Microsoft.ML.Transforms Namespace

Namespace containing data transformation components.

Classes

ApproximatedKernelMappingEstimator	Maps vector columns to a low -dimensional feature space.
ApproximatedKernelTransformer	ITransformer resulting from fitting an ApproximatedKernelMappingEstimator.
ColumnConcatenatingEstimator	Concatenates one or more input columns into a new output column.
ColumnCopyingEstimator	IEstimator<TTransformer> for the ColumnCopyingTransformer.
ColumnCopyingTransformer	ITransformer resulting from fitting a ColumnCopyingEstimator.
ColumnSelectingEstimator	Keeps or drops selected columns from an IDataView.
ColumnSelectingTransformer	ITransformer resulting from fitting an ColumnSelectingEstimator.
CountFeatureSelectingEstimator	Selects the slots for which the count of non-default values is greater than or equal to a threshold.
CustomMappingEstimator<TSrc,TDst>	Applies a custom mapping function to the specified input columns. The result will be in output columns.
CustomMappingFactory<TSrc,TDst>	The base type for custom mapping factories.
CustomMappingFactoryAttributeAttribute	Place this attribute onto a type to cause it to be considered a custom mapping factory.
CustomMappingTransformer<TSrc,TDst>	ITransformer resulting from fitting an CustomMappingEstimator<TSrc,TDst>.
ExpressionEstimator	This estimator applies a user provided expression (specified as a string) to input column values to produce new output column values.
ExpressionTransformer
FeatureContributionCalculatingEstimator	Estimator for FeatureContributionCalculatingTransformer. Computes model-specific per-feature contributions to the score of each input vector.
FeatureContributionCalculatingTransformer	ITransformer resulting from fitting a FeatureContributionCalculatingEstimator.
GaussianKernel	The Gaussian kernel.
GlobalContrastNormalizingEstimator	Normalizes (scales) vectors in the input column applying the global contrast normalization.
HashingEstimator	Estimator for HashingTransformer, which hashes either single valued columns or vector columns. For vector columns, it hashes each slot separately.
HashingEstimator.ColumnOptions	Describes how the transformer handles one column pair.
HashingTransformer	ITransformer resulting from fitting a HashingEstimator.
KernelBase	This class indicates which kernel should be approximated by the ApproximatedKernelTransformer. .
KeyToBinaryVectorMappingEstimator	Estimator for KeyToBinaryVectorMappingTransformer. Converts key types to their corresponding binary representation of the original value.
KeyToBinaryVectorMappingTransformer	ITransformer resulting from fitting a KeyToBinaryVectorMappingEstimator.
KeyToValueMappingEstimator	Estimator for KeyToValueMappingTransformer. Converts the key types back to their original values.
KeyToValueMappingTransformer	ITransformer resulting from fitting a KeyToValueMappingEstimator.
KeyToVectorMappingEstimator	Estimator for KeyToVectorMappingTransformer. Maps the value of a key into a known-sized vector of Single.
KeyToVectorMappingTransformer	ITransformer resulting from fitting a KeyToVectorMappingEstimator.
LaplacianKernel	The Laplacian kernel.
LpNormNormalizingEstimator	Normalizes (scales) vectors in the input column to the unit norm. The type of norm that is used can be specified by the user.
LpNormNormalizingEstimatorBase	Base estimator class for LpNormNormalizingEstimator and GlobalContrastNormalizingEstimator normalizers.
LpNormNormalizingTransformer	ITransformer resulting from fitting a LpNormNormalizingEstimator or GlobalContrastNormalizingEstimator.
MissingValueIndicatorEstimator	IEstimator<TTransformer> for the MissingValueIndicatorTransformer.
MissingValueIndicatorTransformer	ITransformer resulting from fitting a MissingValueIndicatorEstimator.
MissingValueReplacingEstimator	IEstimator<TTransformer> for the MissingValueReplacingTransformer.
MissingValueReplacingTransformer	ITransformer resulting from fitting a MissingValueReplacingEstimator.
MutualInformationFeatureSelectingEstimator	Selects the top k slots across all specified columns ordered by their mutual information with the label column (what you can learn about the label by observing the value of the specified column).
NormalizingEstimator	IEstimator<TTransformer> for the NormalizingTransformer.
NormalizingTransformer	ITransformer resulting from fitting an NormalizingEstimator.
NormalizingTransformer.AffineNormalizerModelParameters<TData>	The model parameters generated by affine normalization transformations.
