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>.

DnnEstimator

The DnnTransformer is used in following two scenarios.

  1. 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.
  2. 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.
DnnModel

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 DnnEstimator object.

DnnTransformer

ITransformer for the DnnEstimator.

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.

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.

TensorFlowEstimator

The TensorFlowTransformer is used in following two scenarios.

  1. 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.
  2. 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 KeyToVectorMappingTransformer. 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

Estimator for ValueToKeyMappingTransformer. Converts input values (words, numbers, etc.) KeyDataViewType.

ValueToKeyMappingTransformer

ITransformer resulting from fitting a ValueToKeyMappingEstimator.

VectorWhiteningEstimator
VectorWhiteningTransformer

Enums

DnnEstimator.Architecture

Image classification model.

DnnEstimator.DnnFramework

Backend DNN training framework.

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.