EstimatorChain

docs\samples\Microsoft.ML.Samples\Dynamic\Trainers\MulticlassClassification\ImageClassification\LearningRateSchedulingCifarResnetTransferLearning.cs (1)

102var pipeline = mlContext.MulticlassClassification.Trainers.

docs\samples\Microsoft.ML.Samples\Dynamic\Trainers\MulticlassClassification\ImageClassification\ResnetV2101TransferLearningEarlyStopping.cs (1)

91var pipeline = mlContext.Transforms.LoadRawImageBytes(

docs\samples\Microsoft.ML.Samples\Dynamic\Trainers\MulticlassClassification\ImageClassification\ResnetV2101TransferLearningTrainTestSplit.cs (1)

82var pipeline = mlContext.MulticlassClassification.Trainers.

SamplesDatasetUtils.cs (1)

91var pipeline = mlContext.Transforms.CopyColumns("Label", "IsOver50K")

TensorFlowEstimatorTests.cs (3)

162var pipe = ML.Transforms.LoadImages("Input", imageFolder, "imagePath") 205var pipe = ML.Transforms.LoadImages("Input", imageFolder, "imagePath") 255var pipe = ML.Transforms.LoadImages("Input", imageFolder, "imagePath")

TensorflowTests.cs (13)

148var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 668var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 823var pipe = _mlContext.Transforms.CopyColumns("Features", "Placeholder") 892var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 1019var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, 1116var pipeline = _mlContext.Transforms.ResizeImages("ResizedImage", imageWidth, imageHeight, nameof(InMemoryImage.LoadedImage)) 1158var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 1350var pipeline = tensorFlowModel.ScoreTensorFlowModel(new[] { "Original_A", "Joined_Splited_Text" }, new[] { "A", "B" }) 1418var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1521var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1679var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1813var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 2062var pipeline = _mlContext.Transforms.LoadImages("Input", imageFolder, "imagePath")

CachingTests.cs (1)

45var pipe = ML.Transforms.CopyColumns("F1", "Features")

CalibratedModelParametersTests.cs (1)

133var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2Important", "X3", "X4Rand")

DatabaseLoaderTests.cs (1)

203var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")

FeatureContributionTests.cs (5)

33var estPipe = ML.Transforms.CalculateFeatureContribution(model) 201var est = ML.Transforms.CalculateFeatureContribution(model, numberOfPositiveContributions: 3, numberOfNegativeContributions: 0) 225var est = ML.Transforms.CalculateFeatureContribution(model, numberOfPositiveContributions: 3, numberOfNegativeContributions: 0) 314var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2VBuffer", "X3Important") 424var dataProcessPipeline = ML.Transforms.CopyColumns(outputColumnName: DefaultColumnNames.Label, inputColumnName: nameof(TaxiTrip.FareAmount))

ImagesTests.cs (3)

50var pipe = new ImageLoadingEstimator(env, imageFolder, ("ImageReal", "ImagePath")) 74var pipe = new ImageLoadingEstimator(env, imageFolder, ("ImageReal", "ImagePath")) 1021var pipe = new ImageLoadingEstimator(env, imageFolder, ("ImageReal", "ImagePath"))

OnnxConversionTest.cs (26)

