API Reference / sklearn / features /

extraction.py

scikit-learn extraction wrappers.

Generated from the FEATURES_EXTRACTION table in specs. Registered under sklearn. hub keys, mirroring the native TuiML features/extraction family.

Classes

AdditiveChi2Sampler

class sklearn.features.extraction.AdditiveChi2Sampler(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn AdditiveChi2Sampler (hub key sklearn.AdditiveChi2Sampler).

Wraps AdditiveChi2Sampler. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

DictionaryLearning

class sklearn.features.extraction.DictionaryLearning(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn DictionaryLearning (hub key sklearn.DictionaryLearning).

Wraps DictionaryLearning. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

FactorAnalysis

class sklearn.features.extraction.FactorAnalysis(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn FactorAnalysis (hub key sklearn.FactorAnalysis).

Wraps FactorAnalysis. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

FastICA

class sklearn.features.extraction.FastICA(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn FastICA (hub key sklearn.FastICA).

Wraps FastICA. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: n_components, random_state.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

GaussianRandomProjection

class sklearn.features.extraction.GaussianRandomProjection(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn GaussianRandomProjection (hub key sklearn.GaussianRandomProjection).

Wraps GaussianRandomProjection. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

IncrementalPCA

class sklearn.features.extraction.IncrementalPCA(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn IncrementalPCA (hub key sklearn.IncrementalPCA).

Wraps IncrementalPCA. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

Isomap

class sklearn.features.extraction.Isomap(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn Isomap (hub key sklearn.Isomap).

Wraps Isomap. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

KernelPCA

class sklearn.features.extraction.KernelPCA(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn KernelPCA (hub key sklearn.KernelPCA).

Wraps KernelPCA. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: n_components, kernel, gamma.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

LatentDirichletAllocation

class sklearn.features.extraction.LatentDirichletAllocation(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn LatentDirichletAllocation (hub key sklearn.LatentDirichletAllocation).

Wraps LatentDirichletAllocation. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

LocallyLinearEmbedding

class sklearn.features.extraction.LocallyLinearEmbedding(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn LocallyLinearEmbedding (hub key sklearn.LocallyLinearEmbedding).

Wraps LocallyLinearEmbedding. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

MiniBatchDictionaryLearning

class sklearn.features.extraction.MiniBatchDictionaryLearning(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn MiniBatchDictionaryLearning (hub key sklearn.MiniBatchDictionaryLearning).

Wraps MiniBatchDictionaryLearning. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

MiniBatchNMF

class sklearn.features.extraction.MiniBatchNMF(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn MiniBatchNMF (hub key sklearn.MiniBatchNMF).

Wraps MiniBatchNMF. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

MiniBatchSparsePCA

class sklearn.features.extraction.MiniBatchSparsePCA(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn MiniBatchSparsePCA (hub key sklearn.MiniBatchSparsePCA).

Wraps MiniBatchSparsePCA. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

NMF

class sklearn.features.extraction.NMF(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn NMF (hub key sklearn.NMF).

Wraps NMF. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: n_components, init, random_state.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

Nystroem

class sklearn.features.extraction.Nystroem(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn Nystroem (hub key sklearn.Nystroem).

Wraps Nystroem. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

PCAExtractor

class sklearn.features.extraction.PCAExtractor(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn PCA (hub key sklearn.PCAExtractor).

Wraps PCA. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: n_components, whiten, random_state.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

PolynomialCountSketch

class sklearn.features.extraction.PolynomialCountSketch(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn PolynomialCountSketch (hub key sklearn.PolynomialCountSketch).

Wraps PolynomialCountSketch. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

RBFSampler

class sklearn.features.extraction.RBFSampler(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn RBFSampler (hub key sklearn.RBFSampler).

Wraps RBFSampler. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

SkewedChi2Sampler

class sklearn.features.extraction.SkewedChi2Sampler(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn SkewedChi2Sampler (hub key sklearn.SkewedChi2Sampler).

Wraps SkewedChi2Sampler. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

SparsePCA

class sklearn.features.extraction.SparsePCA(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn SparsePCA (hub key sklearn.SparsePCA).

Wraps SparsePCA. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

SparseRandomProjection

class sklearn.features.extraction.SparseRandomProjection(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn SparseRandomProjection (hub key sklearn.SparseRandomProjection).

Wraps SparseRandomProjection. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

TruncatedSVD

class sklearn.features.extraction.TruncatedSVD(_SklearnExtractorMixin, FeatureExtractor)

scikit-learn TruncatedSVD (hub key sklearn.TruncatedSVD).

Wraps TruncatedSVD. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: n_components, algorithm, random_state.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.