Use latest numpy and some tests as known failure

rth · rth · commit ca3c1be07277 · 2018-09-17T12:15:13.000+02:00
diff --git a/build_tools/circle/build_test_pypy.sh b/build_tools/circle/build_test_pypy.sh
@@ -18,7 +18,7 @@ source pypy-env/bin/activate
 python --version
 which python
 
-pip install --extra-index https://antocuni.github.io/pypy-wheels/ubuntu numpy==1.14.4 Cython pytest
+pip install --extra-index https://antocuni.github.io/pypy-wheels/ubuntu numpy Cython pytest
 pip install "scipy>=1.1.0" sphinx numpydoc docutils pytest-xdist
 
 ccache -M 512M
@@ -27,5 +27,5 @@ export PATH=/usr/lib/ccache:$PATH
 
 pip install -vv -e . 
 
-pytest -n 1 -Wignore::DeprecationWarning -Wignore::FutureWarning sklearn/
+pytest -n 1 sklearn/
 make test-sphinxext test-doc
diff --git a/sklearn/tests/test_docstring_parameters.py b/sklearn/tests/test_docstring_parameters.py
@@ -17,6 +17,7 @@
 from sklearn.utils.testing import check_docstring_parameters
 from sklearn.utils.testing import _get_func_name
 from sklearn.utils.testing import ignore_warnings
+from sklearn.utils.testing import fails_if_pypy
 from sklearn.utils.deprecation import _is_deprecated
 
 PUBLIC_MODULES = set([pckg[1] for pckg in walk_packages(prefix='sklearn.',
@@ -43,6 +44,7 @@
 ]
 
 
+@fails_if_pypy  # this segfaults with PyPy-6.0
 # numpydoc 0.8.0's docscrape tool raises because of collections.abc under
 # Python 3.7
 @ignore_warnings(category=DeprecationWarning)
diff --git a/sklearn/tests/test_multioutput.py b/sklearn/tests/test_multioutput.py
@@ -14,6 +14,7 @@
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_not_equal
 from sklearn.utils.testing import assert_array_almost_equal
+from sklearn.utils.testing import fails_if_pypy
 from sklearn import datasets
 from sklearn.base import clone
 from sklearn.datasets import make_classification
@@ -162,6 +163,7 @@ def test_multi_target_sample_weights():
 classes = list(map(np.unique, (y1, y2, y3)))
 
 
+@fails_if_pypy  # FIXME
 def test_multi_output_classification_partial_fit_parallelism():
     sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
     mor = MultiOutputClassifier(sgd_linear_clf, n_jobs=-1)
