skip unstable tests on 32bit platform

Author: amueller

sklearn/ensemble/tests/test_forest.py

@@ -29,6 +29,7 @@
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_warns
 from sklearn.utils.testing import ignore_warnings
+from sklearn.utils.testing import skip_if_32bit
 
 from sklearn import datasets
 from sklearn.decomposition import TruncatedSVD
@@ -233,6 +234,7 @@ def check_importances(name, criterion, X, y):
         assert_less(np.abs(importances - importances_bis).mean(), 0.001)
 
 
+@skip_if_32bit
 def test_importances():
     X, y = datasets.make_classification(n_samples=500, n_features=10,
                                         n_informative=3, n_redundant=0,

sklearn/ensemble/tests/test_gradient_boosting.py

@@ -26,6 +26,7 @@
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_true
 from sklearn.utils.testing import assert_warns
+from sklearn.utils.testing import skip_if_32bit
 from sklearn.exceptions import DataConversionWarning
 from sklearn.exceptions import NotFittedError
 
@@ -1030,6 +1031,7 @@ def test_non_uniform_weights_toy_edge_case_clf():
         gb.fit(X, y, sample_weight=sample_weight)
         assert_array_equal(gb.predict([[1, 0]]), [1])
 
+
 def check_sparse_input(EstimatorClass, X, X_sparse, y):
     dense = EstimatorClass(n_estimators=10, random_state=0,
                            max_depth=2).fit(X, y)
@@ -1060,6 +1062,7 @@ def check_sparse_input(EstimatorClass, X, X_sparse, y):
                                   auto.predict_log_proba(X))
 
 
+@skip_if_32bit
 def test_sparse_input():
     ests = (GradientBoostingClassifier, GradientBoostingRegressor)
     sparse_matrices = (csr_matrix, csc_matrix, coo_matrix)

sklearn/feature_selection/tests/test_from_model.py

@@ -5,11 +5,11 @@
 
 from sklearn.utils.testing import assert_less
 from sklearn.utils.testing import assert_greater
-from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_array_almost_equal
 from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_almost_equal
 from sklearn.utils.testing import assert_warns
+from sklearn.utils.testing import skip_if_32bit
 
 from sklearn import datasets
 from sklearn.linear_model import LogisticRegression, SGDClassifier, Lasso
@@ -22,6 +22,7 @@
 data, y = iris.data, iris.target
 rng = np.random.RandomState(0)
 
+
 def test_transform_linear_model():
     for clf in (LogisticRegression(C=0.1),
                 LinearSVC(C=0.01, dual=False),
@@ -62,6 +63,7 @@ def test_input_estimator_unchanged():
     assert_true(transformer.estimator is est)
 
 
+@skip_if_32bit
 def test_feature_importances():
     X, y = datasets.make_classification(
         n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
@@ -88,7 +90,7 @@ def test_feature_importances():
     transformer = SelectFromModel(estimator=est)
     transformer.fit(X, y, sample_weight=sample_weight)
     importances = transformer.estimator_.feature_importances_
-    transformer.fit(X, y, sample_weight=3*sample_weight)
+    transformer.fit(X, y, sample_weight=3 * sample_weight)
     importances_bis = transformer.estimator_.feature_importances_
     assert_almost_equal(importances, importances_bis)
 

sklearn/preprocessing/tests/test_data.py

@@ -29,6 +29,7 @@
 from sklearn.utils.testing import assert_no_warnings
 from sklearn.utils.testing import ignore_warnings
 from sklearn.utils.testing import assert_allclose
+from sklearn.utils.testing import skip_if_32bit
 
 from sklearn.utils.sparsefuncs import mean_variance_axis
 from sklearn.preprocessing.data import _transform_selected
@@ -171,6 +172,7 @@ def test_scale_1d():
         assert_array_equal(scale(X, with_mean=False, with_std=False), X)
 
 
+@skip_if_32bit
 def test_standard_scaler_numerical_stability():
     """Test numerical stability of scaling"""
     # np.log(1e-5) is taken because of its floating point representation
@@ -436,7 +438,7 @@ def test_standard_scaler_trasform_with_partial_fit():
     scaler_incr = StandardScaler()
     for i, batch in enumerate(gen_batches(X.shape[0], 1)):
 
-        X_sofar = X[:(i+1), :]
+        X_sofar = X[:(i + 1), :]
         chunks_copy = X_sofar.copy()
         scaled_batch = StandardScaler().fit_transform(X_sofar)
 

sklearn/utils/testing.py

@@ -16,6 +16,7 @@
 import sys
 import re
 import platform
+import struct
 
 import scipy as sp
 import scipy.io
@@ -686,6 +687,18 @@ def run_test(*args, **kwargs):
     return run_test
 
 
+def skip_if_32bit(func):
+    """Test decorator that skips tests on 32bit platforms."""
+    @wraps(func)
+    def run_test(*args, **kwargs):
+        bits = 8 * struct.calcsize("P")
+        if bits == 32:
+            raise SkipTest('Test skipped on 32bit platforms.')
+        else:
+            return func(*args, **kwargs)
+    return run_test
+
+
 def if_not_mac_os(versions=('10.7', '10.8', '10.9'),
                   message='Multi-process bug in Mac OS X >= 10.7 '
                           '(see issue #636)'):

sklearn/utils/tests/test_extmath.py

@@ -16,6 +16,7 @@
 from sklearn.utils.testing import assert_true
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_raises
+from sklearn.utils.testing import skip_if_32bit
 
 from sklearn.utils.extmath import density
 from sklearn.utils.extmath import logsumexp
@@ -467,6 +468,7 @@ def test_incremental_variance_update_formulas():
     assert_almost_equal(final_count, A.shape[0])
 
 
+@skip_if_32bit
 def test_incremental_variance_numerical_stability():
     # Test Youngs and Cramer incremental variance formulas.