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/ linear_model / _stochastic_gradient.py
# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from joblib import Parallel, delayed
from ..base import clone, is_classifier
from ._base import LinearClassifierMixin, SparseCoefMixin
from ._base import make_dataset
from ..base import BaseEstimator, RegressorMixin
from ..utils import check_array, check_random_state, check_X_y
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import _check_partial_fit_first_call
from ..utils.validation import check_is_fitted, _check_sample_weight
from ..exceptions import ConvergenceWarning
from ..model_selection import StratifiedShuffleSplit, ShuffleSplit
from ._sgd_fast import plain_sgd, average_sgd
from ..utils import compute_class_weight
from ._sgd_fast import Hinge
from ._sgd_fast import SquaredHinge
from ._sgd_fast import Log
from ._sgd_fast import ModifiedHuber
from ._sgd_fast import SquaredLoss
from ._sgd_fast import Huber
from ._sgd_fast import EpsilonInsensitive
from ._sgd_fast import SquaredEpsilonInsensitive
from ..utils.fixes import _joblib_parallel_args
LEARNING_RATE_TYPES = {"constant": 1, "optimal": 2, "invscaling": 3,
"adaptive": 4, "pa1": 5, "pa2": 6}
PENALTY_TYPES = {"none": 0, "l2": 2, "l1": 1, "elasticnet": 3}
DEFAULT_EPSILON = 0.1
# Default value of ``epsilon`` parameter.
MAX_INT = np.iinfo(np.int32).max
class _ValidationScoreCallback:
"""Callback for early stopping based on validation score"""
def __init__(self, estimator, X_val, y_val, sample_weight_val,
classes=None):
self.estimator = clone(estimator)
self.estimator.t_ = 1 # to pass check_is_fitted
if classes is not None:
self.estimator.classes_ = classes
self.X_val = X_val
self.y_val = y_val
self.sample_weight_val = sample_weight_val
def __call__(self, coef, intercept):
est = self.estimator
est.coef_ = coef.reshape(1, -1)
est.intercept_ = np.atleast_1d(intercept)
return est.score(self.X_val, self.y_val, self.sample_weight_val)
class BaseSGD(SparseCoefMixin, BaseEstimator, metaclass=ABCMeta):
"""Base class for SGD classification and regression."""
def __init__(self, loss, penalty='l2', alpha=0.0001, C=1.0,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=0.1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
self.loss = loss
self.penalty = penalty
self.learning_rate = learning_rate
self.epsilon = epsilon
self.alpha = alpha
self.C = C
self.l1_ratio = l1_ratio
self.fit_intercept = fit_intercept
self.shuffle = shuffle
self.random_state = random_state
self.verbose = verbose
self.eta0 = eta0
self.power_t = power_t
self.early_stopping = early_stopping
self.validation_fraction = validation_fraction
self.n_iter_no_change = n_iter_no_change
self.warm_start = warm_start
self.average = average
self.max_iter = max_iter
self.tol = tol
# current tests expect init to do parameter validation
# but we are not allowed to set attributes
self._validate_params()
def set_params(self, **kwargs):
"""Set and validate the parameters of estimator.
Parameters
----------
**kwargs : dict
Estimator parameters.
Returns
-------
self : object
Estimator instance.
"""
super().set_params(**kwargs)
self._validate_params()
return self
@abstractmethod
def fit(self, X, y):
"""Fit model."""
def _validate_params(self, for_partial_fit=False):
"""Validate input params. """
if not isinstance(self.shuffle, bool):
raise ValueError("shuffle must be either True or False")
if not isinstance(self.early_stopping, bool):
raise ValueError("early_stopping must be either True or False")
if self.early_stopping and for_partial_fit:
raise ValueError("early_stopping should be False with partial_fit")
if self.max_iter is not None and self.max_iter <= 0:
raise ValueError("max_iter must be > zero. Got %f" % self.max_iter)
if not (0.0 <= self.l1_ratio <= 1.0):
raise ValueError("l1_ratio must be in [0, 1]")
if self.alpha < 0.0:
raise ValueError("alpha must be >= 0")
if self.n_iter_no_change < 1:
raise ValueError("n_iter_no_change must be >= 1")
if not (0.0 < self.validation_fraction < 1.0):
raise ValueError("validation_fraction must be in range (0, 1)")
if self.learning_rate in ("constant", "invscaling", "adaptive"):
if self.eta0 <= 0.0:
raise ValueError("eta0 must be > 0")
if self.learning_rate == "optimal" and self.alpha == 0:
raise ValueError("alpha must be > 0 since "
"learning_rate is 'optimal'. alpha is used "
"to compute the optimal learning rate.")
