CapyMOAClassifier#

class skactiveml.classifier.CapyMOAClassifier(estimator_class, estimator_param_dict=None, classes=None, missing_label=nan, cost_matrix=None, random_state=None)[source]#

Bases: SkactivemlClassifier

CapyMOA Classifier

Implementation of a wrapper class for CapyMOA [1] classifiers such that missing labels can be handled and the interfaces are compatible with scikit-activeml. Therefore, samples with missing labels are filtered.

Parameters:

estimator_classcapymoa.base.MOAClassifier.__class__: The capymoa classifier class that is used to initialize the capymoa classifier.
estimator_param_dictdict, default=None: Additional arguments for capymoa.base.MOAClassifier. If estimator_param_dict is None, no additional arguments are added. schema is not allowed in this dictionary and will be created internally.
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_capymoa.base.MOAClassifier: initialized MOAClassifier whose predictions and training are wrapped.

References

[1]

Gomes, H.M., Lee, A., Gunasekara, N., Sun, Y., Cassales, G.W., Liu, J., Heyden, M., Cerqueira, V., Bahri, M., Koh, Y.S. and Pfahringer, B., 2025. Capymoa: Efficient machine learning for data streams in python. arXiv preprint arXiv:2502.07432.

Methods

`fit`(X, y)	Fit the module with (re-)initialization 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)	Fit the module without re-initialization.
`predict`(X, **kwargs)	Return class label predictions for the test samples X.
`predict_proba`(X)	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_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

fit(X, y)[source]#

Fit the module with (re-)initialization using X as training data and y as class labels. The model is reinitialized from scratch when using fit

Parameters:

Xmatrix-like, shape (n_samples, n_features): Training data set, usually complete, i.e. including the labeled and unlabeled samples
yarray-like of shape (n_samples, ): Labels of the training data set (possibly including unlabeled ones indicated by self.missing_label)

Returns:

self: CapyMOAClassifier,: CapyMOAClassifier object 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)[source]#

Fit the module without re-initialization. If the module was already initialized, by calling partial_fit or fit, the module will not be re-initialized again.

Parameters:

Xmatrix-like, shape (n_samples, n_features): Training data set, usually complete, i.e. including the labeled and unlabeled samples
yarray-like of shape (n_samples, ): Labels of the training data set (possibly including unlabeled ones indicated by self.missing_label)

Returns:

self: CapyMOAClassifier: CapyMOAClassifier object fitted on the training data.

predict(X, **kwargs)#

Return class label predictions for the test samples X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.

Returns:

ynumpy.ndarray of shape (n_samples,): Predicted class labels of the test samples X.

predict_proba(X)[source]#

Return probability estimates for the input data X.

Parameters:

Xarray-like of shape (n_samples, …): Input samples.

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 = '$UNCHANGED$') → CapyMOAClassifier#

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

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:

selfobject: The updated object.