.9689827 1E-15 1.4415419267167138E-15 0 510 0 -6.4429784 1E-15 1.4415419267167138E-15 0 -511 0 -4.059848 1E-15 1.4415419267167138E-15 0 +511 0 -4.0598474 1E-15 1.4415419267167138E-15 0 512 0 -4.5469956 1E-15 1.4415419267167138E-15 0 -513 0 -5.0985007 1E-15 1.4415419267167138E-15 0 +513 0 -5.0985003 1E-15 1.4415419267167138E-15 0 514 1 10.719854 1 -0 1 515 1 8.6480255 1 -0 1 516 0 -6.53364 1E-15 1.4415419267167138E-15 0 -517 0 -6.046492 1E-15 1.4415419267167138E-15 0 +517 0 -6.0464916 1E-15 1.4415419267167138E-15 0 518 0 -4.8959603 1E-15 1.4415419267167138E-15 0 519 1 6.535843 0.98 0.029146317580716615 1 520 0 -6.3523164 1E-15 1.4415419267167138E-15 0 521 0 -4.9303293 1E-15 1.4415419267167138E-15 0 -522 1 5.502534 0.98 0.029146317580716615 1 +522 1 5.502533 0.98 0.029146317580716615 1 523 1 7.699238 1 -0 1 524 0 -5.0078387 1E-15 1.4415419267167138E-15 0 525 0 -5.189559 1E-15 1.4415419267167138E-15 0 @@ -529,7 +529,7 @@ Instance Label Score Probability Log-loss Assigned 527 0 -4.046695 1E-15 1.4415419267167138E-15 0 528 0 -3.1803741 1E-15 1.4415419267167138E-15 0 529 0 -4.624505 1E-15 1.4415419267167138E-15 0 -530 1 6.515935 0.98 0.029146317580716615 1 +530 1 6.515936 0.98 0.029146317580716615 1 531 0 -4.1128182 1E-15 1.4415419267167138E-15 0 532 0 -5.5593443 1E-15 1.4415419267167138E-15 0 533 0 -5.0078387 1E-15 1.4415419267167138E-15 0 @@ -539,12 +539,12 @@ Instance Label Score Probability Log-loss Assigned 537 0 -3.2791004 1E-15 1.4415419267167138E-15 0 538 0 -4.533843 1E-15 1.4415419267167138E-15 0 539 0 -3.5858517 1E-15 1.4415419267167138E-15 0 -540 0 -3.6101007 1E-15 1.4415419267167138E-15 0 +540 0 -3.6101003 1E-15 1.4415419267167138E-15 0 541 0 -5.494987 1E-15 1.4415419267167138E-15 0 542 0 -4.292143 1E-15 1.4415419267167138E-15 0 543 0 -4.533843 1E-15 1.4415419267167138E-15 0 544 0 -4.589209 1E-15 1.4415419267167138E-15 0 -545 0 -4.059848 1E-15 1.4415419267167138E-15 0 +545 0 -4.0598474 1E-15 1.4415419267167138E-15 0 546 1 11.390259 1 -0 1 547 0 -6.059644 1E-15 1.4415419267167138E-15 0 548 0 -5.5856485 1E-15 1.4415419267167138E-15 0 @@ -554,11 +554,11 @@ Instance Label Score Probability Log-loss Assigned 552 0 -3.1100621 1E-15 1.4415419267167138E-15 0 553 0 -1.7353673 0.071428575 0.10691520887754996 0 554 0 -5.494987 1E-15 1.4415419267167138E-15 0 -555 0 -1.9254994 0.071428575 0.10691520887754996 0 +555 0 -1.925499 0.071428575 0.10691520887754996 0 556 0 -3.424057 1E-15 1.4415419267167138E-15 0 557 0 -3.93614 1E-15 1.4415419267167138E-15 0 558 0 -5.4818344 1E-15 1.4415419267167138E-15 0 -559 0 -4.059848 1E-15 1.4415419267167138E-15 0 +559 0 -4.0598474 1E-15 1.4415419267167138E-15 0 560 0 -3.5726995 1E-15 1.4415419267167138E-15 0 561 0 -3.5726995 1E-15 1.4415419267167138E-15 0 562 0 -5.4686823 1E-15 1.4415419267167138E-15 0 @@ -566,16 +566,16 @@ Instance Label Score Probability Log-loss Assigned 564 0 -3.8496675 1E-15 1.4415419267167138E-15 0 565 1 12.086092 1 -0 1 566 0 -4.227092 1E-15 1.4415419267167138E-15 0 -567 0 -3.6343493 1E-15 1.4415419267167138E-15 0 +567 0 -3.6343489 1E-15 1.4415419267167138E-15 0 568 1 4.1473064 0.9285714 0.10691524360481655 1 569 1 10.713882 1 -0 1 570 1 8.017664 1 -0 1 571 1 11.034657 1 -0 1 572 0 -5.0078387 1E-15 1.4415419267167138E-15 0 573 0 -5.95583 1E-15 1.4415419267167138E-15 0 -574 1 5.950968 0.98 0.029146317580716615 1 +574 1 5.950966 0.98 0.029146317580716615 1 575 0 -3.2791004 1E-15 1.4415419267167138E-15 0 -576 0 -4.059848 1E-15 1.4415419267167138E-15 0 +576 0 -4.0598474 1E-15 1.4415419267167138E-15 0 577 0 -5.95583 1E-15 1.4415419267167138E-15 0 578 0 -5.95583 1E-15 1.4415419267167138E-15 0 579 0 -5.4686823 1E-15 1.4415419267167138E-15 0 @@ -590,52 +590,52 @@ Instance Label Score Probability Log-loss Assigned 588 1 5.463169 0.98 0.029146317580716615 1 589 0 -4.5469956 1E-15 1.4415419267167138E-15 0 590 1 3.9684029 0.9285714 0.10691524360481655 1 -591 1 5.966527 0.98 0.029146317580716615 1 +591 1 5.966526 0.98 0.029146317580716615 1 592 1 5.7801704 0.98 0.029146317580716615 1 -593 0 -4.253397 1E-15 1.4415419267167138E-15 0 -594 1 5.101775 0.98 0.029146317580716615 1 -595 0 -4.059848 1E-15 1.4415419267167138E-15 0 +593 0 -4.2533965 1E-15 1.4415419267167138E-15 0 +594 1 5.101776 0.98 0.029146317580716615 1 +595 0 -4.0598474 1E-15 1.4415419267167138E-15 0 596 0 -4.2402444 1E-15 1.4415419267167138E-15 0 597 0 -2.9855018 1E-15 1.4415419267167138E-15 0 598 0 -5.0078387 1E-15 1.4415419267167138E-15 0 599 0 -3.6294346 1E-15 1.4415419267167138E-15 0 600 0 -5.0078387 1E-15 1.4415419267167138E-15 0 -601 0 -6.046492 1E-15 1.4415419267167138E-15 0 +601 0 -6.0464916 1E-15 1.4415419267167138E-15 0 602 0 -4.533843 1E-15 1.4415419267167138E-15 0 -603 1 4.8058815 0.9285714 0.10691524360481655 1 +603 1 4.8058825 0.9285714 0.10691524360481655 1 604 1 6.1928043 0.98 0.029146317580716615 1 -605 1 9.955452 1 -0 1 +605 1 9.95545 1 -0 1 606 0 -4.715564 1E-15 1.4415419267167138E-15 0 607 0 -6.4429784 1E-15 1.4415419267167138E-15 0 608 1 11.148428 1 -0 1 609 0 -4.5469956 1E-15 1.4415419267167138E-15 0 610 1 8.926189 1 -0 1 -611 1 6.910961 1 -0 1 -612 1 16.893517 1 -0 1 +611 1 6.9109592 1 -0 1 +612 1 16.893515 1 -0 1 613 0 -5.226982 1E-15 1.4415419267167138E-15 0 614 0 -5.5724964 1E-15 1.4415419267167138E-15 0 -615 0 -3.9466457 1E-15 1.4415419267167138E-15 0 +615 0 -3.9466453 1E-15 1.4415419267167138E-15 0 616 0 -5.0078387 1E-15 1.4415419267167138E-15 0 617 0 ? ? ? 0 618 0 -4.533843 1E-15 1.4415419267167138E-15 0 -619 0 -4.059848 1E-15 1.4415419267167138E-15 0 +619 0 -4.0598474 1E-15 1.4415419267167138E-15 0 620 0 -5.0078387 1E-15 1.4415419267167138E-15 0 -621 0 0.35605335 0.6363636 1.4594315756416352 1 +621 0 0.3560543 0.6363636 1.4594315756416352 1 622 0 -2.2074018 0.071428575 0.10691520887754996 0 623 0 -6.4429784 1E-15 1.4415419267167138E-15 0 -624 0 -3.8450823 1E-15 1.4415419267167138E-15 0 +624 0 -3.8450818 1E-15 1.4415419267167138E-15 0 625 0 -3.4678864 1E-15 1.4415419267167138E-15 0 -626 1 5.7601643 0.98 0.029146317580716615 1 +626 1 5.760165 0.98 0.029146317580716615 1 627 0 -4.1699953 1E-15 1.4415419267167138E-15 0 628 0 -5.9689827 1E-15 1.4415419267167138E-15 0 629 0 -5.0209913 1E-15 1.4415419267167138E-15 0 -630 0 -3.358376 1E-15 1.4415419267167138E-15 0 -631 0 -4.059848 1E-15 1.4415419267167138E-15 0 +630 0 -3.3583755 1E-15 1.4415419267167138E-15 0 +631 0 -4.0598474 1E-15 1.4415419267167138E-15 0 632 0 -6.4429784 1E-15 1.4415419267167138E-15 0 633 1 4.3299503 0.9285714 0.10691524360481655 1 634 0 -5.494987 1E-15 1.4415419267167138E-15 0 635 0 -4.61419 1E-15 1.4415419267167138E-15 0 -636 1 10.127518 1 -0 1 +636 1 10.12752 1 -0 1 637 0 -2.4650178 1E-15 1.4415419267167138E-15 0 638 0 -5.0209913 1E-15 1.4415419267167138E-15 0 639 0 -3.93614 1E-15 1.4415419267167138E-15 0 @@ -655,7 +655,7 @@ Instance Label Score Probability Log-loss Assigned 653 0 -4.533843 1E-15 1.4415419267167138E-15 0 654 0 -4.520691 1E-15 1.4415419267167138E-15 0 655 0 -5.0078387 1E-15 1.4415419267167138E-15 0 -656 0 -4.059848 1E-15 1.4415419267167138E-15 0 +656 0 -4.0598474 1E-15 1.4415419267167138E-15 0 657 0 -0.4869156 0.5 1 0 658 1 9.086258 1 -0 1 659 0 -6.4429784 1E-15 1.4415419267167138E-15 0 @@ -665,14 +665,14 @@ Instance Label Score Probability Log-loss Assigned 663 0 -5.3686323 1E-15 1.4415419267167138E-15 0 664 0 -4.3969836 1E-15 1.4415419267167138E-15 0 665 0 -6.4429784 1E-15 1.4415419267167138E-15 0 -666 0 -3.4969325 1E-15 1.4415419267167138E-15 0 +666 0 -3.496932 1E-15 1.4415419267167138E-15 0 667 0 -4.520691 1E-15 1.4415419267167138E-15 0 668 1 2.8043652 0.8095238 0.30485456129516797 1 669 1 8.147335 1 -0 1 670 1 6.4856205 0.98 0.029146317580716615 1 671 0 -4.087837 1E-15 1.4415419267167138E-15 0 672 0 -4.9946866 1E-15 1.4415419267167138E-15 0 -673 0 -3.9078827 1E-15 1.4415419267167138E-15 0 +673 0 -3.9078822 1E-15 1.4415419267167138E-15 0 674 0 -5.95583 1E-15 1.4415419267167138E-15 0 675 0 -4.1401944 1E-15 1.4415419267167138E-15 0 676 0 -5.6622314 1E-15 1.4415419267167138E-15 0 @@ -681,7 +681,7 @@ Instance Label Score Probability Log-loss Assigned 679 0 -5.9689827 1E-15 1.4415419267167138E-15 0 680 1 16.78001 1 -0 1 681 1 9.801077 1 -0 1 -682 0 -3.3756714 1E-15 1.4415419267167138E-15 0 +682 0 -3.3756719 1E-15 1.4415419267167138E-15 0 683 0 -6.4429784 1E-15 1.4415419267167138E-15 0 684 0 -6.4429784 1E-15 1.4415419267167138E-15 0 685 0 -6.4429784 1E-15 1.4415419267167138E-15 0 @@ -690,7 +690,7 @@ Instance Label Score Probability Log-loss Assigned 688 0 -5.0209913 1E-15 1.4415419267167138E-15 0 689 0 -4.1777043 1E-15 1.4415419267167138E-15 0 690 0 -5.832123 1E-15 1.4415419267167138E-15 0 -691 1 4.4967804 0.9285714 0.10691524360481655 1 +691 1 4.4967794 0.9285714 0.10691524360481655 1 692 0 -5.494987 1E-15 1.4415419267167138E-15 0 693 0 -4.6842284 1E-15 1.4415419267167138E-15 0 694 0 -4.9545784 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt index 48a92460de..f49629df35 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt @@ -1,156 +1,156 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.5726995 0.047724232 0.070548673242470078 0 -1 0 3.6101446 0.92095214 3.6611298070520353 1 -2 0 -4.0709443 0.03320216 0.048713845243635014 0 -3 0 2.470542 0.83073896 2.5626781731328263 1 -4 0 -3.4358397 0.05267027 0.078061433982946932 0 -5 1 12.382593 0.99988943 0.0001595227277818751 1 -6 0 -1.4209604 0.20405398 0.32925750806385767 0 +0 0 -3.5726995 0.04772423 0.070548667598666043 0 +1 0 3.6101456 0.92095214 3.6611298070520353 1 +2 0 -4.070944 0.03320217 0.048713861920724008 0 +3 0 2.470542 0.8307389 2.5626776650932253 1 +4 0 -3.4358397 0.052670266 0.078061428309676412 0 +5 1 12.382595 0.99988943 0.0001595227277818751 1 +6 0 -1.4209604 0.20405397 0.32925748105470037 0 7 0 -4.7010875 0.02084881 0.030396453391412736 0 -8 0 -4.6745405 0.021263903 0.031008185910590607 0 -9 0 -4.406417 0.025935683 0.037911059564125327 0 +8 0 -4.6745405 0.021263901 0.031008183164979278 0 +9 0 -4.406417 0.025935682 0.037911056805345573 0 10 0 -5.5593443 0.010982092 0.01593145032913975 0 11 0 -5.4818344 0.011639431 0.016890640083211839 0 -12 1 -0.14206886 0.40347567 1.3094464075888503 0 -13 0 -4.5691886 0.022992346 0.033558231039772665 0 +12 1 -0.14206791 0.40347582 1.3094458747728994 0 +13 0 -4.569188 0.022992352 0.033558239291178633 0 14 1 9.321613 0.998874 0.0016253773809814112 1 -15 1 1.3856993 0.6830734 0.54988747944718552 1 +15 1 1.3856983 0.6830732 0.54988785711378696 1 16 0 -4.533843 0.023602419 0.034459373415090033 0 17 0 -4.046695 0.03379773 0.049602852281362811 0 18 1 7.8903713 0.9966727 0.0048082975514731332 1 -19 0 -3.0987039 0.06699134 0.10003761775121778 0 +19 0 -3.0987039 0.06699133 0.10003760623051484 0 20 1 7.528511 0.9956263 0.0063237968594455725 1 21 1 7.875206 0.9966343 0.0048638619113426269 1 22 0 -5.0078387 0.016592303 0.024138448331127087 0 23 1 ? ? ? 0 -24 0 -5.4686823 0.011754767 0.017059004011272673 0 +24 0 -5.4686823 0.011754766 0.01705900265167648 0 25 1 1.741828 0.73848027 0.43736871808798289 1 -26 0 -4.9710746 0.01705355 0.024815273012732428 0 -27 0 -4.059848 0.03347343 0.049118699463849731 0 +26 0 -4.971074 0.017053556 0.024815281214284793 0 +27 0 -4.0598474 0.03347344 0.049118716145619364 0 28 0 -5.4818344 0.011639431 0.016890640083211839 0 29 0 -5.8557806 0.008789949 0.012737278390963667 0 -30 0 -5.0985007 0.015506633 0.022546608302385175 0 +30 0 -5.0985003 0.015506638 0.022546616491050588 0 31 0 -4.9946866 0.016755885 0.024378447925635292 0 32 1 7.46414 0.9954084 0.0066395103905325147 1 -33 0 -4.6892586 0.02103278 0.030667542438444956 0 -34 0 -4.71424 0.020646105 0.030097814327393193 0 +33 0 -4.689259 0.021032773 0.030667531458592488 0 +34 0 -4.71424 0.020646103 0.030097811583513857 0 35 0 -5.4818344 0.011639431 0.016890640083211839 0 36 1 9.09911 0.99866724 0.0019240484689165348 1 -37 0 -1.113348 0.24456774 0.40462569514389818 0 +37 0 -1.113348 0.24456772 0.40462566668624267 0 38 1 6.140955 0.9875705 0.018044317542393044 1 -39 1 2.5109034 0.83500063 0.26015080155533699 1 +39 1 2.5109034 0.8350006 0.26015090453888573 1 40 0 ? ? ? 0 -41 1 3.3300762 0.90403634 0.14554732340521623 1 +41 1 3.3300762 0.9040363 0.14554741852454292 1 42 1 8.577511 0.99802166 0.0028569650360322531 1 43 1 0.49126053 0.5223421 0.93693314652059867 1 -44 1 8.255751 0.9974761 0.0036458194620463992 1 +44 1 8.255749 0.9974761 0.0036458194620463992 1 45 0 -5.63222 0.010397597 0.015079091662558042 0 -46 1 4.5673847 0.96013206 0.058695238307073898 1 +46 1 4.5673857 0.9601321 0.05869514874509995 1 47 0 -5.95583 0.008152755 0.011810147478593156 0 -48 0 -3.4358397 0.05267027 0.078061433982946932 0 -49 1 5.3666534 0.9778563 0.032305655935016012 1 -50 1 2.5949688 0.8436 0.24536904232329945 1 -51 1 0.12595844 0.4532164 1.1417280158533796 1 +48 0 -3.4358397 0.052670266 0.078061428309676412 0 +49 1 5.3666544 0.9778563 0.032305655935016012 1 +50 1 2.5949688 0.8435999 0.2453691442570726 1 +51 1 0.12595844 0.45321637 1.1417281107212258 1 52 1 5.2992115 0.976721 0.033981595778575298 1 53 1 8.407227 0.99774945 0.0032505195711204126 1 -54 1 7.649309 0.9960077 0.0057712269479890379 1 +54 1 7.6493073 0.9960077 0.0057712269479890379 1 55 1 4.478711 0.957476 0.062691739926295564 1 56 1 5.5541334 0.9807353 0.02806428572372743 1 57 1 1.6657066 0.7271758 0.45962396180677062 1 -58 1 2.5265894 0.8366335 0.25733232225246744 1 -59 1 1.7368536 0.7377509 0.43879434296981179 1 +58 1 2.5265903 0.83663356 0.25733221946992052 1 +59 1 1.7368546 0.737751 0.43879410985231937 1 60 1 2.3288136 0.8150797 0.29498697868205404 1 -61 0 -5.5060835 0.0114297075 0.016584542177967555 0 +61 0 -5.5060835 0.011429707 0.016584540818818418 0 62 1 6.380089 0.98961115 0.015066341261387473 1 -63 1 0.33490086 0.49270463 1.0212050663068744 1 +63 1 0.33490086 0.4927046 1.0212051535714572 1 64 0 -5.95583 0.008152755 0.011810147478593156 0 -65 1 3.8072634 0.9311753 0.1028753176207461 1 +65 1 3.8072643 0.93117535 0.1028752252736623 1 66 0 -4.046695 0.03379773 0.049602852281362811 0 -67 1 4.218014 0.9486548 0.076044929659653121 1 +67 1 4.218014 0.9486547 0.076045020305197497 1 68 1 10.826725 0.9996402 0.00051913703181331893 1 -69 0 -5.271654 0.013623926 0.019790289199966723 0 -70 0 -3.4726496 0.051294282 0.075967452463448076 0 +69 0 -5.2716546 0.01362392 0.019790281026931159 0 +70 0 -3.4726496 0.05129428 0.075967446798405988 0 71 1 7.895046 0.99668443 0.0047913008067115544 1 -72 0 -2.1755848 0.1263537 0.1948787725155719 0 +72 0 -2.1755848 0.12635368 0.19487874790855292 0 73 1 8.9055195 0.9984568 0.0022281210940979582 1 -74 1 2.5993576 0.8440387 0.2446189028997863 1 -75 0 -4.0411606 0.033935104 0.049807988000729261 0 +74 1 2.5993576 0.84403867 0.244619004780572 1 +75 0 -4.0411606 0.0339351 0.049807982437482065 0 76 0 -5.0750337 0.015780753 0.022948364676579087 0 -77 0 -3.499567 0.05030968 0.074470946934380028 0 -78 0 -3.6211972 0.046079826 0.06805955070059394 0 -79 0 -5.391173 0.012457768 0.018085649525652777 0 -80 0 -2.7157316 0.087596945 0.13225681838671866 0 -81 0 -4.2284155 0.029574301 0.043310338334976231 0 -82 0 -3.4452734 0.05231434 0.077519488826293739 0 +77 0 -3.499567 0.050309677 0.074470941275211228 0 +78 0 -3.6211972 0.046079822 0.068059545066518914 0 +79 0 -5.3911724 0.012457772 0.018085654967908992 0 +80 0 -2.7157316 0.08759694 0.13225680660583347 0 +81 0 -4.2284155 0.0295743 0.043310335565852412 0 +82 0 -3.4452734 0.052314337 0.077519483155153973 0 83 0 -2.1223526 0.13087894 0.20237095101991351 0 84 1 9.694054 0.99915093 0.0012254667568283894 1 85 1 6.2895603 0.9888809 0.016131360230936344 1 86 1 2.6168842 0.84578085 0.24164420028856959 1 -87 1 6.919142 0.9930738 0.010027129067340199 1 +87 1 6.91914 0.9930738 0.010027129067340199 1 88 0 -4.046695 0.03379773 0.049602852281362811 0 89 0 -5.085745 0.015655048 0.022764115650391988 0 -90 0 -5.4686823 0.011754767 0.017059004011272673 0 -91 0 -5.189559 0.014486661 0.021052696761167319 0 +90 0 -5.4686823 0.011754766 0.01705900265167648 0 +91 0 -5.189559 0.01448666 0.021052695397802253 0 92 0 -4.046695 0.03379773 0.049602852281362811 0 93 0 -5.95583 0.008152755 0.011810147478593156 0 94 0 -4.9946866 0.016755885 0.024378447925635292 0 -95 0 -5.4686823 0.011754767 0.017059004011272673 0 +95 0 -5.4686823 0.011754766 0.01705900265167648 0 96 0 -5.663555 0.010155837 0.014726683875270435 0 -97 0 -3.5726995 0.047724232 0.070548673242470078 0 +97 0 -3.5726995 0.04772423 0.070548667598666043 0 98 1 8.590233 0.9980406 0.0028295658493644239 1 99 1 10.917194 0.99966407 0.00048472853254951715 1 100 1 4.8476696 0.9675202 0.047636346312542086 1 101 1 -0.84280396 0.28443488 1.8138296783750301 0 -102 0 -3.7530966 0.041876126 0.061715903711063051 0 -103 1 1.7746439 0.7432593 0.42806246416940569 1 +102 0 -3.753096 0.04187614 0.061715926148486977 0 +103 1 1.7746449 0.7432594 0.42806234847449176 1 104 1 12.140858 0.9998672 0.00019160139559387039 1 -105 1 2.5560713 0.83966726 0.25211035808697119 1 +105 1 2.5560703 0.83966714 0.25211056290937062 1 106 1 9.259369 0.99881965 0.0017038920105930569 1 107 1 6.720646 0.99195737 0.011649978820645172 1 108 0 -5.5617743 0.010962089 0.015902272030853013 0 109 1 6.871727 0.992822 0.010393022735684912 1 -110 0 -2.766693 0.08455608 0.12745658613888911 0 -111 1 3.848031 0.93313104 0.099848402613633994 1 +110 0 -2.766693 0.08455607 0.12745657439713692 0 +111 1 3.848032 0.93313104 0.099848402613633994 1 112 1 9.425768 0.9989595 0.0015019320975399703 1 -113 1 9.506624 0.9990213 0.0014126689718023965 1 -114 0 -3.0727458 0.068232656 0.10195832549468188 0 -115 0 -4.6439905 0.021751598 0.03172724574533177 0 -116 0 -0.66188717 0.3131727 0.54198070603227311 0 +113 1 9.506622 0.9990213 0.0014126689718023965 1 +114 0 -3.0727453 0.06823267 0.10195834856678422 0 +115 0 -4.6439905 0.021751596 0.031727242998351647 0 +116 0 -0.66188717 0.31317267 0.54198064343187602 0 117 1 9.617275 0.9991 0.0012989676266954972 1 -118 0 -5.3621607 0.012731451 0.018485525732496611 0 -119 0 -3.9435177 0.036449015 0.053567088295960068 0 -120 0 -4.8696556 0.018392263 0.026781474537826103 0 -121 0 -3.469522 0.05140986 0.076143221101649672 0 +118 0 -5.3621607 0.01273145 0.018485524371555397 0 +119 0 -3.9435177 0.03644901 0.053567082718198315 0 +120 0 -4.8696556 0.018392261 0.026781471800246908 0 +121 0 -3.469522 0.051409855 0.076143215435917344 0 122 1 9.680523 0.99914217 0.0012381182790972595 1 123 1 3.8165932 0.9316275 0.10217485034891999 1 124 1 7.6522446 0.99601656 0.0057583629403995997 1 125 0 -5.95583 0.008152755 0.011810147478593156 0 -126 1 8.564951 0.9980027 0.0028843647430662487 1 +126 1 8.564953 0.9980027 0.0028843647430662487 1 127 0 -4.520691 0.023833437 0.034800759324198648 0 128 1 4.84898 0.9675514 0.04758977495588574 1 -129 0 -5.717684 0.009751258 0.014137132147530099 0 -130 0 -3.4726496 0.051294282 0.075967452463448076 0 +129 0 -5.717684 0.0097512575 0.014137130790684689 0 +130 0 -3.4726496 0.05129428 0.075967446798405988 0 131 0 -4.9946866 0.016755885 0.024378447925635292 0 132 1 8.602232 0.9980583 0.0028039765128859018 1 133 0 -4.810811 0.01921573 0.027992252541605728 0 -134 0 -4.9171767 0.017752616 0.025841674110087472 0 -135 0 -2.7288966 0.08680205 0.13100047471163517 0 +134 0 -4.9171767 0.017752614 0.025841671374291015 0 +135 0 -2.7288966 0.08680204 0.13100046294100468 0 136 0 -4.533843 0.023602419 0.034459373415090033 0 137 0 -5.494987 0.01152521 0.016723922858878242 0 -138 0 -4.2402444 0.029317858 0.04292914403934768 0 +138 0 -4.2402444 0.029317856 0.042929141270955429 0 139 0 ? ? ? 0 140 0 -5.494987 0.01152521 0.016723922858878242 0 141 0 -5.9689827 0.00807247 0.011693373854840414 0 -142 1 4.4324036 0.95602256 0.064883430994521443 1 -143 0 -4.6439905 0.021751598 0.03172724574533177 0 +142 1 4.4324026 0.9560225 0.064883520941486764 1 +143 0 -4.6439905 0.021751596 0.031727242998351647 0 144 0 -5.4818344 0.011639431 0.016890640083211839 0 145 0 ? ? ? 0 146 1 1.3394346 0.67542744 0.56612731001545713 1 -147 0 -5.4154215 0.012233486 0.017758034331297672 0 -148 0 -1.0123739 0.25899458 0.4324439985553144 0 +147 0 -5.4154215 0.0122334855 0.017758032971042548 0 +148 0 -1.012373 0.2589947 0.43244423064898052 0 149 1 11.461615 0.99977773 0.00032069729386163213 1 150 0 -5.5593443 0.010982092 0.01593145032913975 0 -151 1 5.006485 0.9711001 0.042308092038938809 1 +151 1 5.006484 0.9711001 0.042308092038938809 1 152 1 9.715748 0.99916476 0.0012054999542047689 1 153 0 -4.121497 0.03199298 0.046910583933444494 0 154 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -160,17 +160,17 @@ Instance Label Score Probability Log-loss Assigned 158 0 ? ? ? 0 159 1 12.346203 0.99988633 0.0001639947780942108 1 160 1 9.039494 0.99860567 0.002012998795947356 1 -161 0 -3.8496675 0.039033547 0.057442027050147004 0 +161 0 -3.8496675 0.039033543 0.057442021457383786 0 162 0 -4.520691 0.023833437 0.034800759324198648 0 -163 0 -3.3870554 0.05454763 0.080923315355362818 0 +163 0 -3.3870554 0.054547627 0.080923309670827032 0 164 0 ? ? ? 0 -165 0 -3.3999205 0.05404651 0.080158845547395108 0 +165 0 -3.3999205 0.054046508 0.080158839865870704 0 166 1 7.976185 0.9968817 0.004505750926832852 1 167 1 8.355644 0.99765986 0.0033800618776249807 1 168 0 -4.520691 0.023833437 0.034800759324198648 0 169 0 -6.2282124 0.0066409498 0.009612819847748871 0 170 0 -5.494987 0.01152521 0.016723922858878242 0 -171 0 -5.4686823 0.011754767 0.017059004011272673 0 +171 0 -5.4686823 0.011754766 0.01705900265167648 0 172 0 -5.95583 0.008152755 0.011810147478593156 0 173 1 15.1560135 0.9999865 1.9434170443242565E-05 1 174 1 6.1769257 0.9879011 0.017561488134486943 1 @@ -181,72 +181,72 @@ Instance Label Score Probability Log-loss Assigned 179 1 2.290575 0.8106676 0.30281765649168213 1 180 0 -5.5593443 0.010982092 0.01593145032913975 0 181 0 -6.4429784 0.005648197 0.0081717255492616877 0 -182 0 -3.0987039 0.06699134 0.10003761775121778 0 +182 0 -3.0987039 0.06699133 0.10003760623051484 0 183 1 9.1599655 0.9987273 0.0018372561468384253 1 184 1 6.2014637 0.98812157 0.017239546569330137 1 -185 0 -5.0853486 0.01565968 0.022770905093500125 0 +185 0 -5.085348 0.015659686 0.022770913283438728 0 186 1 5.7654095 0.9835412 0.023942621168576837 1 187 1 13.977451 0.99996704 4.7553986690113E-05 1 188 1 9.065283 0.99863267 0.0019739908621602127 1 -189 0 -4.7540584 0.020044118 0.029211295141143595 0 -190 1 11.957216 0.99984735 0.0002202405944450654 1 -191 1 10.956871 0.999674 0.00047036322693564959 1 -192 0 -4.059848 0.03347343 0.049118699463849731 0 -193 0 -5.4686823 0.011754767 0.017059004011272673 0 +189 0 -4.7540584 0.020044116 0.029211292398949824 0 +190 1 11.957218 0.99984735 0.0002202405944450654 1 +191 1 10.956873 0.999674 0.00047036322693564959 1 +192 0 -4.0598474 0.03347344 0.049118716145619364 0 +193 0 -5.4686823 0.011754766 0.01705900265167648 0 194 0 -4.520691 0.023833437 0.034800759324198648 0 195 0 -4.046695 0.03379773 0.049602852281362811 0 196 0 6.8652763 0.992787 7.1151855643261381 1 -197 0 -2.6564164 0.09126051 0.13806131755901335 0 +197 0 -2.6564164 0.0912605 0.13806130573063377 0 198 0 -6.4429784 0.005648197 0.0081717255492616877 0 199 0 -5.0078387 0.016592303 0.024138448331127087 0 200 1 10.36586 0.9994898 0.00073627359086373204 1 201 1 9.869495 0.9992566 0.0010728826448553252 1 -202 0 -5.4686823 0.011754767 0.017059004011272673 0 -203 0 -3.5726995 0.047724232 0.070548673242470078 0 -204 0 -5.4686823 0.011754767 0.017059004011272673 0 -205 1 12.086603 0.99986166 0.00019959967319162043 1 +202 0 -5.4686823 0.011754766 0.01705900265167648 0 +203 0 -3.5726995 0.04772423 0.070548667598666043 0 +204 0 -5.4686823 0.011754766 0.01705900265167648 0 +205 1 12.086601 0.99986166 0.00019959967319162043 1 206 1 5.94417 0.98559827 0.020928376857247459 1 207 0 -5.5593443 0.010982092 0.01593145032913975 0 208 0 -5.5593443 0.010982092 0.01593145032913975 0 -209 0 -3.6633615 0.04469411 0.065965336354842946 0 +209 0 -3.6633615 0.044694107 0.065965330728940397 0 210 1 14.534115 0.99997836 3.1215188826316377E-05 1 211 1 9.64962 0.99912184 0.0012674667953664828 1 -212 0 -5.4686823 0.011754767 0.017059004011272673 0 -213 1 14.52906 0.9999783 3.1301182014901005E-05 1 +212 0 -5.4686823 0.011754766 0.01705900265167648 0 +213 1 14.529058 0.9999783 3.1301182014901005E-05 1 214 1 13.868914 0.9999642 5.1681712271066226E-05 1 215 1 7.643734 0.9959909 0.0057955739072098177 1 216 0 -5.95583 0.008152755 0.011810147478593156 0 -217 0 -5.4686823 0.011754767 0.017059004011272673 0 +217 0 -5.4686823 0.011754766 0.01705900265167648 0 218 1 7.88678 0.99666363 0.0048214119279433977 1 -219 0 -2.511506 0.100795746 0.15327923389796996 0 -220 0 -5.1632547 0.014774316 0.021473856707274121 0 +219 0 -2.511506 0.10079574 0.15327922194416127 0 +220 0 -5.1632547 0.014774315 0.021473855343510994 0 221 1 10.395218 0.999501 0.00072009905980506843 1 -222 1 -2.214662 0.1231175 3.0218922639873447 0 +222 1 -2.214662 0.12311749 3.0218923512935034 0 223 1 5.7424126 0.9832564 0.024360423750620218 1 224 1 9.995327 0.99932426 0.00097521318777220637 1 225 0 -5.95583 0.008152755 0.011810147478593156 0 226 1 10.225868 0.9994326 0.00081878372812222485 1 -227 1 7.459608 0.9953927 0.0066623169983452231 1 +227 1 7.459608 0.9953926 0.0066624033876966004 1 228 0 -5.5593443 0.010982092 0.01593145032913975 0 -229 1 12.666517 0.9999109 0.00012856275965089447 1 +229 1 12.666515 0.9999109 0.00012856275965089447 1 230 1 6.1583214 0.9877312 0.017809586200967932 1 -231 1 8.623034 0.99808866 0.0027601224428665879 1 -232 0 1.2822819 0.66585165 1.5814393559263284 1 +231 1 8.623032 0.99808866 0.0027601224428665879 1 +232 0 1.2822819 0.6658516 1.5814390985815938 1 233 1 6.382519 0.9896301 0.015038709216571163 1 -234 0 -2.8964381 0.07724253 0.115976584697302 0 +234 0 -2.8964386 0.0772425 0.11597653810254285 0 235 0 ? ? ? 0 236 1 11.420414 0.99977064 0.00033093257283884215 1 -237 1 6.5357933 0.9907579 0.01339555438417007 1 +237 1 6.535795 0.9907579 0.01339555438417007 1 238 1 12.422876 0.9998928 0.00015470668910224405 1 239 1 5.902529 0.98514295 0.021595017147241997 1 -240 0 -2.017991 0.14015022 0.21784345766297453 0 +240 0 -2.0179915 0.14015016 0.21784335765559609 0 241 0 -4.0004973 0.034961022 0.051340881416726061 0 242 0 -4.9946866 0.016755885 0.024378447925635292 0 -243 0 -2.6953988 0.088837564 0.13421982380863931 0 -244 0 -5.4686823 0.011754767 0.017059004011272673 0 +243 0 -2.6953983 0.08883759 0.13421985919941723 0 +244 0 -5.4686823 0.011754766 0.01705900265167648 0 245 0 -2.817525 0.081618704 0.12283483473472002 0 -246 1 11.424004 0.9997713 0.00032998645156814019 1 -247 1 3.104393 0.8881221 0.17117009101578401 1 +246 1 11.424002 0.9997713 0.00032998645156814019 1 +247 1 3.104394 0.88812214 0.17116999419202034 1 248 0 -3.0615559 0.068774305 0.10279722872890984 0 249 0 ? ? ? 0 250 0 -6.021953 0.0077569964 0.011234610256342446 0 @@ -258,64 +258,64 @@ Instance Label Score Probability Log-loss Assigned 256 0 -5.494987 0.01152521 0.016723922858878242 0 257 0 -5.0078387 0.016592303 0.024138448331127087 0 258 0 -4.520691 0.023833437 0.034800759324198648 0 -259 0 2.9647493 0.877156 3.025100925818462 1 +259 0 2.9647484 0.8771559 3.0250995258103677 1 260 1 9.870926 0.99925745 0.0010716778711863072 1 261 1 12.206299 0.99987364 0.00018231312886902128 1 -262 1 9.653841 0.99912465 0.0012634216564773898 1 +262 1 9.653839 0.99912465 0.0012634216564773898 1 263 1 8.981979 0.9985436 0.002102643541396716 1 264 1 5.664709 0.9822578 0.025826400239286989 1 -265 0 -2.494875 0.10194498 0.15512426427086881 0 -266 1 7.3661613 0.99505585 0.0071505858244227481 1 -267 1 3.3009596 0.90210295 0.14863601285829425 1 +265 0 -2.494875 0.101944976 0.15512425230176288 0 +266 1 7.3661633 0.99505585 0.0071505858244227481 1 +267 1 3.3009605 0.902103 0.14863591753511315 1 268 1 9.372967 0.998917 0.0015633090806578933 1 -269 0 -5.4686823 0.011754767 0.017059004011272673 0 -270 1 6.031377 0.9865078 0.01959767586907752 1 -271 0 -3.5726995 0.047724232 0.070548673242470078 0 -272 1 3.3009596 0.90210295 0.14863601285829425 1 -273 1 0.21747208 0.4704687 1.0878293505835543 1 -274 0 -4.3236628 0.027569678 0.040333214707074766 0 +269 0 -5.4686823 0.011754766 0.01705900265167648 0 +270 1 6.031378 0.9865078 0.01959767586907752 1 +271 0 -3.5726995 0.04772423 0.070548667598666043 0 +272 1 3.3009605 0.902103 0.14863591753511315 1 +273 1 0.21747303 0.47046885 1.0878288936386407 1 +274 0 -4.323663 0.027569667 0.040333198126582509 0 275 0 ? ? ? 0 276 0 -5.0078387 0.016592303 0.024138448331127087 0 277 0 -5.95583 0.008152755 0.011810147478593156 0 -278 0 -5.4686823 0.011754767 0.017059004011272673 0 -279 1 7.127907 0.9940822 0.0085629246022787941 1 +278 0 -5.4686823 0.011754766 0.01705900265167648 0 +279 1 7.127905 0.9940822 0.0085629246022787941 1 280 0 -4.520691 0.023833437 0.034800759324198648 0 -281 0 -4.6892586 0.02103278 0.030667542438444956 0 +281 0 -4.689259 0.021032773 0.030667531458592488 0 282 1 4.4381247 0.95620465 0.064608669189911136 1 283 1 6.0636253 0.9868295 0.019127222915579972 1 284 1 7.431343 0.99529326 0.006806421628886848 1 285 1 14.218479 0.9999725 3.9642545670177728E-05 1 286 1 15.281265 0.9999877 1.7714321792245208E-05 1 -287 0 -4.9171767 0.017752616 0.025841674110087472 0 +287 0 -4.9171767 0.017752614 0.025841671374291015 0 288 1 2.2163515 0.80187416 0.31855224459431192 1 289 1 8.312019 0.9975813 0.0034936687628036997 1 290 0 -6.4429784 0.005648197 0.0081717255492616877 0 -291 0 -5.4686823 0.011754767 0.017059004011272673 0 +291 0 -5.4686823 0.011754766 0.01705900265167648 0 292 1 ? ? ? 0 -293 1 5.542121 0.9805624 0.028318669172894068 1 +293 1 5.542122 0.9805624 0.028318669172894068 1 294 0 ? ? ? 0 295 1 7.7866364 0.99640125 0.0052012629006476284 1 -296 0 1.823431 0.75025773 2.0014880731401443 1 +296 0 1.823431 0.7502577 2.0014877288199164 1 297 0 ? ? ? 0 298 0 -2.725597 0.08700067 0.13131429030889247 0 299 1 7.8274345 0.9965105 0.0050430801821242759 1 300 1 7.348074 0.9949879 0.0072491063826461647 1 -301 0 -5.4686823 0.011754767 0.017059004011272673 0 +301 0 -5.4686823 0.011754766 0.01705900265167648 0 302 1 15.735764 0.9999913 1.2554788140693439E-05 1 -303 0 -5.4686823 0.011754767 0.017059004011272673 0 +303 0 -5.4686823 0.011754766 0.01705900265167648 0 304 1 5.9607983 0.98577625 0.020667878309752356 1 305 1 8.459471 0.9978367 0.003124349823870973 1 -306 0 -5.4686823 0.011754767 0.017059004011272673 0 -307 0 -5.4686823 0.011754767 0.017059004011272673 0 +306 0 -5.4686823 0.011754766 0.01705900265167648 0 +307 0 -5.4686823 0.011754766 0.01705900265167648 0 308 1 7.422592 0.9952621 0.0068516084791740307 1 -309 0 -1.7474079 0.16675755 0.26319176134218036 0 -310 0 -5.391173 0.012457768 0.018085649525652777 0 +309 0 -1.7474074 0.1667576 0.26319183874281699 0 +310 0 -5.3911724 0.012457772 0.018085654967908992 0 311 0 -6.4429784 0.005648197 0.0081717255492616877 0 312 1 3.6294708 0.9220128 0.11714130634122567 1 313 0 -6.4429784 0.005648197 0.0081717255492616877 0 -314 0 -6.046492 0.0076150266 0.011028204574905828 0 +314 0 -6.0464916 0.007615029 0.011028207959717411 0 315 0 ? ? ? 0 -316 1 3.6177406 0.92137057 0.11814658455212051 1 +316 1 3.6177397 0.9213705 0.11814667788191786 1 317 1 9.215706 0.99877995 0.0017612310516048875 1 318 0 -5.1966968 0.014409561 0.02093983363547448 0 319 0 2.6369457 0.84775543 2.7155373416013662 1 @@ -323,77 +323,77 @@ Instance Label Score Probability Log-loss Assigned 321 0 ? ? ? 0 322 0 -4.520691 0.023833437 0.034800759324198648 0 323 1 5.5612926 0.98083764 0.027913746222999549 1 -324 0 -5.4686823 0.011754767 0.017059004011272673 0 -325 0 -4.192758 0.030360555 0.044479706359279253 0 +324 0 -5.4686823 0.011754766 0.01705900265167648 0 +325 0 -4.192758 0.030360553 0.044479703587910022 0 326 1 4.4103794 0.95531476 0.065951946768905437 1 327 0 -5.95583 0.008152755 0.011810147478593156 0 -328 1 3.373887 0.9068811 0.141014691181642 1 +328 1 3.373888 0.90688115 0.14101459636069669 1 329 1 7.8321342 0.9965229 0.0050251314657524084 1 330 1 5.856251 0.98462033 0.022360562903046506 1 -331 0 -3.2490888 0.06020364 0.08957991749201398 0 +331 0 -3.2490892 0.06020362 0.089579883179530889 0 332 0 -3.1363668 0.06522752 0.097312838862202627 0 333 1 4.914962 0.9690866 0.04530251936714582 1 334 1 5.9119453 0.98524714 0.021442445493118613 1 335 0 -6.4429784 0.005648197 0.0081717255492616877 0 336 1 5.543519 0.9805826 0.028288940560717461 1 -337 0 -5.4686823 0.011754767 0.017059004011272673 0 -338 0 -6.046492 0.0076150266 0.011028204574905828 0 +337 0 -5.4686823 0.011754766 0.01705900265167648 0 +338 0 -6.0464916 0.007615029 0.011028207959717411 0 339 1 5.684025 0.98251134 0.025454033740074194 1 340 1 6.620781 0.9913299 0.012562837713558307 1 -341 0 -5.4686823 0.011754767 0.017059004011272673 0 +341 0 -5.4686823 0.011754766 0.01705900265167648 0 342 0 -5.9689827 0.00807247 0.011693373854840414 0 343 0 -6.4429784 0.005648197 0.0081717255492616877 0 344 1 10.162451 0.99940467 0.00085913711935366719 1 345 0 -6.4429784 0.005648197 0.0081717255492616877 0 -346 0 -3.3358254 0.056586877 0.084038425056544686 0 -347 0 -6.1395845 0.0070995023 0.010278947729356253 0 -348 1 0.15727425 0.45910957 1.1230895756645052 1 +346 0 -3.335825 0.056586895 0.084038453540661334 0 +347 0 -6.1395845 0.007099502 0.010278947052745423 0 +348 1 0.15727425 0.45910954 1.1230896693146193 1 349 1 4.0622606 0.94258505 0.085305293794040227 1 350 0 -3.93614 0.036646076 0.053862170913072119 0 351 0 -4.9946866 0.016755885 0.024378447925635292 0 352 0 0.47192955 0.51868224 1.0549384413455107 1 353 1 8.696342 0.99819183 0.0026109941163622473 1 354 0 -5.95583 0.008152755 0.011810147478593156 0 -355 0 -4.246153 0.029190574 0.042739978357172199 0 -356 1 -0.69921684 0.30711415 1.7031530909424435 0 +355 0 -4.2461534 0.029190563 0.042739961748996601 0 +356 1 -0.69921684 0.30711412 1.7031532309414121 0 357 1 12.852016 0.9999226 0.00011170705633068974 1 358 1 5.582206 0.9811335 0.027478611445726227 1 359 1 5.3672857 0.97786665 0.032290354697391166 1 360 1 15.333874 0.9999882 1.7026382905914664E-05 1 361 1 6.317689 0.9891131 0.0157926107591757 1 362 0 -3.5059962 0.05007717 0.074117778802898118 0 -363 0 -2.0658464 0.13583238 0.2106169271876272 0 +363 0 -2.0658464 0.13583237 0.21061690231070429 0 364 0 -4.9946866 0.016755885 0.024378447925635292 0 365 0 -5.4818344 0.011639431 0.016890640083211839 0 366 1 13.694571 0.9999591 5.899125529457858E-05 1 367 1 11.299244 0.9997486 0.00036275701338483524 1 368 0 -5.8557806 0.008789949 0.012737278390963667 0 -369 0 -5.4592943 0.011837783 0.017180200787587779 0 +369 0 -5.4592943 0.011837782 0.017180199427877362 0 370 0 -3.89473 0.037771333 0.055548313644665219 0 371 0 -5.8557806 0.008789949 0.012737278390963667 0 -372 0 -4.2402444 0.029317858 0.04292914403934768 0 -373 0 -3.7544203 0.04183586 0.061655273456784274 0 -374 0 -4.71424 0.020646105 0.030097814327393193 0 +372 0 -4.2402444 0.029317856 0.042929141270955429 0 +373 0 -3.7544198 0.04183587 0.061655290284144987 0 +374 0 -4.71424 0.020646103 0.030097811583513857 0 375 0 -6.4429784 0.005648197 0.0081717255492616877 0 376 0 -5.95583 0.008152755 0.011810147478593156 0 -377 0 -6.046492 0.0076150266 0.011028204574905828 0 +377 0 -6.0464916 0.007615029 0.011028207959717411 0 378 0 -3.803866 0.040357877 0.059431608857550028 0 -379 0 -2.2557268 0.11979375 0.18408647967245978 0 +379 0 -2.2557268 0.11979374 0.18408646746064505 0 380 0 -6.4429784 0.005648197 0.0081717255492616877 0 381 1 10.07641 0.99936455 0.00091704491714961699 1 -382 0 -3.59721 0.04688631 0.069279781860915407 0 +382 0 -3.5972104 0.04688629 0.069279753666703942 0 383 0 -5.9689827 0.00807247 0.011693373854840414 0 384 0 -5.9689827 0.00807247 0.011693373854840414 0 -385 0 -3.7061968 0.043327074 0.063902325254427392 0 +385 0 -3.7061968 0.04332707 0.063902319636563967 0 386 1 6.0875864 0.98706365 0.018784981568614377 1 -387 0 -2.3345594 0.11363012 0.17401924291232526 0 -388 0 -5.2848067 0.013490496 0.019595145292487764 0 -389 0 -3.3224106 0.057132594 0.084873194157620097 0 +387 0 -2.3345594 0.113630116 0.17401923078542889 0 +388 0 -5.2848067 0.013490495 0.019595143930499406 0 +389 0 -3.322411 0.057132572 0.084873159956897426 0 390 0 -5.6504025 0.010256628 0.014873593254337859 0 391 1 10.030338 0.99934196 0.00094965672085389622 1 392 0 -5.0078387 0.016592303 0.024138448331127087 0 393 0 -6.53364 0.0052747843 0.0076300462926347243 0 -394 0 -5.241206 0.013937826 0.020249479819272553 0 +394 0 -5.241206 0.013937825 0.020249478456666324 0 395 0 -5.0078387 0.016592303 0.024138448331127087 0 396 0 -4.520691 0.023833437 0.034800759324198648 0 397 0 -5.0209913 0.016430287 0.023900783632758753 0 @@ -401,38 +401,38 @@ Instance Label Score Probability Log-loss Assigned 399 0 -5.7283545 0.0096734045 0.014023711114027561 0 400 1 10.056744 0.999355 0.00093081234331069047 1 401 0 -5.494987 0.01152521 0.016723922858878242 0 -402 0 -3.2177973 0.061560772 0.091664774253514145 0 +402 0 -3.2177973 0.06156077 0.091664768526496687 0 403 0 -4.145746 0.031428177 0.046069061057006429 0 -404 0 -5.507669 0.011416126 0.016564721842253591 0 +404 0 -5.507669 0.011416125 0.016564720483123126 0 405 0 -5.95583 0.008152755 0.011810147478593156 0 -406 0 -4.1128182 0.0321975 0.047215431190392593 0 +406 0 -4.1128182 0.032197498 0.047215425637133698 0 407 0 -5.95583 0.008152755 0.011810147478593156 0 -408 0 -3.691061 0.04380549 0.064623974204670379 0 -409 0 -4.71424 0.020646105 0.030097814327393193 0 +408 0 -3.6910605 0.043805506 0.064623996687367499 0 +409 0 -4.71424 0.020646103 0.030097811583513857 0 410 0 -5.95583 0.008152755 0.011810147478593156 0 411 0 ? ? ? 0 412 1 9.230705 0.9987937 0.0017413429278913565 1 -413 0 -3.2791004 0.05892837 0.087623557240190011 0 +413 0 -3.2791004 0.058928367 0.087623551529192406 0 414 1 6.7173824 0.9919376 0.011678759699519599 1 415 0 -0.6668339 0.3123661 0.54028742034074773 0 416 1 8.809383 0.9983402 0.0023965899325754819 1 417 0 -5.95583 0.008152755 0.011810147478593156 0 418 0 -1.8758612 0.15365387 0.2406803006815022 0 419 0 -5.442145 0.011990931 0.017403810256483163 0 -420 0 -2.5893164 0.09557056 0.14492013592578398 0 +420 0 -2.5893164 0.09557055 0.14492012404103641 0 421 1 11.824856 0.99983126 0.00024346198385283565 1 -422 0 -2.8105893 0.08201394 0.12345585262262933 0 -423 0 -3.4726496 0.051294282 0.075967452463448076 0 +422 0 -2.8105888 0.082013965 0.12345588775033869 0 +423 0 -3.4726496 0.05129428 0.075967446798405988 0 424 0 -5.494987 0.01152521 0.016723922858878242 0 425 1 14.817663 0.9999826 2.5109685538071405E-05 1 426 0 -2.8241482 0.08124289 0.12224458409135237 0 -427 1 4.3530817 0.9534221 0.068812983796401356 1 +427 1 4.3530827 0.9534222 0.068812893604113898 1 428 0 -5.95583 0.008152755 0.011810147478593156 0 429 0 -5.4818344 0.011639431 0.016890640083211839 0 430 0 -5.50395 0.011448007 0.016611248346541513 0 -431 0 -2.5834928 0.09595309 0.14553046360362973 0 -432 0 -3.8628197 0.03866103 0.056882875876087251 0 -433 0 -4.4631066 0.024871144 0.036335222703172541 0 +431 0 -2.5834928 0.095953085 0.14553045171385326 0 +432 0 -3.8628197 0.038661025 0.056882870285491223 0 +433 0 -4.4631066 0.024871143 0.036335219947404518 0 434 0 5.0084 0.97114086 5.1148279570464927 1 435 1 7.444332 0.9953392 0.0067398103254229808 1 436 1 3.841199 0.9328069 0.10034962060198541 1 @@ -440,52 +440,52 @@ Instance Label Score Probability Log-loss Assigned 438 0 -3.822938 0.039801266 0.058595061116589527 0 439 0 -4.5469956 0.023373578 0.034121284818622755 0 440 1 10.154686 0.99940115 0.00086421363869066531 1 -441 0 -1.8604465 0.1551807 0.24328528875750627 0 -442 0 -4.932629 0.017549368 0.025543180352955049 0 +441 0 -1.8604469 0.15518063 0.24328518697086191 0 +442 0 -4.9326286 0.017549373 0.02554318855864653 0 443 0 -5.9932313 0.007926505 0.011481092360580692 0 -444 0 -2.442047 0.10567284 0.16112539871549494 0 +444 0 -2.442047 0.10567283 0.16112538669649779 0 445 0 -5.9689827 0.00807247 0.011693373854840414 0 446 0 -6.4429784 0.005648197 0.0081717255492616877 0 447 0 -4.5469956 0.023373578 0.034121284818622755 0 448 0 -6.53364 0.0052747843 0.0076300462926347243 0 449 1 10.298004 0.99946284 0.00077516203531090419 1 -450 0 -4.0136495 0.03462597 0.050840077971040572 0 +450 0 -4.01365 0.034625955 0.050840055702126637 0 451 0 -4.5469956 0.023373578 0.034121284818622755 0 -452 0 -4.8841314 0.018195055 0.026491662451451816 0 +452 0 -4.8841314 0.018195054 0.026491659714422498 0 453 1 8.777971 0.99830025 0.0024543010625143486 1 454 0 -5.6234684 0.010466128 0.015179002444545909 0 -455 1 0.81635284 0.5832275 0.77786931086041744 1 -456 1 10.487387 0.99953467 0.00067149052326743921 1 +455 1 0.81635284 0.58322746 0.77786945830087184 1 +456 1 10.487385 0.99953467 0.00067149052326743921 1 457 1 9.06263 0.9986299 0.0019779518845872311 1 -458 0 -4.253397 0.02903525 0.042509173626544897 0 -459 0 -3.9597979 0.036017757 0.052921523163131159 0 +458 0 -4.2533965 0.029035257 0.042509184696890877 0 +459 0 -3.9597979 0.036017753 0.052921517587864737 0 460 0 -3.93614 0.036646076 0.053862170913072119 0 -461 0 -3.6959996 0.04364884 0.064387638591170013 0 -462 0 -3.4621444 0.05168347 0.076559412170907212 0 -463 0 -4.823963 0.019028598 0.027717016988036743 0 +461 0 -3.6959996 0.043648835 0.064387632971416475 0 +462 0 -3.4621444 0.051683467 0.076559406503540178 0 +463 0 -4.823963 0.019028597 0.027717014248681737 0 464 0 -5.0209913 0.016430287 0.023900783632758753 0 465 1 9.7853985 0.99920774 0.0011434497150273235 1 -466 1 9.541933 0.99904716 0.0013753126588050381 1 +466 1 9.541931 0.99904716 0.0013753126588050381 1 467 1 7.7145195 0.99619967 0.0054931654697981886 1 468 0 -5.0209913 0.016430287 0.023900783632758753 0 469 0 -5.6622314 0.010165936 0.014741403595886524 0 -470 0 -5.0985007 0.015506633 0.022546608302385175 0 -471 0 -3.4621444 0.05168347 0.076559412170907212 0 -472 0 -4.163662 0.0310171 0.04545688944873634 0 +470 0 -5.0985003 0.015506638 0.022546616491050588 0 +471 0 -3.4621444 0.051683467 0.076559406503540178 0 +472 0 -4.163662 0.031017099 0.045456886675489347 0 473 0 -5.0209913 0.016430287 0.023900783632758753 0 474 0 -4.5469956 0.023373578 0.034121284818622755 0 475 0 -5.494987 0.01152521 0.016723922858878242 0 -476 0 -4.727392 0.020445334 0.02980208737260864 0 +476 0 -4.727392 0.020445332 0.029802084629291691 0 477 0 -5.0209913 0.016430287 0.023900783632758753 0 -478 0 -4.4195695 0.025684815 0.03753954391072279 0 +478 0 -4.4195695 0.025684813 0.037539541152653365 0 479 1 8.32148 0.9975986 0.0034686710325429922 1 -480 0 -4.6376576 0.021854054 0.031878354047421072 0 +480 0 -4.6376576 0.021854052 0.031878351300153221 0 481 0 -3.082261 0.067775175 0.10125016271957035 0 482 1 15.481422 0.99998945 1.5220544890042414E-05 1 -483 1 10.906595 0.9996614 0.0004885994477486597 1 -484 0 -4.253397 0.02903525 0.042509173626544897 0 -485 0 -5.023291 0.016402118 0.023859466397713937 0 -486 0 -5.0985007 0.015506633 0.022546608302385175 0 +483 1 10.906593 0.9996614 0.0004885994477486597 1 +484 0 -4.2533965 0.029035257 0.042509184696890877 0 +485 0 -5.0232906 0.016402123 0.023859474593834452 0 +486 0 -5.0985003 0.015506638 0.022546616491050588 0 487 1 13.475906 0.9999518 6.9568659593352019E-05 1 488 1 1.3273258 0.67341053 0.57044180397199062 1 489 1 -0.6232014 0.31951928 1.646025101672679 0 @@ -493,153 +493,153 @@ Instance Label Score Probability Log-loss Assigned 491 1 6.6713333 0.9916534 0.012092158077135179 1 492 0 -4.624505 0.022068353 0.032194464591169099 0 493 1 9.94891 0.99930006 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5.502534 0.97998166 0.029173343678465424 1 +521 0 -4.9303293 0.01757947 0.025587385090042082 0 +522 1 5.502533 0.97998166 0.029173343678465424 1 523 1 7.699238 0.9961555 0.0055571295392400502 1 524 0 -5.0078387 0.016592303 0.024138448331127087 0 -525 0 -5.189559 0.014486661 0.021052696761167319 0 +525 0 -5.189559 0.01448666 0.021052695397802253 0 526 0 -5.0209913 0.016430287 0.023900783632758753 0 527 0 -4.046695 0.03379773 0.049602852281362811 0 -528 0 -3.1803741 0.063221365 0.094219922377553209 0 +528 0 -3.1803741 0.06322136 0.094219910903214188 0 529 0 -4.624505 0.022068353 0.032194464591169099 0 -530 1 6.515935 0.9906189 0.013597970981112992 1 -531 0 -4.1128182 0.0321975 0.047215431190392593 0 +530 1 6.515936 0.9906189 0.013597970981112992 1 +531 0 -4.1128182 0.032197498 0.047215425637133698 0 532 0 -5.5593443 0.010982092 0.01593145032913975 0 533 0 -5.0078387 0.016592303 0.024138448331127087 0 534 0 -5.4818344 0.011639431 0.016890640083211839 0 535 0 -4.5760565 0.022875603 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-640 0 -4.4101357 0.02586451 0.037805647681891714 0 +640 0 -4.4101357 0.025864508 0.037805644923313529 0 641 0 -5.0078387 0.016592303 0.024138448331127087 0 642 0 -5.0078387 0.016592303 0.024138448331127087 0 643 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -649,13 +649,13 @@ Instance Label Score Probability Log-loss Assigned 647 0 -5.832123 0.008947697 0.012966896248755017 0 648 1 12.236198 0.9998765 0.00017818502951934642 1 649 0 -5.0078387 0.016592303 0.024138448331127087 0 -650 0 -3.7496567 0.041980952 0.061873754012903771 0 -651 0 -5.217549 0.014186632 0.020613550094586007 0 -652 0 -3.8628197 0.03866103 0.056882875876087251 0 +650 0 -3.7496567 0.04198095 0.061873748402934067 0 +651 0 -5.217549 0.014186631 0.020613548731635876 0 +652 0 -3.8628197 0.038661025 0.056882870285491223 0 653 0 -4.533843 0.023602419 0.034459373415090033 0 654 0 -4.520691 0.023833437 0.034800759324198648 0 655 0 -5.0078387 0.016592303 0.024138448331127087 0 -656 0 -4.059848 0.03347343 0.049118699463849731 0 +656 0 -4.0598474 0.03347344 0.049118716145619364 0 657 0 -0.4869156 0.3424043 0.6047272426002388 0 658 1 9.086258 0.9986542 0.0019429058245893991 1 659 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -663,36 +663,36 @@ Instance Label Score Probability Log-loss Assigned 661 0 -4.046695 0.03379773 0.049602852281362811 0 662 0 -5.3686323 0.012669891 0.018395571683516153 0 663 0 -5.3686323 0.012669891 0.018395571683516153 0 -664 0 -4.3969836 0.026117077 0.038179748349732472 0 +664 0 -4.3969836 0.026117075 0.038179745590438879 0 665 0 -6.4429784 0.005648197 0.0081717255492616877 0 -666 0 -3.4969325 0.050405253 0.074616140216202456 0 +666 0 -3.496932 0.050405268 0.074616162855156168 0 667 0 -4.520691 0.023833437 0.034800759324198648 0 -668 1 2.8043652 0.86343354 0.21184295985895515 1 +668 1 2.8043652 0.8634335 0.21184305945125079 1 669 1 8.147335 0.99726033 0.0039579294672795707 1 670 1 6.4856205 0.99040276 0.013912762642680866 1 671 0 -4.087837 0.032793265 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0.032454455742594257 0 688 0 -5.0209913 0.016430287 0.023900783632758753 0 -689 0 -4.1777043 0.03069854 0.044982670054638463 0 +689 0 -4.1777043 0.030698538 0.044982667282302893 0 690 0 -5.832123 0.008947697 0.012966896248755017 0 -691 1 4.4967804 0.95803064 0.061856295892801602 1 +691 1 4.4967794 0.9580306 0.061856385651233553 1 692 0 -5.494987 0.01152521 0.016723922858878242 0 -693 0 -4.6842284 0.021111494 0.030783546498899184 0 +693 0 -4.6842284 0.021111492 0.030783543753715333 0 694 0 -4.9545784 0.017264586 0.02512504882755813 0 695 0 -5.9689827 0.00807247 0.011693373854840414 0 696 1 6.7127857 0.9919096 0.011719418003861082 1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt index 68b75e3a8b..c5865349b0 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned 5 1 11.925824 0.99988866 0.00016064073906035849 1 6 0 -0.4527979 0.28799012 0.49003084056125074 0 -8 0 -3.796255 0.026382547 0.038573064761285226 0 -9 0 -3.8130417 0.026036168 0.038059895132779242 0 -10 0 -4.7285223 0.0125917215 0.018281354087045291 0 -11 0 -4.606969 0.013873976 0.02015606392328537 0 -18 1 7.3475924 0.99550986 0.006492485535140746 1 +8 0 -3.7962546 0.02638256 0.03857308408160709 0 +9 0 -3.8130417 0.02603617 0.038059897891843629 0 +10 0 -4.7285223 0.012591722 0.018281355447793919 0 +11 0 -4.606969 0.013873977 0.020156065285803371 0 +18 1 7.3475914 0.99550986 0.006492485535140746 1 20 1 6.1389017 0.9881576 0.017186984398807038 1 21 1 7.1486177 0.9947303 0.0076226814052998944 1 25 1 1.6632223 0.6911742 0.53287870995530984 1 -28 0 -4.606969 0.013873976 0.02015606392328537 0 -31 0 -4.2645645 0.01821836 0.026525908568759592 0 +28 0 -4.606969 0.013873977 0.020156065285803371 0 +31 0 -4.2645645 0.018218363 0.026525911305853887 0 32 1 7.198575 0.99493784 0.0073217047835288053 1 -35 0 -4.606969 0.013873976 0.02015606392328537 0 -37 0 -1.714282 0.12729812 0.19643918434677948 0 +35 0 -4.606969 0.013873977 0.020156065285803371 0 +37 0 -1.7142825 0.12729807 0.19643911044583612 0 40 0 ? ? ? 0 -41 1 2.5451746 0.8203416 0.28570332741079429 1 +41 1 2.5451746 0.82034165 0.28570322258699443 1 44 1 8.165841 0.9976778 0.0033541180091675089 1 -45 0 -4.602255 0.0139262155 0.020232492307431734 0 +45 0 -4.602255 0.013926216 0.020232493670021917 0 46 1 5.6216097 0.9821182 0.026031444169057363 1 -48 0 -3.379683 0.03656119 0.053735055635659848 0 +48 0 -3.379683 0.036561195 0.053735061214071053 0 50 1 2.8003244 0.8487653 0.2365623958486662 1 -51 1 0.14775276 0.39660606 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0.29543816942188406 1 -76 0 -3.8396955 0.02549526 0.037258890759872848 0 -77 0 -3.1438046 0.043905534 0.064774925308085776 0 -79 0 -4.4265766 0.016017534 0.023295486850883066 0 -82 0 -3.1870723 0.042460293 0.062595780681733368 0 -88 0 -3.697434 0.028514326 0.041735373036084926 0 -90 0 -4.54813 0.01454009 0.021130913771998817 0 -91 0 -4.5069323 0.015025148 0.021841204430750202 0 -92 0 -3.697434 0.028514326 0.041735373036084926 0 -93 0 -4.8905344 0.011062949 0.016049403073119514 0 -95 0 -4.54813 0.01454009 0.021130913771998817 0 -96 0 -4.790498 0.011983715 0.017393273625651583 0 -97 0 -3.413869 0.0356 0.052296442645105176 0 +76 0 -3.8396955 0.025495263 0.037258896274938731 0 +77 0 -3.1438046 0.043905538 0.064774930929348148 0 +79 0 -4.4265766 0.016017536 0.023295489581855438 0 +82 0 -3.1870723 0.042460296 0.062595786294511416 0 +88 0 -3.697434 0.028514327 0.041735375802187391 0 +90 0 -4.54813 0.014540091 0.021130915135437803 0 +91 0 -4.5069323 0.015025149 0.021841205794860624 0 +92 0 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0.026802778 0.039195893152521553 0 -643 0 -5.935418 0.006225399 0.0090094253868573462 0 -646 0 -5.426158 0.00923903 0.013391058760089801 0 -647 0 -5.4890733 0.008799812 0.012751633519735472 0 +640 0 -4.039895 0.026802776 0.039195890391283787 0 +643 0 -5.935418 0.0062253983 0.0090094240348259531 0 +646 0 -5.426158 0.009239029 0.013391057403945885 0 +647 0 -5.4890733 0.008799811 0.012751632164192488 0 648 1 8.579456 0.9981042 0.0027376359023654488 1 650 0 -3.6219683 0.036770534 0.054048568515248828 0 -651 0 -4.9650173 0.01319296 0.019160086942209224 0 -655 0 -4.7037654 0.016131794 0.023463022615813851 0 -658 1 7.546404 0.99576104 0.006128529583862446 1 -659 0 -5.935418 0.006225399 0.0090094253868573462 0 -662 0 -5.1684093 0.011276632 0.016361164283329451 0 -663 0 -5.1684093 0.011276632 0.016361164283329451 0 -664 0 -4.2574205 0.02270922 0.033140214869637266 0 -666 0 -3.0702114 0.055484954 0.08235431655806906 0 -667 0 -4.4382315 0.01977694 0.028818008476875413 0 -669 1 6.9817867 0.99342644 0.0095149472206866505 1 -671 0 -3.9565368 0.028554758 0.041795418071838834 0 -672 0 -4.921291 0.013645153 0.019821336171893227 0 +651 0 -4.965017 0.013192964 0.019160092388520032 0 +655 0 -4.7037654 0.016131792 0.023463019884524334 0 +658 1 7.546403 0.99576104 0.006128529583862446 1 +659 0 -5.935418 0.0062253983 0.0090094240348259531 0 +662 0 -5.1684093 0.011276631 0.016361162924390743 0 +663 0 -5.1684093 0.011276631 0.016361162924390743 0 +664 0 -4.2574205 0.022709219 0.033140212119965443 0 +666 0 -3.0702114 0.05548495 0.082354310867892036 0 +667 0 -4.4382315 0.019776938 0.028818005735429076 0 +669 1 6.9817877 0.9934265 0.0095148606603544482 1 +671 0 -3.9565368 0.028554756 0.041795415305621239 0 +672 0 -4.921291 0.013645152 0.019821334809691313 0 673 0 -3.289723 0.047153607 0.069684436821807083 0 -674 0 -5.669884 0.0076494967 0.011078316903702529 0 -675 0 -3.7340279 0.03379261 0.049595209486744243 0 -676 0 -5.419147 0.009289299 0.013464259841054335 0 -677 0 -4.0031805 0.02756116 0.040320577663836212 0 -684 0 -5.935418 0.006225399 0.0090094253868573462 0 -686 0 -5.935418 0.006225399 0.0090094253868573462 0 -687 0 -4.295347 0.022060877 0.032183434683621268 0 -690 0 -5.4890733 0.008799812 0.012751633519735472 0 +674 0 -5.669884 0.007649496 0.011078316226716699 0 +675 0 -3.7340279 0.033792607 0.049595203924317495 0 +676 0 -5.419147 0.009289298 0.013464258484841609 0 +677 0 -4.0031805 0.027561158 0.040320574900445029 0 +684 0 -5.935418 0.0062253983 0.0090094240348259531 0 +686 0 -5.935418 0.0062253983 0.0090094240348259531 0 +687 0 -4.295347 0.022060875 0.032183431935772396 0 +690 0 -5.4890733 0.008799811 0.012751632164192488 0 695 0 -5.4523587 0.00905354 0.013120983569838577 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt index 48a92460de..5b355d6992 100644 --- a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt @@ -1,156 +1,156 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.5726995 0.047724232 0.070548673242470078 0 -1 0 3.6101446 0.92095214 3.6611298070520353 1 -2 0 -4.0709443 0.03320216 0.048713845243635014 0 -3 0 2.470542 0.83073896 2.5626781731328263 1 -4 0 -3.4358397 0.05267027 0.078061433982946932 0 -5 1 12.382593 0.99988943 0.0001595227277818751 1 -6 0 -1.4209604 0.20405398 0.32925750806385767 0 +0 0 -3.5726995 0.04772423 0.070548667598666043 0 +1 0 3.6101456 0.92095214 3.6611298070520353 1 +2 0 -4.070944 0.03320217 0.048713861920724008 0 +3 0 2.470542 0.8307389 2.5626776650932253 1 +4 0 -3.4358397 0.052670266 0.078061428309676412 0 +5 1 12.382595 0.99988943 0.0001595227277818751 1 +6 0 -1.4209604 0.20405397 0.32925748105470037 0 7 0 -4.7010875 0.02084881 0.030396453391412736 0 -8 0 -4.6745405 0.021263903 0.031008185910590607 0 -9 0 -4.406417 0.025935683 0.037911059564125327 0 +8 0 -4.6745405 0.021263901 0.031008183164979278 0 +9 0 -4.406417 0.025935682 0.037911056805345573 0 10 0 -5.5593443 0.010982092 0.01593145032913975 0 11 0 -5.4818344 0.011639431 0.016890640083211839 0 -12 1 -0.14206886 0.40347567 1.3094464075888503 0 -13 0 -4.5691886 0.022992346 0.033558231039772665 0 +12 1 -0.14206791 0.40347582 1.3094458747728994 0 +13 0 -4.569188 0.022992352 0.033558239291178633 0 14 1 9.321613 0.998874 0.0016253773809814112 1 -15 1 1.3856993 0.6830734 0.54988747944718552 1 +15 1 1.3856983 0.6830732 0.54988785711378696 1 16 0 -4.533843 0.023602419 0.034459373415090033 0 17 0 -4.046695 0.03379773 0.049602852281362811 0 18 1 7.8903713 0.9966727 0.0048082975514731332 1 -19 0 -3.0987039 0.06699134 0.10003761775121778 0 +19 0 -3.0987039 0.06699133 0.10003760623051484 0 20 1 7.528511 0.9956263 0.0063237968594455725 1 21 1 7.875206 0.9966343 0.0048638619113426269 1 22 0 -5.0078387 0.016592303 0.024138448331127087 0 23 1 ? ? ? 0 -24 0 -5.4686823 0.011754767 0.017059004011272673 0 +24 0 -5.4686823 0.011754766 0.01705900265167648 0 25 1 1.741828 0.73848027 0.43736871808798289 1 -26 0 -4.9710746 0.01705355 0.024815273012732428 0 -27 0 -4.059848 0.03347343 0.049118699463849731 0 +26 0 -4.971074 0.017053556 0.024815281214284793 0 +27 0 -4.0598474 0.03347344 0.049118716145619364 0 28 0 -5.4818344 0.011639431 0.016890640083211839 0 29 0 -5.8557806 0.008789949 0.012737278390963667 0 -30 0 -5.0985007 0.015506633 0.022546608302385175 0 +30 0 -5.0985003 0.015506638 0.022546616491050588 0 31 0 -4.9946866 0.016755885 0.024378447925635292 0 32 1 7.46414 0.9954084 0.0066395103905325147 1 -33 0 -4.6892586 0.02103278 0.030667542438444956 0 -34 0 -4.71424 0.020646105 0.030097814327393193 0 +33 0 -4.689259 0.021032773 0.030667531458592488 0 +34 0 -4.71424 0.020646103 0.030097811583513857 0 35 0 -5.4818344 0.011639431 0.016890640083211839 0 36 1 9.09911 0.99866724 0.0019240484689165348 1 -37 0 -1.113348 0.24456774 0.40462569514389818 0 +37 0 -1.113348 0.24456772 0.40462566668624267 0 38 1 6.140955 0.9875705 0.018044317542393044 1 -39 1 2.5109034 0.83500063 0.26015080155533699 1 +39 1 2.5109034 0.8350006 0.26015090453888573 1 40 0 ? ? ? 0 -41 1 3.3300762 0.90403634 0.14554732340521623 1 +41 1 3.3300762 0.9040363 0.14554741852454292 1 42 1 8.577511 0.99802166 0.0028569650360322531 1 43 1 0.49126053 0.5223421 0.93693314652059867 1 -44 1 8.255751 0.9974761 0.0036458194620463992 1 +44 1 8.255749 0.9974761 0.0036458194620463992 1 45 0 -5.63222 0.010397597 0.015079091662558042 0 -46 1 4.5673847 0.96013206 0.058695238307073898 1 +46 1 4.5673857 0.9601321 0.05869514874509995 1 47 0 -5.95583 0.008152755 0.011810147478593156 0 -48 0 -3.4358397 0.05267027 0.078061433982946932 0 -49 1 5.3666534 0.9778563 0.032305655935016012 1 -50 1 2.5949688 0.8436 0.24536904232329945 1 -51 1 0.12595844 0.4532164 1.1417280158533796 1 +48 0 -3.4358397 0.052670266 0.078061428309676412 0 +49 1 5.3666544 0.9778563 0.032305655935016012 1 +50 1 2.5949688 0.8435999 0.2453691442570726 1 +51 1 0.12595844 0.45321637 1.1417281107212258 1 52 1 5.2992115 0.976721 0.033981595778575298 1 53 1 8.407227 0.99774945 0.0032505195711204126 1 -54 1 7.649309 0.9960077 0.0057712269479890379 1 +54 1 7.6493073 0.9960077 0.0057712269479890379 1 55 1 4.478711 0.957476 0.062691739926295564 1 56 1 5.5541334 0.9807353 0.02806428572372743 1 57 1 1.6657066 0.7271758 0.45962396180677062 1 -58 1 2.5265894 0.8366335 0.25733232225246744 1 -59 1 1.7368536 0.7377509 0.43879434296981179 1 +58 1 2.5265903 0.83663356 0.25733221946992052 1 +59 1 1.7368546 0.737751 0.43879410985231937 1 60 1 2.3288136 0.8150797 0.29498697868205404 1 -61 0 -5.5060835 0.0114297075 0.016584542177967555 0 +61 0 -5.5060835 0.011429707 0.016584540818818418 0 62 1 6.380089 0.98961115 0.015066341261387473 1 -63 1 0.33490086 0.49270463 1.0212050663068744 1 +63 1 0.33490086 0.4927046 1.0212051535714572 1 64 0 -5.95583 0.008152755 0.011810147478593156 0 -65 1 3.8072634 0.9311753 0.1028753176207461 1 +65 1 3.8072643 0.93117535 0.1028752252736623 1 66 0 -4.046695 0.03379773 0.049602852281362811 0 -67 1 4.218014 0.9486548 0.076044929659653121 1 +67 1 4.218014 0.9486547 0.076045020305197497 1 68 1 10.826725 0.9996402 0.00051913703181331893 1 -69 0 -5.271654 0.013623926 0.019790289199966723 0 -70 0 -3.4726496 0.051294282 0.075967452463448076 0 +69 0 -5.2716546 0.01362392 0.019790281026931159 0 +70 0 -3.4726496 0.05129428 0.075967446798405988 0 71 1 7.895046 0.99668443 0.0047913008067115544 1 -72 0 -2.1755848 0.1263537 0.1948787725155719 0 +72 0 -2.1755848 0.12635368 0.19487874790855292 0 73 1 8.9055195 0.9984568 0.0022281210940979582 1 -74 1 2.5993576 0.8440387 0.2446189028997863 1 -75 0 -4.0411606 0.033935104 0.049807988000729261 0 +74 1 2.5993576 0.84403867 0.244619004780572 1 +75 0 -4.0411606 0.0339351 0.049807982437482065 0 76 0 -5.0750337 0.015780753 0.022948364676579087 0 -77 0 -3.499567 0.05030968 0.074470946934380028 0 -78 0 -3.6211972 0.046079826 0.06805955070059394 0 -79 0 -5.391173 0.012457768 0.018085649525652777 0 -80 0 -2.7157316 0.087596945 0.13225681838671866 0 -81 0 -4.2284155 0.029574301 0.043310338334976231 0 -82 0 -3.4452734 0.05231434 0.077519488826293739 0 +77 0 -3.499567 0.050309677 0.074470941275211228 0 +78 0 -3.6211972 0.046079822 0.068059545066518914 0 +79 0 -5.3911724 0.012457772 0.018085654967908992 0 +80 0 -2.7157316 0.08759694 0.13225680660583347 0 +81 0 -4.2284155 0.0295743 0.043310335565852412 0 +82 0 -3.4452734 0.052314337 0.077519483155153973 0 83 0 -2.1223526 0.13087894 0.20237095101991351 0 84 1 9.694054 0.99915093 0.0012254667568283894 1 85 1 6.2895603 0.9888809 0.016131360230936344 1 86 1 2.6168842 0.84578085 0.24164420028856959 1 -87 1 6.919142 0.9930738 0.010027129067340199 1 +87 1 6.91914 0.9930738 0.010027129067340199 1 88 0 -4.046695 0.03379773 0.049602852281362811 0 89 0 -5.085745 0.015655048 0.022764115650391988 0 -90 0 -5.4686823 0.011754767 0.017059004011272673 0 -91 0 -5.189559 0.014486661 0.021052696761167319 0 +90 0 -5.4686823 0.011754766 0.01705900265167648 0 +91 0 -5.189559 0.01448666 0.021052695397802253 0 92 0 -4.046695 0.03379773 0.049602852281362811 0 93 0 -5.95583 0.008152755 0.011810147478593156 0 94 0 -4.9946866 0.016755885 0.024378447925635292 0 -95 0 -5.4686823 0.011754767 0.017059004011272673 0 +95 0 -5.4686823 0.011754766 0.01705900265167648 0 96 0 -5.663555 0.010155837 0.014726683875270435 0 -97 0 -3.5726995 0.047724232 0.070548673242470078 0 +97 0 -3.5726995 0.04772423 0.070548667598666043 0 98 1 8.590233 0.9980406 0.0028295658493644239 1 99 1 10.917194 0.99966407 0.00048472853254951715 1 100 1 4.8476696 0.9675202 0.047636346312542086 1 101 1 -0.84280396 0.28443488 1.8138296783750301 0 -102 0 -3.7530966 0.041876126 0.061715903711063051 0 -103 1 1.7746439 0.7432593 0.42806246416940569 1 +102 0 -3.753096 0.04187614 0.061715926148486977 0 +103 1 1.7746449 0.7432594 0.42806234847449176 1 104 1 12.140858 0.9998672 0.00019160139559387039 1 -105 1 2.5560713 0.83966726 0.25211035808697119 1 +105 1 2.5560703 0.83966714 0.25211056290937062 1 106 1 9.259369 0.99881965 0.0017038920105930569 1 107 1 6.720646 0.99195737 0.011649978820645172 1 108 0 -5.5617743 0.010962089 0.015902272030853013 0 109 1 6.871727 0.992822 0.010393022735684912 1 -110 0 -2.766693 0.08455608 0.12745658613888911 0 -111 1 3.848031 0.93313104 0.099848402613633994 1 +110 0 -2.766693 0.08455607 0.12745657439713692 0 +111 1 3.848032 0.93313104 0.099848402613633994 1 112 1 9.425768 0.9989595 0.0015019320975399703 1 -113 1 9.506624 0.9990213 0.0014126689718023965 1 -114 0 -3.0727458 0.068232656 0.10195832549468188 0 -115 0 -4.6439905 0.021751598 0.03172724574533177 0 -116 0 -0.66188717 0.3131727 0.54198070603227311 0 +113 1 9.506622 0.9990213 0.0014126689718023965 1 +114 0 -3.0727453 0.06823267 0.10195834856678422 0 +115 0 -4.6439905 0.021751596 0.031727242998351647 0 +116 0 -0.66188717 0.31317267 0.54198064343187602 0 117 1 9.617275 0.9991 0.0012989676266954972 1 -118 0 -5.3621607 0.012731451 0.018485525732496611 0 -119 0 -3.9435177 0.036449015 0.053567088295960068 0 -120 0 -4.8696556 0.018392263 0.026781474537826103 0 -121 0 -3.469522 0.05140986 0.076143221101649672 0 +118 0 -5.3621607 0.01273145 0.018485524371555397 0 +119 0 -3.9435177 0.03644901 0.053567082718198315 0 +120 0 -4.8696556 0.018392261 0.026781471800246908 0 +121 0 -3.469522 0.051409855 0.076143215435917344 0 122 1 9.680523 0.99914217 0.0012381182790972595 1 123 1 3.8165932 0.9316275 0.10217485034891999 1 124 1 7.6522446 0.99601656 0.0057583629403995997 1 125 0 -5.95583 0.008152755 0.011810147478593156 0 -126 1 8.564951 0.9980027 0.0028843647430662487 1 +126 1 8.564953 0.9980027 0.0028843647430662487 1 127 0 -4.520691 0.023833437 0.034800759324198648 0 128 1 4.84898 0.9675514 0.04758977495588574 1 -129 0 -5.717684 0.009751258 0.014137132147530099 0 -130 0 -3.4726496 0.051294282 0.075967452463448076 0 +129 0 -5.717684 0.0097512575 0.014137130790684689 0 +130 0 -3.4726496 0.05129428 0.075967446798405988 0 131 0 -4.9946866 0.016755885 0.024378447925635292 0 132 1 8.602232 0.9980583 0.0028039765128859018 1 133 0 -4.810811 0.01921573 0.027992252541605728 0 -134 0 -4.9171767 0.017752616 0.025841674110087472 0 -135 0 -2.7288966 0.08680205 0.13100047471163517 0 +134 0 -4.9171767 0.017752614 0.025841671374291015 0 +135 0 -2.7288966 0.08680204 0.13100046294100468 0 136 0 -4.533843 0.023602419 0.034459373415090033 0 137 0 -5.494987 0.01152521 0.016723922858878242 0 -138 0 -4.2402444 0.029317858 0.04292914403934768 0 +138 0 -4.2402444 0.029317856 0.042929141270955429 0 139 0 ? ? ? 0 140 0 -5.494987 0.01152521 0.016723922858878242 0 141 0 -5.9689827 0.00807247 0.011693373854840414 0 -142 1 4.4324036 0.95602256 0.064883430994521443 1 -143 0 -4.6439905 0.021751598 0.03172724574533177 0 +142 1 4.4324026 0.9560225 0.064883520941486764 1 +143 0 -4.6439905 0.021751596 0.031727242998351647 0 144 0 -5.4818344 0.011639431 0.016890640083211839 0 145 0 ? ? ? 0 146 1 1.3394346 0.67542744 0.56612731001545713 1 -147 0 -5.4154215 0.012233486 0.017758034331297672 0 -148 0 -1.0123739 0.25899458 0.4324439985553144 0 +147 0 -5.4154215 0.0122334855 0.017758032971042548 0 +148 0 -1.012373 0.2589947 0.43244423064898052 0 149 1 11.461615 0.99977773 0.00032069729386163213 1 150 0 -5.5593443 0.010982092 0.01593145032913975 0 -151 1 5.006485 0.9711001 0.042308092038938809 1 +151 1 5.006484 0.9711001 0.042308092038938809 1 152 1 9.715748 0.99916476 0.0012054999542047689 1 153 0 -4.121497 0.03199298 0.046910583933444494 0 154 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -160,17 +160,17 @@ Instance Label Score Probability Log-loss Assigned 158 0 ? ? ? 0 159 1 12.346203 0.99988633 0.0001639947780942108 1 160 1 9.039494 0.99860567 0.002012998795947356 1 -161 0 -3.8496675 0.039033547 0.057442027050147004 0 +161 0 -3.8496675 0.039033543 0.057442021457383786 0 162 0 -4.520691 0.023833437 0.034800759324198648 0 -163 0 -3.3870554 0.05454763 0.080923315355362818 0 +163 0 -3.3870554 0.054547627 0.080923309670827032 0 164 0 ? ? ? 0 -165 0 -3.3999205 0.05404651 0.080158845547395108 0 +165 0 -3.3999205 0.054046508 0.080158839865870704 0 166 1 7.976185 0.9968817 0.004505750926832852 1 167 1 8.355644 0.99765986 0.0033800618776249807 1 168 0 -4.520691 0.023833437 0.034800759324198648 0 169 0 -6.2282124 0.0066409498 0.009612819847748871 0 170 0 -5.494987 0.01152521 0.016723922858878242 0 -171 0 -5.4686823 0.011754767 0.017059004011272673 0 +171 0 -5.4686823 0.011754766 0.01705900265167648 0 172 0 -5.95583 0.008152755 0.011810147478593156 0 173 1 15.1560135 0.9999865 1.9434170443242565E-05 1 174 1 6.1769257 0.9879011 0.017561488134486943 1 @@ -181,72 +181,72 @@ Instance Label Score Probability Log-loss Assigned 179 1 2.290575 0.8106676 0.30281765649168213 1 180 0 -5.5593443 0.010982092 0.01593145032913975 0 181 0 -6.4429784 0.005648197 0.0081717255492616877 0 -182 0 -3.0987039 0.06699134 0.10003761775121778 0 +182 0 -3.0987039 0.06699133 0.10003760623051484 0 183 1 9.1599655 0.9987273 0.0018372561468384253 1 184 1 6.2014637 0.98812157 0.017239546569330137 1 -185 0 -5.0853486 0.01565968 0.022770905093500125 0 +185 0 -5.085348 0.015659686 0.022770913283438728 0 186 1 5.7654095 0.9835412 0.023942621168576837 1 187 1 13.977451 0.99996704 4.7553986690113E-05 1 188 1 9.065283 0.99863267 0.0019739908621602127 1 -189 0 -4.7540584 0.020044118 0.029211295141143595 0 -190 1 11.957216 0.99984735 0.0002202405944450654 1 -191 1 10.956871 0.999674 0.00047036322693564959 1 -192 0 -4.059848 0.03347343 0.049118699463849731 0 -193 0 -5.4686823 0.011754767 0.017059004011272673 0 +189 0 -4.7540584 0.020044116 0.029211292398949824 0 +190 1 11.957218 0.99984735 0.0002202405944450654 1 +191 1 10.956873 0.999674 0.00047036322693564959 1 +192 0 -4.0598474 0.03347344 0.049118716145619364 0 +193 0 -5.4686823 0.011754766 0.01705900265167648 0 194 0 -4.520691 0.023833437 0.034800759324198648 0 195 0 -4.046695 0.03379773 0.049602852281362811 0 196 0 6.8652763 0.992787 7.1151855643261381 1 -197 0 -2.6564164 0.09126051 0.13806131755901335 0 +197 0 -2.6564164 0.0912605 0.13806130573063377 0 198 0 -6.4429784 0.005648197 0.0081717255492616877 0 199 0 -5.0078387 0.016592303 0.024138448331127087 0 200 1 10.36586 0.9994898 0.00073627359086373204 1 201 1 9.869495 0.9992566 0.0010728826448553252 1 -202 0 -5.4686823 0.011754767 0.017059004011272673 0 -203 0 -3.5726995 0.047724232 0.070548673242470078 0 -204 0 -5.4686823 0.011754767 0.017059004011272673 0 -205 1 12.086603 0.99986166 0.00019959967319162043 1 +202 0 -5.4686823 0.011754766 0.01705900265167648 0 +203 0 -3.5726995 0.04772423 0.070548667598666043 0 +204 0 -5.4686823 0.011754766 0.01705900265167648 0 +205 1 12.086601 0.99986166 0.00019959967319162043 1 206 1 5.94417 0.98559827 0.020928376857247459 1 207 0 -5.5593443 0.010982092 0.01593145032913975 0 208 0 -5.5593443 0.010982092 0.01593145032913975 0 -209 0 -3.6633615 0.04469411 0.065965336354842946 0 +209 0 -3.6633615 0.044694107 0.065965330728940397 0 210 1 14.534115 0.99997836 3.1215188826316377E-05 1 211 1 9.64962 0.99912184 0.0012674667953664828 1 -212 0 -5.4686823 0.011754767 0.017059004011272673 0 -213 1 14.52906 0.9999783 3.1301182014901005E-05 1 +212 0 -5.4686823 0.011754766 0.01705900265167648 0 +213 1 14.529058 0.9999783 3.1301182014901005E-05 1 214 1 13.868914 0.9999642 5.1681712271066226E-05 1 215 1 7.643734 0.9959909 0.0057955739072098177 1 216 0 -5.95583 0.008152755 0.011810147478593156 0 -217 0 -5.4686823 0.011754767 0.017059004011272673 0 +217 0 -5.4686823 0.011754766 0.01705900265167648 0 218 1 7.88678 0.99666363 0.0048214119279433977 1 -219 0 -2.511506 0.100795746 0.15327923389796996 0 -220 0 -5.1632547 0.014774316 0.021473856707274121 0 +219 0 -2.511506 0.10079574 0.15327922194416127 0 +220 0 -5.1632547 0.014774315 0.021473855343510994 0 221 1 10.395218 0.999501 0.00072009905980506843 1 -222 1 -2.214662 0.1231175 3.0218922639873447 0 +222 1 -2.214662 0.12311749 3.0218923512935034 0 223 1 5.7424126 0.9832564 0.024360423750620218 1 224 1 9.995327 0.99932426 0.00097521318777220637 1 225 0 -5.95583 0.008152755 0.011810147478593156 0 226 1 10.225868 0.9994326 0.00081878372812222485 1 -227 1 7.459608 0.9953927 0.0066623169983452231 1 +227 1 7.459608 0.9953926 0.0066624033876966004 1 228 0 -5.5593443 0.010982092 0.01593145032913975 0 -229 1 12.666517 0.9999109 0.00012856275965089447 1 +229 1 12.666515 0.9999109 0.00012856275965089447 1 230 1 6.1583214 0.9877312 0.017809586200967932 1 -231 1 8.623034 0.99808866 0.0027601224428665879 1 -232 0 1.2822819 0.66585165 1.5814393559263284 1 +231 1 8.623032 0.99808866 0.0027601224428665879 1 +232 0 1.2822819 0.6658516 1.5814390985815938 1 233 1 6.382519 0.9896301 0.015038709216571163 1 -234 0 -2.8964381 0.07724253 0.115976584697302 0 +234 0 -2.8964386 0.0772425 0.11597653810254285 0 235 0 ? ? ? 0 236 1 11.420414 0.99977064 0.00033093257283884215 1 -237 1 6.5357933 0.9907579 0.01339555438417007 1 +237 1 6.535795 0.9907579 0.01339555438417007 1 238 1 12.422876 0.9998928 0.00015470668910224405 1 239 1 5.902529 0.98514295 0.021595017147241997 1 -240 0 -2.017991 0.14015022 0.21784345766297453 0 +240 0 -2.0179915 0.14015016 0.21784335765559609 0 241 0 -4.0004973 0.034961022 0.051340881416726061 0 242 0 -4.9946866 0.016755885 0.024378447925635292 0 -243 0 -2.6953988 0.088837564 0.13421982380863931 0 -244 0 -5.4686823 0.011754767 0.017059004011272673 0 +243 0 -2.6953983 0.08883759 0.13421985919941723 0 +244 0 -5.4686823 0.011754766 0.01705900265167648 0 245 0 -2.817525 0.081618704 0.12283483473472002 0 -246 1 11.424004 0.9997713 0.00032998645156814019 1 -247 1 3.104393 0.8881221 0.17117009101578401 1 +246 1 11.424002 0.9997713 0.00032998645156814019 1 +247 1 3.104394 0.88812214 0.17116999419202034 1 248 0 -3.0615559 0.068774305 0.10279722872890984 0 249 0 ? ? ? 0 250 0 -6.021953 0.0077569964 0.011234610256342446 0 @@ -258,64 +258,64 @@ Instance Label Score Probability Log-loss Assigned 256 0 -5.494987 0.01152521 0.016723922858878242 0 257 0 -5.0078387 0.016592303 0.024138448331127087 0 258 0 -4.520691 0.023833437 0.034800759324198648 0 -259 0 2.9647493 0.877156 3.025100925818462 1 +259 0 2.9647484 0.8771559 3.0250995258103677 1 260 1 9.870926 0.99925745 0.0010716778711863072 1 261 1 12.206299 0.99987364 0.00018231312886902128 1 -262 1 9.653841 0.99912465 0.0012634216564773898 1 +262 1 9.653839 0.99912465 0.0012634216564773898 1 263 1 8.981979 0.9985436 0.002102643541396716 1 264 1 5.664709 0.9822578 0.025826400239286989 1 -265 0 -2.494875 0.10194498 0.15512426427086881 0 -266 1 7.3661613 0.99505585 0.0071505858244227481 1 -267 1 3.3009596 0.90210295 0.14863601285829425 1 +265 0 -2.494875 0.101944976 0.15512425230176288 0 +266 1 7.3661633 0.99505585 0.0071505858244227481 1 +267 1 3.3009605 0.902103 0.14863591753511315 1 268 1 9.372967 0.998917 0.0015633090806578933 1 -269 0 -5.4686823 0.011754767 0.017059004011272673 0 -270 1 6.031377 0.9865078 0.01959767586907752 1 -271 0 -3.5726995 0.047724232 0.070548673242470078 0 -272 1 3.3009596 0.90210295 0.14863601285829425 1 -273 1 0.21747208 0.4704687 1.0878293505835543 1 -274 0 -4.3236628 0.027569678 0.040333214707074766 0 +269 0 -5.4686823 0.011754766 0.01705900265167648 0 +270 1 6.031378 0.9865078 0.01959767586907752 1 +271 0 -3.5726995 0.04772423 0.070548667598666043 0 +272 1 3.3009605 0.902103 0.14863591753511315 1 +273 1 0.21747303 0.47046885 1.0878288936386407 1 +274 0 -4.323663 0.027569667 0.040333198126582509 0 275 0 ? ? ? 0 276 0 -5.0078387 0.016592303 0.024138448331127087 0 277 0 -5.95583 0.008152755 0.011810147478593156 0 -278 0 -5.4686823 0.011754767 0.017059004011272673 0 -279 1 7.127907 0.9940822 0.0085629246022787941 1 +278 0 -5.4686823 0.011754766 0.01705900265167648 0 +279 1 7.127905 0.9940822 0.0085629246022787941 1 280 0 -4.520691 0.023833437 0.034800759324198648 0 -281 0 -4.6892586 0.02103278 0.030667542438444956 0 +281 0 -4.689259 0.021032773 0.030667531458592488 0 282 1 4.4381247 0.95620465 0.064608669189911136 1 283 1 6.0636253 0.9868295 0.019127222915579972 1 284 1 7.431343 0.99529326 0.006806421628886848 1 285 1 14.218479 0.9999725 3.9642545670177728E-05 1 286 1 15.281265 0.9999877 1.7714321792245208E-05 1 -287 0 -4.9171767 0.017752616 0.025841674110087472 0 +287 0 -4.9171767 0.017752614 0.025841671374291015 0 288 1 2.2163515 0.80187416 0.31855224459431192 1 289 1 8.312019 0.9975813 0.0034936687628036997 1 290 0 -6.4429784 0.005648197 0.0081717255492616877 0 -291 0 -5.4686823 0.011754767 0.017059004011272673 0 +291 0 -5.4686823 0.011754766 0.01705900265167648 0 292 1 ? ? ? 0 -293 1 5.542121 0.9805624 0.028318669172894068 1 +293 1 5.542122 0.9805624 0.028318669172894068 1 294 0 ? ? ? 0 295 1 7.7866364 0.99640125 0.0052012629006476284 1 -296 0 1.823431 0.75025773 2.0014880731401443 1 +296 0 1.823431 0.7502577 2.0014877288199164 1 297 0 ? ? ? 