{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/94477623","pull_request_review_id":15005649,"id":94477623,"diff_hunk":"@@ -0,0 +1,333 @@\n+# -*- coding: utf-8 -*-\n+\"\"\"K-medoids clustering\"\"\"\n+\n+# Authors: Timo Erkkilä <timo.erkkila@gmail.com>\n+#          Antti Lehmussola <antti.lehmussola@gmail.com>\n+#          Kornel Kiełczewski <kornel.k@plusnet.pl>\n+# License: BSD 3 clause\n+\n+import warnings\n+\n+import numpy as np\n+\n+from ..base import BaseEstimator, ClusterMixin, TransformerMixin\n+from ..metrics.pairwise import pairwise_distances\n+from ..utils import check_array, check_random_state\n+from ..utils.validation import check_is_fitted\n+\n+\n+class KMedoids(BaseEstimator, ClusterMixin, TransformerMixin):\n+    \"\"\"\n+    k-medoids class.\n+\n+    Read more in the :ref:`User Guide <k_medoids>`.\n+\n+    Parameters\n+    ----------\n+    n_clusters : int, optional, default: 8\n+        How many medoids. Must be positive.\n+\n+    distance_metric : string, optional, default: 'euclidean'\n+        What distance metric to use.\n+\n+    init : {'random', 'heuristic'}, optional, default: 'heuristic'\n+        Specify medoid initialization method. Random randomly selects\n+        n_clusters elements from the dataset, while heuristic picks n_clusters\n+        first data points that have the smallest sum distance to every other\n+        point.\n+\n+    max_iter : int, optional, default : 300\n+        Specify the maximum number of iterations when fitting.\n+\n+    random_state : int, optional, default: None\n+        Specify random state for the random number generator.\n+\n+    Attributes\n+    ----------\n+    cluster_centers_ : array, [n_clusters, n_features]\n+        Cluster centers, i.e. medoids (elements from the original dataset)\n+\n+    labels_ :\n+        Labels of each point\n+\n+    inertia_ : float\n+        Sum of distances of samples to their closest cluster center.\n+\n+    Examples\n+    --------\n+\n+    >>> from sklearn.cluster import KMedoids\n+    >>> import numpy as np\n+\n+    >>> X = np.asarray([[1, 2], [1, 4], [1, 0],\n+    ...                 [4, 2], [4, 4], [4, 0]])\n+    >>> kmedoids = KMedoids(n_clusters=2, random_state=0).fit(X)\n+    >>> kmedoids.labels_\n+    array([0, 0, 0, 1, 1, 1])\n+    >>> kmedoids.predict([[0,0], [4,4]])\n+    array([0, 1])\n+    >>> kmedoids.cluster_centers_\n+    array([[1, 2],\n+           [4, 2]])\n+    >>> kmedoids.inertia_\n+    8.0\n+\n+    >>> kmedoids = KMedoids(n_clusters=2, random_state=0,\n+    ...                     distance_metric='manhattan').fit(X)\n+    >>> kmedoids.labels_\n+    array([0, 0, 0, 1, 1, 1])\n+    >>> kmedoids.predict([[0,0], [4,4]])\n+    array([0, 1])\n+    >>> kmedoids.cluster_centers_\n+    array([[1, 2],\n+           [4, 2]])\n+    >>> kmedoids.inertia_\n+    8.0\n+    \"\"\"\n+\n+    def __init__(self, n_clusters=8, distance_metric='euclidean',\n+                 init='heuristic', max_iter=300, random_state=None):\n+        self.n_clusters = n_clusters\n+        self.distance_metric = distance_metric\n+        self.init = init\n+        self.max_iter = max_iter\n+        self.random_state = random_state\n+\n+    def _check_init_args(self):\n+        \"\"\"Validates the input arguments. \"\"\"\n+\n+        # Check n_clusters\n+        if (self.n_clusters is None or self.n_clusters <= 0 or\n+                not isinstance(self.n_clusters, int)):\n+            raise ValueError(\"n_clusters should be a nonnegative integer. \"\n+                             \"%s was given\" % self.n_clusters)\n+\n+        # Check init\n+        init_methods = ['random', 'heuristic']\n+        if self.init not in init_methods:\n+            raise ValueError(\"init needs to be one of \" +\n+                             \"the following: \" +\n+                             \"%s\" % init_methods)\n+\n+        # Check random state\n+        self.random_state_ = check_random_state(self.random_state)\n+\n+    def fit(self, X, y=None):\n+        \"\"\"Fit K-Medoids to the provided data.\n+\n+        Parameters\n+        ----------\n+        X : {array-like, sparse matrix}, shape=(n_samples, n_features).\n+        Dataset to cluster.\n+\n+        Returns\n+        -------\n+        self\n+        \"\"\"\n+\n+        self._check_init_args()\n+\n+        # Check that the array is good and attempt to convert it to\n+        # Numpy array if possible\n+        X = self._check_input(X)\n+\n+        # Apply distance metric to get the distance matrix\n+        distances = pairwise_distances(X, metric=self.distance_metric)\n+\n+        medoid_ics = self._get_initial_medoid_indices(distances,\n+                                                      self.n_clusters)\n+        cluster_ics = self._get_cluster_ics(distances, medoid_ics)\n+\n+        # Old medoids will be stored here for reference\n+        old_medoid_ics = np.zeros((self.n_clusters,))\n+\n+        # Continue the algorithm as long as\n+        # the medoids keep changing and the maximum number\n+        # of iterations is not exceeded\n+        self.n_iter_ = 0\n+        while (not np.all(old_medoid_ics == medoid_ics) and\n+                self.n_iter_ < self.max_iter):\n+            self.n_iter_ += 1\n+\n+            # Keep a copy of the old medoid assignments\n+            old_medoid_ics = np.copy(medoid_ics)\n+\n+            # Get cluster indices\n+            cluster_ics = self._get_cluster_ics(distances, medoid_ics)\n+\n+            # Update medoids with the new cluster indices\n+            self._update_medoid_ics_in_place(distances, cluster_ics,\n+                                             medoid_ics)\n+\n+        # Expose labels_ which are the assignments of\n+        # the training data to clusters\n+        self.labels_ = cluster_ics\n+\n+        # Expose cluster centers, i.e. medoids\n+        self.cluster_centers_ = X[medoid_ics]\n+\n+        # Expose intertia\n+        self.inertia_ = self._compute_inertia(X)\n+\n+        # Return self to enable method chaining\n+        return self\n+\n+    def _check_input(self, X):\n+\n+        X = check_array(X, accept_sparse=['csr', 'csc'])\n+        # Check that the number of clusters is less than or equal 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Adding K-Medoids clustering algorithm revival","user":{"login":"Kornel","id":628457,"avatar_url":"https://avatars.githubusercontent.com/u/628457?v=3","gravatar_id":"","url":"https://api.github.com/users/Kornel","html_url":"https://github.com/Kornel","followers_url":"https://api.github.com/users/Kornel/followers","following_url":"https://api.github.com/users/Kornel/following{/other_user}","gists_url":"https://api.github.com/users/Kornel/gists{/gist_id}","starred_url":"https://api.github.com/users/Kornel/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Kornel/subscriptions","organizations_url":"https://api.github.com/users/Kornel/orgs","repos_url":"https://api.github.com/users/Kornel/repos","events_url":"https://api.github.com/users/Kornel/events{/privacy}","received_events_url":"https://api.github.com/users/Kornel/received_events","type":"User","site_admin":false},"body":"This is a revival of https://github.com/scikit-learn/scikit-learn/pull/5085 \nI've rebased the code and fixed the tests.\n","created_at":"2016-10-18T08:49:52Z","updated_at":"2017-01-03T20:28:30Z","closed_at":null,"merged_at":null,"merge_commit_sha":"706cbceaa4a46ccfddd8a3692b96d8b144d97b31","assignee":{"login":"raghavrv","id":9487348,"avatar_url":"https://avatars.githubusercontent.com/u/9487348?v=3","gravatar_id":"","url":"https://api.github.com/users/raghavrv","html_url":"https://github.com/raghavrv","followers_url":"https://api.github.com/users/raghavrv/followers","following_url":"https://api.github.com/users/raghavrv/following{/other_user}","gists_url":"https://api.github.com/users/raghavrv/gists{/gist_id}","starred_url":"https://api.github.com/users/raghavrv/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/raghavrv/subscriptions","organizations_url":"https://