SklearnClassifier#

class skactiveml.classifier.SklearnClassifier(estimator, include_unlabeled_samples=False, classes=None, missing_label=nan, cost_matrix=None, random_state=None)[source]#

Bases: SkactivemlClassifier, MetaEstimatorMixin

Sklearn Classifier

Implementation of a wrapper class for scikit-learn classifiers such that missing labels can be handled. Therefore, samples with missing labels are filtered.

Parameters:

estimatorsklearn.base.ClassifierMixin with predict_proba method

The scikit-learn classifier to be wrapped.

include_unlabeled_samplesbool, default=False

If False, only labeled samples are passed to the fit method of the estimator.
If True, all samples including the unlabeled ones are passed to the fit method of the estimator. Ensure that your estimator is able to handle unlabeled samples marked by missing_label. Otherwise, missing_label is interpreted as a regular class label. Note that semi-supervised classifiers of sklearn expect missing_label=-1.

classesarray-like of shape (n_classes,), default=None

Holds the label for each class. If None, the classes are determined during fit.

missing_labelscalar or string or np.nan or None, default=np.nan

Value to represent a missing label.

cost_matrixarray-like of shape (n_classes, n_classes)

Cost matrix with cost_matrix[i,j] indicating cost of predicting class classes[j] for a sample of class classes[i]. Can be only set, if classes is not None.

random_stateint or RandomState instance or None, default=None

Determines random number for predict method. Pass an int for reproducible results across multiple method calls.

Attributes:

classes_numpy.ndarray of shape (n_classes,): Holds the label for each class after fitting.
cost_matrix_numpy.ndarray of shape (classes, classes): Cost matrix with cost_matrix_[i,j] indicating cost of predicting class classes_[j] for a sample of class classes_[i].
estimator_sklearn.base.ClassifierMixin with predict_proba method: The scikit-learn classifier after calling the fit method.

Methods

`fit`(X, y[, sample_weight])	Fit the model using X as training data and y as class labels.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit`(X, y[, sample_weight])	Partially fitting the model using X as training data and y as class labels.
`predict`(X, **predict_kwargs)	Return class label predictions for the input data X.
`predict_proba`(X, **predict_proba_kwargs)	Return probability estimates for the input data X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_partial_fit_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `partial_fit` method.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

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

Fit the model using X as training data and y as class labels.

Parameters:

Xarray-like of shape (n_samples, …): The feature matrix representing the samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): It contains the class labels of the training samples. Missing labels are represented the attribute self.missing_label_. In case of multiple labels per sample (i.e., n_outputs > 1), the samples are duplicated.
sample_weightarray-like of shape (n_samples,) or (n_samples, n_outputs): It contains the weights of the training samples’ class labels. It must have the same shape as y.
fit_kwargsdict-like: Further parameters as input to the fit method of the estimator.

Returns:

self: SklearnClassifier,: The SklearnClassifier fitted on the training data.

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.

partial_fit(X, y, sample_weight=None, **fit_kwargs)[source]#

Partially fitting the model using X as training data and y as class labels.

Parameters:

Xarray-like of shape (n_samples, …): The feature matrix representing the samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): It contains the class labels of the training samples. Missing labels are represented the attribute self.missing_label_. In case of multiple labels per sample (i.e., n_outputs > 1), the samples are duplicated.
sample_weightarray-like of shape (n_samples,) or (n_samples, n_outputs): It contains the weights of the training samples’ class labels. It must have the same shape as y.
fit_kwargsdict-like: Further parameters as input to the partial_fit method of the estimator.

Returns:

selfSklearnClassifier,: The SklearnClassifier is fitted on the training data.

predict(X, **predict_kwargs)[source]#

Return class label predictions for the input data X.

Parameters:

Xarray-like of shape (n_samples, …): Input samples.
predict_kwargsdict-like: Further parameters as input to the predict method of the estimator.

Returns:

y_prednumpy.ndarray of shape (n_samples,): Predicted class labels of the input samples.

predict_proba(X, **predict_proba_kwargs)[source]#

Return probability estimates for the input data X.

Parameters:

Xarray-like of shape (n_samples, …): Input samples.
predict_proba_kwargsdict-like: Further parameters as input to the predict_proba method of the estimator.

Returns:

Parray-like of shape (n_samples, classes): The class probabilities of the input samples. Classes are ordered according to the attribute self.classes_.

score(X, y, sample_weight=None)#

Return the mean accuracy on the given test data and labels.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) regarding y.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SklearnClassifier#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

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.

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SklearnClassifier#

Configure whether metadata should be requested to be passed to the partial_fit method.

The options for each parameter are:

True: metadata is requested, and passed to partial_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 partial_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.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SklearnClassifier#

Configure whether metadata should be requested to be passed to the score method.

The options for each parameter are:

True: metadata is requested, and passed to score 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 score.
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.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns: