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codemzs · codemzs · commit 072e61d01fe8 · 2018-05-25T16:57:32.000-07:00
diff --git a/ZBaselines/Common/EntryPoints/core_manifest.json b/ZBaselines/Common/EntryPoints/core_manifest.json
@@ -1437,7 +1437,7 @@
             "Kind": "Struct",
             "Fields": [
               {
-                "Name": "Model",
+                "Name": "PredictorModel",
                 "Type": "PredictorModel",
                 "Desc": "The predictor model",
                 "Required": false,
@@ -3055,7 +3055,7 @@
             "Kind": "Struct",
             "Fields": [
               {
-                "Name": "Model",
+                "Name": "PredictorModel",
                 "Type": "PredictorModel",
                 "Desc": "The predictor model",
                 "Required": false,
diff --git a/src/Microsoft.ML.PipelineInference/PipelinePattern.cs b/src/Microsoft.ML.PipelineInference/PipelinePattern.cs
@@ -152,7 +152,7 @@ public Experiment CreateTrainTestExperiment(IDataView trainData, IDataView testD
                     },
                 Outputs =
                     {
-                        Model = finalOutput
+                        PredictorModel = finalOutput
                     },
                 PipelineId = UniqueId.ToString("N"),
                 Kind = MacroUtils.TrainerKindApiValue<Models.MacroUtilsTrainerKinds>(trainerKind),
@@ -189,7 +189,7 @@ public Models.TrainTestEvaluator.Output AddAsTrainTest(Var<IDataView> trainData,
                     },
                 Outputs =
                     {
-                        Model = finalOutput
+                        PredictorModel = finalOutput
                     },
                 TrainingData = trainData,
                 TestingData = testData,
diff --git a/src/Microsoft.ML/CSharpApi.cs b/src/Microsoft.ML/CSharpApi.cs
@@ -2214,7 +2214,7 @@ public sealed partial class CrossValidationMacroSubGraphOutput
             /// <summary>
             /// The predictor model
             /// </summary>
-            public Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel> Model { get; set; } = new Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel>();
+            public Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel> PredictorModel { get; set; } = new Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel>();
 
             /// <summary>
             /// The transform model
@@ -3400,7 +3400,7 @@ public sealed partial class TrainTestMacroSubGraphOutput
             /// <summary>
             /// The predictor model
             /// </summary>
-            public Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel> Model { get; set; } = new Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel>();
+            public Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel> PredictorModel { get; set; } = new Var<Microsoft.ML.Runtime.EntryPoints.IPredictorModel>();
 
             /// <summary>
             /// Transform model
diff --git a/src/Microsoft.ML/Models/BinaryClassificationEvaluator.cs b/src/Microsoft.ML/Models/BinaryClassificationEvaluator.cs
@@ -4,9 +4,7 @@
 
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Runtime.Data;
-using Microsoft.ML.Runtime.EntryPoints;
 using Microsoft.ML.Transforms;
-using System.Collections.Generic;
 using System.Linq;
 
 namespace Microsoft.ML.Models
@@ -70,7 +68,7 @@ public BinaryClassificationMetrics Evaluate(PredictionModel model, ILearningPipe
 
                 var metric = BinaryClassificationMetrics.FromMetrics(environment, overallMetrics, confusionMatrix);
 
-                Contracts.Assert(metric.Count == 1);
+                Contracts.Check(metric.Count == 1);
 
                 return metric[0];
             }
diff --git a/src/Microsoft.ML/Models/BinaryClassificationMetrics.cs b/src/Microsoft.ML/Models/BinaryClassificationMetrics.cs
@@ -19,7 +19,7 @@ private BinaryClassificationMetrics()
         {
         }
 
-        internal static List<BinaryClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix, int skipRows = 0)
+        internal static List<BinaryClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix, int confusionMatriceStartIndex = 0)
         {
             Contracts.AssertValue(env);
             env.AssertValue(overallMetrics);
@@ -28,41 +28,40 @@ internal static List<BinaryClassificationMetrics> FromMetrics(IHostEnvironment e
             var metricsEnumerable = overallMetrics.AsEnumerable<SerializationClass>(env, true, ignoreMissingColumns: true);
             var enumerator = metricsEnumerable.GetEnumerator();
 
-            while (skipRows-- >= 0)
+            if (!enumerator.MoveNext())
             {
-                if (!enumerator.MoveNext())
-                {
-                    throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");
-                }
+                throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");
             }
 
