lightning.regression.AdaGradRegressor

class lightning.regression.AdaGradRegressor(eta=1.0, alpha=1.0, l1_ratio=0, loss='squared', gamma=1.0, epsilon=0, n_iter=10, shuffle=True, callback=None, n_calls=None, random_state=None)[source]

Estimator for learning linear regressors by AdaGrad.

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

Methods

fit(X, y)
get_params([deep]) Get parameters for this estimator.
n_nonzero([percentage])
predict(X)
score(X, y[, sample_weight]) Returns the coefficient of determination R^2 of the prediction.
set_params(**params) Set the parameters of this estimator.
__init__(eta=1.0, alpha=1.0, l1_ratio=0, loss='squared', gamma=1.0, epsilon=0, n_iter=10, shuffle=True, callback=None, n_calls=None, random_state=None)[source]
get_params(deep=True)

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.

score(X, y, sample_weight=None)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True values for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

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

set_params(**params)

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 :