Append

CachingTests.cs (6)

47.Append(ML.Transforms.NormalizeMeanVariance("Norm2", "F1")); 56.Append(ML.Transforms.NormalizeMinMax("Norm1", "F1")) 57.Append(ML.Transforms.NormalizeMeanVariance("Norm2", "F1")); 75.Append(ML.Transforms.CopyColumns("F1", "Features")) 76.Append(ML.Transforms.NormalizeMinMax("Norm1", "F1")) 77.Append(ML.Transforms.NormalizeMeanVariance("Norm2", "F1"));

CalibratedModelParametersTests.cs (1)

136return pipeline.Append(ML.Transforms.Conversion.ConvertType("Label", outputKind: DataKind.Boolean))

DatabaseLoaderTests.cs (10)

FeatureContributionTests.cs (16)

ImagesTests.cs (4)

52.Append(new ImagePixelExtractingEstimator(env, "ImagePixels", "ImageReal")) 53.Append(new ImageGrayscalingEstimator(env, ("ImageGray", "ImageReal"))); 76.Append(new ImagePixelExtractingEstimator(env, "ImagePixels", "ImageReal")) 77.Append(new ImageGrayscalingEstimator(env, ("ImageGray", "ImageReal")));

OnnxConversionTest.cs (19)

PermutationFeatureImportanceTests.cs (6)

861return pipeline.Append(ML.Transforms.Conversion.ConvertType("Label", outputKind: DataKind.Boolean)) 864return pipeline.Append(ML.Transforms.Conversion.MapValueToKey("Label")) 867return pipeline.Append(ML.Transforms.Conversion.MapValueToKey("GroupId")) 942return pipeline.Append(ML.Transforms.Conversion.ConvertType("Label", outputKind: DataKind.Boolean)) 947return pipeline.Append(ML.Transforms.Conversion.MapValueToKey("Label")) 951return pipeline.Append(ML.Transforms.Conversion.MapValueToKey("GroupId"))

Scenarios\Api\CookbookSamples\CookbookSamplesDynamicApi.cs (15)

Scenarios\Api\Estimators\DecomposableTrainAndPredict.cs (2)

35.Append(ml.MulticlassClassification.Trainers.SdcaMaximumEntropy( 37.Append(new KeyToValueMappingEstimator(ml, "PredictedLabel"));

Scenarios\Api\Estimators\Extensibility.cs (3)

43.Append(new ValueToKeyMappingEstimator(ml, "Label"), TransformerScope.TrainTest) 44.Append(ml.MulticlassClassification.Trainers.SdcaMaximumEntropy( 46.Append(new KeyToValueMappingEstimator(ml, "PredictedLabel"));

Scenarios\Api\Estimators\MultithreadedPrediction.cs (1)

33.Append(ml.BinaryClassification.Trainers.SdcaNonCalibrated(

Scenarios\Api\Estimators\PredictAndMetadata.cs (2)

33.Append(ml.MulticlassClassification.Trainers.SdcaMaximumEntropy( 82.Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(

Scenarios\Api\Estimators\SimpleTrainAndPredict.cs (2)

31.Append(ml.BinaryClassification.Trainers.SdcaNonCalibrated( 68.Append(ml.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(new SymbolicSgdLogisticRegressionBinaryTrainer.Options

Scenarios\IrisPlantClassificationTests.cs (2)

34.Append(mlContext.Transforms.Conversion.MapValueToKey("Label")) 36.Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(

Scenarios\IrisPlantClassificationWithStringLabelTests.cs (3)

38.Append(mlContext.Transforms.Conversion.MapValueToKey("Label", "IrisPlantType"), TransformerScope.TrainTest) 40.Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy( 42.Append(mlContext.Transforms.Conversion.MapKeyToValue("Plant", "PredictedLabel"));

Scenarios\RegressionTest.cs (7)

29.Append(context.Transforms.Categorical.OneHotEncoding(outputColumnName: "RateCodeEncoded", inputColumnName: "RateCode")) 30.Append(context.Transforms.Categorical.OneHotEncoding(outputColumnName: "PaymentTypeEncoded", inputColumnName: "PaymentType")) 31.Append(context.Transforms.NormalizeMeanVariance(outputColumnName: "PassengerCount")) 32.Append(context.Transforms.NormalizeMeanVariance(outputColumnName: "TripTime")) 33.Append(context.Transforms.NormalizeMeanVariance(outputColumnName: "TripDistance")) 34.Append(context.Transforms.Concatenate("Features", "VendorIdEncoded", "RateCodeEncoded", "PaymentTypeEncoded", "PassengerCount", 38var trainingPipeline = dataProcessPipeline.Append(trainer);

ScenariosWithDirectInstantiation\IrisPlantClassificationTests.cs (2)

32.Append(mlContext.Transforms.Conversion.MapValueToKey("Label")) 34.Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(

TrainerEstimators\MetalinearEstimators.cs (5)

33.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); 50.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); 68.Append(ML.Transforms.Conversion.MapKeyToValue("PredictedLabelValue", "PredictedLabel")); 99.Append(ML.MulticlassClassification.Trainers.OneVersusAll(sdcaTrainer)) 100.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel"));

TrainerEstimators\TrainerEstimators.cs (4)

99var pipeWithTrainer = pipe.AppendCacheCheckpoint(Env).Append(trainer); 130var pipeWithTrainer = pipe.AppendCacheCheckpoint(Env).Append(trainer); 172var pipeWithTrainer = pipe.AppendCacheCheckpoint(Env).Append(trainer); 219oneHotPipeline.Append(ML.Transforms.Concatenate("Features", "Features", "LoggedIn"));