             List<BinaryClassificationMetrics> metrics = new List<BinaryClassificationMetrics>();
             var confusionMatrices = ConfusionMatrix.Create(env, confusionMatrix).GetEnumerator();
+
+            int Index = 0;
             do
             {
                 SerializationClass metric = enumerator.Current;
 
-                if (!confusionMatrices.MoveNext())
+                if (Index++ >= confusionMatriceStartIndex && !confusionMatrices.MoveNext())
                 {
                     throw env.Except("Confusion matrices didn't have enough matrices.");
                 }
 
-                metrics.Add( 
+                metrics.Add(
                     new BinaryClassificationMetrics()
-                {
-                    Auc = metric.Auc,
-                    Accuracy = metric.Accuracy,
-                    PositivePrecision = metric.PositivePrecision,
-                    PositiveRecall = metric.PositiveRecall,
-                    NegativePrecision = metric.NegativePrecision,
-                    NegativeRecall = metric.NegativeRecall,
-                    LogLoss = metric.LogLoss,
-                    LogLossReduction = metric.LogLossReduction,
-                    Entropy = metric.Entropy,
-                    F1Score = metric.F1Score,
-                    Auprc = metric.Auprc,
-                    ConfusionMatrix = confusionMatrices.Current,
-                });
+                    {
+                        Auc = metric.Auc,
+                        Accuracy = metric.Accuracy,
+                        PositivePrecision = metric.PositivePrecision,
+                        PositiveRecall = metric.PositiveRecall,
+                        NegativePrecision = metric.NegativePrecision,
+                        NegativeRecall = metric.NegativeRecall,
+                        LogLoss = metric.LogLoss,
+                        LogLossReduction = metric.LogLossReduction,
+                        Entropy = metric.Entropy,
+                        F1Score = metric.F1Score,
+                        Auprc = metric.Auprc,
+                        ConfusionMatrix = confusionMatrices.Current,
+                    });
 
             } while (enumerator.MoveNext());
 
diff --git a/src/Microsoft.ML/Models/ClassificationEvaluator.cs b/src/Microsoft.ML/Models/ClassificationEvaluator.cs
@@ -5,7 +5,6 @@
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Runtime.Data;
 using Microsoft.ML.Transforms;
-using System.Collections.Generic;
 using System.Linq;
 
 namespace Microsoft.ML.Models
@@ -70,7 +69,7 @@ public ClassificationMetrics Evaluate(PredictionModel model, ILearningPipelineLo
 
                 var metric = ClassificationMetrics.FromMetrics(environment, overallMetrics, confusionMatrix);
 
-                Contracts.Assert(metric.Count == 1);
+                Contracts.Check(metric.Count == 1);
 
                 return metric[0];
             }
diff --git a/src/Microsoft.ML/Models/ClassificationMetrics.cs b/src/Microsoft.ML/Models/ClassificationMetrics.cs
@@ -18,27 +18,27 @@ private ClassificationMetrics()
         {
         }
 
-        internal static List<ClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix, int skipRows = 0)
+        internal static List<ClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix, 
+            int confusionMatriceStartIndex = 0)
         {
             Contracts.AssertValue(env);
             env.AssertValue(overallMetrics);
             env.AssertValue(confusionMatrix);
 
