skactiveml.utils.ExtLabelEncoder#

class skactiveml.utils.ExtLabelEncoder(classes=None, missing_label=nan)[source]#

Bases: BaseEstimator

Encode class labels with value between 0 and classes-1 and uses -1 for unlabeled samples. This transformer should be used to encode class labels, i.e. y, and not the input X.

Parameters

classes: array-like, shape (n_classes), default=None: Holds the label for each class.
missing_label: scalar|string|np.nan|None, default=np.nan: Value to represent a missing label.

Attributes

classes_: array-like, shape (n_classes): Holds the label for each class.

Methods

`fit`(y)	Fit label encoder.
`fit_transform`(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`(y)	Transform labels back to original encoding.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(y)	Transform labels to normalized encoding.

fit(y)[source]#

Fit label encoder.

Parameters

y: array-like, shape (n_samples) or (n_samples, n_outputs): Class labels.

Returns

self: returns an instance of self.

fit_transform(y)[source]#

Fit label encoder and return encoded labels.

Parameters

y: array-like, shape (n_samples) or (n_samples, n_outputs): Class labels.

Returns

y: array-like, shape (n_samples) or (n_samples, n_outputs): Class labels.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

inverse_transform(y)[source]#

Transform labels back to original encoding.

Parameters

ynumpy array of shape [n_samples]: Target values.

Returns

y_decnumpy array of shape [n_samples]

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

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

transform(y)[source]#

Transform labels to normalized encoding.

Parameters

yarray-like of shape (n_samples): Target values.

Returns

y_encarray-like of shape (n_samples