_alpha_grid divide by zero when l1_ratio=0 (ElasticNetCV) #7551
Description
Description
According to the docs ElasticNetCV can fit a Ridge penalty by setting l1_ratio=0
This does not work because the grid automatically generated by _alpha_grid sets alpha_max to np.inf in case l1_ratio=0
Steps/Code to Reproduce
from sklearn.linear_model import ElasticNetCV
X = np.array([[1, 2, 4, 5, 8], [3, 5, 7, 7, 8]]).T
y = np.array([12, 10, 11, 21, 5])
est = ElasticNetCV(l1_ratio=0).fit(X, y)
Gives the runtime error
UnboundLocalError: local variable 'best_l1_ratio' referenced before assignment
because
from sklearn.linear_model.coordinate_descent import _alpha_grid
_alpha_grid(X, y, l1_ratio=0)
returns
array([ inf, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan])
glmnet takes care of this by
For alpha=0 (ridge) lambda.max would be ∞∞; hence for this case we pick a value corresponding to a small value for alpha close to zero.)
I could submit a PR where I implement the corresponding logic?
Versions
Darwin-16.0.0-x86_64-i386-64bit
('Python', '2.7.12 |Anaconda custom (x86_64)| (default, Jul 2 2016, 17:43:17) \n[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00)]')
('NumPy', '1.11.1')
('SciPy', '0.18.0')
('Scikit-Learn', '0.18')