             var metricsEnumerable = overallMetrics.AsEnumerable<SerializationClass>(env, true, ignoreMissingColumns: true);
             var enumerator = metricsEnumerable.GetEnumerator();
-            while (skipRows-- >= 0)
+            if (!enumerator.MoveNext())
             {
-                if (!enumerator.MoveNext())
-                {
-                    throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");
-                }
+                throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");
             }
-            
+
             List<ClassificationMetrics> metrics = new List<ClassificationMetrics>();
             var confusionMatrices = ConfusionMatrix.Create(env, confusionMatrix).GetEnumerator();
+
+            int Index = 0;
             do
             {
-                if (!confusionMatrices.MoveNext())
+                if (Index++ >= confusionMatriceStartIndex && !confusionMatrices.MoveNext())
                 {
                     throw env.Except("Confusion matrices didn't have enough matrices.");
                 }
diff --git a/src/Microsoft.ML/Models/CrossValidator.cs b/src/Microsoft.ML/Models/CrossValidator.cs
@@ -17,8 +17,8 @@ public sealed partial class CrossValidator
         /// </summary>
         /// <typeparam name="TInput">Class type that represents input schema.</typeparam>
         /// <typeparam name="TOutput">Class type that represents prediction schema.</typeparam>
-        /// <param name="pipeline">Machine learning pipeline that contain may contain loader, transforms and at least one trainer.</param>
-        /// <returns>List containning metrics and predictor model for each fold</returns>
+        /// <param name="pipeline">Machine learning pipeline may contain loader, transforms and at least one trainer.</param>
+        /// <returns>List containing metrics and predictor model for each fold</returns>
         public CrossValidationOutput<TInput, TOutput> CrossValidate<TInput, TOutput>(LearningPipeline pipeline) 
             where TInput : class
             where TOutput : class, new()
@@ -127,23 +127,20 @@ public CrossValidationOutput<TInput, TOutput> CrossValidate<TInput, TOutput>(Lea
                         cvOutput.BinaryClassificationMetrics = BinaryClassificationMetrics.FromMetrics(
                             environment,
                             experiment.GetOutput(crossValidateOutput.OverallMetrics),
-                            experiment.GetOutput(crossValidateOutput.ConfusionMatrix),
-                            2);
+                            experiment.GetOutput(crossValidateOutput.ConfusionMatrix), 2);
                     }
                     else if(Kind == MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer)
                     {
                         cvOutput.ClassificationMetrics = ClassificationMetrics.FromMetrics(
                             environment,
                             experiment.GetOutput(crossValidateOutput.OverallMetrics),
-                            experiment.GetOutput(crossValidateOutput.ConfusionMatrix),
-                            2);
+                            experiment.GetOutput(crossValidateOutput.ConfusionMatrix), 2);
                     }
                     else if (Kind == MacroUtilsTrainerKinds.SignatureRegressorTrainer)
                     {
                         cvOutput.RegressionMetrics = RegressionMetrics.FromOverallMetrics(
                             environment,
-                            experiment.GetOutput(crossValidateOutput.OverallMetrics),
-                            2);
+                            experiment.GetOutput(crossValidateOutput.OverallMetrics));
                     }
                     else
                     {
diff --git a/src/Microsoft.ML/Models/RegressionMetrics.cs b/src/Microsoft.ML/Models/RegressionMetrics.cs
@@ -19,19 +19,16 @@ private RegressionMetrics()
         {
         }
 
-        internal static List<RegressionMetrics> FromOverallMetrics(IHostEnvironment env, IDataView overallMetrics, int skipRows = 0)
+        internal static List<RegressionMetrics> FromOverallMetrics(IHostEnvironment env, IDataView overallMetrics)
         {
             Contracts.AssertValue(env);
             env.AssertValue(overallMetrics);
 
             var metricsEnumerable = overallMetrics.AsEnumerable<SerializationClass>(env, true, ignoreMissingColumns: true);
             var enumerator = metricsEnumerable.GetEnumerator();
-            while (skipRows-- >= 0)
+            if (!enumerator.MoveNext())
             {
-                if (!enumerator.MoveNext())
-                {
-                    throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");
-                }
+                throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");
             }
 
             List<RegressionMetrics> metrics = new List<RegressionMetrics>();
diff --git a/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationMacro.cs b/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationMacro.cs
@@ -30,7 +30,7 @@ public sealed class SubGraphInput
         public sealed class SubGraphOutput
         {
             [Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)]
-            public Var<IPredictorModel> Model;
+            public Var<IPredictorModel> PredictorModel;
             
             [Argument(ArgumentType.AtMostOnce, HelpText = "The transform model", SortOrder = 2)]
             public Var<ITransformModel> TransformModel;
@@ -203,15 +203,15 @@ public static CommonOutputs.MacroOutput<Output> CrossValidate(
                     VarName = mapping[input.Inputs.Data.VarName]
                 };
 
-                if (input.Outputs.Model != null && mapping.ContainsKey(input.Outputs.Model.VarName))
+                if (input.Outputs.PredictorModel != null && mapping.ContainsKey(input.Outputs.PredictorModel.VarName))
                 {
-                    args.Outputs.Model = new Var<IPredictorModel>
+                    args.Outputs.PredictorModel = new Var<IPredictorModel>
                     {
-                        VarName = mapping[input.Outputs.Model.VarName]
+                        VarName = mapping[input.Outputs.PredictorModel.VarName]
                     };
                 }
                 else
-                    args.Outputs.Model = null;
+                    args.Outputs.PredictorModel = null;
 
                 if (input.Outputs.TransformModel != null && mapping.ContainsKey(input.Outputs.TransformModel.VarName))
                 {
diff --git a/src/Microsoft.ML/Runtime/EntryPoints/TrainTestMacro.cs b/src/Microsoft.ML/Runtime/EntryPoints/TrainTestMacro.cs
@@ -25,7 +25,7 @@ public sealed class SubGraphInput
         public sealed class SubGraphOutput
         {
             [Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)]
-            public Var<IPredictorModel> Model;
+            public Var<IPredictorModel> PredictorModel;
             