76var dynamicPipeline = 153var pipeline = mlContext.Transforms.NormalizeMinMax("Features"). 245var initialPipeline = mlContext.Transforms.ReplaceMissingValues("Features"). 249var pipeline = initialPipeline.Append(estimator); 267var pipeline = new VectorWhiteningEstimator(mlContext, "whitened1", "features") 276private (IDataView, List<IEstimator<ITransformer>>, EstimatorChain<NormalizingTransformer>) GetEstimatorsForOnnxConversionTests() 300var initialPipeline = ML.Transforms.ReplaceMissingValues("Features"). 313var pipelineEstimators = initialPipeline.Append(estimator).Append(calibrator); 390var pipeline = new TextNormalizingEstimator(mlContext, keepDiacritics: true, columns: new[] { ("NormText", "text") }).Append( 572var pipeline = 603var pipeline = 628var pipeline = mlContext.Transforms.ReplaceMissingValues("Features"). 792var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("F2", "F2", Transforms.OneHotEncodingEstimator.OutputKind.Bag) 1167var pipeline = mlContext.Transforms.IndicateMissingValues(new[] { new InputOutputColumnPair("MissingIndicator", "Features"), }) 1541var pipeline = mlContext.Transforms.Text.TokenizeIntoWords("Words", "Text") 1565var pipeline = mlContext.Transforms.Text.TokenizeIntoWords("Words", "Text") 1680var initialPipeline = mlContext.Transforms.ReplaceMissingValues("Features") 1686var pipeline = initialPipeline.Append(estimator); 1898var pipeline = mlContext.Transforms.ReplaceMissingValues("Size").Append(mlContext.Transforms.SelectColumns(new[] { "Size", "Shape", "Thickness", "Label" })); 1956var initialPipeline = mlContext.Transforms.ReplaceMissingValues("MyFeatureVector"). 1960var pipeline = initialPipeline.Append(estimator); 2005var initialPipeline = mlContext.Transforms.ReplaceMissingValues("MyFeatureVector") 2011var pipeline = initialPipeline.Append(estimator); 2202var chain = new EstimatorChain<ITransformer>().Append(pipeline); 2219private void TestPipeline<TLastTransformer, TRow>(EstimatorChain<TLastTransformer> pipeline, IEnumerable<TRow> data, string onnxFileName, ColumnComparison[] columnsToCompare, SchemaDefinition schemaDefinition = null, string onnxTxtName = null, string onnxTxtSubDir = null) 2238private void TestPipeline<TLastTransformer>(EstimatorChain<TLastTransformer> pipeline, IDataView dataView, string onnxFileName, ColumnComparison[] columnsToCompare, string onnxTxtName = null, string onnxTxtSubDir = null)

PermutationFeatureImportanceTests.cs (2)

858var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2Important", "X3", "X4Rand") 938var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2VBuffer", "X3Important")

Scenarios\Api\CookbookSamples\CookbookSamplesDynamicApi.cs (14)

101var pipeline = 167var pipeline = 228var pipeline = 264var finalPipeline = pipeline.Append(mlContext.Transforms.Conversion.MapKeyToValue("Data", "PredictedLabel")); 351var pipeline = context.Transforms.Concatenate("Features", "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", 391var pipeline = context.Transforms.Concatenate("Features", "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", 426var pipeline = context.Transforms.Concatenate("Features", "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", 462var pipeline = context.Transforms.Concatenate("Features", "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", 519var pipeline = 593var pipeline = 612var fullLearningPipeline = pipeline 642var pipeline = 741var estimator = mlContext.Transforms.CustomMapping<InputRow, OutputRow>(CustomMappings.IncomeMapping, nameof(CustomMappings.IncomeMapping)) 779var estimator = mlContext.Transforms.CustomMapping(mapping, null)

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

33var pipeline = new ColumnConcatenatingEstimator(ml, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")

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

41var pipeline = new ColumnConcatenatingEstimator(ml, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")

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

31var pipeline = ml.Transforms.Text.FeaturizeText("Features", "SentimentText")

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

31var pipeline = ml.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") 80var pipeline = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") 115var pipelineUnamed = mlContext.Transforms.Conversion.MapValueToKey("Label")

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

29var pipeline = ml.Transforms.Text.FeaturizeText("Features", "SentimentText") 66var pipeline = ml.Transforms.Text.FeaturizeText("Features", "SentimentText")

Scenarios\IrisPlantClassificationTests.cs (1)

32var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")

Scenarios\IrisPlantClassificationWithStringLabelTests.cs (1)

36var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")

Scenarios\RegressionTest.cs (2)

27var dataProcessPipeline = context.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: "FareAmount") 38var trainingPipeline = dataProcessPipeline.Append(trainer);

Scenarios\WordBagTest.cs (2)

31var textPipeline = 68var textPipeline =

ScenariosWithDirectInstantiation\IrisPlantClassificationTests.cs (1)

30var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")

TrainerEstimators\FAFMEstimator.cs (1)

25var pipeline = mlContext.Transforms.CopyColumns(DefaultColumnNames.Features, nameof(FfmExample.Field0))

TrainerEstimators\LbfgsTests.cs (4)

23var pipeWithTrainer = pipe.Append(trainer); 37var pipeWithTrainer = pipe.Append(trainer); 167var pipeWithTrainer = pipe.Append(trainer); 189var pipeWithTrainer = pipe.Append(trainer);

TrainerEstimators\MetalinearEstimators.cs (1)

97var pipeline = new ColumnConcatenatingEstimator(Env, "Vars", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")

TrainerEstimators\SdcaTests.cs (4)

174var sdcaWithoutWeightMulticlass = mlContext.Transforms.Conversion.MapValueToKey("LabelIndex", "Label"). 178var sdcaWithWeightMulticlass = mlContext.Transforms.Conversion.MapValueToKey("LabelIndex", "Label"). 277var pipeline = mlContext.Transforms.Conversion.MapValueToKey("LabelIndex", "Label"). 311var pipeline = mlContext.Transforms.Conversion.MapValueToKey("LabelIndex", "Label").