0 298 0 -2.725597 0.08700067 0.13131429030889247 0 299 1 7.8274345 0.9965105 0.0050430801821242759 1 300 1 7.348074 0.9949879 0.0072491063826461647 1 -301 0 -5.4686823 0.011754767 0.017059004011272673 0 +301 0 -5.4686823 0.011754766 0.01705900265167648 0 302 1 15.735764 0.9999913 1.2554788140693439E-05 1 -303 0 -5.4686823 0.011754767 0.017059004011272673 0 +303 0 -5.4686823 0.011754766 0.01705900265167648 0 304 1 5.9607983 0.98577625 0.020667878309752356 1 305 1 8.459471 0.9978367 0.003124349823870973 1 -306 0 -5.4686823 0.011754767 0.017059004011272673 0 -307 0 -5.4686823 0.011754767 0.017059004011272673 0 +306 0 -5.4686823 0.011754766 0.01705900265167648 0 +307 0 -5.4686823 0.011754766 0.01705900265167648 0 308 1 7.422592 0.9952621 0.0068516084791740307 1 -309 0 -1.7474079 0.16675755 0.26319176134218036 0 -310 0 -5.391173 0.012457768 0.018085649525652777 0 +309 0 -1.7474074 0.1667576 0.26319183874281699 0 +310 0 -5.3911724 0.012457772 0.018085654967908992 0 311 0 -6.4429784 0.005648197 0.0081717255492616877 0 312 1 3.6294708 0.9220128 0.11714130634122567 1 313 0 -6.4429784 0.005648197 0.0081717255492616877 0 -314 0 -6.046492 0.0076150266 0.011028204574905828 0 +314 0 -6.0464916 0.007615029 0.011028207959717411 0 315 0 ? ? ? 0 -316 1 3.6177406 0.92137057 0.11814658455212051 1 +316 1 3.6177397 0.9213705 0.11814667788191786 1 317 1 9.215706 0.99877995 0.0017612310516048875 1 318 0 -5.1966968 0.014409561 0.02093983363547448 0 319 0 2.6369457 0.84775543 2.7155373416013662 1 @@ -323,77 +323,77 @@ Instance Label Score Probability Log-loss Assigned 321 0 ? ? ? 0 322 0 -4.520691 0.023833437 0.034800759324198648 0 323 1 5.5612926 0.98083764 0.027913746222999549 1 -324 0 -5.4686823 0.011754767 0.017059004011272673 0 -325 0 -4.192758 0.030360555 0.044479706359279253 0 +324 0 -5.4686823 0.011754766 0.01705900265167648 0 +325 0 -4.192758 0.030360553 0.044479703587910022 0 326 1 4.4103794 0.95531476 0.065951946768905437 1 327 0 -5.95583 0.008152755 0.011810147478593156 0 -328 1 3.373887 0.9068811 0.141014691181642 1 +328 1 3.373888 0.90688115 0.14101459636069669 1 329 1 7.8321342 0.9965229 0.0050251314657524084 1 330 1 5.856251 0.98462033 0.022360562903046506 1 -331 0 -3.2490888 0.06020364 0.08957991749201398 0 +331 0 -3.2490892 0.06020362 0.089579883179530889 0 332 0 -3.1363668 0.06522752 0.097312838862202627 0 333 1 4.914962 0.9690866 0.04530251936714582 1 334 1 5.9119453 0.98524714 0.021442445493118613 1 335 0 -6.4429784 0.005648197 0.0081717255492616877 0 336 1 5.543519 0.9805826 0.028288940560717461 1 -337 0 -5.4686823 0.011754767 0.017059004011272673 0 -338 0 -6.046492 0.0076150266 0.011028204574905828 0 +337 0 -5.4686823 0.011754766 0.01705900265167648 0 +338 0 -6.0464916 0.007615029 0.011028207959717411 0 339 1 5.684025 0.98251134 0.025454033740074194 1 340 1 6.620781 0.9913299 0.012562837713558307 1 -341 0 -5.4686823 0.011754767 0.017059004011272673 0 +341 0 -5.4686823 0.011754766 0.01705900265167648 0 342 0 -5.9689827 0.00807247 0.011693373854840414 0 343 0 -6.4429784 0.005648197 0.0081717255492616877 0 344 1 10.162451 0.99940467 0.00085913711935366719 1 345 0 -6.4429784 0.005648197 0.0081717255492616877 0 -346 0 -3.3358254 0.056586877 0.084038425056544686 0 -347 0 -6.1395845 0.0070995023 0.010278947729356253 0 -348 1 0.15727425 0.45910957 1.1230895756645052 1 +346 0 -3.335825 0.056586895 0.084038453540661334 0 +347 0 -6.1395845 0.007099502 0.010278947052745423 0 +348 1 0.15727425 0.45910954 1.1230896693146193 1 349 1 4.0622606 0.94258505 0.085305293794040227 1 350 0 -3.93614 0.036646076 0.053862170913072119 0 351 0 -4.9946866 0.016755885 0.024378447925635292 0 352 0 0.47192955 0.51868224 1.0549384413455107 1 353 1 8.696342 0.99819183 0.0026109941163622473 1 354 0 -5.95583 0.008152755 0.011810147478593156 0 -355 0 -4.246153 0.029190574 0.042739978357172199 0 -356 1 -0.69921684 0.30711415 1.7031530909424435 0 +355 0 -4.2461534 0.029190563 0.042739961748996601 0 +356 1 -0.69921684 0.30711412 1.7031532309414121 0 357 1 12.852016 0.9999226 0.00011170705633068974 1 358 1 5.582206 0.9811335 0.027478611445726227 1 359 1 5.3672857 0.97786665 0.032290354697391166 1 360 1 15.333874 0.9999882 1.7026382905914664E-05 1 361 1 6.317689 0.9891131 0.0157926107591757 1 362 0 -3.5059962 0.05007717 0.074117778802898118 0 -363 0 -2.0658464 0.13583238 0.2106169271876272 0 +363 0 -2.0658464 0.13583237 0.21061690231070429 0 364 0 -4.9946866 0.016755885 0.024378447925635292 0 365 0 -5.4818344 0.011639431 0.016890640083211839 0 366 1 13.694571 0.9999591 5.899125529457858E-05 1 367 1 11.299244 0.9997486 0.00036275701338483524 1 368 0 -5.8557806 0.008789949 0.012737278390963667 0 -369 0 -5.4592943 0.011837783 0.017180200787587779 0 +369 0 -5.4592943 0.011837782 0.017180199427877362 0 370 0 -3.89473 0.037771333 0.055548313644665219 0 371 0 -5.8557806 0.008789949 0.012737278390963667 0 -372 0 -4.2402444 0.029317858 0.04292914403934768 0 -373 0 -3.7544203 0.04183586 0.061655273456784274 0 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0.98706365 0.018784981568614377 1 -387 0 -2.3345594 0.11363012 0.17401924291232526 0 -388 0 -5.2848067 0.013490496 0.019595145292487764 0 -389 0 -3.3224106 0.057132594 0.084873194157620097 0 +387 0 -2.3345594 0.113630116 0.17401923078542889 0 +388 0 -5.2848067 0.013490495 0.019595143930499406 0 +389 0 -3.322411 0.057132572 0.084873159956897426 0 390 0 -5.6504025 0.010256628 0.014873593254337859 0 391 1 10.030338 0.99934196 0.00094965672085389622 1 392 0 -5.0078387 0.016592303 0.024138448331127087 0 393 0 -6.53364 0.0052747843 0.0076300462926347243 0 -394 0 -5.241206 0.013937826 0.020249479819272553 0 +394 0 -5.241206 0.013937825 0.020249478456666324 0 395 0 -5.0078387 0.016592303 0.024138448331127087 0 396 0 -4.520691 0.023833437 0.034800759324198648 0 397 0 -5.0209913 0.016430287 0.023900783632758753 0 @@ -401,38 +401,38 @@ Instance Label Score Probability Log-loss Assigned 399 0 -5.7283545 0.0096734045 0.014023711114027561 0 400 1 10.056744 0.999355 0.00093081234331069047 1 401 0 -5.494987 0.01152521 0.016723922858878242 0 -402 0 -3.2177973 0.061560772 0.091664774253514145 0 +402 0 -3.2177973 0.06156077 0.091664768526496687 0 403 0 -4.145746 0.031428177 0.046069061057006429 0 -404 0 -5.507669 0.011416126 0.016564721842253591 0 +404 0 -5.507669 0.011416125 0.016564720483123126 0 405 0 -5.95583 0.008152755 0.011810147478593156 0 -406 0 -4.1128182 0.0321975 0.047215431190392593 0 +406 0 -4.1128182 0.032197498 0.047215425637133698 0 407 0 -5.95583 0.008152755 0.011810147478593156 0 -408 0 -3.691061 0.04380549 0.064623974204670379 0 -409 0 -4.71424 0.020646105 0.030097814327393193 0 +408 0 -3.6910605 0.043805506 0.064623996687367499 0 +409 0 -4.71424 0.020646103 0.030097811583513857 0 410 0 -5.95583 0.008152755 0.011810147478593156 0 411 0 ? ? ? 0 412 1 9.230705 0.9987937 0.0017413429278913565 1 -413 0 -3.2791004 0.05892837 0.087623557240190011 0 +413 0 -3.2791004 0.058928367 0.087623551529192406 0 414 1 6.7173824 0.9919376 0.011678759699519599 1 415 0 -0.6668339 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0 -2.5834928 0.09595309 0.14553046360362973 0 -432 0 -3.8628197 0.03866103 0.056882875876087251 0 -433 0 -4.4631066 0.024871144 0.036335222703172541 0 +431 0 -2.5834928 0.095953085 0.14553045171385326 0 +432 0 -3.8628197 0.038661025 0.056882870285491223 0 +433 0 -4.4631066 0.024871143 0.036335219947404518 0 434 0 5.0084 0.97114086 5.1148279570464927 1 435 1 7.444332 0.9953392 0.0067398103254229808 1 436 1 3.841199 0.9328069 0.10034962060198541 1 @@ -440,52 +440,52 @@ Instance Label Score Probability Log-loss Assigned 438 0 -3.822938 0.039801266 0.058595061116589527 0 439 0 -4.5469956 0.023373578 0.034121284818622755 0 440 1 10.154686 0.99940115 0.00086421363869066531 1 -441 0 -1.8604465 0.1551807 0.24328528875750627 0 -442 0 -4.932629 0.017549368 0.025543180352955049 0 +441 0 -1.8604469 0.15518063 0.24328518697086191 0 +442 0 -4.9326286 0.017549373 0.02554318855864653 0 443 0 -5.9932313 0.007926505 0.011481092360580692 0 -444 0 -2.442047 0.10567284 0.16112539871549494 0 +444 0 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0.023859466397713937 0 -486 0 -5.0985007 0.015506633 0.022546608302385175 0 +483 1 10.906593 0.9996614 0.0004885994477486597 1 +484 0 -4.2533965 0.029035257 0.042509184696890877 0 +485 0 -5.0232906 0.016402123 0.023859474593834452 0 +486 0 -5.0985003 0.015506638 0.022546616491050588 0 487 1 13.475906 0.9999518 6.9568659593352019E-05 1 488 1 1.3273258 0.67341053 0.57044180397199062 1 489 1 -0.6232014 0.31951928 1.646025101672679 0 @@ -493,153 +493,153 @@ Instance Label Score Probability Log-loss Assigned 491 1 6.6713333 0.9916534 0.012092158077135179 1 492 0 -4.624505 0.022068353 0.032194464591169099 0 493 1 9.94891 0.99930006 0.0010101496965434329 1 -494 0 0.9629116 0.60997653 1.3583671531547612 1 -495 0 -5.0985007 0.015506633 0.022546608302385175 0 +494 0 0.9629116 0.6099765 1.3583669326774681 1 +495 0 -5.0985003 0.015506638 0.022546616491050588 0 496 0 -6.53364 0.0052747843 0.0076300462926347243 0 -497 0 -4.893565 0.018067656 0.02630447000187312 0 +497 0 -4.893565 0.018067654 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0 512 0 -4.5469956 0.023373578 0.034121284818622755 0 -513 0 -5.0985007 0.015506633 0.022546608302385175 0 +513 0 -5.0985003 0.015506638 0.022546616491050588 0 514 1 10.719854 0.9996099 0.00056292303371696174 1 515 1 8.6480255 0.9981245 0.0027083436166447333 1 516 0 -6.53364 0.0052747843 0.0076300462926347243 0 -517 0 -6.046492 0.0076150266 0.011028204574905828 0 +517 0 -6.0464916 0.007615029 0.011028207959717411 0 518 0 -4.8959603 0.01803545 0.026257150944159223 0 519 1 6.535843 0.9907582 0.013395120416830476 1 520 0 -6.3523164 0.0060478863 0.0087517471600555233 0 -521 0 -4.9303293 0.017579472 0.02558738782535638 0 -522 1 5.502534 0.97998166 0.029173343678465424 1 +521 0 -4.9303293 0.01757947 0.025587385090042082 0 +522 1 5.502533 0.97998166 0.029173343678465424 1 523 1 7.699238 0.9961555 0.0055571295392400502 1 524 0 -5.0078387 0.016592303 0.024138448331127087 0 -525 0 -5.189559 0.014486661 0.021052696761167319 0 +525 0 -5.189559 0.01448666 0.021052695397802253 0 526 0 -5.0209913 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0.039033543 0.057442021457383786 0 565 1 12.086092 0.9998616 0.00019968567641750592 1 -566 0 -4.227092 0.029603133 0.043353202239365854 0 -567 0 -3.6343493 0.04564325 0.067399433223387817 0 +566 0 -4.227092 0.029603131 0.04335319947015976 0 +567 0 -3.6343489 0.045643266 0.06739945574937882 0 568 1 4.1473064 0.94597834 0.080120938914498979 1 569 1 10.713882 0.9996081 0.00056550378256940248 1 570 1 8.017664 0.99697804 0.0043663610015818432 1 571 1 11.034657 0.99969274 0.00044335339890107668 1 572 0 -5.0078387 0.016592303 0.024138448331127087 0 573 0 -5.95583 0.008152755 0.011810147478593156 0 -574 1 5.950968 0.9856713 0.020821502205009505 1 -575 0 -3.2791004 0.05892837 0.087623557240190011 0 -576 0 -4.059848 0.03347343 0.049118699463849731 0 +574 1 5.950966 0.9856713 0.020821502205009505 1 +575 0 -3.2791004 0.058928367 0.087623551529192406 0 +576 0 -4.0598474 0.03347344 0.049118716145619364 0 577 0 -5.95583 0.008152755 0.011810147478593156 0 578 0 -5.95583 0.008152755 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0.0010051587149210726 1 -606 0 -4.715564 0.020625811 0.030067920071712086 0 +605 1 9.95545 0.9993035 0.0010051587149210726 1 +606 0 -4.715564 0.02062581 0.030067917327889607 0 607 0 -6.4429784 0.005648197 0.0081717255492616877 0 608 1 11.148428 0.9997181 0.00040671030044872971 1 609 0 -4.5469956 0.023373578 0.034121284818622755 0 610 1 8.926189 0.99848074 0.0021934995672910755 1 -611 1 6.910961 0.993031 0.010089302795775161 1 -612 1 16.893517 0.99999636 5.2454803872987944E-06 1 -613 0 -5.226982 0.014086901 0.020467605961895518 0 -614 0 -5.5724964 0.010874255 0.015774155610380477 0 -615 0 -3.9466457 0.03636577 0.053442453015430637 0 +611 1 6.9109592 0.993031 0.010089302795775161 1 +612 1 16.893515 0.99999636 5.2454803872987944E-06 1 +613 0 -5.226982 0.0140869 0.020467604599083256 0 +614 0 -5.5724964 0.010874254 0.015774154251994583 0 +615 0 -3.9466453 0.03636578 0.053442469747270488 0 616 0 -5.0078387 0.016592303 0.024138448331127087 0 617 0 ? ? ? 0 618 0 -4.533843 0.023602419 0.034459373415090033 0 -619 0 -4.059848 0.03347343 0.049118699463849731 0 +619 0 -4.0598474 0.03347344 0.049118716145619364 0 620 0 -5.0078387 0.016592303 0.024138448331127087 0 -621 0 0.35605335 0.4967156 0.99055422977402885 1 -622 0 -2.2074018 0.12371333 0.19052518105806376 0 +621 0 0.3560543 0.49671578 0.99055474235503638 1 +622 0 -2.2074018 0.12371332 0.19052516879162634 0 623 0 -6.4429784 0.005648197 0.0081717255492616877 0 -624 0 -3.8450823 0.039164227 0.057638228932157462 0 -625 0 -3.4678864 0.0514704 0.076235297856163745 0 -626 1 5.7601643 0.98347664 0.024037311317372478 1 -627 0 -4.1699953 0.03087303 0.045242403057694114 0 +624 0 -3.8450818 0.039164238 0.057638245712729186 0 +625 0 -3.4678864 0.051470395 0.076235292190069803 0 +626 1 5.760165 0.98347664 0.024037311317372478 1 +627 0 -4.1699953 0.030873029 0.045242400284859388 0 628 0 -5.9689827 0.00807247 0.011693373854840414 0 629 0 -5.0209913 0.016430287 0.023900783632758753 0 -630 0 -3.358376 0.05568055 0.082653110243383579 0 -631 0 -4.059848 0.03347343 0.049118699463849731 0 +630 0 -3.3583755 0.055680566 0.08265313300880639 0 +631 0 -4.0598474 0.03347344 0.049118716145619364 0 632 0 -6.4429784 0.005648197 0.0081717255492616877 0 633 1 4.3299503 0.95263666 0.070002027657626642 1 634 0 -5.494987 0.01152521 0.016723922858878242 0 635 0 -4.61419 0.022237854 0.032444542423638763 0 -636 1 10.127518 0.9993887 0.00088219670683892097 1 +636 1 10.12752 0.9993887 0.00088219670683892097 1 637 0 -2.4650178 0.10403733 0.1584894700655759 0 638 0 -5.0209913 0.016430287 0.023900783632758753 0 639 0 -3.93614 0.036646076 0.053862170913072119 0 -640 0 -4.4101357 0.02586451 0.037805647681891714 0 +640 0 -4.4101357 0.025864508 0.037805644923313529 0 641 0 -5.0078387 0.016592303 0.024138448331127087 0 642 0 -5.0078387 0.016592303 0.024138448331127087 0 643 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -649,13 +649,13 @@ Instance Label Score Probability Log-loss Assigned 647 0 -5.832123 0.008947697 0.012966896248755017 0 648 1 12.236198 0.9998765 0.00017818502951934642 1 649 0 -5.0078387 0.016592303 0.024138448331127087 0 -650 0 -3.7496567 0.041980952 0.061873754012903771 0 -651 0 -5.217549 0.014186632 0.020613550094586007 0 -652 0 -3.8628197 0.03866103 0.056882875876087251 0 +650 0 -3.7496567 0.04198095 0.061873748402934067 0 +651 0 -5.217549 0.014186631 0.020613548731635876 0 +652 0 -3.8628197 0.038661025 0.056882870285491223 0 653 0 -4.533843 0.023602419 0.034459373415090033 0 654 0 -4.520691 0.023833437 0.034800759324198648 0 655 0 -5.0078387 0.016592303 0.024138448331127087 0 -656 0 -4.059848 0.03347343 0.049118699463849731 0 +656 0 -4.0598474 0.03347344 0.049118716145619364 0 657 0 -0.4869156 0.3424043 0.6047272426002388 0 658 1 9.086258 0.9986542 0.0019429058245893991 1 659 0 -6.4429784 0.005648197 0.0081717255492616877 0 @@ -663,36 +663,36 @@ Instance Label Score Probability Log-loss Assigned 661 0 -4.046695 0.03379773 0.049602852281362811 0 662 0 -5.3686323 0.012669891 0.018395571683516153 0 663 0 -5.3686323 0.012669891 0.018395571683516153 0 -664 0 -4.3969836 0.026117077 0.038179748349732472 0 +664 0 -4.3969836 0.026117075 0.038179745590438879 0 665 0 -6.4429784 0.005648197 0.0081717255492616877 0 -666 0 -3.4969325 0.050405253 0.074616140216202456 0 +666 0 -3.496932 0.050405268 0.074616162855156168 0 667 0 -4.520691 0.023833437 0.034800759324198648 0 -668 1 2.8043652 0.86343354 0.21184295985895515 1 +668 1 2.8043652 0.8634335 0.21184305945125079 1 669 1 8.147335 0.99726033 0.0039579294672795707 1 670 1 6.4856205 0.99040276 0.013912762642680866 1 671 0 -4.087837 0.032793265 0.048103804028856063 0 672 0 -4.9946866 0.016755885 0.024378447925635292 0 -673 0 -3.9078827 0.03741038 0.055007226361106963 0 +673 0 -3.9078822 0.03741039 0.055007243111104311 0 674 0 -5.95583 0.008152755 0.011810147478593156 0 -675 0 -4.1401944 0.031556632 0.046260409123226896 0 +675 0 -4.1401944 0.03155663 0.046260403573642889 0 676 0 -5.6622314 0.010165936 0.014741403595886524 0 677 0 -4.5469956 0.023373578 0.034121284818622755 0 678 0 -6.4429784 0.005648197 0.0081717255492616877 0 679 0 -5.9689827 0.00807247 0.011693373854840414 0 680 1 16.78001 0.99999607 5.6754386418026423E-06 1 681 1 9.801077 0.9992171 0.0011299384356403619 1 -682 0 -3.3756714 0.05499471 0.08160568921637594 0 +682 0 -3.3756719 0.054994687 0.081605655093025531 0 683 0 -6.4429784 0.005648197 0.0081717255492616877 0 684 0 -6.4429784 0.005648197 0.0081717255492616877 0 685 0 -6.4429784 0.005648197 0.0081717255492616877 0 686 0 -6.4429784 0.005648197 0.0081717255492616877 0 687 0 -4.613783 0.022244573 0.032454455742594257 0 688 0 -5.0209913 0.016430287 0.023900783632758753 0 -689 0 -4.1777043 0.03069854 0.044982670054638463 0 +689 0 -4.1777043 0.030698538 0.044982667282302893 0 690 0 -5.832123 0.008947697 0.012966896248755017 0 -691 1 4.4967804 0.95803064 0.061856295892801602 1 +691 1 4.4967794 0.9580306 0.061856385651233553 1 692 0 -5.494987 0.01152521 0.016723922858878242 0 -693 0 -4.6842284 0.021111494 0.030783546498899184 0 +693 0 -4.6842284 0.021111492 0.030783543753715333 0 694 0 -4.9545784 0.017264586 0.02512504882755813 0 695 0 -5.9689827 0.00807247 0.011693373854840414 0 696 1 6.7127857 0.9919096 0.011719418003861082 1 diff --git a/test/BaselineOutput/Common/Command/linux-arm64/CommandTrainMlrWithStats-summary.txt b/test/BaselineOutput/Common/Command/linux-arm64/CommandTrainMlrWithStats-summary.txt index a62d44920c..a95d31708f 100644 --- a/test/BaselineOutput/Common/Command/linux-arm64/CommandTrainMlrWithStats-summary.txt +++ b/test/BaselineOutput/Common/Command/linux-arm64/CommandTrainMlrWithStats-summary.txt @@ -1,18 +1,18 @@ LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights - Iris-setosa+(Bias) 2.2919443 - Iris-versicolor+(Bias) 0.76702803 - Iris-virginica+(Bias) -3.059023 - Iris-setosa+f3 -3.1749554 - Iris-setosa+f2 -2.887113 - Iris-setosa+f1 0.5143879 - Iris-setosa+f0 -0.058297563 - Iris-versicolor+f1 -0.70855236 - Iris-virginica+f3 3.1559532 - Iris-virginica+f2 1.9136102 - Iris-virginica+f0 0.033977978 + Iris-setosa+(Bias) 2.2949948 + Iris-versicolor+(Bias) 0.76274204 + Iris-virginica+(Bias) -3.0577273 + Iris-setosa+f3 -3.1777203 + Iris-setosa+f2 -2.8901913 + Iris-setosa+f1 0.5150146 + Iris-setosa+f0 -0.06171925 + Iris-versicolor+f1 -0.70386773 + Iris-virginica+f3 3.1538327 + Iris-virginica+f2 1.9106401 + Iris-virginica+f0 0.034127038 *** MODEL STATISTICS SUMMARY *** Count of training examples: 150 -Residual Deviance: 132.03748 +Residual Deviance: 132.03183 Null Deviance: 329.58368 -AIC: 154.03748 +AIC: 154.03183 diff --git a/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-out.txt b/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-out.txt index 15ca05f14a..7192a04a0b 100644 --- a/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-out.txt +++ b/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-out.txt @@ -5,9 +5,9 @@ num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Model trained with 150 training examples. -Residual Deviance: 132.25266 +Residual Deviance: 132.04128 Null Deviance: 329.58368 -AIC: 154.25266 +AIC: 154.04128 Not training a calibrator because it is not needed. Physical memory usage(MB): %Number% Virtual memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-summary.txt b/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-summary.txt index 673f8d8ba6..10ddc21eb6 100644 --- a/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-summary.txt +++ b/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainMlrWithStats-summary.txt @@ -1,18 +1,18 @@ LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights - Iris-setosa+(Bias) 2.2461631 - Iris-versicolor+(Bias) 0.77507037 - Iris-virginica+(Bias) -3.0212474 - Iris-setosa+f3 -3.1763735 - Iris-setosa+f2 -2.8875842 - Iris-setosa+f1 0.51886666 - Iris-setosa+f0 -0.0041847904 - Iris-versicolor+f1 -0.73153543 - Iris-virginica+f3 3.147383 - Iris-virginica+f2 1.9020821 - Iris-virginica+f0 0.0012227822 + Iris-setosa+(Bias) 2.293142 + Iris-versicolor+(Bias) 0.76599276 + Iris-virginica+(Bias) -3.0591424 + Iris-setosa+f3 -3.1762753 + Iris-setosa+f2 -2.8880758 + Iris-setosa+f1 0.5144032 + Iris-setosa+f0 -0.05904869 + Iris-versicolor+f1 -0.70699453 + Iris-virginica+f3 3.154204 + Iris-virginica+f2 1.9121273 + Iris-virginica+f0 0.036154807 *** MODEL STATISTICS SUMMARY *** Count of training examples: 150 -Residual Deviance: 132.25266 +Residual Deviance: 132.04128 Null Deviance: 329.58368 -AIC: 154.25266 +AIC: 154.04128 diff --git a/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainingLrWithStats-out.txt b/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainingLrWithStats-out.txt new file mode 100644 index 0000000000..d735624ab3 --- /dev/null +++ b/test/BaselineOutput/Common/Command/netcoreapp/CommandTrainingLrWithStats-out.txt @@ -0,0 +1,17 @@ +maml.exe Train feat=Num lab=Lab tr=lr{t=- stat=+} loader=text{header+ col=Lab:0 col=Num:9-14} data=%Data% out=%Output% +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Beginning optimization +num vars: 7 +improvement criterion: Mean Improvement +Warning: Premature convergence occurred. The OptimizationTolerance may be set too small. ro equals zero. Is your function linear? +L1 regularization selected 7 of 7 weights. +Model trained with 500 training examples. +Residual Deviance: 458.97424 (on 493 degrees of freedom) +Null Deviance: 539.27637 (on 499 degrees of freedom) +AIC: 472.97424 +Warning: The number of parameters is too large. Cannot hold the variance-covariance matrix in memory. Skipping computation of standard errors and z-statistics of coefficients. Consider choosing a larger L1 regularizerto reduce the number of parameters. +Not training a calibrator because it is not needed. +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/Command/osx-arm64/CommandTrainMlrWithStats-summary.txt b/test/BaselineOutput/Common/Command/osx-arm64/CommandTrainMlrWithStats-summary.txt index a62d44920c..a95d31708f 100644 --- a/test/BaselineOutput/Common/Command/osx-arm64/CommandTrainMlrWithStats-summary.txt +++ b/test/BaselineOutput/Common/Command/osx-arm64/CommandTrainMlrWithStats-summary.txt @@ -1,18 +1,18 @@ LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights - Iris-setosa+(Bias) 2.2919443 - Iris-versicolor+(Bias) 0.76702803 - Iris-virginica+(Bias) -3.059023 - Iris-setosa+f3 -3.1749554 - Iris-setosa+f2 -2.887113 - Iris-setosa+f1 0.5143879 - Iris-setosa+f0 -0.058297563 - Iris-versicolor+f1 -0.70855236 - Iris-virginica+f3 3.1559532 - Iris-virginica+f2 1.9136102 - Iris-virginica+f0 0.033977978 + Iris-setosa+(Bias) 2.2949948 + Iris-versicolor+(Bias) 0.76274204 + Iris-virginica+(Bias) -3.0577273 + Iris-setosa+f3 -3.1777203 + Iris-setosa+f2 -2.8901913 + Iris-setosa+f1 0.5150146 + Iris-setosa+f0 -0.06171925 + Iris-versicolor+f1 -0.70386773 + Iris-virginica+f3 3.1538327 + Iris-virginica+f2 1.9106401 + Iris-virginica+f0 0.034127038 *** MODEL STATISTICS SUMMARY *** Count of training examples: 150 -Residual Deviance: 132.03748 +Residual Deviance: 132.03183 Null Deviance: 329.58368 -AIC: 154.03748 +AIC: 154.03183 diff --git a/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-summary.txt b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-summary.txt index a62d44920c..a95d31708f 100644 --- a/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-summary.txt +++ b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-summary.txt @@ -1,18 +1,18 @@ LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights - Iris-setosa+(Bias) 2.2919443 - Iris-versicolor+(Bias) 0.76702803 - Iris-virginica+(Bias) -3.059023 - Iris-setosa+f3 -3.1749554 - Iris-setosa+f2 -2.887113 - Iris-setosa+f1 0.5143879 - Iris-setosa+f0 -0.058297563 - Iris-versicolor+f1 -0.70855236 - Iris-virginica+f3 3.1559532 - Iris-virginica+f2 1.9136102 - Iris-virginica+f0 0.033977978 + Iris-setosa+(Bias) 2.2949948 + Iris-versicolor+(Bias) 0.76274204 + Iris-virginica+(Bias) -3.0577273 + Iris-setosa+f3 -3.1777203 + Iris-setosa+f2 -2.8901913 + Iris-setosa+f1 0.5150146 + Iris-setosa+f0 -0.06171925 + Iris-versicolor+f1 -0.70386773 + Iris-virginica+f3 3.1538327 + Iris-virginica+f2 1.9106401 + Iris-virginica+f0 0.034127038 *** MODEL STATISTICS SUMMARY *** Count of training examples: 150 -Residual Deviance: 132.03748 +Residual Deviance: 132.03183 Null Deviance: 329.58368 -AIC: 154.03748 +AIC: 154.03183 diff --git a/test/BaselineOutput/Common/EntryPoints/netcoreapp/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/netcoreapp/core_manifest.json new file mode 100644 index 0000000000..fe0fc65c73 --- /dev/null +++ b/test/BaselineOutput/Common/EntryPoints/netcoreapp/core_manifest.json @@ -0,0 +1,30768 @@ +{ + "EntryPoints": [ + { + "Name": "Data.CustomTextLoader", + "Desc": "Import a dataset from a text file", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "InputFile", + "Type": "FileHandle", + "Desc": "Location of the input file", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "CustomSchema", + "Type": "String", + "Desc": "Custom schema to use for parsing", + "Aliases": [ + "schema" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The resulting data view" + } + ] + }, + { + "Name": "Data.DataViewReference", + "Desc": "Pass dataview from memory to experiment", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Pointer to IDataView in memory", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The resulting data view" + } + ] + }, + { + "Name": "Data.IDataViewArrayConverter", + "Desc": "Create an array variable of IDataView", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "The data sets", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "The data set array" + } + ] + }, + { + "Name": "Data.PredictorModelArrayConverter", + "Desc": "Create an array variable of PredictorModel", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputModels", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The model array" + } + ] + }, + { + "Name": "Data.TextLoader", + "Desc": "Import a dataset from a text file", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "InputFile", + "Type": "FileHandle", + "Desc": "Location of the input file", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Arguments", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the column", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Type", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "Text", + "TX", + "TXT", + "BL", + "Bool", + "TS", + "TimeSpan", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "Type of the items in the column", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "R4" + }, + { + "Name": "Source", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Min", + "Type": "Int", + "Desc": "First index in the range", + "Required": true, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "Max", + "Type": "Int", + "Desc": "Last index in the range", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "AutoEnd", + "Type": "Bool", + "Desc": "This range extends to the end of the line, but should be a fixed number of items", + "Aliases": [ + "auto" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "VariableEnd", + "Type": "Bool", + "Desc": "This range extends to the end of the line, which can vary from line to line", + "Aliases": [ + "var" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "AllOther", + "Type": "Bool", + "Desc": "This range includes only other indices not specified", + "Aliases": [ + "other" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "ForceVector", + "Type": "Bool", + "Desc": "Force scalar columns to be treated as vectors of length one", + "Aliases": [ + "vector" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + } + ] + } + }, + "Desc": "Source index range(s) of the column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "KeyCount", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Count", + "Type": "UInt", + "Desc": "Count of valid key values", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ] + }, + "Desc": "For a key column, this defines the range of values", + "Aliases": [ + "key" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "Column groups. Each group is specified as name:type:numeric-ranges, eg, col=Features:R4:1-17,26,35-40", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "AllowQuoting", + "Type": "Bool", + "Desc": "Whether the input may include double-quoted values. This parameter is used to distinguish separator characters in an input value from actual separators. When true, separators within double quotes are treated as part of the input value. When false, all separators, even those within quotes, are treated as delimiting a new column.", + "Aliases": [ + "quote" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "AllowSparse", + "Type": "Bool", + "Desc": "Whether the input may include sparse representations", + "Aliases": [ + "sparse" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "InputSize", + "Type": "Int", + "Desc": "Number of source columns in the text data. Default is that sparse rows contain their size information.", + "Aliases": [ + "size" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Separator", + "Type": { + "Kind": "Array", + "ItemType": "Char" + }, + "Desc": "Source column separator.", + "Aliases": [ + "sep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": [ + "\t" + ] + }, + { + "Name": "Decimal Marker", + "Type": "Char", + "Desc": "Character symbol used to separate the integer part from the fractional part of a number written in decimal form.", + "Aliases": [ + "decimal" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "." + }, + { + "Name": "TrimWhitespace", + "Type": "Bool", + "Desc": "Remove trailing whitespace from lines", + "Aliases": [ + "trim" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "HasHeader", + "Type": "Bool", + "Desc": "Data file has header with feature names. Header is read only if options 'hs' and 'hf' are not specified.", + "Aliases": [ + "header" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseThreads", + "Type": "Bool", + "Desc": "Use separate parsing threads?", + "Aliases": [ + "threads" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "ReadMultilines", + "Type": "Bool", + "Desc": "Escape new line characters inside a quoted field? If AllowQuoting is false, this argument is ignored.", + "Aliases": [ + "multilines" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "HeaderFile", + "Type": "String", + "Desc": "File containing a header with feature names. If specified, header defined in the data file (header+) is ignored.", + "Aliases": [ + "hf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "MaxRows", + "Type": "Int", + "Desc": "Maximum number of rows to produce", + "Aliases": [ + "rows" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "EscapeChar", + "Type": "Char", + "Desc": "Character to use to escape quotes inside quoted fields. It can't be a character used as separator.", + "Aliases": [ + "escapechar" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "\"" + }, + { + "Name": "MissingRealsAsNaNs", + "Type": "Bool", + "Desc": "If true, empty float fields will be loaded as NaN. If false, they'll be loaded as 0. Default is false.", + "Aliases": [ + "missingrealnan" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + } + ] + }, + "Desc": "Arguments", + "Aliases": [ + "args" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The resulting data view" + } + ] + }, + { + "Name": "Models.AnomalyDetectionEvaluator", + "Desc": "Evaluates an anomaly detection scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "K", + "Type": "Int", + "Desc": "Expected number of false positives", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "P", + "Type": "Float", + "Desc": "Expected false positive rate", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01 + }, + { + "Name": "NumTopResults", + "Type": "Int", + "Desc": "Number of top-scored predictions to display", + "Aliases": [ + "n" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 50 + }, + { + "Name": "Stream", + "Type": "Bool", + "Desc": "Whether to calculate metrics in one pass", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxAucExamples", + "Type": "Int", + "Desc": "The number of samples to use for AUC calculation. If 0, AUC is not computed. If -1, the whole dataset is used", + "Aliases": [ + "numauc" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.AnomalyPipelineEnsemble", + "Desc": "Combine anomaly detection models into an ensemble", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Average" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IAnomalyDetectionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.BinaryClassificationEvaluator", + "Desc": "Evaluates a binary classification scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "ProbabilityColumn", + "Type": "String", + "Desc": "Probability column name", + "Aliases": [ + "prob" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Threshold", + "Type": "Float", + "Desc": "Probability value for classification thresholding", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "UseRawScoreThreshold", + "Type": "Bool", + "Desc": "Use raw score value instead of probability for classification thresholding", + "Aliases": [ + "useRawScore" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumRocExamples", + "Type": "Int", + "Desc": "The number of samples to use for p/r curve generation. Specify 0 for no p/r curve generation", + "Aliases": [ + "numpr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100000 + }, + { + "Name": "MaxAucExamples", + "Type": "Int", + "Desc": "The number of samples to use for AUC calculation. If 0, AUC is not computed. If -1, the whole dataset is used", + "Aliases": [ + "numauc" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "NumAuPrcExamples", + "Type": "Int", + "Desc": "The number of samples to use for AUPRC calculation. Specify 0 for no AUPRC calculation", + "Aliases": [ + "numauprc" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100000 + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + }, + { + "Name": "ConfusionMatrix", + "Type": "DataView", + "Desc": "Confusion matrix dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IClassificationEvaluatorOutput", + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.BinaryEnsemble", + "Desc": "Combine binary classifiers into an ensemble", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average", + "Vote" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + }, + { + "Name": "ValidatePipelines", + "Type": "Bool", + "Desc": "Whether to validate that all the pipelines are identical", + "Aliases": [ + "validate" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.BinaryPipelineEnsemble", + "Desc": "Combine binary classification models into an ensemble", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average", + "Vote" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.ClassificationEvaluator", + "Desc": "Evaluates a multi class classification scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "OutputTopKAcc", + "Type": "Int", + "Desc": "Output top-K accuracy.", + "Aliases": [ + "topkacc" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumTopClassesToOutput", + "Type": "Int", + "Desc": "Output top-K classes.", + "Aliases": [ + "topk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 3 + }, + { + "Name": "NumClassesConfusionMatrix", + "Type": "Int", + "Desc": "Maximum number of classes in confusion matrix.", + "Aliases": [ + "nccf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "OutputPerClassStatistics", + "Type": "Bool", + "Desc": "Output per class statistics and confusion matrix.", + "Aliases": [ + "opcs" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + }, + { + "Name": "ConfusionMatrix", + "Type": "DataView", + "Desc": "Confusion matrix dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IClassificationEvaluatorOutput", + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.ClusterEvaluator", + "Desc": "Evaluates a clustering scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Features column name", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "CalculateDbi", + "Type": "Bool", + "Desc": "Calculate DBI? (time-consuming unsupervised metric)", + "Aliases": [ + "dbi" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "NumTopClustersToOutput", + "Type": "Int", + "Desc": "Output top K clusters", + "Aliases": [ + "topk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 3 + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.CrossValidationResultsCombiner", + "Desc": "Combine the metric data views returned from cross validation.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "OverallMetrics", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "Overall metrics datasets", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "PerInstanceMetrics", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "Per instance metrics datasets", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ConfusionMatrix", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "Confusion matrix datasets", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Warnings", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "Warning datasets", + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "SignatureBinaryClassifierTrainer", + "SignatureMulticlassClassificationTrainer", + "SignatureRankerTrainer", + "SignatureRegressorTrainer", + "SignatureMultiOutputRegressorTrainer", + "SignatureAnomalyDetectorTrainer", + "SignatureClusteringTrainer" + ] + }, + "Desc": "Specifies the trainer kind, which determines the evaluator to be used.", + "Required": true, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "SignatureBinaryClassifierTrainer" + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "The label column name", + "Aliases": [ + "Label" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 7.0, + "IsNullable": false, + "Default": "Weight" + }, + { + "Name": "GroupColumn", + "Type": "String", + "Desc": "Column to use for grouping", + "Aliases": [ + "group" + ], + "Required": false, + "SortOrder": 8.0, + "IsNullable": false, + "Default": "GroupId" + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 9.0, + "IsNullable": false, + "Default": "Name" + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + }, + { + "Name": "ConfusionMatrix", + "Type": "DataView", + "Desc": "Confusion matrix dataset" + } + ] + }, + { + "Name": "Models.CrossValidator", + "Desc": "Cross validation for general learning", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data set", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "The transform model from the pipeline before this command. It gets included in the Output.PredictorModel.", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Nodes", + "Type": { + "Kind": "Array", + "ItemType": "Node" + }, + "Desc": "The training subgraph", + "Required": true, + "SortOrder": 3.0, + "IsNullable": false + }, + { + "Name": "Inputs", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for training", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ] + }, + "Desc": "The training subgraph inputs", + "Required": true, + "SortOrder": 4.0, + "IsNullable": false + }, + { + "Name": "Outputs", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + } + ] + }, + "Desc": "The training subgraph outputs", + "Required": true, + "SortOrder": 5.0, + "IsNullable": false + }, + { + "Name": "StratificationColumn", + "Type": "String", + "Desc": "Column to use for stratification", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NumFolds", + "Type": "Int", + "Desc": "Number of folds in k-fold cross-validation", + "Aliases": [ + "k" + ], + "Required": false, + "SortOrder": 7.0, + "IsNullable": false, + "Default": 2 + }, + { + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "SignatureBinaryClassifierTrainer", + "SignatureMulticlassClassificationTrainer", + "SignatureRankerTrainer", + "SignatureRegressorTrainer", + "SignatureMultiOutputRegressorTrainer", + "SignatureAnomalyDetectorTrainer", + "SignatureClusteringTrainer" + ] + }, + "Desc": "Specifies the trainer kind, which determines the evaluator to be used.", + "Required": true, + "SortOrder": 8.0, + "IsNullable": false, + "Default": "SignatureBinaryClassifierTrainer" + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 9.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": "Weight" + }, + { + "Name": "GroupColumn", + "Type": "String", + "Desc": "Column to use for grouping", + "Aliases": [ + "group" + ], + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": "GroupId" + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": "Name" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The final model including the trained predictor model and the model from the transforms, provided as the Input.TransformModel." + }, + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + }, + { + "Name": "ConfusionMatrix", + "Type": "DataView", + "Desc": "Confusion matrix dataset" + } + ] + }, + { + "Name": "Models.CrossValidatorDatasetSplitter", + "Desc": "Split the dataset into the specified number of cross-validation folds (train and test sets)", + "FriendlyName": "Dataset CV Split", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumFolds", + "Type": "Int", + "Desc": "Number of folds to split into", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 2 + }, + { + "Name": "StratificationColumn", + "Type": "String", + "Desc": "Stratification column", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "TrainData", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "Training data (one dataset per fold)" + }, + { + "Name": "TestData", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "Testing data (one dataset per fold)" + } + ] + }, + { + "Name": "Models.DatasetTransformer", + "Desc": "Applies a TransformModel to a dataset.", + "FriendlyName": "Apply Transform Model Output", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "Transform model", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + } + ], + "InputKind": [ + "ITransformInput" + ] + }, + { + "Name": "Models.EnsembleSummary", + "Desc": "Summarize a pipeline ensemble predictor.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor to summarize", + "Aliases": [ + "predictorModel" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "Summaries", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "The summaries of the individual predictors" + }, + { + "Name": "Stats", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "The model statistics of the individual predictors" + } + ] + }, + { + "Name": "Models.FixedPlattCalibrator", + "Desc": "Apply a Platt calibrator with a fixed slope and offset to an input model", + "FriendlyName": "Fixed Sigmoid Calibration", + "ShortName": null, + "Inputs": [ + { + "Name": "Slope", + "Type": "Float", + "Desc": "The slope parameter of the calibration function 1 / (1 + exp(slope * x + offset)", + "Aliases": [ + "slope" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": -1.0 + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UncalibratedPredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor to calibrate", + "Aliases": [ + "uncalibratedPredictorModel" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "The offset parameter of the calibration function 1 / (1 + exp(slope * x + offset)", + "Aliases": [ + "offset" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "MaxRows", + "Type": "Int", + "Desc": "The maximum number of examples to train the calibrator on", + "Aliases": [ + "maxRows" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 1000000000, + "Range": { + "Inf": 0, + "Max": 2147483647 + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ICalibratorInput", + "ITransformInput" + ], + "OutputKind": [ + "ICalibratorOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.MultiClassPipelineEnsemble", + "Desc": "Combine multiclass classifiers into an ensemble", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average", + "Vote" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.MultiOutputRegressionEvaluator", + "Desc": "Evaluates a multi output regression scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "RegressionLossFunction" + }, + "Desc": "Loss function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "SquaredLoss" + } + }, + { + "Name": "SuppressScoresAndLabels", + "Type": "Bool", + "Desc": "Suppress labels and scores in per-instance outputs?", + "Aliases": [ + "noScores" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.NaiveCalibrator", + "Desc": "Apply a Naive calibrator to an input model", + "FriendlyName": "Naive Calibrator", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UncalibratedPredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor to calibrate", + "Aliases": [ + "uncalibratedPredictorModel" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "MaxRows", + "Type": "Int", + "Desc": "The maximum number of examples to train the calibrator on", + "Aliases": [ + "maxRows" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 1000000000, + "Range": { + "Inf": 0, + "Max": 2147483647 + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ICalibratorInput", + "ITransformInput" + ], + "OutputKind": [ + "ICalibratorOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.OneVersusAll", + "Desc": "One-vs-All macro (OVA)", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Nodes", + "Type": { + "Kind": "Array", + "ItemType": "Node" + }, + "Desc": "The subgraph for the binary trainer used to construct the OVA learner. This should be a TrainBinary node.", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "OutputForSubGraph", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Model", + "Type": "PredictorModel", + "Desc": "The predictor model for the subgraph exemplar.", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ] + }, + "Desc": "The training subgraph output.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "UseProbabilities", + "Type": "Bool", + "Desc": "Use probabilities in OVA combiner", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained multiclass model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ] + }, + { + "Name": "Models.OnnxConverter", + "Desc": "Converts the model to ONNX format.", + "FriendlyName": "ONNX Converter.", + "ShortName": null, + "Inputs": [ + { + "Name": "DataFile", + "Type": "String", + "Desc": "The data file", + "Aliases": [ + "data" + ], + "Required": false, + "SortOrder": 0.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Onnx", + "Type": "String", + "Desc": "The path to write the output ONNX to.", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Json", + "Type": "String", + "Desc": "The path to write the output JSON to.", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The 'name' property in the output ONNX. By default this will be the ONNX extension-less name.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Domain", + "Type": "String", + "Desc": "The 'domain' property in the output ONNX.", + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "InputsToDrop", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Array of input column names to drop", + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "OutputsToDrop", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Array of output column names to drop", + "Required": false, + "SortOrder": 8.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Model that needs to be converted to ONNX format.", + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "OnnxVersion", + "Type": { + "Kind": "Enum", + "Values": [ + "Stable", + "Experimental" + ] + }, + "Desc": "The targeted ONNX version. It can be either \"Stable\" or \"Experimental\". If \"Experimental\" is used, produced model can contain components that is not officially supported in ONNX standard.", + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": "Stable" + }, + { + "Name": "PredictiveModel", + "Type": "PredictorModel", + "Desc": "Predictor model that needs to be converted to ONNX format.", + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [] + }, + { + "Name": "Models.OvaModelCombiner", + "Desc": "Combines a sequence of PredictorModels into a single model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "ModelArray", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "Input models", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseProbabilities", + "Type": "Bool", + "Desc": "Use probabilities from learners instead of raw values.", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ] + }, + { + "Name": "Models.PAVCalibrator", + "Desc": "Apply a PAV calibrator to an input model", + "FriendlyName": "PAV Calibration", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UncalibratedPredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor to calibrate", + "Aliases": [ + "uncalibratedPredictorModel" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "MaxRows", + "Type": "Int", + "Desc": "The maximum number of examples to train the calibrator on", + "Aliases": [ + "maxRows" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 1000000000, + "Range": { + "Inf": 0, + "Max": 2147483647 + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ICalibratorInput", + "ITransformInput" + ], + "OutputKind": [ + "ICalibratorOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.PlattCalibrator", + "Desc": "Apply a Platt calibrator to an input model", + "FriendlyName": "Sigmoid Calibration", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UncalibratedPredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor to calibrate", + "Aliases": [ + "uncalibratedPredictorModel" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "MaxRows", + "Type": "Int", + "Desc": "The maximum number of examples to train the calibrator on", + "Aliases": [ + "maxRows" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 1000000000, + "Range": { + "Inf": 0, + "Max": 2147483647 + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ICalibratorInput", + "ITransformInput" + ], + "OutputKind": [ + "ICalibratorOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.QuantileRegressionEvaluator", + "Desc": "Evaluates a quantile regression scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "RegressionLossFunction" + }, + "Desc": "Loss function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "SquaredLoss" + } + }, + { + "Name": "Index", + "Type": "Int", + "Desc": "Quantile index to select", + "Aliases": [ + "ind" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.RankingEvaluator", + "Desc": "Evaluates a ranking scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "GroupIdColumn", + "Type": "String", + "Desc": "Column to use for the group ID", + "Aliases": [ + "group" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "DcgTruncationLevel", + "Type": "Int", + "Desc": "Maximum truncation level for computing (N)DCG", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "LabelGains", + "Type": "String", + "Desc": "Label relevance gains", + "Aliases": [ + "gains" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "0,3,7,15,31" + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.RegressionEnsemble", + "Desc": "Combine regression models into an ensemble", + "FriendlyName": "Regression Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + }, + { + "Name": "ValidatePipelines", + "Type": "Bool", + "Desc": "Whether to validate that all the pipelines are identical", + "Aliases": [ + "validate" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.RegressionEvaluator", + "Desc": "Evaluates a regression scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "RegressionLossFunction" + }, + "Desc": "Loss function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "SquaredLoss" + } + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels.", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Weight column name.", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ScoreColumn", + "Type": "String", + "Desc": "Score column name.", + "Aliases": [ + "score" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "StratColumn", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Stratification column name.", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + } + ], + "InputKind": [ + "IEvaluatorInput" + ], + "OutputKind": [ + "IEvaluatorOutput" + ] + }, + { + "Name": "Models.RegressionPipelineEnsemble", + "Desc": "Combine regression models into an ensemble", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.Summarizer", + "Desc": "Summarize a linear regression predictor.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor to summarize", + "Aliases": [ + "predictorModel" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "Summary", + "Type": "DataView", + "Desc": "The summary of a predictor" + }, + { + "Name": "Stats", + "Type": "DataView", + "Desc": "The training set statistics. Note that this output can be null." + } + ] + }, + { + "Name": "Models.TrainTestEvaluator", + "Desc": "General train test for any supported evaluator", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "train" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "TestingData", + "Type": "DataView", + "Desc": "The data to be used for testing", + "Aliases": [ + "test" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "The aggregated transform model from the pipeline before this command, to apply to the test data, and also include in the final model, together with the predictor model.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Nodes", + "Type": { + "Kind": "Array", + "ItemType": "Node" + }, + "Desc": "The training subgraph", + "Required": true, + "SortOrder": 4.0, + "IsNullable": false + }, + { + "Name": "Inputs", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for training", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ] + }, + "Desc": "The training subgraph inputs", + "Required": true, + "SortOrder": 5.0, + "IsNullable": false + }, + { + "Name": "Outputs", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + } + ] + }, + "Desc": "The training subgraph outputs", + "Required": true, + "SortOrder": 6.0, + "IsNullable": false + }, + { + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "SignatureBinaryClassifierTrainer", + "SignatureMulticlassClassificationTrainer", + "SignatureRankerTrainer", + "SignatureRegressorTrainer", + "SignatureMultiOutputRegressorTrainer", + "SignatureAnomalyDetectorTrainer", + "SignatureClusteringTrainer" + ] + }, + "Desc": "Specifies the trainer kind, which determines the evaluator to be used.", + "Required": false, + "SortOrder": 7.0, + "IsNullable": false, + "Default": "SignatureBinaryClassifierTrainer" + }, + { + "Name": "PipelineId", + "Type": "String", + "Desc": "Identifies which pipeline was run for this train test.", + "Required": false, + "SortOrder": 8.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "IncludeTrainingMetrics", + "Type": "Bool", + "Desc": "Indicates whether to include and output training dataset metrics.", + "Required": false, + "SortOrder": 9.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": "Weight" + }, + { + "Name": "GroupColumn", + "Type": "String", + "Desc": "Column to use for grouping", + "Aliases": [ + "group" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": "GroupId" + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 13.0, + "IsNullable": false, + "Default": "Name" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The final model including the trained predictor model and the model from the transforms, provided as the Input.TransformModel." + }, + { + "Name": "Warnings", + "Type": "DataView", + "Desc": "Warning dataset" + }, + { + "Name": "OverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset" + }, + { + "Name": "ConfusionMatrix", + "Type": "DataView", + "Desc": "Confusion matrix dataset" + }, + { + "Name": "TrainingWarnings", + "Type": "DataView", + "Desc": "Warning dataset for training" + }, + { + "Name": "TrainingOverallMetrics", + "Type": "DataView", + "Desc": "Overall metrics dataset for training" + }, + { + "Name": "TrainingPerInstanceMetrics", + "Type": "DataView", + "Desc": "Per instance metrics dataset for training" + }, + { + "Name": "TrainingConfusionMatrix", + "Type": "DataView", + "Desc": "Confusion matrix dataset for training" + } + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.ExponentialAverage", + "Desc": "Applies a Exponential average on a time series.", + "FriendlyName": "Exponential Average Transform", + "ShortName": "ExpAvg", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column", + "Aliases": [ + "name" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "Decay", + "Type": "Float", + "Desc": "Coefficient d in: d m(y_t) = d * y_t + (1-d) * m(y_(t-1)), it should be in [0, 1].", + "Aliases": [ + "d" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.9 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.IidChangePointDetector", + "Desc": "This transform detects the change-points in an i.i.d. sequence using adaptive kernel density estimation and martingales.", + "FriendlyName": "IID Change Point Detection", + "ShortName": "ichgpnt", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column.", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "Confidence", + "Type": "Float", + "Desc": "The confidence for change point detection in the range [0, 100].", + "Aliases": [ + "cnf" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 95.0 + }, + { + "Name": "ChangeHistoryLength", + "Type": "Int", + "Desc": "The length of the sliding window on p-values for computing the martingale score.", + "Aliases": [ + "wnd" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": 20 + }, + { + "Name": "Martingale", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "Power", + "Mixture" + ] + }, + "Desc": "The martingale used for scoring.", + "Aliases": [ + "mart" + ], + "Required": false, + "SortOrder": 103.0, + "IsNullable": false, + "Default": "Power" + }, + { + "Name": "PowerMartingaleEpsilon", + "Type": "Float", + "Desc": "The epsilon parameter for the Power martingale.", + "Aliases": [ + "eps" + ], + "Required": false, + "SortOrder": 104.0, + "IsNullable": false, + "Default": 0.1 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.IidSpikeDetector", + "Desc": "This transform detects the spikes in a i.i.d. sequence using adaptive kernel density estimation.", + "FriendlyName": "IID Spike Detection", + "ShortName": "ispike", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column.", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "Confidence", + "Type": "Float", + "Desc": "The confidence for spike detection in the range [0, 100].", + "Aliases": [ + "cnf" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 99.0 + }, + { + "Name": "Side", + "Type": { + "Kind": "Enum", + "Values": [ + "Positive", + "Negative", + "TwoSided" + ] + }, + "Desc": "The argument that determines whether to detect positive or negative anomalies, or both.", + "Aliases": [ + "side" + ], + "Required": false, + "SortOrder": 101.0, + "IsNullable": false, + "Default": "TwoSided" + }, + { + "Name": "PvalueHistoryLength", + "Type": "Int", + "Desc": "The size of the sliding window for computing the p-value.", + "Aliases": [ + "wnd" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": 100 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.PercentileThresholdTransform", + "Desc": "Detects the values of time-series that are in the top percentile of the sliding window.", + "FriendlyName": "Percentile Threshold Transform", + "ShortName": "TopPcnt", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column", + "Aliases": [ + "name" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "Percentile", + "Type": "Float", + "Desc": "The percentile value for thresholding in the range [0, 100]", + "Aliases": [ + "pcnt" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "WindowSize", + "Type": "Int", + "Desc": "The size of the sliding window for computing the percentile threshold. The default value is set to 1.", + "Aliases": [ + "wnd" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 1 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.PValueTransform", + "Desc": "This P-Value transform calculates the p-value of the current input in the sequence with regard to the values in the sliding window.", + "FriendlyName": "p-Value Transform", + "ShortName": "PVal", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column", + "Aliases": [ + "name" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed value of the random generator", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "PositiveSide", + "Type": "Bool", + "Desc": "The flag that determines whether the p-values are calculated on the positive side", + "Aliases": [ + "pos" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "WindowSize", + "Type": "Int", + "Desc": "The size of the sliding window for computing the p-value", + "Aliases": [ + "wnd" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "InitialWindowSize", + "Type": "Int", + "Desc": "The size of the initial window for computing the p-value. The default value is set to 0, which means there is no initial window considered.", + "Aliases": [ + "initwnd" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.SlidingWindowTransform", + "Desc": "Returns the last values for a time series [y(t-d-l+1), y(t-d-l+2), ..., y(t-l-1), y(t-l)] where d is the size of the window, l the lag and y is a Float.", + "FriendlyName": "Sliding Window Transform", + "ShortName": "SlideWin", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "WindowSize", + "Type": "Int", + "Desc": "The size of the sliding window for computing the moving average", + "Aliases": [ + "wnd" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 2 + }, + { + "Name": "Lag", + "Type": "Int", + "Desc": "Lag between current observation and last observation from the sliding window", + "Aliases": [ + "l" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "Begin", + "Type": { + "Kind": "Enum", + "Values": [ + "NaNValues", + "FirstValue" + ] + }, + "Desc": "Define how to populate the first rows of the produced series", + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "NaNValues" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.SsaChangePointDetector", + "Desc": "This transform detects the change-points in a seasonal time-series using Singular Spectrum Analysis (SSA).", + "FriendlyName": "SSA Change Point Detection", + "ShortName": "chgpnt", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column.", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "TrainingWindowSize", + "Type": "Int", + "Desc": "The number of points from the beginning of the sequence used for training.", + "Aliases": [ + "twnd" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Confidence", + "Type": "Float", + "Desc": "The confidence for change point detection in the range [0, 100].", + "Aliases": [ + "cnf" + ], + "Required": true, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 95.0 + }, + { + "Name": "SeasonalWindowSize", + "Type": "Int", + "Desc": "An upper bound on the largest relevant seasonality in the input time-series.", + "Aliases": [ + "swnd" + ], + "Required": true, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "ChangeHistoryLength", + "Type": "Int", + "Desc": "The length of the sliding window on p-values for computing the martingale score.", + "Aliases": [ + "wnd" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": 20 + }, + { + "Name": "ErrorFunction", + "Type": { + "Kind": "Enum", + "Values": [ + "SignedDifference", + "AbsoluteDifference", + "SignedProportion", + "AbsoluteProportion", + "SquaredDifference" + ] + }, + "Desc": "The function used to compute the error between the expected and the observed value.", + "Aliases": [ + "err" + ], + "Required": false, + "SortOrder": 103.0, + "IsNullable": false, + "Default": "SignedDifference" + }, + { + "Name": "Martingale", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "Power", + "Mixture" + ] + }, + "Desc": "The martingale used for scoring.", + "Aliases": [ + "mart" + ], + "Required": false, + "SortOrder": 104.0, + "IsNullable": false, + "Default": "Power" + }, + { + "Name": "PowerMartingaleEpsilon", + "Type": "Float", + "Desc": "The epsilon parameter for the Power martingale.", + "Aliases": [ + "eps" + ], + "Required": false, + "SortOrder": 105.0, + "IsNullable": false, + "Default": 0.1 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.SsaForecasting", + "Desc": "This transform forecasts using Singular Spectrum Analysis (SSA).", + "FriendlyName": "SSA Forecasting", + "ShortName": "ssafcst", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column.", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "WindowSize", + "Type": "Int", + "Desc": "The length of the window on the series for building the trajectory matrix (parameter L).", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "SeriesLength", + "Type": "Int", + "Desc": "The length of series that is kept in buffer for modeling (parameter N).", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "TrainSize", + "Type": "Int", + "Desc": "The length of series from the beginning used for training.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "Horizon", + "Type": "Int", + "Desc": "The number of values to forecast.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "ConfidenceLevel", + "Type": "Float", + "Desc": "The confidence level in [0, 1) for forecasting.", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0.95 + }, + { + "Name": "VariableHorizon", + "Type": "Bool", + "Desc": "Set this to true horizon will change at prediction time.", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "ConfidenceLowerBoundColumn", + "Type": "String", + "Desc": "The name of the confidence interval lower bound column.", + "Aliases": [ + "cnfminname" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ConfidenceUpperBoundColumn", + "Type": "String", + "Desc": "The name of the confidence interval upper bound column.", + "Aliases": [ + "cnfmaxnname" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RankSelectionMethod", + "Type": { + "Kind": "Enum", + "Values": [ + "Fixed", + "Exact", + "Fast" + ] + }, + "Desc": "The rank selection method.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Exact" + }, + { + "Name": "Rank", + "Type": "Int", + "Desc": "The desired rank of the subspace used for SSA projection (parameter r). This parameter should be in the range in [1, windowSize]. If set to null, the rank is automatically determined based on prediction error minimization.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxRank", + "Type": "Int", + "Desc": "The maximum rank considered during the rank selection process. If not provided (i.e. set to null), it is set to windowSize - 1.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ShouldStabilize", + "Type": "Bool", + "Desc": "The flag determining whether the model should be stabilized.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "ShouldMaintainInfo", + "Type": "Bool", + "Desc": "The flag determining whether the meta information for the model needs to be maintained.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaxGrowth", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "TimeSpan", + "Type": "Int", + "Desc": "Time span of growth ratio. Must be strictly positive.", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "Growth", + "Type": "Float", + "Desc": "Growth. Must be non-negative.", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0.0 + } + ] + }, + "Desc": "The maximum growth on the exponential trend.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DiscountFactor", + "Type": "Float", + "Desc": "The discount factor in [0,1] used for online updates.", + "Aliases": [ + "disc" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "IsAdaptive", + "Type": "Bool", + "Desc": "The flag determing whether the model is adaptive", + "Aliases": [ + "adp" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "TimeSeriesProcessingEntryPoints.SsaSpikeDetector", + "Desc": "This transform detects the spikes in a seasonal time-series using Singular Spectrum Analysis (SSA).", + "FriendlyName": "SSA Spike Detection", + "ShortName": "spike", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "The name of the source column.", + "Aliases": [ + "src" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The name of the new column.", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "TrainingWindowSize", + "Type": "Int", + "Desc": "The number of points from the beginning of the sequence used for training.", + "Aliases": [ + "twnd" + ], + "Required": true, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Confidence", + "Type": "Float", + "Desc": "The confidence for spike detection in the range [0, 100].", + "Aliases": [ + "cnf" + ], + "Required": true, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 99.0 + }, + { + "Name": "SeasonalWindowSize", + "Type": "Int", + "Desc": "An upper bound on the largest relevant seasonality in the input time-series.", + "Aliases": [ + "swnd" + ], + "Required": true, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "Side", + "Type": { + "Kind": "Enum", + "Values": [ + "Positive", + "Negative", + "TwoSided" + ] + }, + "Desc": "The argument that determines whether to detect positive or negative anomalies, or both.", + "Aliases": [ + "side" + ], + "Required": false, + "SortOrder": 101.0, + "IsNullable": false, + "Default": "TwoSided" + }, + { + "Name": "PvalueHistoryLength", + "Type": "Int", + "Desc": "The size of the sliding window for computing the p-value.", + "Aliases": [ + "wnd" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "ErrorFunction", + "Type": { + "Kind": "Enum", + "Values": [ + "SignedDifference", + "AbsoluteDifference", + "SignedProportion", + "AbsoluteProportion", + "SquaredDifference" + ] + }, + "Desc": "The function used to compute the error between the expected and the observed value.", + "Aliases": [ + "err" + ], + "Required": false, + "SortOrder": 103.0, + "IsNullable": false, + "Default": "SignedDifference" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Trainers.AveragedPerceptronBinaryClassifier", + "Desc": "Averaged Perceptron Binary Classifier.", + "FriendlyName": "Averaged Perceptron", + "ShortName": "ap", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "ClassificationLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "HingeLoss" + } + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.01, + 0.1, + 0.5, + 1.0 + ] + } + }, + { + "Name": "DecreaseLearningRate", + "Type": "Bool", + "Desc": "Decrease learning rate", + "Aliases": [ + "decreaselr" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 Regularization Weight", + "Aliases": [ + "reg", + "L2RegularizerWeight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 0.4 + } + }, + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter", + "numIterations" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 100, + "StepSize": 10.0, + "IsLogScale": true + } + }, + { + "Name": "InitialWeightsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", + "Aliases": [ + "initwts", + "initWtsDiameter" + ], + "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "ResetWeightsAfterXExamples", + "Type": "Int", + "Desc": "Number of examples after which weights will be reset to the current average", + "Aliases": [ + "numreset" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LazyUpdate", + "Type": "Bool", + "Desc": "Instead of updating averaged weights on every example, only update when loss is nonzero", + "Aliases": [ + "lazy", + "DoLazyUpdates" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "RecencyGain", + "Type": "Float", + "Desc": "Extra weight given to more recent updates", + "Aliases": [ + "rg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "RecencyGainMultiplicative", + "Type": "Bool", + "Desc": "Whether Recency Gain is multiplicative (vs. additive)", + "Aliases": [ + "rgm", + "RecencyGainMulti" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Averaged", + "Type": "Bool", + "Desc": "Do averaging?", + "Aliases": [ + "avg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "AveragedTolerance", + "Type": "Float", + "Desc": "The inexactness tolerance for averaging", + "Aliases": [ + "avgtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01 + }, + { + "Name": "InitialWeights", + "Type": "String", + "Desc": "Initial Weights and bias, comma-separated", + "Aliases": [ + "initweights" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Whether to shuffle for each training iteration", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.EnsembleBinaryClassifier", + "Desc": "Train binary ensemble.", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "SamplingType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleSubsetSelector" + }, + "Desc": "Sampling Type", + "Aliases": [ + "st" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": { + "Name": "BootstrapSelector", + "Settings": { + "FeatureSelector": { + "Name": "AllFeatureSelector" + } + } + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "NumModels", + "Type": "Int", + "Desc": "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise.", + "Aliases": [ + "nm" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "SubModelSelectorType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleBinarySubModelSelector" + }, + "Desc": "Algorithm to prune the base learners for selective Ensemble", + "Aliases": [ + "pt" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": { + "Name": "AllSelector" + } + }, + { + "Name": "OutputCombiner", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleBinaryOutputCombiner" + }, + "Desc": "Output combiner", + "Aliases": [ + "oc" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": { + "Name": "Median" + } + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "TrainParallel", + "Type": "Bool", + "Desc": "All the base learners will run asynchronously if the value is true", + "Aliases": [ + "tp" + ], + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Batch size", + "Aliases": [ + "bs" + ], + "Required": false, + "SortOrder": 107.