# raises ValueError if not registered
self._get_penalty_type(self.penalty)
self._get_learning_rate_type(self.learning_rate)
if self.loss not in self.loss_functions:
raise ValueError("The loss %s is not supported. " % self.loss)
def _get_loss_function(self, loss):
"""Get concrete ``LossFunction`` object for str ``loss``. """
try:
loss_ = self.loss_functions[loss]
loss_class, args = loss_[0], loss_[1:]
if loss in ('huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'):
args = (self.epsilon, )
return loss_class(*args)
except KeyError:
raise ValueError("The loss %s is not supported. " % loss)
def _get_learning_rate_type(self, learning_rate):
try:
return LEARNING_RATE_TYPES[learning_rate]
except KeyError:
raise ValueError("learning rate %s "
"is not supported. " % learning_rate)
def _get_penalty_type(self, penalty):
penalty = str(penalty).lower()
try:
return PENALTY_TYPES[penalty]
except KeyError:
raise ValueError("Penalty %s is not supported. " % penalty)
def _allocate_parameter_mem(self, n_classes, n_features, coef_init=None,
intercept_init=None):
"""Allocate mem for parameters; initialize if provided."""
if n_classes > 2:
# allocate coef_ for multi-class
if coef_init is not None:
coef_init = np.asarray(coef_init, order="C")
if coef_init.shape != (n_classes, n_features):
raise ValueError("Provided ``coef_`` does not match "
"dataset. ")
self.coef_ = coef_init
else:
self.coef_ = np.zeros((n_classes, n_features),
dtype=np.float64, order="C")
# allocate intercept_ for multi-class
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, order="C")
if intercept_init.shape != (n_classes, ):
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init
else:
self.intercept_ = np.zeros(n_classes, dtype=np.float64,
order="C")
else:
# allocate coef_ for binary problem
if coef_init is not None:
coef_init = np.asarray(coef_init, dtype=np.float64,
order="C")
coef_init = coef_init.ravel()
if coef_init.shape != (n_features,):
raise ValueError("Provided coef_init does not "
"match dataset.")
self.coef_ = coef_init
else:
self.coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
# allocate intercept_ for binary problem
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, dtype=np.float64)
if intercept_init.shape != (1,) and intercept_init.shape != ():
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init.reshape(1,)
else:
self.intercept_ = np.zeros(1, dtype=np.float64, order="C")
# initialize average parameters
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = np.zeros(self.coef_.shape,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(self.standard_intercept_.shape,
dtype=np.float64,
order="C")
def _make_validation_split(self, y):
"""Split the dataset between training set and validation set.
Parameters
----------
y : array, shape (n_samples, )
Target values.
Returns
-------
validation_mask : array, shape (n_samples, )
Equal to 1 on the validation set, 0 on the training set.
"""
n_samples = y.shape[0]
validation_mask = np.zeros(n_samples, dtype=np.uint8)
if not self.early_stopping:
# use the full set for training, with an empty validation set
return validation_mask
if is_classifier(self):
splitter_type = StratifiedShuffleSplit
else:
splitter_type = ShuffleSplit
cv = splitter_type(test_size=self.validation_fraction,
random_state=self.random_state)
idx_train, idx_val = next(cv.split(np.zeros(shape=(y.shape[0], 1)), y))
if idx_train.shape[0] == 0 or idx_val.shape[0] == 0:
raise ValueError(
"Splitting %d samples into a train set and a validation set "
"with validation_fraction=%r led to an empty set (%d and %d "
"samples). Please either change validation_fraction, increase "
"number of samples, or disable early_stopping."