api.github.com/users/raghavrv/orgs","repos_url":"https://api.github.com/users/raghavrv/repos","events_url":"https://api.github.com/users/raghavrv/events{/privacy}","received_events_url":"https://api.github.com/users/raghavrv/received_events","type":"User","site_admin":false},"assignees":[{"login":"raghavrv","id":9487348,"avatar_url":"https://avatars.githubusercontent.com/u/9487348?v=3","gravatar_id":"","url":"https://api.github.com/users/raghavrv","html_url":"https://github.com/raghavrv","followers_url":"https://api.github.com/users/raghavrv/followers","following_url":"https://api.github.com/users/raghavrv/following{/other_user}","gists_url":"https://api.github.com/users/raghavrv/gists{/gist_id}","starred_url":"https://api.github.com/users/raghavrv/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/raghavrv/subscriptions","organizations_url":"https://api.github.com/users/raghavrv/orgs","repos_url":"https://api.github.com/users/raghavrv/repos","events_url":"https://api.github.com/users/raghavrv/events{/privacy}","received_events_url":"https://api.github.com/users/raghavrv/received_events","type":"User","site_admin":false}],"milestone":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/milestones/21","html_url":"https://github.com/scikit-learn/scikit-learn/milestone/21","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/milestones/21/labels","id":1970916,"number":21,"title":"0.19","description":"","creator":{"login":"amueller","id":449558,"avatar_url":"https://avatars.githubusercontent.com/u/449558?v=3","gravatar_id":"","url":"https://api.github.com/users/amueller","html_url":"https://github.com/amueller","followers_url":"https://api.github.com/users/amueller/followers","following_url":"https://api.github.com/users/amueller/following{/other_user}","gists_url":"https://api.github.com/users/amueller/gists{/gist_id}","starred_url":"https://api.github.com/users/amueller/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amueller/subscriptions","organizations_url":"https://api.github.com/users/amueller/orgs","repos_url":"https://api.github.com/users/amueller/repos","events_url":"https://api.github.com/users/amueller/events{/privacy}","received_events_url":"https://api.github.com/users/amueller/received_events","type":"User","site_admin":false},"open_issues":173,"closed_issues":58,"state":"open","created_at":"2016-08-29T22:34:33Z","updated_at":"2017-01-03T14:09:24Z","due_on":"2017-04-01T07:00:00Z","closed_at":null},"commits_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/7694/commits","review_comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/7694/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/7694/comments","statuses_url":"https://api.github.com/repos/scikit-learn/scikit-learn/statuses/fd342e0c733bc49577cda263ca796a3045d2d534","head":{"label":"Kornel:tk-kmedoids","ref":"tk-kmedoids","sha":"fd342e0c733bc49577cda263ca796a3045d2d534","user":{"login":"Kornel","id":628457,"avatar_url":"https://avatars.githubusercontent.com/u/628457?v=3","gravatar_id":"","url":"https://api.github.com/users/Kornel","html_url":"https://github.com/Kornel","followers_url":"https://api.github.com/users/Kornel/followers","following_url":"https://api.github.com/users/Kornel/following{/other_user}","gists_url":"https://api.github.com/users/Kornel/gists{/gist_id}","starred_url":"https://api.github.com/users/Kornel/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Kornel/subscriptions","organizations_url":"https://api.github.com/users/Kornel/orgs","repos_url":"https://api.github.com/users/Kornel/repos","events_url":"https://api.github.com/users/Kornel/events{/privacy}","received_events_url":"https://api.github.com/users/Kornel/received_events","type":"User","site_admin":false},"repo":{"id":44137052,"name":"scikit-learn","full_name":"Kornel/scikit-learn","owner":{"login":"Kornel","id":628457,"avatar_url":"https://avatars.githubusercontent.com/u/628457?v=3","gravatar_id":"","url":"https://api.git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