TransformsCatalog Class

Definition

Class used by MLContext to create instances of transform components.

public sealed class TransformsCatalog
type TransformsCatalog = class
Public NotInheritable Class TransformsCatalog
Inheritance
TransformsCatalog

Properties

Categorical

The list of operations over categorical data.

Conversion

The list of operations for data type conversion.

FeatureSelection

The list of operations for selecting features based on some criteria.

Text

The list of operations for processing text data.

Extension Methods

CustomMapping<TSrc,TDst>(TransformsCatalog, Action<TSrc,TDst>, String, SchemaDefinition, SchemaDefinition)

Create a CustomMappingEstimator<TSrc,TDst>, which applies a custom mapping of input columns to output columns.

StatefulCustomMapping<TSrc,TDst,TState>(TransformsCatalog, Action<TSrc,TDst,TState>, Action<TState>, String)

Create a StatefulCustomMappingEstimator<TSrc,TDst,TState>, which applies a custom mapping of input columns to output columns, while allowing a per-cursor state.

CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>, Int32, Int32, Boolean)

Create a FeatureContributionCalculatingEstimator that computes model-specific contribution scores for each feature of the input vector.

CalculateFeatureContribution<TModelParameters,TCalibrator>(TransformsCatalog, ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters,TCalibrator>>, Int32, Int32, Boolean)

Create a FeatureContributionCalculatingEstimator that computes model-specific contribution scores for each feature of the input vector. Supports calibrated models.

Expression(TransformsCatalog, String, String, String[])

Creates an ExpressionEstimator.

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])

Create a MissingValueIndicatorEstimator, which copies the data from the column specified in InputColumnName to a new column: OutputColumnName.

IndicateMissingValues(TransformsCatalog, String, String)

Create a MissingValueIndicatorEstimator, which scans the data from the column specified in inputColumnName and fills new column specified in outputColumnName with vector of bools where i-th bool has value of true if i-th element in column data has missing value and false otherwise.

ReplaceMissingValues(TransformsCatalog, InputOutputColumnPair[], MissingValueReplacingEstimator+ReplacementMode, Boolean)

Create a ColumnCopyingEstimator, which copies the data from the column specified in InputColumnName to a new column: OutputColumnName and replaces missing values in it according to replacementMode.

ReplaceMissingValues(TransformsCatalog, String, String, MissingValueReplacingEstimator+ReplacementMode, Boolean)

Create a MissingValueReplacingEstimator, which copies the data from the column specified in inputColumnName to a new column: outputColumnName and replaces missing values in it according to replacementMode.

ConvertToGrayscale(TransformsCatalog, String, String)

Create a ImageGrayscalingEstimator, which converts images in the column specified in InputColumnName to grayscale images in a new column: OutputColumnName.

ConvertToImage(TransformsCatalog, Int32, Int32, String, String, ImagePixelExtractingEstimator+ColorBits, ImagePixelExtractingEstimator+ColorsOrder, Boolean, Single, Single, Int32, Int32, Int32, Int32)

Create a VectorToImageConvertingEstimator, which creates image from the data from the column specified in inputColumnName to a new column: outputColumnName.

ExtractPixels(TransformsCatalog, String, String, ImagePixelExtractingEstimator+ColorBits, ImagePixelExtractingEstimator+ColorsOrder, Boolean, Single, Single, Boolean)

Create a ImagePixelExtractingEstimator, which extracts pixels values from the data specified in column: inputColumnName to a new column: outputColumnName.

LoadImages(TransformsCatalog, String, String, String)

Create a ImageLoadingEstimator, which loads the data from the column specified in inputColumnName as an image to a new column: outputColumnName.

LoadRawImageBytes(TransformsCatalog, String, String, String)

Create a ImageLoadingEstimator, which loads the data from the column specified in inputColumnName as an image of raw bytes to a new column: outputColumnName.

ResizeImages(TransformsCatalog, String, Int32, Int32, String, ImageResizingEstimator+ResizingKind, ImageResizingEstimator+Anchor)

Create a ImageResizingEstimator, which resize the image from the column specified in inputColumnName to a new column: outputColumnName.

ApproximatedKernelMap(TransformsCatalog, String, String, Int32, Boolean, KernelBase, Nullable<Int32>)

Create an ApproximatedKernelMappingEstimator that maps input vectors to a low dimensional feature space where inner products approximate a shift-invariant kernel function.

VectorWhiten(TransformsCatalog, String, String, WhiteningKind, Single, Int32, Int32)

Takes column filled with a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1.

NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32)

Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.

NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32)

Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.

NormalizeGlobalContrast(TransformsCatalog, String, String, Boolean, Boolean, Single)

Create a GlobalContrastNormalizingEstimator, which normalizes columns individually applying global contrast normalization. Setting ensureZeroMean to true, will apply a pre-processing step to make the specified column's mean be the zero vector.

NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.

NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.

NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.

NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.