% (n_samples, self.validation_fraction, idx_train.shape[0],
idx_val.shape[0]))
validation_mask[idx_val] = 1
return validation_mask
def _make_validation_score_cb(self, validation_mask, X, y, sample_weight,
classes=None):
if not self.early_stopping:
return None
return _ValidationScoreCallback(
self, X[validation_mask], y[validation_mask],
sample_weight[validation_mask], classes=classes)
def _prepare_fit_binary(est, y, i):
"""Initialization for fit_binary.
Returns y, coef, intercept, average_coef, average_intercept.
"""
y_i = np.ones(y.shape, dtype=np.float64, order="C")
y_i[y != est.classes_[i]] = -1.0
average_intercept = 0
average_coef = None
if len(est.classes_) == 2:
if not est.average:
coef = est.coef_.ravel()
intercept = est.intercept_[0]
else:
coef = est.standard_coef_.ravel()
intercept = est.standard_intercept_[0]
average_coef = est.average_coef_.ravel()
average_intercept = est.average_intercept_[0]
else:
if not est.average:
coef = est.coef_[i]
intercept = est.intercept_[i]
else:
coef = est.standard_coef_[i]
intercept = est.standard_intercept_[i]
average_coef = est.average_coef_[i]
average_intercept = est.average_intercept_[i]
return y_i, coef, intercept, average_coef, average_intercept
def fit_binary(est, i, X, y, alpha, C, learning_rate, max_iter,
pos_weight, neg_weight, sample_weight, validation_mask=None,
random_state=None):
"""Fit a single binary classifier.
The i'th class is considered the "positive" class.
Parameters
----------
est : Estimator object
The estimator to fit
i : int
Index of the positive class
X : numpy array or sparse matrix of shape [n_samples,n_features]
Training data
y : numpy array of shape [n_samples, ]
Target values
alpha : float
The regularization parameter
C : float
Maximum step size for passive aggressive
learning_rate : string
The learning rate. Accepted values are 'constant', 'optimal',
'invscaling', 'pa1' and 'pa2'.
max_iter : int
The maximum number of iterations (epochs)
pos_weight : float
The weight of the positive class
neg_weight : float
The weight of the negative class
sample_weight : numpy array of shape [n_samples, ]
The weight of each sample
validation_mask : numpy array of shape [n_samples, ] or None
Precomputed validation mask in case _fit_binary is called in the
context of a one-vs-rest reduction.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
"""
# if average is not true, average_coef, and average_intercept will be
# unused
y_i, coef, intercept, average_coef, average_intercept = \
_prepare_fit_binary(est, y, i)
assert y_i.shape[0] == y.shape[0] == sample_weight.shape[0]
random_state = check_random_state(random_state)
dataset, intercept_decay = make_dataset(
X, y_i, sample_weight, random_state=random_state)
penalty_type = est._get_penalty_type(est.penalty)
learning_rate_type = est._get_learning_rate_type(learning_rate)
if validation_mask is None:
validation_mask = est._make_validation_split(y_i)
classes = np.array([-1, 1], dtype=y_i.dtype)
validation_score_cb = est._make_validation_score_cb(
validation_mask, X, y_i, sample_weight, classes=classes)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(MAX_INT)
tol = est.tol if est.tol is not None else -np.inf
if not est.average:
result = plain_sgd(coef, intercept, est.loss_function_,
penalty_type, alpha, C, est.l1_ratio,
dataset, validation_mask, est.early_stopping,
validation_score_cb, int(est.n_iter_no_change),
max_iter, tol, int(est.fit_intercept),
int(est.verbose), int(est.shuffle), seed,
pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay)
else:
standard_coef, standard_intercept, average_coef, average_intercept, \
n_iter_ = average_sgd(coef, intercept, average_coef,
average_intercept, est.loss_function_,
penalty_type, alpha, C, est.l1_ratio,
dataset, validation_mask, est.early_stopping,
validation_score_cb,
int(est.n_iter_no_change), max_iter, tol,
int(est.fit_intercept), int(est.verbose),
int(est.shuffle), seed, pos_weight,
neg_weight, learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay,
est.average)
if len(est.classes_) == 2:
est.average_intercept_[0] = average_intercept
else:
est.average_intercept_[i] = average_intercept
result = standard_coef, standard_intercept, n_iter_
return result
class BaseSGDClassifier(LinearClassifierMixin, BaseSGD, metaclass=ABCMeta):
loss_functions = {
"hinge": (Hinge, 1.0),
"squared_hinge": (SquaredHinge, 1.0),