TrainerEstimators\TreeEnsembleFeaturizerTest.cs (18)

TrainerEstimators\TreeEstimators.cs (6)

311.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); 351.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); 371.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); 396.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); 415.Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); 782.Append(ML.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

Transformers\CategoricalHashTests.cs (6)

91.Append(ML.Transforms.Categorical.OneHotHashEncoding("B", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)) 92.Append(ML.Transforms.Categorical.OneHotHashEncoding("C", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag)) 93.Append(ML.Transforms.Categorical.OneHotHashEncoding("D", "ScalarString", outputKind: OneHotEncodingEstimator.OutputKind.Binary)) 94.Append(ML.Transforms.Categorical.OneHotHashEncoding("E", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Binary)) 95.Append(ML.Transforms.Categorical.OneHotHashEncoding("F", "VarVectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag)) 97.Append(ML.Transforms.Categorical.OneHotHashEncoding("G", "SingleVectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag));

Transformers\CategoricalTests.cs (8)

111.Append(mlContext.Transforms.Concatenate("Features", new string[] { "A", "B" })) 112.Append(mlContext.Transforms.Conversion.MapValueToKey("Label")) 113.Append(mlContext.Transforms.NormalizeSupervisedBinning("Features", fixZero: false, maximumBinCount: 5, labelColumnName: "Label")) 114.Append(mlContext.Transforms.Categorical.OneHotEncoding("Features", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)); 166.Append(ML.Transforms.Categorical.OneHotEncoding("B", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)) 167.Append(ML.Transforms.Categorical.OneHotEncoding("C", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag)) 168.Append(ML.Transforms.Categorical.OneHotEncoding("D", "ScalarString", outputKind: OneHotEncodingEstimator.OutputKind.Binary)) 169.Append(ML.Transforms.Categorical.OneHotEncoding("E", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Binary));

Transformers\ConcatTests.cs (2)

67.Append(ML.Transforms.Concatenate("f3", "float4", "float1")) 68.Append(ML.Transforms.Concatenate("f4", "float6", "vfloat", "float1"));

Transformers\ExpressionTransformerTests.cs (4)

42Append(ML.Transforms.Expression("Expr3", "(x,y)=>x*y", "Float", "Int")). 43Append(ML.Transforms.Expression("Expr4", "(x,y,z)=>abs(x-y)*z", "Float", "FloatVector", "Double")). 44Append(ML.Transforms.Expression("Expr5", "x=>len(concat(upper(x),lower(x)))", "Text")). 45Append(ML.Transforms.Expression("Expr6", "(x,y)=>right(x,y)", "TextVector", "Int"));

Transformers\FeatureSelectionTests.cs (1)

43.Append(ML.Transforms.FeatureSelection.SelectFeaturesBasedOnCount("bag_of_words_count", "bag_of_words", 10)

Transformers\KeyToVectorEstimatorTests.cs (1)

85.Append(ML.Transforms.Conversion.MapKeyToVector("VectorBaggedString", "B", true));

Transformers\NAReplaceTests.cs (3)

137.Append(ML.Transforms.ReplaceMissingValues("C", "VectorFloat", replacementMode: MissingValueReplacingEstimator.ReplacementMode.Mean)) 138.Append(ML.Transforms.ReplaceMissingValues("D", "VectorDouble", replacementMode: MissingValueReplacingEstimator.ReplacementMode.Minimum)) 139.Append(ML.Transforms.ReplaceMissingValues("E", "VectorDouble", replacementMode: MissingValueReplacingEstimator.ReplacementMode.Mode));

Transformers\NormalizerTests.cs (4)

244.Append(context.Transforms.NormalizeMeanVariance( 247.Append(context.Transforms.NormalizeLogMeanVariance( 250.Append(context.Transforms.NormalizeSupervisedBinning( 668.Append(new VectorWhiteningEstimator(ML, "whitened", "features"));

Transformers\TextFeaturizerTests.cs (6)

471.Append(new KeyToValueMappingEstimator(ML, "chars")); 538.Append(ML.Transforms.Text.RemoveDefaultStopWords("NoDefaultStopwords", "words")) 539.Append(ML.Transforms.Text.RemoveStopWords("NoStopWords", "words", "xbox", "this", "is", "a", "the", "THAT", "bY")); 631.Append(new NgramExtractingEstimator(ML, "ngrams", "terms")) 632.Append(new NgramHashingEstimator(ML, "ngramshash", "terms")) 635.Append(new NgramHashingEstimator(ML, "ngramshashinvert", "terms", maximumNumberOfInverts: 2));

Transformers\TextNormalizer.cs (3)

57new TextNormalizingEstimator(ML, caseMode: TextNormalizingEstimator.CaseMode.Upper, columns: new[] { ("UpperText", "text") })).Append( 58new TextNormalizingEstimator(ML, keepDiacritics: true, columns: new[] { ("WithDiacriticsText", "text") })).Append( 59new TextNormalizingEstimator(ML, keepNumbers: false, columns: new[] { ("NoNumberText", "text") })).Append(

Transformers\WordEmbeddingsTests.cs (2)

43.Append(ML.Transforms.Text.RemoveDefaultStopWords("CleanWords", "Words")); 78.Append(ML.Transforms.Text.RemoveDefaultStopWords("CleanWords", "Words"));