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=None, tol=None, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate=’invscaling’, eta0=0.01, power_t=0.25, warm_start=False, average=False, n_iter=None)[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:  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
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the
fit
method, and not the partial_fit. Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.New in version 0.19.
 tol : float or None, optional
The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss  tol). Defaults to None. Defaults to 1e3 from 0.21.
New in version 0.19.
 shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch. Defaults to True.
 verbose : integer, optional
The verbosity level.
 epsilon : float
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_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))
 ‘invscaling’: eta = eta0 / pow(t, power_t) [default]
where t0 is chosen by a heuristic proposed by Leon Bottou.
 eta0 : double, optional
The initial learning rate [default 0.01].
 power_t : double, optional
The exponent for inverse scaling learning rate [default 0.25].
 warm_start : bool, optional
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. average : bool or int, optional
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. n_iter : int, optional
The number of passes over the training data (aka epochs). Defaults to None. Deprecated, will be removed in 0.21.
Changed in version 0.19: Deprecated
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.
See also
Examples
>>> import numpy as np >>> from sklearn import linear_model >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = linear_model.SGDRegressor() >>> clf.fit(X, y) ... SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling', loss='squared_loss', max_iter=None, n_iter=None, penalty='l2', power_t=0.25, random_state=None, shuffle=True, tol=None, verbose=0, warm_start=False)
Methods
densify
()Convert coefficient matrix to dense array format. fit
(X, y[, coef_init, intercept_init, …])Fit linear model with Stochastic Gradient Descent. get_params
([deep])Get parameters for this estimator. partial_fit
(X, y[, sample_weight])Fit linear model with Stochastic Gradient Descent. predict
(X)Predict using the linear model score
(X, y[, sample_weight])Returns the coefficient of determination R^2 of the prediction. set_params
(*args, **kwargs)sparsify
()Convert coefficient matrix to sparse format. 
__init__
(loss=’squared_loss’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate=’invscaling’, eta0=0.01, power_t=0.25, warm_start=False, average=False, n_iter=None)[source]¶

densify
()[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:  self : estimator

fit
(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
 y : numpy array, shape (n_samples,)
Target values
 coef_init : array, shape (n_features,)
The initial coefficients to warmstart the optimization.
 intercept_init : array, shape (1,)
The initial intercept to warmstart the optimization.
 sample_weight : arraylike, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns:  self : returns an instance of self.

get_params
(deep=True)[source]¶ 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.

partial_fit
(X, y, sample_weight=None)[source]¶ Fit linear model with Stochastic Gradient Descent.
Parameters:  X : {arraylike, sparse matrix}, shape (n_samples, n_features)
Subset of training data
 y : numpy array of shape (n_samples,)
Subset of target values
 sample_weight : arraylike, shape (n_samples,), optional
Weights applied to individual samples. If not provided, uniform weights are assumed.
Returns:  self : returns an instance of self.

predict
(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
(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:  X : arraylike, 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.
 y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
 sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns:  score : float
R^2 of self.predict(X) wrt. y.

sparsify
()[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:  self : estimator
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.