fl4health.feature_alignment.string_columns_transformer module

class TextColumnTransformer(transformer)[source]

Bases: BaseEstimator, TransformerMixin

__init__(transformer)[source]

The purpose of this class is to enable the application of text feature transformers from sklearn to a single-column pandas dataframe, which is not supported in the first place.

Parameters:

transformer (TextFeatureTransformer) – Transformer to be applied

fit(x, y=None)[source]

Fit the transformer to the provided dataframe. The dataframe should have a single string column The transformer is fit on the text from the single columns in the x dataframe.

Parameters:

  • x (pd.DataFrame) – Column on which to fit the transformer

  • y (pd.DataFrame | None, optional) – Not used. Defaults to None.

Returns:

The fit transformer

Return type:

TextColumnTransformer

set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → TextColumnTransformer

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in fit.

Returns:
selfobject

The updated object.

set_transform_request(*, x: bool | None | str = '$UNCHANGED$') → TextColumnTransformer

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in transform.

Returns:
selfobject

The updated object.

transform(x)[source]

Transforms the concatenation of a single column of text in the x dataframe.

Parameters:

x (pd.DataFrame) – Dataframe of text-based column to be transformed

Returns:

Transformed dataframe.

Return type:

pd.DataFrame

class TextMulticolumnTransformer(transformer)[source]

Bases: BaseEstimator, TransformerMixin

__init__(transformer)[source]

The purpose of this class is to enable the application of text feature transformers from sklearn to multiple string columns, which is not supported in the first place.

Parameters:

transformer (TextFeatureTransformer) – Transformer to be applied

fit(x, y=None)[source]

Fit the transformer to the provided dataframe. The dataframe should have multiple string columns The transformer is fit on the appended text from all columns in the x dataframe.

Parameters:

  • x (pd.DataFrame) – Columns on which to fit the transformer

  • y (pd.DataFrame | None, optional) – Not used. Defaults to None.

Returns:

The fit transformer

Return type:

TextMulticolumnTransformer

set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → TextMulticolumnTransformer

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in fit.

Returns:
selfobject

The updated object.

set_transform_request(*, x: bool | None | str = '$UNCHANGED$') → TextMulticolumnTransformer

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for x parameter in transform.

Returns:
selfobject

The updated object.

transform(x)[source]

Transforms the concatenation of all columns of text in the x dataframe.

Parameters:

x (pd.DataFrame) – Dataframe of text-based columns to be transformed

Returns:

Transformed dataframe.

Return type:

pd.DataFrame