"perceptron": (Hinge, 0.0),
"log": (Log, ),
"modified_huber": (ModifiedHuber, ),
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=None,
random_state=None, learning_rate="optimal", eta0=0.0,
power_t=0.5, early_stopping=False,
validation_fraction=0.1, n_iter_no_change=5,
class_weight=None, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
self.class_weight = class_weight
self.n_jobs = n_jobs
def _partial_fit(self, X, y, alpha, C,
loss, learning_rate, max_iter,
classes, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C",
accept_large_sparse=False)
n_samples, n_features = X.shape
_check_partial_fit_first_call(self, classes)
n_classes = self.classes_.shape[0]
# Allocate datastructures from input arguments
self._expanded_class_weight = compute_class_weight(self.class_weight,
self.classes_, y)
sample_weight = _check_sample_weight(sample_weight, X)
if getattr(self, "coef_", None) is None or coef_init is not None:
self._allocate_parameter_mem(n_classes, n_features,
coef_init, intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
self.loss_function_ = self._get_loss_function(loss)
if not hasattr(self, "t_"):
self.t_ = 1.0
# delegate to concrete training procedure
if n_classes > 2:
self._fit_multiclass(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight,
max_iter=max_iter)
elif n_classes == 2:
self._fit_binary(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight,
max_iter=max_iter)
else:
raise ValueError(
"The number of classes has to be greater than one;"
" got %d class" % n_classes)
return self
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
self._validate_params()
if hasattr(self, "classes_"):
self.classes_ = None
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C",
accept_large_sparse=False)
# labels can be encoded as float, int, or string literals
# np.unique sorts in asc order; largest class id is positive class
classes = np.unique(y)
if self.warm_start and hasattr(self, "coef_"):
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = 1.0
self._partial_fit(X, y, alpha, C, loss, learning_rate, self.max_iter,
classes, sample_weight, coef_init, intercept_init)
if (self.tol is not None and self.tol > -np.inf
and self.n_iter_ == self.max_iter):
warnings.warn("Maximum number of iteration reached before "
"convergence. Consider increasing max_iter to "
"improve the fit.",
ConvergenceWarning)
return self
def _fit_binary(self, X, y, alpha, C, sample_weight,
learning_rate, max_iter):
"""Fit a binary classifier on X and y. """
coef, intercept, n_iter_ = fit_binary(self, 1, X, y, alpha, C,
learning_rate, max_iter,
self._expanded_class_weight[1],
self._expanded_class_weight[0],
sample_weight,
random_state=self.random_state)
self.t_ += n_iter_ * X.shape[0]
self.n_iter_ = n_iter_
# need to be 2d
if self.average > 0:
if self.average <= self.t_ - 1:
self.coef_ = self.average_coef_.reshape(1, -1)
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_.reshape(1, -1)
self.standard_intercept_ = np.atleast_1d(intercept)
self.intercept_ = self.standard_intercept_
else:
self.coef_ = coef.reshape(1, -1)
# intercept is a float, need to convert it to an array of length 1
self.intercept_ = np.atleast_1d(intercept)
def _fit_multiclass(self, X, y, alpha, C, learning_rate,
sample_weight, max_iter):
"""Fit a multi-class classifier by combining binary classifiers
Each binary classifier predicts one class versus all others. This
strategy is called OvA (One versus All) or OvR (One versus Rest).
"""
# Precompute the validation split using the multiclass labels
# to ensure proper balancing of the classes.
validation_mask = self._make_validation_split(y)
# Use joblib to fit OvA in parallel.
# Pick the random seed for each job outside of fit_binary to avoid
# sharing the estimator random state between threads which could lead
# to non-deterministic behavior
random_state = check_random_state(self.random_state)
seeds = random_state.randint(MAX_INT, size=len(self.classes_))
result = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
**_joblib_parallel_args(require="sharedmem"))(
delayed(fit_binary)(self, i, X, y, alpha, C, learning_rate,
max_iter, self._expanded_class_weight[i],
1., sample_weight,
validation_mask=validation_mask,
random_state=seed)
for i, seed in enumerate(seeds))
# take the maximum of n_iter_ over every binary fit
n_iter_ = 0.
