lightning.classification.SVRGClassifier¶

class lightning.classification.SVRGClassifier(eta=1.0, alpha=1.0, loss='smooth_hinge', gamma=1.0, max_iter=10, n_inner=1.0, tol=0.001, verbose=0, callback=None, random_state=None)[source]¶

Estimator for learning linear classifiers by SVRG.

Solves the following objective:

minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
            + alpha * 0.5 * ||w||^2_2

decision_function(X)¶

fit(X, y)[source]¶

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

n_nonzero(percentage=False)¶

predict(X)¶

property predict_proba¶

score(X, y, sample_weight=None)¶

Return 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 of shape (n_samples, n_features)) – Test samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

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

Return type

float

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: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance