cblearn.preprocessing.MultiColumnLabelEncoder#

class cblearn.preprocessing.MultiColumnLabelEncoder[source]#

Encoder for objects that are a combination of labels in multiple columns.

Extends the function of scikit-learn’s label encoder to 2d arrays. See sklearn.preprocessing.LabelEncoder for more information.

>>> encoder = MultiColumnLabelEncoder()
>>> label_data = [[0.1, 'high'], [0.3, 'low'], [0.1, 'high'], [0.1, 'low']]
>>> encoder.fit(label_data).transform(label_data).tolist()
[0, 2, 0, 1]
>>> encoder.fit_transform(label_data).tolist()
[0, 2, 0, 1]
>>> encoder.inverse_transform([2, 1, 0]).tolist()
[['0.3', 'low'], ['0.1', 'low'], ['0.1', 'high']]

__init__()#

Methods

`__init__`()
`fit`(X[, y])	Fit label encoder.
`fit_transform`(X[, y])	Fit label encoder and return encoded labels.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X[, y])	Transform labels back to original encoding.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, y])	Transform labels to normalized encoding.

fit(X, y=None)[source]#

Fit label encoder.

Parameters:: y (array-like of shape (n_samples,)) – Target values.
Returns:: self – Fitted label encoder.
Return type:: returns an instance of self.

fit_transform(X, y=None)[source]#

Fit label encoder and return encoded labels.

Parameters:: y (array-like of shape (n_samples,)) – Target values.
Returns:: y – Encoded labels.
Return type:: array-like of shape (n_samples,)

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

inverse_transform(X, y=None)[source]#

Transform labels back to original encoding.

Parameters:: y (ndarray of shape (n_samples,)) – Target values.
Returns:: y – Original encoding.
Return type:: ndarray of shape (n_samples,)

set_output(*, transform=None)#

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

”default”: Default output format of a transformer
”pandas”: DataFrame output
”polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

transform(X, y=None)[source]#

Transform labels to normalized encoding.

Parameters:: y (array-like of shape (n_samples,)) – Target values.
Returns:: y – Labels as normalized encodings.
Return type:: array-like of shape (n_samples,)