SlidingWindowClassifier

class skactiveml.classifier.SlidingWindowClassifier(estimator, classes=None, missing_label=nan, cost_matrix=None, window_size=None, only_labeled=False, random_state=None, target_type='auto')

Bases: SkactivemlClassifier, MetaEstimatorMixin

Sliding Window Classifier

Implementation of a wrapper class for SkactivemlClassifier such that the number of training samples can be limited to the latest window_size samples. Furthermore, saves X, y and sample_weight, enabling the use of a partial_fit for any classifier.

Parameters:

estimatorsklearn.base.SkactivemlClassifier

The classifier to be wrapped. If this classifier already implements a partial_fit, this method will be overwritten by this wrapper using the sliding window approach.

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

Holds the label for each class. If None, 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.

window_sizeint, default=None,

Value to represent the estimator sliding window size for X, y and sample weight. If None the window is unrestricted in its size.

only_labeledbool, default=False

If True, unlabeled samples are discarded.

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.

target_type“auto” or “single-output”, default=”auto”

Declared target type. It must remain compatible with the wrapped estimator across incremental updates. The wrapper supports only single-output classification.

Notes

Attributes this wrapper does not hold itself are read from the wrapped estimator, classes_ among them: the wrapper resolves no class vocabulary of its own, so the one its SkactivemlClassifier resolved is the wrapper’s own answer. The fitted attributes it does hold itself, e.g. target_spec_ and the sliding window in X_train_, are never read from the estimator and raise the usual not-fitted error before a fit.

A fit or partial_fit that raises leaves this wrapper exactly as it was, the sliding window included. The window therefore never advances past what the wrapped estimator was trained on, and a rejected update leaves a previously fitted wrapper able to predict as before.

Methods

fit(X, y[, sample_weight])	Fit the model using X as training data and y as class labels.
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_freq(X, **predict_freq_kwargs)	Return class frequency estimates for the test samples 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.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
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.

SlidingWindowClassifier.fit(X, y, sample_weight=None, **fit_kwargs)

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,)

It contains the class labels of the training samples. Missing labels are represented by the attribute self.missing_label_.

sample_weightarray-like of shape (n_samples,), default=None

It contains the weights of the training samples’ class labels.

fit_kwargsdict-like

Further parameters as input to the fit method of the estimator.

Returns:

self: SlidingWindowClassifier,

The SlidingWindowClassifier is fitted on the training data.

SlidingWindowClassifier.partial_fit(X, y, sample_weight=None, **fit_kwargs)

Partially fitting the model using X as training data and y as class labels. If base_estimator has no partial_fit function use fit with the sliding window for X, y and sample_weight.

Parameters:

Xarray-like of shape (n_samples, …)

The feature matrix representing the samples.

yarray-like of shape (n_samples,)

It contains the class labels of the training samples. Missing labels are represented by the attribute self.missing_label_.

sample_weightarray-like of shape (n_samples,), default=None

It contains the weights of the training samples’ class labels.

fit_kwargsdict-like

Further parameters as input to the fit method of the estimator.

Returns:

selfSlidingWindowClassifier,

The SlidingWindowClassifier is fitted on the training data.

SlidingWindowClassifier.predict(X, **predict_kwargs)

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 shape (n_samples,)

Predicted class labels of the input samples.

SlidingWindowClassifier.predict_freq(X, **predict_freq_kwargs)

Return class frequency estimates for the test samples X.

Parameters:

Xarray-like of shape (n_samples, …)

Test samples whose class frequencies are to be estimated.

Returns:

Fnumpy.ndarray of shape (n_samples, classes)

The class frequency estimates of the test samples X. Classes are ordered according to the attribute self.classes_.

SlidingWindowClassifier.predict_proba(X, **predict_proba_kwargs)

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:

Pnumpy.ndarray shape (n_samples, classes)

The class probabilities of the input samples X. Classes are ordered according to the attribute self.classes_.

SlidingWindowClassifier.score(X, y, sample_weight=None)

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

Parameters:

Xarray-like of shape (n_samples, …)

Test samples.

yarray-like of shape (n_samples,)

True class labels of the test samples X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights of the test sample X.

Returns:

scorefloat

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

SlidingWindowClassifier.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.

SlidingWindowClassifier.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.

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

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.

SlidingWindowClassifier.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.

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

Configure whether metadata should be requested to be passed to the partial_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 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.

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

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