`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.

See also

Ridge, ElasticNet, Lasso, sklearn.svm.SVR

Examples

>>> import numpy as np
>>> from sklearn.linear_model import SGDRegressor
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> # Always scale the input. The most convenient way is to use a pipeline.
>>> reg = make_pipeline(StandardScaler(),
...                     SGDRegressor(max_iter=1000, tol=1e-3))
>>> reg.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('sgdregressor', SGDRegressor())])

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])	Perform one epoch of stochastic gradient descent on given samples.
`predict`(X)	Predict using the linear model
`score`(X, y[, sample_weight])	Return the coefficient of determination R^2 of the prediction.
`set_params`(**kwargs)	Set and validate the parameters of estimator.
`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=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()[source]¶

Convert coefficient matrix to dense array format.

Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

Returns

self: Fitted estimator.

fit(X, y, coef_init=None, intercept_init=None, sample_weight=None)[source]¶

Fit linear model with Stochastic Gradient Descent.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features): Training data
yndarray of shape (n_samples,): Target values
coef_initndarray of shape (n_features,), default=None: The initial coefficients to warm-start the optimization.
intercept_initndarray of shape (1,), default=None: The initial intercept to warm-start the optimization.
sample_weightarray-like, shape (n_samples,), default=None: Weights applied to individual samples (1. for unweighted).

Returns

selfreturns an instance of self.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepbool, default=True: 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(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{array-like, sparse matrix}, shape (n_samples, n_features): Subset of training data
ynumpy array of shape (n_samples,): Subset of target values
sample_weightarray-like, shape (n_samples,), default=None: Weights applied to individual samples. If not provided, uniform weights are assumed.

Returns

selfreturns an instance of self.

predict(X)[source]¶

Predict using the linear model

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features)

Returns

ndarray of shape (n_samples,): Predicted target values per element in X.

score(X, y, sample_weight=None)[source]¶

Return 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

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

scorefloat: R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**kwargs)[source]¶

Set and validate the parameters of estimator.

Parameters

**kwargsdict: Estimator parameters.

Returns

selfobject: Estimator instance.

sparsify()[source]¶

Convert coefficient matrix to sparse format.

Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The intercept_ member is not converted.

Returns

self: Fitted estimator.

Notes

For non-sparse 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.

Examples using `sklearn.linear_model.SGDRegressor`¶

Prediction Latency¶

SGD: Penalties¶

sklearn.linear_model.SGDRegressor¶

Examples using sklearn.linear_model.SGDRegressor¶

`sklearn.linear_model`.SGDRegressor¶

Examples using `sklearn.linear_model.SGDRegressor`¶