skmultiflow.core.ClassifierMixin

class skmultiflow.core.ClassifierMixin[source]

Mixin class for all classifiers in scikit-multiflow.

__init__()

Initialize self. See help(type(self)) for accurate signature.

Methods

fit(X, y[, classes, sample_weight])

Fit the model.

partial_fit(X, y[, classes, sample_weight])

Partially (incrementally) fit the model.

predict(X)

Predict classes for the passed data.

predict_proba(X)

Estimates the probability of each sample in X belonging to each of the class-labels.

score(X, y[, sample_weight])

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

fit(X, y, classes=None, sample_weight=None)[source]

Fit the model.

Parameters
  • X (numpy.ndarray of shape (n_samples, n_features)) – The features to train the model.

  • y (numpy.ndarray of shape (n_samples, n_targets)) – An array-like with the class labels of all samples in X.

  • classes (numpy.ndarray, optional (default=None)) – Contains all possible/known class labels. Usage varies depending on the learning method.

  • sample_weight (numpy.ndarray, optional (default=None)) – Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

Returns

Return type

self

partial_fit(X, y, classes=None, sample_weight=None)[source]

Partially (incrementally) fit the model.

Parameters
  • X (numpy.ndarray of shape (n_samples, n_features)) – The features to train the model.

  • y (numpy.ndarray of shape (n_samples)) – An array-like with the class labels of all samples in X.

  • classes (numpy.ndarray, optional (default=None)) – Array with all possible/known class labels. Usage varies depending on the learning method.

  • sample_weight (numpy.ndarray of shape (n_samples), optional (default=None)) – Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

Returns

Return type

self

predict(X)[source]

Predict classes for the passed data.

Parameters

X (numpy.ndarray of shape (n_samples, n_features)) – The set of data samples to predict the class labels for.

Returns

Return type

A numpy.ndarray with all the predictions for the samples in X.

predict_proba(X)[source]

Estimates the probability of each sample in X belonging to each of the class-labels.

Parameters

X (numpy.ndarray of shape (n_samples, n_features)) – The matrix of samples one wants to predict the class probabilities for.

Returns

  • A numpy.ndarray of shape (n_samples, n_labels), in which each outer entry is associated with the X entry of the

  • same index. And where the list in index [i] contains len(self.target_values) elements, each of which represents

  • the probability that the i-th sample of X belongs to a certain class-label.

score(X, y, sample_weight=None)[source]

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

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters
  • X (array-like, shape = (n_samples, n_features)) – Test samples.

  • y (array-like, shape = (n_samples) or (n_samples, n_outputs)) – True labels for X.

  • sample_weight (array-like, shape = [n_samples], optional) – Sample weights.

Returns

score – Mean accuracy of self.predict(X) wrt. y.

Return type

float