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "ShowMetrics", + "Type": "Bool", + "Desc": "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set", + "Aliases": [ + "sm" + ], + "Required": false, + "SortOrder": 108.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.EnsembleClassification", + "Desc": "Train multiclass ensemble.", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "SamplingType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleSubsetSelector" + }, + "Desc": "Sampling Type", + "Aliases": [ + "st" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": { + "Name": "BootstrapSelector", + "Settings": { + "FeatureSelector": { + "Name": "AllFeatureSelector" + } + } + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "NumModels", + "Type": "Int", + "Desc": "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise.", + "Aliases": [ + "nm" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "SubModelSelectorType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleMulticlassSubModelSelector" + }, + "Desc": "Algorithm to prune the base learners for selective Ensemble", + "Aliases": [ + "pt" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": { + "Name": "AllSelectorMultiClass" + } + }, + { + "Name": "OutputCombiner", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleMulticlassOutputCombiner" + }, + "Desc": "Output combiner", + "Aliases": [ + "oc" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": { + "Name": "MultiMedian" + } + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "TrainParallel", + "Type": "Bool", + "Desc": "All the base learners will run asynchronously if the value is true", + "Aliases": [ + "tp" + ], + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Batch size", + "Aliases": [ + "bs" + ], + "Required": false, + "SortOrder": 107.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "ShowMetrics", + "Type": "Bool", + "Desc": "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set", + "Aliases": [ + "sm" + ], + "Required": false, + "SortOrder": 108.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.EnsembleRegression", + "Desc": "Train regression ensemble.", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "SamplingType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleSubsetSelector" + }, + "Desc": "Sampling Type", + "Aliases": [ + "st" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": { + "Name": "BootstrapSelector", + "Settings": { + "FeatureSelector": { + "Name": "AllFeatureSelector" + } + } + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "NumModels", + "Type": "Int", + "Desc": "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise.", + "Aliases": [ + "nm" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "SubModelSelectorType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleRegressionSubModelSelector" + }, + "Desc": "Algorithm to prune the base learners for selective Ensemble", + "Aliases": [ + "pt" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": { + "Name": "AllSelector" + } + }, + { + "Name": "OutputCombiner", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleRegressionOutputCombiner" + }, + "Desc": "Output combiner", + "Aliases": [ + "oc" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": { + "Name": "Median" + } + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "TrainParallel", + "Type": "Bool", + "Desc": "All the base learners will run asynchronously if the value is true", + "Aliases": [ + "tp" + ], + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Batch size", + "Aliases": [ + "bs" + ], + "Required": false, + "SortOrder": 107.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "ShowMetrics", + "Type": "Bool", + "Desc": "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set", + "Aliases": [ + "sm" + ], + "Required": false, + "SortOrder": 108.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastForestBinaryClassifier", + "Desc": "Uses a random forest learner to perform binary classification.", + "FriendlyName": "Fast Forest Classification", + "ShortName": "ff", + "Inputs": [ + { + "Name": "NumberOfTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "MaximumOutputMagnitudePerTree", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0 + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "NumberOfQuantileSamples", + "Type": "Int", + "Desc": "Number of labels to be sampled from each leaf to make the distribution", + "Aliases": [ + "qsc" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "The number of threads to use", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureSelectionSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Aliases": [ + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Aliases": [ + "ps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumCategoricalGroupCountPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The 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"ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "ShuffleLabels", + "Type": "Bool", + "Desc": "Shuffle the labels on every iteration. Useful probably only if using this tree as a tree leaf featurizer for multiclass.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "NumberOfQuantileSamples", + "Type": "Int", + "Desc": "Number of labels to be sampled from each leaf to make the distribution", + "Aliases": [ + "qsc" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "The number of threads to use", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureSelectionSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Aliases": [ + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Aliases": [ + "ps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumCategoricalGroupCountPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastTreeBinaryClassifier", + "Desc": "Uses a logit-boost boosted tree learner to perform binary classification.", + "FriendlyName": "FastTree (Boosted Trees) Classification", + "ShortName": "ftc", + "Inputs": [ + { + "Name": "NumberOfTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" 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+ "Name": "LearningRate", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "UnbalancedSets", + "Type": "Bool", + "Desc": "Option for using derivatives optimized for unbalanced sets", + "Aliases": [ + "us" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Option for using best regression step trees", + "Aliases": [ + "bsr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", + "Aliases": [ + "ls" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumNumberOfLineSearchSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", + "Aliases": [ + "lssteps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MinimumStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", + "Aliases": [ + "minstep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Aliases": [ + "oa" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "GradientDescent" + }, + { + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Aliases": [ + "esr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Aliases": [ + "esmt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Aliases": [ + "pruning" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", + "Aliases": [ + "prtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", + "Aliases": [ + "prth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.004 + }, + { + "Name": "PruningWindowSize", + "Type": "Int", + "Desc": "The moving window size for pruning", + "Aliases": [ + "prws" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", + "Aliases": [ + "shrk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + 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Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastTreeRanker", + "Desc": "Trains gradient boosted decision trees to the LambdaRank quasi-gradient.", + "FriendlyName": "FastTree (Boosted Trees) Ranking", + "ShortName": "ftrank", + "Inputs": [ + { + "Name": "NumberOfTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "GroupId" + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "CustomGains", + "Type": { + "Kind": "Array", + "ItemType": "Float" + }, + "Desc": "Comma-separated list of gains associated to each relevance label.", + "Aliases": [ + "gains" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": [ + 0.0, + 3.0, + 7.0, + 15.0, + 31.0 + ] + }, + { + "Name": "UseDcg", + "Type": "Bool", + "Desc": "Train DCG instead of NDCG", + "Aliases": [ + "dcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "SortingAlgorithm", + "Type": "String", + "Desc": "The sorting algorithm to use for DCG and LambdaMart calculations [DescendingStablePessimistic/DescendingStable/DescendingReverse/DescendingDotNet]", + "Aliases": [ + "sort" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "DescendingStablePessimistic" + }, + { + "Name": "NdcgTruncationLevel", + "Type": "Int", + "Desc": "max-NDCG truncation to use in the LambdaMART algorithm", + "Aliases": [ + "n" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "ShiftedNdcg", + "Type": "Bool", + "Desc": "Use shifted NDCG", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "CostFunctionParam", + "Type": "Char", + "Desc": "Cost function parameter (w/c)", + "Aliases": [ + "cf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "w" + }, + { + "Name": "DistanceWeight2", + "Type": "Bool", + "Desc": "Distance weight 2 adjustment to cost", + "Aliases": [ + "dw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "NormalizeQueryLambdas", + "Type": "Bool", + "Desc": "Normalize query lambdas", + "Aliases": [ + "nql" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Option for using best regression step trees", + "Aliases": [ + "bsr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", + "Aliases": [ + "ls" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumNumberOfLineSearchSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", + "Aliases": [ + "lssteps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MinimumStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", + "Aliases": [ + "minstep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Aliases": [ + "oa" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "GradientDescent" + }, + { + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Aliases": [ + "esr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Aliases": [ + "esmt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Aliases": [ + "pruning" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", + "Aliases": [ + "prtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", + "Aliases": [ + "prth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.004 + }, + { + "Name": "PruningWindowSize", + "Type": "Int", + "Desc": "The moving window size for pruning", + "Aliases": [ + "prws" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", + "Aliases": [ + "shrk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true + } + }, + { + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", + "Aliases": [ + "tdrop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] + } + }, + { + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Aliases": [ + "sr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Aliases": [ + "hl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0 + }, + { + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", + "Aliases": [ + "rs" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", + "Aliases": [ + "fzl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Aliases": [ + "basescores" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal 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Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRankingOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastTreeRegressor", + "Desc": "Trains gradient boosted decision trees to fit target values using least-squares.", + "FriendlyName": "FastTree (Boosted Trees) Regression", + "ShortName": "ftr", + "Inputs": [ + { + "Name": "NumberOfTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Option for using best regression step trees", + "Aliases": [ + "bsr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", + "Aliases": [ + "ls" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumNumberOfLineSearchSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", + "Aliases": [ + "lssteps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MinimumStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", + "Aliases": [ + "minstep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Aliases": [ + "oa" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "GradientDescent" + }, + { + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Aliases": [ + "esr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Aliases": [ + "esmt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Aliases": [ + "pruning" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", + "Aliases": [ + "prtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", + "Aliases": [ + "prth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.004 + }, + { + "Name": "PruningWindowSize", + "Type": "Int", + "Desc": "The moving window size for pruning", + "Aliases": [ + "prws" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", + "Aliases": [ + "shrk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true + } + }, + { + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", + "Aliases": [ + "tdrop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] + } + }, + { + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Aliases": [ + "sr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Aliases": [ + "hl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0 + }, + { + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", + "Aliases": [ + "rs" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", + "Aliases": [ + "fzl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Aliases": [ + "basescores" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal 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null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureSelectionSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Aliases": [ + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Aliases": [ + "ps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumCategoricalGroupCountPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastTreeTweedieRegressor", + "Desc": "Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression.", + "FriendlyName": "FastTree (Boosted Trees) Tweedie Regression", + "ShortName": "fttweedie", + "Inputs": [ + { + "Name": "NumberOfTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Index", + "Type": "Float", + "Desc": "Index parameter for the Tweedie distribution, in the range [1, 2]. 1 is Poisson loss, 2 is gamma loss, and intermediate values are compound Poisson loss.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.5 + }, + { + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Option for using best regression step trees", + "Aliases": [ + "bsr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", + "Aliases": [ + "ls" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumNumberOfLineSearchSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", + "Aliases": [ + "lssteps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MinimumStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", + "Aliases": [ + "minstep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Aliases": [ + "oa" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "GradientDescent" + }, + { + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Aliases": [ + "esr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Aliases": [ + "esmt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Aliases": [ + "pruning" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", + "Aliases": [ + "prtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", + "Aliases": [ + "prth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.004 + }, + { + "Name": "PruningWindowSize", + "Type": "Int", + "Desc": "The moving window size for pruning", + "Aliases": [ + "prws" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", + "Aliases": [ + "shrk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true + } + }, + { + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", + "Aliases": [ + "tdrop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] + } + }, + { + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Aliases": [ + "sr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Aliases": [ + "hl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0 + }, + { + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", + "Aliases": [ + "rs" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", + "Aliases": [ + "fzl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Aliases": [ + "basescores" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Aliases": [ + "basealpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "PositionDiscountFreeform", + "Type": "String", + "Desc": "The discount freeform which specifies the per position discounts of examples in a query (uses a single variable P for position where P=0 is first position)", + "Aliases": [ + "pdff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "The number of threads to use", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureSelectionSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Aliases": [ + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Aliases": [ + "ps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumCategoricalGroupCountPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FieldAwareFactorizationMachineBinaryClassifier", + "Desc": "Train a field-aware factorization machine for binary classification", + "FriendlyName": "Field-aware Factorization Machine", + "ShortName": "ffm", + "Inputs": [ + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Initial learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 0.1, + "SweepRange": { + "RangeType": "Float", + "Min": 0.001, + "Max": 1.0, + "IsLogScale": true + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of training iterations", + "Aliases": [ + "iters", + "iter" + ], + 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The i-th specified string denotes the column containing features form the (i+1)-th field. Note that the first field is specified by \"feat\" instead of \"exfeat\".", + "Aliases": [ + "exfeat" + ], + "Required": false, + "SortOrder": 7.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Whether to shuffle for each training iteration", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 90.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Verbose", + "Type": "Bool", + "Desc": "Report traning progress or not", + "Aliases": [ + "verbose" + ], + "Required": false, + "SortOrder": 91.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Radius", + "Type": "Float", + "Desc": "Radius of initial latent factors", + "Aliases": [ + "rad" + ], + "Required": false, + "SortOrder": 110.0, + "IsNullable": false, + "Default": 0.5, + "SweepRange": { + "RangeType": "Float", + "Min": 0.1, + "Max": 1.0 + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.GeneralizedAdditiveModelBinaryClassifier", + "Desc": "Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It maintains no interactions between features.", + "FriendlyName": "Generalized Additive Model for Binary Classification", + "ShortName": "gam", + "Inputs": [ + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Total number of iterations over all features", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 9500, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 200, + 1500, + 9500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "Minimum number of training instances 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It maintains no interactions between features.", + "FriendlyName": "Generalized Additive Model for Regression", + "ShortName": "gamr", + "Inputs": [ + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Total number of iterations over all features", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 9500, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 200, + 1500, + 9500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "Minimum number of training instances required to form a partition", + "Aliases": [ + "mi" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.002, + "SweepRange": { + "RangeType": "Float", + "Min": 0.001, + "Max": 0.1, + "IsLogScale": true + } + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "PruningMetrics", + "Type": "Int", + "Desc": "Metric for pruning. 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K-means++ improves upon K-means by using a better method for choosing the initial cluster centers.", + "FriendlyName": "KMeans++ Clustering", + "ShortName": "KM", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "K", + "Type": "Int", + "Desc": "The number of clusters", + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 5, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 5, + 10, + 20, + 40 + ] + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Aliases": [ + "nt", + "t", + "threads" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "InitializationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "KMeansPlusPlus", + "Random", + "KMeansYinyang" + ] + }, + "Desc": "Cluster initialization algorithm", + "Aliases": [ + "init" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "KMeansYinyang" + }, + { + "Name": "OptTol", + "Type": "Float", + "Desc": "Tolerance parameter for trainer convergence. 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Avoid the bias of small categories.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 20 + ] + } + }, + { + "Name": "L2CategoricalRegularization", + "Type": "Float", + "Desc": "L2 Regularization for categorical split.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.5, + 1, + 5, + 10 + ] + } + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Sets the random seed for LightGBM to use.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelLightGBM" + }, + "Desc": "Parallel LightGBM Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LightGbmClassifier", + "Desc": "Train a LightGBM multi class model.", + "FriendlyName": "LightGBM Multi Class Classifier", + "ShortName": "LightGBMMC", + "Inputs": [ + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of iterations.", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 50, + 100, + 150, + 200 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Shrinkage rate for trees, used to prevent over-fitting. 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"Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "Booster", + "Type": { + "Kind": "Component", + "ComponentKind": "BoosterParameterFunction" + }, + "Desc": "Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": { + "Name": "gbdt" + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ 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"Verbose", + "Aliases": [ + "v" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Silent", + "Type": "Bool", + "Desc": "Printing running messages.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Number of parallel threads used to run LightGBM.", + "Aliases": [ + "nt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "EarlyStoppingRound", + "Type": "Int", + "Desc": "Rounds of early stopping, 0 will disable it.", + "Aliases": [ + "es" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Number of entries in a batch when loading data.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1048576 + }, + { + "Name": "UseCategoricalSplit", + "Type": "Bool", + "Desc": "Enable categorical split or not.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "HandleMissingValue", + "Type": "Bool", + "Desc": "Enable special handling of missing value or not.", + "Aliases": [ + "hmv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "UseZeroAsMissingValue", + "Type": "Bool", + "Desc": "Enable usage of zero (0) as missing value.", + "Aliases": [ + "uzam" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "MinimumExampleCountPerGroup", + "Type": "Int", + "Desc": "Minimum number of instances per categorical 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Avoid the bias of small categories.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 20 + ] + } + }, + { + "Name": "L2CategoricalRegularization", + "Type": "Float", + "Desc": "L2 Regularization for categorical split.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.5, + 1, + 5, + 10 + ] + } + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Sets the random seed for LightGBM to use.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelLightGBM" + }, + "Desc": "Parallel LightGBM Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LightGbmRanker", + "Desc": "Train a LightGBM ranking model.", + "FriendlyName": "LightGBM Ranking", + "ShortName": "LightGBMRank", + "Inputs": [ + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of iterations.", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 50, + 100, + 150, + 200 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1].", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "Maximum leaves for trees.", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "Minimum number of instances needed in a child.", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 20, + 50 + ] + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "Booster", + "Type": { + "Kind": "Component", + "ComponentKind": "BoosterParameterFunction" + }, + "Desc": "Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": { + "Name": "gbdt" + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ 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255, + 511, + 1023, + 2047, + 4095 + ] + }, + { + "Name": "Sigmoid", + "Type": "Float", + "Desc": "Parameter for the sigmoid function.", + "Aliases": [ + "sigmoid" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.5 + }, + { + "Name": "EvaluationMetric", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "Default", + "MeanAveragedPrecision", + "NormalizedDiscountedCumulativeGain" + ] + }, + "Desc": "Evaluation metrics.", + "Aliases": [ + "em" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "NormalizedDiscountedCumulativeGain" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of bucket bin for features.", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "Verbose", + "Type": "Bool", + "Desc": "Verbose", + "Aliases": [ + "v" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Silent", + "Type": "Bool", + "Desc": "Printing running messages.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Number of parallel threads used to run LightGBM.", + "Aliases": [ + "nt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "EarlyStoppingRound", + "Type": "Int", + "Desc": "Rounds of early stopping, 0 will disable it.", + "Aliases": [ + "es" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Number of entries in a batch when loading data.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1048576 + }, + { + "Name": "UseCategoricalSplit", + "Type": "Bool", + "Desc": "Enable categorical split or not.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "HandleMissingValue", + "Type": "Bool", + "Desc": "Enable special handling of missing value or not.", + "Aliases": [ + "hmv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "UseZeroAsMissingValue", + "Type": "Bool", + "Desc": "Enable usage of zero (0) as missing value.", + "Aliases": [ + "uzam" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "MinimumExampleCountPerGroup", + "Type": "Int", + "Desc": "Minimum number of instances per categorical group.", + "Aliases": [ + "mdpg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100, + "Range": { + "Inf": 0, + "Max": 2147483647 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 50, + 100, + 200 + ] + } + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Max number of categorical thresholds.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 32, + "Range": { + "Inf": 0, + "Max": 2147483647 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 8, + 16, + 32, + 64 + ] + } + }, + { + "Name": "CategoricalSmoothing", + "Type": "Float", + "Desc": "Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 20 + ] + } + }, + { + "Name": "L2CategoricalRegularization", + "Type": "Float", + "Desc": "L2 Regularization for categorical split.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.5, + 1, + 5, + 10 + ] + } + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Sets the random seed for LightGBM to use.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelLightGBM" + }, + "Desc": "Parallel LightGBM Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRankingOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LightGbmRegressor", + "Desc": "LightGBM Regression", + "FriendlyName": "LightGBM Regressor", + "ShortName": "LightGBMR", + "Inputs": [ + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of iterations.", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 50, + 100, + 150, + 200 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1].", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "Maximum leaves for trees.", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "Minimum number of instances needed in a child.", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 20, + 50 + ] + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "Booster", + "Type": { + "Kind": "Component", + "ComponentKind": "BoosterParameterFunction" + }, + "Desc": "Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": { + "Name": "gbdt" + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "EvaluationMetric", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "Default", + "MeanAbsoluteError", + "RootMeanSquaredError", + "MeanSquaredError" + ] + }, + "Desc": "Evaluation metrics.", + "Aliases": [ + "em" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "RootMeanSquaredError" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of bucket bin for features.", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "Verbose", + "Type": "Bool", + "Desc": "Verbose", + "Aliases": [ + "v" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Silent", + "Type": "Bool", + "Desc": "Printing running messages.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Number of parallel threads used to run LightGBM.", + "Aliases": [ + "nt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "EarlyStoppingRound", + "Type": "Int", + "Desc": "Rounds of early stopping, 0 will disable it.", + "Aliases": [ + "es" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Number of entries in a batch when loading data.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1048576 + }, + { + "Name": "UseCategoricalSplit", + "Type": "Bool", + "Desc": "Enable categorical split or not.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "HandleMissingValue", + "Type": "Bool", + "Desc": "Enable special handling of missing value or not.", + "Aliases": [ + "hmv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "UseZeroAsMissingValue", + "Type": "Bool", + "Desc": "Enable usage of zero (0) as missing value.", + "Aliases": [ + "uzam" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + true, + false + ] + } + }, + { + "Name": "MinimumExampleCountPerGroup", + "Type": "Int", + "Desc": "Minimum number of instances per categorical group.", + "Aliases": [ + "mdpg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100, + "Range": { + "Inf": 0, + "Max": 2147483647 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 50, + 100, + 200 + ] + } + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Max number of categorical thresholds.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 32, + "Range": { + "Inf": 0, + "Max": 2147483647 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 8, + 16, + 32, + 64 + ] + } + }, + { + "Name": "CategoricalSmoothing", + "Type": "Float", + "Desc": "Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 20 + ] + } + }, + { + "Name": "L2CategoricalRegularization", + "Type": "Float", + "Desc": "L2 Regularization for categorical split.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.5, + 1, + 5, + 10 + ] + } + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Sets the random seed for LightGBM to use.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelLightGBM" + }, + "Desc": "Parallel LightGBM Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LinearSvmBinaryClassifier", + "Desc": "Train a linear SVM.", + "FriendlyName": "SVM (Pegasos-Linear)", + "ShortName": "svm", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight", + "WeightColumn" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Lambda", + "Type": "Float", + "Desc": "Regularizer constant", + "Aliases": [ + "lambda" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.001, + "SweepRange": { + "RangeType": "Float", + "Min": 1E-05, + "Max": 0.1, + "StepSize": 10.0, + "IsLogScale": true + } + }, + { + "Name": "PerformProjection", + "Type": "Bool", + "Desc": "Perform projection to unit-ball? Typically used with batch size > 1.", + "Aliases": [ + "project" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter", + "numIterations" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 100, + "StepSize": 10.0, + "IsLogScale": true + } + }, + { + "Name": "InitialWeightsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", + "Aliases": [ + "initwts", + "initWtsDiameter" + ], + "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } + }, + { + "Name": "NoBias", + "Type": "Bool", + "Desc": "No bias", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "InitialWeights", + "Type": "String", + "Desc": "Initial Weights and bias, comma-separated", + "Aliases": [ + "initweights" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Whether to shuffle for each training iteration", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Batch size", + "Aliases": [ + "batch" + ], + "Required": false, + "SortOrder": 190.0, + "IsNullable": false, + "Default": 1 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LocalDeepSvmBinaryClassifier", + "Desc": "LD-SVM learns a binary, non-linear SVM classifier with a kernel that is specifically designed to reduce prediction time. LD-SVM learns decision boundaries that are locally linear.", + "FriendlyName": "Local Deep SVM (LDSVM)", + "ShortName": "LDSVM", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "TreeDepth", + "Type": "Int", + "Desc": "Depth of Local Deep SVM tree", + "Aliases": [ + "depth" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 3, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 3, + 5, + 7 + ] + } + }, + { + "Name": "LambdaW", + "Type": "Float", + "Desc": "Regularizer for classifier parameter W", + "Aliases": [ + "lw" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.1, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "LambdaTheta", + "Type": "Float", + "Desc": "Regularizer for kernel parameter Theta", + "Aliases": [ + "lt" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "LambdaThetaprime", + "Type": "Float", + "Desc": "Regularizer for kernel parameter Thetaprime", + "Aliases": [ + "lp" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "Sigma", + "Type": "Float", + "Desc": "Parameter for sigmoid sharpness", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1.0, + 0.1, + 0.01 + ] + } + }, + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter", + "NumIterations" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 15000, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10000, + 15000 + ] + } + }, + { + "Name": "UseBias", + "Type": "Bool", + "Desc": "No bias", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "Cache", + "Type": "Bool", + "Desc": "Whether to cache the data before the first iteration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LogisticRegressionBinaryClassifier", + "Desc": "Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.", + "FriendlyName": "Logistic Regression", + "ShortName": "lr", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "ShowTrainingStatistics", + "Type": "Bool", + "Desc": "Show statistics of training examples.", + "Aliases": [ + "stat", + "ShowTrainingStats" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularization weight", + "Aliases": [ + "l2", + "L2Weight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } + }, + { + "Name": "L1Regularization", + "Type": "Float", + "Desc": "L1 regularization weight", + "Aliases": [ + "l1", + "L1Weight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } + }, + { + "Name": "OptimizationTolerance", + "Type": "Float", + "Desc": "Tolerance parameter for optimization convergence. Low = slower, more accurate", + "Aliases": [ + "ot", + "OptTol" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1E-07, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0001, + 1E-07 + ] + } + }, + { + "Name": "HistorySize", + "Type": "Int", + "Desc": "Memory size for L-BFGS. Low=faster, less accurate", + "Aliases": [ + "m", + "MemorySize" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 5, + 20, + 50 + ] + } + }, + { + "Name": "EnforceNonNegativity", + "Type": "Bool", + "Desc": "Enforce non-negative weights", + "Aliases": [ + "nn" + ], + "Required": false, + "SortOrder": 90.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "InitialWeightsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", + "Aliases": [ + "initwts", + "InitWtsDiameter" + ], + "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } + }, + { + "Name": "MaximumNumberOfIterations", + "Type": "Int", + "Desc": "Maximum iterations.", + "Aliases": [ + "maxiter", + "MaxIterations", + "NumberOfIterations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 2147483647 + } + }, + { + "Name": "StochasticGradientDescentInitilaizationTolerance", + "Type": "Float", + "Desc": "Run SGD to initialize LR weights, converging to this tolerance", + "Aliases": [ + "sgd", + "SgdInitializationTolerance" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Quiet", + "Type": "Bool", + "Desc": "If set to true, produce no output during training.", + "Aliases": [ + "q" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseThreads", + "Type": "Bool", + "Desc": "Whether or not to use threads. Default is true", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Number of threads", + "Aliases": [ + "nt", + "NumThreads" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DenseOptimizer", + "Type": "Bool", + "Desc": "Force densification of the internal optimization vectors", + "Aliases": [ + "do" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LogisticRegressionClassifier", + "Desc": "Maximum entropy classification is a method in statistics used to predict the probabilities of parallel events. The model predicts the probabilities of parallel events by fitting data to a softmax function.", + "FriendlyName": "Multi-class Logistic Regression", + "ShortName": "mlr", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "ShowTrainingStatistics", + "Type": "Bool", + "Desc": "Show statistics of training examples.", + "Aliases": [ + "stat", + "ShowTrainingStats" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularization weight", + "Aliases": [ + "l2", + "L2Weight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } + }, + { + "Name": "L1Regularization", + "Type": "Float", + "Desc": "L1 regularization weight", + "Aliases": [ + "l1", + "L1Weight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } + }, + { + "Name": "OptimizationTolerance", + "Type": "Float", + "Desc": "Tolerance parameter for optimization convergence. Low = slower, more accurate", + "Aliases": [ + "ot", + "OptTol" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1E-07, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0001, + 1E-07 + ] + } + }, + { + "Name": "HistorySize", + "Type": "Int", + "Desc": "Memory size for L-BFGS. Low=faster, less accurate", + "Aliases": [ + "m", + "MemorySize" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 5, + 20, + 50 + ] + } + }, + { + "Name": "EnforceNonNegativity", + "Type": "Bool", + "Desc": "Enforce non-negative weights", + "Aliases": [ + "nn" + ], + "Required": false, + "SortOrder": 90.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "InitialWeightsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", + "Aliases": [ + "initwts", + "InitWtsDiameter" + ], + "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } + }, + { + "Name": "MaximumNumberOfIterations", + "Type": "Int", + "Desc": "Maximum iterations.", + "Aliases": [ + "maxiter", + "MaxIterations", + "NumberOfIterations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 2147483647 + } + }, + { + "Name": "StochasticGradientDescentInitilaizationTolerance", + "Type": "Float", + "Desc": "Run SGD to initialize LR weights, converging to this tolerance", + "Aliases": [ + "sgd", + "SgdInitializationTolerance" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Quiet", + "Type": "Bool", + "Desc": "If set to true, produce no output during training.", + "Aliases": [ + "q" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseThreads", + "Type": "Bool", + "Desc": "Whether or not to use threads. Default is true", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Number of threads", + "Aliases": [ + "nt", + "NumThreads" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DenseOptimizer", + "Type": "Bool", + "Desc": "Force densification of the internal optimization vectors", + "Aliases": [ + "do" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.NaiveBayesClassifier", + "Desc": "Train a MulticlassNaiveBayesTrainer.", + "FriendlyName": "Multiclass Naive Bayes", + "ShortName": "MNB", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.OnlineGradientDescentRegressor", + "Desc": "Train a Online gradient descent perceptron.", + "FriendlyName": "Stochastic Gradient Descent (Regression)", + "ShortName": "ogd", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "RegressionLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "SquaredLoss" + } + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.1, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.01, + 0.1, + 0.5, + 1.0 + ] + } + }, + { + "Name": "DecreaseLearningRate", + "Type": "Bool", + "Desc": "Decrease learning rate", + "Aliases": [ + "decreaselr" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 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"Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Rank", + "Type": "Int", + "Desc": "The number of components in the PCA", + "Aliases": [ + "k" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 40, + 80 + ] + } + }, + { + "Name": "Oversampling", + "Type": "Int", + "Desc": "Oversampling parameter for randomized PCA training", + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 40 + ] + } + }, + { + "Name": "Center", + "Type": "Bool", + "Desc": "If enabled, data is centered to be zero mean", + "Aliases": [ + "center" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed for random number generation", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "IUnsupervisedTrainerWithWeight", + "ITrainerInput" + ], + "OutputKind": [ + "IAnomalyDetectionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.PoissonRegressor", + "Desc": "Train an Poisson regression model.", + "FriendlyName": "Poisson Regression", + "ShortName": "PR", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularization weight", + "Aliases": [ + "l2", + "L2Weight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } + }, + { + "Name": "L1Regularization", + "Type": "Float", + "Desc": "L1 regularization weight", + "Aliases": [ + "l1", + "L1Weight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } + }, + { + "Name": "OptimizationTolerance", + "Type": "Float", + "Desc": "Tolerance parameter for optimization convergence. 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Low=faster, less accurate", + "Aliases": [ + "m", + "MemorySize" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 5, + 20, + 50 + ] + } + }, + { + "Name": "EnforceNonNegativity", + "Type": "Bool", + "Desc": "Enforce non-negative weights", + "Aliases": [ + "nn" + ], + "Required": false, + "SortOrder": 90.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "InitialWeightsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", + "Aliases": [ + "initwts", + "InitWtsDiameter" + ], + "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } + }, + { + "Name": "MaximumNumberOfIterations", + "Type": "Int", + "Desc": "Maximum iterations.", + "Aliases": [ + "maxiter", + "MaxIterations", + "NumberOfIterations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 2147483647 + } + }, + { + "Name": "StochasticGradientDescentInitilaizationTolerance", + "Type": "Float", + "Desc": "Run SGD to initialize LR weights, converging to this tolerance", + "Aliases": [ + "sgd", + "SgdInitializationTolerance" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Quiet", + "Type": "Bool", + "Desc": "If set to true, produce no output during training.", + "Aliases": [ + "q" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseThreads", + "Type": "Bool", + "Desc": "Whether or not to use threads. Default is true", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Number of threads", + "Aliases": [ + "nt", + "NumThreads" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DenseOptimizer", + "Type": "Bool", + "Desc": "Force densification of the internal optimization vectors", + "Aliases": [ + "do" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.StochasticDualCoordinateAscentBinaryClassifier", + "Desc": "Train an SDCA binary model.", + "FriendlyName": "Fast Linear (SA-SDCA)", + "ShortName": "SDCA", + "Inputs": [ + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", + "Aliases": [ + "l2", + "L2Const" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 1E-07, + 1E-06, + 1E-05, + 0.0001, + 0.001, + 0.01 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "L1Threshold", + "Type": "Float", + "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", + "Aliases": [ + "l1" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 0.0, + 0.25, + 0.5, + 0.75, + 1.0 + ] + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "SDCAClassificationLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "LogLoss" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Aliases": [ + "nt", + "t", + "threads", + "NumThreads" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "PositiveInstanceWeight", + "Type": "Float", + "Desc": "Apply weight to the positive class, for imbalanced data", + "Aliases": [ + "piw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "ConvergenceTolerance", + "Type": "Float", + "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", + "Aliases": [ + "tol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.1, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.001, + 0.01, + 0.1, + 0.2 + ] + } + }, + { + "Name": "MaximumNumberOfIterations", + "Type": "Int", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", + "Aliases": [ + "iter", + "MaxIterations", + "NumberOfIterations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 10, + 20, + 100 + ] + } + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Shuffle data every epoch?", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "ConvergenceCheckFrequency", + "Type": "Int", + "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", + "Aliases": [ + "checkFreq", + "CheckFrequency" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "BiasLearningRate", + "Type": "Float", + "Desc": "The learning rate for adjusting bias from being regularized.", + "Aliases": [ + "blr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.01, + 0.1, + 1.0 + ] + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.StochasticDualCoordinateAscentClassifier", + "Desc": "The SDCA linear multi-class classification trainer.", + "FriendlyName": "Fast Linear Multi-class Classification (SA-SDCA)", + "ShortName": "sasdcamc", + "Inputs": [ + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", + "Aliases": [ + "l2", + "L2Const" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 1E-07, + 1E-06, + 1E-05, + 0.0001, + 0.001, + 0.01 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "L1Threshold", + "Type": "Float", + "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", + "Aliases": [ + "l1" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 0.0, + 0.25, + 0.5, + 0.75, + 1.0 + ] + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "SDCAClassificationLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "LogLoss" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Aliases": [ + "nt", + "t", + "threads", + "NumThreads" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ConvergenceTolerance", + "Type": "Float", + "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", + "Aliases": [ + "tol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.1, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.001, + 0.01, + 0.1, + 0.2 + ] + } + }, + { + "Name": "MaximumNumberOfIterations", + "Type": "Int", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", + "Aliases": [ + "iter", + "MaxIterations", + "NumberOfIterations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 10, + 20, + 100 + ] + } + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Shuffle data every epoch?", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "ConvergenceCheckFrequency", + "Type": "Int", + "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", + "Aliases": [ + "checkFreq", + "CheckFrequency" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "BiasLearningRate", + "Type": "Float", + "Desc": "The learning rate for adjusting bias from being regularized.", + "Aliases": [ + "blr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.01, + 0.1, + 1.0 + ] + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.StochasticDualCoordinateAscentRegressor", + "Desc": "The SDCA linear regression trainer.", + "FriendlyName": "Fast Linear Regression (SA-SDCA)", + "ShortName": "sasdcar", + "Inputs": [ + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", + "Aliases": [ + "l2", + "L2Const" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 1E-07, + 1E-06, + 1E-05, + 0.0001, + 0.001, + 0.01 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "L1Threshold", + "Type": "Float", + "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", + "Aliases": [ + "l1" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 0.0, + 0.25, + 0.5, + 0.75, + 1.0 + ] + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "SDCARegressionLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "SquaredLoss" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Aliases": [ + "nt", + "t", + "threads", + "NumThreads" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ConvergenceTolerance", + "Type": "Float", + "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", + "Aliases": [ + "tol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.001, + 0.01, + 0.1, + 0.2 + ] + } + }, + { + "Name": "MaximumNumberOfIterations", + "Type": "Int", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", + "Aliases": [ + "iter", + "MaxIterations", + "NumberOfIterations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 10, + 20, + 100 + ] + } + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Shuffle data every epoch?", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "ConvergenceCheckFrequency", + "Type": "Int", + "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", + "Aliases": [ + "checkFreq", + "CheckFrequency" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "BiasLearningRate", + "Type": "Float", + "Desc": "The learning rate for adjusting bias from being regularized.", + "Aliases": [ + "blr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.01, + 0.1, + 1.0 + ] + } + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.StochasticGradientDescentBinaryClassifier", + "Desc": "Train an Hogwild SGD binary model.", + "FriendlyName": "Hogwild SGD (binary)", + "ShortName": "HogwildSGD", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "ClassificationLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "LogLoss" + } + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 Regularization constant", + "Aliases": [ + "l2", + "L2Weight" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1E-06, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1E-07, + 5E-07, + 1E-06, + 5E-06, + 1E-05 + ] + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic depending on data sparseness. Determinism not guaranteed.", + "Aliases": [ + "nt", + "t", + "threads", + "NumThreads" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "ConvergenceTolerance", + "Type": "Float", + "Desc": "Exponential moving averaged improvement tolerance for convergence", + "Aliases": [ + "tol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0001, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.01, + 0.001, + 0.0001, + 1E-05 + ] + } + }, + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning.", + "Aliases": [ + "iter", + "MaxIterations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 5, + 10, + 20 + ] + } + }, + { + "Name": "InitialLearningRate", + "Type": "Float", + "Desc": "Initial learning rate (only used by SGD)", + "Aliases": [ + "ilr", + "lr", + "InitLearningRate" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01 + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Shuffle data every epoch?", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "PositiveInstanceWeight", + "Type": "Float", + "Desc": "Apply weight to the positive class, for imbalanced data", + "Aliases": [ + "piw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "CheckFrequency", + "Type": "Int", + "Desc": "Convergence check frequency (in terms of number of iterations). Default equals number of threads", + "Aliases": [ + "checkFreq" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.SymSgdBinaryClassifier", + "Desc": "Train a symbolic SGD.", + "FriendlyName": "Symbolic SGD (binary)", + "ShortName": "SymSGD", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of passes over the data.", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 50, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 5, + 10, + 20, + 30, + 40, + 50 + ] + } + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 51.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 10.0, + 1.0, + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularization", + "Aliases": [ + "l2" + ], + "Required": false, + "SortOrder": 52.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-05, + 1E-05, + 1E-06, + 1E-07 + ] + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Determinism not guaranteed. Multi-threading is not supported currently.", + "Aliases": [ + "nt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Tolerance", + "Type": "Float", + "Desc": "Tolerance for difference in average loss in consecutive passes.", + "Aliases": [ + "tol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0001 + }, + { + "Name": "UpdateFrequency", + "Type": "Int", + "Desc": "The number of iterations each thread learns a local model until combining it with the global model. Low value means more updated global model and high value means less cache traffic.", + "Aliases": [ + "freq" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 5, + 20 + ] + } + }, + { + "Name": "MemorySize", + "Type": "Int", + "Desc": "The acceleration memory budget in MB", + "Aliases": [ + "accelMemBudget" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1024 + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Shuffle data?", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "PositiveInstanceWeight", + "Type": "Float", + "Desc": "Apply weight to the positive class, for imbalanced data", + "Aliases": [ + "piw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Transforms.ApproximateBootstrapSampler", + "Desc": "Approximate bootstrap sampling.", + "FriendlyName": "Bootstrap Sample Transform", + "ShortName": "BootstrapSample", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Complement", + "Type": "Bool", + "Desc": "Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform.", + "Aliases": [ + "comp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed. If unspecified random state will be instead derived from the environment.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ShuffleInput", + "Type": "Bool", + "Desc": "Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency.", + "Aliases": [ + "si" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "PoolSize", + "Type": "Int", + "Desc": "When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input.", + "Aliases": [ + "pool" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.BinaryPredictionScoreColumnsRenamer", + "Desc": "For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class.", + "FriendlyName": "Rename Binary Prediction Score Columns", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model used in scoring", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.BinNormalizer", + "Desc": "The values are assigned into equidensity bins and a value is mapped to its bin_number/number_of_bins.", + "FriendlyName": "Binning Normalizer", + "ShortName": "Bin", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "NumBins", + "Type": "Int", + "Desc": "Max number of bins, power of 2 recommended", + "Aliases": [ + "bins" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumBins", + "Type": "Int", + "Desc": "Max number of bins, power of 2 recommended", + "Aliases": [ + "bins" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1024 + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.CategoricalHashOneHotVectorizer", + "Desc": "Converts the categorical value into an indicator array by hashing the value and using the hash as an index in the bag. If the input column is a vector, a single indicator bag is returned for it.", + "FriendlyName": "Categorical Hash Transform", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "OutputKind", + "Type": { + "Kind": "Enum", + "Values": [ + "Bag", + "Indicator", + "Key", + "Binary" + ] + }, + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", + "Aliases": [ + "kind" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumberOfBits", + "Type": "Int", + "Desc": "The number of bits to hash into. Must be between 1 and 30, inclusive.", + "Aliases": [ + "bits" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", + "Aliases": [ + "ord" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaximumNumberOfInverts", + "Type": "Int", + "Desc": "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", + "Aliases": [ + "ih" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:numberOfBits:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 30, inclusive.", + "Aliases": [ + "bits" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 16 + }, + { + "Name": "OutputKind", + "Type": { + "Kind": "Enum", + "Values": [ + "Bag", + "Indicator", + "Key", + "Binary" + ] + }, + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", + "Aliases": [ + "kind" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": "Bag" + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 314489979 + }, + { + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", + "Aliases": [ + "ord" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaximumNumberOfInverts", + "Type": "Int", + "Desc": "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", + "Aliases": [ + "ih" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.CategoricalOneHotVectorizer", + "Desc": "Converts the categorical value into an indicator array by building a dictionary of categories based on the data and using the id in the dictionary as the index in the array.", + "FriendlyName": "Categorical Transform", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "OutputKind", + "Type": { + "Kind": "Enum", + "Values": [ + "Bag", + "Indicator", + "Key", + "Binary" + ] + }, + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), Key (index), or Binary encoded indicator vector", + "Aliases": [ + "kind" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "ByOccurrence", + "ByValue" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, for example, text sorting will be case sensitive (for example, 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of keys to keep per column when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "OutputKind", + "Type": { + "Kind": "Enum", + "Values": [ + "Bag", + "Indicator", + "Key", + "Binary" + ] + }, + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", + "Aliases": [ + "kind" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": "Indicator" + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "ByOccurrence", + "ByValue" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, for example, text sorting will be case sensitive (for example, 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 113.0, + "IsNullable": false, + "Default": "ByOccurrence" + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 114.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.CharacterTokenizer", + "Desc": "Character-oriented tokenizer where text is considered a sequence of characters.", + "FriendlyName": "Character Tokenizer Transform", + "ShortName": "CharToken", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseMarkerChars", + "Type": "Bool", + "Desc": "Whether to mark the beginning/end of each row/slot with start of text character (0x02)/end of text character (0x03)", + "Aliases": [ + "mark" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ColumnConcatenator", + "Desc": "Concatenates one or more columns of the same item type.", + "FriendlyName": "Concat Transform", + "ShortName": "Concat", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:srcs)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ColumnCopier", + "Desc": "Duplicates columns from the dataset", + "FriendlyName": "Copy Columns Transform", + "ShortName": "Copy", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ColumnSelector", + "Desc": "Selects a set of columns, dropping all others", + "FriendlyName": "Select Columns", + "ShortName": null, + "Inputs": [ + { + "Name": "KeepColumns", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of columns to keep.", + "Aliases": [ + "keepcol" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "DropColumns", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of columns to drop.", + "Aliases": [ + "dropcol" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "KeepHidden", + "Type": "Bool", + "Desc": "Specifies whether to keep or remove hidden columns.", + "Aliases": [ + "hidden" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "IgnoreMissing", + "Type": "Bool", + "Desc": "Specifies whether to ignore columns that are missing from the input.", + "Aliases": [ + "ignore" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ColumnTypeConverter", + "Desc": "Converts a column to a different type, using standard conversions.", + "FriendlyName": "Convert Transform", + "ShortName": "Convert", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "ResultType", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "Text", + "TX", + "TXT", + "BL", + "Bool", + "TS", + "TimeSpan", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "The result type", + "Aliases": [ + "type" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Range", + "Type": "String", + "Desc": "For a key column, this defines the range of values", + "Aliases": [ + "key" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:type:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ResultType", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "Text", + "TX", + "TXT", + "BL", + "Bool", + "TS", + "TimeSpan", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "The result type", + "Aliases": [ + "type" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Range", + "Type": "String", + "Desc": "For a key column, this defines the range of values", + "Aliases": [ + "key" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.CombinerByContiguousGroupId", + "Desc": "Groups values of a scalar column into a vector, by a contiguous group ID", + "FriendlyName": "Group Transform", + "ShortName": "Group", + "Inputs": [ + { + "Name": "GroupKey", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to group by", + "Aliases": [ + "g" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to group together", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ConditionalNormalizer", + "Desc": "Normalize the columns only if needed", + "FriendlyName": "Normalize If Needed", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ] + }, + { + "Name": "Transforms.CountTableBuilder", + "Desc": "Transforms the categorical column into the set of features: count of each label class, log-odds for each label class, back-off indicator. The input columns must be keys.", + "FriendlyName": "Count Table Transform", + "ShortName": "Count", + "Inputs": [ + { + "Name": "Columns", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "CountTable", + "Type": { + "Kind": "Component", + "ComponentKind": "CountTableBuilder" + }, + "Desc": "Count table settings", + "Aliases": [ + "table" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "PriorCoefficient", + "Type": "Float", + "Desc": "The coefficient with which to apply the prior smoothing to the features", + "Aliases": [ + "prior" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LaplaceScale", + "Type": "Float", + "Desc": "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", + "Aliases": [ + "laplace" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Seed for the random generator for the laplacian noise.", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "CountTable", + "Type": { + "Kind": "Component", + "ComponentKind": "CountTableBuilder" + }, + "Desc": "Count table settings", + "Aliases": [ + "table" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "CMSketch" + } + }, + { + "Name": "PriorCoefficient", + "Type": "Float", + "Desc": "The coefficient with which to apply the prior smoothing to the features", + "Aliases": [ + "prior" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "LaplaceScale", + "Type": "Float", + "Desc": "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", + "Aliases": [ + "laplace" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Seed for the random generator for the laplacian noise.", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 314489979 + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Label column", + "Aliases": [ + "label", + "lab" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "InitialCountsModel", + "Type": "String", + "Desc": "Optional model file to load counts from. If this is specified all other options are ignored.", + "Aliases": [ + "inmodel", + "extfile" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "SharedTable", + "Type": "Bool", + "Desc": "Keep counts for all columns in one shared count table", + "Aliases": [ + "shared" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.CountTargetEncoder", + "Desc": "Transforms the categorical column into the set of features: count of each label class, log-odds for each label class, back-off indicator. The columns can be of arbitrary type.", + "FriendlyName": "Count Table Transform", + "ShortName": "Count", + "Inputs": [ + { + "Name": "Columns", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "CountTable", + "Type": { + "Kind": "Component", + "ComponentKind": "CountTableBuilder" + }, + "Desc": "Count table settings", + "Aliases": [ + "table" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "PriorCoefficient", + "Type": "Float", + "Desc": "The coefficient with which to apply the prior smoothing to the features", + "Aliases": [ + "prior" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LaplaceScale", + "Type": "Float", + "Desc": "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", + "Aliases": [ + "laplace" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Seed for the random generator for the laplacian noise.", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Combine", + "Type": "Bool", + "Desc": "Whether the values need to be combined for a single hash", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Combine", + "Type": "Bool", + "Desc": "Whether the values need to be combined for a single hash", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CountTable", + "Type": { + "Kind": "Component", + "ComponentKind": "CountTableBuilder" + }, + "Desc": "Count table settings", + "Aliases": [ + "table" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "CMSketch" + } + }, + { + "Name": "PriorCoefficient", + "Type": "Float", + "Desc": "The coefficient with which to apply the prior smoothing to the features", + "Aliases": [ + "prior" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "LaplaceScale", + "Type": "Float", + "Desc": "Laplacian noise diversity/scale-parameter. Suggest keeping it less than 1.", + "Aliases": [ + "laplace" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "Seed for the random generator for the laplacian noise.", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 314489979 + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Label column", + "Aliases": [ + "label", + "lab" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "InitialCountsModel", + "Type": "String", + "Desc": "Optional model file to load counts from. If this is specified all other options are ignored.", + "Aliases": [ + "inmodel", + "extfile" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "SharedTable", + "Type": "Bool", + "Desc": "Keep counts for all columns in one shared count table", + "Aliases": [ + "shared" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "NumberOfBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 31, inclusive.", + "Aliases": [ + "bits" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 31 + }, + { + "Name": "HashingSeed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 314489979 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.DatasetScorer", + "Desc": "Score a dataset with a predictor model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The dataset to be scored", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model to apply to data", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "Suffix", + "Type": "String", + "Desc": "Suffix to append to the score columns", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "ScoredData", + "Type": "DataView", + "Desc": "The scored dataset" + }, + { + "Name": "ScoringTransform", + "Type": "TransformModel", + "Desc": "The scoring transform" + } + ] + }, + { + "Name": "Transforms.DatasetTransformScorer", + "Desc": "Score a dataset with a transform model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The dataset to be scored", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "The transform model to apply to data", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "ScoredData", + "Type": "DataView", + "Desc": "The scored dataset" + }, + { + "Name": "ScoringTransform", + "Type": "TransformModel", + "Desc": "The scoring transform" + } + ] + }, + { + "Name": "Transforms.Dictionarizer", + "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", + "FriendlyName": "Term Transform", + "ShortName": "TermTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "ByOccurrence", + "ByValue" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, for example, text sorting will be case sensitive (for example, 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of keys to keep per column when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "ByOccurrence", + "ByValue" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, for example, text sorting will be case sensitive (for example, 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 113.0, + "IsNullable": false, + "Default": "ByOccurrence" + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 114.