{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/103721313","pull_request_review_id":24517228,"id":103721313,"diff_hunk":"@@ -851,6 +853,230 @@ def test_robust_scaler_iris_quantiles():\n     assert_array_almost_equal(q_range, 1)\n \n \n+def test_quantile_transform_iris():\n+    X = iris.data\n+    normalizer = QuantileTransformer()\n+    X_trans = normalizer.fit_transform(X)\n+    assert_array_almost_equal(np.min(X_trans, axis=0), 0.)\n+    assert_array_almost_equal(np.max(X_trans, axis=0), 1.)\n+    X_trans_inv = normalizer.inverse_transform(X_trans)\n+    assert_array_almost_equal(X, X_trans_inv)\n+\n+\n+def test_quantile_transform_check_error():\n+    X = np.array([[0, 25, 50, 0, 0, 0, 75, 0, 0, 100],\n+                  [2, 4, 0, 0, 6, 8, 0, 10, 0, 0],\n+                  [0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]]).T\n+    X = sparse.csc_matrix(X)\n+    X_neg = np.array([[0, 25, 50, 0, 0, 0, 75, 0, 0, 100],\n+                      [-2, 4, 0, 0, 6, 8, 0, 10, 0, 0],\n+                      [0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]]).T\n+    X_neg = sparse.csc_matrix(X_neg)\n+\n+    assert_raises_regex(ValueError, \"Invalid value for 'n_quantiles'\",\n+                        QuantileTransformer(n_quantiles=0).fit, X_neg)\n+    assert_raises_regex(ValueError, \"Invalid value for 'subsample'\",\n+                        QuantileTransformer(subsample=0).fit, X_neg)\n+\n+    normalizer = QuantileTransformer()\n+    assert_raises_regex(ValueError, \"QuantileTransformer only accepts \"\n+                        \"non-negative sparse matrices\", normalizer.fit, X_neg)\n+    normalizer.fit(X)\n+    assert_raises_regex(ValueError, \"QuantileTransformer only accepts \"\n+                        \"non-negative sparse matrices\",\n+                        normalizer.transform, X_neg)\n+    assert_raises_regex(ValueError, \"QuantileTransformer only accepts \"\n+                        \"non-negative sparse matrices\",\n+                        normalizer.inverse_transform, X_neg)\n+\n+    X_bad_feat = np.array([[0, 25, 50, 0, 0, 0, 75, 0, 0, 100],\n+                           [0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]]).T\n+    assert_raises_regex(ValueError, \"X does not have the same number of \"\n+                        \"feature than the previously fitted data.\",\n+                        normalizer.transform, X_bad_feat)\n+    assert_raises_regex(ValueError, \"X does not have the same number of \"\n+                        \"feature than the previously fitted data.\",\n+                        normalizer.inverse_transform, X_bad_feat)\n+\n+    assert_raises_regex(ValueError, \"'output_distribution' has to be either\"\n+                        \" 'norm' or 'uniform'. Got rnd instead.\",\n+                        QuantileTransformer(\n+                            output_distribution='rnd').fit_transform, X)\n+    assert_raises_regex(ValueError, \"'output_distribution' has to be either\"\n+                        \" 'norm' or 'uniform'. Got rnd instead.\",\n+                        QuantileTransformer(output_distribution='rnd').fit(\n+                            X).inverse_transform, X)\n+\n+\n+def test_quantile_transform_ignore_zeros():\n+    X = np.array([[0, 0, 0, 0, 0],\n+                  [1, 0, 2, 2, 1]]).T\n+    X_sparse = sparse.csc_matrix(X)\n+    nq = QuantileTransformer(ignore_implicit_zeros=True, n_quantiles=5)\n+\n+    # dense case -> warning raise\n+    assert_warns_message(UserWarning, \"'ignore_implicit_zeros' takes effect\"\n+                         \" only with sparse matrix. This parameter has no\"\n+                         \" effect.\", nq.fit, X)\n+\n+    X_gt = np.array([[0, 0, 0, 0, 0],\n+                     [0, 0, 1, 1, 0]]).T\n+    X_trans = nq.fit_transform(X_sparse)\n+    assert_almost_equal(X_gt, X_trans.A)\n+\n+    # consider the case where sparse entries are missing values and user-given\n+    # zeros are to be considered\n+    X_data = np.array([0, 0, 1, 0, 2, 2, 1, 0, 1, 2, 0])\n+    X_col = np.array([0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n+    X_row = np.array([0, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8])\n+    X_sparse = sparse.csc_matrix((X_data, (X_row, X_col)))\n+    X_trans = nq.fit_transform(X_sparse)\n+    X_gt = np.array([[0., 0.5],\n+                     [0., 0.],\n+                     [0., 1.],\n+                     [0., 1.],\n+                     [0., 0.5],\n+                     [0., 0.],\n+                     [0., 0.5],\n+                     [0., 1.],\n+                     [0., 0.]])