for i, (_, intercept, n_iter_i) in enumerate(result):
self.intercept_[i] = intercept
n_iter_ = max(n_iter_, n_iter_i)
self.t_ += n_iter_ * X.shape[0]
self.n_iter_ = n_iter_
if self.average > 0:
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.standard_intercept_ = np.atleast_1d(self.intercept_)
self.intercept_ = self.standard_intercept_
def partial_fit(self, X, y, classes=None, sample_weight=None):
"""Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses ``max_iter = 1``. Therefore, it is not
guaranteed that a minimum of the cost function is reached after calling
it once. Matters such as objective convergence and early stopping
should be handled by the user.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data.
y : numpy array, shape (n_samples,)
Subset of the target values.
classes : array, shape (n_classes,)
Classes across all calls to partial_fit.
Can be obtained by via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn't need to contain all labels in `classes`.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self :
Returns an instance of self.
"""
self._validate_params(for_partial_fit=True)
if self.class_weight in ['balanced']:
raise ValueError("class_weight '{0}' is not supported for "
"partial_fit. In order to use 'balanced' weights,"
" use compute_class_weight('{0}', classes, y). "
"In place of y you can us a large enough sample "
"of the full training set target to properly "
"estimate the class frequency distributions. "
"Pass the resulting weights as the class_weight "
"parameter.".format(self.class_weight))
return self._partial_fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss,
learning_rate=self.learning_rate, max_iter=1,
classes=classes, sample_weight=sample_weight,
coef_init=None, intercept_init=None)
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
y : numpy array, shape (n_samples,)
Target values.
coef_init : array, shape (n_classes, n_features)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (n_classes,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed. These weights will
be multiplied with class_weight (passed through the
constructor) if class_weight is specified.
Returns
-------
self :
Returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init, intercept_init=intercept_init,
sample_weight=sample_weight)
class SGDClassifier(BaseSGDClassifier):
"""Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
This estimator implements regularized linear models with stochastic
gradient descent (SGD) learning: the gradient of the loss is estimated
each sample at a time and the model is updated along the way with a
decreasing strength schedule (aka learning rate). SGD allows minibatch
(online/out-of-core) learning, see the partial_fit method.
For best results using the default learning rate schedule, the data should
have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays
of floating point values for the features. The model it fits can be
controlled with the loss parameter; by default, it fits a linear support
vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, default: 'hinge'
The loss function to be used. Defaults to 'hinge', which gives a
linear SVM.
The possible options are 'hinge', 'log', 'modified_huber',
'squared_hinge', 'perceptron', or a regression loss: 'squared_loss',
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
The 'log' loss gives logistic regression, a probabilistic classifier.
'modified_huber' is another smooth loss that brings tolerance to
outliers as well as probability estimates.
'squared_hinge' is like hinge but is quadratically penalized.
'perceptron' is the linear loss used by the perceptron algorithm.
The other losses are designed for regression but can be useful in
classification as well; see SGDRegressor for a description.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001.
Also used to compute learning_rate when set to 'optimal'.
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
max_iter : int, optional (default=1000)
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.
.. versionadded:: 0.19
tol : float or None, optional (default=1e-3)
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs.
.. versionadded:: 0.19
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
verbose : int, default=0
The verbosity level.
epsilon : float, default=0.1
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
learning_rate : str, optional
The learning rate schedule:
'constant':
eta = eta0
'optimal': [default]
eta = 1.0 / (alpha * (t + t0))
where t0 is chosen by a heuristic proposed by Leon Bottou.
'invscaling':
eta = eta0 / pow(t, power_t)
'adaptive':
eta = eta0, as long as the training keeps decreasing.
Each time n_iter_no_change consecutive epochs fail to decrease the
training loss by tol or fail to increase validation score by tol if
early_stopping is True, the current learning rate is divided by 5.
eta0 : double
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.0 as eta0 is not used by
the default schedule 'optimal'.
power_t : double
The exponent for inverse scaling learning rate [default 0.5].
early_stopping : bool, default=False
Whether to use early stopping to terminate training when validation
score is not improving. If set to True, it will automatically set aside
a stratified fraction of training data as validation and terminate
training when validation score is not improving by at least tol for
n_iter_no_change consecutive epochs.