             [Argument(ArgumentType.AtMostOnce, HelpText = "Transform model", SortOrder = 2)]
             public Var<ITransformModel> TransformModel;
@@ -129,7 +129,7 @@ public static CommonOutputs.MacroOutput<Output> TrainTest(
             subGraphRunContext.RemoveVariable(dataVariable);
 
             // Change the subgraph to use the model variable as output.
-            varName = input.Outputs.UseTransformModel ? input.Outputs.TransformModel.VarName : input.Outputs.Model.VarName;
+            varName = input.Outputs.UseTransformModel ? input.Outputs.TransformModel.VarName : input.Outputs.PredictorModel.VarName;
             if (!subGraphRunContext.TryGetVariable(varName, out dataVariable))
                 throw env.Except($"Invalid variable name '{varName}'.");
 
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
@@ -319,7 +319,7 @@ public void TestCrossValidationMacro()
                     TransformModel = null
                 };
                 crossValidate.Inputs.Data = nop.Data;
-                crossValidate.Outputs.Model = modelCombineOutput.PredictorModel;
+                crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel;
                 var crossValidateOutput = experiment.Add(crossValidate);
 
                 experiment.Compile();
@@ -410,7 +410,7 @@ public void TestCrossValidationMacroWithMultiClass()
                     TransformModel = null
                 };
                 crossValidate.Inputs.Data = nop.Data;
-                crossValidate.Outputs.Model = modelCombineOutput.PredictorModel;
+                crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel;
                 var crossValidateOutput = experiment.Add(crossValidate);
 
                 experiment.Compile();
@@ -541,7 +541,7 @@ public void TestCrossValidationMacroWithStratification()
                     StratificationColumn = "Strat"
                 };
                 crossValidate.Inputs.Data = nop.Data;
-                crossValidate.Outputs.Model = modelCombineOutput.PredictorModel;
+                crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel;
                 var crossValidateOutput = experiment.Add(crossValidate);
                 experiment.Compile();
                 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
@@ -1875,7 +1875,7 @@ public void EntryPointTrainTestMacroNoTransformInput()
                           'Data': '$data1'
                         },
                         'Outputs': {
-                          'Model': '$model'
+                          'PredictorModel': '$model'
                         }
                       },
                       'Outputs': {
@@ -1980,7 +1980,7 @@ public void EntryPointTrainTestMacro()
                           'Data': '$data1'
                         },
                         'Outputs': {
-                          'Model': '$model'
+                          'PredictorModel': '$model'
                         }
                       },
                       'Outputs': {
@@ -2108,7 +2108,7 @@ public void EntryPointChainedTrainTestMacros()
                           'Data': '$data1'
                         },
                         'Outputs': {
-                          'Model': '$model'
+                          'PredictorModel': '$model'
                         }
                       },
                       'Outputs': {
@@ -2141,7 +2141,7 @@ public void EntryPointChainedTrainTestMacros()
                           'Data': '$data4'
                         },
                         'Outputs': {
-                          'Model': '$model2'
+                          'PredictorModel': '$model2'
                         }
                       },
                       'Outputs': {
@@ -2274,7 +2274,7 @@ public void EntryPointChainedCrossValMacros()
                           'Data': '$data6'
                         },
                         'Outputs': {
-                          'Model': '$model'
+                          'PredictorModel': '$model'
                         }
                       },
                       'Outputs': {
@@ -2336,7 +2336,7 @@ public void EntryPointChainedCrossValMacros()
                           'Data': '$data4'
                         },
                         'Outputs': {
-                          'Model': '$model2'
+                          'PredictorModel': '$model2'
                         }
                       },
                       'Outputs': {
diff --git a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs

