Merge pull request scikit-learn#3396 from amueller/less_loud_randomized_lasso

ENH add convergence_warning option to lars_path.

Author: ogrisel

examples/linear_model/plot_sparse_recovery.py

@@ -56,6 +56,7 @@
 from sklearn.metrics import auc, precision_recall_curve
 from sklearn.ensemble import ExtraTreesRegressor
 from sklearn.utils.extmath import pinvh
+from sklearn.utils import ConvergenceWarning
 
 
 def mutual_incoherence(X_relevant, X_irelevant):
@@ -137,6 +138,7 @@ def mutual_incoherence(X_relevant, X_irelevant):
     # as it is specifically set up to be challenging.
     with warnings.catch_warnings():
         warnings.simplefilter('ignore', UserWarning)
+        warnings.simplefilter('ignore', ConvergenceWarning)
         lars_cv = LassoLarsCV(cv=6).fit(X, y)
 
     # Run the RandomizedLasso: we use a paths going down to .1*alpha_max

sklearn/linear_model/randomized_l1.py

@@ -8,6 +8,7 @@
 # License: BSD 3 clause
 import itertools
 from abc import ABCMeta, abstractmethod
+import warnings
 
 import numpy as np
 from scipy.sparse import issparse
@@ -19,7 +20,7 @@
 from ..externals import six
 from ..externals.joblib import Memory, Parallel, delayed
 from ..utils import (as_float_array, check_random_state, safe_asarray,
-                     check_arrays, safe_mask)
+                     check_arrays, safe_mask, ConvergenceWarning)
 from .least_angle import lars_path, LassoLarsIC
 from .logistic import LogisticRegression
 
@@ -158,11 +159,13 @@ def _randomized_lasso(X, y, weights, mask, alpha=1., verbose=False,
     alpha = np.atleast_1d(np.asarray(alpha, dtype=np.float))
 
     X = (1 - weights) * X
-    alphas_, _, coef_ = lars_path(X, y,
-                                  Gram=precompute, copy_X=False,
-                                  copy_Gram=False, alpha_min=np.min(alpha),
-                                  method='lasso', verbose=verbose,
-                                  max_iter=max_iter, eps=eps)
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', ConvergenceWarning)
+        alphas_, _, coef_ = lars_path(X, y,
+                                      Gram=precompute, copy_X=False,
+                                      copy_Gram=False, alpha_min=np.min(alpha),
+                                      method='lasso', verbose=verbose,
+                                      max_iter=max_iter, eps=eps)
 
     if len(alpha) > 1:
         if len(alphas_) > 1:  # np.min(alpha) < alpha_min
@@ -504,8 +507,10 @@ def _lasso_stability_path(X, y, mask, weights, eps):
 
     alpha_max = np.max(np.abs(np.dot(X.T, y))) / X.shape[0]
     alpha_min = eps * alpha_max  # set for early stopping in path
-    alphas, _, coefs = lars_path(X, y, method='lasso', verbose=False,
-                                 alpha_min=alpha_min)
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', ConvergenceWarning)
+        alphas, _, coefs = lars_path(X, y, method='lasso', verbose=False,
+                                     alpha_min=alpha_min)
     # Scale alpha by alpha_max
     alphas /= alphas[0]
     # Sort alphas in assending order