TrainerEstimators\SymSgdClassificationTests.cs (1)

21var pipeWithTrainer = pipe.Append(trainer);

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); 218var oneHotPipeline = pipeline.Append(ML.Transforms.Categorical.OneHotEncoding("LoggedIn"));

TrainerEstimators\TreeEnsembleFeaturizerTest.cs (13)

TrainerEstimators\TreeEstimators.cs (21)

49var pipeWithTrainer = pipe.Append(trainer); 71var pipeWithTrainer = pipe.Append(trainer); 93var pipeWithTrainer = pipe.Append(trainer); 138var pipeWithTrainer = pipe.Append(trainer); 159var pipeWithTrainer = pipe.Append(trainer); 183var pipeWithTrainer = pipe.Append(trainer); 201var pipeWithTrainer = pipe.Append(trainer); 310var pipe = pipeline.Append(trainer) 327var pipe = pipeline.Append(trainer); 350var pipe = pipeline.Append(trainer) 370var pipe = pipeline.Append(trainer) 395var pipe = pipeline.Append(trainer) 414var pipe = pipeline.Append(trainer) 744var pipeline = 781var pipe = pipeline.Append(trainer) 988var estimator = pipeline.Append(ML.BinaryClassification.Trainers.FastTree( 1006var estimator = pipeline.Append(ML.BinaryClassification.Trainers.FastForest( 1027var estimator = pipeline.Append(context.MulticlassClassification.Trainers.OneVersusAll(context.BinaryClassification.Trainers.FastTree())); 1049var trainer = pipeline.Append(ML.BinaryClassification.Trainers.LightGbm( 1084var trainer = pipeline.Append(context.BinaryClassification.Trainers.LightGbm( 1112var trainer = pipeline.Append(context.MulticlassClassification.Trainers.LightGbm(

Transformers\CategoricalHashTests.cs (1)

89var est = ML.Transforms.Text.TokenizeIntoWords("VarVectorString", "ScalarString")

Transformers\CategoricalTests.cs (2)

109var pipe = mlContext.Transforms.Conversion.ConvertType("A", outputKind: DataKind.Single) 164var est = ML.Transforms.Text.TokenizeIntoWords("VarVectorString", "ScalarString")

Transformers\ConcatTests.cs (1)

65var pipe = ML.Transforms.Concatenate("f1", "float1")

Transformers\ConvertTests.cs (1)

338var pipe = ML.Transforms.Categorical.OneHotEncoding(new[] {

Transformers\ExpressionTransformerTests.cs (1)

40var expr = ML.Transforms.Expression("Expr1", "x=>x/2", "Double").

Transformers\FeatureSelectionTests.cs (6)

41var est = new WordBagEstimator(ML, "bag_of_words", "text") 120var est = ML.Transforms.FeatureSelection.SelectFeaturesBasedOnCount("FeatureSelect", "VectorFloat", count: 1) 177var est = ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation("FeatureSelect", "VectorFloat", slotsInOutput: 1, labelColumnName: "Label") 238var pipeline = ML.Transforms.Text.TokenizeIntoWords("Features") 246var pipeline = ML.Transforms.Text.TokenizeIntoWords("Features") 254var pipeline = ML.Transforms.Text.TokenizeIntoWords("Features")

Transformers\HashTests.cs (1)

383var pipeline = ML.Transforms.Concatenate("D", "A")

Transformers\KeyToBinaryVectorEstimatorTest.cs (1)

74var est = ML.Transforms.Conversion.MapKeyToBinaryVector("ScalarString", "A")

Transformers\KeyToValueTests.cs (1)

78var est = ML.Transforms.Conversion.MapKeyToValue("ScalarString", "A")

Transformers\KeyToVectorEstimatorTests.cs (2)

83var est = ML.Transforms.Conversion.MapKeyToVector("ScalarString", "A") 256var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")

Transformers\NAIndicatorTests.cs (1)

139var newpipe = pipe.Append(ML.Transforms.IndicateMissingValues("NAA", "CatA"));

Transformers\NAReplaceTests.cs (1)

135var est = ML.Transforms.ReplaceMissingValues("A", "ScalarFloat", replacementMode: MissingValueReplacingEstimator.ReplacementMode.Maximum)

Transformers\NormalizerTests.cs (5)

238var est = context.Transforms.NormalizeMinMax( 666var est = ML.Transforms.NormalizeLpNorm("lpnorm", "features") 701var est = new VectorWhiteningEstimator(ML, "whitened1", "features") 764var est = ML.Transforms.NormalizeLpNorm("lpNorm1", "features") 824var est = ML.Transforms.NormalizeGlobalContrast("gcnNorm1", "features")

Transformers\SelectColumnsTests.cs (3)

134var chain = est.Append(ColumnSelectingEstimator.KeepColumns(Env, "C", "A")); 157var chain = est.Append(ML.Transforms.SelectColumns(new[] { "B", "A" }, true)); 198var est = new ColumnCopyingEstimator(Env, new[] { ("A", "A"), ("B", "B") }).Append(

Transformers\TextFeaturizerTests.cs (6)

469var est = new WordTokenizingEstimator(ML, "words", "text") 536var est = ML.Transforms.Text.NormalizeText("text") 599var est = new WordBagEstimator(ML, "bag_of_words", "text"). 629var est = new WordTokenizingEstimator(ML, "text", "text") 683var est = new WordBagEstimator(env, "bag_of_words", "text"). 782var pipeline = ML.Transforms.Text.ProduceWordBags("Features")

Transformers\TextNormalizer.cs (1)

56var pipeVariations = new TextNormalizingEstimator(ML, columns: new[] { ("NormText", "text") }).Append(

Transformers\ValueMappingTests.cs (3)

105var estimator = new WordTokenizingEstimator(Env, new[]{ 558var estimator = ML.Transforms.Conversion.MapValue("D", keyValuePairs, "A", true). 640var est = ML.Transforms.Text.TokenizeIntoWords("TokenizeB", "B")

Transformers\WordEmbeddingsTests.cs (2)

41var est = ML.Transforms.Text.NormalizeText("NormalizedText", "SentimentText", keepDiacritics: false, keepPunctuations: false) 76var est = ML.Transforms.Text.NormalizeText("NormalizedText", "SentimentText", keepDiacritics: false, keepPunctuations: false)

TimeSeriesDirectApi.cs (2)

227var pipeline = ml.Transforms.Text.FeaturizeText("Text_Featurized", "Text") 303var pipeline = ml.Transforms.Text.FeaturizeText("Text_Featurized", "Text")

NerTests.cs (6)

69var chain = new EstimatorChain<ITransformer>(); 70var estimator = chain.Append(ML.Transforms.Conversion.MapValueToKey("Label", keyData: labels)) 148var chain = new EstimatorChain<ITransformer>(); 149var estimator = chain.Append(ML.Transforms.Conversion.MapValueToKey("Label", keyData: labels)) 222var chain = new EstimatorChain<ITransformer>(); 223var estimator = chain.Append(ML.Transforms.Conversion.MapValueToKey("Label", keyData: labels))

ObjectDetectionTests.cs (3)

44var chain = new EstimatorChain<ITransformer>(); 46var filteredPipeline = chain.Append(ML.Transforms.Text.TokenizeIntoWords("Labels", separators: new char[] { ',' }), TransformerScope.Training) 63var pipeline = ML.Transforms.Text.TokenizeIntoWords("Labels", separators: new char[] { ',' })

QATests.cs (2)

42var chain = new EstimatorChain<ITransformer>(); 43var estimator = chain.Append(ML.MulticlassClassification.Trainers.QuestionAnswer(maxEpochs: 1));

TextClassificationTests.cs (5)

97var chain = new EstimatorChain<ITransformer>(); 98var estimator = chain.Append(ML.Transforms.Conversion.MapValueToKey("Label", "Sentiment"), TransformerScope.TrainTest) 176var pipeline = 235var estimator = ML.Transforms.Conversion.MapValueToKey("Label", "Sentiment") 320var estimator = ML.MulticlassClassification.Trainers.TextClassification(outputColumnName: "outputColumn", sentence1ColumnName: "Sentence", sentence2ColumnName: "Sentence2", validationSet: preppedData)

Dracula\CountTargetEncodingTransformer.cs (2)

423/// will only be applied if the estimator is part of an <see cref="EstimatorChain{TLastTransformer}"/>, when fitting the next estimator in the chain.</param> 496/// will only be applied if the estimator is part of an <see cref="EstimatorChain{TLastTransformer}"/>, when fitting the next estimator in the chain.</param>

Text\WordBagTransform.cs (2)

464var chain = new EstimatorChain<ITransformer>(); 682var estimator = new EstimatorChain<ITransformer>();