lightning.classification.SAGAClassifier¶

class lightning.classification.SAGAClassifier(eta='auto', alpha=1.0, beta=0.0, loss='smooth_hinge', penalty=None, gamma=1.0, max_iter=10, n_inner=1.0, tol=0.001, verbose=0, callback=None, random_state=None)¶

Estimator for learning linear classifiers by SAGA.

Solves the following objective:

minimize_w 1 / n_samples * sum_i loss(w^T x_i, y_i)

alpha * 0.5 * ||w||^2_2 + beta * penalty(w)

Parameters:

Parameters:	eta : float or string, defaults to ‘auto’ step size for the gradient updates. If set to ‘auto’, this will calculate a step size based on the input data. alpha : float amount of squared L2 regularization beta : float amount of regularization for the penalty term loss : string loss to use in the objective function. Can be one of “smooth_hinge”, “squared_hinge” or “log” (for logistic loss). penalty : string or Penalty object penalty term to use in the objective function. Can be “l1” or a custom Penalty object (object defined in lightning/impl/sag_fast.pxd) gamma : float gamma parameter in the “smooth_hinge” loss (not used for other loss functions) max_iter : int maximum number of outer iterations (also known as epochs). tol : float stopping criterion tolerance. verbose : int verbosity level. Set positive to print progress information. callback : callable or None if given, callback(self) will be called on each outer iteration (epoch). random_state: int or RandomState : Pseudo-random number generator state used for random sampling.

eta : float or string, defaults to ‘auto’

step size for the gradient updates. If set to ‘auto’, this will calculate a step size based on the input data.

alpha : float

amount of squared L2 regularization

beta : float

amount of regularization for the penalty term

loss : string

loss to use in the objective function. Can be one of “smooth_hinge”, “squared_hinge” or “log” (for logistic loss).

penalty : string or Penalty object

penalty term to use in the objective function. Can be “l1” or a custom Penalty object (object defined in lightning/impl/sag_fast.pxd)

gamma : float

gamma parameter in the “smooth_hinge” loss (not used for other loss functions)

max_iter : int

maximum number of outer iterations (also known as epochs).

tol : float

stopping criterion tolerance.

verbose : int

verbosity level. Set positive to print progress information.

callback : callable or None

if given, callback(self) will be called on each outer iteration (epoch).

random_state: int or RandomState :

Pseudo-random number generator state used for random sampling.

Methods

`decision_function`(X)
`fit`(X, y)
`get_params`([deep])	Get parameters for this estimator.
`n_nonzero`([percentage])
`predict`(X)
`predict_proba`(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='auto', alpha=1.0, beta=0.0, loss='smooth_hinge', penalty=None, gamma=1.0, max_iter=10, n_inner=1.0, tol=0.001, verbose=0, callback=None, random_state=None)¶

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

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.

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:

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.

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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :