lightning.classification.FistaClassifier

class lightning.classification.FistaClassifier(C=1.0, alpha=1.0, loss='squared_hinge', penalty='l1', multiclass=False, max_iter=100, max_steps=30, eta=2.0, sigma=1e-05, callback=None, verbose=0)

Estimator for learning linear classifiers by FISTA.

The objective functions considered take the form

minimize F(W) = C * L(W) + alpha * R(W),

where L(W) is a loss term and R(W) is a penalty term.

Parameters

• loss (str, 'squared_hinge', 'log', 'modified_huber', 'squared') – The loss function to be used.

• penalty (str or Penalty object, {'l2', 'l1', 'l1/l2', 'tv1d', 'simplex'}) –

  The penalty or constraint to be used.

  • l2: ridge

  • l1: lasso

  • l1/l2: group lasso

  • tv1d: 1-dimensional total variation (also known as fused lasso)

  • simplex: simplex constraint

  The method can also take an arbitrary Penalty object, i.e., an instance that implements methods projection regularization method (see file penalty.py)

• multiclass (bool) – Whether to use a direct multiclass formulation (True) or one-vs-rest (False).

• C (float) – Weight of the loss term.

• alpha (float) – Weight of the penalty term.

• max_iter (int) – Maximum number of iterations to perform.

• max_steps (int) – Maximum number of steps to use during the line search.

• sigma (float) – Constant used in the line search sufficient decrease condition.

• eta (float) – Decrease factor for line-search procedure. For example, eta=2. will decrease the step size by a factor of 2 at each iteration of the line-search routine.

• callback (callable) – Callback function.

• verbose (int) – Verbosity level.

decision_function(X)

fit(X, y)

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