.. versionadded:: 0.20
validation_fraction : float, default=0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if early_stopping is True.
.. versionadded:: 0.20
n_iter_no_change : int, default=5
Number of iterations with no improvement to wait before early stopping.
.. versionadded:: 0.20
class_weight : dict, {class_label: weight} or "balanced" or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes
are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``.
warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
See :term:`the Glossary <warm_start>`.
Repeatedly calling fit or partial_fit when warm_start is True can
result in a different solution than when calling fit a single time
because of the way the data is shuffled.
If a dynamic learning rate is used, the learning rate is adapted
depending on the number of samples already seen. Calling ``fit`` resets
this counter, while ``partial_fit`` will result in increasing the
existing counter.
average : bool or int, default=False
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10`` will begin averaging after seeing 10
samples.
Attributes
----------
coef_ : array, shape (1, n_features) if n_classes == 2 else (n_classes,\
n_features)
Weights assigned to the features.
intercept_ : array, shape (1,) if n_classes == 2 else (n_classes,)
Constants in decision function.
n_iter_ : int
The actual number of iterations to reach the stopping criterion.
For multiclass fits, it is the maximum over every binary fit.
loss_function_ : concrete ``LossFunction``
classes_ : array of shape (n_classes,)
t_ : int
Number of weight updates performed during training.
Same as ``(n_iter_ * n_samples)``.
See Also
--------
sklearn.svm.LinearSVC: Linear support vector classification.
LogisticRegression: Logistic regression.
Perceptron: Inherits from SGDClassifier. ``Perceptron()`` is equivalent to
``SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant",
penalty=None)``.
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> Y = np.array([1, 1, 2, 2])
>>> clf = linear_model.SGDClassifier(max_iter=1000, tol=1e-3)
>>> clf.fit(X, Y)
SGDClassifier()
>>> print(clf.predict([[-0.8, -1]]))
[1]
"""
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
fit_intercept=True, max_iter=1000, tol=1e-3, shuffle=True,
verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=None,
random_state=None, learning_rate="optimal", eta0=0.0,
power_t=0.5, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, class_weight=None, warm_start=False,
average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon, n_jobs=n_jobs,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, class_weight=class_weight,
warm_start=warm_start, average=average)
def _check_proba(self):
if self.loss not in ("log", "modified_huber"):
raise AttributeError("probability estimates are not available for"
" loss=%r" % self.loss)
@property
def predict_proba(self):
"""Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (one-vs.-rest)
estimates by simple normalization, as recommended by Zadrozny and
Elkan.
Binary probability estimates for loss="modified_huber" are given by
(clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions
it is necessary to perform proper probability calibration by wrapping
the classifier with
:class:`sklearn.calibration.CalibratedClassifierCV` instead.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Input data for prediction.
Returns
-------
array, shape (n_samples, n_classes)
Returns the probability of the sample for each class in the model,
where classes are ordered as they are in `self.classes_`.
References
----------
Zadrozny and Elkan, "Transforming classifier scores into multiclass
probability estimates", SIGKDD'02,
http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf
The justification for the formula in the loss="modified_huber"
case is in the appendix B in:
http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
"""
self._check_proba()
return self._predict_proba
def _predict_proba(self, X):
check_is_fitted(self)
if self.loss == "log":
return self._predict_proba_lr(X)
elif self.loss == "modified_huber":
binary = (len(self.classes_) == 2)
scores = self.decision_function(X)
if binary:
prob2 = np.ones((scores.shape[0], 2))
prob = prob2[:, 1]
else:
prob = scores
np.clip(scores, -1, 1, prob)
prob += 1.
prob /= 2.
if binary:
prob2[:, 0] -= prob
prob = prob2
else:
# the above might assign zero to all classes, which doesn't
# normalize neatly; work around this to produce uniform
# probabilities
prob_sum = prob.sum(axis=1)
all_zero = (prob_sum == 0)
if np.any(all_zero):
prob[all_zero, :] = 1
prob_sum[all_zero] = len(self.classes_)
# normalize
prob /= prob_sum.reshape((prob.shape[0], -1))
return prob
else:
raise NotImplementedError("predict_(log_)proba only supported when"
" loss='log' or loss='modified_huber' "
"(%r given)" % self.loss)
@property
def predict_log_proba(self):
"""Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss="modified_huber", probability estimates may be hard zeros
and ones, so taking the logarithm is not possible.
