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

Methods

decision_function(X)
fit(X, y)
get_params([deep]) Get parameters for this estimator.
n_nonzero([percentage])
predict(X)
score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
__init__(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]
get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

score(X, y, sample_weight=None)

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 : float

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

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :