TransformsCatalog TransformsCatalog 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
TransformsCatalogTransformsCatalogTransformsCatalog

Properties

Categorical Categorical Categorical

The list of operations over categorical data.

Conversion Conversion Conversion

The list of operations for data type conversion.

FeatureSelection FeatureSelection FeatureSelection

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

Text Text Text

The list of operations for processing text data.

Extension Methods

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

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

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[]) IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[]) 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) IndicateMissingValues(TransformsCatalog, String, String) 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) ReplaceMissingValues(TransformsCatalog, InputOutputColumnPair[], MissingValueReplacingEstimator+ReplacementMode, Boolean) 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) ReplaceMissingValues(TransformsCatalog, String, String, MissingValueReplacingEstimator+ReplacementMode, Boolean) 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) ConvertToGrayscale(TransformsCatalog, String, String) 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) ConvertToImage(TransformsCatalog, Int32, Int32, String, String, ImagePixelExtractingEstimator+ColorBits, ImagePixelExtractingEstimator+ColorsOrder, Boolean, Single, Single, Int32, Int32, Int32, Int32) 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) ExtractPixels(TransformsCatalog, String, String, ImagePixelExtractingEstimator+ColorBits, ImagePixelExtractingEstimator+ColorsOrder, Boolean, Single, Single, Boolean) 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) LoadImages(TransformsCatalog, String, String, String) 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.

ResizeImages(TransformsCatalog, String, Int32, Int32, String, ImageResizingEstimator+ResizingKind, ImageResizingEstimator+Anchor) ResizeImages(TransformsCatalog, String, Int32, Int32, String, ImageResizingEstimator+ResizingKind, ImageResizingEstimator+Anchor) 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>) ApproximatedKernelMap(TransformsCatalog, String, String, Int32, Boolean, KernelBase, Nullable<Int32>) 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) VectorWhiten(TransformsCatalog, String, String, WhiteningKind, Single, Int32, Int32) 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) NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32) 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) NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32) 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) NormalizeGlobalContrast(TransformsCatalog, String, String, Boolean, Boolean, Single) 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[], Int64, Boolean) NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) 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, String, Int64, Boolean) NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean) 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) NormalizeLpNorm(TransformsCatalog, String, String, LpNormNormalizingEstimatorBase+NormFunction, Boolean) 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) NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean) 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) NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean) 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) NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) 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) NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean) NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)

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

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

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

Initializes a new instance of PrincipalComponentAnalyzer.

Concatenate(TransformsCatalog, String, String[]) Concatenate(TransformsCatalog, String, String[]) Concatenate(TransformsCatalog, String, String[])

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

CopyColumns(TransformsCatalog, String, String) CopyColumns(TransformsCatalog, String, String) 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[]) DropColumns(TransformsCatalog, String[]) 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[]) SelectColumns(TransformsCatalog, String[]) SelectColumns(TransformsCatalog, String[])

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

SelectColumns(TransformsCatalog, String[], Boolean) SelectColumns(TransformsCatalog, String[], Boolean) SelectColumns(TransformsCatalog, String[], Boolean)

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

Applies to