Original file line number	Diff line number	Diff line change
`@@ -18,27 +18,27 @@ private ClassificationMetrics()`
`18`	`18`	`{`
`19`	`19`	`}`
`20`	`20`
`21`		`- internal static List<ClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix, int skipRows = 0)`
	`21`	`+ internal static List<ClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix,`
	`22`	`+ int confusionMatriceStartIndex = 0)`
`22`	`23`	`{`
`23`	`24`	`Contracts.AssertValue(env);`
`24`	`25`	`env.AssertValue(overallMetrics);`
`25`	`26`	`env.AssertValue(confusionMatrix);`
`26`	`27`
`27`	`28`	`var metricsEnumerable = overallMetrics.AsEnumerable<SerializationClass>(env, true, ignoreMissingColumns: true);`
`28`	`29`	`var enumerator = metricsEnumerable.GetEnumerator();`
`29`		`- while (skipRows-- >= 0)`
	`30`	`+ if (!enumerator.MoveNext())`
`30`	`31`	`{`
`31`		`- if (!enumerator.MoveNext())`
`32`		`- {`
`33`		`- throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");`
`34`		`- }`
	`32`	`+ throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");`
`35`	`33`	`}`
`36`		`-`
	`34`	`+`
`37`	`35`	`List<ClassificationMetrics> metrics = new List<ClassificationMetrics>();`
`38`	`36`	`var confusionMatrices = ConfusionMatrix.Create(env, confusionMatrix).GetEnumerator();`
	`37`	`+`
	`38`	`+ int Index = 0;`
`39`	`39`	`do`
`40`	`40`	`{`
`41`		`- if (!confusionMatrices.MoveNext())`
	`41`	`+ if (Index++ >= confusionMatriceStartIndex && !confusionMatrices.MoveNext())`
`42`	`42`	`{`
`43`	`43`	`throw env.Except("Confusion matrices didn't have enough matrices.");`
`44`	`44`	`}`
Original file line number	Diff line number	Diff line change
`@@ -19,19 +19,16 @@ private RegressionMetrics()`
`19`	`19`	`{`
`20`	`20`	`}`
`21`	`21`
`22`		`- internal static List<RegressionMetrics> FromOverallMetrics(IHostEnvironment env, IDataView overallMetrics, int skipRows = 0)`
	`22`	`+ internal static List<RegressionMetrics> FromOverallMetrics(IHostEnvironment env, IDataView overallMetrics)`
`23`	`23`	`{`
`24`	`24`	`Contracts.AssertValue(env);`
`25`	`25`	`env.AssertValue(overallMetrics);`
`26`	`26`
`27`	`27`	`var metricsEnumerable = overallMetrics.AsEnumerable<SerializationClass>(env, true, ignoreMissingColumns: true);`
`28`	`28`	`var enumerator = metricsEnumerable.GetEnumerator();`
`29`		`- while (skipRows-- >= 0)`
	`29`	`+ if (!enumerator.MoveNext())`
`30`	`30`	`{`
`31`		`- if (!enumerator.MoveNext())`
`32`		`- {`
`33`		`- throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");`
`34`		`- }`
	`31`	`+ throw env.Except("The overall RegressionMetrics didn't have sufficient rows.");`
`35`	`32`	`}`
`36`	`33`
`37`	`34`	`List<RegressionMetrics> metrics = new List<RegressionMetrics>();`
Original file line number	Diff line number	Diff line change
`@@ -30,7 +30,7 @@ public sealed class SubGraphInput`
`30`	`30`	`public sealed class SubGraphOutput`
`31`	`31`	`{`
`32`	`32`	`[Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)]`
`33`		`- public Var<IPredictorModel> Model;`
	`33`	`+ public Var<IPredictorModel> PredictorModel;`
`34`	`34`
`35`	`35`	`[Argument(ArgumentType.AtMostOnce, HelpText = "The transform model", SortOrder = 2)]`
`36`	`36`	`public Var<ITransformModel> TransformModel;`
`@@ -203,15 +203,15 @@ public static CommonOutputs.MacroOutput<Output> CrossValidate(`
`203`	`203`	`VarName = mapping[input.Inputs.Data.VarName]`
`204`	`204`	`};`
`205`	`205`
`206`		`- if (input.Outputs.Model != null && mapping.ContainsKey(input.Outputs.Model.VarName))`
	`206`	`+ if (input.Outputs.PredictorModel != null && mapping.ContainsKey(input.Outputs.PredictorModel.VarName))`
`207`	`207`	`{`
`208`		`- args.Outputs.Model = new Var<IPredictorModel>`
	`208`	`+ args.Outputs.PredictorModel = new Var<IPredictorModel>`
`209`	`209`	`{`
`210`		`- VarName = mapping[input.Outputs.Model.VarName]`
	`210`	`+ VarName = mapping[input.Outputs.PredictorModel.VarName]`
`211`	`211`	`};`
`212`	`212`	`}`
`213`	`213`	`else`
`214`		`- args.Outputs.Model = null;`
	`214`	`+ args.Outputs.PredictorModel = null;`
`215`	`215`
`216`	`216`	`if (input.Outputs.TransformModel != null && mapping.ContainsKey(input.Outputs.TransformModel.VarName))`
`217`	`217`	`{`