API Reference / sklearn / preprocessing /

text.py

scikit-learn text wrappers.

Generated from the PREPROCESSING_TEXT table in specs. Registered under sklearn. hub keys, mirroring the native TuiML preprocessing/text family.

Classes

CountVectorizer

class sklearn.preprocessing.text.CountVectorizer(_SklearnTransformerMixin, Transformer)

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

Wraps CountVectorizer. 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: max_features, ngram_range, stop_words.

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.

DictVectorizer

class sklearn.preprocessing.text.DictVectorizer(_SklearnTransformerMixin, Transformer)

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

Wraps DictVectorizer. 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.

FeatureHasher

class sklearn.preprocessing.text.FeatureHasher(_SklearnTransformerMixin, Transformer)

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

Wraps FeatureHasher. 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.

HashingVectorizer

class sklearn.preprocessing.text.HashingVectorizer(_SklearnTransformerMixin, Transformer)

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

Wraps HashingVectorizer. 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.

PatchExtractor

class sklearn.preprocessing.text.PatchExtractor(_SklearnTransformerMixin, Transformer)

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

Wraps PatchExtractor. 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.

TfidfTransformer

class sklearn.preprocessing.text.TfidfTransformer(_SklearnTransformerMixin, Transformer)

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

Wraps TfidfTransformer. 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.

TfidfVectorizer

class sklearn.preprocessing.text.TfidfVectorizer(_SklearnTransformerMixin, Transformer)

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

Wraps TfidfVectorizer. 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: max_features, ngram_range, stop_words.

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.