See ``predict_proba`` for details.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data for prediction.
Returns
-------
T : array-like, shape (n_samples, n_classes)
Returns the log-probability of the sample for each class in the
model, where classes are ordered as they are in
`self.classes_`.
"""
self._check_proba()
return self._predict_log_proba
def _predict_log_proba(self, X):
return np.log(self.predict_proba(X))
class BaseSGDRegressor(RegressorMixin, BaseSGD):
loss_functions = {
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON,
random_state=None, learning_rate="invscaling", eta0=0.01,
power_t=0.25, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
max_iter, sample_weight, coef_init, intercept_init):
X, y = check_X_y(X, y, "csr", copy=False, order='C', dtype=np.float64,
accept_large_sparse=False)
y = y.astype(np.float64, copy=False)
n_samples, n_features = X.shape
sample_weight = _check_sample_weight(sample_weight, X)
# Allocate datastructures from input arguments
if getattr(self, "coef_", None) is None:
self._allocate_parameter_mem(1, n_features, coef_init,
intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
if self.average > 0 and getattr(self, "average_coef_", None) is None:
self.average_coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(1, dtype=np.float64, order="C")
self._fit_regressor(X, y, alpha, C, loss, learning_rate,
sample_weight, max_iter)
return self
def partial_fit(self, X, y, sample_weight=None):
"""Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses ``max_iter = 1``. Therefore, it is not
guaranteed that a minimum of the cost function is reached after calling
it once. Matters such as objective convergence and early stopping
should be handled by the user.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of training data
y : numpy array of shape (n_samples,)
Subset of target values
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self : returns an instance of self.
"""
self._validate_params(for_partial_fit=True)
return self._partial_fit(X, y, self.alpha, C=1.0,
loss=self.loss,
learning_rate=self.learning_rate, max_iter=1,
sample_weight=sample_weight, coef_init=None,
intercept_init=None)
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
self._validate_params()
if self.warm_start and getattr(self, "coef_", None) is not None:
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_intercept_ = self.intercept_
self.standard_coef_ = self.coef_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = 1.0
self._partial_fit(X, y, alpha, C, loss, learning_rate,
self.max_iter, sample_weight, coef_init,
intercept_init)
if (self.tol is not None and self.tol > -np.inf
and self.n_iter_ == self.max_iter):
warnings.warn("Maximum number of iteration reached before "
"convergence. Consider increasing max_iter to "
"improve the fit.",
ConvergenceWarning)
return self
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_features,)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (1,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init,
intercept_init=intercept_init,
sample_weight=sample_weight)
def _decision_function(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
check_is_fitted(self)
X = check_array(X, accept_sparse='csr')
scores = safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_
return scores.ravel()
def predict(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
return self._decision_function(X)
def _fit_regressor(self, X, y, alpha, C, loss, learning_rate,
sample_weight, max_iter):
dataset, intercept_decay = make_dataset(X, y, sample_weight)
loss_function = self._get_loss_function(loss)
penalty_type = self._get_penalty_type(self.penalty)
learning_rate_type = self._get_learning_rate_type(learning_rate)
if not hasattr(self, "t_"):
self.t_ = 1.0
validation_mask = self._make_validation_split(y)
validation_score_cb = self._make_validation_score_cb(
validation_mask, X, y, sample_weight)
random_state = check_random_state(self.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
tol = self.tol if self.tol is not None else -np.inf
if self.average > 0:
self.standard_coef_, self.standard_intercept_, \
self.average_coef_, self.average_intercept_, self.n_iter_ =\
average_sgd(self.standard_coef_,
self.standard_intercept_[0],
self.average_coef_,
self.average_intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
validation_mask, self.early_stopping,
validation_score_cb,
int(self.n_iter_no_change),
max_iter, tol,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay, self.average)
self.average_intercept_ = np.atleast_1d(self.average_intercept_)
self.standard_intercept_ = np.atleast_1d(self.standard_intercept_)
self.t_ += self.n_iter_ * X.shape[0]
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.intercept_ = self.standard_intercept_
else:
self.coef_, self.intercept_, self.n_iter_ = \
plain_sgd(self.coef_,
self.intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
validation_mask, self.early_stopping,
validation_score_cb,
int(self.n_iter_no_change),
max_iter, tol,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay)
self.t_ += self.n_iter_ * X.shape[0]
self.intercept_ = np.atleast_1d(self.intercept_)
class SGDRegressor(BaseSGDRegressor):
"""Linear model fitted by minimizing a regularized empirical loss with SGD
SGD stands for Stochastic Gradient Descent: the gradient of the loss is
estimated each sample at a time and the model is updated along the way with
a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
This implementation works with data represented as dense numpy arrays of
floating point values for the features.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, default: 'squared_loss'
The loss function to be used. The possible values are 'squared_loss',
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
The 'squared_loss' refers to the ordinary least squares fit.
