instancelib.feature_extraction.textinstance module

class instancelib.feature_extraction.textinstance.TextInstanceVectorizer(vectorizer)

Bases: BaseVectorizer[Instance[Any, str, ndarray, Any]]

Parameters:

vectorizer (BaseVectorizer[str]) –

fit(x_data, **kwargs)

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)

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) –

property fitted: bool

Check if the vectorizer has been fitted

Returns:

True if the vectorizer has been fitted

Return type:

bool

transform(x_data, **kwargs)

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) –