ENH sparsify and densify methods for CD models

Author: larsmans

sklearn/linear_model/coordinate_descent.py

@@ -12,7 +12,7 @@
 import numpy as np
 from scipy import sparse
 
-from .base import LinearModel, _pre_fit
+from .base import LinearModel, _pre_fit, SparseCoefMixin
 from ..base import RegressorMixin
 from .base import center_data, sparse_center_data
 from ..utils import array2d, atleast2d_or_csc
@@ -515,7 +515,7 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
 # ElasticNet model
 
 
-class ElasticNet(LinearModel, RegressorMixin):
+class ElasticNet(LinearModel, RegressorMixin, SparseCoefMixin):
     """Linear Model trained with L1 and L2 prior as regularizer
 
     Minimizes the objective function::
@@ -699,7 +699,10 @@ def fit(self, X, y):
 
     @property
     def sparse_coef_(self):
-        """ sparse representation of the fitted coef """
+        """Sparse representation of the fitted coef_."""
+        warnings.warn("sparse_coef_ is deprecated and will be removed in 0.17."
+                      " Call the sparsify method instead.",
+                      DeprecationWarning)
         return sparse.csr_matrix(self.coef_)
 
     def decision_function(self, X):