Infinite loop or never returns for logistic regression in nearly degenerate case

[MarvinT]

Description

Logistic Regression never returns on fitting with nearly degenerate data.

Steps/Code to Reproduce

import sklearn.linear_model
import numpy as np
model = sklearn.linear_model.LogisticRegression()
num_pts = 15
x = np.zeros((num_pts*2, 2))
x[3] = 3.7491010398553741e-208
y = np.append(np.zeros(num_pts), np.ones(num_pts))
model.fit(x, y)

Expected Results

Return or throw error.

Actual Results

Never returns.

Versions

Linux-2.6.32-573.18.1.el6.x86_64-x86_64-with-redhat-6.7-Carbon
('Python', '2.7.12 |Anaconda 2.0.1 (64-bit)| (default, Jul 2 2016, 17:42:40) \n[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]')
('NumPy', '1.11.0')
('SciPy', '0.17.0')
('Scikit-Learn', '0.17.1')

[agramfort]

option 1 : have it fixed in liblinear

option 2 : use a different solver eg lbfgs

[amueller]

They're gonna fix it. Which means we need to update our code...