\n+    assert_almost_equal(X_gt, X_trans.A)\n+\n+    nq = QuantileTransformer(ignore_implicit_zeros=True, n_quantiles=5)\n+    X_data = np.array([-1, -1, 1, 0, 0, 0, 1, -1, 1])\n+    X_col = np.array([0, 0, 1, 1, 1, 1, 1, 1, 1])\n+    X_row = np.array([0, 4, 0, 1, 2, 3, 4, 5, 6])\n+    X_sparse = sparse.csc_matrix((X_data, (X_row, X_col)))\n+    X_trans = nq.fit_transform(X_sparse)\n+    X_gt = np.array([[0, 1],\n+                     [0, 0.5],\n+                     [0, 0.5],\n+                     [0, 0.5],\n+                     [0, 1],\n+                     [0, 0],\n+                     [0, 1]])\n+    assert_almost_equal(X_gt, X_trans.A)\n+    assert_almost_equal(X_sparse.A, nq.inverse_transform(X_trans).A)\n+\n+\n+def test_quantile_transform_dense_toy():\n+    X = np.array([[0, 25, 50, 75, 100],\n+                  [2, 4, 6, 8, 10],\n+                  [2.6, 4.1, 2.3, 9.5, 0.1]]).T\n+\n+    normalizer = QuantileTransformer()\n+    normalizer.fit(X)\n+\n+    X_trans = normalizer.fit_transform(X)\n+    assert_almost_equal(np.min(X_trans, axis=0), 0.)\n+    assert_almost_equal(np.max(X_trans, axis=0), 1.)\n+\n+    X_test = np.array([\n+        [-1,  1,  0],\n+        [101, 11, 10],\n+    ])\n+    expected = np.array([\n+        [0, 0, 0],\n+        [1, 1, 1],\n+    ])\n+    assert_array_almost_equal(normalizer.transform(X_test), expected)\n+\n+    X_trans_inv = normalizer.inverse_transform(X_trans)\n+    assert_array_almost_equal(X, X_trans_inv)\n+\n+    # test subsampling\n+    # FIXME: there is not comparison for the moment\n+    random_state = 42\n+    normalizer.set_params(**{'subsample': 3,\n+                             'n_quantiles': 2,\n+                             'random_state': random_state})\n+    X_trans = normalizer.fit_transform(X)\n+\n+\n+def test_quantile_transform_sparse_toy():\n+    X = np.array([[0, 25, 50, 0, 0, 0, 75, 0, 0, 100],\n+                  [2, 4, 0, 0, 6, 8, 0, 10, 0, 0],\n+                  [0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]]).T\n+\n+    X = sparse.csc_matrix(X)\n+\n+    normalizer = QuantileTransformer()\n+    normalizer.fit(X)\n+\n+    X_trans = normalizer.fit_transform(X)\n+    assert_array_almost_equal(np.min(X_trans.toarray(), axis=0), 0.)\n+    assert_array_almost_equal(np.max(X_trans.toarray(), axis=0), 1.)\n+\n+    X_trans_inv = normalizer.inverse_transform(X_trans)\n+    assert_array_almost_equal(X.toarray(), X_trans_inv.toarray())\n+\n+    normalizer_dense = QuantileTransformer().fit(X.toarray())","path":"sklearn/preprocessing/tests/test_data.py","position":183,"original_position":183,"commit_id":"3ff2eef4138222316e04481edac4a77afadb88c1","original_commit_id":"3ff2eef4138222316e04481edac4a77afadb88c1","user":{"login":"ogrisel","id":89061,"avatar_url":"https://avatars3.githubusercontent.com/u/89061?v=3","gravatar_id":"","url":"https://api.github.com/users/ogrisel","html_url":"https://github.com/ogrisel","followers_url":"https://api.github.com/users/ogrisel/followers","following_url":"https://api.github.com/users/ogrisel/following{/other_user}","gists_url":"https://api.github.com/users/ogrisel/gists{/gist_id}","starred_url":"https://api.github.com/users/ogrisel/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ogrisel/subscriptions","organizations_url":"https://api.github.com/users/ogrisel/orgs","repos_url":"https://api.github.com/users/ogrisel/repos","events_url":"https://api.github.com/users/ogrisel/events{/privacy}","received_events_url":"https://api.github.com/users/ogrisel/received_events","type":"User","site_admin":false},"body":"This should be renamed to `transformer_dense` for the sake of consistency.","created_at":"2017-03-01T16:10:29Z","updated_at":"2017-03-01T16:10:29Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/8363#discussion_r103721313","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/8363","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/103721313"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/8363#discussion_r103721313"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/8363"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/8363","id":106356842,"html_url":"https://github.com/scikit-learn/scikit-learn/pull/8363","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/8363.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/8363.