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.FeatureCombiner", + "Desc": "Combines all the features into one feature column.", + "FriendlyName": "Feature Combiner", + "ShortName": "fc", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Features", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Features", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.FeatureContributionCalculationTransformer", + "Desc": "For each data point, calculates the contribution of individual features to the model prediction.", + "FriendlyName": "Feature Contribution Calculation", + "ShortName": null, + "Inputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model to apply to data", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Name of feature column", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "Top", + "Type": "Int", + "Desc": "Number of top contributions", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "Bottom", + "Type": "Int", + "Desc": "Number of bottom contributions", + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "Normalize", + "Type": "Bool", + "Desc": "Whether or not output of Features contribution should be normalized", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.FeatureSelectorByCount", + "Desc": "Selects the slots for which the count of non-default values is greater than or equal to a threshold.", + "FriendlyName": "Count Feature Selection Transform", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to use for feature selection", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Count", + "Type": "Int", + "Desc": "If the count of non-default values for a slot is greater than or equal to this threshold, the slot is preserved", + "Aliases": [ + "c" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.FeatureSelectorByMutualInformation", + "Desc": "Selects the top k slots across all specified columns ordered by their mutual information with the label column.", + "FriendlyName": "Mutual Information Feature Selection Transform", + "ShortName": "MIFeatureSelection", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to use for feature selection", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "SlotsInOutput", + "Type": "Int", + "Desc": "The maximum number of slots to preserve in output", + "Aliases": [ + "topk", + "numSlotsToKeep" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 1000 + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "NumBins", + "Type": "Int", + "Desc": "Max number of bins for R4/R8 columns, power of 2 recommended", + "Aliases": [ + "bins" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 256 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.GlobalContrastNormalizer", + "Desc": "Performs a global contrast normalization on input values: Y = (s * X - M) / D, where s is a scale, M is mean and D is either L2 norm or standard deviation.", + "FriendlyName": "Global Contrast Normalization Transform", + "ShortName": "Gcn", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "UseStdDev", + "Type": "Bool", + "Desc": "Normalize by standard deviation rather than L2 norm", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale features by this value", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseStdDev", + "Type": "Bool", + "Desc": "Normalize by standard deviation rather than L2 norm", + "Aliases": [ + "useStd" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale features by this value", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.HashConverter", + "Desc": "Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. ", + "FriendlyName": "Hash Join Transform", + "ShortName": "HashJoin", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Join", + "Type": "Bool", + "Desc": "Whether the values need to be combined for a single hash", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "CustomSlotMap", + "Type": "String", + "Desc": "Which slots should be combined together. Example: 0,3,5;0,1;3;2,1,0. Overrides 'join'.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NumberOfBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 31, inclusive.", + "Aliases": [ + "bits" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", + "Aliases": [ + "ord" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 31, inclusive.", + "Aliases": [ + "bits" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 31 + }, + { + "Name": "Join", + "Type": "Bool", + "Desc": "Whether the values need to be combined for a single hash", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 314489979 + }, + { + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", + "Aliases": [ + "ord" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ImageGrayscale", + "Desc": "Convert image into grayscale.", + "FriendlyName": "Image Greyscale Transform", + "ShortName": "ImageGrayscaleTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ImageLoader", + "Desc": "Load images from files.", + "FriendlyName": "Image Loader Transform", + "ShortName": "ImageLoaderTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ImageFolder", + "Type": "String", + "Desc": "Folder where to search for images", + "Aliases": [ + "folder" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ImagePixelExtractor", + "Desc": "Extract color plane(s) from an image. Options include scaling, offset and conversion to floating point.", + "FriendlyName": "Image Pixel Extractor Transform", + "ShortName": "ImagePixelExtractor", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "UseAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", + "Aliases": [ + "alpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "UseRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "UseGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "UseBlue", + "Type": "Bool", + "Desc": "Whether to use blue channel", + "Aliases": [ + "blue" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Order", + "Type": { + "Kind": "Enum", + "Values": [ + "ARGB", + "ARBG", + "ABRG", + "ABGR", + "AGRB", + "AGBR" + ] + }, + "Desc": "Order of channels", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Interleave", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in specified order", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Convert", + "Type": "Bool", + "Desc": "Whether to convert to floating point", + "Aliases": [ + "conv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", + "Aliases": [ + "alpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "UseGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "UseBlue", + "Type": "Bool", + "Desc": "Whether to use blue channel", + "Aliases": [ + "blue" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Order", + "Type": { + "Kind": "Enum", + "Values": [ + "ARGB", + "ARBG", + "ABRG", + "ABGR", + "AGRB", + "AGBR" + ] + }, + "Desc": "Order of colors.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "ARGB" + }, + { + "Name": "Interleave", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in specified order", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Convert", + "Type": "Bool", + "Desc": "Whether to convert to floating point", + "Aliases": [ + "conv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ImageResizer", + "Desc": "Scales an image to specified dimensions using one of the three scale types: isotropic with padding, isotropic with cropping or anisotropic. In case of isotropic padding, transparent color is used to pad resulting image.", + "FriendlyName": "Image Resizer Transform", + "ShortName": "ImageScalerTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "ImageWidth", + "Type": "Int", + "Desc": "Width of the resized image", + "Aliases": [ + "width" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ImageHeight", + "Type": "Int", + "Desc": "Height of the resized image", + "Aliases": [ + "height" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Resizing", + "Type": { + "Kind": "Enum", + "Values": [ + "IsoPad", + "IsoCrop", + "Fill" + ] + }, + "Desc": "Resizing method", + "Aliases": [ + "scale" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "CropAnchor", + "Type": { + "Kind": "Enum", + "Values": [ + "Right", + "Left", + "Top", + "Bottom", + "Center" + ] + }, + "Desc": "Anchor for cropping", + "Aliases": [ + "anchor" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ImageWidth", + "Type": "Int", + "Desc": "Resized width of the image", + "Aliases": [ + "width" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "ImageHeight", + "Type": "Int", + "Desc": "Resized height of the image", + "Aliases": [ + "height" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "Resizing", + "Type": { + "Kind": "Enum", + "Values": [ + "IsoPad", + "IsoCrop", + "Fill" + ] + }, + "Desc": "Resizing method", + "Aliases": [ + "scale" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "IsoCrop" + }, + { + "Name": "CropAnchor", + "Type": { + "Kind": "Enum", + "Values": [ + "Right", + "Left", + "Top", + "Bottom", + "Center" + ] + }, + "Desc": "Anchor for cropping", + "Aliases": [ + "anchor" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Center" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.KeyToTextConverter", + "Desc": "KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata.", + "FriendlyName": "Key To Value Transform", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LabelColumnKeyBooleanConverter", + "Desc": "Transforms the label to either key or bool (if needed) to make it suitable for classification.", + "FriendlyName": "Prepare Classification Label", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "The label column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Convert the key values to text", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LabelIndicator", + "Desc": "Label remapper used by OVA", + "FriendlyName": "LabelIndicator", + "ShortName": "LabelIndictator", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "ClassIndex", + "Type": "Int", + "Desc": "The positive example class for binary classification.", + "Aliases": [ + "index" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ClassIndex", + "Type": "Int", + "Desc": "Label of the positive class.", + "Aliases": [ + "index" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LabelToFloatConverter", + "Desc": "Transforms the label to float to make it suitable for regression.", + "FriendlyName": "Prepare Regression Label", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "The label column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LightLda", + "Desc": "The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation.", + "FriendlyName": "Latent Dirichlet Allocation Transform", + "ShortName": "LightLda", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "NumTopic", + "Type": "Int", + "Desc": "The number of topics", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "AlphaSum", + "Type": "Float", + "Desc": "Dirichlet prior on document-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Beta", + "Type": "Float", + "Desc": "Dirichlet prior on vocab-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Mhstep", + "Type": "Int", + "Desc": "Number of Metropolis Hasting step", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LikelihoodInterval", + "Type": "Int", + "Desc": "Compute log likelihood over local dataset on this iteration interval", + "Aliases": [ + "llInterval" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of training threads", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumMaxDocToken", + "Type": "Int", + "Desc": "The threshold of maximum count of tokens per doc", + "Aliases": [ + "maxNumToken" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumSummaryTermPerTopic", + "Type": "Int", + "Desc": "The number of words to summarize the topic", + "Aliases": [ + "ns" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumBurninIterations", + "Type": "Int", + "Desc": "The number of burn-in iterations", + "Aliases": [ + "burninIter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": 10 + }, + { + "Name": "ResetRandomGenerator", + "Type": "Bool", + "Desc": "Reset the random number generator for each document", + "Aliases": [ + "reset" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "OutputTopicWordSummary", + "Type": "Bool", + "Desc": "Whether to output the topic-word summary in text format when saving the model to disk", + "Aliases": [ + "summary" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:srcs)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 49.0, + "IsNullable": false + }, + { + "Name": "NumTopic", + "Type": "Int", + "Desc": "The number of topics", + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 40, + 100, + 200 + ] + } + }, + { + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of training threads. Default value depends on number of logical processors.", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "NumMaxDocToken", + "Type": "Int", + "Desc": "The threshold of maximum count of tokens per doc", + "Aliases": [ + "maxNumToken" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 512 + }, + { + "Name": "AlphaSum", + "Type": "Float", + "Desc": "Dirichlet prior on document-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 100, + 200 + ] + } + }, + { + "Name": "Beta", + "Type": "Float", + "Desc": "Dirichlet prior on vocab-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.01, + 0.015, + 0.07, + 0.02 + ] + } + }, + { + "Name": "Mhstep", + "Type": "Int", + "Desc": "Number of Metropolis Hasting step", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 4, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 2, + 4, + 8, + 16 + ] + } + }, + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 200, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 100, + 200, + 300, + 400 + ] + } + }, + { + "Name": "LikelihoodInterval", + "Type": "Int", + "Desc": "Compute log likelihood over local dataset on this iteration interval", + "Aliases": [ + "llInterval" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "NumSummaryTermPerTopic", + "Type": "Int", + "Desc": "The number of words to summarize the topic", + "Aliases": [ + "ns" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "NumBurninIterations", + "Type": "Int", + "Desc": "The number of burn-in iterations", + "Aliases": [ + "burninIter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 30, + 40 + ] + } + }, + { + "Name": "ResetRandomGenerator", + "Type": "Bool", + "Desc": "Reset the random number generator for each document", + "Aliases": [ + "reset" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "OutputTopicWordSummary", + "Type": "Bool", + "Desc": "Whether to output the topic-word summary in text format when saving the model to disk", + "Aliases": [ + "summary" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LogMeanVarianceNormalizer", + "Desc": "Normalizes the data based on the computed mean and variance of the logarithm of the data.", + "FriendlyName": "LogMeanVar Normalizer", + "ShortName": "LogMeanVar", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseCdf", + "Type": "Bool", + "Desc": "Whether to use CDF as the output", + "Aliases": [ + "cdf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LpNormalizer", + "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm.", + "FriendlyName": "Lp-Norm Normalizer", + "ShortName": "lpnorm", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Norm", + "Type": { + "Kind": "Enum", + "Values": [ + "L2", + "StandardDeviation", + "L1", + "Infinity" + ] + }, + "Desc": "The norm to use to normalize each sample", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Norm", + "Type": { + "Kind": "Enum", + "Values": [ + "L2", + "StandardDeviation", + "L1", + "Infinity" + ] + }, + "Desc": "The norm to use to normalize each sample", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": "L2" + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ManyHeterogeneousModelCombiner", + "Desc": "Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "TransformModels", + "Type": { + "Kind": "Array", + "ItemType": "TransformModel" + }, + "Desc": "Transform model", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model" + } + ] + }, + { + "Name": "Transforms.MeanVarianceNormalizer", + "Desc": "Normalizes the data based on the computed mean and variance of the data.", + "FriendlyName": "MeanVar Normalizer", + "ShortName": "MeanVar", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseCdf", + "Type": "Bool", + "Desc": "Whether to use CDF as the output", + "Aliases": [ + "cdf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.MinMaxNormalizer", + "Desc": "Normalizes the data based on the observed minimum and maximum values of the data.", + "FriendlyName": "Min-Max Normalizer", + "ShortName": "MinMax", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.MissingValueHandler", + "Desc": "Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if the input column type is numeric.", + "FriendlyName": "NA Handle Transform", + "ShortName": "NAHandle", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum" + ] + }, + "Desc": "The replacement method to utilize", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ImputeBySlot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", + "Aliases": [ + "slot" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ConcatIndicator", + "Type": "Bool", + "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Aliases": [ + "ind" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:rep:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ReplaceWith", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum" + ] + }, + "Desc": "The replacement method to utilize", + "Aliases": [ + "kind" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "DefaultValue" + }, + { + "Name": "ImputeBySlot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", + "Aliases": [ + "slot" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Concat", + "Type": "Bool", + "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Aliases": [ + "ind" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.MissingValueIndicator", + "Desc": "Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing.", + "FriendlyName": "NA Indicator Transform", + "ShortName": "NAInd", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.MissingValuesDropper", + "Desc": "Removes NAs from vector columns.", + "FriendlyName": "NA Drop Transform", + "ShortName": "NADrop", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "Columns to drop the NAs for", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.MissingValuesRowDropper", + "Desc": "Filters out rows that contain missing values.", + "FriendlyName": "NA Filter", + "ShortName": "NAFilter", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Column", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Complement", + "Type": "Bool", + "Desc": "If true, keep only rows that contain NA values, and filter the rest.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.MissingValueSubstitutor", + "Desc": "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).", + "FriendlyName": "NA Replace Transform", + "ShortName": "NARep", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "ReplacementString", + "Type": "String", + "Desc": "Replacement value for NAs (uses default value if not given)", + "Aliases": [ + "rep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum", + "SpecifiedValue", + "Mode" + ] + }, + "Desc": "The replacement method to utilize", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Slot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:rep:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ReplacementKind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum", + "SpecifiedValue", + "Mode" + ] + }, + "Desc": "The replacement method to utilize", + "Aliases": [ + "kind" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "DefaultValue" + }, + { + "Name": "ImputeBySlot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", + "Aliases": [ + "slot" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ModelCombiner", + "Desc": "Combines a sequence of TransformModels into a single model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "TransformModel" + }, + "Desc": "Input models", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputModel", + "Type": "TransformModel", + "Desc": "Combined model" + } + ] + }, + { + "Name": "Transforms.NGramTranslator", + "Desc": "Produces a bag of counts of n-grams (sequences of consecutive values of length 1-n) in a given vector of keys. It does so by building a dictionary of n-grams and using the id in the dictionary as the index in the bag.", + "FriendlyName": "NGram Transform", + "ShortName": "NgramTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "NgramLength", + "Type": "Int", + "Desc": "Maximum n-gram length", + "Aliases": [ + "ngram" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to include all n-gram lengths up to NgramLength or only NgramLength", + "Aliases": [ + "all" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an n-gram", + "Aliases": [ + "skips" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxNumTerms", + "Type": { + "Kind": "Array", + "ItemType": "Int" + }, + "Desc": "Maximum number of n-grams to store in the dictionary", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Weighting", + "Type": { + "Kind": "Enum", + "Values": [ + "Tf", + "Idf", + "TfIdf" + ] + }, + "Desc": "Statistical measure used to evaluate how important a word is to a document in a corpus", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NgramLength", + "Type": "Int", + "Desc": "Maximum n-gram length", + "Aliases": [ + "ngram" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2 + }, + { + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to store all n-gram lengths up to ngramLength, or only ngramLength", + "Aliases": [ + "all" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an n-gram", + "Aliases": [ + "skips" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MaxNumTerms", + "Type": { + "Kind": "Array", + "ItemType": "Int" + }, + "Desc": "Maximum number of n-grams to store in the dictionary", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": [ + 10000000 + ] + }, + { + "Name": "Weighting", + "Type": { + "Kind": "Enum", + "Values": [ + "Tf", + "Idf", + "TfIdf" + ] + }, + "Desc": "The weighting criteria", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Tf" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.NoOperation", + "Desc": "Does nothing.", + "FriendlyName": "No Op", + "ShortName": "Nop", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.OptionalColumnCreator", + "Desc": "If the source column does not exist after deserialization, create a column with the right type and default values.", + "FriendlyName": "Optional Column Transform", + "ShortName": "optional", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "New column definition(s)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.PcaCalculator", + "Desc": "PCA is a dimensionality-reduction transform which computes the projection of a numeric vector onto a low-rank subspace.", + "FriendlyName": "Principal Component Analysis Transform", + "ShortName": "Pca", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "The name of the weight column", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Rank", + "Type": "Int", + "Desc": "The number of components in the PCA", + "Aliases": [ + "k" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Oversampling", + "Type": "Int", + "Desc": "Oversampling parameter for randomized PCA training", + "Aliases": [ + "over" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Center", + "Type": "Bool", + "Desc": "If enabled, data is centered to be zero mean", + "Aliases": [ + "center" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed for random number generation", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "The name of the weight column", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Rank", + "Type": "Int", + "Desc": "The number of components in the PCA", + "Aliases": [ + "k" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 20 + }, + { + "Name": "Oversampling", + "Type": "Int", + "Desc": "Oversampling parameter for randomized PCA training", + "Aliases": [ + "over" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 20 + }, + { + "Name": "Center", + "Type": "Bool", + "Desc": "If enabled, data is centered to be zero mean", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed for random number generation", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.PermutationFeatureImportance", + "Desc": "Permutation Feature Importance (PFI)", + "FriendlyName": "PFI", + "ShortName": "PFI", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The path to the model file", + "Aliases": [ + "path" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "UseFeatureWeightFilter", + "Type": "Bool", + "Desc": "Use feature weights to pre-filter features", + "Aliases": [ + "usefw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "NumberOfExamplesToUse", + "Type": "Int", + "Desc": "Limit the number of examples to evaluate on", + "Aliases": [ + "numexamples" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "PermutationCount", + "Type": "Int", + "Desc": "The number of permutations to perform", + "Aliases": [ + "permutations" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + } + ], + "Outputs": [ + { + "Name": "Metrics", + "Type": "DataView", + "Desc": "The PFI metrics" + } + ], + "InputKind": [ + "ITransformInput" + ] + }, + { + "Name": "Transforms.PredictedLabelColumnOriginalValueConverter", + "Desc": "Transforms a predicted label column to its original values, unless it is of type bool.", + "FriendlyName": "Convert Predicted Label", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictedLabelColumn", + "Type": "String", + "Desc": "The predicted label column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RandomNumberGenerator", + "Desc": "Adds a column with a generated number sequence.", + "FriendlyName": "Generate Number Transform", + "ShortName": "Generate", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "UseCounter", + "Type": "Bool", + "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Aliases": [ + "cnt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:seed)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseCounter", + "Type": "Bool", + "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Aliases": [ + "cnt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 42 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RobustScalingNormalizer", + "Desc": "Optionally centers the data and scales based on the range of data and the quantile min and max values provided. This method is more robust to outliers.", + "FriendlyName": "Robust Scaling Normalizer", + "ShortName": "RobScal", + "Inputs": [ + { + "Name": "CenterData", + "Type": "Bool", + "Desc": "Should the data be centered around 0", + "Aliases": [ + "center" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "QuantileMin", + "Type": "UInt", + "Desc": "Minimum quantile value. Defaults to 25", + "Aliases": [ + "qmin" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 25 + }, + { + "Name": "QuantileMax", + "Type": "UInt", + "Desc": "Maximum quantile value. Defaults to 75", + "Aliases": [ + "qmax" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 75 + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RowRangeFilter", + "Desc": "Filters a dataview on a column of type Single, Double or Key (contiguous). Keeps the values that are in the specified min/max range. NaNs are always filtered out. If the input is a Key type, the min/max are considered percentages of the number of values.", + "FriendlyName": "Range Filter", + "ShortName": "RangeFilter", + "Inputs": [ + { + "Name": "Column", + "Type": "String", + "Desc": "Column", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Min", + "Type": "Float", + "Desc": "Minimum value (0 to 1 for key types)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Max", + "Type": "Float", + "Desc": "Maximum value (0 to 1 for key types)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Complement", + "Type": "Bool", + "Desc": "If true, keep the values that fall outside the range.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "IncludeMin", + "Type": "Bool", + "Desc": "If true, include in the range the values that are equal to min.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "IncludeMax", + "Type": "Bool", + "Desc": "If true, include in the range the values that are equal to max.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RowSkipAndTakeFilter", + "Desc": "Allows limiting input to a subset of rows at an optional offset. Can be used to implement data paging.", + "FriendlyName": "Skip and Take Filter", + "ShortName": "SkipTake", + "Inputs": [ + { + "Name": "Skip", + "Type": "Int", + "Desc": "Number of items to skip", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Take", + "Type": "Int", + "Desc": "Number of items to take", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RowSkipFilter", + "Desc": "Allows limiting input to a subset of rows by skipping a number of rows.", + "FriendlyName": "Skip Filter", + "ShortName": "Skip", + "Inputs": [ + { + "Name": "Count", + "Type": "Int", + "Desc": "Number of items to skip", + "Aliases": [ + "c", + "n", + "s" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RowTakeFilter", + "Desc": "Allows limiting input to a subset of rows by taking N first rows.", + "FriendlyName": "Take Filter", + "ShortName": "Take", + "Inputs": [ + { + "Name": "Count", + "Type": "Int", + "Desc": "Number of items to take", + "Aliases": [ + "c", + "n", + "t" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 9223372036854775807 + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ScoreColumnSelector", + "Desc": "Selects only the last score columns and the extra columns specified in the arguments.", + "FriendlyName": "Choose Columns By Indices", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ExtraColumns", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Extra columns to write", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.Scorer", + "Desc": "Turn the predictor model into a transform model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model to turn into a transform", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "ScoredData", + "Type": "DataView", + "Desc": "The scored dataset" + }, + { + "Name": "ScoringTransform", + "Type": "TransformModel", + "Desc": "The scoring transform" + } + ] + }, + { + "Name": "Transforms.Segregator", + "Desc": "Un-groups vector columns into sequences of rows, inverse of Group transform", + "FriendlyName": "Un-group Transform", + "ShortName": "Ungroup", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to unroll, or 'pivot'", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "Mode", + "Type": { + "Kind": "Enum", + "Values": [ + "Inner", + "Outer", + "First" + ] + }, + "Desc": "Specifies how to unroll multiple pivot columns of different size.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Inner" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.SentimentAnalyzer", + "Desc": "Uses a pretrained sentiment model to score input strings", + "FriendlyName": "Sentiment Analyzing Transform", + "ShortName": "Senti", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column.", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column.", + "Aliases": [ + "dst" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TensorFlowScorer", + "Desc": "Transforms the data using the TensorFlow model.", + "FriendlyName": "TensorFlowTransform", + "ShortName": "TFTransform", + "Inputs": [ + { + "Name": "ModelLocation", + "Type": "String", + "Desc": "TensorFlow model used by the transform. Please see https://www.tensorflow.org/mobile/prepare_models for more details.", + "Required": true, + "SortOrder": 0.0, + "IsNullable": false + }, + { + "Name": "InputColumns", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "The names of the model inputs", + "Aliases": [ + "inputs" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "OutputColumns", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "The name of the outputs", + "Aliases": [ + "outputs" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Number of samples to use for mini-batch training.", + "Required": false, + "SortOrder": 9.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "AddBatchDimensionInputs", + "Type": "Bool", + "Desc": "Add a batch dimension to the input e.g. input = [224, 224, 3] => [-1, 224, 224, 3].", + "Required": false, + "SortOrder": 16.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TreatOutputAsBatched", + "Type": "Bool", + "Desc": "If the first dimension of the output is unknown, should it be treated as batched or not. e.g. output = [-1] will be read as a vector of unknown length when this is false.", + "Required": false, + "SortOrder": 17.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TextFeaturizer", + "Desc": "A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are normalized counts of (word and/or character) n-grams in a given tokenized text.", + "FriendlyName": "Text Transform", + "ShortName": "Text", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + }, + "Desc": "New column definition (optional form: name:srcs).", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Language", + "Type": { + "Kind": "Enum", + "Values": [ + "English", + "French", + "German", + "Dutch", + "Italian", + "Spanish", + "Japanese" + ] + }, + "Desc": "Dataset language or 'AutoDetect' to detect language per row.", + "Aliases": [ + "lang" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "English" + }, + { + "Name": "StopWordsRemover", + "Type": { + "Kind": "Component", + "ComponentKind": "StopWordsRemover" + }, + "Desc": "Stopwords remover.", + "Aliases": [ + "remover" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "TextCase", + "Type": { + "Kind": "Enum", + "Values": [ + "Lower", + "Upper", + "None" + ] + }, + "Desc": "Casing text using the rules of the invariant culture.", + "Aliases": [ + "case" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Lower" + }, + { + "Name": "KeepDiacritics", + "Type": "Bool", + "Desc": "Whether to keep diacritical marks or remove them.", + "Aliases": [ + "diac" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "KeepPunctuations", + "Type": "Bool", + "Desc": "Whether to keep punctuation marks or remove them.", + "Aliases": [ + "punc" + ], + "Required": false, + "SortOrder": 7.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "KeepNumbers", + "Type": "Bool", + "Desc": "Whether to keep numbers or remove them.", + "Aliases": [ + "num" + ], + "Required": false, + "SortOrder": 8.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "OutputTokensColumnName", + "Type": "String", + "Desc": "Column containing the transformed text tokens.", + "Aliases": [ + "tokens", + "showtext", + "showTransformedText" + ], + "Required": false, + "SortOrder": 9.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Dictionary", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "ByOccurrence", + "ByValue" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value, items are sorted according to their default comparison, for example, text sorting will be case sensitive (for example, 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "ByOccurrence" + }, + { + "Name": "DropUnknowns", + "Type": "Bool", + "Desc": "Drop unknown terms instead of mapping them to NA term.", + "Aliases": [ + "dropna" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": false + } + ] + }, + "Desc": "A dictionary of allowed terms.", + "Aliases": [ + "dict" + ], + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WordFeatureExtractor", + "Type": { + "Kind": "Component", + "ComponentKind": "NgramExtractor" + }, + "Desc": "Ngram feature extractor to use for words (WordBag/WordHashBag).", + "Aliases": [ + "wordExtractor" + ], + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": { + "Name": "NGram", + "Settings": { + "NgramLength": 2, + "MaxNumTerms": [ + 10000000 + ] + } + } + }, + { + "Name": "CharFeatureExtractor", + "Type": { + "Kind": "Component", + "ComponentKind": "NgramExtractor" + }, + "Desc": "Ngram feature extractor to use for characters (WordBag/WordHashBag).", + "Aliases": [ + "charExtractor" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": { + "Name": "NGram", + "Settings": { + "NgramLength": 3, + "AllLengths": false, + "MaxNumTerms": [ + 10000000 + ] + } + } + }, + { + "Name": "VectorNormalizer", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "L1", + "L2", + "Infinity" + ] + }, + "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm.", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 13.0, + "IsNullable": false, + "Default": "L2" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TextToKeyConverter", + "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", + "FriendlyName": "To Key", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "ByOccurrence", + "ByValue" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, for example, text sorting will be case sensitive (for example, 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of keys to keep per column when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "ByOccurrence", + "ByValue" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, for example, text sorting will be case sensitive (for example, 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 113.0, + "IsNullable": false, + "Default": "ByOccurrence" + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 114.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TrainTestDatasetSplitter", + "Desc": "Split the dataset into train and test sets", + "FriendlyName": "Dataset Train-Test Split", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Fraction", + "Type": "Float", + "Desc": "Fraction of training data", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0.8 + }, + { + "Name": "StratificationColumn", + "Type": "String", + "Desc": "Stratification column", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "TrainData", + "Type": "DataView", + "Desc": "Training data" + }, + { + "Name": "TestData", + "Type": "DataView", + "Desc": "Testing data" + } + ] + }, + { + "Name": "Transforms.TreeLeafFeaturizer", + "Desc": "Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices.", + "FriendlyName": "Tree Ensemble Featurization Transform", + "ShortName": "TreeFeat", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Trainer to use", + "Required": true, + "SortOrder": 10.0, + "IsNullable": false + }, + { + "Name": "Suffix", + "Type": "String", + "Desc": "Output column: The suffix to append to the default column names", + "Aliases": [ + "ex" + ], + "Required": false, + "SortOrder": 101.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "LabelPermutationSeed", + "Type": "Int", + "Desc": "If specified, determines the permutation seed for applying this featurizer to a multiclass problem.", + "Aliases": [ + "lps" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "IFeaturizerInput", + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TwoHeterogeneousModelCombiner", + "Desc": "Combines a TransformModel and a PredictorModel into a single PredictorModel.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "Transform model", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model" + } + ] + }, + { + "Name": "Transforms.VectorToImage", + "Desc": "Converts vector array into image type.", + "FriendlyName": "Vector To Image Transform", + "ShortName": "VectorToImageConverter", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "ContainsAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", + "Aliases": [ + "alpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ContainsRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ContainsGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ContainsBlue", + "Type": "Bool", + "Desc": "Whether to use blue channel", + "Aliases": [ + "blue" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Order", + "Type": { + "Kind": "Enum", + "Values": [ + "ARGB", + "ARBG", + "ABRG", + "ABGR", + "AGRB", + "AGBR" + ] + }, + "Desc": "Order of channels", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Interleave", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in specified order", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ImageWidth", + "Type": "Int", + "Desc": "Width of the image", + "Aliases": [ + "width" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ImageHeight", + "Type": "Int", + "Desc": "Height of the image", + "Aliases": [ + "height" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DefaultAlpha", + "Type": "Int", + "Desc": "Default value for alpha channel. Will be used if ContainsAlpha set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DefaultRed", + "Type": "Int", + "Desc": "Default value for red channel. Will be used if ContainsRed set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DefaultGreen", + "Type": "Int", + "Desc": "Default value for green channel. Will be used if ContainsGreen set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DefaultBlue", + "Type": "Int", + "Desc": "Default value for blue channel. Will be used if ContainsGreen set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ContainsAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", + "Aliases": [ + "alpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "ContainsRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "ContainsGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "ContainsBlue", + "Type": "Bool", + "Desc": "Whether to use blue channel", + "Aliases": [ + "blue" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Order", + "Type": { + "Kind": "Enum", + "Values": [ + "ARGB", + "ARBG", + "ABRG", + "ABGR", + "AGRB", + "AGBR" + ] + }, + "Desc": "Order of colors.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "ARGB" + }, + { + "Name": "Interleave", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in specified order", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "ImageWidth", + "Type": "Int", + "Desc": "Width of the image", + "Aliases": [ + "width" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "ImageHeight", + "Type": "Int", + "Desc": "Height of the image", + "Aliases": [ + "height" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "DefaultAlpha", + "Type": "Int", + "Desc": "Default value for alpha channel. Will be used if ContainsAlpha set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "DefaultRed", + "Type": "Int", + "Desc": "Default value for red channel. Will be used if ContainsRed set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "DefaultGreen", + "Type": "Int", + "Desc": "Default value for green channel. Will be used if ContainsGreen set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "DefaultBlue", + "Type": "Int", + "Desc": "Default value for blue channel. Will be used if ContainsBlue set to false", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.WordEmbeddings", + "Desc": "Word Embeddings transform is a text featurizer which converts vectors of text tokens into sentence vectors using a pre-trained model", + "FriendlyName": "Word Embeddings Transform", + "ShortName": "WordEmbeddings", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 0.0, + "IsNullable": false + }, + { + "Name": "ModelKind", + "Type": { + "Kind": "Enum", + "Values": [ + "GloVe50D", + "GloVe100D", + "GloVe200D", + "GloVe300D", + "GloVeTwitter25D", + "GloVeTwitter50D", + "GloVeTwitter100D", + "GloVeTwitter200D", + "FastTextWikipedia300D", + "SentimentSpecificWordEmbedding" + ] + }, + "Desc": "Pre-trained model used to create the vocabulary", + "Aliases": [ + "model" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": "SentimentSpecificWordEmbedding" + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "CustomLookupTable", + "Type": "String", + "Desc": "Filename for custom word embedding model", + "Aliases": [ + "dataFile" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.WordTokenizer", + "Desc": "The input to this transform is text, and the output is a vector of text containing the words (tokens) in the original text. The separator is space, but can be specified as any other character (or multiple characters) if needed.", + "FriendlyName": "Tokenize Text Transform", + "ShortName": "TokenizeTextTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "TermSeparators", + "Type": "String", + "Desc": "Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character.", + "Aliases": [ + "sep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "CharArrayTermSeparators", + "Type": { + "Kind": "Array", + "ItemType": "Char" + }, + "Desc": "Array of single character term separator(s). By default uses space character separator.", + "Aliases": [ + "sep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + } + ], + "Components": [ + { + "Kind": "BoosterParameterFunction", + "Components": [ + { + "Name": "dart", + "Desc": "Dropouts meet Multiple Additive Regresion Trees. See https://arxiv.org/abs/1505.01866", + "FriendlyName": "Tree Dropout Tree Booster", + "Settings": [ + { + "Name": "TreeDropFraction", + "Type": "Float", + "Desc": "The drop ratio for trees. Range:(0,1).", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.1, + "Range": { + "Inf": 0.0, + "Max": 1.0 + } + }, + { + "Name": "MaximumNumberOfDroppedTreesPerRound", + "Type": "Int", + "Desc": "Maximum number of dropped trees in a boosting round.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1, + "Range": { + "Inf": 0, + "Max": 2147483647 + } + }, + { + "Name": "SkipDropFraction", + "Type": "Float", + "Desc": "Probability for not dropping in a boosting round.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.5, + "Range": { + "Inf": 0.0, + "Max": 1.0 + } + }, + { + "Name": "XgboostDartMode", + "Type": "Bool", + "Desc": "True will enable xgboost dart mode.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UniformDrop", + "Type": "Bool", + "Desc": "True will enable uniform drop.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MinimumSplitGain", + "Type": "Float", + "Desc": "Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "Range": { + "Min": 0.0 + } + }, + { + "Name": "MaximumTreeDepth", + "Type": "Int", + "Desc": "Maximum depth of a tree. 0 means no limit. However, tree still grows by best-first.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0, + "Range": { + "Max": 2147483647, + "Min": 0 + } + }, + { + "Name": "MinimumChildWeight", + "Type": "Float", + "Desc": "Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.1, + "Range": { + "Min": 0.0 + } + }, + { + "Name": "SubsampleFrequency", + "Type": "Int", + "Desc": "Subsample frequency for bagging. 0 means no subsample. Specifies the frequency at which the bagging occurs, where if this is set to N, the subsampling will happen at every N iterations.This must be set with Subsample as this specifies the amount to subsample.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0, + "Range": { + "Max": 2147483647, + "Min": 0 + } + }, + { + "Name": "SubsampleFraction", + "Type": "Float", + "Desc": "Subsample ratio of the training instance. Setting it to 0.5 means that LightGBM randomly collected half of the data instances to grow trees and this will prevent overfitting. Range: (0,1].", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "Range": { + "Inf": 0.0, + "Max": 1.0 + } + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "Subsample ratio of columns when constructing each tree. Range: (0,1].", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "Range": { + "Inf": 0.0, + "Max": 1.0 + } + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularization term on weights, increasing this value will make model more conservative.", + "Aliases": [ + "l2" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.5, + 1.0 + ] + } + }, + { + "Name": "L1Regularization", + "Type": "Float", + "Desc": "L1 regularization term on weights, increase this value will make model more conservative.", + "Aliases": [ + "l1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "Range": { + "Min": 0.0 + }, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.5, + 1.0 + ] + } + } + ] + }, + { + "Name": "gbdt", + "Desc": "Traditional Gradient Boosting Decision Tree.", + "FriendlyName": "Tree Booster", + "Settings": [ + { + "Name": "MinimumSplitGain", + "Type": "Float", + "Desc": "Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "Range": { + "Min": 0.0 + } + }, + { + "Name": "MaximumTreeDepth", + "Type": "Int", + "Desc": "Maximum depth of a tree. 0 means no limit. However, tree still grows by best-first.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0, + "Range": { + "Max": 2147483647, + "Min": 0 + } + }, + { + "Name": "MinimumChildWeight", + "Type": "Float", + "Desc": "Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.1, + "Range": { + "Min": 0.0 + } + }, + { + "Name": "SubsampleFrequency", + "Type": "Int", + "Desc": "Subsample frequency for bagging. 0 means no subsample. Specifies the frequency at which the bagging occurs, where if this is set to N, the subsampling will happen at every N iterations.This must be set with Subsample as this specifies the amount to subsample.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0, + "Range": { + "Max": 2147483647, + "Min": 0 + } + }, + { + "Name": "SubsampleFraction", + "Type": "Float", + "Desc": "Subsample ratio of the training instance. Setting it to 0.5 means that LightGBM randomly collected half of the data instances to grow trees and this will prevent overfitting. Range: (0,1].", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "Range": { + "Inf": 0.0, + "Max": 1.0 + } + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "Subsample ratio of columns when constructing each tree. 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"CustomGains", + "Type": { + "Kind": "Array", + "ItemType": "Float" + }, + "Desc": "Comma-separated list of gains associated to each relevance label.", + "Aliases": [ + "gains" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": [ + 0.0, + 3.0, + 7.0, + 15.0, + 31.0 + ] + }, + { + "Name": "UseDcg", + "Type": "Bool", + "Desc": "Train DCG instead of NDCG", + "Aliases": [ + "dcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "SortingAlgorithm", + "Type": "String", + "Desc": "The sorting algorithm to use for DCG and LambdaMart calculations [DescendingStablePessimistic/DescendingStable/DescendingReverse/DescendingDotNet]", + "Aliases": [ + "sort" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "DescendingStablePessimistic" + }, + { + "Name": "NdcgTruncationLevel", + "Type": "Int", + "Desc": "max-NDCG truncation to use in the LambdaMART algorithm", + "Aliases": [ + "n" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "ShiftedNdcg", + "Type": "Bool", + "Desc": "Use shifted NDCG", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "CostFunctionParam", + "Type": "Char", + "Desc": "Cost function parameter (w/c)", + "Aliases": [ + "cf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "w" + }, + { + "Name": "DistanceWeight2", + "Type": "Bool", + "Desc": "Distance weight 2 adjustment to cost", + "Aliases": [ + "dw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "NormalizeQueryLambdas", + "Type": "Bool", + "Desc": "Normalize query lambdas", + "Aliases": [ + "nql" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Option for using 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"Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Aliases": [ + "oa" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "GradientDescent" + }, + { + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Aliases": [ + "esr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Aliases": [ + "esmt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Aliases": [ + "pruning" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", + "Aliases": [ + "prtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", + "Aliases": [ + "prth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.004 + }, + { + "Name": "PruningWindowSize", + "Type": "Int", + "Desc": "The moving window size for pruning", + "Aliases": [ + "prws" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", + "Aliases": [ + "shrk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true + } + }, + { + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", + "Aliases": [ + "tdrop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] + } + }, + { + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Aliases": [ + "sr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Aliases": [ + "hl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0 + }, + { + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", + "Aliases": [ + "rs" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", + "Aliases": [ + "fzl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Aliases": [ + "basescores" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Aliases": [ + "basealpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "PositionDiscountFreeform", + "Type": "String", + "Desc": "The discount freeform which specifies the per position discounts of examples in a query (uses a single variable P for position where P=0 is first position)", + "Aliases": [ + "pdff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "The number of threads to use", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureSelectionSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Aliases": [ + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Aliases": [ + "ps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumCategoricalGroupCountPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ] + }, + { + "Name": "FastTreeRegression", + "Desc": "Trains gradient boosted decision trees to fit target values using least-squares.", + "FriendlyName": "FastTree (Boosted Trees) Regression", + "Settings": [ + { + "Name": "NumberOfTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Option for using best regression step trees", + "Aliases": [ + "bsr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", + "Aliases": [ + "ls" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumNumberOfLineSearchSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", + "Aliases": [ + "lssteps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MinimumStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", + "Aliases": [ + "minstep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Aliases": [ + "oa" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "GradientDescent" + }, + { + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Aliases": [ + "esr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Aliases": [ + "esmt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Aliases": [ + "pruning" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", + "Aliases": [ + "prtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", + "Aliases": [ + "prth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.004 + }, + { + "Name": "PruningWindowSize", + "Type": "Int", + "Desc": "The moving window size for pruning", + "Aliases": [ + "prws" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", + "Aliases": [ + "shrk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true + } + }, + { + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", + "Aliases": [ + "tdrop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] + } + }, + { + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Aliases": [ + "sr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Aliases": [ + "hl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0 + }, + { + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", + "Aliases": [ + "rs" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", + "Aliases": [ + "fzl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Aliases": [ + "basescores" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Aliases": [ + "basealpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "PositionDiscountFreeform", + "Type": "String", + "Desc": "The discount freeform which specifies the per position discounts of examples in a query (uses a single variable P for position where P=0 is first position)", + "Aliases": [ + "pdff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "The number of threads to use", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureSelectionSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Aliases": [ + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Aliases": [ + "ps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumCategoricalGroupCountPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ] + }, + { + "Name": "FastTreeTweedieRegression", + "Desc": "Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression.", + "FriendlyName": "FastTree (Boosted Trees) Tweedie Regression", + "Settings": [ + { + "Name": "NumberOfTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumberOfLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "MinimumExampleCountPerLeaf", + "Type": "Int", + "Desc": "The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data", + "Aliases": [ + "mil" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "RowGroupColumnName", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Index", + "Type": "Float", + "Desc": "Index parameter for the Tweedie distribution, in the range [1, 2]. 1 is Poisson loss, 2 is gamma loss, and intermediate values are compound Poisson loss.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.5 + }, + { + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Option for using best regression step trees", + "Aliases": [ + "bsr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", + "Aliases": [ + "ls" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumNumberOfLineSearchSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", + "Aliases": [ + "lssteps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MinimumStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", + "Aliases": [ + "minstep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Aliases": [ + "oa" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "GradientDescent" + }, + { + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Aliases": [ + "esr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Aliases": [ + "esmt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Aliases": [ + "pruning" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", + "Aliases": [ + "prtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", + "Aliases": [ + "prth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.004 + }, + { + "Name": "PruningWindowSize", + "Type": "Int", + "Desc": "The moving window size for pruning", + "Aliases": [ + "prws" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", + "Aliases": [ + "shrk" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true + } + }, + { + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", + "Aliases": [ + "tdrop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] + } + }, + { + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Aliases": [ + "sr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Aliases": [ + "hl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0 + }, + { + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", + "Aliases": [ + "rs" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", + "Aliases": [ + "fzl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Aliases": [ + "basescores" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Aliases": [ + "basealpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "PositionDiscountFreeform", + "Type": "String", + "Desc": "The discount freeform which specifies the per position discounts of examples in a query (uses a single variable P for position where P=0 is first position)", + "Aliases": [ + "pdff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Aliases": [ + "parag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "Single" + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "The number of threads to use", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureSelectionSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Aliases": [ + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Aliases": [ + "ps" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", + "Aliases": [ + "cat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MaximumCategoricalGroupCountPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Aliases": [ + "mcg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MaximumCategoricalSplitPointCount", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Aliases": [ + "maxcat" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 64 + }, + { + "Name": "MinimumExampleFractionForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical example percentage in a bin to consider for a split.", + "Aliases": [ + "mdop" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.001 + }, + { + "Name": "MinimumExamplesForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical example count in a bin to consider for a split.", + "Aliases": [ + "mdo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100 + }, + { + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Aliases": [ + "bias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Aliases": [ + "bundle" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "None" + }, + { + "Name": "MaximumBinCountPerFeature", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", + "Aliases": [ + "sp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", + "Aliases": [ + "ffup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", + "Aliases": [ + "frup" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Aliases": [ + "smtemp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "ExecutionTime", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", + "Aliases": [ + "et" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "MemoryStatistics", + "Type": "Bool", + "Desc": "Print memory statistics to stdout", + "Aliases": [ + "memstats" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Aliases": [ + "ff" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Aliases": [ + "bag" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "BaggingExampleFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", + "Aliases": [ + "bagfrac" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.7 + }, + { + "Name": "FeatureFractionPerSplit", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", + "Aliases": [ + "sf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", + "Aliases": [ + "cmp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", + "Aliases": [ + "graph" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", + "Aliases": [ + "graphtv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", + "Aliases": [ + "tf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2147483647 + } + ] + } + ] + }, + { + "Kind": "NgramExtractor", + "Components": [ + { + "Name": "NGram", + "Desc": "Extracts NGrams from text and convert them to vector using dictionary.", + "FriendlyName": "NGram Extractor Transform", + "Aliases": [ + "NGramExtractorTransform", + "NGramExtractor" + ], + "Settings": [ + { + "Name": "NgramLength", + "Type": "Int", + "Desc": "Ngram length", + "Aliases": [ + "ngram" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an n-gram", + "Aliases": [ + "skips" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to include all n-gram lengths up to NgramLength or only NgramLength", + "Aliases": [ + "all" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxNumTerms", + "Type": { + "Kind": "Array", + "ItemType": "Int" + }, + "Desc": "Maximum number of n-grams to store in the dictionary", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": [ + 10000000 + ] + }, + { + "Name": "Weighting", + "Type": { + "Kind": "Enum", + "Values": [ + "Tf", + "Idf", + "TfIdf" + ] + }, + "Desc": "The weighting criteria", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Tf" + }, + { + "Name": "TermSeparator", + "Type": "Char", + "Desc": "Separator used to separate terms/frequency pairs.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "\u0000" + }, + { + "Name": "FreqSeparator", + "Type": "Char", + "Desc": "Separator used to separate terms from their frequency.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "\u0000" + } + ] + }, + { + "Name": "NGramHash", + "Desc": "Extracts NGrams from text and convert them to vector using hashing trick.", + "FriendlyName": "NGram Hash Extractor Transform", + "Aliases": [ + "NGramHashExtractorTransform", + "NGramHashExtractor" + ], + "Settings": [ + { + "Name": "NumberOfBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 30, inclusive.", + "Aliases": [ + "bits" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 16 + }, + { + "Name": "NgramLength", + "Type": "Int", + "Desc": "Ngram length", + "Aliases": [ + "ngram" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an n-gram", + "Aliases": [ + "skips" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to include all n-gram lengths up to ngramLength or only ngramLength", + "Aliases": [ + "all" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 314489979 + }, + { + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each source column should be included in the hash (when there are multiple source columns).", + "Aliases": [ + "ord" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaximumNumberOfInverts", + "Type": "Int", + "Desc": "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", + "Aliases": [ + "ih" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ] + } + ] + }, + { + "Kind": "ParallelLightGBM", + "Components": [ + { + "Name": "Single", + "Desc": "Single node machine learning process.", + "FriendlyName": "Single", + "Settings": [] + } + ] + }, + { + "Kind": "ParallelTraining", + "Components": [ + { + "Name": "Single", + "Desc": "Single node machine learning process.", + "FriendlyName": "Single", + "Settings": [] + } + ] + }, + { + "Kind": "PartitionedPathParser", + "Components": [ + { + "Name": "ParquetPathParser", + "Desc": "Extract name/value pairs from Parquet formatted directory names. Example path: Year=2018/Month=12/data1.parquet", + "FriendlyName": "Parquet Partitioned Path Parser", + "Aliases": [ + "ParqPP" + ], + "Settings": [] + }, + { + "Name": "SimplePathParser", + "Desc": "A simple parser that extracts directory names as column values. Column names are defined as arguments.", + "FriendlyName": "Simple Partitioned Path Parser", + "Aliases": [ + "SmplPP" + ], + "Settings": [ + { + "Name": "Columns", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the column.", + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "Type", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "Text", + "TX", + "TXT", + "BL", + "Bool", + "TS", + "TimeSpan", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "Data type of the column.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Source", + "Type": "Int", + "Desc": "Index of the directory representing this column.", + "Required": true, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ] + } + }, + "Desc": "Column definitions used to override the Partitioned Path Parser. Expected with the format name:type:numeric-source, for example, col=MyFeature:R4:1", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Type", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "Text", + "TX", + "TXT", + "BL", + "Bool", + "TS", + "TimeSpan", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "Data type of each column.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Text" + } + ] + } + ] + }, + { + "Kind": "RegressionLossFunction", + "Components": [ + { + "Name": "PoissonLoss", + "Desc": "Poisson loss.", + "FriendlyName": "Poisson Loss", + "Settings": [] + }, + { + "Name": "SquaredLoss", + "Desc": "Squared loss.", + "FriendlyName": "Squared Loss", + "Aliases": [ + "L2" + ], + "Settings": [] + }, + { + "Name": "TweedieLoss", + "Desc": "Tweedie loss.", + "FriendlyName": "Tweedie Loss", + "Aliases": [ + "tweedie" + ], + "Settings": [ + { + "Name": "Index", + "Type": "Float", + "Desc": "Index parameter for the Tweedie distribution, in the range [1, 2]. 1 is Poisson loss, 2 is gamma loss, and intermediate values are compound Poisson loss.