lightning.regression.SGDRegressor

class lightning.regression.SGDRegressor(loss='squared', penalty='l2', alpha=0.01, learning_rate='pegasos', eta0=0.03, power_t=0.5, epsilon=0.01, fit_intercept=True, intercept_decay=1.0, max_iter=10, shuffle=True, random_state=None, callback=None, n_calls=100, verbose=0)

Estimator for learning linear regressors by SGD.

Parameters

• loss (str, 'squared', 'epsilon_insensitive', 'huber') – Loss function to be used.

• penalty (str, 'l2', 'l1', 'l1/l2') –

  The penalty to be used.

  • l2: ridge

  • l1: lasso

  • l1/l2: group lasso

• alpha (float) – Weight of the penalty term.

• learning_rate ('pegasos', 'constant', 'invscaling') – Learning schedule to use.

• eta0 (float) – Step size.

• power_t (float) – Power to be used (when learning_rate=’invscaling’).

• epsilon (float) – Value to be used for epsilon-insensitive loss.

• fit_intercept (bool) – Whether to fit the intercept or not.

• intercept_decay (float) – Value by which the intercept is multiplied (to regularize it).

• max_iter (int) – Maximum number of iterations to perform.

• shuffle (bool) – Whether to shuffle data.

• callback (callable) – Callback function.

• n_calls (int) – Frequency with which callback must be called.

• random_state (RandomState or int) – The seed of the pseudo random number generator to use.

• verbose (int) – Verbosity level.

fit(X, y)

Fit model according to X and y.

Parameters

• X (array-like, shape = [n_samples, n_features]) – Training vectors, where n_samples is the number of samples and n_features is the number of features.

• y (array-like, shape = [n_samples] or [n_samples, n_targets]) – Target values.

Returns

self – Returns self.

Return type

regressor

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

n_nonzero(percentage=False)

predict(X)

Perform regression on an array of test vectors X.

Parameters

X (array-like, shape = [n_samples, n_features]) –

Returns

p – Predicted target values for X

Return type

array, shape = [n_samples]

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 (array-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 with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) wrt. y.

Return type

float

Notes

The \(R^2\) 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(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance