{"action":"created","comment":{"url":"https://api.github.com/repos/metric-learn/metric-learn/pulls/comments/282912685","pull_request_review_id":236143057,"id":282912685,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDI4MjkxMjY4NQ==","diff_hunk":"@@ -5,86 +5,108 @@\n from sklearn import clone\n from sklearn.utils.testing import set_random_state\n from test.test_utils import ids_metric_learners, metric_learners\n+import string\n+\n+\n+def remove_spaces(s):\n+  s = s.replace(' ', '')\n+  s = s.replace('\\n', '')\n+  return s\n \n \n class TestStringRepr(unittest.TestCase):\n \n   def test_covariance(self):\n-    self.assertEqual(str(metric_learn.Covariance()),\n-                     \"Covariance(preprocessor=None)\")\n+    self.assertEqual(remove_spaces(str(metric_learn.Covariance())),\n+                     remove_spaces(\"Covariance(preprocessor=None)\"))\n \n   def test_lmnn(self):\n     self.assertRegexpMatches(\n         str(metric_learn.LMNN()),\n         r\"(python_)?LMNN\\(convergence_tol=0.001, k=3, learn_rate=1e-07, \"\n-        r\"max_iter=1000,\\n      min_iter=50, preprocessor=None, \"\n-        r\"regularization=0.5, use_pca=True,\\n      verbose=False\\)\")\n+        r\"max_iter=1000,\\n(\\ +)min_iter=50, preprocessor=None, \"\n+        r\"regularization=0.5, use_pca=True,\\n(\\ +)verbose=False\\)\")\n \n   def test_nca(self):\n-    self.assertEqual(str(metric_learn.NCA()),\n-                     \"NCA(max_iter=100, num_dims=None, preprocessor=None, \"\n-                     \"tol=None, verbose=False)\")\n+    self.assertEqual(remove_spaces(str(metric_learn.NCA())),\n+                     remove_spaces(\n+                       \"NCA(max_iter=100, num_dims=None, preprocessor=None, \"\n+                       \"tol=None, verbose=False)\"))\n \n   def test_lfda(self):\n-    self.assertEqual(str(metric_learn.LFDA()),\n-                     \"LFDA(embedding_type='weighted', k=None, num_dims=None, \"\n-                     \"preprocessor=None)\")\n+    self.assertEqual(remove_spaces(str(metric_learn.LFDA())),\n+                     remove_spaces(\n+                       \"LFDA(embedding_type='weighted', k=None, \"\n+                       \"num_dims=None, \"\n+                       \"preprocessor=None)\"))\n \n   def test_itml(self):\n-    self.assertEqual(str(metric_learn.ITML()), \"\"\"\n+    self.assertEqual(remove_spaces(str(metric_learn.ITML())),\n+                     remove_spaces(\"\"\"\n ITML(A0=None, convergence_threshold=0.001, gamma=1.0, max_iter=1000,\n    preprocessor=None, verbose=False)\n-\"\"\".strip('\\n'))\n-    self.assertEqual(str(metric_learn.ITML_Supervised()), \"\"\"\n+\"\"\".strip('\\n')))\n+    self.assertEqual(remove_spaces(str(metric_learn.ITML_Supervised())),\n+                     remove_spaces(\"\"\"\n ITML_Supervised(A0=None, bounds='deprecated', convergence_threshold=0.001,\n         gamma=1.0, max_iter=1000, num_constraints=None,\n         num_labeled='deprecated', preprocessor=None, verbose=False)\n-\"\"\".strip('\\n'))\n+\"\"\".strip('\\n')))","path":"test/test_base_metric.py","position":64,"original_position":64,"commit_id":"b4d8c5fddcce0da5c2dcd325dbad79bcccd8fc07","original_commit_id":"b4d8c5fddcce0da5c2dcd325dbad79bcccd8fc07","user":{"login":"wdevazelhes","id":31916524,"node_id":"MDQ6VXNlcjMxOTE2NTI0","avatar_url":"https://avatars2.githubusercontent.com/u/31916524?v=4","gravatar_id":"","url":"https://api.github.com/users/wdevazelhes","html_url":"https://github.com/wdevazelhes","followers_url":"https://api.github.com/users/wdevazelhes/followers","following_url":"https://api.github.com/users/wdevazelhes/following{/other_user}","gists_url":"https://api.github.com/users/wdevazelhes/gists{/gist_id}","starred_url":"https://api.github.com/users/wdevazelhes/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wdevazelhes/subscriptions","organizations_url":"https://api.github.com/users/wdevazelhes/orgs","repos_url":"https://api.github.com/users/wdevazelhes/repos","events_url":"https://api.github.com/users/wdevazelhes/events{/privacy}","received_events_url":"https://api.github.com/users/wdevazelhes/received_events","type":"User","site_admin":false},"body":"That's right indeed","created_at":"2019-05-10T14:39:48Z","updated_at":"2019-05-10T14:39:48Z","html_url":"https://github.com/metric-learn/metric-learn/pull/199#discussion_r282912685","pull_request_url":"https://api.github.com/repos/metric-learn/metric-learn/pulls/199","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/metric-learn/metric-learn/pulls/comments/282912685"},"html":{"href":"https://github.com/metric-learn/metric-learn/pull/199#discussion_r282912685"},"pull_request":{"href":"https://api.github.com/repos/metric-learn/metric-learn/pulls/199"}},"in_reply_to_id":282896935},"pull_request":{"url":"https://api.github.com/repos/metric-learn/metric-learn/pulls/199","id":277665663,"node_id":"MDExOlB1bGxSZXF1ZXN0Mjc3NjY1NjYz","html_url":"https://github.com/metric-learn/metric-learn/pull/199","diff_url":"https://github.com/metric-learn/metric-learn/pull/199.diff","patch_url":"https://github.com/metric-learn/metric-learn/pull/199.patch","issue_url":"https://api.github.com/repos/metric-learn/metric-learn/issues/199","number":199,"state":"open","locked":false,"title":"[MRG] Be compatible with newer 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their last version 0.21, which does not support python<3.5, so I think we can update the travis script to download the 0.20.3 for python3.4 and python2.7, but still use the newest release for python3.6 (so that we test the behaviour on the new scikit-learn), what do you think ? \r\n\r\nAlso, there were a few changes that need to be made to work with scikit-learn's new version, which this PR 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