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.5 + } + ] + } + ] + }, + { + "Kind": "SDCAClassificationLossFunction", + "Components": [ + { + "Name": "HingeLoss", + "Desc": "Hinge loss.", + "FriendlyName": "Hinge loss", + "Aliases": [ + "Hinge" + ], + "Settings": [ + { + "Name": "Margin", + "Type": "Float", + "Desc": "Margin value", + "Aliases": [ + "marg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + } + ] + }, + { + "Name": "LogLoss", + "Desc": "Log loss.", + "FriendlyName": "Log loss", + "Aliases": [ + "Logistic", + "CrossEntropy" + ], + "Settings": [] + }, + { + "Name": "SmoothedHingeLoss", + "Desc": "Smoothed Hinge loss.", + "FriendlyName": "Smoothed Hinge Loss", + "Aliases": [ + "SmoothedHinge" + ], + "Settings": [ + { + "Name": "SmoothingConst", + "Type": "Float", + "Desc": "Smoothing constant", + "Aliases": [ + "smooth" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + } + ] + } + ] + }, + { + "Kind": "SDCARegressionLossFunction", + "Components": [ + { + "Name": "SquaredLoss", + "Desc": "Squared loss.", + "FriendlyName": "Squared Loss", + "Aliases": [ + "L2" + ], + "Settings": [] + } + ] + }, + { + "Kind": "StopWordsRemover", + "Components": [ + { + "Name": "Custom", + "Desc": "Remover with list of stopwords specified by the user.", + "FriendlyName": "Custom Stopwords Remover", + "Aliases": [ + "CustomStopWordsRemover", + "CustomStopWords" + ], + "Settings": [ + { + "Name": "Stopword", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of stopwords", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + }, + { + "Name": "Predefined", + "Desc": "Remover with predefined list of stop words.", + "FriendlyName": "Predefined Stopwords List Remover", + "Aliases": [ + "PredefinedStopWordsRemover", + "PredefinedStopWords" + ], + "Settings": [] + } + ] + } + ], + "EntryPointKinds": [ + { + "Kind": "IAnomalyDetectionOutput", + "Settings": [] + }, + { + "Kind": "IBinaryClassificationOutput", + "Settings": [] + }, + { + "Kind": "ICalibratorInput", + "Settings": [ + { + "Name": "UncalibratedPredictorModel", + "Type": "PredictorModel" + }, + { + "Name": "MaxRows", + "Type": "Int" + }, + { + "Name": "Data", + "Type": "DataView" + } + ] + }, + { + "Kind": "ICalibratorOutput", + "Settings": [] + }, + { + "Kind": "IClassificationEvaluatorOutput", + "Settings": [ + { + "Name": "ConfusionMatrix", + "Type": "DataView" + }, + { + "Name": "Warnings", + "Type": "DataView" + }, + { + "Name": "OverallMetrics", + "Type": "DataView" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView" + } + ] + }, + { + "Kind": "IClusteringOutput", + "Settings": [] + }, + { + "Kind": "IEvaluatorInput", + "Settings": [ + { + "Name": "Data", + "Type": "DataView" + }, + { + "Name": "NameColumn", + "Type": "String" + } + ] + }, + { + "Kind": "IEvaluatorOutput", + "Settings": [ + { + "Name": "Warnings", + "Type": "DataView" + }, + { + "Name": "OverallMetrics", + "Type": "DataView" + }, + { + "Name": "PerInstanceMetrics", + "Type": "DataView" + } + ] + }, + { + "Kind": "IFeaturizerInput", + "Settings": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel" + }, + { + "Name": "Data", + "Type": "DataView" + } + ] + }, + { + "Kind": "IMulticlassClassificationOutput", + "Settings": [] + }, + { + "Kind": "IRankingOutput", + "Settings": [] + }, + { + "Kind": "IRegressionOutput", + "Settings": [] + }, + { + "Kind": "ITrainerInput", + "Settings": [ + { + "Name": "TrainingData", + "Type": "DataView" + }, + { + "Name": "FeatureColumn", + "Type": "String" + } + ] + }, + { + "Kind": "ITrainerInputWithGroupId", + "Settings": [ + { + "Name": "GroupIdColumn", + "Type": "String" + }, + { + "Name": "WeightColumn", + "Type": "String" + }, + { + "Name": "LabelColumn", + "Type": "String" + }, + { + "Name": "TrainingData", + "Type": "DataView" + }, + { + "Name": "FeatureColumn", + "Type": "String" + } + ] + }, + { + "Kind": "ITrainerInputWithLabel", + "Settings": [ + { + "Name": "LabelColumn", + "Type": "String" + }, + { + "Name": "TrainingData", + "Type": "DataView" + }, + { + "Name": "FeatureColumn", + "Type": "String" + } + ] + }, + { + "Kind": "ITrainerInputWithWeight", + "Settings": [ + { + "Name": "WeightColumn", + "Type": "String" + }, + { + "Name": "LabelColumn", + "Type": "String" + }, + { + "Name": "TrainingData", + "Type": "DataView" + }, + { + "Name": "FeatureColumn", + "Type": "String" + } + ] + }, + { + "Kind": "ITrainerOutput", + "Settings": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel" + } + ] + }, + { + "Kind": "ITransformInput", + "Settings": [ + { + "Name": "Data", + "Type": "DataView" + } + ] + }, + { + "Kind": "ITransformOutput", + "Settings": [ + { + "Name": "OutputData", + "Type": "DataView" + }, + { + "Name": "Model", + "Type": "TransformModel" + } + ] + }, + { + "Kind": "IUnsupervisedTrainerWithWeight", + "Settings": [ + { + "Name": "WeightColumn", + "Type": "String" + }, + { + "Name": "TrainingData", + "Type": "DataView" + }, + { + "Name": "FeatureColumn", + "Type": "String" + } + ] + } + ] +} \ No newline at end of file diff --git a/test/BaselineOutput/Common/FeatureContribution/linux-arm/SDCARegression.tsv b/test/BaselineOutput/Common/FeatureContribution/linux-arm/SDCARegression.tsv new file mode 100644 index 0000000000..8fd1a40627 --- /dev/null +++ b/test/BaselineOutput/Common/FeatureContribution/linux-arm/SDCARegression.tsv @@ -0,0 +1,17 @@ +#@ TextLoader{ +#@ sep=tab +#@ col=X1:R4:0 +#@ col=X2VBuffer:R4:1-4 +#@ col=X3Important:R4:5 +#@ col=Label:R4:6 +#@ col=Features:R4:7-12 +#@ col=Features:R4:13-18 +#@ col=FeatureContributions:R4:19-24 +#@ col=FeatureContributions:R4:25-30 +#@ col=FeatureContributions:R4:31-36 +#@ col=FeatureContributions:R4:37-42 +#@ } +950 757 692 720 297 7515 184479 950 757 692 720 297 7515 0.95669687 0.760804 0.7872582 0.75471693 0.29789367 0.7578661 0.06895084 0 0 0.05886949 0 1 18 11:1 17:141970.22 +459 961 0 659 274 2147 66495 459 961 0 659 274 2147 0.46223566 0.96582913 0 0.69077563 0.27482447 0.21651876 0 0.24562529 0 0.1885993 0 1 18 11:1 17:40560.223 +672 275 0 65 195 9818 208477 672 275 0 65 195 9818 0.6767372 0.2763819 0 0.06813417 0.19558676 0.99011695 0.037332855 0.015370621 0 0 0 1 18 11:1 17:185477.53 +186 301 0 681 526 1456 46093 186 301 0 681 526 1456 0.18731117 0.30251256 0 0.71383643 0.52758276 0.1468334 0 0 0 0.28739053 0.17624094 1 18 11:1 17:27506.14 diff --git a/test/BaselineOutput/Common/FeatureContribution/netcoreapp/SDCARegression.tsv b/test/BaselineOutput/Common/FeatureContribution/netcoreapp/SDCARegression.tsv new file mode 100644 index 0000000000..22cb0165ee --- /dev/null +++ b/test/BaselineOutput/Common/FeatureContribution/netcoreapp/SDCARegression.tsv @@ -0,0 +1,17 @@ +#@ TextLoader{ +#@ sep=tab +#@ col=X1:R4:0 +#@ col=X2VBuffer:R4:1-4 +#@ col=X3Important:R4:5 +#@ col=Label:R4:6 +#@ col=Features:R4:7-12 +#@ col=Features:R4:13-18 +#@ col=FeatureContributions:R4:19-24 +#@ col=FeatureContributions:R4:25-30 +#@ col=FeatureContributions:R4:31-36 +#@ col=FeatureContributions:R4:37-42 +#@ } +950 757 692 720 297 7515 184479 950 757 692 720 297 7515 0.956696868 0.760804 0.7872582 0.754716933 0.297893673 0.7578661 0.06895079 0 0 0.0588694736 0 1 18 11:1 17:141970.234 +459 961 0 659 274 2147 66495 459 961 0 659 274 2147 0.462235659 0.965829134 0 0.690775633 0.27482447 0.21651876 0 0.245625839 0 0.188599259 0 1 18 11:1 17:40560.2266 +672 275 0 65 195 9818 208477 672 275 0 65 195 9818 0.6767372 0.2763819 0 0.06813417 0.195586756 0.990116954 0.0373328254 0.0153706567 0 0 0 1 18 11:1 17:185477.48 +186 301 0 681 526 1456 46093 186 301 0 681 526 1456 0.187311172 0.302512556 0 0.713836432 0.527582765 0.1468334 0 0 0 0.287390471 0.176241338 1 18 11:1 17:27506.1426 diff --git a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer-out.txt index 4fb66c0381..72c7ab0891 100644 --- a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 124 | 10 | 0.9254 - negative || 5 | 215 | 0.9773 + positive || 134 | 0 | 1.0000 + negative || 10 | 210 | 0.9545 ||====================== -Precision || 0.9612 | 0.9556 | -OVERALL 0/1 ACCURACY: 0.957627 -LOG LOSS/instance: 0.166325 +Precision || 0.9306 | 1.0000 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: 0.125103 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.826202 -AUC: 0.994199 +LOG-LOSS REDUCTION (RIG): 0.869275 +AUC: 0.994369 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 100 | 5 | 0.9524 + positive || 94 | 11 | 0.8952 negative || 4 | 220 | 0.9821 ||====================== -Precision || 0.9615 | 0.9778 | -OVERALL 0/1 ACCURACY: 0.972644 -LOG LOSS/instance: 0.182604 +Precision || 0.9592 | 0.9524 | +OVERALL 0/1 ACCURACY: 0.954407 +LOG LOSS/instance: 0.283104 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.797882 -AUC: 0.961054 +LOG-LOSS REDUCTION (RIG): 0.686643 +AUC: 0.951786 OVERALL RESULTS --------------------------------------- -AUC: 0.977627 (0.0166) -Accuracy: 0.965136 (0.0075) -Positive precision: 0.961389 (0.0001) -Positive recall: 0.938877 (0.0135) -Negative precision: 0.966667 (0.0111) -Negative recall: 0.979708 (0.0024) -Log-loss: 0.174464 (0.0081) -Log-loss reduction: 0.812042 (0.0142) -F1 Score: 0.949952 (0.0070) -AUPRC: 0.979909 (0.0095) +AUC: 0.973077 (0.0213) +Accuracy: 0.963079 (0.0087) +Positive precision: 0.944870 (0.0143) +Positive recall: 0.947619 (0.0524) +Negative precision: 0.976190 (0.0238) +Negative recall: 0.968344 (0.0138) +Log-loss: 0.204103 (0.0790) +Log-loss reduction: 0.777959 (0.0913) +F1 Score: 0.945069 (0.0190) +AUPRC: 0.974864 (0.0146) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer.txt index 5acbf41e17..67a5cd0824 100644 --- a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-def-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.526149 0.9999277 0.00010431124866801515 1 -6 0 -1.1218108 0.0914504 0.13836281897248148 0 -8 0 -1.6203799 0.027515754 0.040253216049532151 0 -9 0 -1.669663 0.024350794 0.035565573924712414 0 -10 0 -1.8151165 0.016943678 0.02465402036879874 0 -11 0 -1.8067945 0.017300142 0.02517724745610924 0 -18 1 2.0538619 0.9969428 0.0044173368194155311 1 -20 1 1.8093374 0.99431825 0.0082204124602030648 1 -21 1 2.1534398 0.9976257 0.0034294513286414175 1 -25 1 -0.6382538 0.25631282 1.9640224701655145 0 -28 0 -1.8067945 0.017300142 0.02517724745610924 0 -31 0 -1.728947 0.021011736 0.030636530178365998 0 -32 1 2.5068038 0.9990328 0.0013960564827661087 1 -35 0 -1.8067945 0.017300142 0.02517724745610924 0 -37 0 -1.1458213 0.08649767 0.13051968926295085 0 +5 1 2.1765673 0.9969219 0.0044476126500663854 1 +6 0 0.51526934 0.6339415 1.4498537580847979 1 +8 0 -1.1061414 0.010390314 0.015068474240466503 0 +9 0 -1.1771077 0.0083269235 0.012063507149562485 0 +10 0 -1.1235644 0.009841091 0.014268015322017972 0 +11 0 -1.1607295 0.008763769 0.012699173604545824 0 +18 1 1.4996887 0.97464174 0.03705608571006886 1 +20 1 1.5068367 0.9751921 0.036241612300444467 1 +21 1 1.785187 0.98952216 0.01519607991864815 1 +25 1 0.9082803 0.8565223 0.22343725022691355 1 +28 0 -1.1607295 0.008763769 0.012699173604545824 0 +31 0 -1.1561694 0.00888939 0.01288202095789585 0 +32 1 1.8661491 0.99186087 0.011790334377083218 1 +35 0 -1.1607295 0.008763769 0.012699173604545824 0 +37 0 -1.0896765 0.010937171 0.015865925646523532 0 40 0 ? ? ? 0 -41 1 -0.20914128 0.50675654 0.98063528051212701 0 -44 1 2.8598988 0.999606 0.00056851466206539339 1 -45 0 -1.7839876 0.018315233 0.026668266391998201 0 -46 1 1.2672604 0.9777957 0.0323950043331281 1 -48 0 -1.5373471 0.033772714 0.049565500871283592 0 -50 1 0.9638393 0.95314443 0.069233250884230338 1 -51 1 -0.85461456 0.1657656 2.5927834616592653 0 -52 1 1.3377451 0.98137456 0.027124219099611876 1 -54 1 2.3109157 0.99840856 0.0022977972822397406 1 -56 1 1.6193739 0.9908179 0.013308155996179006 1 -60 1 -0.41534865 0.37804702 1.4033624154398172 0 -63 1 -0.7428849 0.20890367 2.2590902549815062 0 -64 0 -1.8474231 0.015627015 0.022723030236888715 0 -66 0 -1.6305369 0.026832342 0.039239720183993521 0 -68 1 2.9473996 0.9996847 0.00045496584261690929 1 -69 0 -1.7663403 0.019140588 0.027881727370439507 0 -70 0 -1.4818044 0.03870289 0.05694569877155152 0 -71 1 2.8194919 0.9995634 0.00063002400430274086 1 -72 0 -1.0894251 0.09853413 0.14965522272949733 0 -73 1 2.1321487 0.9974938 0.0036202156435655515 1 -74 1 0.8495918 0.9383063 0.091869185987172225 1 -76 0 -1.6012264 0.028850798 0.042235133868308392 0 -77 0 -1.5217263 0.035094522 0.051540471690820183 0 -79 0 -1.7627866 0.019311147 0.028132615082685872 0 -82 0 -1.5285081 0.03451464 0.050673710939356742 0 -88 0 -1.6305369 0.026832342 0.039239720183993521 0 -90 0 -1.7732897 0.018811302 0.027397479222316581 0 -91 0 -1.7983022 0.017671494 0.02572252962241716 0 -92 0 -1.6305369 0.026832342 0.039239720183993521 0 -93 0 -1.8474231 0.015627015 0.022723030236888715 0 -95 0 -1.7732897 0.018811302 0.027397479222316581 0 -96 0 -1.8405538 0.015898272 0.023120638748319003 0 -97 0 -1.5770282 0.030627552 0.04487701744231537 0 -98 1 2.854033 0.9996001 0.00057703118380684083 1 -99 1 2.7723851 0.9995078 0.00071029118780069692 1 -100 1 1.6455318 0.99140424 0.01245467275034115 1 -102 0 -1.5401378 0.033541683 0.049220584188514341 0 -104 1 4.042039 0.99998057 2.8033444452050102E-05 1 -105 1 -0.17070323 0.5311762 0.91273756280616514 0 -106 1 2.352503 0.9985682 0.002067163879089538 1 -108 0 -1.8354541 0.016102642 0.023420275836834568 0 -109 1 1.7236166 0.9929427 0.010217642001234576 1 -111 1 1.080668 0.9647726 0.051739187127587655 1 -112 1 2.1199894 0.99741524 0.003733841442281771 1 -113 1 3.2178142 0.9998416 0.00022858305058355181 1 -115 0 -1.6449932 0.025888044 0.037840502738395523 0 -117 1 2.5524666 0.99913883 0.0012429379358114413 1 -120 0 -1.6473104 0.025739722 0.037620848572117747 0 -121 0 -1.5082824 0.036271907 0.053301934714094418 0 -122 1 3.113542 0.9997934 0.00029807672479589532 1 -123 1 1.3110919 0.9800931 0.029009264450288733 1 -125 0 -1.8474231 0.015627015 0.022723030236888715 0 -128 1 1.1243547 0.9683626 0.046380690271674324 1 -129 0 -1.5529418 0.032501105 0.047668081687226582 0 -131 0 -1.728947 0.021011736 0.030636530178365998 0 -132 1 2.495432 0.9990044 0.0014370285632838195 1 -133 0 -1.7545773 0.019710878 0.028720780875823325 0 -137 0 -1.840337 0.015906906 0.023133295326727574 0 -138 0 -1.6268921 0.027075663 0.039600482255918001 0 -141 0 -1.8808964 0.014369044 0.020880527660383778 0 -144 0 -1.8067945 0.017300142 0.02517724745610924 0 +41 1 1.2700887 0.9491162 0.075343413107988241 1 +44 1 2.0909412 0.99597317 0.0058212163617048923 1 +45 0 -1.1382513 0.009400513 0.01362622107446259 0 +46 1 1.2644507 0.9482519 0.076657733004513645 1 +48 0 -1.1775705 0.008314899 0.012046014185871317 0 +50 1 1.1130487 0.91919714 0.12155378237949821 1 +51 1 0.757234 0.7876933 0.34429405070592511 1 +52 1 1.437172 0.96929395 0.044993845382617442 1 +54 1 1.6779271 0.9853739 0.021256815131055602 1 +56 1 1.4246802 0.9681014 0.046769956459304179 1 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-1.1390419 0.0093773585 0.013592499522272915 0 +150 0 -1.1235644 0.009841091 0.014268015322017972 0 +151 1 1.3686883 0.9621836 0.055615893306992133 1 +152 1 1.9984549 0.99461913 0.0077839138364596099 1 +154 0 -1.1511469 0.009029816 0.013086444039771647 0 +156 0 -1.1348325 0.009501292 0.013773002152591649 0 +161 0 -1.1600993 0.008781025 0.012724289765809726 0 164 0 ? ? ? 0 -167 1 2.826551 0.99957114 0.00061884029247210139 1 -169 0 -1.9158832 0.013160857 0.019113154122289679 0 -171 0 -1.7732897 0.018811302 0.027397479222316581 0 -173 1 4.37442 0.99999166 1.2038835790403619E-05 1 -174 1 1.5860469 0.9900131 0.014480446740836076 1 -176 0 -1.728947 0.021011736 0.030636530178365998 0 -177 1 2.0442426 0.9968673 0.004526626072621099 1 -179 1 0.69324994 0.9108409 0.13472897458542998 1 -180 0 -1.8151165 0.016943678 0.02465402036879874 0 -181 0 -1.917501 0.013107483 0.019035126752815424 0 -183 1 2.46993 0.9989377 0.0015333519660518796 1 -187 1 3.709254 0.99995464 6.5440882835427928E-05 1 -188 1 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-1.120567 0.012248385 0.017779794496594212 0 diff --git a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer-out.txt index 3511d8cc47..63ce6e37a4 100644 --- a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 121 | 13 | 0.9030 - negative || 14 | 206 | 0.9364 + positive || 126 | 8 | 0.9403 + negative || 12 | 208 | 0.9455 ||====================== -Precision || 0.8963 | 0.9406 | -OVERALL 0/1 ACCURACY: 0.923729 -LOG LOSS/instance: 0.332232 +Precision || 0.9130 | 0.9630 | +OVERALL 0/1 ACCURACY: 0.943503 +LOG LOSS/instance: 0.262484 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.652840 -AUC: 0.969878 +LOG-LOSS REDUCTION (RIG): 0.725722 +AUC: 0.980801 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 102 | 3 | 0.9714 - negative || 54 | 170 | 0.7589 + positive || 103 | 2 | 0.9810 + negative || 36 | 188 | 0.8393 ||====================== -Precision || 0.6538 | 0.9827 | -OVERALL 0/1 ACCURACY: 0.826748 -LOG LOSS/instance: 0.402185 +Precision || 0.7410 | 0.9895 | +OVERALL 0/1 ACCURACY: 0.884498 +LOG LOSS/instance: 0.231456 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.554836 -AUC: 0.968878 +LOG-LOSS REDUCTION (RIG): 0.743810 +AUC: 0.984396 OVERALL RESULTS --------------------------------------- -AUC: 0.969378 (0.0005) -Accuracy: 0.875238 (0.0485) -Positive precision: 0.775071 (0.1212) -Positive recall: 0.937207 (0.0342) -Negative precision: 0.961649 (0.0210) -Negative recall: 0.847646 (0.0887) -Log-loss: 0.367208 (0.0350) -Log-loss reduction: 0.603838 (0.0490) -F1 Score: 0.840619 (0.0590) -AUPRC: 0.962365 (0.0123) +AUC: 0.982598 (0.0018) +Accuracy: 0.914001 (0.0295) +Positive precision: 0.827025 (0.0860) +Positive recall: 0.960625 (0.0203) +Negative precision: 0.976218 (0.0133) +Negative recall: 0.892370 (0.0531) +Log-loss: 0.246970 (0.0155) +Log-loss reduction: 0.734766 (0.0090) +F1 Score: 0.885366 (0.0411) +AUPRC: 0.972638 (0.0101) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer.txt index a14120bfd8..87f4bbc2ab 100644 --- a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.8534284 0.9966877 0.00478655555839757 1 -6 0 1.9441669 0.9089837 3.4577313685410616 1 -8 0 -1.3064579 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0.023836725074621097 0 +676 0 -0.6909238 0.004859244 0.007027495218564289 0 +677 0 -0.53125983 0.007538426 0.010916849925431595 0 +684 0 -0.6033769 0.006183063 0.0089479660528151827 0 +686 0 -0.6033769 0.006183063 0.0089479660528151827 0 +687 0 -0.44247046 0.009617808 0.013942721511381454 0 +690 0 -0.5071147 0.008055119 0.011668137472837622 0 +695 0 -0.60704285 0.0061210385 0.008857929435335957 0 diff --git a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt index 637b1e19ce..017b8f5a34 100644 --- a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -8,28 +8,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 220 | 19 | 0.9205 - negative || 25 | 419 | 0.9437 + positive || 216 | 23 | 0.9038 + negative || 10 | 434 | 0.9775 ||====================== -Precision || 0.8980 | 0.9566 | -OVERALL 0/1 ACCURACY: 0.935578 -LOG LOSS/instance: 0.315898 +Precision || 0.9558 | 0.9497 | +OVERALL 0/1 ACCURACY: 0.951684 +LOG LOSS/instance: 0.251640 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.661780 -AUC: 0.963945 +LOG-LOSS REDUCTION (RIG): 0.730579 +AUC: 0.969024 OVERALL RESULTS --------------------------------------- -AUC: 0.963945 (0.0000) -Accuracy: 0.935578 (0.0000) -Positive precision: 0.897959 (0.0000) -Positive recall: 0.920502 (0.0000) -Negative precision: 0.956621 (0.0000) -Negative recall: 0.943694 (0.0000) -Log-loss: 0.315898 (0.0000) -Log-loss reduction: 0.661780 (0.0000) -F1 Score: 0.909091 (0.0000) -AUPRC: 0.951812 (0.0000) +AUC: 0.969024 (0.0000) +Accuracy: 0.951684 (0.0000) +Positive precision: 0.955752 (0.0000) +Positive recall: 0.903766 (0.0000) +Negative precision: 0.949672 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.251640 (0.0000) +Log-loss reduction: 0.730579 (0.0000) +F1 Score: 0.929032 (0.0000) +AUPRC: 0.965033 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer.txt index 50ffce0106..b78d5de043 100644 --- a/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/linux-arm64/LDSVM-nob-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -1.9210272 0.009717667 0.014088192920503215 0 -1 0 1.5968752 0.8927986 3.2216043827830356 1 -2 0 -1.2856477 0.032107096 0.047080670997454895 0 -3 0 2.7900536 0.9879544 6.3753473131417593 1 -4 0 -1.6028564 0.017738463 0.025820887678307834 0 -5 1 3.5799701 0.9973251 0.0038642031538066459 1 -6 0 1.7048188 0.91105425 3.4909305754333388 1 -7 0 -1.3287921 0.029634012 0.043399110868457463 0 -8 0 -1.5092304 0.021151947 0.030843167633737175 0 -9 0 -1.1032926 0.044938717 0.066334786043110966 0 -10 0 -0.80262655 0.0772644 0.11601077400748538 0 -11 0 -1.1343119 0.042454563 0.062587148254938621 0 -12 1 0.52196103 0.5147654 0.95801305991064523 1 -13 0 -0.65906256 0.09931112 0.15089924629648419 0 -14 1 2.0290787 0.95017695 0.073731879050449603 1 -15 1 -1.232694 0.035415977 4.8194558383867481 0 -16 0 -1.46132 0.023139516 0.033775564687304624 0 -17 0 -1.7614868 0.013148509 0.019095101663794155 0 -18 1 2.5355208 0.9805259 0.028372340077133067 1 -19 0 -2.0753126 0.007247647 0.010494219405683425 0 -20 1 1.3964187 0.85009974 0.23429597194071752 1 -21 1 1.3561343 0.83999085 0.25155447483025511 1 -22 0 -1.3004504 0.031236883 0.045784155844925269 0 +0 0 -2.1649945 0.009562437 0.01386206474900401 0 +1 0 0.88093066 0.8628806 2.8664952624817146 1 +2 0 -1.677961 0.02648804 0.038729391193614529 0 +3 0 1.7938383 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0.015905373504591969 1 +692 0 -1.4761332 0.04012283 0.059078289752994735 0 +693 0 -1.8977997 0.016759487 0.024383733602070419 0 +694 0 -1.6757553 0.026609309 0.038909117412749217 0 +695 0 -1.3411404 0.052765768 0.078206875603933218 0 +696 1 1.7864851 0.97737575 0.033014791291851482 1 +697 1 1.5218714 0.96094424 0.057475382675620579 1 +698 1 1.5541373 0.96344036 0.05373273501846821 1 diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer-out.txt index 703a4d879b..c3c9d949bb 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 133 | 1 | 0.9925 - negative || 10 | 210 | 0.9545 + positive || 134 | 0 | 1.0000 + negative || 9 | 211 | 0.9591 ||====================== -Precision || 0.9301 | 0.9953 | -OVERALL 0/1 ACCURACY: 0.968927 -LOG LOSS/instance: 0.138800 +Precision || 0.9371 | 1.0000 | +OVERALL 0/1 ACCURACY: 0.974576 +LOG LOSS/instance: 0.117268 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.854963 -AUC: 0.994267 +LOG-LOSS REDUCTION (RIG): 0.877462 +AUC: 0.994607 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 101 | 4 | 0.9619 - negative || 4 | 220 | 0.9821 + positive || 99 | 6 | 0.9429 + negative || 3 | 221 | 0.9866 ||====================== -Precision || 0.9619 | 0.9821 | -OVERALL 0/1 ACCURACY: 0.975684 -LOG LOSS/instance: 0.157194 +Precision || 0.9706 | 0.9736 | +OVERALL 0/1 ACCURACY: 0.972644 +LOG LOSS/instance: 0.176988 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.826008 -AUC: 0.982185 +LOG-LOSS REDUCTION (RIG): 0.804098 +AUC: 0.984396 OVERALL RESULTS --------------------------------------- -AUC: 0.988226 (0.0060) -Accuracy: 0.972305 (0.0034) -Positive precision: 0.945987 (0.0159) -Positive recall: 0.977221 (0.0153) -Negative precision: 0.988702 (0.0066) -Negative recall: 0.968344 (0.0138) -Log-loss: 0.147997 (0.0092) -Log-loss reduction: 0.840485 (0.0145) -F1 Score: 0.961097 (0.0008) -AUPRC: 0.986066 (0.0034) +AUC: 0.989501 (0.0051) +Accuracy: 0.973610 (0.0010) +Positive precision: 0.953826 (0.0168) +Positive recall: 0.971429 (0.0286) +Negative precision: 0.986784 (0.0132) +Negative recall: 0.972849 (0.0138) +Log-loss: 0.147128 (0.0299) +Log-loss reduction: 0.840780 (0.0367) +F1 Score: 0.962015 (0.0055) +AUPRC: 0.986587 (0.0035) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer.txt index 1055c7a08f..137b8873b1 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.192721 0.9995258 0.00068430925266268561 1 -6 0 0.31271142 0.5096692 1.0281726628467021 1 -8 0 -1.3462902 0.012771749 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a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-out.txt index 0d6047d595..bdeb0ffe66 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 123 | 11 | 0.9179 - negative || 17 | 203 | 0.9227 + positive || 125 | 9 | 0.9328 + negative || 13 | 207 | 0.9409 ||====================== -Precision || 0.8786 | 0.9486 | -OVERALL 0/1 ACCURACY: 0.920904 -LOG LOSS/instance: 0.352056 +Precision || 0.9058 | 0.9583 | +OVERALL 0/1 ACCURACY: 0.937853 +LOG LOSS/instance: 0.309511 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.632125 -AUC: 0.961330 +LOG-LOSS REDUCTION (RIG): 0.676582 +AUC: 0.971744 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 104 | 1 | 0.9905 - negative || 44 | 180 | 0.8036 + positive || 102 | 3 | 0.9714 + negative || 22 | 202 | 0.9018 ||====================== -Precision || 0.7027 | 0.9945 | -OVERALL 0/1 ACCURACY: 0.863222 -LOG LOSS/instance: 0.139853 +Precision || 0.8226 | 0.9854 | +OVERALL 0/1 ACCURACY: 0.924012 +LOG LOSS/instance: 0.299203 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.845202 -AUC: 0.993580 +LOG-LOSS REDUCTION (RIG): 0.668823 +AUC: 0.976488 OVERALL RESULTS --------------------------------------- -AUC: 0.977455 (0.0161) -Accuracy: 0.892063 (0.0288) -Positive precision: 0.790637 (0.0879) -Positive recall: 0.954193 (0.0363) -Negative precision: 0.971537 (0.0229) -Negative recall: 0.863149 (0.0596) -Log-loss: 0.245954 (0.1061) -Log-loss reduction: 0.738663 (0.1065) -F1 Score: 0.859972 (0.0378) -AUPRC: 0.964976 (0.0234) +AUC: 0.974116 (0.0024) +Accuracy: 0.930933 (0.0069) +Positive precision: 0.864189 (0.0416) +Positive recall: 0.952132 (0.0193) +Negative precision: 0.971850 (0.0135) +Negative recall: 0.921347 (0.0196) +Log-loss: 0.304357 (0.0052) +Log-loss reduction: 0.672702 (0.0039) +F1 Score: 0.904974 (0.0141) +AUPRC: 0.966608 (0.0115) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-rp.txt index 0a55cda83b..71dce23763 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.977455 0.892063 0.790637 0.954193 0.971537 0.863149 0.245954 0.738663 0.859972 0.964976 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 +0.974116 0.930933 0.864189 0.952132 0.97185 0.921347 0.304357 0.672702 0.904974 0.966608 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer.txt index 64f3d10f02..393b27e5fe 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.798577 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0.00974897 0.014133798382209318 0 +677 0 -0.98767114 0.0076238294 0.011041001927285387 0 +684 0 -0.74154985 0.013856358 0.020130289400489415 0 +686 0 -0.74154985 0.013856358 0.020130289400489415 0 +687 0 -0.8366627 0.011004546 0.015964204955816238 0 +690 0 -0.71363914 0.014823757 0.021546256617030777 0 +695 0 -0.8119284 0.01168489 0.016956997114979482 0 diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-out.txt index 650237ba34..81921808c4 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -8,28 +8,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 226 | 13 | 0.9456 - negative || 22 | 422 | 0.9505 + positive || 228 | 11 | 0.9540 + negative || 15 | 429 | 0.9662 ||====================== -Precision || 0.9113 | 0.9701 | -OVERALL 0/1 ACCURACY: 0.948755 -LOG LOSS/instance: 0.259132 +Precision || 0.9383 | 0.9750 | +OVERALL 0/1 ACCURACY: 0.961933 +LOG LOSS/instance: 0.188873 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.722558 -AUC: 0.971682 +LOG-LOSS REDUCTION (RIG): 0.797781 +AUC: 0.980974 OVERALL RESULTS --------------------------------------- -AUC: 0.971682 (0.0000) -Accuracy: 0.948755 (0.0000) -Positive precision: 0.911290 (0.0000) -Positive recall: 0.945607 (0.0000) -Negative precision: 0.970115 (0.0000) -Negative recall: 0.950450 (0.0000) -Log-loss: 0.259132 (0.0000) -Log-loss reduction: 0.722558 (0.0000) -F1 Score: 0.928131 (0.0000) -AUPRC: 0.961861 (0.0000) +AUC: 0.980974 (0.0000) +Accuracy: 0.961933 (0.0000) +Positive precision: 0.938272 (0.0000) +Positive recall: 0.953975 (0.0000) +Negative precision: 0.975000 (0.0000) +Negative recall: 0.966216 (0.0000) +Log-loss: 0.188873 (0.0000) +Log-loss reduction: 0.797781 (0.0000) +F1 Score: 0.946058 (0.0000) +AUPRC: 0.976643 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-rp.txt index d831e9838d..384fb65087 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.971682 0.948755 0.91129 0.945607 0.970115 0.95045 0.259132 0.722558 0.928131 0.961861 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 +0.980974 0.961933 0.938272 0.953975 0.975 0.966216 0.188873 0.797781 0.946058 0.976643 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer.txt index 758a96884d..8a1cffa058 100644 --- a/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-nob-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -1.9328692 0.00879855 0.012749796760162687 0 -1 0 1.7016087 0.92350006 3.7083975930171791 1 -2 0 -1.3935351 0.025243035 0.036885536024252437 0 -3 0 2.6855175 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0.050867463428077943 0 +684 0 -0.9823508 0.034644295 0.050867463428077943 0 +685 0 -0.9823508 0.034644295 0.050867463428077943 0 +686 0 -0.9823508 0.034644295 0.050867463428077943 0 +687 0 -1.0711788 0.028311372 0.041434009989270069 0 +688 0 -1.2739269 0.01778492 0.025889121808373532 0 +689 0 -1.9011282 0.0041396352 0.0059846268911291807 0 +690 0 -1.1489447 0.023701586 0.034605907302734731 0 +691 1 2.4506643 0.99122626 0.01271369237054799 1 +692 0 -1.1901796 0.021563126 0.031449319333433555 0 +693 0 -1.4852631 0.010907938 0.015823285189527934 0 +694 0 -1.3193941 0.016014215 0.02329062026634221 0 +695 0 -1.093269 0.02691992 0.039369557847489002 0 +696 1 2.2754688 0.9868245 0.019134542609123055 1 +697 1 1.9577334 0.9726328 0.040032812989940499 1 +698 1 2.009111 0.97566503 0.035542170842136504 1 diff --git a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer-out.txt index 4fb66c0381..72c7ab0891 100644 --- a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 124 | 10 | 0.9254 - negative || 5 | 215 | 0.9773 + positive || 134 | 0 | 1.0000 + negative || 10 | 210 | 0.9545 ||====================== -Precision || 0.9612 | 0.9556 | -OVERALL 0/1 ACCURACY: 0.957627 -LOG LOSS/instance: 0.166325 +Precision || 0.9306 | 1.0000 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: 0.125103 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.826202 -AUC: 0.994199 +LOG-LOSS REDUCTION (RIG): 0.869275 +AUC: 0.994369 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 100 | 5 | 0.9524 + positive || 94 | 11 | 0.8952 negative || 4 | 220 | 0.9821 ||====================== -Precision || 0.9615 | 0.9778 | -OVERALL 0/1 ACCURACY: 0.972644 -LOG LOSS/instance: 0.182604 +Precision || 0.9592 | 0.9524 | +OVERALL 0/1 ACCURACY: 0.954407 +LOG LOSS/instance: 0.283104 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.797882 -AUC: 0.961054 +LOG-LOSS REDUCTION (RIG): 0.686643 +AUC: 0.951786 OVERALL RESULTS --------------------------------------- -AUC: 0.977627 (0.0166) -Accuracy: 0.965136 (0.0075) -Positive precision: 0.961389 (0.0001) -Positive recall: 0.938877 (0.0135) -Negative precision: 0.966667 (0.0111) -Negative recall: 0.979708 (0.0024) -Log-loss: 0.174464 (0.0081) -Log-loss reduction: 0.812042 (0.0142) -F1 Score: 0.949952 (0.0070) -AUPRC: 0.979909 (0.0095) +AUC: 0.973077 (0.0213) +Accuracy: 0.963079 (0.0087) +Positive precision: 0.944870 (0.0143) +Positive recall: 0.947619 (0.0524) +Negative precision: 0.976190 (0.0238) +Negative recall: 0.968344 (0.0138) +Log-loss: 0.204103 (0.0790) +Log-loss reduction: 0.777959 (0.0913) +F1 Score: 0.945069 (0.0190) +AUPRC: 0.974864 (0.0146) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer.txt index 5acbf41e17..156b46d6d4 100644 --- a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.526149 0.9999277 0.00010431124866801515 1 -6 0 -1.1218108 0.0914504 0.13836281897248148 0 -8 0 -1.6203799 0.027515754 0.040253216049532151 0 -9 0 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-1.120567 0.012248385 0.017779794496594212 0 diff --git a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer-out.txt index 3511d8cc47..63ce6e37a4 100644 --- a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 121 | 13 | 0.9030 - negative || 14 | 206 | 0.9364 + positive || 126 | 8 | 0.9403 + negative || 12 | 208 | 0.9455 ||====================== -Precision || 0.8963 | 0.9406 | -OVERALL 0/1 ACCURACY: 0.923729 -LOG LOSS/instance: 0.332232 +Precision || 0.9130 | 0.9630 | +OVERALL 0/1 ACCURACY: 0.943503 +LOG LOSS/instance: 0.262484 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.652840 -AUC: 0.969878 +LOG-LOSS REDUCTION (RIG): 0.725722 +AUC: 0.980801 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 102 | 3 | 0.9714 - negative || 54 | 170 | 0.7589 + positive || 103 | 2 | 0.9810 + negative || 36 | 188 | 0.8393 ||====================== -Precision || 0.6538 | 0.9827 | -OVERALL 0/1 ACCURACY: 0.826748 -LOG LOSS/instance: 0.402185 +Precision || 0.7410 | 0.9895 | +OVERALL 0/1 ACCURACY: 0.884498 +LOG LOSS/instance: 0.231456 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.554836 -AUC: 0.968878 +LOG-LOSS REDUCTION (RIG): 0.743810 +AUC: 0.984396 OVERALL RESULTS --------------------------------------- -AUC: 0.969378 (0.0005) -Accuracy: 0.875238 (0.0485) -Positive precision: 0.775071 (0.1212) -Positive recall: 0.937207 (0.0342) -Negative precision: 0.961649 (0.0210) -Negative recall: 0.847646 (0.0887) -Log-loss: 0.367208 (0.0350) -Log-loss reduction: 0.603838 (0.0490) -F1 Score: 0.840619 (0.0590) -AUPRC: 0.962365 (0.0123) +AUC: 0.982598 (0.0018) +Accuracy: 0.914001 (0.0295) +Positive precision: 0.827025 (0.0860) +Positive recall: 0.960625 (0.0203) +Negative precision: 0.976218 (0.0133) +Negative recall: 0.892370 (0.0531) +Log-loss: 0.246970 (0.0155) +Log-loss reduction: 0.734766 (0.0090) +F1 Score: 0.885366 (0.0411) +AUPRC: 0.972638 (0.0101) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer.txt index 671d56e7a7..87f4bbc2ab 100644 --- a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.8534284 0.9966877 0.00478655555839757 1 -6 0 1.9441669 0.9089837 3.4577313685410616 1 -8 0 -1.3064579 0.029403893 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-0.2785446 0.015055314 0.021885388643877877 0 +647 0 -0.5071147 0.008055119 0.011668137472837622 0 +648 1 2.42801 0.9647293 0.051803897820601692 1 +650 0 0.16968758 0.050187703 0.074285660668636233 1 +651 0 -0.30934447 0.013842333 0.020109771754367942 0 +655 0 -0.75042504 0.0041245935 0.005962836132079292 0 +658 1 2.9719381 0.99194974 0.011661074995093706 1 +659 0 -0.6033769 0.006183063 0.0089479660528151827 0 +662 0 -0.61207336 0.006036931 0.0087358461427175585 0 +663 0 -0.61207336 0.006036931 0.0087358461427175585 0 +664 0 -0.5567181 0.0070292326 0.010176848738108852 0 +666 0 0.001853267 0.032141745 0.047132317958526702 1 +667 0 -2.7659492 1.5664978E-05 2.2599963799406927E-05 0 +669 1 2.7991824 0.9870792 0.018762243866583983 1 +671 0 -0.36638606 0.011845023 0.017190771215094193 0 +672 0 -2.4942868 3.3220316E-05 4.792758160595997E-05 0 +673 0 0.08195717 0.039800398 0.0585937569614534 1 +674 0 -0.7580547 0.004038772 0.0058385146185466067 0 +675 0 -0.24743536 0.016386613 0.023836725074621097 0 +676 0 -0.6909238 0.004859244 0.007027495218564289 0 +677 0 -0.53125983 0.007538426 0.010916849925431595 0 +684 0 -0.6033769 0.006183063 0.0089479660528151827 0 +686 0 -0.6033769 0.006183063 0.0089479660528151827 0 +687 0 -0.44247046 0.009617808 0.013942721511381454 0 +690 0 -0.5071147 0.008055119 0.011668137472837622 0 +695 0 -0.60704285 0.0061210385 0.008857929435335957 0 diff --git a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt index 637b1e19ce..017b8f5a34 100644 --- a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -8,28 +8,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 220 | 19 | 0.9205 - negative || 25 | 419 | 0.9437 + positive || 216 | 23 | 0.9038 + negative || 10 | 434 | 0.9775 ||====================== -Precision || 0.8980 | 0.9566 | -OVERALL 0/1 ACCURACY: 0.935578 -LOG LOSS/instance: 0.315898 +Precision || 0.9558 | 0.9497 | +OVERALL 0/1 ACCURACY: 0.951684 +LOG LOSS/instance: 0.251640 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.661780 -AUC: 0.963945 +LOG-LOSS REDUCTION (RIG): 0.730579 +AUC: 0.969024 OVERALL RESULTS --------------------------------------- -AUC: 0.963945 (0.0000) -Accuracy: 0.935578 (0.0000) -Positive precision: 0.897959 (0.0000) -Positive recall: 0.920502 (0.0000) -Negative precision: 0.956621 (0.0000) -Negative recall: 0.943694 (0.0000) -Log-loss: 0.315898 (0.0000) -Log-loss reduction: 0.661780 (0.0000) -F1 Score: 0.909091 (0.0000) -AUPRC: 0.951812 (0.0000) +AUC: 0.969024 (0.0000) +Accuracy: 0.951684 (0.0000) +Positive precision: 0.955752 (0.0000) +Positive recall: 0.903766 (0.0000) +Negative precision: 0.949672 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.251640 (0.0000) +Log-loss reduction: 0.730579 (0.0000) +F1 Score: 0.929032 (0.0000) +AUPRC: 0.965033 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer.txt index 50ffce0106..b78d5de043 100644 --- a/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -1.9210272 0.009717667 0.014088192920503215 0 -1 0 1.5968752 0.8927986 3.2216043827830356 1 -2 0 -1.2856477 0.032107096 0.047080670997454895 0 -3 0 2.7900536 0.9879544 6.3753473131417593 1 -4 0 -1.6028564 0.017738463 0.025820887678307834 0 -5 1 3.5799701 0.9973251 0.0038642031538066459 1 -6 0 1.7048188 0.91105425 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0.015905373504591969 1 +692 0 -1.4761332 0.04012283 0.059078289752994735 0 +693 0 -1.8977997 0.016759487 0.024383733602070419 0 +694 0 -1.6757553 0.026609309 0.038909117412749217 0 +695 0 -1.3411404 0.052765768 0.078206875603933218 0 +696 1 1.7864851 0.97737575 0.033014791291851482 1 +697 1 1.5218714 0.96094424 0.057475382675620579 1 +698 1 1.5541373 0.96344036 0.05373273501846821 1 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-out.txt index 4fb66c0381..72c7ab0891 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 124 | 10 | 0.9254 - negative || 5 | 215 | 0.9773 + positive || 134 | 0 | 1.0000 + negative || 10 | 210 | 0.9545 ||====================== -Precision || 0.9612 | 0.9556 | -OVERALL 0/1 ACCURACY: 0.957627 -LOG LOSS/instance: 0.166325 +Precision || 0.9306 | 1.0000 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: 0.125103 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.826202 -AUC: 0.994199 +LOG-LOSS REDUCTION (RIG): 0.869275 +AUC: 0.994369 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 100 | 5 | 0.9524 + positive || 94 | 11 | 0.8952 negative || 4 | 220 | 0.9821 ||====================== -Precision || 0.9615 | 0.9778 | -OVERALL 0/1 ACCURACY: 0.972644 -LOG LOSS/instance: 0.182604 +Precision || 0.9592 | 0.9524 | +OVERALL 0/1 ACCURACY: 0.954407 +LOG LOSS/instance: 0.283104 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.797882 -AUC: 0.961054 +LOG-LOSS REDUCTION (RIG): 0.686643 +AUC: 0.951786 OVERALL RESULTS --------------------------------------- -AUC: 0.977627 (0.0166) -Accuracy: 0.965136 (0.0075) -Positive precision: 0.961389 (0.0001) -Positive recall: 0.938877 (0.0135) -Negative precision: 0.966667 (0.0111) -Negative recall: 0.979708 (0.0024) -Log-loss: 0.174464 (0.0081) -Log-loss reduction: 0.812042 (0.0142) -F1 Score: 0.949952 (0.0070) -AUPRC: 0.979909 (0.0095) +AUC: 0.973077 (0.0213) +Accuracy: 0.963079 (0.0087) +Positive precision: 0.944870 (0.0143) +Positive recall: 0.947619 (0.0524) +Negative precision: 0.976190 (0.0238) +Negative recall: 0.968344 (0.0138) +Log-loss: 0.204103 (0.0790) +Log-loss reduction: 0.777959 (0.0913) +F1 Score: 0.945069 (0.0190) +AUPRC: 0.974864 (0.0146) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-rp.txt index d540f25184..480e3e3ca1 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.977627 0.965136 0.961389 0.938877 0.966667 0.979708 0.174464 0.812042 0.949952 0.979909 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 +0.973077 0.963079 0.94487 0.947619 0.97619 0.968344 0.204103 0.777959 0.945069 0.974864 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer.txt index 5acbf41e17..67a5cd0824 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.526149 0.9999277 0.00010431124866801515 1 -6 0 -1.1218108 0.0914504 0.13836281897248148 0 -8 0 -1.6203799 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-1.0737815 0.014207697 0.020644377630023358 0 +695 0 -1.120567 0.012248385 0.017779794496594212 0 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-out.txt index 3511d8cc47..63ce6e37a4 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 121 | 13 | 0.9030 - negative || 14 | 206 | 0.9364 + positive || 126 | 8 | 0.9403 + negative || 12 | 208 | 0.9455 ||====================== -Precision || 0.8963 | 0.9406 | -OVERALL 0/1 ACCURACY: 0.923729 -LOG LOSS/instance: 0.332232 +Precision || 0.9130 | 0.9630 | +OVERALL 0/1 ACCURACY: 0.943503 +LOG LOSS/instance: 0.262484 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.652840 -AUC: 0.969878 +LOG-LOSS REDUCTION (RIG): 0.725722 +AUC: 0.980801 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 102 | 3 | 0.9714 - negative || 54 | 170 | 0.7589 + positive || 103 | 2 | 0.9810 + negative || 36 | 188 | 0.8393 ||====================== -Precision || 0.6538 | 0.9827 | -OVERALL 0/1 ACCURACY: 0.826748 -LOG LOSS/instance: 0.402185 +Precision || 0.7410 | 0.9895 | +OVERALL 0/1 ACCURACY: 0.884498 +LOG LOSS/instance: 0.231456 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.554836 -AUC: 0.968878 +LOG-LOSS REDUCTION (RIG): 0.743810 +AUC: 0.984396 OVERALL RESULTS --------------------------------------- -AUC: 0.969378 (0.0005) -Accuracy: 0.875238 (0.0485) -Positive precision: 0.775071 (0.1212) -Positive recall: 0.937207 (0.0342) -Negative precision: 0.961649 (0.0210) -Negative recall: 0.847646 (0.0887) -Log-loss: 0.367208 (0.0350) -Log-loss reduction: 0.603838 (0.0490) -F1 Score: 0.840619 (0.0590) -AUPRC: 0.962365 (0.0123) +AUC: 0.982598 (0.0018) +Accuracy: 0.914001 (0.0295) +Positive precision: 0.827025 (0.0860) +Positive recall: 0.960625 (0.0203) +Negative precision: 0.976218 (0.0133) +Negative recall: 0.892370 (0.0531) +Log-loss: 0.246970 (0.0155) +Log-loss reduction: 0.734766 (0.0090) +F1 Score: 0.885366 (0.0411) +AUPRC: 0.972638 (0.0101) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-rp.txt index 55c052227f..00dfdd4bd7 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.969378 0.875238 0.775071 0.937207 0.961649 0.847646 0.367208 0.603838 0.840619 0.962365 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 +0.982598 0.914001 0.827025 0.960625 0.976218 0.89237 0.24697 0.734766 0.885366 0.972638 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer.txt index 5638d5909d..87f4bbc2ab 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.8534284 0.9966877 0.00478655555839757 1 -6 0 1.9441669 0.9089837 3.4577313685410616 1 -8 0 -1.3064579 0.029403893 0.04305702174461324 0 -9 0 -0.92505693 0.056439757 0.083813463651964668 0 -10 0 -0.75773776 0.07460297 0.11185562463682144 0 -11 0 -0.9906934 0.050519254 0.074789349927547044 0 -18 1 2.413366 0.95844084 0.061238710589369388 1 -20 1 1.568261 0.83627826 0.25794503634600341 1 -21 1 1.7287251 0.8718046 0.19792328720715538 1 -25 1 2.2633896 0.9463779 0.079511751428780225 1 -28 0 -0.9906934 0.050519254 0.074789349927547044 0 -31 0 -1.2457733 0.032655496 0.04789832154160352 0 -32 1 -0.24883446 0.16654387 2.5860258312595557 0 -35 0 -0.9906934 0.050519254 0.074789349927547044 0 -37 0 -2.2412913 0.0056849807 0.0082250953708129255 0 +5 1 3.4806793 0.9974079 0.0037444458239151209 1 +6 0 1.6657805 0.9159839 3.5731906350538307 1 +8 0 -2.0404513 0.0074762525 0.010826473754901748 0 +9 0 -1.1990678 0.03781906 0.055619875908389405 0 +10 0 -0.99956495 0.05495863 0.081550609492380702 0 +11 0 -1.291973 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0.0058385146185466067 0 +675 0 -0.24743536 0.016386613 0.023836725074621097 0 +676 0 -0.6909238 0.004859244 0.007027495218564289 0 +677 0 -0.53125983 0.007538426 0.010916849925431595 0 +684 0 -0.6033769 0.006183063 0.0089479660528151827 0 +686 0 -0.6033769 0.006183063 0.0089479660528151827 0 +687 0 -0.44247046 0.009617808 0.013942721511381454 0 +690 0 -0.5071147 0.008055119 0.011668137472837622 0 +695 0 -0.60704285 0.0061210385 0.008857929435335957 0 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-out.txt index 637b1e19ce..017b8f5a34 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -8,28 +8,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 220 | 19 | 0.9205 - negative || 25 | 419 | 0.9437 + positive || 216 | 23 | 0.9038 + negative || 10 | 434 | 0.9775 ||====================== -Precision || 0.8980 | 0.9566 | -OVERALL 0/1 ACCURACY: 0.935578 -LOG LOSS/instance: 0.315898 +Precision || 0.9558 | 0.9497 | +OVERALL 0/1 ACCURACY: 0.951684 +LOG LOSS/instance: 0.251640 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.661780 -AUC: 0.963945 +LOG-LOSS REDUCTION (RIG): 0.730579 +AUC: 0.969024 OVERALL RESULTS --------------------------------------- -AUC: 0.963945 (0.0000) -Accuracy: 0.935578 (0.0000) -Positive precision: 0.897959 (0.0000) -Positive recall: 0.920502 (0.0000) -Negative precision: 0.956621 (0.0000) -Negative recall: 0.943694 (0.0000) -Log-loss: 0.315898 (0.0000) -Log-loss reduction: 0.661780 (0.0000) -F1 Score: 0.909091 (0.0000) -AUPRC: 0.951812 (0.0000) +AUC: 0.969024 (0.0000) +Accuracy: 0.951684 (0.0000) +Positive precision: 0.955752 (0.0000) +Positive recall: 0.903766 (0.0000) +Negative precision: 0.949672 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.251640 (0.0000) +Log-loss reduction: 0.730579 (0.0000) +F1 Score: 0.929032 (0.0000) +AUPRC: 0.965033 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-rp.txt index 8f8e4e2508..c9e5f4b580 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.963945 0.935578 0.897959 0.920502 0.956621 0.943694 0.315898 0.66178 0.909091 0.951812 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 +0.969024 0.951684 0.955752 0.903766 0.949672 0.977477 0.25164 0.730579 0.929032 0.965033 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer.txt index 50ffce0106..b78d5de043 100644 --- a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -1.9210272 0.009717667 0.014088192920503215 0 -1 0 1.5968752 0.8927986 3.2216043827830356 1 -2 0 -1.2856477 0.032107096 0.047080670997454895 0 -3 0 2.7900536 0.9879544 6.3753473131417593 1 -4 0 -1.6028564 0.017738463 0.025820887678307834 0 -5 1 3.5799701 0.9973251 0.0038642031538066459 1 -6 0 1.7048188 0.91105425 3.4909305754333388 1 -7 0 -1.3287921 0.029634012 0.043399110868457463 0 -8 0 -1.5092304 0.021151947 0.030843167633737175 0 -9 0 -1.1032926 0.044938717 0.066334786043110966 0 -10 0 -0.80262655 0.0772644 0.11601077400748538 0 -11 0 -1.1343119 0.042454563 0.062587148254938621 0 -12 1 0.52196103 0.5147654 0.95801305991064523 1 -13 0 -0.65906256 0.09931112 0.15089924629648419 0 -14 1 2.0290787 0.95017695 0.073731879050449603 1 -15 1 -1.232694 0.035415977 4.8194558383867481 0 -16 0 -1.46132 0.023139516 0.033775564687304624 0 -17 0 -1.7614868 0.013148509 0.019095101663794155 0 -18 1 2.5355208 0.9805259 0.028372340077133067 1 -19 0 -2.0753126 0.007247647 0.010494219405683425 0 -20 1 1.3964187 0.85009974 0.23429597194071752 1 -21 1 1.3561343 0.83999085 0.25155447483025511 1 -22 0 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-2.261975 0.0077937013 0.011287979078845841 0 +20 1 0.9822598 0.88645047 0.17388807223703645 1 +21 1 1.4631535 0.9559766 0.064952781769457887 1 +22 0 -1.7020811 0.025196556 0.036816746749580007 0 23 1 ? ? ? 0 -24 0 -1.2505093 0.034267645 0.050304682317859895 0 -25 1 1.905938 0.9377377 0.092743655124549157 1 -26 0 -0.53328854 0.12305661 0.18944438539766473 0 -27 0 -1.616954 0.017273678 0.025138395680476627 0 -28 0 -1.1343119 0.042454563 0.062587148254938621 0 -29 0 -0.49451151 0.13130563 0.2030794156871249 0 -30 0 -0.8590751 0.069893956 0.10453288386536097 0 -31 0 -1.4261553 0.024713239 0.036101621382306076 0 -32 1 -0.12503237 0.2348392 2.0902548417650344 0 -33 0 -1.3707117 0.027408555 0.040094193550119166 0 -34 0 -1.1915295 0.03821279 0.056210355363241475 0 -35 0 -1.1343119 0.042454563 0.062587148254938621 0 -36 1 1.6662233 0.90487367 0.14421170558259205 1 -37 0 -2.4657376 0.0034420658 0.0049744173016865612 0 -38 1 3.3093688 0.99551463 0.0064855752173072238 1 -39 1 1.1984813 0.7950998 0.3307921470429056 1 +24 0 -1.6619154 0.027382618 0.040055719878614657 0 +25 1 1.0788547 0.9055504 0.14313312343145604 1 +26 0 -1.0241482 0.098559596 0.14969597891777689 0 +27 0 -1.9310751 0.015632035 0.022730387306330355 0 +28 0 -1.5691804 0.033156335 0.0486454652406801 0 +29 0 -1.1701434 0.0741986 0.11122535626440654 0 +30 0 -1.282179 0.059398387 0.088344288098015269 0 +31 0 -1.8063252 0.02028676 0.029568556729297705 0 +32 1 0.37225828 0.680768 0.55476484365918155 1 +33 0 -1.8774829 0.017486785 0.025451282270853446 0 +34 0 -1.6551952 0.027765948 0.040624430703498714 0 +35 0 -1.5691804 0.033156335 0.0486454652406801 0 +36 1 1.6817615 0.9718886 0.0411371338030391 1 +37 0 -2.2746136 0.007588516 0.010989664948068162 0 +38 1 2.190717 0.9902991 0.014063758527318863 1 +39 1 0.58784926 0.7713482 0.37454587048651583 1 40 0 ? ? ? 0 -41 1 -0.5083777 0.12830329 2.9623699364656852 0 -42 1 2.8106928 0.9884162 0.016809445882683193 1 -43 1 -2.5014713 0.0032149102 8.2810058620236937 0 -44 1 1.118996 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b/test/BaselineOutput/Common/LogisticRegression/linux-arm64/LogisticRegression-non-negative-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 12.634016 0.9999967 4.7295306510363196E-06 1 -6 0 -1.3840265 0.2003631 0.32258304597672599 0 -8 0 -4.72443 0.008797685 0.01274853746288432 0 -9 0 -5.1366463 0.005843026 0.0084544286227711037 0 -10 0 -5.6093 0.0036502592 0.0052758458554607087 0 -11 0 -5.8696203 0.002815993 0.0040683501047807172 0 -18 1 7.267023 0.9993023 0.001006879741116309 1 -20 1 7.564518 0.99948174 0.00074788839258190052 1 -21 1 7.3895073 0.9993827 0.0008908871693913803 1 -25 1 1.3487854 0.793931 0.33291445228970418 1 -28 0 -5.8696203 0.002815993 0.0040683501047807172 0 -31 0 -5.1809826 0.0055910437 0.0080888046329900768 0 -32 1 7.291664 0.99931926 0.00098244136155410146 1 -35 0 -5.8696203 0.002815993 0.0040683501047807172 0 -37 0 -1.0239582 0.2642571 0.44272638610457016 0 +5 1 12.634104 0.9999967 4.7295306510363196E-06 1 +6 0 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a/test/BaselineOutput/Common/LogisticRegression/netcoreapp/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/netcoreapp/LogisticRegression-non-negative-CV-breast-cancer.txt index 156a695123..d2fa8eb18a 100644 --- a/test/BaselineOutput/Common/LogisticRegression/netcoreapp/LogisticRegression-non-negative-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LogisticRegression/netcoreapp/LogisticRegression-non-negative-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 12.634052 0.9999967 4.7295306510363196E-06 1 -6 0 -1.3831959 0.20049621 0.32282322512958106 0 -8 0 -4.7247458 0.008794933 0.012744531847526374 0 -9 0 -5.136714 0.005842633 0.0084538582850831219 0 -10 0 -5.6094384 0.0036497563 0.0052751176456810279 0 -11 0 -5.869679 0.0028158284 0.004068111950303187 0 -18 1 7.2676487 0.99930274 0.0010062773817144412 1 -20 1 7.5638084 0.9994814 0.00074840460816191399 1 -21 1 7.3895473 0.99938273 0.00089080112495110294 1 -25 1 1.3491688 0.7939937 0.33280051379712122 1 -28 0 -5.869679 0.0028158284 0.004068111950303187 0 -31 0 -5.1811585 0.0055900654 0.0080873852307543487 0 -32 1 7.2914295 0.99931914 0.00098261346137125155 1 -35 0 -5.869679 0.0028158284 0.004068111950303187 0 -37 0 -1.0245552 0.26414105 0.44249884478738094 0 +5 1 12.6340475 0.9999967 4.7295306510363196E-06 1 +6 0 -1.383316 0.20047696 0.32278848500188356 0 +8 0 -4.7247133 0.008795215 0.012744942575086885 0 +9 0 -5.1366987 0.0058427216 0.0084539866786188071 0 +10 0 -5.60942 0.003649824 0.0052752157516993657 0 +11 0 -5.8696694 0.0028158552 0.0040681506882989755 0 +18 1 7.267564 0.9993027 0.0010063634330421671 1 +20 1 7.56392 0.99948144 0.00074831857221908471 1 +21 1 7.389552 0.99938273 0.00089080112495110294 1 +25 1 1.3491125 0.7939845 0.33281730074743887 1 +28 0 -5.8696694 0.0028158552 0.0040681506882989755 0 +31 0 -5.1811304 0.005590222 0.0080876122269247976 0 +32 1 7.2914686 0.99931914 0.00098261346137125155 1 +35 0 -5.8696694 0.0028158552 0.0040681506882989755 0 +37 0 -1.024457 0.26416016 0.44253629840217606 0 40 0 ? ? ? 0 -41 1 2.7845802 0.94183683 0.086450949311470543 1 -44 1 7.8202686 0.9995986 0.00057918182856106851 1 -45 0 -5.7772603 0.0030876263 0.0044613942665847131 0 -46 1 4.3640785 0.98743355 0.018244426563395755 1 -48 0 -3.8105168 0.021657312 0.031588203058819132 0 -50 1 2.3512 0.91302955 0.13126653903574156 1 -51 1 -0.38567162 0.4047597 1.3048624219352793 0 -52 1 4.762245 0.991526 0.012277480154992908 1 -54 1 7.3692417 0.99937004 0.00090912870659733396 1 -56 1 4.515278 0.9891778 0.015698199387500632 1 -60 1 2.0932302 0.8902435 0.16772814544847323 1 -63 1 -0.43255043 0.39351746 1.345500430582274 0 -64 0 -6.235624 0.0019545793 0.0028226212236700603 0 -66 0 -4.449269 0.011552098 0.016763167178581874 0 -68 1 12.420467 0.99999595 5.8474219794828371E-06 1 -69 0 -5.3731294 0.004618163 0.0066780331962603507 0 -70 0 -3.9443603 0.018995773 0.027668742142448771 0 -71 1 8.000187 0.9996647 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+691 1 2.8413935 0.9448721 0.081809067347296954 1 +692 0 -5.9340563 0.0026407284 0.0038148048797132152 0 +693 0 -5.130742 0.005877423 0.0085043455317272768 0 +694 0 -5.4755497 0.00417046 0.006029283392418496 0 +695 0 -6.470736 0.001545693 0.0022316888622042515 0 +696 1 5.102537 0.99395543 0.0087469287128904821 1 +697 1 3.3553085 0.96627825 0.049489399322663288 1 +698 1 4.276967 0.9863054 0.019893639574599043 1 diff --git a/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-CV-breast-cancer.txt index 38ba3a740f..efb86df100 100644 --- a/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 12.634016 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0.031553435554311934 0 +667 0 -5.231364 0.0053177993 0.0076924342384671104 0 +669 1 6.017091 0.9975692 0.0035111674317070724 1 +671 0 -4.909486 0.007322269 0.010602666547192814 0 +672 0 -5.867675 0.0028214615 0.004076261773822842 0 +673 0 -4.2514973 0.014042881 0.020403192715518718 0 +674 0 -6.853299 0.0010548523 0.0015226333560959162 0 +675 0 -4.269416 0.013796935 0.020043358111662083 0 +676 0 -6.511878 0.001483483 0.0021418026346892508 0 +677 0 -4.657368 0.009402169 0.013628632693424379 0 +684 0 -7.202613 0.00074408355 0.0010738852230407912 0 +686 0 -7.202613 0.00074408355 0.0010738852230407912 0 +687 0 -5.1562004 0.005730529 0.0082911847451536194 0 +690 0 -6.6233063 0.0013272655 0.0019161112588203986 0 +695 0 -6.5663013 0.0014050149 0.0020284333366327612 0 diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer.txt index 38ba3a740f..efb86df100 100644 --- a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 12.634016 0.9999967 4.7295306510363196E-06 1 -6 0 -1.3840265 0.2003631 0.32258304597672599 0 -8 0 -4.72443 0.008797685 0.01274853746288432 0 -9 0 -5.1366463 0.005843026 0.0084544286227711037 0 -10 0 -5.6093 0.0036502592 0.0052758458554607087 0 -11 0 -5.8696203 0.002815993 0.0040683501047807172 0 -18 1 7.267023 0.9993023 0.001006879741116309 1 -20 1 7.564518 0.99948174 0.00074788839258190052 1 -21 1 7.3895073 0.9993827 0.0008908871693913803 1 -25 1 1.3487854 0.793931 0.33291445228970418 1 -28 0 -5.8696203 0.002815993 0.0040683501047807172 0 -31 0 -5.1809826 0.0055910437 0.0080888046329900768 0 -32 1 7.291664 0.99931926 0.00098244136155410146 1 -35 0 -5.8696203 0.002815993 0.0040683501047807172 0 -37 0 -1.0239582 0.2642571 0.44272638610457016 0 +5 1 12.634104 0.9999967 4.7295306510363196E-06 1 +6 0 -1.3828411 0.20055309 0.32292586396939055 0 +8 0 -4.7249002 0.008793586 0.01274257174335309 0 +9 0 -5.1367483 0.0058424333 0.0084535683860366782 0 +10 0 -5.609509 0.0036494997 0.0052747461239813281 0 +11 0 -5.8697157 0.0028157253 0.0040679627248160844 0 +18 1 7.2679424 0.9993029 0.0010060192277620589 1 +20 1 7.563551 0.99948126 0.00074857668006296475 1 +21 1 7.3895855 0.99938273 0.00089080112495110294 1 +25 1 1.3493443 0.7940224 0.33274842134324101 1 +28 0 -5.8697157 0.0028157253 0.0040679627248160844 0 +31 0 -5.181243 0.0055895965 0.0080867049180407633 0 +32 1 7.291338 0.9993191 0.00098269951128752541 1 +35 0 -5.8697157 0.0028157253 0.0040679627248160844 0 +37 0 -1.0248013 0.26409322 0.44240506894055126 0 40 0 ? ? ? 0 -41 1 2.784809 0.9418494 0.086431684776605053 1 -44 1 7.8207245 0.9995988 0.00057892375102128462 1 -45 0 -5.7771015 0.003088115 0.0044621015121553741 0 -46 1 4.363323 0.9874242 0.018258099080425954 1 -48 0 -3.8101325 0.021665458 0.031600214495233307 0 -50 1 2.3506079 0.9129825 0.1313408508726798 1 -51 1 -0.38570404 0.4047519 1.3048902531945354 0 -52 1 4.7617655 0.991522 0.012283290824972246 1 -54 1 7.3694706 0.9993702 0.00090887057002802098 1 -56 1 4.514043 0.9891646 0.015717498447270977 1 -60 1 2.0933666 0.89025676 0.16770660536078968 1 -63 1 -0.43266487 0.39349017 1.345600516084799 0 -64 0 -6.2355585 0.0019547066 0.0028228053227955028 0 -66 0 -4.4491057 0.011553961 0.016765885815999977 0 -68 1 12.42049 0.99999595 5.8474219794828371E-06 1 -69 0 -5.372943 0.0046190205 0.0066792757321367446 0 -70 0 -3.944561 0.018992033 0.027663241712138437 0 -71 1 8.002766 0.99966556 0.00048257802859336444 1 -72 0 -1.4694653 0.18702391 0.29871516855965657 0 -73 1 8.232639 0.9997342 0.00038348628329455832 1 -74 1 1.5337801 0.8225587 0.28180945594824713 1 -76 0 -4.994693 0.006728226 0.0097395799853625745 0 -77 0 -4.450124 0.01154234 0.016748924320874031 0 -79 0 -5.807241 0.0029967043 0.0043298212867696499 0 -82 0 -4.08207 0.016592547 0.024138806297648339 0 -88 0 -4.4491057 0.011553961 0.016765885815999977 0 -90 0 -5.546921 0.0038842994 0.0056147713181484132 0 -91 0 -5.8728333 0.0028069853 0.0040553180261770671 0 -92 0 -4.4491057 0.011553961 0.016765885815999977 0 -93 0 -6.2355585 0.0019547066 0.0028228053227955028 0 -95 0 -5.546921 0.0038842994 0.0056147713181484132 0 -96 0 -6.238772 0.0019484477 0.0028137579050843478 0 -97 0 -4.083167 0.016574655 0.024112557476670533 0 -98 1 8.655586 0.9998259 0.00025120253004900898 1 -99 1 11.468365 0.99998957 1.5048560434990871E-05 1 -100 1 3.7766733 0.97761387 0.032663346614426464 1 -102 0 -4.0354834 0.01737008 0.025279927497574458 0 -104 1 12.723648 0.999997 4.2995726784359523E-06 1 -105 1 2.7624168 0.94061077 0.088330249233447317 1 -106 1 8.566938 0.99980974 0.00027451042551103868 1 -108 0 -5.8876033 0.0027659447 0.0039959435227908723 0 -109 1 7.138527 0.9992067 0.0011449127273942801 1 -111 1 2.7874622 0.94199455 0.086209385178054376 1 -112 1 9.633659 0.9999345 9.4507562065679747E-05 1 -113 1 9.298004 0.9999084 0.00013217472169822292 1 -115 0 -5.0740213 0.006218298 0.0089991161843039711 0 -117 1 11.032467 0.99998385 2.3303837404214046E-05 1 -120 0 -4.8138123 0.008051504 0.011662880569639303 0 -121 0 -3.9854646 0.018244753 0.026564690979039515 0 -122 1 9.10256 0.99988866 0.00016064073906035849 1 -123 1 3.8359165 0.9788744 0.030804357887786005 1 -125 0 -6.2355585 0.0019547066 0.0028228053227955028 0 -128 1 3.930893 0.98075163 0.028040261476875789 1 -129 0 -6.4251633 0.0016176477 0.0023356619743196646 0 -131 0 -5.1809826 0.0055910437 0.0080888046329900768 0 -132 1 8.021416 0.99967176 0.00047363196654040616 1 -133 0 -5.3297043 0.004822137 0.0069737005123077563 0 -137 0 -6.19232 0.002040903 0.0029474094708446014 0 -138 0 -4.7241216 0.008800375 0.012752453623473303 0 -141 0 -6.5582585 0.0014163447 0.0020448018331478031 0 -144 0 -5.8696203 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0.0019161112588203986 0 +695 0 -6.5663013 0.0014050149 0.0020284333366327612 0 diff --git a/test/BaselineOutput/Common/MulticlassLogisticRegression/linux-arm/LogisticRegression-Non-Negative-TrainTest-iris-out.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/linux-arm/LogisticRegression-Non-Negative-TrainTest-iris-out.txt new file mode 100644 index 0000000000..327ee94382 --- /dev/null +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/linux-arm/LogisticRegression-Non-Negative-TrainTest-iris-out.txt @@ -0,0 +1,68 @@ +maml.exe TrainTest test=%Data% tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Not adding a normalizer. +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 13 of 15 weights. +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 48 | 2 | 0.9600 + 2 || 0 | 1 | 49 | 0.9800 + ||======================== +Precision ||1.0000 |0.9796 |0.9608 | +Accuracy(micro-avg): 0.980000 +Accuracy(macro-avg): 0.980000 +Log-loss: 0.095473 +Log-loss reduction: 0.913096 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.980000 (0.0000) +Accuracy(macro-avg): 0.980000 (0.0000) +Log-loss: 0.095473 (0.0000) +Log-loss reduction: 0.913096 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Building term dictionary' started. +[1] (%Time%) 150 examples Total Terms: 3 +[1] 'Building term dictionary' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 1.0986123085021973 +[3] (%Time%) 1 iterations Loss: 1.0638996362686157 Improvement: 0.03471 +[3] (%Time%) 2 iterations Loss: 1.0165412425994873 Improvement: 0.04483 +[3] (%Time%) 3 iterations Loss: 0.9443140625953674 Improvement: 0.0657 +[3] (%Time%) 4 iterations Loss: 0.6682092547416687 Improvement: 0.2241 +[3] (%Time%) 5 iterations Loss: 0.5532791018486023 Improvement: 0.1421 +[3] (%Time%) 6 iterations Loss: 0.42720934748649597 Improvement: 0.1301 +[3] (%Time%) 7 iterations Loss: 0.33543163537979126 Improvement: 0.1014 +[3] (%Time%) 8 iterations Loss: 0.27138790488243103 Improvement: 0.07337 +[3] (%Time%) 9 iterations Loss: 0.21875371038913727 Improvement: 0.05782 +[3] (%Time%) 10 iterations Loss: 0.1928301453590393 Improvement: 0.0339 +[3] (%Time%) 11 iterations Loss: 0.18482188880443573 Improvement: 0.01448 +[3] (%Time%) 12 iterations Loss: 0.1825772225856781 Improvement: 0.005304 +[3] (%Time%) 13 iterations Loss: 0.18094274401664734 Improvement: 0.002552 +[3] (%Time%) 14 iterations Loss: 0.1789129674434662 Improvement: 0.00216 +[3] (%Time%) 15 iterations Loss: 0.17135457694530487 Improvement: 0.006209 +[3] (%Time%) 16 iterations Loss: 0.1576078236103058 Improvement: 0.01186 +[3] (%Time%) 17 iterations Loss: 0.15354663133621216 Improvement: 0.006011 +[3] (%Time%) 18 iterations Loss: 0.15137210488319397 Improvement: 0.003134 +[3] (%Time%) 19 iterations Loss: 0.1468994915485382 Improvement: 0.004138 +[3] (%Time%) 20 iterations Loss: 0.1437908560037613 Improvement: 0.003366 +[3] (%Time%) 21 iterations Loss: 0.14152587950229645 Improvement: 0.00254 +[3] (%Time%) 22 iterations Loss: 0.1407100260257721 Improvement: 0.001247 +[3] (%Time%) 23 iterations Loss: 0.14007723331451416 Improvement: 0.0007863 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Saving model' started. +[4] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/MulticlassLogisticRegression/linux-arm/LogisticRegression-Non-Negative-TrainTest-iris.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/linux-arm/LogisticRegression-Non-Negative-TrainTest-iris.txt new file mode 100644 index 0000000000..29213c1449 --- /dev/null +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/linux-arm/LogisticRegression-Non-Negative-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.0055160384679411277 0.99449915 0.005502038 5.1773974E-11 0 1 2 +1 0 0 0.023864730958411882 0.9764178 0.023581393 1.0470911E-09 0 1 2 +2 0 0 0.0093345440359657913 0.9907089 0.009291213 3.3886932E-10 0 1 2 +3 0 0 0.023485169105185572 0.97678846 0.023210973 2.9965708E-09 0 1 2 +4 0 0 0.0040397473706441957 0.9959684 0.0040320004 4.1783424E-11 0 1 2 +5 0 0 0.0045280297264245848 0.9954822 0.004518237 6.792683E-11 0 1 2 +6 0 0 0.0069865892171306161 0.99303776 0.006961399 5.4606175E-10 0 1 2 +7 0 0 0.010116555796003303 0.98993444 0.010064013 2.2703014E-10 0 1 2 +8 0 0 0.030149466155855287 0.9703005 0.029698554 6.8450254E-09 0 1 2 +9 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 +10 0 0 0.0043411169825802809 0.9956683 0.0043306584 1.7070477E-11 0 1 2 +11 0 0 0.013576521322325076 0.9865152 0.013485671 8.031634E-10 0 1 2 +12 0 0 0.023572921206410413 0.97670275 0.023298172 8.6137114E-10 0 1 2 +13 0 0 0.0085086580176359974 0.99152744 0.008472632 4.1598563E-10 0 1 2 +14 0 0 0.0007095469417312554 0.9992907 0.00070933235 1.5176845E-13 0 1 2 +15 0 0 0.00055310522407816078 0.99944705 0.00055347907 6.048726E-13 0 1 2 +16 0 0 0.0012550477831004697 0.99874574 0.0012540518 3.9664222E-12 0 1 2 +17 0 0 0.0055160384679411277 0.99449915 0.0055009676 8.31305E-11 0 1 2 +18 0 0 0.0063400554390819954 0.99368 0.0063216076 3.0053654E-11 0 1 2 +19 0 0 0.0030975582921822227 0.99690723 0.0030934948 3.8560665E-11 0 1 2 +20 0 0 0.020122528904036908 0.9800786 0.019920269 3.064467E-10 0 1 2 +21 0 0 0.0041771031887722987 0.9958316 0.0041694827 1.0134493E-10 0 1 2 +22 0 0 0.0010643302939017342 0.99893624 0.0010639144 7.4156245E-12 0 1 2 +23 0 0 0.02605629788130279 0.97428024 0.025720494 4.7576445E-09 0 1 2 +24 0 0 0.035209627128022683 0.965403 0.03459665 6.6329586E-09 0 1 2 +25 0 0 0.045448274212285186 0.955569 0.04443145 3.216728E-09 0 1 2 +26 0 0 0.013916015058125409 0.98618037 0.013818858 1.1871322E-09 0 1 2 +27 0 0 0.0076942651707202743 0.99233526 0.0076657226 7.9652826E-11 0 1 2 +28 0 0 0.007534024516302436 0.9924943 0.0075039454 6.411831E-11 0 1 2 +29 0 0 0.024288162137741393 0.9760044 0.02399573 2.8173113E-09 0 1 2 +30 0 0 0.033046722035427502 0.96749336 0.03250687 3.46561E-09 0 1 2 +31 0 0 0.010627754848336371 0.9894285 0.010573362 1.9241796E-10 0 1 2 +32 0 0 0.0012779052922244011 0.9987229 0.0012773476 2.5791548E-12 0 1 2 +33 0 0 0.00071336435395046255 0.9992869 0.0007130818 5.394398E-13 0 1 2 +34 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 +35 0 0 0.0070380898792076199 0.9929866 0.007013349 7.24567E-11 0 1 2 +36 0 0 0.0042114001992704566 0.99579746 0.004201503 8.375258E-12 0 1 2 +37 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 +38 0 0 0.016315454412074732 0.9838169 0.01618225 2.0803645E-09 0 1 2 +39 0 0 0.010242464293569139 0.9898098 0.010190028 1.7190988E-10 0 1 2 +40 0 0 0.0039520171010716818 0.9960558 0.003945863 5.4001064E-11 0 1 2 +41 0 0 0.12730980592945779 0.88046086 0.119539626 7.181209E-08 0 1 2 +42 0 0 0.0089912491214826969 0.99104905 0.0089505045 7.80488E-10 0 1 2 +43 0 0 0.010326412100003242 0.9897267 0.010272516 1.874473E-09 0 1 2 +44 0 0 0.011150365797478341 0.98891157 0.011090281 1.0552612E-09 0 1 2 +45 0 0 0.02356529293651816 0.9767102 0.023289332 2.2207163E-09 0 1 2 +46 0 0 0.0042686243571466928 0.9957405 0.0042613307 4.8837566E-11 0 1 2 +47 0 0 0.012689595644547294 0.9873906 0.012608856 9.079638E-10 0 1 2 +48 0 0 0.0042877197577386979 0.99572146 0.0042767935 2.2542229E-11 0 1 2 +49 0 0 0.0099010247223913338 0.9901478 0.009852205 1.827634E-10 0 1 2 +50 1 1 0.0090408081662831513 0.99099994 0.007567417 0.0014314867 1 2 0 +51 1 1 0.034511583658046456 0.96607715 0.031063149 0.002861517 1 2 0 +52 1 1 0.04304121150554157 0.9578719 0.04158025 0.00054618425 1 2 0 +53 1 1 0.13135529742102009 0.87690616 0.122118875 0.0009759098 1 2 0 +54 1 1 0.074932084511623434 0.9278065 0.07160018 0.00059335935 1 2 0 +55 1 1 0.1890525667021884 0.827743 0.1714483 0.00080681086 1 2 0 +56 1 1 0.11877056857344356 0.8880115 0.1100965 0.0018910082 1 2 0 +57 1 1 0.025358193239084965 0.9749606 0.01495395 0.010085882 1 0 2 +58 1 1 0.019096356683429662 0.9810848 0.01808086 0.0008358488 1 2 0 +59 1 1 0.15862658959532094 0.85331494 0.14217468 0.0045111584 1 2 0 +60 1 1 0.038318653357595697 0.9624062 0.035282373 0.0023114693 1 2 0 +61 1 1 0.065311423102221738 0.9367757 0.058971565 0.0042529916 1 2 0 +62 1 1 0.010269141347627856 0.9897834 0.009452066 0.0007660691 1 2 0 +63 1 1 0.17183304995881135 0.84211975 0.1573262 0.00055418257 1 2 0 +64 1 1 0.030368791940673646 0.9700877 0.023280893 0.0066331304 1 0 2 +65 1 1 0.00975415306436043 0.99029326 0.0068194265 0.0028855118 1 2 0 +66 1 1 0.39629741859747109 0.67280656 0.32598528 0.0012099475 1 2 0 +67 1 1 0.012058247747154605 0.98801416 0.009438771 0.0025457656 1 2 0 +68 1 1 0.3368797650804119 0.7139947 0.28589886 0.00010822646 1 2 0 +69 1 1 0.021083825783822494 0.9791369 0.018163126 0.0027012476 1 2 0 +70 1 2 1.0416832150942417 0.6467134 0.35286024 0.00042478985 2 1 0 +71 1 1 0.013755801712349471 0.9863384 0.00910499 0.0045568454 1 2 0 +72 1 1 0.58514020696109537 0.55702776 0.44291526 5.669436E-05 1 2 0 +73 1 1 0.084747339774486161 0.9187444 0.080806226 0.00044783644 1 2 0 +74 1 1 0.012447376934475682 0.9876298 0.010109726 0.0022619804 1 2 0 +75 1 1 0.013278939705380893 0.98680884 0.011033021 0.0021575652 1 2 0 +76 1 1 0.04319766018798589 0.95772207 0.041966196 0.0003101741 1 2 0 +77 1 1 0.30690002516055565 0.73572415 0.2640443 0.00023127515 1 2 0 +78 1 1 0.16997907799864528 0.84368247 0.1552497 0.0010691443 1 2 0 +79 1 1 0.014675126081593009 0.985432 0.013093817 0.0014749394 1 0 2 +80 1 1 0.022987361370576598 0.97727484 0.01993399 0.00278951 1 2 0 +81 1 1 0.012458783376888797 0.9876185 0.008495432 0.003886289 1 2 0 +82 1 1 0.016071326268181901 0.9840571 0.011121038 0.004822609 1 2 0 +83 1 2 1.707477571538714 0.8186583 0.18132259 1.8361572E-05 2 1 0 +84 1 1 0.62495808673272679 0.53528386 0.4637305 0.0009871349 1 2 0 +85 1 1 0.11122891687722759 0.8947339 0.1001901 0.0050765327 1 2 0 +86 1 1 0.036052681021758022 0.9645895 0.03433999 0.0010723303 1 2 0 +87 1 1 0.063015011349415673 0.9389294 0.060807556 0.00026142705 1 2 0 +88 1 1 0.044994341810109487 0.9560029 0.037784368 0.0062121437 1 2 0 +89 1 1 0.092109667787323657 0.9120051 0.086147815 0.0018485248 1 2 0 +90 1 1 0.20783613448356417 0.81234014 0.18704556 0.0006146012 1 2 0 +91 1 1 0.10023498504355982 0.9046248 0.09426649 0.0011077761 1 2 0 +92 1 1 0.022636421664178376 0.97761786 0.01980763 0.0025746985 1 2 0 +93 1 1 0.020406702516074512 0.9798001 0.0109967375 0.009203869 1 0 2 +94 1 1 0.10109711620080604 0.90384525 0.09442052 0.0017330045 1 2 0 +95 1 1 0.031333905261789932 0.9691519 0.02633722 0.0045093265 1 2 0 +96 1 1 0.054904961042088807 0.9465751 0.050160136 0.0032641925 1 2 0 +97 1 1 0.020442959924209147 0.9797646 0.017932491 0.0023010836 1 2 0 +98 1 1 0.053829215135703323 0.9475939 0.05015344 0.00225256 1 0 2 +99 1 1 0.046024295999239594 0.95501876 0.04161595 0.0033648298 1 2 0 +100 2 2 0.00071718178074236128 0.9992831 0.0007153486 2.3431886E-08 2 1 0 +101 2 2 0.029472378645200227 0.9709577 0.029038547 3.0171875E-06 2 1 0 +102 2 2 0.0393600403713796 0.9614045 0.038595207 6.408885E-07 2 1 0 +103 2 2 0.042228681999026033 0.9586505 0.04134742 1.4739417E-06 2 1 0 +104 2 2 0.0064353741478142112 0.9935853 0.006415478 1.5842724E-07 2 1 0 +105 2 2 0.011154163002271514 0.9889078 0.011091466 1.8252322E-08 2 1 0 +106 2 2 0.038921380516359183 0.9618263 0.038155746 1.6744794E-05 2 1 0 +107 2 2 0.050231903865451351 0.95100886 0.04899014 1.6252797E-07 2 1 0 +108 2 2 0.028682695065745022 0.97172475 0.028276091 1.5199748E-07 2 1 0 +109 2 2 0.011791212573983524 0.98827803 0.011722299 6.1125087E-07 2 1 0 +110 2 2 0.31905363806129555 0.72683656 0.27304813 0.000116325275 2 1 0 +111 2 2 0.07549667881010734 0.9272828 0.072712734 3.6851195E-06 2 1 0 +112 2 2 0.076789840257624864 0.92608446 0.07391078 4.6068785E-06 2 1 0 +113 2 2 0.013851165145911459 0.9862443 0.013756296 1.0960175E-06 2 1 0 +114 2 2 0.0033950556005307988 0.9966107 0.0033878458 4.755091E-07 2 1 0 +115 2 2 0.03064347778045929 0.9698213 0.030172396 6.850469E-06 2 1 0 +116 2 2 0.12950899319139736 0.8785267 0.121466115 7.857308E-06 2 1 0 +117 2 2 0.029441808148158395 0.9709874 0.029012194 3.762749E-07 2 1 0 +118 2 2 0.00083159205288146009 0.99916875 0.00083038886 1.5524602E-10 2 1 0 +119 2 2 0.18149944608911653 0.8340187 0.16597763 5.176206E-06 2 1 0 +120 2 2 0.027599360244525448 0.972778 0.027219418 1.605549E-06 2 1 0 +121 2 2 0.027206434952982018 0.9731603 0.026830971 7.3364695E-06 2 1 0 +122 2 2 0.011005419791721024 0.9890549 0.010943153 7.0800863E-09 2 1 0 +123 2 2 0.36999896999840359 0.69073504 0.30920705 5.735062E-05 2 1 0 +124 2 2 0.047847764215344722 0.9532789 0.046716593 3.8119588E-06 2 1 0 +125 2 2 0.20049289074676657 0.8183273 0.18166876 3.932513E-06 2 1 0 +126 2 2 0.47024913514888667 0.6248466 0.3750236 0.00013107271 2 1 0 +127 2 2 0.37044139869226145 0.6904295 0.30942416 0.00014464991 2 1 0 +128 2 2 0.011402157721508203 0.9886626 0.011337829 2.959167E-07 2 1 0 +129 2 2 0.61341094273731411 0.5415007 0.45849052 1.03566035E-05 2 1 0 +130 2 2 0.074195879113551133 0.9284898 0.07150847 3.3010758E-07 2 1 0 +131 2 2 0.36711882425272957 0.6927273 0.3072634 1.0183718E-05 2 1 0 +132 2 2 0.0071143853619847876 0.99291086 0.007090189 1.8508953E-07 2 1 0 +133 2 1 0.70938490731195092 0.507985 0.4919467 6.684E-05 1 2 0 +134 2 2 0.085842140845050208 0.9177391 0.082260855 1.222486E-06 2 1 0 +135 2 2 0.040000927528960964 0.96078855 0.039211515 3.1772186E-07 2 1 0 +136 2 2 0.0066146987210017709 0.99340713 0.0065912344 1.0530171E-06 2 1 0 +137 2 2 0.11827848787180953 0.8884486 0.11154141 9.8605E-06 2 1 0 +138 2 2 0.40225979543659157 0.66880697 0.3309761 0.00021604139 2 1 0 +139 2 2 0.17508696641630789 0.839384 0.1605988 1.8394523E-05 2 1 0 +140 2 2 0.011779813743593867 0.9882893 0.011711036 7.23893E-07 2 1 0 +141 2 2 0.21421052275020513 0.80717844 0.19276378 5.790216E-05 2 1 0 +142 2 2 0.029472378645200227 0.9709577 0.029038547 3.0171875E-06 2 1 0 +143 2 2 0.0095519990430713575 0.9904935 0.009506778 2.986748E-07 2 1 0 +144 2 2 0.0073394639370607329 0.9926874 0.0073135537 5.9723027E-07 2 1 0 +145 2 2 0.071865099775285421 0.93065643 0.06933218 1.1476711E-05 2 1 0 +146 2 2 0.12049862656713971 0.8864783 0.11351164 8.385402E-06 2 1 0 +147 2 2 0.15922545922324882 0.85280406 0.14717093 2.4967045E-05 2 1 0 +148 2 2 0.017231011314152864 0.9829166 0.017076416 5.251435E-06 2 1 0 +149 2 2 0.10884858930380203 0.8968662 0.103108585 2.5997891E-05 2 1 0 diff --git a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-out.