instancelib.feature_extraction.textsklearn module

class instancelib.feature_extraction.textsklearn.SklearnVectorizer(vectorizer, storage_location=None, filename=None)[source]

Bases: BaseVectorizer[str], SaveableInnerModel

Parameters:

vectorizer (BaseEstimator) –
storage_location (Optional[str]) –
filename (Optional[str]) –

fit(x_data, **kwargs)[source]

Fit the vectorizer according to the data in the given Sequence.

Parameters:: x_data (Sequence[DT]) – A Sequence of examples with type DT.
Returns:: A fitted vectorizer for data with type DT
Return type:: BaseVectorizer[DT]

Examples

Assume the creation of a vectorizer and a sequence of data examples in the variable data_list

>>> vectorizer = BaseVectorizer[DT]()
>>> vectorizer = vectorizer.fit(data_list)

Parameters:: kwargs (Any) –

fit_transform(x_data, **kwargs)[source]

Transform a list of data to a feature matrix. The transformation is based on the data contained in the parameter x_data. Subsequent transformations with transform() will be based on the fit of the data provided in this call.

Parameters:: x_data (Sequence[DT]) – A sequence of raw data examples with length n_examples
Returns:: A feature matrix with shape (n_examples, n_features)
Return type:: npt.NDArray[Any]

Examples

Assume the vectorizer is fitted

>>> x_mat = vectorizer.fit_transform(x_data)

Parameters:: kwargs (Any) –

innermodel: BaseEstimator

property name: str

transform(x_data, **kwargs)[source]

Transform a list raw data points to a feature matrix according to the fitted vectorizer

Parameters:: x_data (Sequence[DT]) – A sequence of raw data examples with length n_examples
Returns:: A feature matrix with shape (n_examples, n_features)
Return type:: npt.NDArray[Any]

Examples

Assume the vectorizer is fitted

>>> x_mat = vectorizer.transform(x_data)

Parameters:: kwargs (Any) –