Linear regression with combined L1 and L2 priors as regularizer.
Minimizes the objective function:
1 / (2 * n_samples) * y  Xw^2_2
+ alpha * l1_ratio * w_1
+ 0.5 * alpha * (1  l1_ratio) * w^2_2
If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to:
a * L1 + b * L2
where:
alpha = a + b and l1_ratio = a / (a + b)
The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio = 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable, unless you supply your own sequence of alpha.
Read more in the User Guide.
Parameters: 

alpha : float, optional 
Constant that multiplies the penalty terms. Defaults to 1.0. See the notes for the exact mathematical meaning of this parameter.``alpha = 0`` is equivalent to an ordinary least square, solved by the LinearRegression object. For numerical reasons, using alpha = 0 with the Lasso object is not advised. Given this, you should use the LinearRegression object. 
l1_ratio : float 
The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1 . For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1 , the penalty is a combination of L1 and L2. 
fit_intercept : bool 
Whether the intercept should be estimated or not. If False , the data is assumed to be already centered. 
normalize : boolean, optional, default False 
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False . 
precompute : True  False  arraylike 
Whether to use a precomputed Gram matrix to speed up calculations. The Gram matrix can also be passed as argument. For sparse input this option is always True to preserve sparsity. 
max_iter : int, optional 
The maximum number of iterations 
copy_X : boolean, optional, default True 
If True , X will be copied; else, it may be overwritten. 
tol : float, optional 
The tolerance for the optimization: if the updates are smaller than tol , the optimization code checks the dual gap for optimality and continues until it is smaller than tol . 
warm_start : bool, optional 
When set to True , reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. 
positive : bool, optional 
When set to True , forces the coefficients to be positive. 
random_state : int, RandomState instance or None, optional, default None 
The seed of the pseudo random number generator that selects a random feature to update. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random . Used when selection == ‘random’. 
selection : str, default ‘cyclic’ 
If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4. 
Attributes: 

coef_ : array, shape (n_features,)  (n_targets, n_features) 
parameter vector (w in the cost function formula) 
sparse_coef_ : scipy.sparse matrix, shape (n_features, 1)  (n_targets, n_features) 
sparse representation of the fitted coef_ 
intercept_ : float  array, shape (n_targets,) 
independent term in decision function. 
n_iter_ : arraylike, shape (n_targets,) 
number of iterations run by the coordinate descent solver to reach the specified tolerance. 
See also

ElasticNetCV
 Elastic net model with best model selection by crossvalidation.

SGDRegressor
 implements elastic net regression with incremental training.

SGDClassifier
 implements logistic regression with elastic net penalty (
SGDClassifier(loss="log", penalty="elasticnet")
).
Notes
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
Examples
>>> from sklearn.linear_model import ElasticNet
>>> from sklearn.datasets import make_regression
>>>
>>> X, y = make_regression(n_features=2, random_state=0)
>>> regr = ElasticNet(random_state=0)
>>> regr.fit(X, y)
ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,
max_iter=1000, normalize=False, positive=False, precompute=False,
random_state=0, selection='cyclic', tol=0.0001, warm_start=False)
>>> print(regr.coef_)
[18.83816048 64.55968825]
>>> print(regr.intercept_)
1.451...
>>> print(regr.predict([[0, 0]]))
[1.451...]
Methods
fit (X, y[, check_input])  Fit model with coordinate descent. 
get_params ([deep])  Get parameters for this estimator. 
path (X, y[, l1_ratio, eps, n_alphas, …])  Compute elastic net path with coordinate descent 
predict (X)  Predict using the linear model 
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__(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection=’cyclic’)
[source]

fit(X, y, check_input=True)
[source]

Fit model with coordinate descent.
Parameters: 

X : ndarray or scipy.sparse matrix, (n_samples, n_features) 
Data 
y : ndarray, shape (n_samples,) or (n_samples, n_targets) 
Target. Will be cast to X’s dtype if necessary 
check_input : boolean, (default=True) 
Allow to bypass several input checking. Don’t use this parameter unless you know what you do. 
Notes
Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortrancontiguous numpy array if necessary.
To avoid memory reallocation it is advised to allocate the initial data in memory directly using that format.

get_params(deep=True)
[source]

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. 

static path(X, y, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, precompute=’auto’, Xy=None, copy_X=True, coef_init=None, verbose=False, return_n_iter=False, positive=False, check_input=True, **params)
[source]

Compute elastic net path with coordinate descent
The elastic net optimization function varies for mono and multioutputs.
For monooutput tasks it is:
1 / (2 * n_samples) * y  Xw^2_2
+ alpha * l1_ratio * w_1
+ 0.5 * alpha * (1  l1_ratio) * w^2_2
For multioutput tasks it is:
(1 / (2 * n_samples)) * Y  XW^Fro_2
+ alpha * l1_ratio * W_21
+ 0.5 * alpha * (1  l1_ratio) * W_Fro^2
Where:
W_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the User Guide.
Parameters: 

X : {arraylike}, shape (n_samples, n_features) 
Training data. Pass directly as Fortrancontiguous data to avoid unnecessary memory duplication. If y is monooutput then X can be sparse. 
y : ndarray, shape (n_samples,) or (n_samples, n_outputs) 
Target values 
l1_ratio : float, optional 
float between 0 and 1 passed to elastic net (scaling between l1 and l2 penalties). l1_ratio=1 corresponds to the Lasso 
eps : float 
Length of the path. eps=1e3 means that alpha_min / alpha_max = 1e3 
n_alphas : int, optional 
Number of alphas along the regularization path 
alphas : ndarray, optional 
List of alphas where to compute the models. If None alphas are set automatically 
precompute : True  False  ‘auto’  arraylike 
Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument. 
Xy : arraylike, optional 
Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed. 
copy_X : boolean, optional, default True 
If True , X will be copied; else, it may be overwritten. 
coef_init : array, shape (n_features, )  None 
The initial values of the coefficients. 
verbose : bool or integer 
Amount of verbosity. 
return_n_iter : bool 
whether to return the number of iterations or not. 
positive : bool, default False 
If set to True, forces coefficients to be positive. (Only allowed when y.ndim == 1 ). 
check_input : bool, default True 
Skip input validation checks, including the Gram matrix when provided assuming there are handled by the caller when check_input=False. 
**params : kwargs 
keyword arguments passed to the coordinate descent solver. 
Returns: 

alphas : array, shape (n_alphas,) 
The alphas along the path where models are computed. 
coefs : array, shape (n_features, n_alphas) or (n_outputs, n_features, n_alphas) 
Coefficients along the path. 
dual_gaps : array, shape (n_alphas,) 
The dual gaps at the end of the optimization for each alpha. 
n_iters : arraylike, shape (n_alphas,) 
The number of iterations taken by the coordinate descent optimizer to reach the specified tolerance for each alpha. (Is returned when return_n_iter is set to True). 
Notes
For an example, see examples/linear_model/plot_lasso_coordinate_descent_path.py.

predict(X)
[source]

Predict using the linear model
Parameters: 

X : array_like or sparse matrix, shape (n_samples, n_features) 
Samples. 
Returns: 

C : array, shape (n_samples,) 
Returns predicted values. 

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

Returns 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: 

X : arraylike, shape = (n_samples, n_features) 
Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. 
y : arraylike, shape = (n_samples) or (n_samples, n_outputs) 
True values for X. 
sample_weight : arraylike, shape = [n_samples], optional 
Sample weights. 
Returns: 

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

set_params(**params)
[source]

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.

sparse_coef_

sparse representation of the fitted coef_