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-out.txt new file mode 100644 index 0000000000..10721bd5ce --- /dev/null +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-out.txt @@ -0,0 +1,68 @@ +maml.exe TrainTest test=%Data% tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Not adding a normalizer. +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 48 | 2 | 0.9600 + 2 || 0 | 2 | 48 | 0.9600 + ||======================== +Precision ||1.0000 |0.9600 |0.9600 | +Accuracy(micro-avg): 0.973333 +Accuracy(macro-avg): 0.973333 +Log-loss: 0.099452 +Log-loss reduction: 0.909475 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.973333 (0.0000) +Accuracy(macro-avg): 0.973333 (0.0000) +Log-loss: 0.099452 (0.0000) +Log-loss reduction: 0.909475 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Building term dictionary' started. +[1] (%Time%) 150 examples Total Terms: 3 +[1] 'Building term dictionary' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 1.0986123085021973 +[3] (%Time%) 1 iterations Loss: 1.0638996362686157 Improvement: 0.03471 +[3] (%Time%) 2 iterations Loss: 1.0165412425994873 Improvement: 0.04483 +[3] (%Time%) 3 iterations Loss: 0.9443140625953674 Improvement: 0.0657 +[3] (%Time%) 4 iterations Loss: 0.6682096123695374 Improvement: 0.2241 +[3] (%Time%) 5 iterations Loss: 0.553279459476471 Improvement: 0.1421 +[3] (%Time%) 6 iterations Loss: 0.42720964550971985 Improvement: 0.1301 +[3] (%Time%) 7 iterations Loss: 0.3354315459728241 Improvement: 0.1014 +[3] (%Time%) 8 iterations Loss: 0.2713882327079773 Improvement: 0.07337 +[3] (%Time%) 9 iterations Loss: 0.21875490248203278 Improvement: 0.05782 +[3] (%Time%) 10 iterations Loss: 0.19283106923103333 Improvement: 0.0339 +[3] (%Time%) 11 iterations Loss: 0.1848226934671402 Improvement: 0.01448 +[3] (%Time%) 12 iterations Loss: 0.18257759511470795 Improvement: 0.005304 +[3] (%Time%) 13 iterations Loss: 0.18094150722026825 Improvement: 0.002553 +[3] (%Time%) 14 iterations Loss: 0.17891088128089905 Improvement: 0.002161 +[3] (%Time%) 15 iterations Loss: 0.17133614420890808 Improvement: 0.006221 +[3] (%Time%) 16 iterations Loss: 0.1575724482536316 Improvement: 0.01188 +[3] (%Time%) 17 iterations Loss: 0.15349344909191132 Improvement: 0.006029 +[3] (%Time%) 18 iterations Loss: 0.15131297707557678 Improvement: 0.003143 +[3] (%Time%) 19 iterations Loss: 0.14684413373470306 Improvement: 0.004137 +[3] (%Time%) 20 iterations Loss: 0.14376644790172577 Improvement: 0.003343 +[3] (%Time%) 21 iterations Loss: 0.14153197407722473 Improvement: 0.002512 +[3] (%Time%) 22 iterations Loss: 0.1407090276479721 Improvement: 0.001245 +[3] (%Time%) 23 iterations Loss: 0.14069536328315735 Improvement: 0.0003215 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Saving model' started. +[4] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..c07eb23a28 --- /dev/null +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +MulticlassLogisticRegression +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /l2 /l1 /ot /nt /nn Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.973333 0.973333 0.099452 0.909475 0.1 0.001 0.001 1 + MulticlassLogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /l2:0.1;/l1:0.001;/ot:0.001;/nt:1;/nn:+ + diff --git a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris.txt index ebaccc4d62..d44cac07f2 100644 --- a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.0055160384679411277 0.99449915 0.005502038 5.1773974E-11 0 1 2 -1 0 0 0.023864730958411882 0.9764178 0.023581393 1.0470911E-09 0 1 2 -2 0 0 0.0093345440359657913 0.9907089 0.009291213 3.3886932E-10 0 1 2 -3 0 0 0.023485169105185572 0.97678846 0.023210973 2.9965708E-09 0 1 2 -4 0 0 0.0040397473706441957 0.9959684 0.0040320004 4.1783424E-11 0 1 2 -5 0 0 0.0045280297264245848 0.9954822 0.004518237 6.792683E-11 0 1 2 -6 0 0 0.0069865892171306161 0.99303776 0.006961399 5.4606175E-10 0 1 2 -7 0 0 0.010116555796003303 0.98993444 0.010064013 2.2703014E-10 0 1 2 -8 0 0 0.030149466155855287 0.9703005 0.029698554 6.8450254E-09 0 1 2 -9 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 -10 0 0 0.0043411169825802809 0.9956683 0.0043306584 1.7070477E-11 0 1 2 -11 0 0 0.013576521322325076 0.9865152 0.013485671 8.031634E-10 0 1 2 -12 0 0 0.023572921206410413 0.97670275 0.023298172 8.6137114E-10 0 1 2 -13 0 0 0.0085086580176359974 0.99152744 0.008472632 4.1598563E-10 0 1 2 -14 0 0 0.0007095469417312554 0.9992907 0.00070933235 1.5176845E-13 0 1 2 -15 0 0 0.00055310522407816078 0.99944705 0.00055347907 6.048726E-13 0 1 2 -16 0 0 0.0012550477831004697 0.99874574 0.0012540518 3.9664222E-12 0 1 2 -17 0 0 0.0055160384679411277 0.99449915 0.0055009676 8.31305E-11 0 1 2 -18 0 0 0.0063400554390819954 0.99368 0.0063216076 3.0053654E-11 0 1 2 -19 0 0 0.0030975582921822227 0.99690723 0.0030934948 3.8560665E-11 0 1 2 -20 0 0 0.020122528904036908 0.9800786 0.019920269 3.064467E-10 0 1 2 -21 0 0 0.0041771031887722987 0.9958316 0.0041694827 1.0134493E-10 0 1 2 -22 0 0 0.0010643302939017342 0.99893624 0.0010639144 7.4156245E-12 0 1 2 -23 0 0 0.02605629788130279 0.97428024 0.025720494 4.7576445E-09 0 1 2 -24 0 0 0.035209627128022683 0.965403 0.03459665 6.6329586E-09 0 1 2 -25 0 0 0.045448274212285186 0.955569 0.04443145 3.216728E-09 0 1 2 -26 0 0 0.013916015058125409 0.98618037 0.013818858 1.1871322E-09 0 1 2 -27 0 0 0.0076942651707202743 0.99233526 0.0076657226 7.9652826E-11 0 1 2 -28 0 0 0.007534024516302436 0.9924943 0.0075039454 6.411831E-11 0 1 2 -29 0 0 0.024288162137741393 0.9760044 0.02399573 2.8173113E-09 0 1 2 -30 0 0 0.033046722035427502 0.96749336 0.03250687 3.46561E-09 0 1 2 -31 0 0 0.010627754848336371 0.9894285 0.010573362 1.9241796E-10 0 1 2 -32 0 0 0.0012779052922244011 0.9987229 0.0012773476 2.5791548E-12 0 1 2 -33 0 0 0.00071336435395046255 0.9992869 0.0007130818 5.394398E-13 0 1 2 -34 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 -35 0 0 0.0070380898792076199 0.9929866 0.007013349 7.24567E-11 0 1 2 -36 0 0 0.0042114001992704566 0.99579746 0.004201503 8.375258E-12 0 1 2 -37 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 -38 0 0 0.016315454412074732 0.9838169 0.01618225 2.0803645E-09 0 1 2 -39 0 0 0.010242464293569139 0.9898098 0.010190028 1.7190988E-10 0 1 2 -40 0 0 0.0039520171010716818 0.9960558 0.003945863 5.4001064E-11 0 1 2 -41 0 0 0.12730980592945779 0.88046086 0.119539626 7.181209E-08 0 1 2 -42 0 0 0.0089912491214826969 0.99104905 0.0089505045 7.80488E-10 0 1 2 -43 0 0 0.010326412100003242 0.9897267 0.010272516 1.874473E-09 0 1 2 -44 0 0 0.011150365797478341 0.98891157 0.011090281 1.0552612E-09 0 1 2 -45 0 0 0.02356529293651816 0.9767102 0.023289332 2.2207163E-09 0 1 2 -46 0 0 0.0042686243571466928 0.9957405 0.0042613307 4.8837566E-11 0 1 2 -47 0 0 0.012689595644547294 0.9873906 0.012608856 9.079638E-10 0 1 2 -48 0 0 0.0042877197577386979 0.99572146 0.0042767935 2.2542229E-11 0 1 2 -49 0 0 0.0099010247223913338 0.9901478 0.009852205 1.827634E-10 0 1 2 -50 1 1 0.0090408081662831513 0.99099994 0.007567417 0.0014314867 1 2 0 -51 1 1 0.034511583658046456 0.96607715 0.031063149 0.002861517 1 2 0 -52 1 1 0.04304121150554157 0.9578719 0.04158025 0.00054618425 1 2 0 -53 1 1 0.13135529742102009 0.87690616 0.122118875 0.0009759098 1 2 0 -54 1 1 0.074932084511623434 0.9278065 0.07160018 0.00059335935 1 2 0 -55 1 1 0.1890525667021884 0.827743 0.1714483 0.00080681086 1 2 0 -56 1 1 0.11877056857344356 0.8880115 0.1100965 0.0018910082 1 2 0 -57 1 1 0.025358193239084965 0.9749606 0.01495395 0.010085882 1 0 2 -58 1 1 0.019096356683429662 0.9810848 0.01808086 0.0008358488 1 2 0 -59 1 1 0.15862658959532094 0.85331494 0.14217468 0.0045111584 1 2 0 -60 1 1 0.038318653357595697 0.9624062 0.035282373 0.0023114693 1 2 0 -61 1 1 0.065311423102221738 0.9367757 0.058971565 0.0042529916 1 2 0 -62 1 1 0.010269141347627856 0.9897834 0.009452066 0.0007660691 1 2 0 -63 1 1 0.17183304995881135 0.84211975 0.1573262 0.00055418257 1 2 0 -64 1 1 0.030368791940673646 0.9700877 0.023280893 0.0066331304 1 0 2 -65 1 1 0.00975415306436043 0.99029326 0.0068194265 0.0028855118 1 2 0 -66 1 1 0.39629741859747109 0.67280656 0.32598528 0.0012099475 1 2 0 -67 1 1 0.012058247747154605 0.98801416 0.009438771 0.0025457656 1 2 0 -68 1 1 0.3368797650804119 0.7139947 0.28589886 0.00010822646 1 2 0 -69 1 1 0.021083825783822494 0.9791369 0.018163126 0.0027012476 1 2 0 -70 1 2 1.0416832150942417 0.6467134 0.35286024 0.00042478985 2 1 0 -71 1 1 0.013755801712349471 0.9863384 0.00910499 0.0045568454 1 2 0 -72 1 1 0.58514020696109537 0.55702776 0.44291526 5.669436E-05 1 2 0 -73 1 1 0.084747339774486161 0.9187444 0.080806226 0.00044783644 1 2 0 -74 1 1 0.012447376934475682 0.9876298 0.010109726 0.0022619804 1 2 0 -75 1 1 0.013278939705380893 0.98680884 0.011033021 0.0021575652 1 2 0 -76 1 1 0.04319766018798589 0.95772207 0.041966196 0.0003101741 1 2 0 -77 1 1 0.30690002516055565 0.73572415 0.2640443 0.00023127515 1 2 0 -78 1 1 0.16997907799864528 0.84368247 0.1552497 0.0010691443 1 2 0 -79 1 1 0.014675126081593009 0.985432 0.013093817 0.0014749394 1 0 2 -80 1 1 0.022987361370576598 0.97727484 0.01993399 0.00278951 1 2 0 -81 1 1 0.012458783376888797 0.9876185 0.008495432 0.003886289 1 2 0 -82 1 1 0.016071326268181901 0.9840571 0.011121038 0.004822609 1 2 0 -83 1 2 1.707477571538714 0.8186583 0.18132259 1.8361572E-05 2 1 0 -84 1 1 0.62495808673272679 0.53528386 0.4637305 0.0009871349 1 2 0 -85 1 1 0.11122891687722759 0.8947339 0.1001901 0.0050765327 1 2 0 -86 1 1 0.036052681021758022 0.9645895 0.03433999 0.0010723303 1 2 0 -87 1 1 0.063015011349415673 0.9389294 0.060807556 0.00026142705 1 2 0 -88 1 1 0.044994341810109487 0.9560029 0.037784368 0.0062121437 1 2 0 -89 1 1 0.092109667787323657 0.9120051 0.086147815 0.0018485248 1 2 0 -90 1 1 0.20783613448356417 0.81234014 0.18704556 0.0006146012 1 2 0 -91 1 1 0.10023498504355982 0.9046248 0.09426649 0.0011077761 1 2 0 -92 1 1 0.022636421664178376 0.97761786 0.01980763 0.0025746985 1 2 0 -93 1 1 0.020406702516074512 0.9798001 0.0109967375 0.009203869 1 0 2 -94 1 1 0.10109711620080604 0.90384525 0.09442052 0.0017330045 1 2 0 -95 1 1 0.031333905261789932 0.9691519 0.02633722 0.0045093265 1 2 0 -96 1 1 0.054904961042088807 0.9465751 0.050160136 0.0032641925 1 2 0 -97 1 1 0.020442959924209147 0.9797646 0.017932491 0.0023010836 1 2 0 -98 1 1 0.053829215135703323 0.9475939 0.05015344 0.00225256 1 0 2 -99 1 1 0.046024295999239594 0.95501876 0.04161595 0.0033648298 1 2 0 -100 2 2 0.00071718178074236128 0.9992831 0.0007153486 2.3431886E-08 2 1 0 -101 2 2 0.029472378645200227 0.9709577 0.029038547 3.0171875E-06 2 1 0 -102 2 2 0.0393600403713796 0.9614045 0.038595207 6.408885E-07 2 1 0 -103 2 2 0.042228681999026033 0.9586505 0.04134742 1.4739417E-06 2 1 0 -104 2 2 0.0064353741478142112 0.9935853 0.006415478 1.5842724E-07 2 1 0 -105 2 2 0.011154163002271514 0.9889078 0.011091466 1.8252322E-08 2 1 0 -106 2 2 0.038921380516359176 0.9618263 0.038155746 1.6744794E-05 2 1 0 -107 2 2 0.050231903865451351 0.95100886 0.04899014 1.6252797E-07 2 1 0 -108 2 2 0.028682695065745022 0.97172475 0.028276091 1.5199748E-07 2 1 0 -109 2 2 0.011791212573983524 0.98827803 0.011722299 6.1125087E-07 2 1 0 -110 2 2 0.31905363806129555 0.72683656 0.27304813 0.000116325275 2 1 0 -111 2 2 0.07549667881010734 0.9272828 0.072712734 3.6851195E-06 2 1 0 -112 2 2 0.076789840257624864 0.92608446 0.07391078 4.6068785E-06 2 1 0 -113 2 2 0.013851165145911459 0.9862443 0.013756296 1.0960175E-06 2 1 0 -114 2 2 0.0033950556005307988 0.9966107 0.0033878458 4.755091E-07 2 1 0 -115 2 2 0.03064347778045929 0.9698213 0.030172396 6.850469E-06 2 1 0 -116 2 2 0.12950899319139736 0.8785267 0.121466115 7.857308E-06 2 1 0 -117 2 2 0.029441808148158395 0.9709874 0.029012194 3.762749E-07 2 1 0 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0.0048637461544768163 0.99514806 0.0048530777 2.1329287E-11 0 1 2 +11 0 0 0.014535897462350887 0.98556924 0.014430009 9.1312186E-10 0 1 2 +12 0 0 0.024089826265429194 0.976198 0.023801321 9.860573E-10 0 1 2 +13 0 0 0.0097446432661685064 0.9903027 0.009698148 5.199548E-10 0 1 2 +14 0 0 0.00088880209796682742 0.9991116 0.0008885167 2.214887E-13 0 1 2 +15 0 0 0.00071718178074236128 0.9992831 0.00071511255 8.3642067E-13 0 1 2 +16 0 0 0.0015487737280443089 0.9984524 0.0015481457 5.322795E-12 0 1 2 +17 0 0 0.0061950852433387601 0.99382406 0.0061750147 1.0156496E-10 0 1 2 +18 0 0 0.0067634503253886933 0.9932594 0.006742421 3.5515146E-11 0 1 2 +19 0 0 0.0036468762471827455 0.99635977 0.0036403935 4.8154106E-11 0 1 2 +20 0 0 0.020011910373325573 0.980187 0.019812541 3.4069497E-10 0 1 2 +21 0 0 0.0048027147543415524 0.9952088 0.00479181 1.225549E-10 0 1 2 +22 0 0 0.00140569018309729 0.9985953 0.0014052592 1.0557051E-11 0 1 2 +23 0 0 0.025959029382547023 0.974375 0.025625352 4.9580686E-09 0 1 2 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0.96842384 0.022214318 0.00935996 1 2 0 +96 1 1 0.050395938246597986 0.9508529 0.042172417 0.0069737583 1 2 0 +97 1 1 0.021072381409417223 0.9791481 0.015627518 0.005223911 1 2 0 +98 1 1 0.086358994837316017 0.9172649 0.08074706 0.0019872745 1 0 2 +99 1 1 0.043498989321880621 0.9574335 0.035394125 0.0071709203 1 2 0 +100 2 2 0.0011253129694704825 0.9988753 0.0011242428 1.1564506E-07 2 1 0 +101 2 2 0.040019973135030099 0.96077025 0.03921765 1.1391113E-05 2 1 0 +102 2 2 0.052360548423487648 0.94898665 0.051010333 2.9822752E-06 2 1 0 +103 2 2 0.056476572865404355 0.9450886 0.05490551 6.142361E-06 2 1 0 +104 2 2 0.0091857704417590124 0.9908563 0.009142892 7.379481E-07 2 1 0 +105 2 2 0.014972700003367621 0.98513883 0.014861965 1.0640279E-07 2 1 0 +106 2 2 0.052852650255712383 0.94851977 0.05142669 5.3088323E-05 2 1 0 +107 2 2 0.064285257313123734 0.93773746 0.06226263 8.3501254E-07 2 1 0 +108 2 2 0.036701220902616828 0.9639641 0.03603608 7.256182E-07 2 1 0 +109 2 2 0.017375952834142096 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a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-CV-iris.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-CV-iris.txt index a077f1e9b6..eabf5f533c 100644 --- a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-CV-iris.txt +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-CV-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -5 0 0 0.0039186265649996796 0.99608904 0.003910809 2.0865299E-11 0 1 2 -6 0 0 0.0066394791182328066 0.9933825 0.006617098 2.152395E-10 0 1 2 -8 0 0 0.025494595703479419 0.97482765 0.025172155 2.838915E-09 0 1 2 -9 0 0 0.019190772567240676 0.9809922 0.01900817 2.7058017E-10 0 1 2 -10 0 0 0.0035562493991592786 0.99645007 0.003550249 4.9272335E-12 0 1 2 -11 0 0 0.012517929979546309 0.9875601 0.0124394335 2.8008076E-10 0 1 2 -18 0 0 0.0047769616990695207 0.99523443 0.004765985 8.127231E-12 0 1 2 -20 0 0 0.014707546996037419 0.9854001 0.014600216 8.914893E-11 0 1 2 -21 0 0 0.003647833406238951 0.9963588 0.003641376 3.5084515E-11 0 1 2 -25 0 0 0.032317619176197167 0.968199 0.03180108 1.10023E-09 0 1 2 -28 0 0 0.0057745087199158104 0.99424213 0.0057580057 2.0235997E-11 0 1 2 -31 0 0 0.0071621105155360203 0.9928635 0.0071367947 6.242954E-11 0 1 2 -32 0 0 0.0014066451993830036 0.99859434 0.0014055507 7.3242096E-13 0 1 2 -35 0 0 0.0052270768592320212 0.99478656 0.0052133226 2.5026661E-11 0 1 2 -37 0 0 0.019190772567240676 0.9809922 0.01900817 2.7058017E-10 0 1 2 -40 0 0 0.0033063652339419308 0.9966991 0.003300911 1.895811E-11 0 1 2 -41 0 0 0.077088846429646946 0.9258076 0.0741926 3.3343277E-08 0 1 2 -44 0 0 0.010642032182541446 0.9894144 0.0105858175 3.5036918E-10 0 1 2 -45 0 0 0.017220520546649732 0.9829269 0.017073102 8.5334684E-10 0 1 2 -46 0 0 0.0041847046934752278 0.99582404 0.0041763177 1.5150974E-11 0 1 2 -48 0 0 0.0036583023932260659 0.9963484 0.0036516767 6.700264E-12 0 1 2 -50 1 1 0.0098977138531054429 0.9901511 0.005578102 0.0042707166 1 2 0 -51 1 1 0.031642446182477788 0.96885294 0.024089899 0.007057201 1 2 0 -52 1 1 0.03573585794885261 0.9648951 0.033440154 0.001664746 1 2 0 -54 1 1 0.072409637391112461 0.9301498 0.06803778 0.0018123286 1 2 0 -56 1 1 0.093649650619783725 0.91060174 0.08499099 0.0044072797 1 2 0 -60 1 1 0.042489542543681612 0.9584005 0.03626706 0.005332458 1 2 0 -63 1 1 0.137502875229945 0.87153184 0.1271067 0.0013614136 1 2 0 -64 1 1 0.054985501307998973 0.9464989 0.048023954 0.0054772357 1 0 2 -66 1 1 0.32485007538570837 0.7226357 0.27483696 0.0025274218 1 2 0 -68 1 1 0.41495870735351531 0.66036755 0.33927307 0.00035943443 1 2 0 -69 1 1 0.021870392416744027 0.97836703 0.015370483 0.006262353 1 2 0 -70 1 2 0.95191086445840967 0.61301166 0.38600272 0.0009856796 2 1 0 -71 1 1 0.019884826671693976 0.9803116 0.011781351 0.007907056 1 0 2 -72 1 1 0.59602090426171883 0.55099976 0.44882512 0.00017505078 1 2 0 -73 1 1 0.061366106483072287 0.94047886 0.058446974 0.001074166 1 2 0 -74 1 1 0.01440358171033043 0.98569965 0.008161617 0.0061388216 1 2 0 -76 1 1 0.038602842055599461 0.96213275 0.036847353 0.0010197883 1 2 0 -77 1 1 0.29142764060847787 0.7471961 0.25208423 0.00071971776 1 2 0 -79 1 1 0.031789553326302268 0.9687104 0.030109784 0.0011796883 1 0 2 -82 1 1 0.020876629838716645 0.9793398 0.011350486 0.009309866 1 0 2 -88 1 1 0.040395748253186578 0.9604093 0.027647337 0.01194328 1 2 0 -90 1 1 0.166073088823076 0.8469843 0.15168492 0.0013306275 1 2 0 -91 1 1 0.078327406928112728 0.92466164 0.07274388 0.0025945483 1 2 0 -92 1 1 0.023790137724984652 0.9764906 0.017208781 0.006300524 1 2 0 -93 1 1 0.031245715484219467 0.9692374 0.022426113 0.0083365 1 0 2 -95 1 1 0.026741054389737642 0.9736133 0.017711794 0.008674911 1 2 0 -96 1 1 0.04639434288106066 0.9546654 0.038535807 0.006798673 1 2 0 -97 1 1 0.020153545641826632 0.9800482 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0.039539583 1.5799143E-06 2 1 0 -120 2 2 0.02318047647021565 0.9770861 0.022909554 4.537731E-06 2 1 0 -121 2 2 0.023067445323975651 0.9771966 0.022788834 1.4752109E-05 2 1 0 -122 2 2 0.010407897593236567 0.9896461 0.010353751 3.0826786E-08 2 1 0 -123 2 2 0.3268478109707687 0.7211935 0.27864572 0.00016074626 2 1 0 -125 2 2 0.24618388448271417 0.78177845 0.21820657 1.498082E-05 2 1 0 -128 2 2 0.0093602944019322582 0.9906834 0.009315706 7.625066E-07 2 1 0 -129 2 1 0.72903371465671218 0.517586 0.48237488 3.915742E-05 1 2 0 -131 2 2 0.50473360924795707 0.60366637 0.3962909 4.2747015E-05 2 1 0 -132 2 2 0.0054111588821948668 0.99460346 0.0053958558 4.5496841E-07 2 1 0 -133 2 1 0.80138632910758123 0.5511005 0.44870648 0.00019297059 1 2 0 -137 2 2 0.13871576668047261 0.8704754 0.12949541 2.8931609E-05 2 1 0 -138 2 2 0.41129950849971153 0.6627884 0.33667925 0.0005324046 2 1 0 -141 2 2 0.15623071913140255 0.8553618 0.14447537 0.00016272673 2 1 0 -144 2 2 0.0057010728771068496 0.99431515 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0.0055899402333488843 0.99442565 0.0055738376 4.1337103E-07 2 1 0 +105 2 2 0.010776502140667386 0.98928136 0.010718488 7.397515E-08 2 1 0 +106 2 2 0.035450012508994544 0.965171 0.034799412 2.9794508E-05 2 1 0 +108 2 2 0.025392551945450037 0.9749271 0.025072392 4.9133615E-07 2 1 0 +109 2 2 0.010955823550611778 0.989104 0.010894274 1.8224963E-06 2 1 0 +111 2 2 0.06487274446443414 0.9371867 0.062802896 1.01739515E-05 2 1 0 +112 2 2 0.065966109277555141 0.9361626 0.06382388 1.3540421E-05 2 1 0 +113 2 2 0.010098914216835852 0.9899519 0.010046053 2.241711E-06 2 1 0 +115 2 2 0.025224193337550441 0.9750913 0.024892442 1.6148571E-05 2 1 0 +117 2 2 0.040342314546391055 0.9604606 0.039537944 1.5796084E-06 2 1 0 +120 2 2 0.02318047647021565 0.9770861 0.02290926 4.5370043E-06 2 1 0 +121 2 2 0.023067445323975651 0.9771966 0.022788497 1.4749999E-05 2 1 0 +122 2 2 0.010407475995621918 0.9896465 0.0103532225 3.0820733E-08 2 1 0 +123 2 2 0.32683946363533112 0.7211995 0.27863985 0.00016072435 2 1 0 +125 2 2 0.24617290564122887 0.78178704 0.21819793 1.4978335E-05 2 1 0 +128 2 2 0.0093598130805993083 0.99068385 0.009315497 7.6238445E-07 2 1 0 +129 2 1 0.72900344169343156 0.51757145 0.48238948 3.9151782E-05 1 2 0 +131 2 2 0.50471494799167183 0.60367763 0.39627966 4.27398E-05 2 1 0 +132 2 2 0.0054106794579163063 0.99460393 0.0053957785 4.5489728E-07 2 1 0 +133 2 1 0.80135750397993166 0.5510876 0.4487194 0.00019294629 1 2 0 +137 2 2 0.13871001490885845 0.8704804 0.12949048 2.8926946E-05 2 1 0 +138 2 2 0.41129015581014772 0.6627946 0.336673 0.00053233345 2 1 0 +141 2 2 0.15623044039725695 0.85536206 0.14447519 0.00016270345 2 1 0 +144 2 2 0.0057010728771068496 0.99431515 0.0056835874 1.4548094E-06 2 1 0 +145 2 2 0.050842734596971917 0.9504281 0.049542528 2.9353128E-05 2 1 0 +147 2 2 0.14442252350825718 0.86552197 0.13440919 6.884588E-05 2 1 0 +0 0 0 0.0039052826303189398 0.99610233 0.0038976613 4.373769E-13 0 1 2 +1 0 0 0.01266768309090782 0.9874122 0.012587332 1.2107598E-11 0 1 2 +2 0 0 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2 +26 0 0 0.010642996060550395 0.98941344 0.010586246 1.640988E-11 0 1 2 +27 0 0 0.0051164761595189259 0.9948966 0.005103707 6.8218895E-13 0 1 2 +29 0 0 0.018854160559333675 0.98132247 0.018677382 4.5549585E-11 0 1 2 +30 0 0 0.021645552448278319 0.97858703 0.021413254 5.2732024E-11 0 1 2 +33 0 0 0.00068855143500750813 0.9993117 0.00068878755 2.6637945E-15 0 1 2 +34 0 0 0.014088646318605779 0.98601013 0.01399014 9.314752E-12 0 1 2 +36 0 0 0.0021105014325534205 0.9978917 0.0021083653 4.4323682E-14 0 1 2 +38 0 0 0.012737285781813377 0.9873435 0.012656637 3.3930508E-11 0 1 2 +39 0 0 0.0067491682510599756 0.99327356 0.006726153 1.6541226E-12 0 1 2 +42 0 0 0.0084113983536726177 0.9916239 0.00837582 1.2086278E-11 0 1 2 +43 0 0 0.0090751521004575654 0.9909659 0.009034118 2.9838947E-11 0 1 2 +47 0 0 0.010281546721274558 0.9897711 0.010228623 1.3024835E-11 0 1 2 +49 0 0 0.0063428746789303565 0.9936772 0.006322958 1.7793256E-12 0 1 2 +53 1 1 0.10957930479889894 0.8962111 0.10103965 0.0027492002 1 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0.9508778 0.042176057 0.0069461297 1 2 0 +86 1 1 0.023870469129919129 0.9764122 0.020375451 0.0032124338 1 2 0 +87 1 1 0.067410212010544465 0.93481165 0.06390876 0.0012797081 1 2 0 +89 1 1 0.063293670471973457 0.9386678 0.056962185 0.0043699616 1 2 0 +94 1 1 0.064283159763128858 0.93773943 0.058663167 0.0035974565 1 2 0 +101 2 2 0.016423241110482573 0.9837109 0.016285963 2.9112189E-06 2 1 0 +103 2 2 0.021625026364756963 0.9786071 0.021391531 1.5351787E-06 2 1 0 +107 2 2 0.015677938925009325 0.9844443 0.015555596 2.0633152E-07 2 1 0 +110 2 2 0.31733201991188631 0.728089 0.2716928 0.00021816233 2 1 0 +114 2 2 0.0015687724332034386 0.99843246 0.001567381 3.0577573E-07 2 1 0 +116 2 2 0.083694952996644006 0.91971177 0.08027698 1.1447655E-05 2 1 0 +118 2 2 8.2973107700441223E-05 0.99991703 8.340552E-05 8.99174E-11 2 1 0 +119 2 2 0.083391697723947486 0.9199907 0.08000028 8.9567075E-06 2 1 0 +124 2 2 0.030993488916279836 0.9694819 0.03051392 4.4014096E-06 2 1 0 +126 2 2 0.42598822225339628 0.65312403 0.34659016 0.00028579155 2 1 0 +127 2 2 0.37475535349032807 0.68745744 0.31228298 0.00025961632 2 1 0 +130 2 2 0.023461371183951175 0.9768117 0.023187635 5.152995E-07 2 1 0 +134 2 2 0.039185036833512002 0.96157277 0.038425777 1.4153605E-06 2 1 0 +135 2 2 0.012777794001199237 0.9873035 0.012695913 4.7814564E-07 2 1 0 +136 2 2 0.0041899719117273965 0.9958188 0.004180388 7.360644E-07 2 1 0 +139 2 2 0.12075187531073932 0.88625383 0.113711946 3.4401823E-05 2 1 0 +140 2 2 0.0054893680446388747 0.9945257 0.0054734107 6.795554E-07 2 1 0 +142 2 2 0.016423241110482573 0.9837109 0.016285963 2.9112189E-06 2 1 0 +143 2 2 0.0041432263192192472 0.99586535 0.004134322 2.5040606E-07 2 1 0 +146 2 2 0.062243437538287393 0.9396541 0.060331948 1.4102009E-05 2 1 0 +148 2 2 0.013613196562563339 0.98647904 0.013516276 4.4905573E-06 2 1 0 +149 2 2 0.092099668430092721 0.91201425 0.08795362 3.2270033E-05 2 1 0 diff --git a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-TrainTest-iris.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-TrainTest-iris.txt index 1f136318b1..74dc392a4f 100644 --- a/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/MulticlassLogisticRegression-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.0011148704666220459 0.99888575 0.0011145204 1.3776857E-13 0 1 2 -1 0 0 0.0070600595001449043 0.9929648 0.007035469 5.980848E-12 0 1 2 -2 0 0 0.0023107383603406979 0.9976919 0.002307759 1.30608E-12 0 1 2 -3 0 0 0.0071973505498515739 0.9928285 0.0071717617 1.5530932E-11 0 1 2 -4 0 0 0.00077247645619236299 0.9992278 0.0007718982 9.492881E-14 0 1 2 -5 0 0 0.00070763824108637647 0.9992926 0.00070762733 1.7460614E-13 0 1 2 -6 0 0 0.0015249548965825181 0.9984762 0.0015239433 1.9716312E-12 0 1 2 -7 0 0 0.0023698852189009997 0.9976329 0.0023674963 7.53184E-13 0 1 2 -8 0 0 0.01027010486628236 0.98978245 0.010217615 4.4595366E-11 0 1 2 -9 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2 -10 0 0 0.00077915735755335308 0.99922115 0.0007790165 3.6408105E-14 0 1 2 -11 0 0 0.0034885388576309144 0.99651754 0.0034823413 2.83667E-12 0 1 2 -12 0 0 0.0075292801506795124 0.992499 0.0075008543 4.1048614E-12 0 1 2 -13 0 0 0.0024023278603287428 0.99760056 0.0023991016 1.5549362E-12 0 1 2 -14 0 0 8.1065602621708377E-05 0.99991894 8.0906575E-05 1.6902284E-16 0 1 2 -15 0 0 5.149973917474053E-05 0.9999485 5.1647687E-05 6.3881086E-16 0 1 2 -16 0 0 0.00015259953334155612 0.9998474 0.00015233555 7.692787E-15 0 1 2 -17 0 0 0.0010471460236901183 0.9989534 0.0010464673 2.6370047E-13 0 1 2 -18 0 0 0.0010852740224894391 0.9989153 0.0010850534 7.665227E-14 0 1 2 -19 0 0 0.00050831837325152984 0.9994918 0.00050798786 8.2925454E-14 0 1 2 -20 0 0 0.0050735213310076762 0.9949393 0.005060825 1.1999263E-12 0 1 2 -21 0 0 0.00068950576665922968 0.99931073 0.00068974565 3.0276918E-13 0 1 2 -22 0 0 0.00016690693355841423 0.9998331 0.00016714552 1.24774365E-14 0 1 2 -23 0 0 0.0060968509418567766 0.9939217 0.006078474 3.6398856E-11 0 1 2 -24 0 0 0.011000658927949667 0.9890596 0.010940627 2.9289283E-11 0 1 2 -25 0 0 0.015137646970205936 0.98497635 0.015023758 2.1512653E-11 0 1 2 -26 0 0 0.0030670062937996402 0.9969377 0.003062062 6.018899E-12 0 1 2 -27 0 0 0.0016346217426049894 0.9983667 0.0016332197 2.287227E-13 0 1 2 -28 0 0 0.0016097859543283302 0.9983915 0.0016089765 1.9991067E-13 0 1 2 -29 0 0 0.0072956329818799339 0.9927309 0.007268736 1.3519734E-11 0 1 2 -30 0 0 0.010519988636096064 0.98953515 0.010464923 1.956453E-11 0 1 2 -31 0 0 0.0020780682572110079 0.9979241 0.0020754703 9.251467E-13 0 1 2 -32 0 0 0.0001897991996934539 0.9998102 0.00019020819 2.480733E-15 0 1 2 -33 0 0 8.392686160427524E-05 0.9999161 8.372972E-05 5.0225263E-16 0 1 2 -34 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2 -35 0 0 0.0015668620918511083 0.99843436 0.0015660885 2.637719E-13 0 1 2 -36 0 0 0.00075529720050108832 0.999245 0.00075494894 2.1162744E-14 0 1 2 -37 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2 -38 0 0 0.0048484729816518539 0.99516326 0.0048371274 1.0765147E-11 0 1 2 -39 0 0 0.0023670174117273336 0.9976358 0.0023640736 5.7310146E-13 0 1 2 -40 0 0 0.00071431870928224299 0.99928594 0.0007139834 1.5880794E-13 0 1 2 -41 0 0 0.058616606694306482 0.94306827 0.05693195 1.3674992E-09 0 1 2 -42 0 0 0.0023202972115890941 0.9976824 0.002317787 2.964662E-12 0 1 2 -43 0 0 0.0018701744864162277 0.9981316 0.0018682877 1.1569887E-11 0 1 2 -44 0 0 0.0022153938202876546 0.99778706 0.0022129146 3.5982877E-12 0 1 2 -45 0 0 0.0066394791182328066 0.9933825 0.0066177957 1.5050398E-11 0 1 2 -46 0 0 0.00079442815684458367 0.9992059 0.0007942036 9.45553E-14 0 1 2 -47 0 0 0.0033950556005307988 0.9966107 0.0033896863 3.7430263E-12 0 1 2 -48 0 0 0.00078011177567713298 0.9992202 0.00078014436 4.7848332E-14 0 1 2 -49 0 0 0.0023355318196242821 0.9976672 0.0023323793 6.583122E-13 0 1 2 -50 1 1 0.0060477374096205563 0.9939705 0.0040119863 0.002017312 1 2 0 -51 1 1 0.023358802522272765 0.9769119 0.018574875 0.0045133037 1 2 0 -52 1 1 0.03167006942034075 0.9688262 0.030503416 0.0006706227 1 2 0 -53 1 1 0.085153806889826739 0.918371 0.080720015 0.0009088988 1 2 0 -54 1 1 0.063849951086753426 0.93814576 0.061179116 0.0006753106 1 2 0 -55 1 1 0.089629429284646664 0.9142699 0.08488794 0.00084198435 1 2 0 -56 1 1 0.08483648364691608 0.9186625 0.07830901 0.0030285995 1 2 0 -57 1 1 0.019588462132268222 0.98060215 0.016944302 0.0024533693 1 0 2 -58 1 1 0.0099349166147827024 0.9901143 0.008975584 0.0009100606 1 2 0 -59 1 1 0.092321899949188477 0.9118116 0.08203742 0.006150943 1 2 0 -60 1 1 0.014485823445928967 0.9856186 0.012574332 0.0018072706 1 2 0 -61 1 1 0.045088304308168195 0.95591307 0.037453618 0.006633371 1 2 0 -62 1 1 0.0040779297962239254 0.9959304 0.00350171 0.00056805444 1 2 0 -63 1 1 0.10219954324186892 0.9028494 0.09655303 0.0005976357 1 2 0 -64 1 1 0.04381778370401148 0.95712835 0.040380865 0.0024907354 1 0 2 -65 1 1 0.0080509348386470451 0.9919814 0.004386741 0.0036320223 1 0 2 -66 1 1 0.25712513878794285 0.77327144 0.22504078 0.0016878267 1 2 0 -67 1 1 0.0046958738522607582 0.99531513 0.0024410768 0.002243941 1 0 2 -68 1 1 0.42020607515071323 0.65691143 0.3430131 7.5479635E-05 1 2 0 -69 1 1 0.0088743981357345281 0.99116486 0.006175138 0.002660179 1 2 0 -70 1 2 1.119156119237483 0.67287296 0.32655525 0.00057173334 2 1 0 -71 1 1 0.010574322077426965 0.9894814 0.006254719 0.0042638187 1 0 2 -72 1 1 0.61366271154662444 0.5413644 0.45859504 4.0561474E-05 1 2 0 -73 1 1 0.03377105479336788 0.9667928 0.03281732 0.0003900002 1 2 0 -74 1 1 0.0076222497957970196 0.9924067 0.004714336 0.0028790454 1 2 0 -75 1 1 0.0093469378209280057 0.9906966 0.006279014 0.0030244375 1 2 0 -76 1 1 0.03111313795737956 0.9693659 0.030329114 0.00030520634 1 2 0 -77 1 1 0.36622901478232017 0.693344 0.30640054 0.00025561816 1 2 0 -78 1 1 0.12305449703206547 0.8842155 0.11444254 0.0013420355 1 2 0 -79 1 1 0.017178073477986378 0.9829686 0.016676014 0.00035517136 1 0 2 -80 1 1 0.0099248633048492161 0.9901242 0.007182131 0.002693587 1 2 0 -81 1 1 0.0061249768712999088 0.99389374 0.0037273536 0.0023786854 1 0 2 -82 1 1 0.01018477684487269 0.9898669 0.0059374967 0.0041955533 1 0 2 -83 1 2 1.7848181575763675 0.8321596 0.16782758 1.2941472E-05 2 1 0 -84 1 1 0.40771368629306376 0.6651693 0.33338308 0.0014476714 1 2 0 -85 1 1 0.073538094082338953 0.92910075 0.061388977 0.009510201 1 2 0 -86 1 1 0.02568532133252191 0.97464174 0.023907999 0.0014502493 1 2 0 -87 1 1 0.047872087095895285 0.9532557 0.046538867 0.00020534953 1 2 0 -88 1 1 0.023007854444924543 0.9772548 0.014006073 0.008739044 1 2 0 -89 1 1 0.051250957163473519 0.9500402 0.047992557 0.0019672096 1 2 0 -90 1 1 0.088376539509711596 0.9154161 0.08403013 0.0005538561 1 2 0 -91 1 1 0.054428086534615831 0.9470266 0.051642187 0.0013313727 1 2 0 -92 1 1 0.011075027413584841 0.9889861 0.008220732 0.002793185 1 2 0 -93 1 1 0.01437425449213319 0.98572856 0.011792642 0.002478861 1 0 2 -94 1 1 0.049662849884455844 0.9515502 0.046534896 0.0019149618 1 2 0 -95 1 1 0.013598453800202021 0.9864936 0.00785744 0.0056490703 1 2 0 -96 1 1 0.025311486855273613 0.97500616 0.020882485 0.004111272 1 2 0 -97 1 1 0.01101301311502036 0.9890474 0.00809153 0.002861004 1 2 0 -98 1 1 0.081760169588047965 0.92149293 0.0778774 0.00062952057 1 0 2 -99 1 1 0.023015234494193421 0.9772476 0.018569632 0.0041829064 1 2 0 -100 2 2 0.00011158612051911557 0.9998884 0.00011122978 4.3882338E-09 2 1 0 -101 2 2 0.013729575340933843 0.98636425 0.0136345485 1.266714E-06 2 1 0 -102 2 2 0.010888574253525379 0.9891705 0.010829585 1.6294389E-07 2 1 0 -103 2 2 0.025930890575031128 0.9744024 0.025596872 6.894996E-07 2 1 0 -104 2 2 0.0015621459522632778 0.9984391 0.0015607552 3.6407382E-08 2 1 0 -105 2 2 0.0026502530714843367 0.99735326 0.0026466271 2.7263904E-09 2 1 0 -106 2 2 0.030433677359253137 0.97002476 0.0299635 1.159951E-05 2 1 0 -107 2 2 0.024480795647284091 0.9758164 0.024183862 4.4924544E-08 2 1 0 -108 2 2 0.011575682088038546 0.98849106 0.01150899 3.3041104E-08 2 1 0 -109 2 2 0.0019807750421713521 0.9980212 0.0019784786 1.6299222E-07 2 1 0 -110 2 2 0.1668877744415341 0.8462946 0.15360819 9.722446E-05 2 1 0 -111 2 2 0.031444061158942889 0.96904516 0.030953335 1.3455377E-06 2 1 0 -112 2 2 0.023395533219334613 0.976876 0.023122288 1.6099556E-06 2 1 0 -113 2 2 0.004405951750777239 0.99560374 0.0043958076 3.048161E-07 2 1 0 -114 2 2 0.00051786001087093976 0.9994823 0.00051763566 9.5351375E-08 2 1 0 -115 2 2 0.0073614401801424031 0.9926656 0.0073315674 2.597062E-06 2 1 0 -116 2 2 0.085010512269807784 0.9185026 0.08149283 4.67563E-06 2 1 0 -117 2 2 0.010475174804130676 0.9895795 0.0104205115 1.4943078E-07 2 1 0 -118 2 2 8.392686160427524E-05 0.9999161 8.4219195E-05 7.1131295E-12 2 1 0 -119 2 2 0.14243270625738888 0.8672459 0.13275184 2.228693E-06 2 1 0 -120 2 2 0.0058665359402536059 0.99415064 0.0058489824 4.4902038E-07 2 1 0 -121 2 2 0.011418315077759932 0.9886466 0.011349821 3.4921036E-06 2 1 0 -122 2 2 0.0027408576136189505 0.9972629 0.0027368125 8.6288726E-10 2 1 0 -123 2 2 0.21712063090760428 0.8048329 0.19513033 3.696039E-05 2 1 0 -124 2 2 0.018555186660661513 0.9816159 0.018382292 1.7941145E-06 2 1 0 -125 2 2 0.13317062821825568 0.8753157 0.12468223 2.215571E-06 2 1 0 -126 2 2 0.30929185172157869 0.7339665 0.26592687 0.00010682235 2 1 0 -127 2 2 0.28011468210882728 0.7556971 0.24416323 0.00013955706 2 1 0 -128 2 2 0.0030803390471882929 0.9969244 0.0030754893 6.93042E-08 2 1 0 -129 2 2 0.5607895853823287 0.5707582 0.42923495 6.927903E-06 2 1 0 -130 2 2 0.026384817227264001 0.9739602 0.026039718 7.690115E-08 2 1 0 -131 2 2 0.22129675374786231 0.8014788 0.19851315 7.966131E-06 2 1 0 -132 2 2 0.0015125382870987553 0.9984886 0.0015110222 3.6266687E-08 2 1 0 -133 2 1 0.80010965478119345 0.55066234 0.4492797 5.7917074E-05 1 2 0 -134 2 2 0.10865733918867169 0.89703774 0.10296163 7.100352E-07 2 1 0 -135 2 2 0.0060692055433251568 0.9939492 0.0060508684 4.8325873E-08 2 1 0 -136 2 2 0.0014610229435586669 0.99854004 0.0014594739 3.6343258E-07 2 1 0 -137 2 2 0.085431109138005057 0.9181164 0.08187672 6.785138E-06 2 1 0 -138 2 2 0.31214448263984934 0.7318758 0.26789987 0.00022450527 2 1 0 -139 2 2 0.059142087199894701 0.94257283 0.057418868 8.503209E-06 2 1 0 -140 2 2 0.0018873131884692992 0.99811447 0.0018852964 1.5602878E-07 2 1 0 -141 2 2 0.047074804913225708 0.95401603 0.0459597 2.4441237E-05 2 1 0 -142 2 2 0.013729575340933843 0.98636425 0.0136345485 1.266714E-06 2 1 0 -143 2 2 0.0020265834982710466 0.99797547 0.0020240443 7.197046E-08 2 1 0 -144 2 2 0.0010843193130689751 0.99891627 0.0010831588 1.3605347E-07 2 1 0 -145 2 2 0.014166328018634792 0.98593354 0.0140630165 3.5111582E-06 2 1 0 -146 2 2 0.04495238264372909 0.956043 0.04395422 2.8865643E-06 2 1 0 -147 2 2 0.067826276148074821 0.9344228 0.065563805 1.3508331E-05 2 1 0 -148 2 2 0.005008703171980048 0.9950038 0.0049937237 2.5133447E-06 2 1 0 -149 2 2 0.081482913321255249 0.92174846 0.07823061 2.0680885E-05 2 1 0 +0 0 0 0.0011100967873528965 0.9988905 0.0011098513 1.3853537E-13 0 1 2 +1 0 0 0.0070448127720684432 0.99297994 0.007020332 6.009378E-12 0 1 2 +2 0 0 0.0023012393424285556 0.9977014 0.0022987302 1.3132493E-12 0 1 2 +3 0 0 0.0071735168643098715 0.99285215 0.0071482384 1.561395E-11 0 1 2 +4 0 0 0.0007686588183116556 0.99923164 0.0007680596 9.548995E-14 0 1 2 +5 0 0 0.00070382085072599819 0.9992964 0.0007038784 1.7574012E-13 0 1 2 +6 0 0 0.0015173138878857594 0.99848384 0.0015159593 1.984035E-12 0 1 2 +7 0 0 0.0023612818220528371 0.9976415 0.0023586082 7.572865E-13 0 1 2 +8 0 0 0.010239573820286053 0.9898127 0.010187215 4.4828266E-11 0 1 2 +9 0 0 0.0076141416338779428 0.9924148 0.0075850827 3.5899447E-12 0 1 2 +10 0 0 0.00077629410864748507 0.999224 0.0007757293 3.6610746E-14 0 1 2 +11 0 0 0.0034742436669066589 0.9965318 0.0034680672 2.8527846E-12 0 1 2 +12 0 0 0.0075121045342651342 0.99251604 0.0074843164 4.123805E-12 0 1 2 +13 0 0 0.0023918122664198516 0.99761105 0.0023891912 1.5632746E-12 0 1 2 +14 0 0 8.0111851446806079E-05 0.9999199 8.048216E-05 1.7000186E-16 0 1 2 +15 0 0 5.149973917474053E-05 0.9999485 5.1264044E-05 6.434332E-16 0 1 2 +16 0 0 0.00015164571393867562 0.99984837 0.00015141307 7.743812E-15 0 1 2 +17 0 0 0.0010423726677040194 0.9989582 0.0010418568 2.6524404E-13 0 1 2 +18 0 0 0.0010814551902763913 0.9989191 0.0010809758 7.708184E-14 0 1 2 +19 0 0 0.00050545589971897188 0.9994947 0.0005050637 8.3460514E-14 0 1 2 +20 0 0 0.0050620790031108715 0.9949507 0.0050492333 1.2057628E-12 0 1 2 +21 0 0 0.00068664277443630751 0.9993136 0.00068597856 3.04746E-13 0 1 2 +22 0 0 0.00016595310050876666 0.99983406 0.00016600745 1.2559383E-14 0 1 2 +23 0 0 0.0060768214445233283 0.9939416 0.0060588485 3.6614014E-11 0 1 2 +24 0 0 0.010962512572068527 0.98909736 0.010902246 2.945331E-11 0 1 2 +25 0 0 0.015114773021998094 0.9849989 0.015001287 2.1611067E-11 0 1 2 +26 0 0 0.0030546303096060656 0.99695003 0.0030498332 6.054951E-12 0 1 2 +27 0 0 0.0016288903521015773 0.99837244 0.0016271285 2.29958E-13 0 1 2 +28 0 0 0.0016050099119122884 0.9983963 0.0016034979 2.0095434E-13 0 1 2 +29 0 0 0.0072708363197615542 0.99275553 0.0072445245 1.3592701E-11 0 1 2 +30 0 0 0.010493304889420576 0.98956156 0.0104382755 1.966337E-11 0 1 2 +31 0 0 0.0020713786723451175 0.99793077 0.00206902 9.302355E-13 0 1 2 +32 0 0 0.00018884534480816681 0.9998112 0.00018895416 2.496587E-15 0 1 2 +33 0 0 8.2973107700441223E-05 0.99991703 8.316866E-05 5.0553383E-16 0 1 2 +34 0 0 0.0076141416338779428 0.9924148 0.0075850827 3.5899447E-12 0 1 2 +35 0 0 0.0015621459522632778 0.9984391 0.0015609234 2.6512367E-13 0 1 2 +36 0 0 0.00075243401991187603 0.99924785 0.0007524573 2.1270185E-14 0 1 2 +37 0 0 0.0076141416338779428 0.9924148 0.0075850827 3.5899447E-12 0 1 2 +38 0 0 0.0048313433476961402 0.9951803 0.004819271 1.0823887E-11 0 1 2 +39 0 0 0.0023584140395519126 0.99764436 0.0023558387 5.7615056E-13 0 1 2 +40 0 0 0.00071050129341987335 0.99928975 0.0007105087 1.5976344E-13 0 1 2 +41 0 0 0.058613762568067093 0.94307095 0.056928936 1.3731134E-09 0 1 2 +42 0 0 0.0023097824802412793 0.9976929 0.0023068404 2.9820896E-12 0 1 2 +43 0 0 0.0018606199368352383 0.9981411 0.0018590544 1.1648272E-11 0 1 2 +44 0 0 0.0022039244129900587 0.9977985 0.0022013872 3.6220575E-12 0 1 2 +45 0 0 0.0066223187777712305 0.99339956 0.0066003366 1.512848E-11 0 1 2 +46 0 0 0.00079061043515947244 0.9992097 0.00078995415 9.513509E-14 0 1 2 +47 0 0 0.0033817186504715878 0.996624 0.0033761603 3.763931E-12 0 1 2 +48 0 0 0.00077724852403853091 0.99922305 0.00077664276 4.8120887E-14 0 1 2 +49 0 0 0.0023269884618295112 0.9976757 0.0023243777 6.6178313E-13 0 1 2 +50 1 1 0.0060296277781084866 0.9939885 0.0040062875 0.0020049622 1 2 0 +51 1 1 0.023376923644402699 0.9768942 0.018610137 0.0044954917 1 2 0 +52 1 1 0.031613839401899838 0.96888065 0.030452834 0.0006662728 1 2 0 +53 1 1 0.085075602344142798 0.91844285 0.08065317 0.0009038801 1 2 0 +54 1 1 0.063746903397246202 0.93824244 0.061086345 0.0006710334 1 2 0 +55 1 1 0.089693777542918976 0.9142111 0.084950805 0.00083812367 1 2 0 +56 1 1 0.085069177512258412 0.91844875 0.07853346 0.0030178856 1 2 0 +57 1 1 0.019546522245977516 0.9806433 0.016897747 0.0024591105 1 0 2 +58 1 1 0.0099081882770904756 0.99014074 0.008955239 0.0009040547 1 2 0 +59 1 1 0.092630883817733156 0.9115299 0.0823354 0.006134713 1 2 0 +60 1 1 0.014457703268027379 0.9856463 0.012556279 0.0017973994 1 2 0 +61 1 1 0.045186017212082943 0.95581967 0.03756768 0.006612597 1 2 0 +62 1 1 0.0040554869713392292 0.9959527 0.0034838247 0.00056346314 1 2 0 +63 1 1 0.10219478993047783 0.90285367 0.09655194 0.0005945007 1 2 0 +64 1 1 0.043745547946894225 0.9571975 0.0403017 0.002500794 1 0 2 +65 1 1 0.0080270808006034106 0.99200505 0.0043637045 0.003631049 1 0 2 +66 1 1 0.25794669864881997 0.7726364 0.22568193 0.0016817307 1 2 0 +67 1 1 0.004681082317533663 0.99532986 0.0024284304 0.0022418906 1 0 2 +68 1 1 0.4186439374882594 0.6579384 0.3419866 7.495434E-05 1 2 0 +69 1 1 0.0088572595356648746 0.99118185 0.006171344 0.002646689 1 2 0 +70 1 2 1.1223549691918893 0.67391926 0.32551232 0.0005684842 2 1 0 +71 1 1 0.01054667309481289 0.98950875 0.006225767 0.0042653275 1 0 2 +72 1 1 0.6119784875601737 0.5422769 0.45768276 4.030501E-05 1 2 0 +73 1 1 0.033711439152709112 0.96685046 0.032761887 0.00038760784 1 2 0 +74 1 1 0.0076026701985775378 0.99242616 0.004710559 0.002863219 1 2 0 +75 1 1 0.0093264821417960372 0.9907169 0.0062752664 0.003007812 1 2 0 +76 1 1 0.030996808891834982 0.96947867 0.030218327 0.00030285056 1 2 0 +77 1 1 0.3659636701213016 0.693528 0.30621776 0.00025408115 1 2 0 +78 1 1 0.12320159568004112 0.8840854 0.114578724 0.0013359514 1 2 0 +79 1 1 0.017106038862047986 0.98303944 0.016605409 0.00035523012 1 0 2 +80 1 1 0.0099058405518300738 0.99014306 0.007177006 0.0026797957 1 2 0 +81 1 1 0.0061030277839462773 0.99391556 0.003708176 0.0023763352 1 0 2 +82 1 1 0.010160932065713054 0.9898905 0.0059115994 0.0041978564 1 0 2 +83 1 2 1.7840146780311217 0.83202463 0.16796248 1.2869889E-05 2 1 0 +84 1 1 0.40920238010603066 0.6641798 0.33437788 0.0014422628 1 2 0 +85 1 1 0.073859680860476701 0.928802 0.06170616 0.0094919205 1 2 0 +86 1 1 0.025666241017175091 0.97466034 0.023897348 0.0014421142 1 2 0 +87 1 1 0.047637449122048356 0.9534794 0.046317182 0.00020365046 1 2 0 +88 1 1 0.023036216088822219 0.9772271 0.014055676 0.008717236 1 2 0 +89 1 1 0.051268336038284672 0.9500237 0.04801822 0.0019581784 1 2 0 +90 1 1 0.088363647401615383 0.9154279 0.08402101 0.00055107626 1 2 0 +91 1 1 0.054470508133182577 0.94698644 0.051688343 0.0013252622 1 2 0 +92 1 1 0.011057368917507868 0.98900354 0.008217381 0.0027790256 1 2 0 +93 1 1 0.014335132717320211 0.9857671 0.011750958 0.0024818496 1 0 2 +94 1 1 0.049708577777940119 0.9515067 0.046586514 0.0019068395 1 2 0 +95 1 1 0.013599843477539789 0.9864922 0.007876568 0.005631309 1 2 0 +96 1 1 0.025343398572549754 0.97497505 0.020928022 0.0040969974 1 2 0 +97 1 1 0.010998730483251765 0.98906153 0.008091495 0.0028467982 1 2 0 +98 1 1 0.08160397308321414 0.9216369 0.077731304 0.0006317606 1 0 2 +99 1 1 0.0230314585956332 0.97723174 0.018601017 0.0041670897 1 2 0 +100 2 2 0.00011063234023480313 0.9998894 0.00011070922 4.351013E-09 2 1 0 +101 2 2 0.013711446914811879 0.9863821 0.013616532 1.2585332E-06 2 1 0 +102 2 2 0.01091575062028363 0.9891436 0.010856191 1.6200677E-07 2 1 0 +103 2 2 0.025952361637010559 0.9743815 0.025617588 6.854851E-07 2 1 0 +104 2 2 0.0015611907874603366 0.99844 0.0015597419 3.6159353E-08 2 1 0 +105 2 2 0.0026664489273118116 0.9973371 0.00266266 2.7131466E-09 2 1 0 +106 2 2 0.030318717531012056 0.9701363 0.02985217 1.1521343E-05 2 1 0 +107 2 2 0.024636260966751176 0.97566473 0.02433561 4.4725848E-08 2 1 0 +108 2 2 0.01164243488614073 0.9884251 0.011575128 3.289459E-08 2 1 0 +109 2 2 0.0019750416673904813 0.9980269 0.0019724772 1.617013E-07 2 1 0 +110 2 2 0.16647675820074742 0.8466425 0.15326092 9.659204E-05 2 1 0 +111 2 2 0.031490808813879896 0.96899986 0.030998776 1.3378963E-06 2 1 0 +112 2 2 0.023404136451361482 0.9768676 0.023130763 1.5998768E-06 2 1 0 +113 2 2 0.0044012222026529255 0.99560845 0.004391366 3.0283564E-07 2 1 0 +114 2 2 0.00051595167606361596 0.9994842 0.0005152728 9.4579214E-08 2 1 0 +115 2 2 0.0073308777219288574 0.9926959 0.007301546 2.5768245E-06 2 1 0 +116 2 2 0.085065867766488085 0.9184518 0.081543766 4.6483183E-06 2 1 0 +117 2 2 0.010478969445945856 0.98957574 0.010424239 1.4843683E-07 2 1 0 +118 2 2 8.4880616417756642E-05 0.9999151 8.498341E-05 7.0823174E-12 2 1 0 +119 2 2 0.14308164654434841 0.8666833 0.13331467 2.218199E-06 2 1 0 +120 2 2 0.0058594012796195543 0.99415773 0.0058418275 4.458627E-07 2 1 0 +121 2 2 0.011374968132936548 0.9886895 0.011307055 3.4667921E-06 2 1 0 +122 2 2 0.0027647054244224279 0.9972391 0.0027604867 8.594396E-10 2 1 0 +123 2 2 0.21725987041570646 0.8047208 0.19524235 3.6747544E-05 2 1 0 +124 2 2 0.018522762201905772 0.98164773 0.018350681 1.7817254E-06 2 1 0 +125 2 2 0.1335644992952405 0.87497103 0.12502682 2.2033246E-06 2 1 0 +126 2 2 0.30912157107031513 0.7340915 0.26580217 0.00010620019 2 1 0 +127 2 2 0.27955964400421202 0.7561166 0.24374475 0.00013872533 2 1 0 +128 2 2 0.0030812956641316051 0.99692345 0.0030768916 6.886699E-08 2 1 0 +129 2 2 0.56300973710059499 0.56949246 0.4305007 6.883423E-06 2 1 0 +130 2 2 0.026556982677033589 0.97379255 0.026207458 7.655899E-08 2 1 0 +131 2 2 0.22159337865988013 0.8012411 0.19875106 7.916578E-06 2 1 0 +132 2 2 0.0015125382870987553 0.9984886 0.0015109567 3.6027792E-08 2 1 0 +133 2 1 0.80118038716036721 0.5511436 0.4487989 5.757113E-05 1 2 0 +134 2 2 0.10909272224640143 0.8966473 0.10335219 7.0668176E-07 2 1 0 +135 2 2 0.0060958914358296692 0.99392265 0.0060769496 4.806114E-08 2 1 0 +136 2 2 0.0014524273624274906 0.9985486 0.0014510206 3.6032796E-07 2 1 0 +137 2 2 0.085394364767160641 0.9181501 0.08184301 6.7438123E-06 2 1 0 +138 2 2 0.31136751231271187 0.73244464 0.26733193 0.00022317962 2 1 0 +139 2 2 0.05913209594350867 0.94258225 0.05740952 8.44917E-06 2 1 0 +140 2 2 0.0018834912921805113 0.9981183 0.0018814779 1.5487406E-07 2 1 0 +141 2 2 0.046989901440894453 0.95409703 0.04587865 2.427137E-05 2 1 0 +142 2 2 0.013711446914811879 0.9863821 0.013616532 1.2585332E-06 2 1 0 +143 2 2 0.0020237166754501316 0.9979783 0.002021656 7.145971E-08 2 1 0 +144 2 2 0.0010795457796386584 0.99892104 0.001079097 1.3495621E-07 2 1 0 +145 2 2 0.014142509019330281 0.985957 0.014039472 3.4864931E-06 2 1 0 +146 2 2 0.045035866284553396 0.9559632 0.044033695 2.8706036E-06 2 1 0 +147 2 2 0.067760959715770355 0.9344838 0.06550267 1.3421985E-05 2 1 0 +148 2 2 0.0049781526314697216 0.9950342 0.0049631833 2.4921453E-06 2 1 0 +149 2 2 0.081277817729632454 0.9219375 0.07804192 2.0546579E-05 2 1 0 diff --git a/test/BaselineOutput/Common/MulticlassLogisticRegression/osx-arm64/LogisticRegression-Non-Negative-TrainTest-iris.txt b/test/BaselineOutput/Common/MulticlassLogisticRegression/osx-arm64/LogisticRegression-Non-Negative-TrainTest-iris.txt index 29213c1449..46a4057a13 100644 --- a/test/BaselineOutput/Common/MulticlassLogisticRegression/osx-arm64/LogisticRegression-Non-Negative-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/MulticlassLogisticRegression/osx-arm64/LogisticRegression-Non-Negative-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.0055160384679411277 0.99449915 0.005502038 5.1773974E-11 0 1 2 -1 0 0 0.023864730958411882 0.9764178 0.023581393 1.0470911E-09 0 1 2 -2 0 0 0.0093345440359657913 0.9907089 0.009291213 3.3886932E-10 0 1 2 -3 0 0 0.023485169105185572 0.97678846 0.023210973 2.9965708E-09 0 1 2 -4 0 0 0.0040397473706441957 0.9959684 0.0040320004 4.1783424E-11 0 1 2 -5 0 0 0.0045280297264245848 0.9954822 0.004518237 6.792683E-11 0 1 2 -6 0 0 0.0069865892171306161 0.99303776 0.006961399 5.4606175E-10 0 1 2 -7 0 0 0.010116555796003303 0.98993444 0.010064013 2.2703014E-10 0 1 2 -8 0 0 0.030149466155855287 0.9703005 0.029698554 6.8450254E-09 0 1 2 -9 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 -10 0 0 0.0043411169825802809 0.9956683 0.0043306584 1.7070477E-11 0 1 2 -11 0 0 0.013576521322325076 0.9865152 0.013485671 8.031634E-10 0 1 2 -12 0 0 0.023572921206410413 0.97670275 0.023298172 8.6137114E-10 0 1 2 -13 0 0 0.0085086580176359974 0.99152744 0.008472632 4.1598563E-10 0 1 2 -14 0 0 0.0007095469417312554 0.9992907 0.00070933235 1.5176845E-13 0 1 2 -15 0 0 0.00055310522407816078 0.99944705 0.00055347907 6.048726E-13 0 1 2 -16 0 0 0.0012550477831004697 0.99874574 0.0012540518 3.9664222E-12 0 1 2 -17 0 0 0.0055160384679411277 0.99449915 0.0055009676 8.31305E-11 0 1 2 -18 0 0 0.0063400554390819954 0.99368 0.0063216076 3.0053654E-11 0 1 2 -19 0 0 0.0030975582921822227 0.99690723 0.0030934948 3.8560665E-11 0 1 2 -20 0 0 0.020122528904036908 0.9800786 0.019920269 3.064467E-10 0 1 2 -21 0 0 0.0041771031887722987 0.9958316 0.0041694827 1.0134493E-10 0 1 2 -22 0 0 0.0010643302939017342 0.99893624 0.0010639144 7.4156245E-12 0 1 2 -23 0 0 0.02605629788130279 0.97428024 0.025720494 4.7576445E-09 0 1 2 -24 0 0 0.035209627128022683 0.965403 0.03459665 6.6329586E-09 0 1 2 -25 0 0 0.045448274212285186 0.955569 0.04443145 3.216728E-09 0 1 2 -26 0 0 0.013916015058125409 0.98618037 0.013818858 1.1871322E-09 0 1 2 -27 0 0 0.0076942651707202743 0.99233526 0.0076657226 7.9652826E-11 0 1 2 -28 0 0 0.007534024516302436 0.9924943 0.0075039454 6.411831E-11 0 1 2 -29 0 0 0.024288162137741393 0.9760044 0.02399573 2.8173113E-09 0 1 2 -30 0 0 0.033046722035427502 0.96749336 0.03250687 3.46561E-09 0 1 2 -31 0 0 0.010627754848336371 0.9894285 0.010573362 1.9241796E-10 0 1 2 -32 0 0 0.0012779052922244011 0.9987229 0.0012773476 2.5791548E-12 0 1 2 -33 0 0 0.00071336435395046255 0.9992869 0.0007130818 5.394398E-13 0 1 2 -34 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 -35 0 0 0.0070380898792076199 0.9929866 0.007013349 7.24567E-11 0 1 2 -36 0 0 0.0042114001992704566 0.99579746 0.004201503 8.375258E-12 0 1 2 -37 0 0 0.024381664764845521 0.97591317 0.02408588 8.098426E-10 0 1 2 -38 0 0 0.016315454412074732 0.9838169 0.01618225 2.0803645E-09 0 1 2 -39 0 0 0.010242464293569139 0.9898098 0.010190028 1.7190988E-10 0 1 2 -40 0 0 0.0039520171010716818 0.9960558 0.003945863 5.4001064E-11 0 1 2 -41 0 0 0.12730980592945779 0.88046086 0.119539626 7.181209E-08 0 1 2 -42 0 0 0.0089912491214826969 0.99104905 0.0089505045 7.80488E-10 0 1 2 -43 0 0 0.010326412100003242 0.9897267 0.010272516 1.874473E-09 0 1 2 -44 0 0 0.011150365797478341 0.98891157 0.011090281 1.0552612E-09 0 1 2 -45 0 0 0.02356529293651816 0.9767102 0.023289332 2.2207163E-09 0 1 2 -46 0 0 0.0042686243571466928 0.9957405 0.0042613307 4.8837566E-11 0 1 2 -47 0 0 0.012689595644547294 0.9873906 0.012608856 9.079638E-10 0 1 2 -48 0 0 0.0042877197577386979 0.99572146 0.0042767935 2.2542229E-11 0 1 2 -49 0 0 0.0099010247223913338 0.9901478 0.009852205 1.827634E-10 0 1 2 -50 1 1 0.0090408081662831513 0.99099994 0.007567417 0.0014314867 1 2 0 -51 1 1 0.034511583658046456 0.96607715 0.031063149 0.002861517 1 2 0 -52 1 1 0.04304121150554157 0.9578719 0.04158025 0.00054618425 1 2 0 -53 1 1 0.13135529742102009 0.87690616 0.122118875 0.0009759098 1 2 0 -54 1 1 0.074932084511623434 0.9278065 0.07160018 0.00059335935 1 2 0 -55 1 1 0.1890525667021884 0.827743 0.1714483 0.00080681086 1 2 0 -56 1 1 0.11877056857344356 0.8880115 0.1100965 0.0018910082 1 2 0 -57 1 1 0.025358193239084965 0.9749606 0.01495395 0.010085882 1 0 2 -58 1 1 0.019096356683429662 0.9810848 0.01808086 0.0008358488 1 2 0 -59 1 1 0.15862658959532094 0.85331494 0.14217468 0.0045111584 1 2 0 -60 1 1 0.038318653357595697 0.9624062 0.035282373 0.0023114693 1 2 0 -61 1 1 0.065311423102221738 0.9367757 0.058971565 0.0042529916 1 2 0 -62 1 1 0.010269141347627856 0.9897834 0.009452066 0.0007660691 1 2 0 -63 1 1 0.17183304995881135 0.84211975 0.1573262 0.00055418257 1 2 0 -64 1 1 0.030368791940673646 0.9700877 0.023280893 0.0066331304 1 0 2 -65 1 1 0.00975415306436043 0.99029326 0.0068194265 0.0028855118 1 2 0 -66 1 1 0.39629741859747109 0.67280656 0.32598528 0.0012099475 1 2 0 -67 1 1 0.012058247747154605 0.98801416 0.009438771 0.0025457656 1 2 0 -68 1 1 0.3368797650804119 0.7139947 0.28589886 0.00010822646 1 2 0 -69 1 1 0.021083825783822494 0.9791369 0.018163126 0.0027012476 1 2 0 -70 1 2 1.0416832150942417 0.6467134 0.35286024 0.00042478985 2 1 0 -71 1 1 0.013755801712349471 0.9863384 0.00910499 0.0045568454 1 2 0 -72 1 1 0.58514020696109537 0.55702776 0.44291526 5.669436E-05 1 2 0 -73 1 1 0.084747339774486161 0.9187444 0.080806226 0.00044783644 1 2 0 -74 1 1 0.012447376934475682 0.9876298 0.010109726 0.0022619804 1 2 0 -75 1 1 0.013278939705380893 0.98680884 0.011033021 0.0021575652 1 2 0 -76 1 1 0.04319766018798589 0.95772207 0.041966196 0.0003101741 1 2 0 -77 1 1 0.30690002516055565 0.73572415 0.2640443 0.00023127515 1 2 0 -78 1 1 0.16997907799864528 0.84368247 0.1552497 0.0010691443 1 2 0 -79 1 1 0.014675126081593009 0.985432 0.013093817 0.0014749394 1 0 2 -80 1 1 0.022987361370576598 0.97727484 0.01993399 0.00278951 1 2 0 -81 1 1 0.012458783376888797 0.9876185 0.008495432 0.003886289 1 2 0 -82 1 1 0.016071326268181901 0.9840571 0.011121038 0.004822609 1 2 0 -83 1 2 1.707477571538714 0.8186583 0.18132259 1.8361572E-05 2 1 0 -84 1 1 0.62495808673272679 0.53528386 0.4637305 0.0009871349 1 2 0 -85 1 1 0.11122891687722759 0.8947339 0.1001901 0.0050765327 1 2 0 -86 1 1 0.036052681021758022 0.9645895 0.03433999 0.0010723303 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+149 2 2 0.14454648917846522 0.8654147 0.13449843 8.879521E-05 2 1 0 diff --git a/test/BaselineOutput/Common/SymSGD/netcoreapp/SymSGD-CV-breast-cancer.txt b/test/BaselineOutput/Common/SymSGD/netcoreapp/SymSGD-CV-breast-cancer.txt new file mode 100644 index 0000000000..d68782c62c --- /dev/null +++ b/test/BaselineOutput/Common/SymSGD/netcoreapp/SymSGD-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 1000.1891 1 -0 1 +6 0 95.69922 1 Infinity 1 +8 0 -334.17828 0 -0 0 +9 0 -235.6391 0 -0 0 +10 0 -277.29367 0 -0 0 +11 0 -322.8632 0 -0 0 +18 1 646.0083 1 -0 1 +20 1 198.0318 1 -0 1 +21 1 459.70312 1 -0 1 +25 1 124.40161 1 -0 1 +28 0 -322.8632 0 -0 0 +31 0 -300.14307 0 -0 0 +32 1 450.40417 1 -0 1 +35 0 -322.8632 0 -0 0 +37 0 -82.94397 9.503676E-37 -0 0 +40 0 ? ? ? 0 +41 1 193.508 1 -0 1 +44 1 640.1284 1 -0 1 +45 0 -325.427 0 -0 0 +46 1 662.572 1 -0 1 +48 0 -313.28973 0 -0 0 +50 1 277.65234 1 -0 1 +51 1 44.778015 1 -0 1 +52 1 271.5711 1 -0 1 +54 1 311.60034 1 -0 1 +56 1 904.77344 1 -0 1 +60 1 94.159424 1 -0 1 +63 1 -9.789154 5.6053155E-05 14.12284488867504 0 +64 0 -328.27826 0 -0 0 +66 0 -289.31293 0 -0 0 +68 1 540.3833 1 -0 1 +69 0 -294.4256 0 -0 0 +70 0 -261.91486 0 -0 0 +71 1 360.4336 1 -0 1 +72 0 -18.042511 1.45961065E-08 2.105773068336081E-08 0 +73 1 337.30237 1 -0 1 +74 1 314.5249 1 -0 1 +76 0 -261.22357 0 -0 0 +77 0 -165.81848 0 -0 0 +79 0 -351.12762 0 -0 0 +82 0 -207.50394 0 -0 0 +88 0 -289.31293 0 -0 0 +90 0 -305.55814 0 -0 0 +91 0 -352.15662 0 -0 0 +92 0 -289.31293 0 -0 0 +93 0 -328.27826 0 -0 0 +95 0 -305.55814 0 -0 0 +96 0 -357.5717 0 -0 0 +97 0 -283.8979 0 -0 0 +98 1 375.3628 1 -0 1 +99 1 541.3288 1 -0 1 +100 1 40.636658 1 -0 1 +102 0 -266.9614 0 -0 0 +104 1 509.3629 1 -0 1 +105 1 -0.046447754 0.48839015 1.0338939978132953 0 +106 1 971.9457 1 -0 1 +108 0 -306.01318 0 -0 0 +109 1 561.1019 1 -0 1 +111 1 429.27478 1 -0 1 +112 1 352.87836 1 -0 1 +113 1 807.48413 1 -0 1 +115 0 -172.66177 0 -0 0 +117 1 497.8714 1 -0 1 +120 0 -295.19498 0 -0 0 +121 0 -168.33484 0 -0 0 +122 1 868.4481 1 -0 1 +123 1 257.67218 1 -0 1 +125 0 -328.27826 0 -0 0 +128 1 268.114 1 -0 1 +129 0 -577.114 0 -0 0 +131 0 -300.14307 0 -0 0 +132 1 745.2488 1 -0 1 +133 0 -306.31558 0 -0 0 +137 0 -340.16824 0 -0 0 +138 0 -289.68152 0 -0 0 +141 0 -345.5833 0 -0 0 +144 0 -322.8632 0 -0 0 +145 0 ? ? ? 0 +147 0 -311.95856 0 -0 0 +150 0 -277.29367 0 -0 0 +151 1 243.12073 1 -0 1 +152 1 862.843 1 -0 1 +154 0 -350.99835 0 -0 0 +156 0 -229.53273 0 -0 0 +161 0 -278.1804 0 -0 0 +164 0 ? ? ? 0 +167 1 283.807 1 -0 1 +169 0 -334.7223 0 -0 0 +171 0 -305.55814 0 -0 0 +173 1 1010.343 1 -0 1 +174 1 608.9114 1 -0 1 +176 0 -300.14307 0 -0 0 +177 1 562.58374 1 -0 1 +179 1 177.81586 1 -0 1 +180 0 -277.29367 0 -0 0 +181 0 -350.99835 0 -0 0 +183 1 808.6741 1 -0 1 +187 1 524.15204 1 -0 1 +188 1 673.8281 1 -0 1 +189 0 -184.46106 0 -0 0 +191 1 429.5337 1 -0 1 +192 0 -306.61798 0 -0 0 +196 0 363.26575 1 Infinity 1 +198 0 -350.99835 0 -0 0 +199 0 -317.44812 0 -0 0 +201 1 687.1731 1 -0 1 +202 0 -305.55814 0 -0 0 +204 0 -305.55814 0 -0 0 +205 1 999.5823 1 -0 1 +206 1 667.10315 1 -0 1 +207 0 -277.29367 0 -0 0 +209 0 -255.63342 0 -0 0 +210 1 1057.675 1 -0 1 +211 1 905.6708 1 -0 1 +212 0 -305.55814 0 -0 0 +216 0 -328.27826 0 -0 0 +218 1 708.58105 1 -0 1 +219 0 -233.7797 0 -0 0 +223 1 528.3917 1 -0 1 +226 1 694.28674 1 -0 1 +228 0 -277.29367 0 -0 0 +233 1 352.99603 1 -0 1 +237 1 444.02045 1 -0 1 +239 1 448.62347 1 -0 1 +240 0 -219.04243 0 -0 0 +241 0 -290.44034 0 -0 0 +242 0 -300.14307 0 -0 0 +244 0 -305.55814 0 -0 0 +246 1 1022.4103 1 -0 1 +247 1 407.5221 1 -0 1 +248 0 -233.65024 0 -0 0 +249 0 ? ? ? 0 +250 0 -252.25284 0 -0 0 +252 0 294.28015 1 Infinity 1 +254 1 734.71045 1 -0 1 +257 0 -317.44812 0 -0 0 +258 0 -294.728 0 -0 0 +259 0 569.26843 1 Infinity 1 +260 1 521.4542 1 -0 1 +262 1 895.22437 1 -0 1 +267 1 399.35107 1 -0 1 +268 1 728.25024 1 -0 1 +269 0 -305.55814 0 -0 0 +271 0 -283.8979 0 -0 0 +272 1 399.35107 1 -0 1 +275 0 ? ? ? 0 +276 0 -317.44812 0 -0 0 +277 0 -328.27826 0 -0 0 +278 0 -305.55814 0 -0 0 +279 1 440.3501 1 -0 1 +280 0 -294.728 0 -0 0 +283 1 553.0797 1 -0 1 +284 1 419.6612 1 -0 1 +285 1 976.37036 1 -0 1 +288 1 33.66107 1 -0 1 +290 0 -350.99835 0 -0 0 +291 0 -305.55814 0 -0 0 +293 1 380.2743 1 -0 1 +296 0 123.29089 1 Infinity 1 +297 0 ? ? ? 0 +299 1 205.4273 1 -0 1 +300 1 383.03644 1 -0 1 +301 0 -305.55814 0 -0 0 +303 0 -305.55814 0 -0 0 +304 1 251.54071 1 -0 1 +308 1 568.57874 1 -0 1 +309 0 -70.95117 1.5356689E-31 -0 0 +311 0 -350.99835 0 -0 0 +312 1 -108.10928 0 Infinity 0 +314 0 -300.0138 0 -0 0 +316 1 460.54492 1 -0 1 +317 1 708.1575 1 -0 1 +319 0 149.38269 1 Infinity 1 +321 0 ? ? ? 0 +323 1 373.12366 1 -0 1 +327 0 -328.27826 0 -0 0 +328 1 573.4951 1 -0 1 +329 1 455.49634 1 -0 1 +331 0 -281.04663 0 -0 0 +332 0 -208.72833 0 -0 0 +333 1 386.81335 1 -0 1 +336 1 410.70654 1 -0 1 +338 0 -300.0138 0 -0 0 +343 0 -350.99835 0 -0 0 +344 1 453.73303 1 -0 1 +346 0 -248.58038 0 -0 0 +347 0 -300.46884 0 -0 0 +348 1 -168.91534 0 Infinity 0 +349 1 214.19824 1 -0 1 +350 0 -353.31488 0 -0 0 +352 0 26.112091 1 Infinity 1 +353 1 631.7583 1 -0 1 +354 0 -328.27826 0 -0 0 +355 0 -329.16498 0 -0 0 +358 1 578.1035 1 -0 1 +360 1 916.9276 1 -0 1 +361 1 671.8186 1 -0 1 +366 1 942.1826 1 -0 1 +368 0 -306.29526 0 -0 0 +370 0 -165.15805 0 -0 0 +371 0 -306.29526 0 -0 0 +373 0 -318.60638 0 -0 0 +376 0 -328.27826 0 -0 0 +377 0 -300.0138 0 -0 0 +378 0 -370.54523 0 -0 0 +379 0 -120.58423 0 -0 0 +381 1 631.9988 1 -0 1 +383 0 -345.5833 0 -0 0 +384 0 -345.5833 0 -0 0 +387 0 -126.6597 0 -0 0 +388 0 -311.73065 0 -0 0 +389 0 -279.16577 0 -0 0 +391 1 929.01294 1 -0 1 +392 0 -317.44812 0 -0 0 +395 0 -317.44812 0 -0 0 +396 0 -294.728 0 -0 0 +398 0 -229.92163 0 -0 0 +399 0 -229.9013 0 -0 0 +404 0 -287.44864 0 -0 0 +406 0 -213.28755 0 -0 0 +409 0 -295.09656 0 -0 0 +413 0 -261.54633 0 -0 0 +414 1 634.50854 1 -0 1 +415 0 -74.0965 6.611506E-33 -0 0 +416 1 576.37305 1 -0 1 +418 0 -138.57281 0 -0 0 +419 0 -277.58878 0 -0 0 +422 0 -69.55682 6.1923467E-31 -0 0 +423 0 -261.91486 0 -0 0 +428 0 -328.27826 0 -0 0 +429 0 -322.8632 0 -0 0 +430 0 -164.35779 0 -0 0 +434 0 679.9729 1 Infinity 1 +436 1 574.03687 1 -0 1 +439 0 -329.3381 0 -0 0 +440 1 412.9361 1 -0 1 +441 0 -132.80533 0 -0 0 +442 0 -285.80685 0 -0 0 +449 1 771.35254 1 -0 1 +450 0 -307.74536 0 -0 0 +451 0 -329.3381 0 -0 0 +452 0 -364.14502 0 -0 0 +453 1 535.7859 1 -0 1 +454 0 -221.05986 0 -0 0 +455 1 22.2547 1 -0 1 +456 1 616.36194 1 -0 1 +457 1 621.3196 1 -0 1 +464 0 -334.75317 0 -0 0 +465 1 656.32996 1 -0 1 +466 1 772.76086 1 -0 1 +467 1 661.41406 1 -0 1 +474 0 -329.3381 0 -0 0 +480 0 -301.07364 0 -0 0 +482 1 755.6924 1 -0 1 +483 1 852.1958 1 -0 1 +484 0 -306.98654 0 -0 0 +487 1 937.19653 1 -0 1 +489 1 24.864746 1 -0 1 +492 0 -283.7686 0 -0 0 +493 1 900.6575 1 -0 1 +495 0 -289.18365 0 -0 0 +497 0 -258.35922 0 -0 0 +501 0 -312.03305 0 -0 0 +502 0 -324.02142 0 -0 0 +504 0 -350.99835 0 -0 0 +507 0 -217.24591 0 -0 0 +510 0 -350.99835 0 -0 0 +513 0 -289.18365 0 -0 0 +514 1 719.33826 1 -0 1 +517 0 -300.0138 0 -0 0 +519 1 782.5841 1 -0 1 +520 0 -379.26282 0 -0 0 +521 0 -363.01764 0 -0 0 +522 1 289.018 1 -0 1 +523 1 448.80402 1 -0 1 +527 0 -289.31293 0 -0 0 +528 0 -291.2083 0 -0 0 +529 0 -283.7686 0 -0 0 +531 0 -213.28755 0 -0 0 +532 0 -277.29367 0 -0 0 +533 0 -317.44812 0 -0 0 +534 0 -322.8632 0 -0 0 +535 0 -272.8434 0 -0 0 +538 0 -312.03305 0 -0 0 +539 0 -301.20294 0 -0 0 +540 0 -262.03384 0 -0 0 +541 0 -340.16824 0 -0 0 +544 0 -290.14847 0 -0 0 +546 1 1038.1329 1 -0 1 +547 0 -317.31885 0 -0 0 +548 0 -311.90378 0 -0 0 +549 1 527.9298 1 -0 1 +557 0 -353.31488 0 -0 0 +558 0 -322.8632 0 -0 0 +559 0 -306.61798 0 -0 0 +560 0 -283.8979 0 -0 0 +561 0 -283.8979 0 -0 0 +563 0 -317.44812 0 -0 0 +565 1 843.4319 1 -0 1 +566 0 -272.37646 0 -0 0 +569 1 725.87305 1 -0 1 +577 0 -328.27826 0 -0 0 +578 0 -328.27826 0 -0 0 +581 1 767.5625 1 -0 1 +582 1 963.59705 1 -0 1 +584 0 -417.54453 0 -0 0 +586 1 1060.5945 1 -0 1 +590 1 609.25183 1 -0 1 +593 0 -306.98654 0 -0 0 +594 1 768.8346 1 -0 1 +600 0 -317.44812 0 -0 0 +602 0 -312.03305 0 -0 0 +604 1 240.23865 1 -0 1 +606 0 -346.74155 0 -0 0 +607 0 -350.99835 0 -0 0 +609 0 -329.3381 0 -0 0 +612 1 1086.2559 1 -0 1 +613 0 -170.07529 0 -0 0 +614 0 -294.59872 0 -0 0 +617 0 ? ? ? 0 +618 0 -312.03305 0 -0 0 +619 0 -306.61798 0 -0 0 +621 0 -20.488556 1.2645419E-09 1.8243483518208995E-09 0 +622 0 -297.4131 0 -0 0 +624 0 -290.34192 0 -0 0 +627 0 -167.2467 0 -0 0 +629 0 -334.75317 0 -0 0 +633 1 371.80853 1 -0 1 +634 0 -340.16824 0 -0 0 +638 0 -334.75317 0 -0 0 +639 0 -353.31488 0 -0 0 +641 0 -317.44812 0 -0 0 +642 0 -317.44812 0 -0 0 +644 0 -345.5833 0 -0 0 +645 0 -317.44812 0 -0 0 +649 0 -317.44812 0 -0 0 +652 0 -295.48544 0 -0 0 +653 0 -312.03305 0 -0 0 +654 0 -294.728 0 -0 0 +656 0 -306.61798 0 -0 0 +657 0 -79.233826 3.883173E-35 -0 0 +660 0 -328.27826 0 -0 0 +661 0 -289.31293 0 -0 0 +665 0 -350.99835 0 -0 0 +668 1 325.23853 1 -0 1 +670 1 794.5818 1 -0 1 +678 0 -350.99835 0 -0 0 +679 0 -345.5833 0 -0 0 +680 1 1116.7966 1 -0 1 +681 1 948.44995 1 -0 1 +682 0 -272.76532 0 -0 0 +683 0 -350.99835 0 -0 0 +685 0 -350.99835 0 -0 0 +688 0 -334.75317 0 -0 0 +689 0 -331.03958 0 -0 0 +691 1 734.78345 1 -0 1 +692 0 -340.16824 0 -0 0 +693 0 -314.59686 0 -0 0 +694 0 -323.84854 0 -0 0 +696 1 745.32556 1 -0 1 +697 1 641.42883 1 -0 1 +698 1 668.19824 1 -0 1 +0 0 -655.84644 0 -0 0 +1 0 352.09045 1 Infinity 1 +2 0 -559.3663 0 -0 0 +3 0 1740.6991 1 Infinity 1 +4 0 -568.0045 0 -0 0 +7 0 -495.54675 0 -0 0 +12 1 241.31317 1 -0 1 +13 0 -462.8861 0 -0 0 +14 1 942.8898 1 -0 1 +15 1 16.798767 0.99999994 8.5991327994145617E-08 1 +16 0 -553.6886 0 -0 0 +17 0 -643.05927 0 -0 0 +19 0 -668.63354 0 -0 0 +22 0 -540.9015 0 -0 0 +23 1 ? ? ? 0 +24 0 -604.6978 0 -0 0 +26 0 -270.65833 0 -0 0 +27 0 -566.47577 0 -0 0 +29 0 -182.08011 0 -0 0 +30 0 -411.08737 0 -0 0 +33 0 -579.9576 0 -0 0 +34 0 -418.96326 0 -0 0 +36 1 1591.2499 1 -0 1 +38 1 1299.1012 1 -0 1 +39 1 138.43103 1 -0 1 +42 1 1330.6154 1 -0 1 +43 1 -316.8241 0 Infinity 0 +47 0 -515.32715 0 -0 0 +49 1 1846.6259 1 -0 1 +53 1 -274.02063 0 Infinity 0 +55 1 1099.3195 1 -0 1 +57 1 -608.20197 0 Infinity 0 +58 1 -331.38577 0 Infinity 0 +59 1 272.1659 1 -0 1 +61 0 -444.42126 0 -0 0 +62 1 1275.4248 1 -0 1 +65 1 -452.49982 0 Infinity 0 +67 1 413.8687 1 -0 1 +75 0 -419.58133 0 -0 0 +78 0 -488.46042 0 -0 0 +80 0 -582.7864 0 -0 0 +81 0 -516.16125 0 -0 0 +83 0 -916.849 0 -0 0 +84 1 897.4657 1 -0 1 +85 1 746.3778 1 -0 1 +86 1 635.3164 1 -0 1 +87 1 1356.6456 1 -0 1 +89 0 -620.40094 0 -0 0 +94 0 -617.485 0 -0 0 +101 1 841.3364 1 -0 1 +103 1 -1044.8297 0 Infinity 0 +107 1 1459.1467 1 -0 1 +110 0 -263.18948 0 -0 0 +114 0 -85.66412 6.2595604E-38 -0 0 +116 0 -16.924438 4.4648793E-08 6.4414593835410614E-08 0 +118 0 -543.7728 0 -0 0 +119 0 -418.93723 0 -0 0 +124 1 404.9671 1 -0 1 +126 1 567.6393 1 -0 1 +127 0 -630.2721 0 -0 0 +130 0 -322.59937 0 -0 0 +134 0 -670.71545 0 -0 0 +135 0 -421.6544 0 -0 0 +136 0 -553.6886 0 -0 0 +139 0 ? ? ? 0 +140 0 -451.53076 0 -0 0 +142 1 488.31128 1 -0 1 +143 0 -142.33295 0 -0 0 +146 1 204.00928 1 -0 1 +148 0 -941.34155 0 -0 0 +149 1 752.699 1 -0 1 +153 0 -322.50616 0 -0 0 +155 1 1089.5981 1 -0 1 +157 0 -617.485 0 -0 0 +158 0 ? ? ? 0 +159 1 1923.4984 1 -0 1 +160 1 1494.5034 1 -0 1 +162 0 -630.2721 0 -0 0 +163 0 -310.77014 0 -0 0 +165 0 -490.5103 0 -0 0 +166 1 1570.0204 1 -0 1 +168 0 -630.2721 0 -0 0 +170 0 -451.53076 0 -0 0 +172 0 -515.32715 0 -0 0 +175 1 1194.7078 1 -0 1 +178 0 -643.05927 0 -0 0 +182 0 -668.63354 0 -0 0 +184 1 1070.4229 1 -0 1 +185 0 -487.67093 0 -0 0 +186 1 1482.4264 1 -0 1 +190 1 2258.6045 1 -0 1 +193 0 -604.6978 0 -0 0 +194 0 -630.2721 0 -0 0 +195 0 -643.05927 0 -0 0 +197 0 -543.2643 0 -0 0 +200 1 1415.3917 1 -0 1 +203 0 -655.84644 0 -0 0 +208 0 -474.88376 0 -0 0 +213 1 2248.6738 1 -0 1 +214 1 2270.229 1 -0 1 +215 1 1644.9979 1 -0 1 +217 0 -604.6978 0 -0 0 +220 0 -567.1704 0 -0 0 +221 1 221.4364 1 -0 1 +222 1 -65.19629 4.848545E-29 94.058362912090217 0 +224 1 1288.9967 1 -0 1 +225 0 -515.32715 0 -0 0 +227 1 1841.0248 1 -0 1 +229 1 1514.782 1 -0 1 +230 1 930.4315 1 -0 1 +231 1 1347.1566 1 -0 1 +232 0 355.9265 1 Infinity 1 +234 0 50.925537 1 Infinity 1 +235 0 ? ? ? 0 +236 1 1204.765 1 -0 1 +238 1 2421.6172 1 -0 1 +243 0 -499.81567 0 -0 0 +245 0 -547.4132 0 -0 0 +251 1 963.10803 1 -0 1 +253 1 1330.6154 1 -0 1 +255 1 1480.2573 1 -0 1 +256 0 -451.53076 0 -0 0 +261 1 1146.0453 1 -0 1 +263 1 671.7936 1 -0 1 +264 1 168.76099 1 -0 1 +265 0 -208.3888 0 -0 0 +266 1 1778.7516 1 -0 1 +270 1 1587.8641 1 -0 1 +273 1 71.01257 1 -0 1 +274 0 -548.629 0 -0 0 +281 0 -579.9576 0 -0 0 +282 1 1006.2108 1 -0 1 +286 1 1933.3766 1 -0 1 +287 0 -670.71545 0 -0 0 +289 1 1586.0116 1 -0 1 +292 1 ? ? ? 0 +294 0 ? ? ? 0 +295 1 906.5337 1 -0 1 +298 0 -764.4797 0 -0 0 +302 1 1682.4965 1 -0 1 +305 1 1757.9471 1 -0 1 +306 0 -604.6978 0 -0 0 +307 0 -604.6978 0 -0 0 +310 0 -657.92834 0 -0 0 +313 0 -425.95645 0 -0 0 +315 0 ? ? ? 0 +318 0 -994.1401 0 -0 0 +320 1 276.64636 1 -0 1 +322 0 -630.2721 0 -0 0 +324 0 -604.6978 0 -0 0 +325 0 -115.24805 0 -0 0 +326 1 -15.441406 1.9673534E-07 22.27724050916763 0 +330 1 698.8905 1 -0 1 +334 1 1535.5616 1 -0 1 +335 0 -425.95645 0 -0 0 +337 0 -604.6978 0 -0 0 +339 1 1217.4391 1 -0 1 +340 1 493.83777 1 -0 1 +341 0 -604.6978 0 -0 0 +342 0 -438.74362 0 -0 0 +345 0 -425.95645 0 -0 0 +351 0 -617.485 0 -0 0 +356 1 -20.483704 1.2706928E-09 29.551737594786047 0 +357 1 1071.4106 1 -0 1 +359 1 718.9486 1 -0 1 +362 0 -396.35065 0 -0 0 +363 0 412.75226 1 Infinity 1 +364 0 -617.485 0 -0 0 +365 0 -528.1143 0 -0 0 +367 1 1990.4601 1 -0 1 +369 0 -141.63672 0 -0 0 +372 0 -431.7504 0 -0 0 +374 0 -418.96326 0 -0 0 +375 0 -425.95645 0 -0 0 +380 0 -425.95645 0 -0 0 +382 0 -248.73444 0 -0 0 +385 0 -124.03369 0 -0 0 +386 1 986.1404 1 -0 1 +390 0 -477.79977 0 -0 0 +393 0 -296.1424 0 -0 0 +394 0 -131.02173 0 -0 0 +397 0 -464.3179 0 -0 0 +400 1 1161.0793 1 -0 1 +401 0 -451.53076 0 -0 0 +402 0 -41.741272 7.44725E-19 -0 0 +403 0 -238.81316 0 -0 0 +405 0 -515.32715 0 -0 0 +407 0 -515.32715 0 -0 0 +408 0 -106.25546 0 -0 0 +410 0 -515.32715 0 -0 0 +411 0 ? ? ? 0 +412 1 1142.7122 1 -0 1 +417 0 -515.32715 0 -0 0 +420 0 -151.03839 0 -0 0 +421 1 1101.1173 1 -0 1 +424 0 -451.53076 0 -0 0 +425 1 1700.8898 1 -0 1 +426 0 413.44397 1 Infinity 1 +427 1 1406.7853 1 -0 1 +431 0 -758.77673 0 -0 0 +432 0 -484.83264 0 -0 0 +433 0 -114.108734 0 -0 0 +435 1 1690.2623 1 -0 1 +437 0 -464.3179 0 -0 0 +438 0 -145.38675 0 -0 0 +443 0 -355.0506 0 -0 0 +444 0 -508.6529 0 -0 0 +445 0 -438.74362 0 -0 0 +446 0 -425.95645 0 -0 0 +447 0 -477.10504 0 -0 0 +448 0 -296.1424 0 -0 0 +458 0 -355.16684 0 -0 0 +459 0 -233.22867 0 -0 0 +460 0 -402.05032 0 -0 0 +461 0 -167.90726 0 -0 0 +462 0 -414.83746 0 -0 0 +463 0 -382.6748 0 -0 0 +468 0 -464.3179 0 -0 0 +469 0 -393.38895 0 -0 0 +470 0 -411.08737 0 -0 0 +471 0 -414.83746 0 -0 0 +472 0 -360.07812 0 -0 0 +473 0 -464.3179 0 -0 0 +475 0 -451.53076 0 -0 0 +476 0 -342.3797 0 -0 0 +477 0 -464.3179 0 -0 0 +478 0 -336.676 0 -0 0 +479 1 1756.934 1 -0 1 +481 0 38.273315 1 Infinity 1 +485 0 -79.27588 3.723259E-35 -0 0 +486 0 -411.08737 0 -0 0 +488 1 1032.361 1 -0 1 +490 0 -425.95645 0 -0 0 +491 1 1566.1005 1 -0 1 +494 0 -82.79584 1.1021094E-36 -0 0 +496 0 -296.1424 0 -0 0 +498 0 -553.6886 0 -0 0 +499 0 -553.6886 0 -0 0 +500 0 -668.63354 0 -0 0 +503 0 -643.05927 0 -0 0 +505 0 -170.6727 0 -0 0 +506 1 1927.5609 1 -0 1 +508 0 -477.10504 0 -0 0 +509 0 -438.74362 0 -0 0 +511 0 -566.47577 0 -0 0 +512 0 -477.10504 0 -0 0 +515 1 1918.6837 1 -0 1 +516 0 -296.1424 0 -0 0 +518 0 -292.0652 0 -0 0 +524 0 -540.9015 0 -0 0 +525 0 -414.0034 0 -0 0 +526 0 -464.3179 0 -0 0 +530 1 944.2931 1 -0 1 +536 0 -655.84644 0 -0 0 +537 0 -533.9082 0 -0 0 +542 0 -196.24692 0 -0 0 +543 0 -553.6886 0 -0 0 +545 0 -566.47577 0 -0 0 +550 0 -540.9015 0 -0 0 +551 0 -604.6978 0 -0 0 +552 0 -338.1051 0 -0 0 +553 0 240.83246 1 Infinity 1 +554 0 -451.53076 0 -0 0 +555 0 119.92975 1 Infinity 1 +556 0 -136.76703 0 -0 0 +562 0 -604.6978 0 -0 0 +564 0 -561.4162 0 -0 0 +567 0 -411.87683 0 -0 0 +568 1 595.40564 1 -0 1 +570 1 542.1084 1 -0 1 +571 1 1942.8524 1 -0 1 +572 0 -540.9015 0 -0 0 +573 0 -515.32715 0 -0 0 +574 1 1153.9879 1 -0 1 +575 0 -533.9082 0 -0 0 +576 0 -566.47577 0 -0 0 +579 0 -604.6978 0 -0 0 +580 0 -444.53754 0 -0 0 +583 0 -451.53076 0 -0 0 +585 0 -425.95645 0 -0 0 +587 0 -484.83264 0 -0 0 +588 1 962.9796 1 -0 1 +589 0 -477.10504 0 -0 0 +591 1 1292.7548 1 -0 1 +592 1 495.96814 1 -0 1 +595 0 -566.47577 0 -0 0 +596 0 -431.7504 0 -0 0 +597 0 -411.97003 0 -0 0 +598 0 -540.9015 0 -0 0 +599 0 158.95264 1 Infinity 1 +601 0 -385.51306 0 -0 0 +603 1 666.12964 1 -0 1 +605 1 1270.6947 1 -0 1 +608 1 1017.25073 1 -0 1 +610 1 401.65607 1 -0 1 +611 1 1604.3878 1 -0 1 +615 0 -309.81223 0 -0 0 +616 0 -540.9015 0 -0 0 +620 0 -540.9015 0 -0 0 +623 0 -425.95645 0 -0 0 +625 0 -124.750854 0 -0 0 +626 1 592.06555 1 -0 1 +628 0 -438.74362 0 -0 0 +630 0 -105.58676 0 -0 0 +631 0 -566.47577 0 -0 0 +632 0 -425.95645 0 -0 0 +635 0 -85.716156 5.942189E-38 -0 0 +636 1 933.86743 1 -0 1 +637 0 98.515686 1 Infinity 1 +640 0 -389.26315 0 -0 0 +643 0 -425.95645 0 -0 0 +646 0 -163.5892 0 -0 0 +647 0 -350.9017 0 -0 0 +648 1 893.8413 1 -0 1 +650 0 -331.09985 0 -0 0 +651 0 -299.84335 0 -0 0 +655 0 -540.9015 0 -0 0 +658 1 1548.1578 1 -0 1 +659 0 -425.95645 0 -0 0 +662 0 -271.4508 0 -0 0 +663 0 -271.4508 0 -0 0 +664 0 -465.8467 0 -0 0 +666 0 -209.53821 0 -0 0 +667 0 -630.2721 0 -0 0 +669 1 2239.6367 1 -0 1 +671 0 -452.3157 0 -0 0 +672 0 -617.485 0 -0 0 +673 0 -204.12277 0 -0 0 +674 0 -515.32715 0 -0 0 +675 0 -98.50333 1.67E-43 -0 0 +676 0 -393.38895 0 -0 0 +677 0 -477.10504 0 -0 0 +684 0 -425.95645 0 -0 0 +686 0 -425.95645 0 -0 0 +687 0 -377.61526 0 -0 0 +690 0 -350.9017 0 -0 0 +695 0 -438.74362 0 -0 0 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt index 357e702af8..078266882b 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt @@ -88,28 +88,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 232 | 7 | 0.9707 + positive || 231 | 8 | 0.9665 negative || 9 | 435 | 0.9797 ||====================== -Precision || 0.9627 | 0.9842 | -OVERALL 0/1 ACCURACY: 0.976574 -LOG LOSS/instance: 0.112829 +Precision || 0.9625 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.112863 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.879198 -AUC: 0.996259 +LOG-LOSS REDUCTION (RIG): 0.879162 +AUC: 0.996249 OVERALL RESULTS --------------------------------------- -AUC: 0.996259 (0.0000) -Accuracy: 0.976574 (0.0000) -Positive precision: 0.962656 (0.0000) -Positive recall: 0.970711 (0.0000) -Negative precision: 0.984163 (0.0000) +AUC: 0.996249 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.962500 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981941 (0.0000) Negative recall: 0.979730 (0.0000) -Log-loss: 0.112829 (0.0000) -Log-loss reduction: 0.879198 (0.0000) -F1 Score: 0.966667 (0.0000) -AUPRC: 0.992451 (0.0000) +Log-loss: 0.112863 (0.0000) +Log-loss reduction: 0.879162 (0.0000) +F1 Score: 0.964509 (0.0000) +AUPRC: 0.992435 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer.txt index 90e103c1e2..54dcc900e1 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/linux-arm64/WE-Hetero-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.2653842 0.015952274 0.023199807675989368 0 -1 0 2.3943 0.8926791 3.2199969729837079 1 -2 0 -3.5103545 0.012222463 0.017741934441503331 0 -3 0 2.3016274 0.8824897 3.0891406473343408 1 -4 0 -2.964328 0.02209367 0.032231814122084979 0 -5 1 9.490734 0.99995196 6.9310673199979037E-05 1 -6 0 -1.4822237 0.10377876 0.1580731722532151 0 -7 0 -3.8577213 0.008365857 0.012120149713014752 0 -8 0 -3.6269126 0.010764261 0.015613732363990333 0 -9 0 -3.6241956 0.010796209 0.015660326004814831 0 -10 0 -4.5891733 0.0037519955 0.0054231655326512475 0 -11 0 -4.310881 0.0050926157 0.0073658631702685686 0 -12 1 -0.49860787 0.2551422 1.9706264900233927 0 -13 0 -3.926801 0.0077570654 0.011234710461103907 0 -14 1 6.8671675 0.9991336 0.001250511714607687 1 -15 1 1.0072587 0.64314145 0.63679202513504418 1 -16 0 -3.7142038 0.009786176 0.014188003892544484 0 -17 0 -3.5423803 0.011803397 0.017129998433039258 0 -18 1 6.1876116 0.9981689 0.0026441611286903101 1 -19 0 -2.7536023 0.02771238 0.040544943786311564 0 -20 1 4.5410404 0.98885375 0.016170926765872271 1 -21 1 6.042017 0.99785066 0.0031041843706052876 1 -22 0 -3.9912 0.0072291996 0.01046741171687173 0 +0 0 -3.2653844 0.01609207 0.023404773580146997 0 +1 0 2.3942997 0.8930092 3.2244411568032052 1 +2 0 -3.5103545 0.012332842 0.017903156367001596 0 +3 0 2.3300252 0.8860532 3.1335677427618682 1 +4 0 -2.9643283 0.02227979 0.032506420017895478 0 +5 1 9.490736 0.9999518 6.9568659593352019E-05 1 +6 0 -1.6140785 0.09161906 0.13863065882610109 0 +7 0 -3.8577213 0.008444489 0.012234552658491686 0 +8 0 -3.6269126 0.010862814 0.015757469294557003 0 +9 0 -3.6241956 0.010895023 0.015804447933838124 0 +10 0 -4.5891733 0.003790069 0.0054783017558651982 0 +11 0 -4.3108807 0.0051428624 0.0074387267544352214 0 +12 1 -0.49860787 0.256326 1.9639483279716188 0 +13 0 -3.9268007 0.007830536 0.011341538789164538 0 +14 1 6.8671675 0.9991329 0.0012515445057059728 1 +15 1 1.0072587 0.64423734 0.63433581425753083 1 +16 0 -3.7142036 0.009876682 0.014319874162656562 0 +17 0 -3.5423803 0.011910398 0.017286219944511169 0 +18 1 6.1876116 0.9981685 0.0026446780232283022 1 +19 0 -2.7876728 0.026937503 0.039395627305407972 0 +20 1 4.5410404 0.98886913 0.016148491102969308 1 +21 1 6.0276127 0.99781626 0.0031539090959622827 1 +22 0 -3.9911995 0.0072981557 0.010567622171575815 0 23 1 ? ? ? 0 -24 0 -4.5614243 0.0038681172 0.0055913344991073621 0 -25 1 0.95733976 0.63041174 0.66563368347093177 1 -26 0 -3.9880347 0.007254291 0.010503874761927334 0 -27 0 -3.4372077 0.013235498 0.019222278344108352 0 -28 0 -4.310881 0.0050926157 0.0073658631702685686 0 -29 0 -4.1751075 0.005910179 0.0085518826419797075 0 -30 0 -4.12895 0.006216839 0.0089969982438126914 0 -31 0 -4.1808977 0.005872787 0.0084976174910000781 0 -32 1 5.927128 0.9975611 0.0035228907965977533 1 -33 0 -4.0466037 0.006803707 0.0098492183881150851 0 -34 0 -3.6587865 0.010396388 0.015077329328009545 0 -35 0 -4.310881 0.0050926157 0.0073658631702685686 0 -36 1 7.264369 0.99944067 0.00080716835563049752 1 -37 0 -1.6018027 0.092140414 0.13945891409760244 0 -38 1 4.131503 0.9826025 0.02532021885827818 1 -39 1 1.4290619 0.74156404 0.43135681746337273 1 +24 0 -4.561424 0.0039072633 0.0056480307271758777 0 +25 1 0.95733976 0.6315355 0.66306432895578327 1 +26 0 -3.9880342 0.0073234625 0.010604401090954271 0 +27 0 -3.4372077 0.013353979 0.019395513403520501 0 +28 0 -4.3108807 0.0051428624 0.0074387267544352214 0 +29 0 -4.175107 0.0059676734 0.0086353248944514086 0 +30 0 -4.12895 0.0062770187 0.0090843650252649174 0 +31 0 -4.180897 0.0059299525 0.0085805794795519568 0 +32 1 5.927128 0.9975613 0.0035226321919329248 1 +33 0 -4.0466037 0.006868991 0.0099440513860409134 0 +34 0 -3.6587865 0.010491926 0.015216616062819949 0 +35 0 -4.3108807 0.0051428624 0.0074387267544352214 0 +36 1 7.264369 0.99944 0.00080811478988956963 1 +37 0 -1.5557506 0.09710908 0.14737639041341738 0 +38 1 4.131503 0.982633 0.025275412463056989 1 +39 1 1.4290624 0.7424016 0.42972827539403025 1 40 0 ? ? ? 0 -41 1 2.4566758 0.8990924 0.1534587434575343 1 -42 1 6.960308 0.9992181 0.001128475438458492 1 -43 1 0.5260675 0.5146095 0.95844996063218424 1 -44 1 6.5539103 0.99877656 0.0017661385528755422 1 -45 0 -4.5121536 0.0040831906 0.0059028582649527928 0 -46 1 3.7554107 0.9738954 0.038161308108430485 1 -47 0 -4.6772075 0.0034060962 0.0049223459274076384 0 -48 0 -2.964328 0.02209367 0.032231814122084979 0 -49 1 4.9304743 0.99271655 0.010546249330477866 1 -50 1 1.8948941 0.8274625 0.27323417318850318 1 -51 1 -0.28067923 0.3034168 1.7206271802708712 0 -52 1 4.19266 0.9837185 0.023682539397784308 1 -53 1 5.4079657 0.995685 0.006238725823479274 1 -54 1 4.82276 0.99180555 0.011870791402737474 1 -55 1 4.2160063 0.9841257 0.023085534049573704 1 -56 1 5.2144136 0.99466395 0.0077188999857641426 1 -57 1 1.0327675 0.6495705 0.62244192548725341 1 -58 1 1.4143667 0.7384466 0.43743451024071284 1 -59 1 1.0671694 0.6581552 0.60350026168407533 1 -60 1 1.8759899 0.8244663 0.27846759091542378 1 -61 0 -4.3741302 0.0047511896 0.0068708530099692734 0 -62 1 5.7681594 0.9970952 0.0041967995833654863 1 -63 1 0.13153982 0.40695703 1.2970516233714999 1 -64 0 -4.6772075 0.0034060962 0.0049223459274076384 0 -65 1 2.4733298 0.9007462 0.15080739241425892 1 -66 0 -3.5423803 0.011803397 0.017129998433039258 0 -67 1 3.0271864 0.94354695 0.083833786891299319 1 -68 1 7.686926 0.9996489 0.0005065778344789803 1 -69 0 -4.360554 0.0048225005 0.0069742277361959345 0 -70 0 -2.8253188 0.025659462 0.037502004316218585 0 -71 1 5.8321104 0.99729246 0.0039114535608829881 1 -72 0 -2.5583076 0.034143705 0.050119541229370387 0 -73 1 6.3776765 0.99851453 0.0021446691246249767 1 -74 1 2.3740892 0.8905254 0.1672713326871515 1 -75 0 -3.6002493 0.011081863 0.016076996059940792 0 -76 0 -3.7219477 0.009703779 0.014067960469962591 0 -77 0 -2.7897956 0.026657125 0.038979988799380721 0 -78 0 -3.2013326 0.017099671 0.024882967480084003 0 -79 0 -4.2326174 0.005549056 0.0080278898201274346 0 -80 0 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-4.3785825 0.004775005 0.0069053756819761388 0 +691 1 3.916963 0.97810405 0.031940141424689809 1 +692 0 -4.163023 0.0060471655 0.0087507008750277743 0 +693 0 -3.8830423 0.008214092 0.011899368056717551 0 +694 0 -3.7162507 0.009854654 0.014287776989780327 0 +695 0 -4.4408646 0.004459773 0.0064484827624683733 0 +696 1 5.3014183 0.9951529 0.0070099032168673631 1 +697 1 3.0674176 0.9460074 0.0800766703186329 1 +698 1 3.9357042 0.9785419 0.031294452528203641 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-out.txt index 28ed905c91..7534310404 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-out.txt @@ -25,23 +25,23 @@ TRUTH ||====================== ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 -LOG LOSS/instance: 0.112168 +LOG LOSS/instance: 0.110238 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.879907 -AUC: 0.996240 +LOG-LOSS REDUCTION (RIG): 0.881972 +AUC: 0.996325 OVERALL RESULTS --------------------------------------- -AUC: 0.996240 (0.0000) +AUC: 0.996325 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) -Log-loss: 0.112168 (0.0000) -Log-loss reduction: 0.879907 (0.0000) +Log-loss: 0.110238 (0.0000) +Log-loss reduction: 0.881972 (0.0000) F1 Score: 0.962500 (0.0000) -AUPRC: 0.992400 (0.0000) +AUPRC: 0.992721 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-rp.txt index 5a83bbedba..38d5dd673b 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ WeightedEnsemble AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.99624 0.973646 0.958506 0.966527 0.9819 0.977477 0.112168 0.879907 0.9625 0.9924 AvgPer 3 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:AvgPer;/nm:3 +0.996325 0.973646 0.958506 0.966527 0.9819 0.977477 0.110238 0.881972 0.9625 0.992721 AvgPer 3 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:AvgPer;/nm:3 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer.txt index 59ef27a617..f3feea2b57 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-AvgPer-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.482872 0.02253428 0.032881986053906846 0 -1 0 2.9472694 0.89578646 3.2623854205088092 1 -2 0 -4.2678914 0.011065353 0.016052909740335439 0 -3 0 2.986452 0.899107 3.3091017167851975 1 -4 0 -3.5019517 0.022150515 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+21 1 7.010025 0.99764335 0.0034039375442231515 1 +22 0 -5.10437 0.005542395 0.0080182267005007406 0 23 1 ? ? ? 