'huber' modifies 'squared_loss' to focus less on getting outliers
correct by switching from squared to linear loss past a distance of
epsilon. 'epsilon_insensitive' ignores errors less than epsilon and is
linear past that; this is the loss function used in SVR.
'squared_epsilon_insensitive' is the same but becomes squared loss past
a tolerance of epsilon.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001
Also used to compute learning_rate when set to 'optimal'.
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
max_iter : int, optional (default=1000)
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.
.. versionadded:: 0.19
tol : float or None, optional (default=1e-3)
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs.
.. versionadded:: 0.19
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
verbose : integer, default=0
The verbosity level.
epsilon : float, default=0.1
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
learning_rate : string, optional
The learning rate schedule:
'constant':
eta = eta0
'optimal':
eta = 1.0 / (alpha * (t + t0))
where t0 is chosen by a heuristic proposed by Leon Bottou.
'invscaling': [default]
eta = eta0 / pow(t, power_t)
'adaptive':
eta = eta0, as long as the training keeps decreasing.
Each time n_iter_no_change consecutive epochs fail to decrease the
training loss by tol or fail to increase validation score by tol if
early_stopping is True, the current learning rate is divided by 5.
eta0 : double
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.01.
power_t : double
The exponent for inverse scaling learning rate [default 0.25].
early_stopping : bool, default=False
Whether to use early stopping to terminate training when validation
score is not improving. If set to True, it will automatically set aside
a fraction of training data as validation and terminate
training when validation score is not improving by at least tol for
n_iter_no_change consecutive epochs.
.. versionadded:: 0.20
validation_fraction : float, default=0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if early_stopping is True.
.. versionadded:: 0.20
n_iter_no_change : int, default=5
Number of iterations with no improvement to wait before early stopping.
.. versionadded:: 0.20
warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
See :term:`the Glossary <warm_start>`.
Repeatedly calling fit or partial_fit when warm_start is True can
result in a different solution than when calling fit a single time
because of the way the data is shuffled.
If a dynamic learning rate is used, the learning rate is adapted
depending on the number of samples already seen. Calling ``fit`` resets
this counter, while ``partial_fit`` will result in increasing the
existing counter.
average : bool or int, default=False
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10`` will begin averaging after seeing 10
samples.
Attributes
----------
coef_ : array, shape (n_features,)
Weights assigned to the features.
intercept_ : array, shape (1,)
The intercept term.
average_coef_ : array, shape (n_features,)
Averaged weights assigned to the features.
average_intercept_ : array, shape (1,)
The averaged intercept term.
n_iter_ : int
The actual number of iterations to reach the stopping criterion.
t_ : int
Number of weight updates performed during training.
Same as ``(n_iter_ * n_samples)``.
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> clf = linear_model.SGDRegressor(max_iter=1000, tol=1e-3)
>>> clf.fit(X, y)
SGDRegressor()
See also
--------
Ridge, ElasticNet, Lasso, sklearn.svm.SVR
"""
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON,
random_state=None, learning_rate="invscaling", eta0=0.01,
power_t=0.25, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)