skactiveml.classifier.SklearnClassifier#

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

Bases: SkactivemlClassifier, MetaEstimatorMixin

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

Parameters
estimatorsklearn.base.ClassifierMixin with predict_proba method

scikit-learn classifier that is able to deal with missing labels.

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

Holds the label for each class. If none, the classes are determined during the 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_array-like of shape (n_classes,)

Holds the label for each class after fitting.

cost_matrix_array-like 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])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_partial_fit_request(*[, sample_weight])

Request metadata passed to the partial_fit method.

set_score_request(*[, sample_weight])

Request metadata 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
Xmatrix-like, shape (n_samples, n_features)

The sample matrix X is the feature matrix representing the samples.

yarray-like, shape (n_samples) or (n_samples, n_outputs)

It contains the class labels of the training samples. Missing labels are represented the attribute ‘missing_label’. In case of multiple labels per sample (i.e., n_outputs > 1), the samples are duplicated.

sample_weightarray-like, 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 is 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
Xmatrix-like, shape (n_samples, n_features)

The sample matrix X is the feature matrix representing the samples.

yarray-like, shape (n_samples) or (n_samples, n_outputs)

It contains the class labels of the training samples. Missing labels are represented the attribute ‘missing_label’. In case of multiple labels per sample (i.e., n_outputs > 1), the samples are duplicated.

sample_weightarray-like, 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
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, shape (n_samples, n_features)

Input samples.

predict_kwargsdict-like

Further parameters as input to the ‘predict’ method of the ‘estimator’.

Returns
yarray-like, 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, shape (n_samples, n_features)

Input samples.

predict_proba_kwargsdict-like

Further parameters as input to the ‘predict_proba’ method of the ‘estimator’.

Returns
Parray-like, shape (n_samples, classes)

The class probabilities of the input samples. Classes are ordered by lexicographic order.

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: Union[bool, None, str] = '$UNCHANGED$') → SklearnClassifier#

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.

New 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
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: Union[bool, None, str] = '$UNCHANGED$') → SklearnClassifier#

Request metadata passed to the partial_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 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.

New 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
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: Union[bool, None, str] = '$UNCHANGED$') → SklearnClassifier#

Request metadata passed to the score 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 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.

New 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
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns
selfobject

The updated object.

Examples using skactiveml.classifier.SklearnClassifier#

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

Density-weighted Uncertainty Sampling

Density-weighted Uncertainty Sampling

Dual strategy for Active Learning

Dual strategy for Active Learning