0 -24 0 -5.7595983 0.002824923 0.0040812697914632779 0 -25 1 1.2282028 0.63836163 0.64755415040416864 1 -26 0 -4.4920645 0.0090201 0.013072299802577814 0 -27 0 -3.7689247 0.017406758 0.025333778099074687 0 -28 0 -5.476469 0.0036632805 0.0052947005483042842 0 -29 0 -5.952343 0.0023665943 0.0034183203055802051 0 -30 0 -4.7488623 0.007134092 0.010329207933888177 0 -31 0 -5.1904163 0.0047619957 0.0068865174780414111 0 -32 1 6.630596 0.99610066 0.0056365490645245509 1 -33 0 -4.8385086 0.0065725357 0.0095134628469589824 0 -34 0 -4.7176075 0.0073408717 0.010629702792356348 0 -35 0 -5.476469 0.0036632805 0.0052947005483042842 0 -36 1 8.456722 0.9992721 0.0010505084408715286 1 -37 0 -1.3033309 0.14642769 0.22841472084732017 0 -38 1 4.9043865 0.9811732 0.027420241139352593 1 -39 1 0.99844265 0.58823794 0.76552825403251523 1 +24 0 -5.9414234 0.0025572449 0.0036940497744623112 0 +25 1 0.897665 0.5933967 0.7529311345918871 1 +26 0 -4.6209464 0.008651597 0.012535923114455177 0 +27 0 -4.0081143 0.015174806 0.022060425445491177 0 +28 0 -5.652498 0.0033406552 0.0048276149428202837 0 +29 0 -6.039849 0.00233462 0.0033720826473683787 0 +30 0 -4.9185214 0.006578372 0.0095219383371659885 0 +31 0 -5.3906345 0.0042553055 0.0061522072207014699 0 +32 1 6.630597 0.9966525 0.0048375462257031088 1 +33 0 -5.0246115 0.0059654796 0.0086321410137232147 0 +34 0 -4.889058 0.0067594172 0.0097848851791912557 0 +35 0 -5.652498 0.0033406552 0.0048276149428202837 0 +36 1 8.000793 0.99905866 0.0013587005999149109 1 +37 0 -1.692574 0.11662218 0.17889748507231804 0 +38 1 4.9043856 0.9836149 0.02383447391076737 1 +39 1 0.99844265 0.6157415 0.69960330804376314 1 40 0 ? ? ? 0 -41 1 3.018794 0.9017767 0.14915790635460019 1 -42 1 7.673623 0.9985041 0.0021597400725719149 1 -43 1 0.637537 0.50608635 0.9825445333891023 1 -44 1 8.207812 0.9990848 0.0013210014038608894 1 -45 0 -5.777409 0.0027790968 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0.0444930893633559 0 +674 0 -3.1699133 0.005674961 0.0082105575200780885 0 +675 0 -2.0441408 0.023279706 0.033982622004851887 0 +676 0 -2.9698434 0.0073042074 0.010576417192379716 0 +677 0 -2.3910747 0.01511084 0.021966723054784913 0 +684 0 -3.299302 0.004819355 0.0069696676947273014 0 +686 0 -3.299302 0.004819355 0.0069696676947273014 0 +687 0 -2.4359035 0.014286714 0.020760022876868865 0 +690 0 -3.05244 0.006581785 0.0095268953013750626 0 +695 0 -3.1115277 0.006108973 0.0088404158141125212 0 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..242ef9cd77 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer-out.txt @@ -0,0 +1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 130 instances with missing features during training (over 10 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 170 instances with missing features during training (over 10 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 190 instances with missing features during training (over 10 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 231 | 8 | 0.9665 + negative || 9 | 435 | 0.9797 + ||====================== +Precision || 0.9625 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.112669 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.879370 +AUC: 0.996287 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996287 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.962500 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981941 (0.0000) +Negative recall: 0.979730 (0.0000) +Log-loss: 0.112669 (0.0000) +Log-loss reduction: 0.879370 (0.0000) +F1 Score: 0.964509 (0.0000) +AUPRC: 0.992508 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..3895e807ec --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996287 0.97511 0.9625 0.966527 0.981941 0.97973 0.112669 0.87937 0.964509 0.992508 svm,ap 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:svm,ap;/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..e02bc0a582 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Hetero-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.2653847 0.01606247 0.023361373063565786 0 +1 0 2.3942997 0.89365447 3.233168642152529 1 +2 0 -3.5103545 0.012305525 0.017863255262247732 0 +3 0 2.330025 0.8867252 3.1421009895524099 1 +4 0 -2.964328 0.022249075 0.032461098556400524 0 +5 1 9.490736 0.9999526 6.836472348564471E-05 1 +6 0 -1.6140783 0.09167324 0.13871671132075303 0 +7 0 -3.8577213 0.008421281 0.012200786382637996 0 +8 0 -3.6269126 0.01083681 0.015719541380870845 0 +9 0 -3.6241956 0.010868987 0.015766472600222039 0 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-2.9305813 0.023073524 0.033678106944583668 0 +674 0 -4.6772075 0.0034271795 0.0049528669508363014 0 +675 0 -3.0559897 0.020152379 0.029370685732283856 0 +676 0 -4.3682995 0.0048120874 0.0069591320626229613 0 +677 0 -3.6090312 0.01105032 0.016030979963451743 0 +678 0 -4.7929907 0.0030174674 0.0043598665120151139 0 +679 0 -4.4408646 0.0044435263 0.0064249385978414108 0 +680 1 12.114309 0.9999974 3.7836232804574336E-06 1 +681 1 7.529873 0.999588 0.00059449451188820618 1 +682 0 -3.1010995 0.019192826 0.027958563399165529 0 +683 0 -4.7929907 0.0030174674 0.0043598665120151139 0 +684 0 -4.7929907 0.0030174674 0.0043598665120151139 0 +685 0 -4.7929907 0.0030174674 0.0043598665120151139 0 +686 0 -4.7929907 0.0030174674 0.0043598665120151139 0 +687 0 -3.6626296 0.010422478 0.015115365276159273 0 +688 0 -3.8860273 0.008164489 0.011827214924006168 0 +689 0 -3.6151924 0.010976288 0.015922983970064867 0 +690 0 -4.3785825 0.0047580656 0.0068808203063086652 0 +691 1 3.916964 0.97829807 0.031654002126526269 1 +692 0 -4.163023 0.006027712 0.0087224649837247988 0 +693 0 -3.8830423 0.008191198 0.011866066162447262 0 +694 0 -3.716251 0.009829708 0.014251430548210188 0 +695 0 -4.4408646 0.0044435263 0.0064249385978414108 0 +696 1 5.3014183 0.99520665 0.0069319633534266831 1 +697 1 3.0674176 0.94640434 0.079471408542772501 1 +698 1 3.9357042 0.97873265 0.03101327353736098 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt index 357e702af8..078266882b 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt @@ -88,28 +88,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 232 | 7 | 0.9707 + positive || 231 | 8 | 0.9665 negative || 9 | 435 | 0.9797 ||====================== -Precision || 0.9627 | 0.9842 | -OVERALL 0/1 ACCURACY: 0.976574 -LOG LOSS/instance: 0.112829 +Precision || 0.9625 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.112863 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.879198 -AUC: 0.996259 +LOG-LOSS REDUCTION (RIG): 0.879162 +AUC: 0.996249 OVERALL RESULTS --------------------------------------- -AUC: 0.996259 (0.0000) -Accuracy: 0.976574 (0.0000) -Positive precision: 0.962656 (0.0000) -Positive recall: 0.970711 (0.0000) -Negative precision: 0.984163 (0.0000) +AUC: 0.996249 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.962500 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981941 (0.0000) Negative recall: 0.979730 (0.0000) -Log-loss: 0.112829 (0.0000) -Log-loss reduction: 0.879198 (0.0000) -F1 Score: 0.966667 (0.0000) -AUPRC: 0.992451 (0.0000) +Log-loss: 0.112863 (0.0000) +Log-loss reduction: 0.879162 (0.0000) +F1 Score: 0.964509 (0.0000) +AUPRC: 0.992435 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer.txt index 9b29d47b50..54dcc900e1 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.2653842 0.015952274 0.023199807675989368 0 -1 0 2.3943 0.8926791 3.2199969729837079 1 -2 0 -3.5103545 0.012222463 0.017741934441503331 0 -3 0 2.3016274 0.8824897 3.0891406473343408 1 -4 0 -2.964328 0.02209367 0.032231814122084979 0 -5 1 9.490734 0.99995196 6.9310673199979037E-05 1 -6 0 -1.4822237 0.10377876 0.1580731722532151 0 -7 0 -3.8577213 0.008365857 0.012120149713014752 0 -8 0 -3.6269126 0.010764261 0.015613732363990333 0 -9 0 -3.6241956 0.010796209 0.015660326004814831 0 -10 0 -4.5891733 0.0037519955 0.0054231655326512475 0 -11 0 -4.310881 0.0050926157 0.0073658631702685686 0 -12 1 -0.49860787 0.2551422 1.9706264900233927 0 -13 0 -3.926801 0.0077570654 0.011234710461103907 0 -14 1 6.8671675 0.9991336 0.001250511714607687 1 -15 1 1.0072587 0.64314145 0.63679202513504418 1 -16 0 -3.7142038 0.009786176 0.014188003892544484 0 -17 0 -3.5423803 0.011803397 0.017129998433039258 0 -18 1 6.1876116 0.9981689 0.0026441611286903101 1 -19 0 -2.7536023 0.02771238 0.040544943786311564 0 -20 1 4.5410404 0.98885375 0.016170926765872271 1 -21 1 6.042017 0.99785066 0.0031041843706052876 1 -22 0 -3.9912 0.0072291996 0.01046741171687173 0 +0 0 -3.2653844 0.01609207 0.023404773580146997 0 +1 0 2.3942997 0.8930092 3.2244411568032052 1 +2 0 -3.5103545 0.012332842 0.017903156367001596 0 +3 0 2.3300252 0.8860532 3.1335677427618682 1 +4 0 -2.9643283 0.02227979 0.032506420017895478 0 +5 1 9.490736 0.9999518 6.9568659593352019E-05 1 +6 0 -1.6140785 0.09161906 0.13863065882610109 0 +7 0 -3.8577213 0.008444489 0.012234552658491686 0 +8 0 -3.6269126 0.010862814 0.015757469294557003 0 +9 0 -3.6241956 0.010895023 0.015804447933838124 0 +10 0 -4.5891733 0.003790069 0.0054783017558651982 0 +11 0 -4.3108807 0.0051428624 0.0074387267544352214 0 +12 1 -0.49860787 0.256326 1.9639483279716188 0 +13 0 -3.9268007 0.007830536 0.011341538789164538 0 +14 1 6.8671675 0.9991329 0.0012515445057059728 1 +15 1 1.0072587 0.64423734 0.63433581425753083 1 +16 0 -3.7142036 0.009876682 0.014319874162656562 0 +17 0 -3.5423803 0.011910398 0.017286219944511169 0 +18 1 6.1876116 0.9981685 0.0026446780232283022 1 +19 0 -2.7876728 0.026937503 0.039395627305407972 0 +20 1 4.5410404 0.98886913 0.016148491102969308 1 +21 1 6.0276127 0.99781626 0.0031539090959622827 1 +22 0 -3.9911995 0.0072981557 0.010567622171575815 0 23 1 ? ? ? 0 -24 0 -4.5614243 0.0038681172 0.0055913344991073621 0 -25 1 0.95733976 0.63041174 0.66563368347093177 1 -26 0 -3.9880347 0.007254291 0.010503874761927334 0 -27 0 -3.4372077 0.013235498 0.019222278344108352 0 -28 0 -4.310881 0.0050926157 0.0073658631702685686 0 -29 0 -4.1751075 0.005910179 0.0085518826419797075 0 -30 0 -4.12895 0.006216839 0.0089969982438126914 0 -31 0 -4.1808977 0.005872787 0.0084976174910000781 0 -32 1 5.927128 0.9975611 0.0035228907965977533 1 -33 0 -4.0466037 0.006803707 0.0098492183881150851 0 -34 0 -3.6587865 0.010396388 0.015077329328009545 0 -35 0 -4.310881 0.0050926157 0.0073658631702685686 0 -36 1 7.264369 0.99944067 0.00080716835563049752 1 -37 0 -1.6018027 0.092140414 0.13945891409760244 0 -38 1 4.131503 0.9826025 0.02532021885827818 1 -39 1 1.4290619 0.74156404 0.43135681746337273 1 +24 0 -4.561424 0.0039072633 0.0056480307271758777 0 +25 1 0.95733976 0.6315355 0.66306432895578327 1 +26 0 -3.9880342 0.0073234625 0.010604401090954271 0 +27 0 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5.3014183 0.9951529 0.0070099032168673631 1 +697 1 3.0674176 0.9460074 0.0800766703186329 1 +698 1 3.9357042 0.9785419 0.031294452528203641 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-out.txt index 357e702af8..078266882b 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-out.txt @@ -88,28 +88,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 232 | 7 | 0.9707 + positive || 231 | 8 | 0.9665 negative || 9 | 435 | 0.9797 ||====================== -Precision || 0.9627 | 0.9842 | -OVERALL 0/1 ACCURACY: 0.976574 -LOG LOSS/instance: 0.112829 +Precision || 0.9625 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.112863 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.879198 -AUC: 0.996259 +LOG-LOSS REDUCTION (RIG): 0.879162 +AUC: 0.996249 OVERALL RESULTS --------------------------------------- -AUC: 0.996259 (0.0000) -Accuracy: 0.976574 (0.0000) -Positive precision: 0.962656 (0.0000) -Positive recall: 0.970711 (0.0000) -Negative precision: 0.984163 (0.0000) +AUC: 0.996249 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.962500 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981941 (0.0000) Negative recall: 0.979730 (0.0000) -Log-loss: 0.112829 (0.0000) -Log-loss reduction: 0.879198 (0.0000) -F1 Score: 0.966667 (0.0000) -AUPRC: 0.992451 (0.0000) +Log-loss: 0.112863 (0.0000) +Log-loss reduction: 0.879162 (0.0000) +F1 Score: 0.964509 (0.0000) +AUPRC: 0.992435 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-rp.txt index 1ac6423566..a14165fac2 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ WeightedEnsemble AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.996259 0.976574 0.962656 0.970711 0.984163 0.97973 0.112829 0.879198 0.966667 0.992451 svm,ap 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:svm,ap;/nm:20 +0.996249 0.97511 0.9625 0.966527 0.981941 0.97973 0.112863 0.879162 0.964509 0.992435 svm,ap 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:svm,ap;/nm:20 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer.txt index 90e103c1e2..54dcc900e1 100644 --- a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -3.2653842 0.015952274 0.023199807675989368 0 -1 0 2.3943 0.8926791 3.2199969729837079 1 -2 0 -3.5103545 0.012222463 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-3.8830423 0.008214092 0.011899368056717551 0 +694 0 -3.7162507 0.009854654 0.014287776989780327 0 +695 0 -4.4408646 0.004459773 0.0064484827624683733 0 +696 1 5.3014183 0.9951529 0.0070099032168673631 1 +697 1 3.0674176 0.9460074 0.0800766703186329 1 +698 1 3.9357042 0.9785419 0.031294452528203641 1 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris-out.txt index c8c52a6463..bacf0a2018 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in %Time% Beginning training model 3 of 5 Beginning optimization @@ -44,15 +44,15 @@ TRUTH ||======================== Precision ||1.0000 |0.9375 |0.9038 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 -Log-loss: 0.433321 -Log-loss reduction: 0.605574 +Log-loss: 0.433342 +Log-loss reduction: 0.605555 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) -Log-loss: 0.433321 (0.0000) -Log-loss reduction: 0.605574 (0.0000) +Log-loss: 0.433342 (0.0000) +Log-loss reduction: 0.605555 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris.txt index 4b60908a66..3f8132df21 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Average-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.19958509625104848 0.8190705 0.16951077 0.01141867 0 1 2 -1 0 0 0.22949171958730707 0.79493755 0.19322549 0.011837031 0 1 2 -2 0 0 0.20844157984769851 0.81184846 0.1771891 0.010962385 0 1 2 -3 0 0 0.2315363463785047 0.79331386 0.19423674 0.012449473 0 1 2 -4 0 0 0.19394222266281516 0.8237055 0.16497608 0.011318412 0 1 2 -5 0 0 0.25615477277829379 0.77402216 0.20474723 0.021230659 0 1 2 -6 0 0 0.23071794889050412 0.7939634 0.19154221 0.014494381 0 1 2 -7 0 0 0.21332435396586477 0.80789405 0.17988764 0.012218421 0 1 2 -8 0 0 0.23541836887303985 0.79024017 0.19788687 0.011873004 0 1 2 -9 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -10 0 0 0.19656631015650861 0.82154685 0.16647743 0.011975767 0 1 2 -11 0 0 0.22154100919058439 0.80128306 0.18575905 0.012957819 0 1 2 -12 0 0 0.20380232774069329 0.8156236 0.1750274 0.009349017 0 1 2 -13 0 0 0.1808222416849333 0.8345837 0.15767018 0.0077461596 0 1 2 -14 0 0 0.16079850921766828 0.8514636 0.13880378 0.00973264 0 1 2 -15 0 0 0.20740288151214764 0.81269217 0.1691007 0.018207159 0 1 2 -16 0 0 0.21909979965662399 0.80324155 0.18003035 0.01672817 0 1 2 -17 0 0 0.2250250868760485 0.7984962 0.1870559 0.014447969 0 1 2 -18 0 0 0.23366391641909409 0.7916278 0.1913674 0.017004812 0 1 2 -19 0 0 0.21538798103516299 0.8062286 0.17876595 0.015005595 0 1 2 -20 0 0 0.23104359040697584 0.79370487 0.19246776 0.013827393 0 1 2 -21 0 0 0.24970905734884308 0.7790274 0.20189607 0.019076502 0 1 2 -22 0 0 0.16525463650896541 0.8476778 0.14351057 0.008811684 0 1 2 -23 0 0 0.33745519484716036 0.71358395 0.2589257 0.027490431 0 1 2 -24 0 0 0.24877482798679529 0.77975553 0.20474195 0.015502517 0 1 2 -25 0 0 0.24794503212625785 0.78040284 0.20624886 0.013348286 0 1 2 -26 0 0 0.28121802414883634 0.75486374 0.22453001 0.02060628 0 1 2 -27 0 0 0.20765755811531678 0.8124852 0.17536946 0.012145374 0 1 2 -28 0 0 0.20539224708657894 0.81432784 0.17415427 0.011517986 0 1 2 -29 0 0 0.23412760766040333 0.79126084 0.19560409 0.01313511 0 1 2 -30 0 0 0.24087635296706686 0.7859388 0.20082402 0.013237218 0 1 2 -31 0 0 0.2712309592171685 0.7624404 0.21805911 0.019500589 0 1 2 -32 0 0 0.15532962919885376 0.8561329 0.13477181 0.009095248 0 1 2 -33 0 0 0.16434959721587869 0.84844536 0.14075178 0.010802942 0 1 2 -34 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -35 0 0 0.20080315237761032 0.81807345 0.17158608 0.010340452 0 1 2 -36 0 0 0.19233578372186541 0.8250298 0.16419326 0.010777007 0 1 2 -37 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -38 0 0 0.22021810241220668 0.8023438 0.18655506 0.011101251 0 1 2 -39 0 0 0.21344026560531973 0.8078004 0.17997126 0.012228421 0 1 2 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0.20382178 0.009751147 2 1 0 +144 2 2 0.19277506025299521 0.82466745 0.16827284 0.007059779 2 1 0 +145 2 2 0.29645658975590794 0.7434479 0.24201272 0.014539455 2 1 0 +146 2 2 0.53949958828252265 0.58303994 0.3820177 0.03494235 2 1 0 +147 2 2 0.42791751097099723 0.6518652 0.32011598 0.028018856 2 1 0 +148 2 2 0.26540671026209578 0.766894 0.21922064 0.013885461 2 1 0 +149 2 2 0.55568989581257633 0.57367635 0.38143882 0.044884887 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris-out.txt index 994b20dc27..1ba9c30bb3 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 20 finished in %Time% Beginning training model 3 of 20 Beginning optimization @@ -35,25 +35,25 @@ Beginning training model 6 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 6 of 20 finished in %Time% Beginning training model 7 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 7 of 20 finished in %Time% Beginning training model 8 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 13 of 15 weights. Trainer 8 of 20 finished in %Time% Beginning training model 9 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 9 of 20 finished in %Time% Beginning training model 10 of 20 Beginning optimization @@ -77,7 +77,7 @@ Beginning training model 13 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 10 of 15 weights. Trainer 13 of 20 finished in %Time% Beginning training model 14 of 20 Beginning optimization @@ -101,7 +101,7 @@ Beginning training model 17 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 10 of 15 weights. Trainer 17 of 20 finished in %Time% Beginning training model 18 of 20 Beginning optimization @@ -128,21 +128,21 @@ Confusion table PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 - 1 || 0 | 46 | 4 | 0.9200 + 1 || 0 | 45 | 5 | 0.9000 2 || 0 | 4 | 46 | 0.9200 ||======================== -Precision ||1.0000 |0.9200 |0.9200 | -Accuracy(micro-avg): 0.946667 -Accuracy(macro-avg): 0.946667 -Log-loss: 0.435234 -Log-loss reduction: 0.603833 +Precision ||1.0000 |0.9184 |0.9020 | +Accuracy(micro-avg): 0.940000 +Accuracy(macro-avg): 0.940000 +Log-loss: 0.435527 +Log-loss reduction: 0.603567 OVERALL RESULTS --------------------------------------- -Accuracy(micro-avg): 0.946667 (0.0000) -Accuracy(macro-avg): 0.946667 (0.0000) -Log-loss: 0.435234 (0.0000) -Log-loss reduction: 0.603833 (0.0000) +Accuracy(micro-avg): 0.940000 (0.0000) +Accuracy(macro-avg): 0.940000 (0.0000) +Log-loss: 0.435527 (0.0000) +Log-loss reduction: 0.603567 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris.txt index 0c2a0340f9..72c90f52ec 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Bootstrap-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.19132521950387102 0.82586396 0.16326165 0.011272334 0 1 2 -1 0 0 0.21058122478131439 0.81011325 0.17777567 0.011607351 0 1 2 -2 0 0 0.19377139152550549 0.8238462 0.1655918 0.010643643 0 1 2 -3 0 0 0.21394833989369735 0.8073901 0.18037504 0.012178002 0 1 2 -4 0 0 0.18749378711852813 0.82903427 0.1599744 0.011120496 0 1 2 -5 0 0 0.25456562337805144 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0.04797053 2 1 0 +124 2 2 0.34462436216934128 0.70848644 0.27463168 0.018299839 2 1 0 +125 2 2 0.46338154549363286 0.62915254 0.34375423 0.027498497 2 1 0 +126 2 2 0.61966344680337626 0.5381255 0.4106491 0.05175575 2 1 0 +127 2 2 0.59607239709968218 0.5509714 0.39871454 0.051560596 2 1 0 +128 2 2 0.37182414910820849 0.6894755 0.29307303 0.01788643 2 1 0 +129 2 2 0.61114457663022292 0.5427293 0.41413823 0.044528842 2 1 0 +130 2 2 0.42056926126063154 0.6566729 0.3243283 0.019460702 2 1 0 +131 2 2 0.30791995604881894 0.73497415 0.24856502 0.015736412 2 1 0 +132 2 2 0.33130505988078957 0.7179861 0.26795352 0.014453676 2 1 0 +133 2 1 0.80810775086649034 0.4835313 0.44570065 0.0761718 1 2 0 +134 2 1 0.81831409848191228 0.4921103 0.4411748 0.06758197 1 2 0 +135 2 2 0.25892683675007677 0.7718795 0.22045654 0.0084484685 2 1 0 +136 2 2 0.24278134065062493 0.784443 0.20540413 0.010239118 2 1 0 +137 2 2 0.52018345139854039 0.5944115 0.3704331 0.037139595 2 1 0 +138 2 2 0.60968926151378566 0.54351974 0.4032047 0.05451999 2 1 0 +139 2 2 0.37530034382512217 0.6870829 0.292382 0.020991728 2 1 0 +140 2 2 0.25220476443377965 0.7770856 0.2135016 0.009631263 2 1 0 +141 2 2 0.31830537126239522 0.72738063 0.25660747 0.016346755 2 1 0 +142 2 2 0.52679836662001223 0.5904925 0.37430802 0.03595 2 1 0 +143 2 2 0.26291633199749292 0.7688062 0.22215395 0.010095732 2 1 0 +144 2 2 0.21255758023756524 0.80851376 0.18454349 0.0074395556 2 1 0 +145 2 2 0.31499351332366721 0.7297936 0.25534606 0.0152454395 2 1 0 +146 2 2 0.54724744046009344 0.5785401 0.38578716 0.036145456 2 1 0 +147 2 2 0.44573342075513955 0.64035445 0.33139628 0.028928222 2 1 0 +148 2 2 0.28786710238614632 0.74986124 0.23571481 0.014580249 2 1 0 +149 2 2 0.57108791250922109 0.56491053 0.38996506 0.046190515 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris-out.txt index ef1f8ddb78..6a249314e1 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris-out.txt @@ -17,19 +17,19 @@ Beginning training model 3 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 3 of 5 finished in %Time% Beginning training model 4 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 4 of 5 finished in %Time% Beginning training model 5 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 13 of 15 weights. Trainer 5 of 5 finished in %Time% The number of instances used for stacking trainer is 43 Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. @@ -50,15 +50,15 @@ TRUTH ||======================== Precision ||1.0000 |1.0000 |0.7692 | Accuracy(micro-avg): 0.900000 Accuracy(macro-avg): 0.900000 -Log-loss: 0.431634 -Log-loss reduction: 0.607110 +Log-loss: 0.430540 +Log-loss reduction: 0.608106 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.900000 (0.0000) Accuracy(macro-avg): 0.900000 (0.0000) -Log-loss: 0.431634 (0.0000) -Log-loss reduction: 0.607110 (0.0000) +Log-loss: 0.430540 (0.0000) +Log-loss reduction: 0.608106 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris.txt index c917d0baca..cef73f7623 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Stacking-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.18456843242389695 0.83146304 0.1227522 0.045784764 0 1 2 -1 0 0 0.20214058599237553 0.81698006 0.13414726 0.04887256 0 1 2 -2 0 0 0.18886780960827929 0.82789594 0.12566118 0.046442788 0 1 2 -3 0 0 0.2042374594143592 0.81526875 0.13539806 0.04933317 0 1 2 -4 0 0 0.18129421451257496 0.8341899 0.12062066 0.045189448 0 1 2 -5 0 0 0.22910474623581079 0.79524523 0.14964026 0.05511457 0 1 2 -6 0 0 0.20599247898241843 0.8138392 0.13618556 0.049975187 0 1 2 -7 0 0 0.19339539089320237 0.82415605 0.128386 0.047457952 0 1 2 -8 0 0 0.20560263135927859 0.81415653 0.13639703 0.049446475 0 1 2 -9 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 -10 0 0 0.18359826846964028 0.8322701 0.12201076 0.045719206 0 1 2 -11 0 0 0.1991312532671079 0.81944233 0.13198684 0.048570868 0 1 2 -12 0 0 0.18431827795758612 0.83167106 0.12293197 0.045396894 0 1 2 -13 0 0 0.16957589938046716 0.8440227 0.113465235 0.042512156 0 1 2 -14 0 0 0.16156841381666529 0.8508083 0.10780533 0.0413863 0 1 2 -15 0 0 0.19535709636238854 0.8225409 0.12864546 0.04881365 0 1 2 -16 0 0 0.20104048481461023 0.8178793 0.13259548 0.04952511 0 1 2 -17 0 0 0.20257448411033235 0.81662565 0.13397187 0.04940252 0 1 2 -18 0 0 0.2108950743623737 0.80985904 0.13882692 0.05131394 0 1 2 -19 0 0 0.19749954306525297 0.8207805 0.1305829 0.048636604 0 1 2 -20 0 0 0.2059590826256649 0.8138664 0.13622546 0.04990815 0 1 2 -21 0 0 0.22258117574077119 0.80045 0.14595445 0.05359562 0 1 2 -22 0 0 0.16275285950798724 0.8498012 0.10876235 0.041436434 0 1 2 -23 0 0 0.29209403096048014 0.7466983 0.18707061 0.06623104 0 1 2 -24 0 0 0.21896408768070941 0.80335057 0.14426711 0.05238235 0 1 2 -25 0 0 0.21536299293777614 0.8062487 0.14238434 0.05136709 0 1 2 -26 0 0 0.24497624859436443 0.7827231 0.1597444 0.057532523 0 1 2 -27 0 0 0.19007467541054762 0.8268974 0.12622653 0.04687606 0 1 2 -28 0 0 0.18795964100158014 0.82864815 0.12495545 0.04639634 0 1 2 -29 0 0 0.20677255717266285 0.8132046 0.13690357 0.049891777 0 1 2 -30 0 0 0.21094225229927066 0.80982083 0.13956985 0.050609313 0 1 2 -31 0 0 0.23705788916645681 0.7889456 0.15500115 0.056053244 0 1 2 -32 0 0 0.15830972718006323 0.85358536 0.105717234 0.040697433 0 1 2 -33 0 0 0.16458939467986103 0.8482419 0.10965086 0.04210731 0 1 2 -34 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 -35 0 0 0.18375848842038933 0.83213675 0.12241189 0.04545127 0 1 2 -36 0 0 0.17973684252969455 0.83549005 0.119678274 0.044831615 0 1 2 -37 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 -38 0 0 0.1956789610087275 0.8222762 0.13009433 0.047629397 0 1 2 -39 0 0 0.1934814578219369 0.8240851 0.12843771 0.047477078 0 1 2 -40 0 0 0.1963727608748696 0.8217059 0.1301112 0.04818288 0 1 2 -41 0 0 0.2497403510890685 0.779003 0.16397238 0.057024583 0 1 2 -42 0 0 0.18862701420916578 0.8280953 0.12551515 0.046389602 0 1 2 -43 0 0 0.30319543938694477 0.73845476 0.19266923 0.06887592 0 1 2 -44 0 0 0.25054767014146001 0.7783744 0.16248465 0.05914096 0 1 2 -45 0 0 0.22318220567341671 0.7999691 0.1471228 0.052908104 0 1 2 -46 0 0 0.18541167729254801 0.8307622 0.12309762 0.046140097 0 1 2 -47 0 0 0.19442318111477996 0.8233094 0.12917219 0.04751831 0 1 2 -48 0 0 0.18351913487149632 0.83233595 0.1219628 0.04570115 0 1 2 -49 0 0 0.19122122437689801 0.82594985 0.12708125 0.046968836 0 1 2 -50 1 2 0.83534678639095294 0.4504996 0.43372405 0.11577626 2 1 0 -51 1 2 0.86767375122309398 0.4695561 0.41992727 0.11051655 2 1 0 -52 1 2 0.93160046459105528 0.50976956 0.39392275 0.09630774 2 1 0 -53 1 1 0.65279786243084215 0.5205872 0.32883742 0.15057541 1 2 0 -54 1 2 0.85588285603383563 0.47106034 0.4249079 0.10403175 2 1 0 -55 1 1 0.72444018792155385 0.48459578 0.3803149 0.13508928 1 2 0 -56 1 2 0.98572873730563182 0.533935 0.3731672 0.09289769 2 1 0 -57 1 1 0.67065499709518706 0.5113735 0.28466457 0.20396191 1 0 2 -58 1 1 0.74477313673948764 0.474842 0.39399037 0.1311677 1 2 0 -59 1 1 0.70437252875256884 0.4944187 0.35925198 0.14632927 1 2 0 -60 1 1 0.61578638696316768 0.5402159 0.24439119 0.2153929 1 0 2 -61 1 1 0.80743109299301685 0.44600233 0.43339732 0.120600276 1 2 0 -62 1 1 0.60669673129301405 0.5451487 0.24562658 0.20922473 1 2 0 -63 1 1 0.81010570244336411 0.44481105 0.44143718 0.1137517 1 2 0 -64 1 1 0.67750966457140038 0.5078802 0.30291897 0.18920082 1 2 0 -65 1 1 0.78691518322194398 0.45524698 0.4174812 0.12727173 1 2 0 -66 1 2 0.8492664122325263 0.46270448 0.4277286 0.109566994 2 1 0 -67 1 1 0.64189813167902954 0.5262925 0.2533001 0.22040735 1 2 0 -68 1 1 0.79679939034537706 0.4507694 0.44593748 0.10329327 1 2 0 -69 1 1 0.63095605796758758 0.53208286 0.26473647 0.20318066 1 2 0 -70 1 2 1.1786959306692437 0.6199267 0.3076797 0.072393574 2 1 0 -71 1 1 0.68700913557299137 0.50307846 0.3367396 0.16018195 1 2 0 -72 1 2 0.88352334949034161 0.49275747 0.41332406 0.09391846 2 1 0 -73 1 1 0.70206740334268469 0.49555972 0.3631541 0.14128613 1 2 0 -74 1 1 0.71553457377668617 0.48893067 0.36550486 0.14556457 1 2 0 -75 1 1 0.77974019879306522 0.45852512 0.41534883 0.12612611 1 2 0 -76 1 2 0.82134963252744819 0.45131367 0.43983763 0.108848765 2 1 0 -77 1 2 1.1169302363810012 0.5973121 0.32728294 0.075404994 2 1 0 -78 1 2 0.84441164135652436 0.46174592 0.42981017 0.10844384 2 1 0 -79 1 1 0.66995193368367634 0.5117332 0.27248898 0.21577789 1 0 2 -80 1 1 0.62519005874700628 0.5351597 0.2573923 0.20744793 1 2 0 -81 1 1 0.63644896084609348 0.5291682 0.24347588 0.22735596 1 0 2 -82 1 1 0.65414397006754765 0.5198869 0.29469875 0.18541425 1 2 0 -83 1 2 1.0172899730630536 0.5572187 0.3615735 0.08120789 2 1 0 -84 1 2 0.84819043086685031 0.46207583 0.42818907 0.109735176 2 1 0 -85 1 2 0.95335282800352295 0.51472425 0.38544652 0.0998292 2 1 0 -86 1 2 0.89508029756596508 0.48877975 0.4085748 0.1026454 2 1 0 -87 1 1 0.68518916551236386 0.5039949 0.36442462 0.13158056 1 2 0 -88 1 1 0.70499279569103135 0.49411213 0.34656 0.1593278 1 2 0 -89 1 1 0.66563580799433686 0.51394665 0.33148995 0.15456343 1 2 0 -90 1 1 0.66580375299970151 0.51386034 0.3328713 0.15326834 1 2 0 -91 1 1 0.80308522395849768 0.44794482 0.43358922 0.11846605 1 2 0 -92 1 1 0.64934596286916624 0.5223873 0.30157614 0.1760366 1 2 0 -93 1 1 0.66061191350309834 0.51653516 0.27903017 0.20443459 1 0 2 -94 1 1 0.69199747208392948 0.5005752 0.3514926 0.14793213 1 2 0 -95 1 1 0.68305063190899151 0.50507385 0.32116494 0.17376122 1 2 0 -96 1 1 0.7054436305258599 0.49388942 0.35452142 0.15158923 1 2 0 -97 1 1 0.71484602970268085 0.48926744 0.3649459 0.14578669 1 2 0 -98 1 1 0.68343046582533751 0.50488204 0.28716582 0.20795216 1 0 2 -99 1 1 0.69195746458728125 0.5005952 0.34436232 0.15504251 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0.036257982 2 1 0 +144 2 2 0.17203380019624837 0.8419507 0.12520038 0.032848984 2 1 0 +145 2 2 0.23676464803252026 0.789177 0.16939414 0.04142889 2 1 0 +146 2 2 0.4211462560021087 0.6562941 0.28131506 0.062390868 2 1 0 +147 2 2 0.3329620285977431 0.7167974 0.22978072 0.053421956 2 1 0 +148 2 2 0.21891734588438697 0.8033881 0.15723217 0.039379735 2 1 0 +149 2 2 0.44342805097505028 0.6418324 0.29147354 0.06669417 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Voting-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Voting-TrainTest-iris-out.txt index d54a5ba13e..8077ff4b6f 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Voting-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/linux-arm64/WE-Voting-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in %Time% Beginning training model 3 of 5 Beginning optimization diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-out.txt index 87cb55abe8..947924d424 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-out.txt @@ -44,15 +44,15 @@ TRUTH ||======================== Precision ||1.0000 |0.9375 |0.9038 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 -Log-loss: 0.433392 -Log-loss reduction: 0.605510 +Log-loss: 0.433400 +Log-loss reduction: 0.605503 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) -Log-loss: 0.433392 (0.0000) -Log-loss reduction: 0.605510 (0.0000) +Log-loss: 0.433400 (0.0000) +Log-loss reduction: 0.605503 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-rp.txt index a3d2f79fe5..488aae593f 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-rp.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris-rp.txt @@ -1,4 +1,4 @@ WeightedEnsembleMulticlass Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.946667 0.946667 0.433392 0.60551 MultiAverage mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:mlr{t-};/nm:5 +0.946667 0.946667 0.4334 0.605503 MultiAverage mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:mlr{t-};/nm:5 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris.txt index d9f16cd7c2..843eed7f66 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Average-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.1995347399327862 0.81911176 0.16947956 0.011408707 0 1 2 -1 0 0 0.22944163401069054 0.79497737 0.19319294 0.011829739 0 1 2 -2 0 0 0.20840244858665047 0.81188023 0.17716658 0.0109532075 0 1 2 -3 0 0 0.23148277745006318 0.79335636 0.19420494 0.012438736 0 1 2 -4 0 0 0.19389359685209501 0.82374555 0.16494733 0.01130717 0 1 2 -5 0 0 0.25602002069266661 0.77412647 0.20466755 0.021206044 0 1 2 -6 0 0 0.2306506863851141 0.7940168 0.19150472 0.014478475 0 1 2 -7 0 0 0.21326688271208111 0.8079405 0.17985189 0.012207716 0 1 2 -8 0 0 0.23537507529361423 0.7902744 0.19786206 0.011863577 0 1 2 -9 0 0 0.2059855213161193 0.81384486 0.17629413 0.009860988 0 1 2 -10 0 0 0.19650580384489832 0.82159656 0.1664384 0.011965177 0 1 2 -11 0 0 0.22147755951658693 0.8013339 0.18572126 0.012944865 0 1 2 -12 0 0 0.20377273133806445 0.8156477 0.17500915 0.009343221 0 1 2 -13 0 0 0.18081681390014581 0.8345882 0.15767181 0.0077399695 0 1 2 -14 0 0 0.16075720855189568 0.8514988 0.13877651 0.009724712 0 1 2 -15 0 0 0.20729412091966262 0.81278056 0.16903628 0.018183162 0 1 2 -16 0 0 0.21900979288305267 0.80331385 0.17997597 0.016710162 0 1 2 -17 0 0 0.22495216027283546 0.7985544 0.18701091 0.014434678 0 1 2 -18 0 0 0.2335565532640983 0.7917128 0.19129768 0.016989535 0 1 2 -19 0 0 0.2153080656759096 0.806293 0.17871873 0.014988273 0 1 2 -20 0 0 0.23096331521848035 0.7937686 0.19241396 0.013817437 0 1 2 -21 0 0 0.24959942221453638 0.7791128 0.20183216 0.019055037 0 1 2 -22 0 0 0.16523656566316319 0.84769315 0.1435049 0.008802018 0 1 2 -23 0 0 0.33727896509923982 0.7137097 0.25882468 0.02746568 0 1 2 -24 0 0 0.24868295112435151 0.7798272 0.20468676 0.015486002 0 1 2 -25 0 0 0.24787629539957912 0.7804565 0.20620346 0.013340117 0 1 2 -26 0 0 0.28109516869097023 0.7549565 0.22445743 0.020586135 0 1 2 -27 0 0 0.207598284266892 0.8125334 0.17533147 0.012135103 0 1 2 -28 0 0 0.20534028005767088 0.81437016 0.17412049 0.011509328 0 1 2 -29 0 0 0.2340644842080086 0.7913108 0.19556604 0.013123191 0 1 2 -30 0 0 0.24081128540522556 0.78598994 0.20078325 0.013226831 0 1 2 -31 0 0 0.27111471805594867 0.762529 0.21798526 0.019485744 0 1 2 -32 0 0 0.15529321819210176 0.8561641 0.13475163 0.009084328 0 1 2 -33 0 0 0.16429901734220378 0.8484883 0.1407213 0.010790507 0 1 2 -34 0 0 0.2059855213161193 0.81384486 0.17629413 0.009860988 0 1 2 -35 0 0 0.20076723315534722 0.81810284 0.17156358 0.010333608 0 1 2 -36 0 0 0.1922879583805806 0.82506925 0.16416083 0.010770038 0 1 2 -37 0 0 0.2059855213161193 0.81384486 0.17629413 0.009860988 0 1 2 -38 0 0 0.22018274187250014 0.80237216 0.18653594 0.011091883 0 1 2 -39 0 0 0.21338145961265487 0.8078479 0.17993386 0.012218325 0 1 2 -40 0 0 0.21624608735187567 0.80553705 0.18088369 0.013579297 0 1 2 -41 0 0 0.30090695435667597 0.74014664 0.2452245 0.014628872 0 1 2 -42 0 0 0.2080697111998088 0.8121504 0.17692481 0.010924849 0 1 2 -43 0 0 0.346583722689219 0.7070996 0.2608438 0.032056604 0 1 2 -44 0 0 0.28356813015038723 0.7530918 0.22298017 0.023928061 0 1 2 -45 0 0 0.25792702699646886 0.7726516 0.21243982 0.014908629 0 1 2 -46 0 0 0.19841976452676924 0.82002556 0.16739146 0.012582937 0 1 2 -47 0 0 0.21653714682186442 0.8053026 0.18307057 0.011626892 0 1 2 -48 0 0 0.19639800430898482 0.82168514 0.16636033 0.011954611 0 1 2 -49 0 0 0.21097471142350205 0.80979455 0.1786267 0.01157874 0 1 2 -50 1 1 0.72280073006483325 0.4853909 0.39585024 0.1187588 1 2 0 -51 1 1 0.75095120185317177 0.47191745 0.4169356 0.111146964 1 2 0 -52 1 1 0.77019012421610067 0.46292505 0.44817072 0.08890428 1 2 0 -53 1 1 0.57142170157377936 0.564722 0.27788973 0.15738833 1 2 0 -54 1 1 0.71239956520915249 0.49046588 0.4126091 0.09692501 1 2 0 -55 1 1 0.63475024085845666 0.53006786 0.32890567 0.14102645 1 2 0 -56 1 2 0.82745693803480713 0.47644606 0.4371596 0.08639438 2 1 0 -57 1 1 0.60893856307030114 0.5439279 0.30701622 0.14905585 1 0 2 -58 1 1 0.64963193956024912 0.52223796 0.34073702 0.13702507 1 2 0 -59 1 1 0.63381208431825353 0.5305654 0.31399345 0.15544118 1 2 0 -60 1 1 0.54428602163438411 0.5802559 0.2640427 0.15570138 1 0 2 -61 1 1 0.70940332391985461 0.49193764 0.38475576 0.123306684 1 2 0 -62 1 1 0.53804079688154416 0.5838911 0.22874148 0.18736747 1 0 2 -63 1 1 0.6879192376376434 0.5026208 0.38552287 0.11185632 1 2 0 -64 1 1 0.6400093702291928 0.5272875 0.26100343 0.21170907 1 2 0 -65 1 1 0.69585737753817489 0.49864674 0.36727938 0.1340739 1 2 0 -66 1 1 0.72690633660133497 0.48340216 0.40959644 0.1070014 1 2 0 -67 1 1 0.59085655296881212 0.5538527 0.245508 0.20063941 1 0 2 -68 1 1 0.64115557553219493 0.52668345 0.38411126 0.08920532 1 2 0 -69 1 1 0.5749363545422802 0.5627407 0.22431958 0.21293981 1 0 2 -70 1 2 0.94975261920255172 0.55785084 0.3868367 0.055312466 2 1 0 -71 1 1 0.62600890570944601 0.5347217 0.29016036 0.175118 1 2 0 -72 1 1 0.70250976124137288 0.49534056 0.42644 0.078219526 1 2 0 -73 1 1 0.61466942923271062 0.54081964 0.30954513 0.14963524 1 2 0 -74 1 1 0.64122925176637446 0.52664465 0.31644267 0.15691268 1 2 0 -75 1 1 0.68437217417595786 0.5044068 0.36432958 0.13126366 1 2 0 -76 1 1 0.68371561310761098 0.5047381 0.39147708 0.1037849 1 2 0 -77 1 2 0.88400151338475097 0.5295436 0.41312647 0.05732989 2 1 0 -78 1 1 0.71607086970050315 0.48866853 0.4069286 0.104402855 1 2 0 -79 1 1 0.61714056045559618 0.53948486 0.29775333 0.16276182 1 0 2 -80 1 1 0.56736638067214262 0.5670168 0.22821276 0.20477043 1 0 2 -81 1 1 0.57782266402365767 0.5611188 0.26693997 0.17194131 1 0 2 -82 1 1 0.60233825031974264 0.5475299 0.24703716 0.20543304 1 2 0 -83 1 2 0.79663312659522156 0.48736793 0.45084435 0.061787795 2 1 0 -84 1 1 0.72666586419426138 0.48351842 0.40933466 0.10714693 1 2 0 -85 1 2 0.82134007875793547 0.46225557 0.43984184 0.097902596 2 1 0 -86 1 1 0.75394179064401501 0.47050825 0.4311025 0.09838925 1 2 0 -87 1 1 0.57947561020607474 0.56019205 0.30843455 0.13137347 1 2 0 -88 1 1 0.64942321187796115 0.522347 0.30190894 0.17574409 1 2 0 -89 1 1 0.59264806316544294 0.55286133 0.28258952 0.16454916 1 2 0 -90 1 1 0.58849175026178779 0.555164 0.2811874 0.16364865 1 2 0 -91 1 1 0.69355788128380058 0.4997947 0.3802913 0.11991403 1 2 0 -92 1 1 0.58944720965663988 0.5546338 0.25229675 0.1930695 1 2 0 -93 1 1 0.59601841622955909 0.55100113 0.3007882 0.14821072 1 0 2 -94 1 1 0.61682053844790008 0.53965753 0.3026878 0.15765478 1 2 0 -95 1 1 0.63319057823429603 0.53089523 0.27506185 0.19404295 1 2 0 -96 1 1 0.63915390942847394 0.52773875 0.30764136 0.16461992 1 2 0 -97 1 1 0.64112151201164758 0.5267014 0.3161953 0.15710333 1 2 0 -98 1 1 0.63046071020020678 0.5323465 0.31035924 0.15729432 1 0 2 -99 1 1 0.62664693219770329 0.5343806 0.29743573 0.16818376 1 2 0 -100 2 2 0.17846706690757469 0.8365516 0.1575896 0.005858842 2 1 0 -101 2 2 0.51421816746098636 0.5979679 0.36742076 0.03461136 2 1 0 -102 2 2 0.31769228863039173 0.7278267 0.25737938 0.014793965 2 1 0 -103 2 2 0.49804364886014763 0.6077184 0.3591608 0.033120845 2 1 0 -104 2 2 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1 0.61434843411645212 0.5409933 0.30960724 0.14939952 1 2 0 +74 1 1 0.64091477925576956 0.5268103 0.3165122 0.15667751 1 2 0 +75 1 1 0.68407148232842552 0.5045585 0.3643849 0.13105662 1 2 0 +76 1 1 0.68334853791760064 0.5049234 0.39145765 0.10361897 1 2 0 +77 1 2 0.88366807271212022 0.529507 0.41326424 0.05722883 2 1 0 +78 1 1 0.7158362810587191 0.48878318 0.4069887 0.104228094 1 2 0 +79 1 1 0.61674355844515727 0.5396991 0.2974386 0.16286233 1 0 2 +80 1 1 0.56702543043296505 0.56721014 0.22793916 0.2048507 1 0 2 +81 1 1 0.57744553142383837 0.56133044 0.26664442 0.17202519 1 0 2 +82 1 1 0.6020230385750599 0.5477025 0.24713679 0.20516074 1 2 0 +83 1 2 0.79632572715929228 0.48733798 0.45098296 0.061679114 2 1 0 +84 1 1 0.72652207687105719 0.48358795 0.40945554 0.10695656 1 2 0 +85 1 2 0.82122862485727066 0.46239072 0.43989086 0.09771848 2 1 0 +86 1 1 0.75365249217115637 0.47064438 0.43113437 0.09822126 1 2 0 +87 1 1 0.57910423546049083 0.5604001 0.30840737 0.13119255 1 2 0 +88 1 1 0.64918954334483836 0.52246904 0.30205613 0.17547482 1 2 0 +89 1 1 0.59237350572858261 0.55301315 0.28266603 0.1643209 1 2 0 +90 1 1 0.58820287537271632 0.5553244 0.2812703 0.16340537 1 2 0 +91 1 1 0.69330931833735343 0.49991894 0.3803693 0.11971181 1 2 0 +92 1 1 0.58912829945004608 0.5548107 0.25237796 0.1928114 1 2 0 +93 1 1 0.5956565275778597 0.55120057 0.30049133 0.14830817 1 0 2 +94 1 1 0.61655836684835097 0.53979903 0.30278113 0.15741988 1 2 0 +95 1 1 0.63291139686141251 0.53104347 0.2752038 0.19375281 1 2 0 +96 1 1 0.63890354510303216 0.5278709 0.30776128 0.16436782 1 2 0 +97 1 1 0.64082585318732821 0.52685714 0.31627795 0.15686497 1 2 0 +98 1 1 0.63011893676892994 0.53252846 0.31005707 0.15741442 1 0 2 +99 1 1 0.62638462526698035 0.5345208 0.29754353 0.16793567 1 2 0 +100 2 2 0.17854573045386624 0.8364858 0.15766709 0.0058471747 2 1 0 +101 2 2 0.51430867978572659 0.5979138 0.3675367 0.034549545 2 1 0 +102 2 2 0.31788033494603257 0.72768986 0.25754195 0.0147682 2 1 0 +103 2 2 0.49816605941844655 0.607644 0.35929748 0.033058584 2 1 0 +104 2 2 0.28867732934052415 0.7492539 0.23813371 0.012612431 2 1 0 +105 2 2 0.26640780607662956 0.76612663 0.22432148 0.009551965 2 1 0 +106 2 2 0.75887424088069566 0.4681932 0.46393743 0.06786944 2 1 0 +107 2 2 0.41949732562943226 0.6573772 0.3207701 0.021852715 2 1 0 +108 2 2 0.50032345268755207 0.6063345 0.3665964 0.027069187 2 1 0 +109 2 2 0.16477470975062117 0.84808475 0.14619362 0.005721667 2 1 0 +110 2 2 0.42691933359896422 0.6525162 0.3165834 0.030900424 2 1 0 +111 2 2 0.4892665138472555 0.6130759 0.35624778 0.030676395 2 1 0 +112 2 2 0.35161356283616868 0.70355195 0.2775808 0.018867258 2 1 0 +113 2 2 0.48197340426799445 0.6175635 0.35378543 0.028651152 2 1 0 +114 2 2 0.27773285264554587 0.75749916 0.23051243 0.011988522 2 1 0 +115 2 2 0.28053556200171892 0.7553791 0.23040111 0.014219812 2 1 0 +116 2 2 0.50337609743580891 0.6044864 0.35984895 0.03566469 2 1 0 +117 2 2 0.20076104031638917 0.8181079 0.17369609 0.008196038 2 1 0 +118 2 2 0.20559582282446537 0.8141621 0.18121485 0.00462307 2 1 0 +119 2 1 0.86029078998818231 0.50738615 0.42303905 0.06957479 1 2 0 +120 2 2 0.25276446799502034 0.7766508 0.2123049 0.01104436 2 1 0 +121 2 2 0.47454626861507559 0.6221673 0.34556285 0.032269914 2 1 0 +122 2 2 0.30340661307510058 0.73829883 0.250859 0.010842184 2 1 0 +123 2 2 0.60325288306884739 0.5470293 0.40604225 0.04692855 2 1 0 +124 2 2 0.31998820004798173 0.7261576 0.2561329 0.017709536 2 1 0 +125 2 2 0.43294150179254648 0.64859843 0.32371324 0.02768834 2 1 0 +126 2 2 0.61062798868369261 0.54300976 0.4063266 0.050663676 2 1 0 +127 2 2 0.58257581061288266 0.55845803 0.3914896 0.050052296 2 1 0 +128 2 2 0.3537892399929789 0.7020229 0.28082222 0.017154936 2 1 0 +129 2 2 0.58781333237576538 0.55554074 0.39976707 0.044692274 2 1 0 +130 2 2 0.39661586555721279 0.67259234 0.3079466 0.0194611 2 1 0 +131 2 2 0.28003683676996688 0.7557559 0.22855721 0.01568691 2 1 0 +132 2 2 0.31315469739923252 0.7311368 0.2551107 0.0137525005 2 1 0 +133 2 1 0.79106931217684229 0.47124082 0.45335975 0.075399496 1 2 0 +134 2 1 0.80270726491286482 0.48509088 0.44811416 0.06679502 1 2 0 +135 2 2 0.23508888702042546 0.7905006 0.20126441 0.008235063 2 1 0 +136 2 2 0.22165847237718772 0.80118895 0.18909346 0.009717573 2 1 0 +137 2 2 0.49712418518998785 0.60827744 0.3552923 0.036430355 2 1 0 +138 2 2 0.59698402120269867 0.55046934 0.39661556 0.052915093 2 1 0 +139 2 2 0.35516605880793273 0.701057 0.27853647 0.02040651 2 1 0 +140 2 2 0.23232518613015027 0.7926883 0.19812645 0.00918522 2 1 0 +141 2 2 0.29922712331098988 0.741391 0.24288689 0.015722154 2 1 0 +142 2 2 0.51430867978572659 0.5979138 0.3675367 0.034549545 2 1 0 +143 2 2 0.24024026789369451 0.7864389 0.20380802 0.009753074 2 1 0 +144 2 2 0.19273906686103115 0.82469714 0.16824202 0.0070608924 2 1 0 +145 2 2 0.29640936881831181 0.743483 0.24197637 0.014540671 2 1 0 +146 2 2 0.53944786083097751 0.5830701 0.38198867 0.034941282 2 1 0 +147 2 2 0.42785935868312264 0.6519031 0.3200786 0.028018346 2 1 0 +148 2 2 0.26532168585094545 0.7669592 0.21915643 0.013884395 2 1 0 +149 2 2 0.55556054938948174 0.57375056 0.38137275 0.044876736 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-out.txt index 690fc87b19..dad57accee 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-out.txt @@ -41,25 +41,25 @@ Beginning training model 7 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 7 of 20 finished in %Time% Beginning training model 8 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 13 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 8 of 20 finished in %Time% Beginning training model 9 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 9 of 20 finished in %Time% Beginning training model 10 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 10 of 20 finished in %Time% Beginning training model 11 of 20 Beginning optimization @@ -77,7 +77,7 @@ Beginning training model 13 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 10 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 13 of 20 finished in %Time% Beginning training model 14 of 20 Beginning optimization @@ -101,7 +101,7 @@ Beginning training model 17 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 10 of 15 weights. Trainer 17 of 20 finished in %Time% Beginning training model 18 of 20 Beginning optimization @@ -134,15 +134,15 @@ TRUTH ||======================== Precision ||1.0000 |0.9184 |0.9020 | Accuracy(micro-avg): 0.940000 Accuracy(macro-avg): 0.940000 -Log-loss: 0.435613 -Log-loss reduction: 0.603488 +Log-loss: 0.435078 +Log-loss reduction: 0.603975 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.940000 (0.0000) Accuracy(macro-avg): 0.940000 (0.0000) -Log-loss: 0.435613 (0.0000) -Log-loss reduction: 0.603488 (0.0000) +Log-loss: 0.435078 (0.0000) +Log-loss reduction: 0.603975 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-rp.txt index 7948bf23ec..f02650755c 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-rp.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris-rp.txt @@ -1,4 +1,4 @@ WeightedEnsembleMulticlass Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.94 0.94 0.435613 0.603488 mlr{t-} 20 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /bp:mlr{t-};/nm:20 +0.94 0.94 0.435078 0.603975 mlr{t-} 20 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /bp:mlr{t-};/nm:20 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris.txt index d97c7e23c3..319212e3c8 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Bootstrap-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.19248787194951467 0.8249043 0.16391772 0.0112357875 0 1 2 -1 0 0 0.21089485356602972 0.8098592 0.17780837 0.011608226 0 1 2 -2 0 0 0.19409556864309002 0.8235792 0.16561954 0.010706931 0 1 2 -3 0 0 0.21419006839095034 0.80719495 0.18040752 0.012171622 0 1 2 -4 0 0 0.18833345903842305 0.82833844 0.16069299 0.011115085 0 1 2 -5 0 0 0.25671045116465646 0.7735922 0.20349255 0.020969568 0 1 2 -6 0 0 0.22057586389907957 0.8020568 0.18351358 0.014028646 0 1 2 -7 0 0 0.2040367914342095 0.81543237 0.17245781 0.011987341 0 1 2 -8 0 0 0.21469098821931784 0.8067907 0.1807341 0.011613437 0 1 2 -9 0 0 0.18990947648168735 0.827034 0.16333845 0.009730559 0 1 2 -10 0 0 0.19308170709676575 0.8244146 0.16429962 0.011793552 0 1 2 -11 0 0 0.21188952031136538 0.8090541 0.17814124 0.012651183 0 1 2 -12 0 0 0.18612689123641185 0.83016825 0.16011591 0.009217645 0 1 2 -13 0 0 0.16365933251526441 0.8490312 0.1433618 0.0075960676 0 1 2 -14 0 0 0.16113954990336041 0.8511733 0.13809414 0.00960567 0 1 2 -15 0 0 0.21012891014784502 0.81047976 0.16992986 0.017931463 0 1 2 -16 0 0 0.21873203245527259 0.803537 0.17837805 0.01636998 0 1 2 -17 0 0 0.21780768458867614 0.8042801 0.18159494 0.014109329 0 1 2 -18 0 0 0.23289034631477437 0.79224044 0.19024733 0.016867355 0 1 2 -19 0 0 0.21337843455473515 0.80785036 0.17651081 0.014678294 0 1 2 -20 0 0 0.22228998975816838 0.80068314 0.18572956 0.0136960875 0 1 2 -21 0 0 0.24632448530288342 0.78166854 0.19899875 0.018468987 0 1 2 -22 0 0 0.15908638273424053 0.8529227 0.13780464 0.0085659865 0 1 2 -23 0 0 0.32499574971623529 0.7225304 0.2499164 0.02643568 0 1 2 -24 0 0 0.23875897794580808 0.7876047 0.19739422 0.015246329 0 1 2 -25 0 0 0.22896249883151112 0.79535836 0.19072814 0.013115302 0 1 2 -26 0 0 0.27143494156468528 0.7622849 0.21734704 0.019897442 0 1 2 -27 0 0 0.20061861560427344 0.81822443 0.17000848 0.011955516 0 1 2 -28 0 0 0.19645364366816578 0.8216394 0.1669108 0.011345269 0 1 2 -29 0 0 0.21918239337856113 0.8031752 0.18391031 0.012841332 0 1 2 -30 0 0 0.22350078104580848 0.79971427 0.18718359 0.012965914 0 1 2 -31 0 0 0.26231798849203691 0.76926637 0.21132784 0.019094171 0 1 2 -32 0 0 0.15546038557804362 0.856021 0.13391201 0.009047328 0 1 2 -33 0 0 0.16523494844385578 0.8476945 0.14044563 0.010695541 0 1 2 -34 0 0 0.18990947648168735 0.827034 0.16333845 0.009730559 0 1 2 -35 0 0 0.18712876342819015 0.82933694 0.16043445 0.010163587 0 1 2 -36 0 0 0.18586687170692034 0.83038414 0.15897799 0.0106012 0 1 2 -37 0 0 0.18990947648168735 0.827034 0.16333845 0.009730559 0 1 2 -38 0 0 0.20150773594974408 0.81749725 0.17104076 0.010840238 0 1 2 -39 0 0 0.20432212486997534 0.81519973 0.17266783 0.012038765 0 1 2 -40 0 0 0.20901646458364931 0.8113819 0.17520803 0.013237229 0 1 2 -41 0 0 0.27330587248955635 0.76086 0.22409466 0.014374888 0 1 2 -42 0 0 0.19328185173953721 0.8242496 0.16500969 0.01065601 0 1 2 -43 0 0 0.33922807226712121 0.71231997 0.25515014 0.030492488 0 1 2 -44 0 0 0.28280339175118763 0.75366795 0.22098917 0.023443494 0 1 2 -45 0 0 0.23839851113284732 0.78788865 0.19637284 0.014528947 0 1 2 -46 0 0 0.19617540530110095 0.82186806 0.16539367 0.012385834 0 1 2 -47 0 0 0.20186513723566452 0.81720513 0.17134875 0.0113613885 0 1 2 -48 0 0 0.19283201629927799 0.8246205 0.16403821 0.011762289 0 1 2 -49 0 0 0.19942057437754529 0.8192053 0.16932516 0.011371488 0 1 2 -50 1 1 0.69241014278315771 0.50036865 0.38920426 0.11844209 1 2 0 -51 1 1 0.72255516581343215 0.4855101 0.41205072 0.11150342 1 2 0 -52 1 1 0.74117353476279446 0.47655433 0.44188732 0.089149326 1 2 0 -53 1 1 0.58850613714593147 0.555156 0.2788548 0.1635042 1 2 0 -54 1 1 0.69292428403176232 0.50011146 0.4086107 0.098359935 1 2 0 -55 1 1 0.63232735505457305 0.5313537 0.3208812 0.14350468 1 2 0 -56 1 2 0.79957813256768062 0.47135997 0.44951856 0.08678871 2 1 0 -57 1 1 0.64545404818887431 0.5244244 0.31833512 0.14914165 1 0 2 -58 1 1 0.63723447138971445 0.5287527 0.3334638 0.13977997 1 2 0 -59 1 1 0.64251816210224766 0.5259663 0.3096367 0.15934971 1 2 0 -60 1 1 0.58106958714568646 0.5592998 0.27867922 0.15798523 1 0 2 -61 1 1 0.69328499599477678 0.4999311 0.3815279 0.12497688 1 2 0 -62 1 1 0.5580943155087128 0.57229865 0.23871075 0.18836403 1 0 2 -63 1 1 0.66931338309259314 0.51206005 0.37796557 0.11335312 1 2 0 -64 1 1 0.64360156033662053 0.52539676 0.25747693 0.2162116 1 2 0 -65 1 1 0.67333124866058358 0.5100068 0.36244223 0.13508126 1 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0.020977434 2 1 0 +140 2 2 0.25179778547921933 0.7774019 0.2131693 0.009634234 2 1 0 +141 2 2 0.31732326044803261 0.72809535 0.255903 0.016349927 2 1 0 +142 2 2 0.52498610781518373 0.5915636 0.37326652 0.035954762 2 1 0 +143 2 2 0.2625028787149627 0.76912415 0.22089085 0.010096038 2 1 0 +144 2 2 0.21257984430279722 0.80849576 0.18431029 0.007449273 2 1 0 +145 2 2 0.31396969120914769 0.73054117 0.25461537 0.015248882 2 1 0 +146 2 2 0.54647730899814428 0.5789858 0.38529754 0.036144704 2 1 0 +147 2 2 0.44535400270453684 0.64059746 0.33105242 0.028909232 2 1 0 +148 2 2 0.28674282658820827 0.75070477 0.23462024 0.014580021 2 1 0 +149 2 2 0.57039810426121063 0.56530035 0.38914245 0.04617037 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris-out.txt index 0ce2612e66..a8db8d83ca 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris-out.txt @@ -23,13 +23,13 @@ Beginning training model 4 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 4 of 5 finished in %Time% Beginning training model 5 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 13 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 5 of 5 finished in %Time% The number of instances used for stacking trainer is 43 Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. @@ -50,15 +50,15 @@ TRUTH ||======================== Precision ||1.0000 |1.0000 |0.7692 | Accuracy(micro-avg): 0.900000 Accuracy(macro-avg): 0.900000 -Log-loss: 0.430568 -Log-loss reduction: 0.608080 +Log-loss: 0.431318 +Log-loss reduction: 0.607397 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.900000 (0.0000) Accuracy(macro-avg): 0.900000 (0.0000) -Log-loss: 0.430568 (0.0000) -Log-loss reduction: 0.608080 (0.0000) +Log-loss: 0.431318 (0.0000) +Log-loss reduction: 0.607397 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris.txt index 0124babe3c..cb0a108c7b 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.18481972504974498 0.8312541 0.12271381 0.04603213 0 1 2 -1 0 0 0.20170184420340331 0.8173386 0.1337135 0.048947915 0 1 2 -2 0 0 0.18861319452864489 0.82810676 0.12531842 0.04657473 0 1 2 -3 0 0 0.20393994424857698 0.81551135 0.13505594 0.049432613 0 1 2 -4 0 0 0.18162509260959864 0.8339139 0.120627165 0.045458846 0 1 2 -5 0 0 0.23097517962067832 0.79375917 0.15060978 0.05563101 0 1 2 -6 0 0 0.20621873937701451 0.8136551 0.13616554 0.050179407 0 1 2 -7 0 0 0.19360066190337119 0.8239869 0.12833208 0.047681026 0 1 2 -8 0 0 0.20488191609285844 0.8147435 0.13579899 0.04945746 0 1 2 -9 0 0 0.18602063544070438 0.83025646 0.12377789 0.045965694 0 1 2 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0.84635375139263191 0.4617478 0.42897624 0.109276086 2 1 0 +85 1 2 0.95640345671185811 0.51693135 0.38427246 0.098796226 2 1 0 +86 1 2 0.8983674344224869 0.49105656 0.40723395 0.10170938 2 1 0 +87 1 1 0.68226050149047401 0.5054731 0.36294943 0.13157739 1 2 0 +88 1 1 0.70310641817484731 0.4950451 0.34704182 0.15791313 1 2 0 +89 1 1 0.66196269503814287 0.5158379 0.32977995 0.15438214 1 2 0 +90 1 1 0.66271578141285847 0.5154496 0.33186498 0.15268537 1 2 0 +91 1 1 0.803457735189547 0.447778 0.43468067 0.117541365 1 2 0 +92 1 1 0.64652834530683412 0.5238613 0.30091637 0.17522238 1 2 0 +93 1 1 0.65820931543122041 0.5177777 0.2792198 0.20300257 1 0 2 +94 1 1 0.68919201891211956 0.5019815 0.35070387 0.14731465 1 2 0 +95 1 1 0.68111852319146848 0.50605065 0.32201785 0.17193149 1 2 0 +96 1 1 0.7035482121570098 0.49482644 0.35471654 0.15045694 1 2 0 +97 1 1 0.71410250814994625 0.48963135 0.36584362 0.144525 1 2 0 +98 1 1 0.68081774821669361 0.5062029 0.28690857 0.20688856 1 0 2 +99 1 1 0.68959996964588033 0.50177675 0.34411222 0.15411112 1 2 0 +100 2 2 0.16440678365436223 0.84839684 0.11974012 0.031862963 2 1 0 +101 2 2 0.40609917468075751 0.6662441 0.27254024 0.061215565 2 1 0 +102 2 2 0.2458655470066283 0.78202736 0.17552033 0.04245227 2 1 0 +103 2 2 0.38472685224582337 0.6806365 0.2604263 0.058937266 2 1 0 +104 2 2 0.2290053657821704 0.79532427 0.16432573 0.040349994 2 1 0 +105 2 2 0.20723032216584331 0.8128324 0.14978161 0.037386052 2 1 0 +106 2 2 0.64208411177086122 0.52619463 0.3864971 0.087308206 2 1 0 +107 2 2 0.31060960376714142 0.733 0.21705876 0.049941264 2 1 0 +108 2 2 0.37698654218512184 0.6859253 0.2572235 0.056851283 2 1 0 +109 2 2 0.15675327318419499 0.85491496 0.11422711 0.030858016 2 1 0 +110 2 2 0.33752728259911918 0.7135325 0.23190069 0.054566808 2 1 0 +111 2 2 0.37954021815835165 0.6841759 0.2577698 0.058054265 2 1 0 +112 2 2 0.27358957649361698 0.7606442 0.19330837 0.04604751 2 1 0 +113 2 2 0.37882923236196364 0.6846625 0.25766626 0.057671204 2 1 0 +114 2 2 0.22780084487132377 0.7962828 0.16349928 0.040218007 2 1 0 +115 2 2 0.22935316561817776 0.7950477 0.16424085 0.040711533 2 1 0 +116 2 2 0.390922276423114 0.6764327 0.2635034 0.06006389 2 1 0 +117 2 2 0.17341210812093677 0.84079105 0.12598889 0.033220116 2 1 0 +118 2 2 0.1699034872854441 0.84374624 0.12392624 0.03232753 2 1 0 +119 2 2 0.71178950557259157 0.49076518 0.41827542 0.090959296 2 1 0 +120 2 2 0.2076823544060864 0.8124651 0.14978218 0.03775265 2 1 0 +121 2 2 0.37819217830109125 0.6850988 0.2563862 0.05851496 2 1 0 +122 2 2 0.22715042885378808 0.7968009 0.16345328 0.03974589 2 1 0 +123 2 2 0.48446990757238495 0.61602366 0.31367502 0.07030122 2 1 0 +124 2 2 0.25222631815403124 0.77706885 0.17927824 0.043652885 2 1 0 +125 2 2 0.32760903367813859 0.7206447 0.22665083 0.052704424 2 1 0 +126 2 2 0.49496663552114289 0.60959125 0.3182506 0.0721581 2 1 0 +127 2 2 0.47115948268649155 0.624278 0.3054865 0.07023542 2 1 0 +128 2 2 0.27345684220388478 0.76074517 0.19353299 0.04572179 2 1 0 +129 2 2 0.45305048544352328 0.63568604 0.29742348 0.06689058 2 1 0 +130 2 2 0.29535913915082623 0.74426425 0.20762253 0.048113324 2 1 0 +131 2 2 0.22103501146332244 0.8016886 0.15847385 0.039837632 2 1 0 +132 2 2 0.24559996217679675 0.7822351 0.17544875 0.04231613 2 1 0 +133 2 2 0.65255813858606682 0.520712 0.3895665 0.089721434 2 1 0 +134 2 2 0.6490835661934814 0.5225244 0.39125416 0.08622134 2 1 0 +135 2 2 0.19220784522232714 0.82513535 0.1393483 0.035516433 2 1 0 +136 2 2 0.19151187493999464 0.8257098 0.13857752 0.03571268 2 1 0 +137 2 2 0.38734780852959166 0.67885494 0.2611855 0.05995947 2 1 0 +138 2 2 0.48648283435051609 0.6147849 0.31299442 0.07222071 2 1 0 +139 2 2 0.27773836068681401 0.757495 0.19575231 0.046752684 2 1 0 +140 2 2 0.19576718204038143 0.82220364 0.14167166 0.036124658 2 1 0 +141 2 2 0.24180960538509974 0.78520566 0.17248924 0.042305063 2 1 0 +142 2 2 0.40609917468075751 0.6662441 0.27254024 0.061215565 2 1 0 +143 2 2 0.19891262656195247 0.8196215 0.1438337 0.036544785 2 1 0 +144 2 2 0.17320611965414481 0.84096426 0.12591036 0.03312536 2 1 0 +145 2 2 0.23877812475853602 0.7875896 0.1706328 0.041777547 2 1 0 +146 2 2 0.42437484648962892 0.6541786 0.2831175 0.0627038 2 1 0 +147 2 2 0.3349058975761407 0.7154054 0.23090635 0.05368829 2 1 0 +148 2 2 0.22024848673187941 0.8023194 0.15802814 0.039652426 2 1 0 +149 2 2 0.44465947397136207 0.64104253 0.29216596 0.06679159 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris-out.txt index c8c52a6463..bacf0a2018 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in %Time% Beginning training model 3 of 5 Beginning optimization @@ -44,15 +44,15 @@ TRUTH ||======================== Precision ||1.0000 |0.9375 |0.9038 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 -Log-loss: 0.433321 -Log-loss reduction: 0.605574 +Log-loss: 0.433342 +Log-loss reduction: 0.605555 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) -Log-loss: 0.433321 (0.0000) -Log-loss reduction: 0.605574 (0.0000) +Log-loss: 0.433342 (0.0000) +Log-loss reduction: 0.605555 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris.txt index 4b60908a66..8972ae9e11 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.19958509625104848 0.8190705 0.16951077 0.01141867 0 1 2 -1 0 0 0.22949171958730707 0.79493755 0.19322549 0.011837031 0 1 2 -2 0 0 0.20844157984769851 0.81184846 0.1771891 0.010962385 0 1 2 -3 0 0 0.2315363463785047 0.79331386 0.19423674 0.012449473 0 1 2 -4 0 0 0.19394222266281516 0.8237055 0.16497608 0.011318412 0 1 2 -5 0 0 0.25615477277829379 0.77402216 0.20474723 0.021230659 0 1 2 -6 0 0 0.23071794889050412 0.7939634 0.19154221 0.014494381 0 1 2 -7 0 0 0.21332435396586477 0.80789405 0.17988764 0.012218421 0 1 2 -8 0 0 0.23541836887303985 0.79024017 0.19788687 0.011873004 0 1 2 -9 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -10 0 0 0.19656631015650861 0.82154685 0.16647743 0.011975767 0 1 2 -11 0 0 0.22154100919058439 0.80128306 0.18575905 0.012957819 0 1 2 -12 0 0 0.20380232774069329 0.8156236 0.1750274 0.009349017 0 1 2 -13 0 0 0.1808222416849333 0.8345837 0.15767018 0.0077461596 0 1 2 -14 0 0 0.16079850921766828 0.8514636 0.13880378 0.00973264 0 1 2 -15 0 0 0.20740288151214764 0.81269217 0.1691007 0.018207159 0 1 2 -16 0 0 0.21909979965662399 0.80324155 0.18003035 0.01672817 0 1 2 -17 0 0 0.2250250868760485 0.7984962 0.1870559 0.014447969 0 1 2 -18 0 0 0.23366391641909409 0.7916278 0.1913674 0.017004812 0 1 2 -19 0 0 0.21538798103516299 0.8062286 0.17876595 0.015005595 0 1 2 -20 0 0 0.23104359040697584 0.79370487 0.19246776 0.013827393 0 1 2 -21 0 0 0.24970905734884308 0.7790274 0.20189607 0.019076502 0 1 2 -22 0 0 0.16525463650896541 0.8476778 0.14351057 0.008811684 0 1 2 -23 0 0 0.33745519484716036 0.71358395 0.2589257 0.027490431 0 1 2 -24 0 0 0.24877482798679529 0.77975553 0.20474195 0.015502517 0 1 2 -25 0 0 0.24794503212625785 0.78040284 0.20624886 0.013348286 0 1 2 -26 0 0 0.28121802414883634 0.75486374 0.22453001 0.02060628 0 1 2 -27 0 0 0.20765755811531678 0.8124852 0.17536946 0.012145374 0 1 2 -28 0 0 0.20539224708657894 0.81432784 0.17415427 0.011517986 0 1 2 -29 0 0 0.23412760766040333 0.79126084 0.19560409 0.01313511 0 1 2 -30 0 0 0.24087635296706686 0.7859388 0.20082402 0.013237218 0 1 2 -31 0 0 0.2712309592171685 0.7624404 0.21805911 0.019500589 0 1 2 -32 0 0 0.15532962919885376 0.8561329 0.13477181 0.009095248 0 1 2 -33 0 0 0.16434959721587869 0.84844536 0.14075178 0.010802942 0 1 2 -34 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -35 0 0 0.20080315237761032 0.81807345 0.17158608 0.010340452 0 1 2 -36 0 0 0.19233578372186541 0.8250298 0.16419326 0.010777007 0 1 2 -37 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -38 0 0 0.22021810241220668 0.8023438 0.18655506 0.011101251 0 1 2 -39 0 0 0.21344026560531973 0.8078004 0.17997126 0.012228421 0 1 2 -40 0 0 0.21630905795028774 0.8054863 0.18092151 0.013592168 0 1 2 -41 0 0 0.30096606575453844 0.7401029 0.24526386 0.014633313 0 1 2 -42 0 0 0.20810486617361554 0.81212187 0.17694256 0.0109356595 0 1 2 -43 0 0 0.34678419522506876 0.7069579 0.26095027 0.03209188 0 1 2 -44 0 0 0.28372706912227269 0.7529721 0.22306935 0.02395857 0 1 2 -45 0 0 0.25799900398330489 0.772596 0.21248487 0.014919168 0 1 2 -46 0 0 0.1984840939936815 0.8199728 0.16743004 0.012597151 0 1 2 -47 0 0 0.21658237113948361 0.8052662 0.1830964 0.011637499 0 1 2 -48 0 0 0.19645719830870367 0.8216365 0.1663978 0.011965793 0 1 2 -49 0 0 0.21102469023036374 0.8097541 0.17865822 0.011587797 0 1 2 -50 1 1 0.72219582983911745 0.4856846 0.3957767 0.1185387 1 2 0 -51 1 1 0.75046978711973689 0.4721447 0.41692048 0.11093482 1 2 0 -52 1 1 0.76958914038145454 0.46320334 0.44806728 0.0887294 1 2 0 -53 1 1 0.57095107325625116 0.56498784 0.27784678 0.15716541 1 2 0 -54 1 1 0.71182454522463745 0.490748 0.41250286 0.09674921 1 2 0 -55 1 1 0.63432438157018267 0.53029364 0.32892045 0.14078592 1 2 0 -56 1 2 0.82703428884420771 0.4764425 0.4373444 0.08621321 2 1 0 -57 1 1 0.6084864208677675 0.5441739 0.3067269 0.14909923 1 0 2 -58 1 1 0.64905915539487302 0.5225372 0.34067142 0.13679142 1 2 0 -59 1 1 0.63344041453667532 0.5307626 0.31403926 0.15519817 1 2 0 -60 1 1 0.54383148046665453 0.58051974 0.26378644 0.15569383 1 0 2 -61 1 1 0.70897643660265774 0.49214768 0.38476583 0.123086534 1 2 0 -62 1 1 0.53748409573418543 0.58421624 0.22847545 0.18730839 1 0 2 -63 1 1 0.6874510444439752 0.5028562 0.38549486 0.11164905 1 2 0 -64 1 1 0.63955301833531797 0.52752817 0.26105487 0.21141699 1 2 0 -65 1 1 0.69528760745748575 0.49893093 0.36722904 0.13384007 1 2 0 -66 1 1 0.7265596703602144 0.48356977 0.40963522 0.10679509 1 2 0 -67 1 1 0.59033377266304388 0.5541423 0.24519745 0.20066026 1 0 2 -68 1 1 0.64053684151642798 0.5270094 0.38393053 0.089060105 1 2 0 -69 1 1 0.57444151987848757 0.5630192 0.22403803 0.21294275 1 0 2 -70 1 2 0.94941076927349677 0.55784464 0.38696897 0.05518642 2 1 0 -71 1 1 0.62548781460243952 0.5350004 0.29014286 0.17485681 1 2 0 -72 1 1 0.70192272007371137 0.49563143 0.42629296 0.07807569 1 2 0 -73 1 1 0.61417194723606394 0.54108876 0.3095269 0.14938432 1 2 0 -74 1 1 0.64068071477248156 0.5269336 0.3164059 0.15666047 1 2 0 -75 1 1 0.68380796394306786 0.5046915 0.3642716 0.13103694 1 2 0 -76 1 1 0.68309004860794009 0.50505394 0.3913477 0.103598416 1 2 0 -77 1 2 0.88339710333663513 0.5294137 0.41337624 0.0572101 2 1 0 -78 1 1 0.71561893779063213 0.48888943 0.4069018 0.10420883 1 2 0 -79 1 1 0.61660375052510907 0.53977454 0.29744694 0.16277865 1 0 2 -80 1 1 0.56687706299463814 0.5672943 0.22793761 0.20476814 1 0 2 -81 1 1 0.577317162489008 0.5614025 0.26665047 0.171947 1 0 2 -82 1 1 0.60183881197342548 0.5478034 0.24704412 0.2051525 1 2 0 -83 1 2 0.79614745102428552 0.48727614 0.45106336 0.061660558 2 1 0 -84 1 1 0.72636216626290295 0.4836653 0.4093965 0.10693822 1 2 0 -85 1 2 0.82100250307584133 0.46231195 0.43999034 0.09769779 2 1 0 -86 1 1 0.75338201554753248 0.4707717 0.4310279 0.0982004 1 2 0 -87 1 1 0.57888302932035285 0.5605241 0.3083013 0.1311746 1 2 0 -88 1 1 0.64901124808319288 0.5225622 0.30197474 0.17546313 1 2 0 -89 1 1 0.59219632846582471 0.55311114 0.2825799 0.16430902 1 2 0 -90 1 1 0.58805734238203022 0.5554052 0.2812 0.16339482 1 2 0 -91 1 1 0.69310188205590872 0.50002265 0.38028446 0.119692944 1 2 0 -92 1 1 0.58894654035017713 0.55491155 0.25228664 0.19280186 1 2 0 -93 1 1 0.59555693924738862 0.55125546 0.30050763 0.14823692 1 0 2 -94 1 1 0.6163831455564952 0.5398936 0.30269963 0.15740676 1 2 0 -95 1 1 0.63274574343740053 0.53113145 0.27512455 0.19374405 1 2 0 -96 1 1 0.63872120368860863 0.52796715 0.30767828 0.16435464 1 2 0 -97 1 1 0.64060798380199047 0.52697194 0.31617925 0.15684882 1 2 0 -98 1 1 0.62999145933178791 0.53259635 0.31007034 0.15733333 1 0 2 -99 1 1 0.62619752826924102 0.5346208 0.2974563 0.1679229 1 2 0 -100 2 2 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0.72634060031506553 0.48367572 0.4093322 0.106992066 1 2 0 +85 1 2 0.82103494820623479 0.46227762 0.43997607 0.097746335 2 1 0 +86 1 1 0.75361278982728497 0.47066307 0.4311017 0.09823523 1 2 0 +87 1 1 0.57911699884625145 0.560393 0.30839 0.1312171 1 2 0 +88 1 1 0.64908984009311788 0.52252114 0.30194747 0.17553142 1 2 0 +89 1 1 0.59230172575550921 0.55305284 0.28257605 0.16437112 1 2 0 +90 1 1 0.58815854781610788 0.555349 0.2811957 0.16345541 1 2 0 +91 1 1 0.69323426673785327 0.49995646 0.3803033 0.11974024 1 2 0 +92 1 1 0.58910176399956904 0.5548254 0.25230795 0.1928667 1 2 0 +93 1 1 0.59568983403173437 0.5511822 0.3005962 0.14822163 1 0 2 +94 1 1 0.61648195908214887 0.5398403 0.30269247 0.15746729 1 2 0 +95 1 1 0.6328543802553106 0.53107375 0.2751131 0.19381313 1 2 0 +96 1 1 0.63882010425534663 0.52791494 0.30766746 0.16441761 1 2 0 +97 1 1 0.64078467381927184 0.52687883 0.3162181 0.15690307 1 2 0 +98 1 1 0.63012240653126628 0.5325266 0.31016228 0.1573111 1 0 2 +99 1 1 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1 0 +129 2 2 0.58771527300706905 0.5555952 0.39971742 0.044687316 2 1 0 +130 2 2 0.39651643967513822 0.6726592 0.30788618 0.019454643 2 1 0 +131 2 2 0.27997847645811702 0.7558 0.22851907 0.015680907 2 1 0 +132 2 2 0.31318624739295159 0.73111373 0.25513503 0.0137512265 2 1 0 +133 2 1 0.79111940470998876 0.4712534 0.45333704 0.07540964 1 2 0 +134 2 1 0.80269609195143132 0.4850783 0.44811916 0.06680262 1 2 0 +135 2 2 0.23503889731429836 0.7905401 0.20122902 0.008230896 2 1 0 +136 2 2 0.22172297513146652 0.80113727 0.18914513 0.009717601 2 1 0 +137 2 2 0.49716935925050121 0.60824996 0.35531783 0.036432166 2 1 0 +138 2 2 0.59713519106387447 0.55038613 0.3966885 0.052925397 2 1 0 +139 2 2 0.35518671915074979 0.70104253 0.27855316 0.020404344 2 1 0 +140 2 2 0.23235736926966191 0.7926628 0.19815338 0.009183794 2 1 0 +141 2 2 0.29927198511493391 0.74135774 0.2429217 0.015720611 2 1 0 +142 2 2 0.51442213081049648 0.597846 0.3675995 0.03455456 2 1 0 +143 2 2 0.2402552745375453 0.7864271 0.20382178 0.009751147 2 1 0 +144 2 2 0.19277506025299521 0.82466745 0.16827284 0.007059779 2 1 0 +145 2 2 0.29645658975590794 0.7434479 0.24201272 0.014539455 2 1 0 +146 2 2 0.53949958828252265 0.58303994 0.3820177 0.03494235 2 1 0 +147 2 2 0.42791751097099723 0.6518652 0.32011598 0.028018856 2 1 0 +148 2 2 0.26540671026209578 0.766894 0.21922064 0.013885461 2 1 0 +149 2 2 0.55568989581257633 0.57367635 0.38143882 0.044884887 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris-out.txt index 994b20dc27..1ba9c30bb3 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 20 finished in %Time% Beginning training model 3 of 20 Beginning optimization @@ -35,25 +35,25 @@ Beginning training model 6 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 6 of 20 finished in %Time% Beginning training model 7 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 7 of 20 finished in %Time% Beginning training model 8 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 13 of 15 weights. Trainer 8 of 20 finished in %Time% Beginning training model 9 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 9 of 20 finished in %Time% Beginning training model 10 of 20 Beginning optimization @@ -77,7 +77,7 @@ Beginning training model 13 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 10 of 15 weights. Trainer 13 of 20 finished in %Time% Beginning training model 14 of 20 Beginning optimization @@ -101,7 +101,7 @@ Beginning training model 17 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 10 of 15 weights. Trainer 17 of 20 finished in %Time% Beginning training model 18 of 20 Beginning optimization @@ -128,21 +128,21 @@ Confusion table PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 - 1 || 0 | 46 | 4 | 0.9200 + 1 || 0 | 45 | 5 | 0.9000 2 || 0 | 4 | 46 | 0.9200 ||======================== -Precision ||1.0000 |0.9200 |0.9200 | -Accuracy(micro-avg): 0.946667 -Accuracy(macro-avg): 0.946667 -Log-loss: 0.435234 -Log-loss reduction: 0.603833 +Precision ||1.0000 |0.9184 |0.9020 | +Accuracy(micro-avg): 0.940000 +Accuracy(macro-avg): 0.940000 +Log-loss: 0.435527 +Log-loss reduction: 0.603567 OVERALL RESULTS --------------------------------------- -Accuracy(micro-avg): 0.946667 (0.0000) -Accuracy(macro-avg): 0.946667 (0.0000) -Log-loss: 0.435234 (0.0000) -Log-loss reduction: 0.603833 (0.0000) +Accuracy(micro-avg): 0.940000 (0.0000) +Accuracy(macro-avg): 0.940000 (0.0000) +Log-loss: 0.435527 (0.0000) +Log-loss reduction: 0.603567 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris.txt index fb2167d15d..72c90f52ec 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.19132521950387102 0.82586396 0.16326165 0.011272334 0 1 2 -1 0 0 0.21058122478131439 0.81011325 0.17777567 0.011607351 0 1 2 -2 0 0 0.19377139152550549 0.8238462 0.1655918 0.010643643 0 1 2 -3 0 0 0.21394833989369735 0.8073901 0.18037504 0.012178002 0 1 2 -4 0 0 0.18749378711852813 0.82903427 0.1599744 0.011120496 0 1 2 -5 0 0 0.25456562337805144 0.7752532 0.20198968 0.021105848 0 1 2 -6 0 0 0.2199577562989645 0.8025527 0.18342122 0.013983405 0 1 2 -7 0 0 0.20268324355207298 0.81653684 0.17203337 0.012051117 0 1 2 -8 0 0 0.21455993600206141 0.80689645 0.18077749 0.011551563 0 1 2 -9 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 -10 0 0 0.19181567954629949 0.825459 0.16280192 0.011902858 0 1 2 -11 0 0 0.21056930558990042 0.8101229 0.17773885 0.012758404 0 1 2 -12 0 0 0.1857633708736893 0.8304701 0.16012534 0.009209672 0 1 2 -13 0 0 0.16367716426818307 0.8490161 0.14334759 0.007550855 0 1 2 -14 0 0 0.1592486638167529 0.8527843 0.1366698 0.009606908 0 1 2 -15 0 0 0.20768748980943236 0.8124609 0.16827278 0.017978072 0 1 2 -16 0 0 0.21681237325434061 0.805081 0.17695686 0.016417108 0 1 2 -17 0 0 0.21684939173278467 0.8050512 0.181056 0.014152357 0 1 2 -18 0 0 0.23136175078772675 0.7934524 0.18847735 0.01705836 0 1 2 -19 0 0 0.21197144694890696 0.8089878 0.175268 0.014743009 0 1 2 -20 0 0 0.22015815366279803 0.8023919 0.18439388 0.013820611 0 1 2 -21 0 0 0.24543394080765951 0.78236496 0.19777209 0.018589469 0 1 2 -22 0 0 0.15849039000781612 0.85343117 0.13754007 0.008510644 0 1 2 -23 0 0 0.323425302027813 0.723666 0.24987355 0.026793782 0 1 2 -24 0 0 0.23711069983685371 0.78890395 0.19630879 0.015339675 0 1 2 -25 0 0 0.22815863427568761 0.795998 0.19058642 0.013238922 0 1 2 -26 0 0 0.27027251016522602 0.7631715 0.21731177 0.020077024 0 1 2 -27 0 0 0.1992888890650909 0.81931317 0.16926673 0.012075843 0 1 2 -28 0 0 0.19507242562593269 0.82277507 0.16646528 0.011425154 0 1 2 -29 0 0 0.21828009480014346 0.80390024 0.18383104 0.012923741 0 1 2 -30 0 0 0.22282477688327434 0.80025506 0.18709216 0.013062084 0 1 2 -31 0 0 0.26074300389946803 0.7704789 0.21109545 0.019286428 0 1 2 -32 0 0 0.15387950553049615 0.8573753 0.13263428 0.0090501495 0 1 2 -33 0 0 0.16340172022875207 0.84924996 0.13898855 0.010684636 0 1 2 -34 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 -35 0 0 0.186637363615415 0.8297446 0.16040832 0.010106187 0 1 2 -36 0 0 0.18440564571580689 0.8315984 0.1581842 0.010682614 0 1 2 -37 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 -38 0 0 0.20158721223460938 0.8174323 0.17107035 0.010779634 0 1 2 -39 0 0 0.20284662392296379 0.81640345 0.17213078 0.0121171195 0 1 2 -40 0 0 0.2082159904393574 0.8120316 0.17468612 0.01320599 0 1 2 -41 0 0 0.27327101245786778 0.76088655 0.22326237 0.014362189 0 1 2 -42 0 0 0.19324670783718506 0.8242786 0.16498175 0.010594476 0 1 2 -43 0 0 0.3385856409638423 0.71277773 0.25433904 0.030721603 0 1 2 -44 0 0 0.28146251667171029 0.7546792 0.21964729 0.023636864 0 1 2 -45 0 0 0.2383924590629562 0.7878934 0.19639453 0.014524944 0 1 2 -46 0 0 0.1947321443118433 0.8230551 0.16418305 0.012469268 0 1 2 -47 0 0 0.20147857192484991 0.8175211 0.1713196 0.011324062 0 1 2 -48 0 0 0.19160297775405896 0.8256346 0.16265854 0.011878425 0 1 2 -49 0 0 0.19840900700849953 0.8200344 0.16919632 0.011390395 0 1 2 -50 1 1 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0.54351974 0.4032047 0.05451999 2 1 0 +139 2 2 0.37530034382512217 0.6870829 0.292382 0.020991728 2 1 0 +140 2 2 0.25220476443377965 0.7770856 0.2135016 0.009631263 2 1 0 +141 2 2 0.31830537126239522 0.72738063 0.25660747 0.016346755 2 1 0 +142 2 2 0.52679836662001223 0.5904925 0.37430802 0.03595 2 1 0 +143 2 2 0.26291633199749292 0.7688062 0.22215395 0.010095732 2 1 0 +144 2 2 0.21255758023756524 0.80851376 0.18454349 0.0074395556 2 1 0 +145 2 2 0.31499351332366721 0.7297936 0.25534606 0.0152454395 2 1 0 +146 2 2 0.54724744046009344 0.5785401 0.38578716 0.036145456 2 1 0 +147 2 2 0.44573342075513955 0.64035445 0.33139628 0.028928222 2 1 0 +148 2 2 0.28786710238614632 0.74986124 0.23571481 0.014580249 2 1 0 +149 2 2 0.57108791250922109 0.56491053 0.38996506 0.046190515 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris-out.txt index 2f6636f68a..6a249314e1 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris-out.txt @@ -17,19 +17,19 @@ Beginning training model 3 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 3 of 5 finished in %Time% Beginning training model 4 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 4 of 5 finished in %Time% Beginning training model 5 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 13 of 15 weights. Trainer 5 of 5 finished in %Time% The number of instances used for stacking trainer is 43 Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. @@ -50,15 +50,15 @@ TRUTH ||======================== Precision ||1.0000 |1.0000 |0.7692 | Accuracy(micro-avg): 0.900000 Accuracy(macro-avg): 0.900000 -Log-loss: 0.431560 -Log-loss reduction: 0.607177 +Log-loss: 0.430540 +Log-loss reduction: 0.608106 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.900000 (0.0000) Accuracy(macro-avg): 0.900000 (0.0000) -Log-loss: 0.431560 (0.0000) -Log-loss reduction: 0.607177 (0.0000) +Log-loss: 0.430540 (0.0000) +Log-loss reduction: 0.608106 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris.txt index fcc92e4dee..08b0714284 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.18450140782674077 0.83151877 0.12272505 0.04575624 0 1 2 -1 0 0 0.202071935546666 0.81703615 0.13412096 0.04884281 0 1 2 -2 0 0 0.18880035225550124 0.8279518 0.1256342 0.046414033 0 1 2 -3 0 0 0.20416859176588947 0.8153249 0.13537185 0.049303208 0 1 2 -4 0 0 0.18122748044971973 0.83424556 0.12059335 0.045161154 0 1 2 -5 0 0 0.22903324527471447 0.7953021 0.14961572 0.055082098 0 1 2 -6 0 0 0.20592305096525618 0.8138957 0.13615948 0.04994493 0 1 2 -7 0 0 0.19332741048831092 0.8242121 0.12835923 0.047428735 0 1 2 -8 0 0 0.20553374283892295 0.8142126 0.13637091 0.0494165 0 1 2 -9 0 0 0.18629577502982836 0.83002806 0.12415345 0.045818496 0 1 2 -10 0 0 0.1835313088636242 0.8323258 0.121983536 0.045690667 0 1 2 -11 0 0 0.19906280909583152 0.8194984 0.13196035 0.048541173 0 1 2 -12 0 0 0.18425155677923813 0.83172655 0.12290477 0.045368597 0 1 2 -13 0 0 0.16951107258028258 0.8440774 0.11343753 0.042485084 0 1 2 -14 0 0 0.16150424413069714 0.8508629 0.10777754 0.041359637 0 1 2 -15 0 0 0.19528818542366277 0.82259756 0.12861885 0.048783675 0 1 2 -16 0 0 0.200971108254794 0.81793606 0.13256915 0.049494926 0 1 2 -17 0 0 0.20250558492011145 0.8166819 0.13394552 0.049372446 0 1 2 -18 0 0 0.21082493717570058 0.80991584 0.13880116 0.051283058 0 1 2 -19 0 0 0.19743106525114842 0.8208367 0.13055637 0.048606824 0 1 2 -20 0 0 0.20588994985233811 0.81392264 0.13619934 0.049877897 0 1 2 -21 0 0 0.22251036306250224 0.8005067 0.14592941 0.0535638 0 1 2 -22 0 0 0.16268875404395641 0.84985566 0.108734556 0.04140978 0 1 2 -23 0 0 0.29202035587769903 0.74675333 0.1870521 0.066194445 0 1 2 -24 0 0 0.21889375323594415 0.8034071 0.14424184 0.05235111 0 1 2 -25 0 0 0.21529342877203145 0.8063048 0.14235884 0.051336333 0 1 2 -26 0 0 0.24490406066264753 0.78277963 0.15972123 0.05749923 0 1 2 -27 0 0 0.19000699244459801 0.82695335 0.1261996 0.046847068 0 1 2 -28 0 0 0.18789217295644908 0.82870406 0.12492844 0.046367567 0 1 2 -29 0 0 0.20670344143158395 0.8132608 0.13687758 0.0498616 0 1 2 -30 0 0 0.21087262698353662 0.8098772 0.13954405 0.05057884 0 1 2 -31 0 0 0.23698619502285928 0.7890022 0.15497725 0.05602051 0 1 2 -32 0 0 0.15824625502481904 0.85363954 0.105689384 0.040671058 0 1 2 -33 0 0 0.16452482005987762 0.8482967 0.1096231 0.042080294 0 1 2 -34 0 0 0.18629577502982836 0.83002806 0.12415345 0.045818496 0 1 2 -35 0 0 0.18369151808560824 0.8321925 0.1223847 0.04542292 0 1 2 -36 0 0 0.17967035498463491 0.8355456 0.119650885 0.04480348 0 1 2 -37 0 0 0.18629577502982836 0.83002806 0.12415345 0.045818496 0 1 2 -38 0 0 0.19561075271128853 0.82233226 0.13006766 0.047600165 0 1 2 -39 0 0 0.1934134715661279 0.82414114 0.12841092 0.04744785 0 1 2 -40 0 0 0.19630428764153024 0.82176214 0.13008459 0.04815333 0 1 2 -41 0 0 0.24966881304910607 0.77905875 0.16394956 0.05699172 0 1 2 -42 0 0 0.18855978901676593 0.828151 0.1254881 0.046360835 0 1 2 -43 0 0 0.30312110329957714 0.73850965 0.19265203 0.068838306 0 1 2 -44 0 0 0.25047469606135375 0.7784312 0.16246194 0.05910696 0 1 2 -45 0 0 0.22311187194730808 0.80002534 0.14709783 0.05287672 0 1 2 -46 0 0 0.18534445267106384 0.83081806 0.1230705 0.046111383 0 1 2 -47 0 0 0.19435527558972726 0.82336533 0.1291455 0.04748909 0 1 2 -48 0 0 0.18345189413793689 0.8323919 0.12193558 0.04567262 0 1 2 -49 0 0 0.19115353592732157 0.82600576 0.12705435 0.04693981 0 1 2 -50 1 2 0.83499826792922327 0.45035413 0.43387523 0.11577052 2 1 0 -51 1 2 0.86731491930829419 0.46941003 0.42007798 0.11051202 2 1 0 -52 1 2 0.93121802480515314 0.5096219 0.39407343 0.09630473 2 1 0 -53 1 1 0.65251532862736505 0.5207343 0.32870632 0.15055935 1 2 0 -54 1 2 0.85552072736691076 0.47091198 0.4250618 0.104026146 2 1 0 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0.17203401257679538 0.84195054 0.12520038 0.03284899 2 1 0 +145 2 2 0.23676464803252026 0.789177 0.1693942 0.04142891 2 1 0 +146 2 2 0.42114643764216764 0.656294 0.2813151 0.062390883 2 1 0 +147 2 2 0.332962278060079 0.71679723 0.22978069 0.053421956 2 1 0 +148 2 2 0.21891734588438697 0.8033881 0.15723224 0.03937976 2 1 0 +149 2 2 0.44342823670780318 0.6418323 0.29147357 0.0666942 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Voting-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Voting-TrainTest-iris-out.txt index d54a5ba13e..8077ff4b6f 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Voting-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Voting-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in %Time% Beginning training model 3 of 5 Beginning optimization diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-out.txt index c8c52a6463..bacf0a2018 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in %Time% Beginning training model 3 of 5 Beginning optimization @@ -44,15 +44,15 @@ TRUTH ||======================== Precision ||1.0000 |0.9375 |0.9038 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 -Log-loss: 0.433321 -Log-loss reduction: 0.605574 +Log-loss: 0.433342 +Log-loss reduction: 0.605555 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) -Log-loss: 0.433321 (0.0000) -Log-loss reduction: 0.605574 (0.0000) +Log-loss: 0.433342 (0.0000) +Log-loss reduction: 0.605555 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-rp.txt index ac49f32b03..e4fb552ba6 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-rp.