lightning.regression.SDCARegressor¶

class lightning.regression.SDCARegressor(alpha=1.0, l1_ratio=0, loss='squared', max_iter=100, tol=0.001, callback=None, n_calls=None, verbose=0, random_state=None)[source]¶

Estimator for learning linear regressors by (proximal) SDCA.

Solves the following objective:

minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
            + alpha * l1_ratio * ||w||_1
            + alpha * (1 - l1_ratio) * 0.5 * ||w||^2_2

Parameters

loss (string, {'squared', 'absolute'}) – Loss function to use in the model.
alpha (float) – Amount of regularization (see model formulation above).
l1_ratio (float) – Ratio between the L1 and L2 regularization (see model formulation above).
tol (float) – stopping criterion tolerance.
max_iter (int) – maximum number of outer iterations (also known as epochs).
verbose (int) – verbosity level. Set positive to print progress information.
callback (callable or None) – if given, callback(self) will be called on each outer iteration (epoch).
random_state (int or RandomState) – Pseudo-random number generator state used for random sampling.

fit(X, y)[source]¶

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)¶

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