NormalizingTransformer.BinNormalizerModelParameters<TData>	The model parameters generated by buckettizing the data into bins with monotonically increasing UpperBounds. The Density value is constant from bin to bin, for most cases. ///
NormalizingTransformer.CdfNormalizerModelParameters<TData>	The model parameters generated by cumulative distribution normalization transformations. The cumulative density function is parameterized by Mean and the StandardDeviation as observed during fitting.
NormalizingTransformer.NormalizerModelParametersBase	Base class for all the data normalizer models like NormalizingTransformer.AffineNormalizerModelParameters<TData>, NormalizingTransformer.BinNormalizerModelParameters<TData>, NormalizingTransformer.CdfNormalizerModelParameters<TData>.
OneHotEncodingEstimator	Converts one or more input columns of categorical values into as many output columns of one-hot encoded vectors.
OneHotEncodingTransformer	ITransformer resulting from fitting a OneHotEncodingEstimator.
OneHotHashEncodingEstimator	Converts one or more input columns of categorical values into as many output columns of hash-based one-hot encoded vectors.
OneHotHashEncodingTransformer	ITransformer resulting from fitting a OneHotHashEncodingEstimator.
PrincipalComponentAnalysisTransformer	PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace.
PrincipalComponentAnalyzer	PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace.
StatefulCustomMappingEstimator<TSrc,TDst,TState>	Applies a custom mapping function to the specified input columns, while allowing a per-cursor state. The result will be in output columns.
StatefulCustomMappingFactory<TSrc,TDst,TState>	The base type for stateful custom mapping factories.
StatefulCustomMappingTransformer<TSrc,TDst,TState>	ITransformer resulting from fitting an StatefulCustomMappingEstimator<TSrc,TDst,TState>.
TensorFlowEstimator	The TensorFlowTransformer is used in following two scenarios. Scoring with pretrained TensorFlow model: In this mode, the transform extracts hidden layers' values from a pre-trained Tensorflow model and uses outputs as features in ML.Net pipeline. Retraining of TensorFlow model: In this mode, the transform retrains a TensorFlow model using the user data passed through ML.Net pipeline. Once the model is trained, it's outputs can be used as features for scoring.
TensorFlowModel	This class holds the information related to TensorFlow model and session. It provides some convenient methods to query model schema as well as creation of TensorFlowEstimator object.
TensorFlowTransformer	ITransformer for the TensorFlowEstimator.
TransformInputBase	The base class for all transform inputs.
TypeConvertingEstimator	Estimator for TypeConvertingTransformer. Converts the underlying input column type to a new type. The input and output column types need to be compatible. PrimitiveDataViewType
TypeConvertingTransformer	ITransformer resulting from fitting a TypeConvertingEstimator.
ValueMappingEstimator	Estimator for ValueMappingTransformer creating a key-value map using the pairs of values in the input data PrimitiveDataViewType
ValueMappingEstimator<TKey,TValue>	Estimator for ValueMappingTransformer creating a key-value map using the pairs of values in the input data PrimitiveDataViewType
ValueMappingTransformer	ITransformer resulting from fitting a ValueMappingEstimator.
ValueToKeyMappingEstimator	IEstimator<TTransformer> for the ValueToKeyMappingTransformer. Converts a set of categorical values (for example, US state abbreviations) into numerical key values (e.g. 1-50). The numerical key can be used directly by classification algorithms.
ValueToKeyMappingTransformer	ITransformer resulting from fitting a ValueToKeyMappingEstimator.
VectorWhiteningEstimator
VectorWhiteningTransformer

Interfaces

IFunctionProvider

This interface enables extending the ExprTransform language with additional functions.

Enums

ErrId
LpNormNormalizingEstimatorBase.NormFunction	The kind of unit norm vectors are rescaled to. This enumeration is serialized.
MissingValueReplacingEstimator.ReplacementMode	The possible ways to replace missing values.
OneHotEncodingEstimator.OutputKind
ValueToKeyMappingEstimator.KeyOrdinality	Controls how the order of the output keys.
WhiteningKind	Which vector whitening technique to use. ZCA whitening ensures that the average covariance between whitened and original variables is maximal. In contrast, PCA whitening lead to maximally compressed whitened variables, as measured by squared covariance.

Delegates

SignatureFunctionProvider