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-rp.txt @@ -1,4 +1,4 @@ WeightedEnsembleMulticlass Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.946667 0.946667 0.433321 0.605574 MultiAverage mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:mlr{t-};/nm:5 +0.946667 0.946667 0.433342 0.605555 MultiAverage mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:mlr{t-};/nm:5 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris.txt index 4b60908a66..3f8132df21 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.19958509625104848 0.8190705 0.16951077 0.01141867 0 1 2 -1 0 0 0.22949171958730707 0.79493755 0.19322549 0.011837031 0 1 2 -2 0 0 0.20844157984769851 0.81184846 0.1771891 0.010962385 0 1 2 -3 0 0 0.2315363463785047 0.79331386 0.19423674 0.012449473 0 1 2 -4 0 0 0.19394222266281516 0.8237055 0.16497608 0.011318412 0 1 2 -5 0 0 0.25615477277829379 0.77402216 0.20474723 0.021230659 0 1 2 -6 0 0 0.23071794889050412 0.7939634 0.19154221 0.014494381 0 1 2 -7 0 0 0.21332435396586477 0.80789405 0.17988764 0.012218421 0 1 2 -8 0 0 0.23541836887303985 0.79024017 0.19788687 0.011873004 0 1 2 -9 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -10 0 0 0.19656631015650861 0.82154685 0.16647743 0.011975767 0 1 2 -11 0 0 0.22154100919058439 0.80128306 0.18575905 0.012957819 0 1 2 -12 0 0 0.20380232774069329 0.8156236 0.1750274 0.009349017 0 1 2 -13 0 0 0.1808222416849333 0.8345837 0.15767018 0.0077461596 0 1 2 -14 0 0 0.16079850921766828 0.8514636 0.13880378 0.00973264 0 1 2 -15 0 0 0.20740288151214764 0.81269217 0.1691007 0.018207159 0 1 2 -16 0 0 0.21909979965662399 0.80324155 0.18003035 0.01672817 0 1 2 -17 0 0 0.2250250868760485 0.7984962 0.1870559 0.014447969 0 1 2 -18 0 0 0.23366391641909409 0.7916278 0.1913674 0.017004812 0 1 2 -19 0 0 0.21538798103516299 0.8062286 0.17876595 0.015005595 0 1 2 -20 0 0 0.23104359040697584 0.79370487 0.19246776 0.013827393 0 1 2 -21 0 0 0.24970905734884308 0.7790274 0.20189607 0.019076502 0 1 2 -22 0 0 0.16525463650896541 0.8476778 0.14351057 0.008811684 0 1 2 -23 0 0 0.33745519484716036 0.71358395 0.2589257 0.027490431 0 1 2 -24 0 0 0.24877482798679529 0.77975553 0.20474195 0.015502517 0 1 2 -25 0 0 0.24794503212625785 0.78040284 0.20624886 0.013348286 0 1 2 -26 0 0 0.28121802414883634 0.75486374 0.22453001 0.02060628 0 1 2 -27 0 0 0.20765755811531678 0.8124852 0.17536946 0.012145374 0 1 2 -28 0 0 0.20539224708657894 0.81432784 0.17415427 0.011517986 0 1 2 -29 0 0 0.23412760766040333 0.79126084 0.19560409 0.01313511 0 1 2 -30 0 0 0.24087635296706686 0.7859388 0.20082402 0.013237218 0 1 2 -31 0 0 0.2712309592171685 0.7624404 0.21805911 0.019500589 0 1 2 -32 0 0 0.15532962919885376 0.8561329 0.13477181 0.009095248 0 1 2 -33 0 0 0.16434959721587869 0.84844536 0.14075178 0.010802942 0 1 2 -34 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -35 0 0 0.20080315237761032 0.81807345 0.17158608 0.010340452 0 1 2 -36 0 0 0.19233578372186541 0.8250298 0.16419326 0.010777007 0 1 2 -37 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 -38 0 0 0.22021810241220668 0.8023438 0.18655506 0.011101251 0 1 2 -39 0 0 0.21344026560531973 0.8078004 0.17997126 0.012228421 0 1 2 -40 0 0 0.21630905795028774 0.8054863 0.18092151 0.013592168 0 1 2 -41 0 0 0.30096606575453844 0.7401029 0.24526386 0.014633313 0 1 2 -42 0 0 0.20810486617361554 0.81212187 0.17694256 0.0109356595 0 1 2 -43 0 0 0.34678419522506876 0.7069579 0.26095027 0.03209188 0 1 2 -44 0 0 0.28372706912227269 0.7529721 0.22306935 0.02395857 0 1 2 -45 0 0 0.25799900398330489 0.772596 0.21248487 0.014919168 0 1 2 -46 0 0 0.1984840939936815 0.8199728 0.16743004 0.012597151 0 1 2 -47 0 0 0.21658237113948361 0.8052662 0.1830964 0.011637499 0 1 2 -48 0 0 0.19645719830870367 0.8216365 0.1663978 0.011965793 0 1 2 -49 0 0 0.21102469023036374 0.8097541 0.17865822 0.011587797 0 1 2 -50 1 1 0.72219582983911745 0.4856846 0.3957767 0.1185387 1 2 0 -51 1 1 0.75046978711973689 0.4721447 0.41692048 0.11093482 1 2 0 -52 1 1 0.76958914038145454 0.46320334 0.44806728 0.0887294 1 2 0 -53 1 1 0.57095107325625116 0.56498784 0.27784678 0.15716541 1 2 0 -54 1 1 0.71182454522463745 0.490748 0.41250286 0.09674921 1 2 0 -55 1 1 0.63432438157018267 0.53029364 0.32892045 0.14078592 1 2 0 -56 1 2 0.82703428884420771 0.4764425 0.4373444 0.08621321 2 1 0 -57 1 1 0.6084864208677675 0.5441739 0.3067269 0.14909923 1 0 2 -58 1 1 0.64905915539487302 0.5225372 0.34067142 0.13679142 1 2 0 -59 1 1 0.63344041453667532 0.5307626 0.31403926 0.15519817 1 2 0 -60 1 1 0.54383148046665453 0.58051974 0.26378644 0.15569383 1 0 2 -61 1 1 0.70897643660265774 0.49214768 0.38476583 0.123086534 1 2 0 -62 1 1 0.53748409573418543 0.58421624 0.22847545 0.18730839 1 0 2 -63 1 1 0.6874510444439752 0.5028562 0.38549486 0.11164905 1 2 0 -64 1 1 0.63955301833531797 0.52752817 0.26105487 0.21141699 1 2 0 -65 1 1 0.69528760745748575 0.49893093 0.36722904 0.13384007 1 2 0 -66 1 1 0.7265596703602144 0.48356977 0.40963522 0.10679509 1 2 0 -67 1 1 0.59033377266304388 0.5541423 0.24519745 0.20066026 1 0 2 -68 1 1 0.64053684151642798 0.5270094 0.38393053 0.089060105 1 2 0 -69 1 1 0.57444151987848757 0.5630192 0.22403803 0.21294275 1 0 2 -70 1 2 0.94941076927349677 0.55784464 0.38696897 0.05518642 2 1 0 -71 1 1 0.62548781460243952 0.5350004 0.29014286 0.17485681 1 2 0 -72 1 1 0.70192272007371137 0.49563143 0.42629296 0.07807569 1 2 0 -73 1 1 0.61417194723606394 0.54108876 0.3095269 0.14938432 1 2 0 -74 1 1 0.64068071477248156 0.5269336 0.3164059 0.15666047 1 2 0 -75 1 1 0.68380796394306786 0.5046915 0.3642716 0.13103694 1 2 0 -76 1 1 0.68309004860794009 0.50505394 0.3913477 0.103598416 1 2 0 -77 1 2 0.88339710333663513 0.5294137 0.41337624 0.0572101 2 1 0 -78 1 1 0.71561893779063213 0.48888943 0.4069018 0.10420883 1 2 0 -79 1 1 0.61660375052510907 0.53977454 0.29744694 0.16277865 1 0 2 -80 1 1 0.56687706299463814 0.5672943 0.22793761 0.20476814 1 0 2 -81 1 1 0.577317162489008 0.5614025 0.26665047 0.171947 1 0 2 -82 1 1 0.60183881197342548 0.5478034 0.24704412 0.2051525 1 2 0 -83 1 2 0.79614745102428552 0.48727614 0.45106336 0.061660558 2 1 0 -84 1 1 0.72636216626290295 0.4836653 0.4093965 0.10693822 1 2 0 -85 1 2 0.82100250307584133 0.46231195 0.43999034 0.09769779 2 1 0 -86 1 1 0.75338201554753248 0.4707717 0.4310279 0.0982004 1 2 0 -87 1 1 0.57888302932035285 0.5605241 0.3083013 0.1311746 1 2 0 -88 1 1 0.64901124808319288 0.5225622 0.30197474 0.17546313 1 2 0 -89 1 1 0.59219632846582471 0.55311114 0.2825799 0.16430902 1 2 0 -90 1 1 0.58805734238203022 0.5554052 0.2812 0.16339482 1 2 0 -91 1 1 0.69310188205590872 0.50002265 0.38028446 0.119692944 1 2 0 -92 1 1 0.58894654035017713 0.55491155 0.25228664 0.19280186 1 2 0 -93 1 1 0.59555693924738862 0.55125546 0.30050763 0.14823692 1 0 2 -94 1 1 0.6163831455564952 0.5398936 0.30269963 0.15740676 1 2 0 -95 1 1 0.63274574343740053 0.53113145 0.27512455 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0.17863163 0.011586516 0 1 2 +50 1 1 0.72248298139251754 0.48554516 0.3958799 0.118574955 1 2 0 +51 1 1 0.75062943328508813 0.47206932 0.41695213 0.110978656 1 2 0 +52 1 1 0.76986313597504419 0.46307644 0.44816485 0.08875871 1 2 0 +53 1 1 0.57107071425742828 0.56492025 0.2778556 0.1572242 1 2 0 +54 1 1 0.71204385939409542 0.49064037 0.4125758 0.09678384 1 2 0 +55 1 1 0.63442172411868747 0.530242 0.32891673 0.14084128 1 2 0 +56 1 2 0.82714196189142419 0.4764505 0.4372973 0.086252235 2 1 0 +57 1 1 0.6086055991642263 0.54410905 0.30681697 0.14907405 1 0 2 +58 1 1 0.64929986733753797 0.5224114 0.3407538 0.13683484 1 2 0 +59 1 1 0.63346736690140448 0.5307483 0.31398654 0.15526515 1 2 0 +60 1 1 0.54396825241678914 0.58044034 0.26386887 0.1556908 1 0 2 +61 1 1 0.70906448839267 0.49210435 0.3847564 0.123139285 1 2 0 +62 1 1 0.53770031002083307 0.58408993 0.22854295 0.18736719 1 0 2 +63 1 1 0.68759104075640942 0.5027858 0.38552144 0.11169286 1 2 0 +64 1 1 0.63965912021616089 0.5274722 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0.017708195 2 1 0 +125 2 2 0.43288259716046124 0.64863664 0.32368 0.027683413 2 1 0 +126 2 2 0.61073622497129809 0.542951 0.40637872 0.050670326 2 1 0 +127 2 2 0.58270346873861856 0.55838674 0.3915527 0.05006062 2 1 0 +128 2 2 0.3538174285615992 0.7020031 0.28084314 0.017153699 2 1 0 +129 2 2 0.58771527300706905 0.5555952 0.39971742 0.044687316 2 1 0 +130 2 2 0.39651643967513822 0.6726592 0.30788618 0.019454643 2 1 0 +131 2 2 0.27997847645811702 0.7558 0.22851907 0.015680907 2 1 0 +132 2 2 0.31318624739295159 0.73111373 0.25513503 0.0137512265 2 1 0 +133 2 1 0.79111940470998876 0.4712534 0.45333704 0.07540964 1 2 0 +134 2 1 0.80269609195143132 0.4850783 0.44811916 0.06680262 1 2 0 +135 2 2 0.23503889731429836 0.7905401 0.20122902 0.008230896 2 1 0 +136 2 2 0.22172297513146652 0.80113727 0.18914513 0.009717601 2 1 0 +137 2 2 0.49716935925050121 0.60824996 0.35531783 0.036432166 2 1 0 +138 2 2 0.59713519106387447 0.55038613 0.3966885 0.052925397 2 1 0 +139 2 2 0.35518671915074979 0.70104253 0.27855316 0.020404344 2 1 0 +140 2 2 0.23235736926966191 0.7926628 0.19815338 0.009183794 2 1 0 +141 2 2 0.29927198511493391 0.74135774 0.2429217 0.015720611 2 1 0 +142 2 2 0.51442213081049648 0.597846 0.3675995 0.03455456 2 1 0 +143 2 2 0.2402552745375453 0.7864271 0.20382178 0.009751147 2 1 0 +144 2 2 0.19277506025299521 0.82466745 0.16827284 0.007059779 2 1 0 +145 2 2 0.29645658975590794 0.7434479 0.24201272 0.014539455 2 1 0 +146 2 2 0.53949958828252265 0.58303994 0.3820177 0.03494235 2 1 0 +147 2 2 0.42791751097099723 0.6518652 0.32011598 0.028018856 2 1 0 +148 2 2 0.26540671026209578 0.766894 0.21922064 0.013885461 2 1 0 +149 2 2 0.55568989581257633 0.57367635 0.38143882 0.044884887 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-out.txt index 994b20dc27..1ba9c30bb3 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 20 finished in %Time% Beginning training model 3 of 20 Beginning optimization @@ -35,25 +35,25 @@ Beginning training model 6 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 6 of 20 finished in %Time% Beginning training model 7 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 7 of 20 finished in %Time% Beginning training model 8 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 13 of 15 weights. Trainer 8 of 20 finished in %Time% Beginning training model 9 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 9 of 20 finished in %Time% Beginning training model 10 of 20 Beginning optimization @@ -77,7 +77,7 @@ Beginning training model 13 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 10 of 15 weights. Trainer 13 of 20 finished in %Time% Beginning training model 14 of 20 Beginning optimization @@ -101,7 +101,7 @@ Beginning training model 17 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 10 of 15 weights. Trainer 17 of 20 finished in %Time% Beginning training model 18 of 20 Beginning optimization @@ -128,21 +128,21 @@ Confusion table PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 - 1 || 0 | 46 | 4 | 0.9200 + 1 || 0 | 45 | 5 | 0.9000 2 || 0 | 4 | 46 | 0.9200 ||======================== -Precision ||1.0000 |0.9200 |0.9200 | -Accuracy(micro-avg): 0.946667 -Accuracy(macro-avg): 0.946667 -Log-loss: 0.435234 -Log-loss reduction: 0.603833 +Precision ||1.0000 |0.9184 |0.9020 | +Accuracy(micro-avg): 0.940000 +Accuracy(macro-avg): 0.940000 +Log-loss: 0.435527 +Log-loss reduction: 0.603567 OVERALL RESULTS --------------------------------------- -Accuracy(micro-avg): 0.946667 (0.0000) -Accuracy(macro-avg): 0.946667 (0.0000) -Log-loss: 0.435234 (0.0000) -Log-loss reduction: 0.603833 (0.0000) +Accuracy(micro-avg): 0.940000 (0.0000) +Accuracy(macro-avg): 0.940000 (0.0000) +Log-loss: 0.435527 (0.0000) +Log-loss reduction: 0.603567 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-rp.txt index b3236f6187..b0472d02ed 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-rp.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-rp.txt @@ -1,4 +1,4 @@ WeightedEnsembleMulticlass Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.946667 0.946667 0.435234 0.603833 mlr{t-} 20 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /bp:mlr{t-};/nm:20 +0.94 0.94 0.435527 0.603567 mlr{t-} 20 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /bp:mlr{t-};/nm:20 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris.txt index fb2167d15d..72c90f52ec 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.19132521950387102 0.82586396 0.16326165 0.011272334 0 1 2 -1 0 0 0.21058122478131439 0.81011325 0.17777567 0.011607351 0 1 2 -2 0 0 0.19377139152550549 0.8238462 0.1655918 0.010643643 0 1 2 -3 0 0 0.21394833989369735 0.8073901 0.18037504 0.012178002 0 1 2 -4 0 0 0.18749378711852813 0.82903427 0.1599744 0.011120496 0 1 2 -5 0 0 0.25456562337805144 0.7752532 0.20198968 0.021105848 0 1 2 -6 0 0 0.2199577562989645 0.8025527 0.18342122 0.013983405 0 1 2 -7 0 0 0.20268324355207298 0.81653684 0.17203337 0.012051117 0 1 2 -8 0 0 0.21455993600206141 0.80689645 0.18077749 0.011551563 0 1 2 -9 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 -10 0 0 0.19181567954629949 0.825459 0.16280192 0.011902858 0 1 2 -11 0 0 0.21056930558990042 0.8101229 0.17773885 0.012758404 0 1 2 -12 0 0 0.1857633708736893 0.8304701 0.16012534 0.009209672 0 1 2 -13 0 0 0.16367716426818307 0.8490161 0.14334759 0.007550855 0 1 2 -14 0 0 0.1592486638167529 0.8527843 0.1366698 0.009606908 0 1 2 -15 0 0 0.20768748980943236 0.8124609 0.16827278 0.017978072 0 1 2 -16 0 0 0.21681237325434061 0.805081 0.17695686 0.016417108 0 1 2 -17 0 0 0.21684939173278467 0.8050512 0.181056 0.014152357 0 1 2 -18 0 0 0.23136175078772675 0.7934524 0.18847735 0.01705836 0 1 2 -19 0 0 0.21197144694890696 0.8089878 0.175268 0.014743009 0 1 2 -20 0 0 0.22015815366279803 0.8023919 0.18439388 0.013820611 0 1 2 -21 0 0 0.24543394080765951 0.78236496 0.19777209 0.018589469 0 1 2 -22 0 0 0.15849039000781612 0.85343117 0.13754007 0.008510644 0 1 2 -23 0 0 0.323425302027813 0.723666 0.24987355 0.026793782 0 1 2 -24 0 0 0.23711069983685371 0.78890395 0.19630879 0.015339675 0 1 2 -25 0 0 0.22815863427568761 0.795998 0.19058642 0.013238922 0 1 2 -26 0 0 0.27027251016522602 0.7631715 0.21731177 0.020077024 0 1 2 -27 0 0 0.1992888890650909 0.81931317 0.16926673 0.012075843 0 1 2 -28 0 0 0.19507242562593269 0.82277507 0.16646528 0.011425154 0 1 2 -29 0 0 0.21828009480014346 0.80390024 0.18383104 0.012923741 0 1 2 -30 0 0 0.22282477688327434 0.80025506 0.18709216 0.013062084 0 1 2 -31 0 0 0.26074300389946803 0.7704789 0.21109545 0.019286428 0 1 2 -32 0 0 0.15387950553049615 0.8573753 0.13263428 0.0090501495 0 1 2 -33 0 0 0.16340172022875207 0.84924996 0.13898855 0.010684636 0 1 2 -34 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 -35 0 0 0.186637363615415 0.8297446 0.16040832 0.010106187 0 1 2 -36 0 0 0.18440564571580689 0.8315984 0.1581842 0.010682614 0 1 2 -37 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 -38 0 0 0.20158721223460938 0.8174323 0.17107035 0.010779634 0 1 2 -39 0 0 0.20284662392296379 0.81640345 0.17213078 0.0121171195 0 1 2 -40 0 0 0.2082159904393574 0.8120316 0.17468612 0.01320599 0 1 2 -41 0 0 0.27327101245786778 0.76088655 0.22326237 0.014362189 0 1 2 -42 0 0 0.19324670783718506 0.8242786 0.16498175 0.010594476 0 1 2 -43 0 0 0.3385856409638423 0.71277773 0.25433904 0.030721603 0 1 2 -44 0 0 0.28146251667171029 0.7546792 0.21964729 0.023636864 0 1 2 -45 0 0 0.2383924590629562 0.7878934 0.19639453 0.014524944 0 1 2 -46 0 0 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0.4411748 0.06758197 1 2 0 +135 2 2 0.25892683675007677 0.7718795 0.22045654 0.0084484685 2 1 0 +136 2 2 0.24278134065062493 0.784443 0.20540413 0.010239118 2 1 0 +137 2 2 0.52018345139854039 0.5944115 0.3704331 0.037139595 2 1 0 +138 2 2 0.60968926151378566 0.54351974 0.4032047 0.05451999 2 1 0 +139 2 2 0.37530034382512217 0.6870829 0.292382 0.020991728 2 1 0 +140 2 2 0.25220476443377965 0.7770856 0.2135016 0.009631263 2 1 0 +141 2 2 0.31830537126239522 0.72738063 0.25660747 0.016346755 2 1 0 +142 2 2 0.52679836662001223 0.5904925 0.37430802 0.03595 2 1 0 +143 2 2 0.26291633199749292 0.7688062 0.22215395 0.010095732 2 1 0 +144 2 2 0.21255758023756524 0.80851376 0.18454349 0.0074395556 2 1 0 +145 2 2 0.31499351332366721 0.7297936 0.25534606 0.0152454395 2 1 0 +146 2 2 0.54724744046009344 0.5785401 0.38578716 0.036145456 2 1 0 +147 2 2 0.44573342075513955 0.64035445 0.33139628 0.028928222 2 1 0 +148 2 2 0.28786710238614632 0.74986124 0.23571481 0.014580249 2 1 0 +149 2 2 0.57108791250922109 0.56491053 0.38996506 0.046190515 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-out.txt index ef1f8ddb78..6a249314e1 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-out.txt @@ -17,19 +17,19 @@ Beginning training model 3 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 3 of 5 finished in %Time% Beginning training model 4 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 12 of 15 weights. +L1 regularization selected 11 of 15 weights. Trainer 4 of 5 finished in %Time% Beginning training model 5 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 13 of 15 weights. Trainer 5 of 5 finished in %Time% The number of instances used for stacking trainer is 43 Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. @@ -50,15 +50,15 @@ TRUTH ||======================== Precision ||1.0000 |1.0000 |0.7692 | Accuracy(micro-avg): 0.900000 Accuracy(macro-avg): 0.900000 -Log-loss: 0.431634 -Log-loss reduction: 0.607110 +Log-loss: 0.430540 +Log-loss reduction: 0.608106 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.900000 (0.0000) Accuracy(macro-avg): 0.900000 (0.0000) -Log-loss: 0.431634 (0.0000) -Log-loss reduction: 0.607110 (0.0000) +Log-loss: 0.430540 (0.0000) +Log-loss reduction: 0.608106 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris.txt index 542ce8658d..1baaddbf42 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris.txt @@ -1,151 +1,151 @@ Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class -0 0 0 0.18456843242389695 0.83146304 0.12275226 0.045784786 0 1 2 -1 0 0 0.20214058599237553 0.81698006 0.13414729 0.04887257 0 1 2 -2 0 0 0.18886780960827929 0.82789594 0.12566121 0.0464428 0 1 2 -3 0 0 0.2042374594143592 0.81526875 0.13539809 0.04933318 0 1 2 -4 0 0 0.18129421451257496 0.8341899 0.12062063 0.04518944 0 1 2 -5 0 0 0.22910497108966421 0.79524505 0.14964029 0.055114582 0 1 2 -6 0 0 0.20599247898241843 0.8138392 0.13618557 0.04997521 0 1 2 -7 0 0 0.19339539089320237 0.82415605 0.12838604 0.047457963 0 1 2 -8 0 0 0.20560263135927859 0.81415653 0.13639703 0.049446475 0 1 2 -9 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 -10 0 0 0.18359826846964028 0.8322701 0.12201079 0.045719218 0 1 2 -11 0 0 0.1991312532671079 0.81944233 0.13198687 0.04857088 0 1 2 -12 0 0 0.18431827795758612 0.83167106 0.122932024 0.045396913 0 1 2 -13 0 0 0.16957589938046716 0.8440227 0.11346526 0.042512167 0 1 2 -14 0 0 0.16156841381666529 0.8508083 0.10780533 0.0413863 0 1 2 -15 0 0 0.19535709636238854 0.8225409 0.12864554 0.04881367 0 1 2 -16 0 0 0.20104048481461023 0.8178793 0.13259548 0.04952511 0 1 2 -17 0 0 0.20257448411033235 0.81662565 0.13397184 0.04940251 0 1 2 -18 0 0 0.2108950743623737 0.80985904 0.1388269 0.05131394 0 1 2 -19 0 0 0.19749954306525297 0.8207805 0.1305829 0.048636615 0 1 2 -20 0 0 0.2059590826256649 0.8138664 0.13622543 0.049908135 0 1 2 -21 0 0 0.22258117574077119 0.80045 0.14595445 0.05359562 0 1 2 -22 0 0 0.16275285950798724 0.8498012 0.108762376 0.041436445 0 1 2 -23 0 0 0.29209403096048014 0.7466983 0.18707062 0.06623104 0 1 2 -24 0 0 0.21896408768070941 0.80335057 0.14426717 0.052382372 0 1 2 -25 0 0 0.21536328865125401 0.8062485 0.14238437 0.0513671 0 1 2 -26 0 0 0.24497624859436443 0.7827231 0.1597444 0.057532523 0 1 2 -27 0 0 0.19007467541054762 0.8268974 0.12622653 0.04687606 0 1 2 -28 0 0 0.18795964100158014 0.82864815 0.12495548 0.046396352 0 1 2 -29 0 0 0.20677255717266285 0.8132046 0.13690367 0.049891815 0 1 2 -30 0 0 0.21094225229927066 0.80982083 0.13956985 0.050609313 0 1 2 -31 0 0 0.23705788916645681 0.7889456 0.15500115 0.056053244 0 1 2 -32 0 0 0.15830993666579354 0.8535852 0.10571726 0.04069744 0 1 2 -33 0 0 0.16458939467986103 0.8482419 0.10965086 0.042107303 0 1 2 -34 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 -35 0 0 0.18375848842038933 0.83213675 0.12241189 0.04545127 0 1 2 -36 0 0 0.17973684252969455 0.83549005 0.119678274 0.044831615 0 1 2 -37 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 -38 0 0 0.1956789610087275 0.8222762 0.13009433 0.04762942 0 1 2 -39 0 0 0.1934814578219369 0.8240851 0.12843774 0.04747709 0 1 2 -40 0 0 0.1963727608748696 0.8217059 0.13011126 0.0481829 0 1 2 -41 0 0 0.2497403510890685 0.779003 0.16397238 0.057024594 0 1 2 -42 0 0 0.18862701420916578 0.8280953 0.12551515 0.046389602 0 1 2 -43 0 0 0.30319543938694477 0.73845476 0.19266933 0.06887595 0 1 2 -44 0 0 0.25054767014146001 0.7783744 0.16248466 0.059140977 0 1 2 -45 0 0 0.22318220567341671 0.7999691 0.1471228 0.052908104 0 1 2 -46 0 0 0.18541167729254801 0.8307622 0.12309762 0.046140097 0 1 2 -47 0 0 0.19442318111477996 0.8233094 0.12917225 0.04751833 0 1 2 -48 0 0 0.18351913487149632 0.83233595 0.1219628 0.04570115 0 1 2 -49 0 0 0.19122122437689801 0.82594985 0.1270813 0.046968848 0 1 2 -50 1 2 0.83534671767831947 0.4504997 0.43372408 0.115776286 2 1 0 -51 1 2 0.86767360928270354 0.46955615 0.41992733 0.11051658 2 1 0 -52 1 2 0.93160031328057413 0.50976956 0.3939228 0.09630776 2 1 0 -53 1 1 0.65279797692587294 0.52058715 0.32883742 0.15057544 1 2 0 -54 1 2 0.8558829963104696 0.47106034 0.42490783 0.10403175 2 1 0 -55 1 1 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0.2194564188937434 0.80295515 0.14442296 0.05262182 0 1 2 +25 0 0 0.21494981878172878 0.8065819 0.14198802 0.0514301 0 1 2 +26 0 0 0.24577165070800933 0.7821008 0.16011554 0.057783637 0 1 2 +27 0 0 0.19038748932418323 0.82663876 0.12622954 0.047131795 0 1 2 +28 0 0 0.18805977254660364 0.8285652 0.12482488 0.046609845 0 1 2 +29 0 0 0.20663000661012179 0.8133205 0.13665743 0.05002196 0 1 2 +30 0 0 0.21065664271165035 0.81005216 0.13924377 0.050704047 0 1 2 +31 0 0 0.23786115445111095 0.78831214 0.15536134 0.056326643 0 1 2 +32 0 0 0.15886285256730598 0.85311335 0.10583072 0.041055936 0 1 2 +33 0 0 0.16545343736843707 0.8475093 0.10996151 0.04252926 0 1 2 +34 0 0 0.18591403201907866 0.830345 0.12370853 0.04594655 0 1 2 +35 0 0 0.18341544711019692 0.83242226 0.122001536 0.04557609 0 1 2 +36 0 0 0.17992855400900115 0.8353299 0.11959151 0.045078475 0 1 2 +37 0 0 0.18591403201907866 0.830345 0.12370853 0.04594655 0 1 2 +38 0 0 0.19496130358440647 0.8228665 0.1294759 0.04765765 0 1 2 +39 0 0 0.19364645219995238 0.82394916 0.12835562 0.04769524 0 1 2 +40 0 0 0.19668958314222432 0.8214456 0.13012631 0.048428062 0 1 2 +41 0 0 0.24734924174209014 0.78086793 0.16247886 0.056653164 0 1 2 +42 0 0 0.18821646400604544 0.82843536 0.1250735 0.046491038 0 1 2 +43 0 0 0.30505612494040585 0.737082 0.19371146 0.06920647 0 1 2 +44 0 0 0.25262641192631019 0.776758 0.1635937 0.059648313 0 1 2 +45 0 0 0.22270010168843449 0.80035484 0.14670305 0.052942175 0 1 2 +46 0 0 0.1861340710892852 0.8301623 0.12334507 0.046492632 0 1 2 +47 0 0 0.19411706356419201 0.8235615 0.12880456 0.047633827 0 1 2 +48 0 0 0.18407298665537969 0.8318751 0.12210441 0.046020597 0 1 2 +49 0 0 0.19114754665828765 0.8260107 0.1268496 0.04713962 0 1 2 +50 1 2 0.83673939262963537 0.45193356 0.43312046 0.11494588 2 1 0 +51 1 2 0.86770710777353577 0.4701126 0.41991326 0.109974146 2 1 0 +52 1 2 0.93418015288521949 0.51126885 0.39290786 0.09582327 2 1 0 +53 1 1 0.64787252840007592 0.5231576 0.32513994 0.15170242 1 2 0 +54 1 2 0.85530403181892489 0.4708453 0.4251539 0.1040008 2 1 0 +55 1 1 0.72048475722606919 0.48651636 0.37823516 0.13524848 1 2 0 +56 1 2 0.98731359975440414 0.5350068 0.37257624 0.09241684 2 1 0 +57 1 1 0.66748045719924243 0.5129995 0.2861536 0.20084697 1 0 2 +58 1 1 0.7431537925578906 0.47561157 0.393557 0.13083136 1 2 0 +59 1 1 0.69909034236712653 0.49703723 0.35606247 0.14690027 1 2 0 +60 1 1 0.61320765843493341 0.5416108 0.24694625 0.21144293 1 0 2 +61 1 1 0.80477681340081808 0.44718772 0.43233594 0.12047644 1 2 0 +62 1 1 0.6031903416378529 0.5470635 0.24245861 0.21047784 1 2 0 +63 1 1 0.80814292312261826 0.44568497 0.44062257 0.113692455 1 2 0 +64 1 1 0.67277872169448072 0.51028866 0.30068505 0.18902634 1 2 0 +65 1 1 0.78628156083692546 0.45553553 0.41785353 0.12661098 1 2 0 +66 1 2 0.84632457310327791 0.46142864 0.42898875 0.10958265 2 1 0 +67 1 1 0.6373582531736357 0.52868724 0.25097573 0.22033703 1 2 0 +68 1 1 0.79356232181273612 0.45223093 0.44376975 0.10399931 1 2 0 +69 1 1 0.62659540219150156 0.53440815 0.26171228 0.20387949 1 2 0 +70 1 2 1.1801212945612525 0.62052447 0.30724147 0.07223395 2 1 0 +71 1 1 0.68328703737154262 0.50495446 0.33493194 0.16011359 1 2 0 +72 1 2 0.88211060454285306 0.4918638 0.4139084 0.09422791 2 1 0 +73 1 1 0.69879400429831884 0.49718454 0.3615793 0.14123622 1 2 0 +74 1 1 0.71286299269118358 0.49023864 0.36453193 0.14522941 1 2 0 +75 1 1 0.77864003107153634 0.45902985 0.4152896 0.1256806 1 2 0 +76 1 2 0.8213647425762034 0.45147192 0.439831 0.108697206 2 1 0 +77 1 2 1.1205735207790843 0.59874195 0.32609272 0.07516524 2 1 0 +78 1 2 0.8423781447381492 0.46086276 0.43068507 0.10845222 2 1 0 +79 1 1 0.66587555577664381 0.51382345 0.27274686 0.21342967 1 0 2 +80 1 1 0.62096953599728122 0.53742313 0.254104 0.20847283 1 2 0 +81 1 1 0.63268278886482698 0.5311649 0.24450144 0.22433369 1 0 2 +82 1 1 0.64973911629850245 0.522182 0.29223257 0.1855855 1 2 0 +83 1 2 1.0165786599708186 0.5567998 0.36183077 0.08136935 2 1 0 +84 1 2 0.84465969535270868 0.46047688 0.42970356 0.109819606 2 1 0 +85 1 2 0.95367630550936477 0.5154051 0.38532186 0.09927302 2 1 0 +86 1 2 0.89634562281333741 0.48975435 0.40805814 0.10218742 2 1 0 +87 1 1 0.68173814091576967 0.5057372 0.36195385 0.13230896 1 2 0 +88 1 1 0.70062163189048132 0.4962767 0.34466654 0.15905687 1 2 0 +89 1 1 0.66080660077991882 0.5164346 0.32821947 0.15534595 1 2 0 +90 1 1 0.66105985481861962 0.51630384 0.3298916 0.15380464 1 2 0 +91 1 1 0.80115057192814798 0.44881228 0.43293905 0.11824857 1 2 0 +92 1 1 0.64499747750623426 0.52466387 0.29888114 0.176455 1 2 0 +93 1 1 0.65776161330378657 0.51800954 0.28080425 0.20118622 1 0 2 +94 1 1 0.68749585061462215 0.50283366 0.3488738 0.14829257 1 2 0 +95 1 1 0.67858831186500035 0.5073327 0.31937033 0.17329699 1 2 0 +96 1 1 0.70128620602118619 0.495947 0.35253498 0.15151796 1 2 0 +97 1 1 0.71177578153743348 0.49077192 0.36369538 0.14553276 1 2 0 +98 1 1 0.67999031846275859 0.5066219 0.28830498 0.20507313 1 0 2 +99 1 1 0.68764474479701121 0.5027588 0.34208116 0.15515994 1 2 0 +100 2 2 0.16334915301332609 0.8492946 0.11910573 0.031599615 2 1 0 +101 2 2 0.40349818314773872 0.66797924 0.27107194 0.06094874 2 1 0 +102 2 2 0.24416306506638097 0.7833599 0.17449318 0.042146955 2 1 0 +103 2 2 0.38306751588647814 0.68176687 0.2594857 0.058747455 2 1 0 +104 2 2 0.22725748045347199 0.7967156 0.16325499 0.040029448 2 1 0 +105 2 2 0.20588973015834619 0.8139228 0.14897631 0.037100896 2 1 0 +106 2 2 0.64017532690764556 0.5272 0.38546926 0.08733083 2 1 0 +107 2 2 0.30903721290498132 0.73415345 0.21614915 0.04969741 2 1 0 +108 2 2 0.3742356171953844 0.68781483 0.25564197 0.056543257 2 1 0 +109 2 2 0.15601799789188972 0.8555438 0.113819085 0.030637234 2 1 0 +110 2 2 0.33605932129883082 0.7145807 0.23106545 0.054353748 2 1 0 +111 2 2 0.37701078665804577 0.6859087 0.25632346 0.057767972 2 1 0 +112 2 2 0.27167893063706217 0.7620989 0.19216375 0.045737263 2 1 0 +113 2 2 0.37539933081353255 0.6870149 0.25568938 0.05729574 2 1 0 +114 2 2 0.2254385625759445 0.7981661 0.16201293 0.03982088 2 1 0 +115 2 2 0.22769425901021159 0.7963677 0.16323063 0.040401716 2 1 0 +116 2 2 0.38956585567194119 0.6773509 0.2627405 0.059908684 2 1 0 +117 2 2 0.17295836593084912 0.84117264 0.12578449 0.03304281 2 1 0 +118 2 2 0.16852548860591923 0.8449097 0.123072796 0.032017503 2 1 0 +119 2 2 0.70996822423470063 0.49165982 0.41727066 0.09106962 2 1 0 +120 2 2 0.20625375608101151 0.8136266 0.14891486 0.03745854 2 1 0 +121 2 2 0.37559030535389232 0.6868837 0.25489673 0.058219526 2 1 0 +122 2 2 0.22552773088215228 0.7980949 0.16246906 0.039435945 2 1 0 +123 2 2 0.48231493554580024 0.6173526 0.31250232 0.07014498 2 1 0 +124 2 2 0.25092794131040175 0.77807844 0.17851919 0.043402437 2 1 0 +125 2 2 0.32669426420547754 0.72130424 0.22614858 0.052547175 2 1 0 +126 2 2 0.49320729911530842 0.61066467 0.31728864 0.07204676 2 1 0 +127 2 2 0.47003634080980566 0.62497956 0.30485395 0.070166476 2 1 0 +128 2 2 0.27117897343655767 0.76248 0.192149 0.04537086 2 1 0 +129 2 2 0.45264756774399528 0.6359422 0.29717746 0.06688029 2 1 0 +130 2 2 0.29341223926537774 0.74571466 0.20647036 0.04781495 2 1 0 +131 2 2 0.2207152880774497 0.801945 0.1583615 0.03969353 2 1 0 +132 2 2 0.24339555106317839 0.78396136 0.17408688 0.04195166 2 1 0 +133 2 2 0.65322479100237496 0.520365 0.38965574 0.08997938 2 1 0 +134 2 2 0.64929712905598536 0.52241284 0.39114103 0.08644619 2 1 0 +135 2 2 0.19078847254617268 0.82630736 0.1384796 0.035213117 2 1 0 +136 2 2 0.19030284183980689 0.82670873 0.13785057 0.035440713 2 1 0 +137 2 2 0.3862692192365339 0.67958754 0.2605812 0.059831236 2 1 0 +138 2 2 0.48542370971435655 0.6154364 0.31239364 0.072169885 2 1 0 +139 2 2 0.27598668140911997 0.75882304 0.1947137 0.046463266 2 1 0 +140 2 2 0.19423149379963675 0.82346725 0.14072263 0.03581011 2 1 0 +141 2 2 0.23991707338026233 0.7866931 0.1713347 0.04197225 2 1 0 +142 2 2 0.40349818314773872 0.66797924 0.27107194 0.06094874 2 1 0 +143 2 2 0.19757412604060867 0.8207193 0.14302261 0.036258016 2 1 0 +144 2 2 0.17203401257679538 0.84195054 0.12520038 0.032849003 2 1 0 +145 2 2 0.23676464803252026 0.789177 0.16939415 0.04142891 2 1 0 +146 2 2 0.4211462560021087 0.6562941 0.28131506 0.062390868 2 1 0 +147 2 2 0.332962278060079 0.71679723 0.22978072 0.053421956 2 1 0 +148 2 2 0.21891734588438697 0.8033881 0.15723218 0.03937976 2 1 0 +149 2 2 0.44342814384142243 0.64183235 0.29147345 0.06669417 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-out.txt index d54a5ba13e..8077ff4b6f 100644 --- a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-out.txt +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-out.txt @@ -11,7 +11,7 @@ Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement -L1 regularization selected 11 of 15 weights. +L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in %Time% Beginning training model 3 of 5 Beginning optimization diff --git a/test/Directory.Build.props b/test/Directory.Build.props index cd90d4e943..f5c381e57d 100644 --- a/test/Directory.Build.props +++ b/test/Directory.Build.props @@ -2,9 +2,9 @@ - net6.0;net462;net8.0 - net6.0 - net6.0 + net8.0;net462;net9.0 + net8.0 + net8.0 $(TestTargetFramework) win-x64 true diff --git a/test/Microsoft.Data.Analysis.Interactive.Tests/Microsoft.Data.Analysis.Interactive.Tests.csproj b/test/Microsoft.Data.Analysis.Interactive.Tests/Microsoft.Data.Analysis.Interactive.Tests.csproj index c49ffd82d1..e9abe83cb4 100644 --- a/test/Microsoft.Data.Analysis.Interactive.Tests/Microsoft.Data.Analysis.Interactive.Tests.csproj +++ b/test/Microsoft.Data.Analysis.Interactive.Tests/Microsoft.Data.Analysis.Interactive.Tests.csproj @@ -1,7 +1,7 @@  - net6.0 + net8.0 $(NoWarn);MSML_ExtendBaseTestClass diff --git a/test/Microsoft.Data.Analysis.PerformanceTests/Microsoft.Data.Analysis.PerformanceTests.csproj b/test/Microsoft.Data.Analysis.PerformanceTests/Microsoft.Data.Analysis.PerformanceTests.csproj index fa3a7b571a..41ea50c39d 100644 --- a/test/Microsoft.Data.Analysis.PerformanceTests/Microsoft.Data.Analysis.PerformanceTests.csproj +++ b/test/Microsoft.Data.Analysis.PerformanceTests/Microsoft.Data.Analysis.PerformanceTests.csproj @@ -3,7 +3,7 @@ Exe disable - net6.0 + net8.0 false diff --git a/test/Microsoft.Data.Analysis.Tests/ArrowIntegrationTests.cs b/test/Microsoft.Data.Analysis.Tests/ArrowIntegrationTests.cs index 1eaec8ef20..fa960a1f0e 100644 --- a/test/Microsoft.Data.Analysis.Tests/ArrowIntegrationTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/ArrowIntegrationTests.cs @@ -10,12 +10,18 @@ using Apache.Arrow; using Apache.Arrow.Ipc; using Apache.Arrow.Types; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class ArrowIntegrationTests + public class ArrowIntegrationTests : BaseTestClass { + public ArrowIntegrationTests(ITestOutputHelper output) : base(output, true) + { + } + [Fact] public void TestArrowIntegration() { @@ -60,7 +66,9 @@ public void TestArrowIntegration() foreach (RecordBatch batch in recordBatches) { RecordBatchComparer.CompareBatches(originalBatch, batch); + batch.Dispose(); } + originalBatch.Dispose(); } [Fact] @@ -103,7 +111,7 @@ RecordBatch CreateRecordBatch(string prependColumnNamesWith = "") return ret; } - RecordBatch originalBatch = CreateRecordBatch(); + using RecordBatch originalBatch = CreateRecordBatch(); ArrowBuffer.BitmapBuilder validityBitmapBuilder = new ArrowBuffer.BitmapBuilder(); for (int i = 0; i < originalBatch.Length; i++) { @@ -114,17 +122,18 @@ RecordBatch CreateRecordBatch(string prependColumnNamesWith = "") StructType structType = new StructType(originalBatch.Schema.FieldsList); StructArray structArray = new StructArray(structType, originalBatch.Length, originalBatch.Arrays.Cast(), validityBitmap); Schema schema = new Schema.Builder().Field(new Field("Struct", structType, false)).Build(); - RecordBatch recordBatch = new RecordBatch(schema, new[] { structArray }, originalBatch.Length); + using RecordBatch recordBatch = new RecordBatch(schema, new[] { structArray }, originalBatch.Length); DataFrame df = DataFrame.FromArrowRecordBatch(recordBatch); DataFrameIOTests.VerifyColumnTypes(df, testArrowStringColumn: true); IEnumerable recordBatches = df.ToArrowRecordBatches(); - RecordBatch expected = CreateRecordBatch("Struct_"); + using RecordBatch expected = CreateRecordBatch("Struct_"); foreach (RecordBatch batch in recordBatches) { RecordBatchComparer.CompareBatches(expected, batch); + batch.Dispose(); } } @@ -141,18 +150,20 @@ public async Task TestEmptyDataFrameRecordBatch() foreach (RecordBatch recordBatch in recordBatches) { foundARecordBatch = true; - MemoryStream stream = new MemoryStream(); - ArrowStreamWriter writer = new ArrowStreamWriter(stream, recordBatch.Schema); + using MemoryStream stream = new MemoryStream(); + using ArrowStreamWriter writer = new ArrowStreamWriter(stream, recordBatch.Schema); await writer.WriteRecordBatchAsync(recordBatch); stream.Position = 0; - ArrowStreamReader reader = new ArrowStreamReader(stream); + using ArrowStreamReader reader = new ArrowStreamReader(stream); RecordBatch readRecordBatch = reader.ReadNextRecordBatch(); while (readRecordBatch != null) { RecordBatchComparer.CompareBatches(recordBatch, readRecordBatch); + readRecordBatch.Dispose(); readRecordBatch = reader.ReadNextRecordBatch(); } + recordBatch.Dispose(); } Assert.True(foundARecordBatch); } @@ -160,7 +171,7 @@ public async Task TestEmptyDataFrameRecordBatch() [Fact] public void TestMutationOnArrowColumn() { - RecordBatch originalBatch = new RecordBatch.Builder() + using RecordBatch originalBatch = new RecordBatch.Builder() .Append("Column1", false, col => col.Int32(array => array.AppendRange(Enumerable.Range(0, 10)))).Build(); DataFrame df = DataFrame.FromArrowRecordBatch(originalBatch); Assert.Equal(1, df.Columns["Column1"][1]); @@ -176,7 +187,7 @@ public void TestEmptyArrowColumns() // 1. Data + Empty null bitmaps // 2. Empty Data + Null bitmaps // 3. Empty Data + Empty null bitmaps - RecordBatch originalBatch = new RecordBatch.Builder() + using RecordBatch originalBatch = new RecordBatch.Builder() .Append("EmptyNullBitMapColumn", false, col => col.Int32(array => array.AppendRange(Enumerable.Range(0, 10)))) .Append("EmptyDataColumn", true, new Int32Array( valueBuffer: ArrowBuffer.Empty, @@ -200,7 +211,7 @@ public void TestEmptyArrowColumns() Assert.Null(df.Columns["EmptyDataColumn"][i]); } - RecordBatch batch1 = new RecordBatch.Builder() + using RecordBatch batch1 = new RecordBatch.Builder() .Append("EmptyDataAndNullColumns", false, col => col.Int32(array => array.Clear())).Build(); DataFrame emptyDataFrame = DataFrame.FromArrowRecordBatch(batch1); Assert.Equal(0, emptyDataFrame.Rows.Count); @@ -213,7 +224,7 @@ public void TestInconsistentNullBitMapLength() { // Arrow allocates buffers of length 64 by default. 64 * 8 = 512 bits in the NullBitMapBuffer. Anything lesser than 512 will not trigger a throw Int32Array int32 = new Int32Array.Builder().AppendRange(Enumerable.Range(0, 520)).Build(); - RecordBatch originalBatch = new RecordBatch.Builder() + using RecordBatch originalBatch = new RecordBatch.Builder() .Append("EmptyDataColumn", true, new Int32Array( valueBuffer: int32.ValueBuffer, nullBitmapBuffer: new ArrowBuffer.Builder().Append(0x00).Build(), diff --git a/test/Microsoft.Data.Analysis.Tests/ArrowStringColumnTests.cs b/test/Microsoft.Data.Analysis.Tests/ArrowStringColumnTests.cs index c2f2c9040c..df4c50ff40 100644 --- a/test/Microsoft.Data.Analysis.Tests/ArrowStringColumnTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/ArrowStringColumnTests.cs @@ -8,17 +8,22 @@ using System.Text; using System.Threading.Tasks; using Apache.Arrow; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class ArrowStringColumnTests + public class ArrowStringColumnTests : BaseTestClass { + public ArrowStringColumnTests(ITestOutputHelper output) : base(output, true) + { + } [Fact] public void TestBasicArrowStringColumn() { - StringArray strArray = new StringArray.Builder().Append("foo").Append("bar").Build(); + using StringArray strArray = new StringArray.Builder().Append("foo").Append("bar").Build(); Memory dataMemory = new byte[] { 102, 111, 111, 98, 97, 114 }; Memory nullMemory = new byte[] { 0, 0, 0, 0 }; Memory offsetMemory = new byte[] { 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0 }; @@ -55,7 +60,7 @@ public void TestArrowStringColumnWithNulls() [Fact] public void TestArrowStringColumnClone() { - StringArray strArray = new StringArray.Builder().Append("foo").Append("bar").Build(); + using StringArray strArray = new StringArray.Builder().Append("foo").Append("bar").Build(); Memory dataMemory = new byte[] { 102, 111, 111, 98, 97, 114 }; Memory nullMemory = new byte[] { 0, 0, 0, 0 }; Memory offsetMemory = new byte[] { 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0 }; diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrame.IOTests.cs b/test/Microsoft.Data.Analysis.Tests/DataFrame.IOTests.cs index 441c581ef6..d9a3897137 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrame.IOTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrame.IOTests.cs @@ -17,11 +17,17 @@ using System.Threading; using Microsoft.ML.Data; using System.Threading.Tasks; +using Xunit.Abstractions; +using Microsoft.ML.TestFramework; namespace Microsoft.Data.Analysis.Tests { - public class DataFrameIOTests + public class DataFrameIOTests : BaseTestClass { + public DataFrameIOTests(ITestOutputHelper output) : base(output, true) + { + } + internal static void VerifyColumnTypes(DataFrame df, bool testArrowStringColumn = false) { foreach (DataFrameColumn column in df.Columns) diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.cs b/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.cs index 03854c7c5e..26bd5ab2c6 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.cs @@ -9,12 +9,17 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public partial class DataFrameColumnTests + public partial class DataFrameColumnTests : BaseTestClass { + public DataFrameColumnTests(ITestOutputHelper output) : base(output, true) + { + } [Fact] public void AddByteDataFrameColumnToByteDataFrameColumn() { diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.tt b/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.tt index 08597cc83b..598853b73d 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.tt +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameColumn.BinaryOperationTests.tt @@ -15,12 +15,17 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public partial class DataFrameColumnTests + public partial class DataFrameColumnTests : BaseTestClass { + public DataFrameColumnTests(ITestOutputHelper output) : base(output, true) + { + } <# bool supportedInPlace(string type1, string type2) { diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameGroupByTests.cs b/test/Microsoft.Data.Analysis.Tests/DataFrameGroupByTests.cs index e1eba9da8d..d598776b95 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameGroupByTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameGroupByTests.cs @@ -6,12 +6,18 @@ using System.Collections.Generic; using System.Linq; using System.Text; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class DataFrameGroupByTests + public class DataFrameGroupByTests : BaseTestClass { + public DataFrameGroupByTests(ITestOutputHelper output) : base(output, true) + { + } + [Fact] public void TestGroupingWithTKeyTypeofString() { diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameIDataViewTests.cs b/test/Microsoft.Data.Analysis.Tests/DataFrameIDataViewTests.cs index 83cfb67fec..806ce1d968 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameIDataViewTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameIDataViewTests.cs @@ -10,11 +10,17 @@ using Microsoft.ML.Data; using Xunit; using Microsoft.ML.Trainers; +using Xunit.Abstractions; +using Microsoft.ML.TestFramework; namespace Microsoft.Data.Analysis.Tests { - public partial class DataFrameIDataViewTests + public partial class DataFrameIDataViewTests : BaseTestClass { + public DataFrameIDataViewTests(ITestOutputHelper output) : base(output, true) + { + } + [Fact] public void TestIDataView() { diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameJoinExtensionsTests.cs b/test/Microsoft.Data.Analysis.Tests/DataFrameJoinExtensionsTests.cs index ed39dff49f..86c95a3217 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameJoinExtensionsTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameJoinExtensionsTests.cs @@ -4,12 +4,18 @@ using System.Collections.ObjectModel; using System.Linq; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class DataFrameJoinExtensionsTests + public class DataFrameJoinExtensionsTests : BaseTestClass { + public DataFrameJoinExtensionsTests(ITestOutputHelper output) : base(output, true) + { + } + [Fact] public void GetSortedListsIntersection_EmptyCollections_EmptyResult() { diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameTests.BinaryOperations.cs b/test/Microsoft.Data.Analysis.Tests/DataFrameTests.BinaryOperations.cs index 752a6f0f6e..eb0183b0c8 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameTests.BinaryOperations.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameTests.BinaryOperations.cs @@ -8,12 +8,18 @@ using System.Text; using System.Threading.Tasks; using Apache.Arrow; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public partial class DataFrameTests + public partial class DataFrameTests : BaseTestClass { + public DataFrameTests(ITestOutputHelper output) : base(output, true) + { + } + [Fact] public void TestBinaryOperations() { @@ -244,7 +250,7 @@ public void TestBinaryOperationsOnArrowStringColumn() { strArrayBuilder.Append(i.ToString()); } - StringArray strArray = strArrayBuilder.Build(); + using StringArray strArray = strArrayBuilder.Build(); ArrowStringDataFrameColumn stringColumn = new ArrowStringDataFrameColumn("String", strArray.ValueBuffer.Memory, strArray.ValueOffsetsBuffer.Memory, strArray.NullBitmapBuffer.Memory, strArray.Length, strArray.NullCount); df.Columns.Insert(0, stringColumn); diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameTests.Computations.cs b/test/Microsoft.Data.Analysis.Tests/DataFrameTests.Computations.cs index 792460e1c9..71ec940095 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameTests.Computations.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameTests.Computations.cs @@ -7,11 +7,13 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public partial class DataFrameTests + public partial class DataFrameTests : BaseTestClass { [Fact] public void TestComputations() diff --git a/test/Microsoft.Data.Analysis.Tests/DataFrameTests.cs b/test/Microsoft.Data.Analysis.Tests/DataFrameTests.cs index 85b56e0b75..2d75caef72 100644 --- a/test/Microsoft.Data.Analysis.Tests/DataFrameTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/DataFrameTests.cs @@ -8,11 +8,13 @@ using System.Text; using Microsoft.ML; using Microsoft.ML.Data; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public partial class DataFrameTests + public partial class DataFrameTests : BaseTestClass { [Fact] public void TestIndexer() diff --git a/test/Microsoft.Data.Analysis.Tests/Microsoft.Data.Analysis.Tests.csproj b/test/Microsoft.Data.Analysis.Tests/Microsoft.Data.Analysis.Tests.csproj index f982aabf87..d37043ec31 100644 --- a/test/Microsoft.Data.Analysis.Tests/Microsoft.Data.Analysis.Tests.csproj +++ b/test/Microsoft.Data.Analysis.Tests/Microsoft.Data.Analysis.Tests.csproj @@ -3,6 +3,7 @@ $(NoWarn);MSML_ParameterLocalVarName;MSML_PrivateFieldName;MSML_ExtendBaseTestClass;MSML_GeneralName preview + $(DefineConstants);SKIP_MKL @@ -10,6 +11,8 @@ + + diff --git a/test/Microsoft.Data.Analysis.Tests/PrimitiveDataFrameColumnTests.cs b/test/Microsoft.Data.Analysis.Tests/PrimitiveDataFrameColumnTests.cs index ebf882defa..0111a7a316 100644 --- a/test/Microsoft.Data.Analysis.Tests/PrimitiveDataFrameColumnTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/PrimitiveDataFrameColumnTests.cs @@ -4,13 +4,19 @@ using System; using System.Linq; +using Microsoft.ML.TestFramework; using Microsoft.ML.TestFramework.Attributes; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class PrimitiveDataFrameColumnTests + public class PrimitiveDataFrameColumnTests : BaseTestClass { + public PrimitiveDataFrameColumnTests(ITestOutputHelper output) : base(output, true) + { + } + [X64Fact("32-bit doesn't allow to allocate more than 2 Gb")] public void TestGetterAndSetterForColumnsGreaterThanMaxCapacity() { diff --git a/test/Microsoft.Data.Analysis.Tests/Properties/AssemblyInfo.cs b/test/Microsoft.Data.Analysis.Tests/Properties/AssemblyInfo.cs new file mode 100644 index 0000000000..70da298f72 --- /dev/null +++ b/test/Microsoft.Data.Analysis.Tests/Properties/AssemblyInfo.cs @@ -0,0 +1,7 @@ +// 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 Xunit; + +[assembly: CollectionBehavior(DisableTestParallelization = true)] diff --git a/test/Microsoft.Data.Analysis.Tests/StringDataFrameColumnTests.cs b/test/Microsoft.Data.Analysis.Tests/StringDataFrameColumnTests.cs index e14d5c928f..b9e640e095 100644 --- a/test/Microsoft.Data.Analysis.Tests/StringDataFrameColumnTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/StringDataFrameColumnTests.cs @@ -7,12 +7,18 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class StringDataFrameColumnTests + public class StringDataFrameColumnTests : BaseTestClass { + public StringDataFrameColumnTests(ITestOutputHelper output) : base(output, true) + { + } + [Fact] public void TestColumnClone() { diff --git a/test/Microsoft.Data.Analysis.Tests/TextFieldParserTests.cs b/test/Microsoft.Data.Analysis.Tests/TextFieldParserTests.cs index 30511173fc..1bcbaaa6c9 100644 --- a/test/Microsoft.Data.Analysis.Tests/TextFieldParserTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/TextFieldParserTests.cs @@ -5,12 +5,18 @@ using System; using System.IO; using System.Text; +using Microsoft.ML.TestFramework; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class TextFieldParserTests + public class TextFieldParserTests : BaseTestClass { + public TextFieldParserTests(ITestOutputHelper output) : base(output, true) + { + } + private Stream GetStream(string streamData) { return new MemoryStream(Encoding.Default.GetBytes(streamData)); diff --git a/test/Microsoft.Data.Analysis.Tests/VBufferColumnTests.cs b/test/Microsoft.Data.Analysis.Tests/VBufferColumnTests.cs index e727a1f2af..e84b324987 100644 --- a/test/Microsoft.Data.Analysis.Tests/VBufferColumnTests.cs +++ b/test/Microsoft.Data.Analysis.Tests/VBufferColumnTests.cs @@ -8,14 +8,20 @@ using System.Text; using System.Threading.Tasks; using Microsoft.ML.Data; +using Microsoft.ML.TestFramework; using Microsoft.ML.TestFramework.Attributes; using Xunit; +using Xunit.Abstractions; namespace Microsoft.Data.Analysis.Tests { - public class VBufferColumnTests + public class VBufferColumnTests : BaseTestClass { + public VBufferColumnTests(ITestOutputHelper output) : base(output, true) + { + } + [Fact] public void TestVBufferColumn_Creation() { @@ -61,6 +67,8 @@ public void TestVBufferColumn_Indexer_MoreThanMaxInt() { Assert.Equal(originalValues[i], values[i]); } + + vBufferColumn = null; } } } diff --git a/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj b/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj index 149962617d..9f393e0873 100644 --- a/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj +++ b/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj @@ -5,7 +5,7 @@ None - + true diff --git a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt index d56e742f3b..b75a426ee8 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt +++ b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 diff --git a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.test.ConsoleApp.csproj.approved.txt b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.test.ConsoleApp.csproj.approved.txt index 7e7c94d918..ef3297ad59 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.test.ConsoleApp.csproj.approved.txt +++ b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureCodeGeneratorTest.test.ConsoleApp.csproj.approved.txt @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 diff --git a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt index 12a18142d2..9be23b26b9 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt +++ b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 diff --git a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.received.txt b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.received.txt index 4637958288..27e748537e 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.received.txt +++ b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureImageCodeGeneratorTest.received.txt @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 diff --git a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureObjectDetectionCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureObjectDetectionCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt index 12a18142d2..9be23b26b9 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureObjectDetectionCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt +++ b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.AzureObjectDetectionCodeGeneratorTest.CodeGenTest.ConsoleApp.csproj.approved.txt @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 diff --git a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.ConsoleAppProjectFileContentTest.approved.txt b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.ConsoleAppProjectFileContentTest.approved.txt index b6aaf2b243..ab60beee0e 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.ConsoleAppProjectFileContentTest.approved.txt +++ b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.ConsoleAppProjectFileContentTest.approved.txt @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 diff --git a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.Recommendation_GenerateConsoleAppProjectContents_VerifyPredictProject.approved.txt b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.Recommendation_GenerateConsoleAppProjectContents_VerifyPredictProject.approved.txt index 2ffb1661f0..d6141f8276 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.Recommendation_GenerateConsoleAppProjectContents_VerifyPredictProject.approved.txt +++ b/test/Microsoft.ML.CodeGenerator.Tests/ApprovalTests/ConsoleCodeGeneratorTests.Recommendation_GenerateConsoleAppProjectContents_VerifyPredictProject.approved.txt @@ -2,7 +2,7 @@ Exe - net6.0 + net8.0 diff --git a/test/Microsoft.ML.CodeGenerator.Tests/Microsoft.ML.CodeGenerator.Tests.csproj b/test/Microsoft.ML.CodeGenerator.Tests/Microsoft.ML.CodeGenerator.Tests.csproj index af3f6b1d13..8360a90458 100644 --- a/test/Microsoft.ML.CodeGenerator.Tests/Microsoft.ML.CodeGenerator.Tests.csproj +++ b/test/Microsoft.ML.CodeGenerator.Tests/Microsoft.ML.CodeGenerator.Tests.csproj @@ -5,7 +5,7 @@ None - + true diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs index 80acaf5d70..6553f8d289 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs @@ -5208,7 +5208,7 @@ public void TestCrossValidationMacro() foldGetter(ref fold); Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold)); if (w == 1) - Assert.Equal(1.585, stdev, .001); + Assert.Equal(1.585, stdev, .01); else Assert.Equal(1.39, stdev, 0.01); isWeightedGetter(ref isWeighted); diff --git a/test/Microsoft.ML.CpuMath.PerformanceTests/Microsoft.ML.CpuMath.PerformanceTests.csproj b/test/Microsoft.ML.CpuMath.PerformanceTests/Microsoft.ML.CpuMath.PerformanceTests.csproj index 525256afff..e1be32c678 100644 --- a/test/Microsoft.ML.CpuMath.PerformanceTests/Microsoft.ML.CpuMath.PerformanceTests.csproj +++ b/test/Microsoft.ML.CpuMath.PerformanceTests/Microsoft.ML.CpuMath.PerformanceTests.csproj @@ -3,7 +3,7 @@ Exe false - net6.0 + net8.0 false false + + $(NoWarn);NU1605 diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index 2e7b194fd1..5e9197eb34 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -353,7 +353,7 @@ public void BinaryClassifierLogisticRegressionGaussianNormTest() { var binaryPredictors = new[] { TestLearners.logisticRegressionGaussianNorm }; var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest(); - RunAllTests(binaryPredictors, binaryClassificationDatasets, digitsOfPrecision: 4); + RunAllTests(binaryPredictors, binaryClassificationDatasets, digitsOfPrecision: 2); Done(); } @@ -981,7 +981,7 @@ public void FastTreeBinaryClassificationCategoricalSplitTest() foreach (var learner in learners) { foreach (TestDataset dataset in binaryClassificationDatasets) - Run_TrainTest(learner, dataset, extraTag: "Cat", summary: true, saveAsIni: true); + Run_TrainTest(learner, dataset, extraTag: "Cat", summary: true, saveAsIni: true, digitsOfPrecision: 3); } }); Done(); @@ -1618,7 +1618,7 @@ public void DefaultCalibratorPerceptronTest() public void PAVCalibratorPerceptronTest() { var datasets = GetDatasetsForCalibratorTest(); - RunAllTests(new[] { TestLearners.perceptronDefault }, datasets, new[] { "cali=PAV" }, "PAVcalibration"); + RunAllTests(new[] { TestLearners.perceptronDefault }, datasets, new[] { "cali=PAV" }, "PAVcalibration", digitsOfPrecision: 3); Done(); } @@ -2209,7 +2209,7 @@ public void EnsemblesDefaultTest() { // This one does CV as well as TrainTest. var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 tp=-"), "WE-Default"); - RunOneAllTests(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 5, parseOption: NumberParseOption.UseSingle); + RunOneAllTests(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 3, parseOption: NumberParseOption.UseSingle); Done(); } @@ -2249,7 +2249,7 @@ public void EnsemblesStackingCombinerTest() public void EnsemblesAveragerCombinerTest() { var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 oc=Average tp=-"), "WE-Average"); - Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 3, parseOption: NumberParseOption.UseSingle); Done(); } @@ -2289,7 +2289,7 @@ public void EnsemblesAllDataSetSelectorTest() public void EnsemblesRandomSubSpaceSelectorTest() { var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-"), "WE-RandomFeature"); - Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 5, parseOption: NumberParseOption.UseSingle); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 3, parseOption: NumberParseOption.UseSingle); Done(); } diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs index 3fce5c1bc2..15a5955d1e 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs @@ -118,7 +118,7 @@ private IEnumerable GetConfigurationDirs() // on x64 vs x86 and dotnet core 3.1 vs others, so we have 4 combination: // x64-netcore3.1, x86-netcore3.1, x64-rest, x86-rest. In some cases x64 vs x86 // have different results, in some cases netcore 3.1 vs rest have different results, - // the most complicate situation is 12 combinations (x64 vs x86, net6.0 vs rest, + // the most complicate situation is 12 combinations (x64 vs x86, net8.0 vs rest, // win vs linux vs osx) have different results. // So use list of string to return different configurations and test will try to search // through this list and use the one file first found, make sure we don't have baseline file @@ -147,6 +147,8 @@ private IEnumerable GetConfigurationDirs() { if (RuntimeInformation.ProcessArchitecture == Architecture.Arm64) configurationDirs.Add("win-arm"); + if (!Environment.Is64BitProcess) + configurationDirs.Add("win-x86"); } // This needs to come after win-arm but before win-x64 and win-x86 @@ -163,8 +165,6 @@ private IEnumerable GetConfigurationDirs() { if (Environment.Is64BitProcess) configurationDirs.Add("win-x64"); - else - configurationDirs.Add("win-x86"); } diff --git a/test/Microsoft.ML.TestFramework/BaseTestClass.cs b/test/Microsoft.ML.TestFramework/BaseTestClass.cs index 6fc4350eea..b60ec26049 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestClass.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestClass.cs @@ -22,14 +22,12 @@ public class BaseTestClass : IDisposable { public string TestName { get; set; } public string FullTestName { get; set; } + public bool GcAfterTests { get; set; } public ChannelMessageKind MessageKindToLog; static BaseTestClass() { - // specific to use tls 1.2 as https://aka.ms/mlnet-resources/ only accpets tls 1.2 or newer - ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; - AppDomain.CurrentDomain.UnhandledException += (sender, e) => { // Write to stdout because stderr does not show up in the test output @@ -41,7 +39,7 @@ static BaseTestClass() DataDir = Path.Combine(RootDir, "test", "data"); } - public BaseTestClass(ITestOutputHelper output) + public BaseTestClass(ITestOutputHelper output, bool gcAfterTests = false) { //This locale is currently set for tests only so that the produced output //files can be compared on systems with other locales to give set of known @@ -63,6 +61,7 @@ public BaseTestClass(ITestOutputHelper output) ITest test = (ITest)output.GetType().GetField("test", BindingFlags.NonPublic | BindingFlags.Instance).GetValue(output); FullTestName = test.TestCase.TestMethod.TestClass.Class.Name + "." + test.TestCase.TestMethod.Method.Name; TestName = test.TestCase.TestMethod.Method.Name; + GcAfterTests = gcAfterTests; MessageKindToLog = ChannelMessageKind.Error; var attributes = test.TestCase.TestMethod.Method.GetCustomAttributes(typeof(LogMessageKind)); @@ -90,10 +89,14 @@ protected virtual void Initialize() protected virtual void Cleanup() { + if (GcAfterTests) + GC.Collect(2, GCCollectionMode.Forced, true); } protected static string RootDir { get; } + protected string OutDir { get; } + protected static string DataDir { get; } protected ITestOutputHelper Output { get; } @@ -104,6 +107,7 @@ public static string GetDataPath(string name) return null; return Path.GetFullPath(Path.Combine(DataDir, name)); } + public static string GetDataPath(string subDir, string name) { if (string.IsNullOrWhiteSpace(name)) @@ -115,14 +119,17 @@ protected string GetOutputPath(string name) { return TestCommon.GetOutputPath(OutDir, name); } + protected string GetOutputPath(string subDir, string name) { return TestCommon.GetOutputPath(OutDir, subDir, name); } + protected string DeleteOutputPath(string subDir, string name) { return TestCommon.DeleteOutputPath(OutDir, subDir, name); } + protected string DeleteOutputPath(string name) { return TestCommon.DeleteOutputPath(OutDir, name); diff --git a/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs b/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs index 452e4ffa4a..5c2989994d 100644 --- a/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs +++ b/test/Microsoft.ML.TestFramework/DataPipe/Parquet.cs @@ -20,7 +20,7 @@ protected override void Initialize() public void TestParquetPrimitiveDataTypes() { string pathData = GetDataPath(@"Parquet", "alltypes.parquet"); - TestCore(pathData, false, new[] { "loader=Parquet{bigIntDates=+}" }); + TestCore(pathData, false, new[] { "loader=Parquet{bigIntDates=+}" }, digitsOfPrecision: 2); Done(); } diff --git a/test/Microsoft.ML.TestFramework/GlobalBase.cs b/test/Microsoft.ML.TestFramework/GlobalBase.cs index 016e3dfd9b..2d0afbf501 100644 --- a/test/Microsoft.ML.TestFramework/GlobalBase.cs +++ b/test/Microsoft.ML.TestFramework/GlobalBase.cs @@ -25,6 +25,7 @@ public static void AssemblyInit() var prev = Contracts.SetAssertHandler(AssertHandler); Contracts.Check(prev == null, "Expected to replace null assertion handler!"); +#if !SKIP_MKL // Only do this for x86/x64. This will cause crashes in places IntelMKL is not supported. if (RuntimeInformation.ProcessArchitecture == Architecture.X64 || RuntimeInformation.ProcessArchitecture == Architecture.X86) { @@ -36,6 +37,7 @@ public static void AssemblyInit() // See https://github.com/dotnet/machinelearning/issues/1073 Mkl.PptrfInternal(Mkl.Layout.RowMajor, Mkl.UpLo.Up, 0, Array.Empty()); } +#endif } private static class Mkl diff --git a/test/Microsoft.ML.TestFramework/TestCommandBase.cs b/test/Microsoft.ML.TestFramework/TestCommandBase.cs index 60dba561f5..479c033b07 100644 --- a/test/Microsoft.ML.TestFramework/TestCommandBase.cs +++ b/test/Microsoft.ML.TestFramework/TestCommandBase.cs @@ -1144,12 +1144,12 @@ public void CommandTrainingLrWithStats() string trainData = GetDataPath("adult.tiny.with-schema.txt"); OutputPath trainModel = ModelPath(); - TestCore("train", trainData, loaderArgs, extraArgs, digitsOfPrecision: 5); + TestCore("train", trainData, loaderArgs, extraArgs, digitsOfPrecision: 3); _step++; // Save model summary. OutputPath modelSummary = CreateOutputPath("summary.txt"); - TestInCore("savemodel", null, trainModel, "", digitsOfPrecision: 4, modelSummary.Arg("sum")); + TestInCore("savemodel", null, trainModel, "", digitsOfPrecision: 3, modelSummary.Arg("sum")); Done(); } diff --git a/test/Microsoft.ML.TestFrameworkCommon/Properties/AssemblyInfo.cs b/test/Microsoft.ML.TestFrameworkCommon/Properties/AssemblyInfo.cs new file mode 100644 index 0000000000..d74297b8f7 --- /dev/null +++ b/test/Microsoft.ML.TestFrameworkCommon/Properties/AssemblyInfo.cs @@ -0,0 +1,9 @@ +// 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.Runtime.CompilerServices; +using Microsoft.ML; + +[assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.Predictor.Tests" + PublicKey.Value)] +[assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.TimeSeries.Tests" + PublicKey.Value)] +[assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.Tests" + PublicKey.TestValue)] diff --git a/test/Microsoft.ML.Tokenizers.Data.Tests/Microsoft.ML.Tokenizers.Data.Tests.csproj b/test/Microsoft.ML.Tokenizers.Data.Tests/Microsoft.ML.Tokenizers.Data.Tests.csproj index 0bb5927412..51dced3ca5 100644 --- a/test/Microsoft.ML.Tokenizers.Data.Tests/Microsoft.ML.Tokenizers.Data.Tests.csproj +++ b/test/Microsoft.ML.Tokenizers.Data.Tests/Microsoft.ML.Tokenizers.Data.Tests.csproj @@ -7,7 +7,7 @@ enable - + true diff --git a/test/Microsoft.ML.Tokenizers.Tests/Microsoft.ML.Tokenizers.Tests.csproj b/test/Microsoft.ML.Tokenizers.Tests/Microsoft.ML.Tokenizers.Tests.csproj index e0d08c93aa..8e1f741552 100644 --- a/test/Microsoft.ML.Tokenizers.Tests/Microsoft.ML.Tokenizers.Tests.csproj +++ b/test/Microsoft.ML.Tokenizers.Tests/Microsoft.ML.Tokenizers.Tests.csproj @@ -7,13 +7,13 @@ enable - - - - + true + + + diff --git a/test/Microsoft.ML.TorchSharp.Tests/Microsoft.ML.TorchSharp.Tests.csproj b/test/Microsoft.ML.TorchSharp.Tests/Microsoft.ML.TorchSharp.Tests.csproj index 0d5f6541a8..5fed431948 100644 --- a/test/Microsoft.ML.TorchSharp.Tests/Microsoft.ML.TorchSharp.Tests.csproj +++ b/test/Microsoft.ML.TorchSharp.Tests/Microsoft.ML.TorchSharp.Tests.csproj @@ -8,7 +8,7 @@ - + true diff --git a/test/Microsoft.ML.TorchSharp.Tests/QATests.cs b/test/Microsoft.ML.TorchSharp.Tests/QATests.cs index 18e77f179e..a1a971df79 100644 --- a/test/Microsoft.ML.TorchSharp.Tests/QATests.cs +++ b/test/Microsoft.ML.TorchSharp.Tests/QATests.cs @@ -26,7 +26,7 @@ private class TestSingleSentenceData public int AnswerIndex; } - [Fact] + [Fact(Skip = "Skip in CI build, uses too much memory.")] public void TestSimpleQA() { var dataView = ML.Data.LoadFromEnumerable(