sklearn.linear_model
.SGDRegressor¶

class
sklearn.linear_model.
SGDRegressor
(loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, 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)[source]¶ 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 User Guide.
 Parameters
 lossstr, 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.
 penaltystr, ‘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’.
 alphafloat
Constant that multiplies the regularization term. Defaults to 0.0001 Also used to compute learning_rate when set to ‘optimal’.
 l1_ratiofloat
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_interceptbool
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
 max_iterint, 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 thepartial_fit
method.New in version 0.19.
 tolfloat or None, optional (default=1e3)
The stopping criterion. If it is not None, the iterations will stop when (loss > best_loss  tol) for
n_iter_no_change
consecutive epochs.New in version 0.19.
 shufflebool, optional
Whether or not the training data should be shuffled after each epoch. Defaults to True.
 verboseinteger, default=0
The verbosity level.
 epsilonfloat, default=0.1
Epsilon in the epsiloninsensitive 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 epsiloninsensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold. random_stateint, 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_ratestring, 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.
 eta0double
The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.01.
 power_tdouble
The exponent for inverse scaling learning rate [default 0.25].
 early_stoppingbool, 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.
New in version 0.20.
 validation_fractionfloat, 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.
New in version 0.20.
 n_iter_no_changeint, default=5
Number of iterations with no improvement to wait before early stopping.
New in version 0.20.
 warm_startbool, default=False
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.
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, whilepartial_fit
will result in increasing the existing counter. averagebool 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. Soaverage=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)
.
See also
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=1e3) >>> clf.fit(X, y) SGDRegressor()
Methods
densify
(self)Convert coefficient matrix to dense array format.
fit
(self, X, y[, coef_init, intercept_init, …])Fit linear model with Stochastic Gradient Descent.
get_params
(self[, deep])Get parameters for this estimator.
partial_fit
(self, X, y[, sample_weight])Perform one epoch of stochastic gradient descent on given samples.
predict
(self, X)Predict using the linear model
score
(self, X, y[, sample_weight])Returns the coefficient of determination R^2 of the prediction.
set_params
(self, \*args, \*\*kwargs)Set the parameters of this estimator.
sparsify
(self)Convert coefficient matrix to sparse format.

__init__
(self, loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, 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)[source]¶ Initialize self. See help(type(self)) for accurate signature.

densify
(self)[source]¶ Convert coefficient matrix to dense array format.
Converts the
coef_
member (back) to a numpy.ndarray. This is the default format ofcoef_
and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a noop. Returns
 selfestimator

fit
(self, X, y, coef_init=None, intercept_init=None, sample_weight=None)[source]¶ Fit linear model with Stochastic Gradient Descent.
 Parameters
 X{arraylike, sparse matrix}, shape (n_samples, n_features)
Training data
 ynumpy array, shape (n_samples,)
Target values
 coef_initarray, shape (n_features,)
The initial coefficients to warmstart the optimization.
 intercept_initarray, shape (1,)
The initial intercept to warmstart the optimization.
 sample_weightarraylike, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
 Returns
 selfreturns an instance of self.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

partial_fit
(self, X, y, sample_weight=None)[source]¶ 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{arraylike, sparse matrix}, shape (n_samples, n_features)
Subset of training data
 ynumpy array of shape (n_samples,)
Subset of target values
 sample_weightarraylike, shape (n_samples,), optional
Weights applied to individual samples. If not provided, uniform weights are assumed.
 Returns
 selfreturns an instance of self.

predict
(self, X)[source]¶ Predict using the linear model
 Parameters
 X{arraylike, sparse matrix}, shape (n_samples, n_features)
 Returns
 array, shape (n_samples,)
Predicted target values per element in X.

score
(self, X, y, sample_weight=None)[source]¶ Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  u/v), where u is the residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
 Parameters
 Xarraylike, shape = (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.
 yarraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike, shape = [n_samples], optional
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor will usemultioutput='uniform_average'
from version 0.23 to keep consistent withr2_score
. This will influence thescore
method of all the multioutput regressors (except forMultiOutputRegressor
). To specify the default value manually and avoid the warning, please either callr2_score
directly or make a custom scorer withmake_scorer
(the builtin scorer'r2'
usesmultioutput='uniform_average'
).

set_params
(self, *args, **kwargs)[source]¶ 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

sparsify
(self)[source]¶ Convert coefficient matrix to sparse format.
Converts the
coef_
member to a scipy.sparse matrix, which for L1regularized models can be much more memory and storageefficient than the usual numpy.ndarray representation.The
intercept_
member is not converted. Returns
 selfestimator
Notes
For nonsparse models, i.e. when there are not many zeros in
coef_
, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with(coef_ == 0).sum()
, must be more than 50% for this to provide significant benefits.After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.