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/8363","number":8363,"state":"open","locked":false,"title":"[WIP] Quantile scaler","user":{"login":"glemaitre","id":7454015,"avatar_url":"https://avatars1.githubusercontent.com/u/7454015?v=3","gravatar_id":"","url":"https://api.github.com/users/glemaitre","html_url":"https://github.com/glemaitre","followers_url":"https://api.github.com/users/glemaitre/followers","following_url":"https://api.github.com/users/glemaitre/following{/other_user}","gists_url":"https://api.github.com/users/glemaitre/gists{/gist_id}","starred_url":"https://api.github.com/users/glemaitre/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/glemaitre/subscriptions","organizations_url":"https://api.github.com/users/glemaitre/orgs","repos_url":"https://api.github.com/users/glemaitre/repos","events_url":"https://api.github.com/users/glemaitre/events{/privacy}","received_events_url":"https://api.github.com/users/glemaitre/received_events","type":"User","site_admin":false},"body":"<!--\r\nThanks for contributing a pull request! Please ensure you have taken a look at\r\nthe contribution guidelines: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md#Contributing-Pull-Requests\r\n-->\r\n#### Reference Issue\r\n\r\nCont'd of #2176 \r\n\r\n#### What does this implement/fix? Explain your changes.\r\n\r\nImplementation of quantile normalizer\r\n\r\n#### Any other comments?\r\n\r\n\r\n<!--\r\nPlease be aware that we are a loose team of volunteers so patience is\r\nnecessary; assistance handling other issues is very welcome. We value\r\nall user contributions, no matter how minor they are. If we are slow to\r\nreview, either the pull request needs some benchmarking, tinkering,\r\nconvincing, etc. or more likely the reviewers are simply busy. In either\r\ncase, we ask for your understanding during the review process.\r\nFor more information, see our FAQ on this topic:\r\nhttp://scikit-learn.org/dev/faq.html#why-is-my-pull-request-not-getting-any-attention.\r\n\r\nThanks for contributing!\r\n-->\r\n","created_at":"2017-02-15T17:14:46Z","updated_at":"2017-03-01T16:10:29Z","closed_at":null,"merged_at":null,"merge_commit_sha":"338cbe5862a45faac33a0f18f10a2b6f883eaac6","assignee":null,"assignees":[],"milestone":null,"commits_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/8363/commits","review_comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/8363/comments","review_comment_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments{/number}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/8363/comments","statuses_url":"https://api.github.com/repos/scikit-learn/scikit-learn/statuses/3ff2eef4138222316e04481edac4a77afadb88c1","head":{"label":"glemaitre:quantile_scaler","ref":"quantile_scaler","sha":"3ff2eef4138222316e04481edac4a77afadb88c1","user":{"login":"glemaitre","id":7454015,"avatar_url":"https://avatars1.githubusercontent.com/u/7454015?v=3","gravatar_id":"","url":"https://api.github.com/users/glemaitre","html_url":"https://github.com/glemaitre","followers_url":"https://api.github.com/users/glemaitre/followers","following_url":"https://api.github.com/users/glemaitre/following{/other_user}","gists_url":"https://api.github.com/users/glemaitre/gists{/gist_id}","starred_url":"https://api.github.com/users/glemaitre/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/glemaitre/subscriptions","organizations_url":"https://api.github.com/users/glemaitre/orgs","repos_url":"https://api.github.com/users/glemaitre/repos","events_url":"https://api.github.com/users/glemaitre/events{/privacy}","received_events_url":"https://api.github.com/users/glemaitre/received_events","type":"User","site_admin":false},"repo":{"id":49886894,"name":"scikit-learn","full_name":"glemaitre/scikit-learn","owner":{"login":"glemaitre","id":7454015,"avatar_url":"https://avatars1.githubusercontent.com/u/7454015?v=3","gravatar_id":"","url":"https://api.github.com/users/glemaitre","html_url":"https://github.com/glemaitre","followers_url":"https://api.github.com/users/glemaitre/followers","following_url":"https://api.github.com/users/glemaitre/following{/other_user}","gists_url":"https://api.github.com/users/glemaitre/gists{/gist_id}","starred_url":"https://api.github.com/users/glemaitre/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/glemaitre/subscriptions","organizations_url":"https://api.github.com/users/glemaitre/orgs","repos_url":"https://api.github.com/users/glemaitre/repos","events_url":"https://api.github.com/users/glemaitre/events{/privacy}","received_events_url":"https://api.github.com/users/glemaitre/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/glemaitre/scikit-learn","description":"scikit-learn: 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