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]

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. 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. 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 :