cblearn.preprocessing.SharedColumnEncoder#

class cblearn.preprocessing.SharedColumnEncoder(encoder)[source]#

Wrapper to share an encoder across all columns.

>>> encoder = SharedColumnEncoder(LabelEncoder())
>>> label_data = [[0.1, 0.3, 0.4], [0.4, 0.1, 0.3], [0.5, 0.3, 0.3]]
>>> encoder.fit(label_data).transform(label_data).tolist()
[[0, 1, 2], [2, 0, 1], [3, 1, 1]]
>>> encoder.fit_transform(label_data).tolist()
[[0, 1, 2], [2, 0, 1], [3, 1, 1]]
>>> encoder.inverse_transform([[2, 2], [1, 0], [0, 1]]).tolist()
[[0.4, 0.4], [0.3, 0.1], [0.1, 0.3]]

__init__(encoder)[source]#

Methods

`__init__`(encoder)
`fit`(X[, y])
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X[, y])
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, y])

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

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

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

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