{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/201850761","pull_request_review_id":136434773,"id":201850761,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIwMTg1MDc2MQ==","diff_hunk":"@@ -95,27 +95,18 @@ def test_dbscan_optics_parity():\n     op = OPTICS(min_samples=10).fit(X)\n     core_optics, labels_optics = op.extract_dbscan(0.3)\n \n-    op_h = metrics.homogeneity_score(labels_true, labels_optics)\n-    op_c = metrics.completeness_score(labels_true, labels_optics)\n-    op_V = metrics.v_measure_score(labels_true, labels_optics)\n-    op_m = metrics.adjusted_mutual_info_score(labels_true, labels_optics)\n-    op_s = metrics.silhouette_score(X, labels_optics)\n-\n-    op_metrics = np.array([op_h, op_c, op_V, op_m, op_s])\n-\n-    # calculate dbscan labels and cluster metrics\n+    # calculate dbscan labels\n     db = DBSCAN(eps=0.3, min_samples=10).fit(X)\n \n-    db_h = metrics.homogeneity_score(labels_true, db.labels_)\n-    db_c = metrics.completeness_score(labels_true, db.labels_)\n-    db_V = metrics.v_measure_score(labels_true, db.labels_)\n-    db_m = metrics.adjusted_mutual_info_score(labels_true, db.labels_)\n-    db_s = metrics.silhouette_score(X, db.labels_)\n+    confusion_m = contingency_matrix(db.labels_, labels_optics)\n+    agree = np.max(confusion_m, axis=1)\n+    disagree = np.sum(confusion_m) - np.sum(agree)","path":"sklearn/cluster/tests/test_optics.py","position":null,"original_position":48,"commit_id":"33d74d76c73dd255a9ed744d05504375afa96fa0","original_commit_id":"33d74d76c73dd255a9ed744d05504375afa96fa0","user":{"login":"jnothman","id":78827,"node_id":"MDQ6VXNlcjc4ODI3","avatar_url":"https://avatars2.githubusercontent.com/u/78827?v=4","gravatar_id":"","url":"https://api.github.com/users/jnothman","html_url":"https://github.com/jnothman","followers_url":"https://api.github.com/users/jnothman/followers","following_url":"https://api.github.com/users/jnothman/following{/other_user}","gists_url":"https://api.github.com/users/jnothman/gists{/gist_id}","starred_url":"https://api.github.com/users/jnothman/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jnothman/subscriptions","organizations_url":"https://api.github.com/users/jnothman/orgs","repos_url":"https://api.github.com/users/jnothman/repos","events_url":"https://api.github.com/users/jnothman/events{/privacy}","received_events_url":"https://api.github.com/users/jnothman/received_events","type":"User","site_admin":false},"body":"The number of agreements can be over-stated using this calculation.\r\n\r\n```\r\n[[5],\r\n [5],\r\n [5],\r\n [5]]\r\n```\r\n\r\nwill be regarded as a perfect match to\r\n\r\n```\r\n[[20]]\r\n```\r\n\r\nYou can fix this by using the max-sum assignment... or just by checking it is valid for axis=1 as well as axis=0. (Or `agree = min((... axis=1), (... axis=0))`)","created_at":"2018-07-11T21:49:02Z","updated_at":"2018-07-11T21:50:30Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/1984#discussion_r201850761","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/1984","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/201850761"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/1984#discussion_r201850761"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/1984"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/1984","id":5849419,"node_id":"MDExOlB1bGxSZXF1ZXN0NTg0OTQxOQ==","html_url":"https://github.com/scikit-learn/scikit-learn/pull/1984","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/1984.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/1984.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/1984","number":1984,"state":"open","locked":false,"title":"[MRG] Implementation of OPTICS","user":{"login":"espg","id":3896161,"node_id":"MDQ6VXNlcjM4OTYxNjE=","avatar_url":"https://avatars3.githubusercontent.com/u/3896161?v=4","gravatar_id":"","url":"https://api.github.com/users/espg","html_url":"https://github.com/espg","followers_url":"https://api.github.com/users/espg/followers","following_url":"https://api.github.com/users/espg/following{/other_user}","gists_url":"https://api.github.com/users/espg/gists{/gist_id}","starred_url":"https://api.github.com/users/espg/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/espg/subscriptions","organizations_url":"https://api.github.com/users/espg/orgs","repos_url":"https://api.github.com/users/espg/repos","events_url":"https://api.github.com/users/espg/events{/privacy}","received_events_url":"https://api.github.com/users/espg/received_events","type":"User","site_admin":false},"body":"Equivalent results to DBSCAN, but allows execution on arbitrarily large datasets. After initial construction, allows multiple 'scans' to quickly extract DBSCAN clusters at variable epsilon distances\n\nAlgorithm is tested and validated, but not yet optimized. Tested on 70K points (successful); currently testing on 2 x 10^9 LiDAR points (pending)\n\nFirst commit; would appreciate any advice on changes to help code conform to the scikit-learn API\n\nExample usage in examples 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