wildboar.linear_model._kernel_logistic#

Module Contents#

Classes#

KernelLogisticRegression

A simple kernel logistic implementation using a Nystroem kernel approximation
class wildboar.linear_model._kernel_logistic.KernelLogisticRegression(kernel=None, *, kernel_params=None, n_components=100, penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)#

Bases: sklearn.linear_model.LogisticRegression

A simple kernel logistic implementation using a Nystroem kernel approximation

Warning

This kernel method is not specialized for temporal classification.

See also

wildboar.datasets.outlier.EmmottLabeler

Synthetic outlier dataset construction

fit(x, y, sample_weight=None)#

Fit the model according to the given training data.

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.

  • y (array-like of shape (n_samples,)) – Target vector relative to X.

  • sample_weight (array-like of shape (n_samples,) default=None) –

    Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

    New in version 0.17: sample_weight support to LogisticRegression.

Returns:

Fitted estimator.

Return type:

self

Notes

The SAGA solver supports both float64 and float32 bit arrays.

decision_function(x)#

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The data matrix for which we want to get the confidence scores.

Returns:

scores – Confidence scores per (n_samples, n_classes) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

Return type:

ndarray of shape (n_samples,) or (n_samples, n_classes)