NormalizeLpNorm(TransformsCatalog, String, String, LpNormNormalizingEstimatorBase+NormFunction, Boolean)

Create a LpNormNormalizingEstimator, which normalizes (scales) vectors in the input column to the unit norm. The type of norm that is used is defined by norm. Setting ensureZeroMean to true, will apply a pre-processing step to make the specified column's mean be a zero vector.

NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean)

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.

NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean)

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.

NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)

Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.

NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)

Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.

NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32)

Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales the data according to the quantile range (defaults to the interquartile range).

NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32)

Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales the data according to the quantile range (defaults to the interquartile range).

NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32)

Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the labelColumnName column.

NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32)

Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the labelColumnName column.

ApplyOnnxModel(TransformsCatalog, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the input column. Input/output columns are determined based on the input/output columns of the provided ONNX model. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.

ApplyOnnxModel(TransformsCatalog, String, Nullable<Int32>, Boolean)

Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the input column. Input/output columns are determined based on the input/output columns of the provided ONNX model. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.

ApplyOnnxModel(TransformsCatalog, String, String, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnName column. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.

ApplyOnnxModel(TransformsCatalog, String, String, String, Nullable<Int32>, Boolean)

Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnName column. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.

ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.

ApplyOnnxModel(TransformsCatalog, String[], String[], String, Nullable<Int32>, Boolean)

Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.

DnnFeaturizeImage(TransformsCatalog, String, Func<DnnImageFeaturizerInput,EstimatorChain<ColumnCopyingTransformer>>, String)

Create DnnImageFeaturizerEstimator, which applies one of the pre-trained DNN models in DnnImageModelSelector to featurize an image.

ProjectToPrincipalComponents(TransformsCatalog, String, String, String, Int32, Int32, Boolean, Nullable<Int32>)

Initializes a new instance of PrincipalComponentAnalyzer.

DetectAnomalyBySrCnn(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, Int32, Double)

Create SrCnnAnomalyEstimator, which detects timeseries anomalies using SRCNN algorithm.

DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)

Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA).

DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
Obsolete.

Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA).

DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)

Create IidChangePointEstimator, which predicts change points in an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)
Obsolete.

Create IidChangePointEstimator, which predicts change points in an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)

Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)
Obsolete.

Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

DetectSpikeBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, AnomalySide, ErrorFunction)

Create SsaSpikeEstimator, which predicts spikes in time series using Singular Spectrum Analysis (SSA).

DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction)
Obsolete.

Create SsaSpikeEstimator, which predicts spikes in time series using Singular Spectrum Analysis (SSA).

Concatenate(TransformsCatalog, String, String[])

Create a ColumnConcatenatingEstimator, which concatenates one or more input columns into a new output column.

CopyColumns(TransformsCatalog, String, String)

Create a ColumnCopyingEstimator, which copies the data from the column specified in inputColumnName to a new column: outputColumnName.

DropColumns(TransformsCatalog, String[])

Create a ColumnSelectingEstimator, which drops a given list of columns from an IDataView. Any column not specified will be maintained in the output.

SelectColumns(TransformsCatalog, String[])

Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others.

SelectColumns(TransformsCatalog, String[], Boolean)

Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others.

FeaturizeByFastForestBinary(TransformsCatalog, FastForestBinaryFeaturizationEstimator+Options)

Create FastForestBinaryFeaturizationEstimator, which uses FastForestBinaryTrainer to train TreeEnsembleModelParameters to create tree-based features.

FeaturizeByFastForestRegression(TransformsCatalog, FastForestRegressionFeaturizationEstimator+Options)

Create FastForestRegressionFeaturizationEstimator, which uses FastForestRegressionTrainer to train TreeEnsembleModelParameters to create tree-based features.

FeaturizeByFastTreeBinary(TransformsCatalog, FastTreeBinaryFeaturizationEstimator+Options)

Create FastTreeBinaryFeaturizationEstimator, which uses FastTreeBinaryTrainer to train TreeEnsembleModelParameters to create tree-based features.

FeaturizeByFastTreeRanking(TransformsCatalog, FastTreeRankingFeaturizationEstimator+Options)

Create FastTreeRankingFeaturizationEstimator, which uses FastTreeRankingTrainer to train TreeEnsembleModelParameters to create tree-based features.

FeaturizeByFastTreeRegression(TransformsCatalog, FastTreeRegressionFeaturizationEstimator+Options)

Create FastTreeRegressionFeaturizationEstimator, which uses FastTreeRegressionTrainer to train TreeEnsembleModelParameters to create tree-based features.

FeaturizeByFastTreeTweedie(TransformsCatalog, FastTreeTweedieFeaturizationEstimator+Options)

Create FastTreeTweedieFeaturizationEstimator, which uses FastTreeTweedieTrainer to train TreeEnsembleModelParameters to create tree-based features.

FeaturizeByPretrainTreeEnsemble(TransformsCatalog, PretrainedTreeFeaturizationEstimator+Options)

Create PretrainedTreeFeaturizationEstimator, which produces tree-based features given a TreeEnsembleModelParameters.

Applies to