Merge pull request scikit-learn#3090 from ogrisel/learning-curves-warnings

[MRG] FIX: remove deprecation warnings in learning curves under Python 3

Author: ogrisel

sklearn/learning_curve.py

@@ -132,7 +132,7 @@ def learning_curve(estimator, X, y, train_sizes=np.linspace(0.1, 1.0, 5),
             verbose, parameters=None, fit_params=None, return_train_score=True)
             for train, test in cv for n_train_samples in train_sizes_abs)
         out = np.array(out)[:, :2]
-        n_cv_folds = out.shape[0] / n_unique_ticks
+        n_cv_folds = out.shape[0] // n_unique_ticks
         out = out.reshape(n_cv_folds, n_unique_ticks, 2)
 
     out = np.asarray(out).transpose((2, 1, 0))
@@ -297,7 +297,7 @@ def validation_curve(estimator, X, y, param_name, param_range, cv=None,
 
     out = np.asarray(out)[:, :2]
     n_params = len(param_range)
-    n_cv_folds = out.shape[0] / n_params
+    n_cv_folds = out.shape[0] // n_params
     out = out.reshape(n_cv_folds, n_params, 2).transpose((2, 1, 0))
 
     return out[0], out[1]

sklearn/tests/test_learning_curve.py

@@ -5,6 +5,7 @@
 import sys
 from sklearn.externals.six.moves import cStringIO as StringIO
 import numpy as np
+import warnings
 from sklearn.base import BaseEstimator
 from sklearn.learning_curve import learning_curve, validation_curve
 from sklearn.utils.testing import assert_raises
@@ -84,8 +85,11 @@ def test_learning_curve():
                                n_redundant=0, n_classes=2,
                                n_clusters_per_class=1, random_state=0)
     estimator = MockImprovingEstimator(20)
-    train_sizes, train_scores, test_scores = learning_curve(
-        estimator, X, y, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
+    with warnings.catch_warnings(record=True) as w:
+        train_sizes, train_scores, test_scores = learning_curve(
+            estimator, X, y, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
+    if len(w) > 0:
+      raise RuntimeError("Unexpected warning: %r" % w[0].message)
     assert_equal(train_scores.shape, (10, 3))
     assert_equal(test_scores.shape, (10, 3))
     assert_array_equal(train_sizes, np.linspace(2, 20, 10))
@@ -239,8 +243,12 @@ def test_validation_curve():
                                n_redundant=0, n_classes=2,
                                n_clusters_per_class=1, random_state=0)
     param_range = np.linspace(0, 1, 10)
-    train_scores, test_scores = validation_curve(MockEstimatorWithParameter(),
-                                                 X, y, param_name="param",
-                                                 param_range=param_range, cv=2)
+    with warnings.catch_warnings(record=True) as w:
+        train_scores, test_scores = validation_curve(
+          MockEstimatorWithParameter(), X, y, param_name="param",
+          param_range=param_range, cv=2)
+    if len(w) > 0:
+      raise RuntimeError("Unexpected warning: %r" % w[0].message)
+
     assert_array_almost_equal(train_scores.mean(axis=1), param_range)
     assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)