{"action":"created","comment":{"url":"https://api.github.com/repos/apache/spark/pulls/comments/194190113","pull_request_review_id":127325463,"id":194190113,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDE5NDE5MDExMw==","diff_hunk":"@@ -1156,6 +1159,216 @@ def getKeepLastCheckpoint(self):\n         return self.getOrDefault(self.keepLastCheckpoint)\n \n \n+@inherit_doc\n+class PowerIterationClustering(HasMaxIter, HasWeightCol, JavaParams, JavaMLReadable,\n+                               JavaMLWritable):\n+    \"\"\"\n+    .. note:: Experimental\n+\n+    Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by\n+    <a href=http://www.icml2010.org/papers/387.pdf>Lin and Cohen</a>. From the abstract:\n+    PIC finds a very low-dimensional embedding of a dataset using truncated power\n+    iteration on a normalized pair-wise similarity matrix of the data.\n+\n+    This class is not yet an Estimator/Transformer, use `assignClusters` method to run the\n+    PowerIterationClustering algorithm.\n+\n+    .. seealso:: `Wikipedia on Spectral clustering \\\n+    <http://en.wikipedia.org/wiki/Spectral_clustering>`_\n+\n+    >>> from pyspark.sql.types import StructField, StructType\n+    >>> import math\n+    >>> def genCircle(r, n):\n+    ...     points = []\n+    ...     for i in range(0, n):\n+    ...         theta = 2.0 * math.pi * i / n\n+    ...         points.append((r * math.cos(theta), r * math.sin(theta)))\n+    ...     return points\n+    >>> def sim(x, y):\n+    ...     dist = (x[0] - y[0]) * (x[0] - y[0]) + (x[1] - y[1]) * (x[1] - y[1])\n+    ...     return math.exp(-dist / 2.0)\n+    >>> r1 = 1.0\n+    >>> n1 = 10\n+    >>> r2 = 4.0\n+    >>> n2 = 40\n+    >>> n = n1 + n2\n+    >>> points = genCircle(r1, n1) + genCircle(r2, n2)\n+    >>> data = [(i, j, sim(points[i], points[j])) for i in range(1, n) for j in range(0, i)]\n+    >>> rdd = sc.parallelize(data, 2)\n+    >>> schema = StructType([StructField(\"src\", LongType(), False), \\\n+                 StructField(\"dst\", LongType(),  True), \\\n+                 StructField(\"weight\", DoubleType(), True)])\n+    >>> df = spark.createDataFrame(rdd, schema)\n+    >>> pic = PowerIterationClustering()\n+    >>> assignments = pic.setK(2).setMaxIter(40).setWeightCol(\"weight\").assignClusters(df)\n+    >>> result = sorted(assignments.collect(), key=lambda x: x.id)\n+    >>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster\n+    True\n+    >>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster\n+    True\n+    >>> pic_path = temp_path + \"/pic\"\n+    >>> pic.save(pic_path)\n+    >>> pic2 = PowerIterationClustering.load(pic_path)\n+    >>> pic2.getK()\n+    2\n+    >>> pic2.getMaxIter()\n+    40\n+    >>> assignments2 = pic2.assignClusters(df)\n+    >>> result2 = sorted(assignments2.collect(), key=lambda x: x.id)\n+    >>> result2[0].cluster == result2[1].cluster == result2[2].cluster == result2[3].cluster\n+    True\n+    >>> result2[4].cluster == result2[5].cluster == result2[6].cluster == result2[7].cluster\n+    True\n+    >>> pic3 = PowerIterationClustering(k=4, initMode=\"degree\", srcCol=\"source\", dstCol=\"dest\")\n+    >>> pic3.getSrcCol()\n+    'source'\n+    >>> pic3.getDstCol()\n+    'dest'\n+    >>> pic3.getK()\n+    4\n+    >>> pic3.getMaxIter()\n+    20\n+    >>> pic3.getInitMode()\n+    'degree'\n+\n+    .. versionadded:: 2.4.0\n+    \"\"\"\n+\n+    k = Param(Params._dummy(), \"k\",\n+              \"The number of clusters to create. Must be > 1.\",\n+              typeConverter=TypeConverters.toInt)\n+    initMode = Param(Params._dummy(), \"initMode\",\n+                     \"The initialization algorithm. This can be either \" +\n+                     \"'random' to use a random vector as vertex properties, or 'degree' to use \" +\n+                     \"a normalized sum of similarities with other vertices.  Supported options: \" +\n+                     \"'random' and 'degree'.\",\n+                     typeConverter=TypeConverters.toString)\n+    srcCol = Param(Params._dummy(), \"srcCol\",\n+                   \"Name of the input column for source vertex IDs.\",\n+                   typeConverter=TypeConverters.toString)\n+    dstCol = Param(Params._dummy(), \"dstCol\",\n+                   \"Name of the input column for destination vertex IDs.\",\n+                   typeConverter=TypeConverters.toString)\n+\n+    @keyword_only\n+    def __init__(self, k=2, maxIter=20, initMode=\"random\", srcCol=\"src\", dstCol=\"dst\",\n+                 weightCol=None):\n+        \"\"\"\n+        __init__(self, k=2, maxIter=20, initMode=\"random\", srcCol=\"src\", dstCol=\"dst\",\\\n+                 weightCol=None)\n+        \"\"\"\n+        super(PowerIterationClustering, self).__init__()\n+        self._java_obj = self._new_java_obj(\n+            \"org.apache.spark.ml.clustering.PowerIterationClustering\", self.uid)\n+        self._setDefault(k=2, maxIter=20, initMode=\"random\", srcCol=\"src\", dstCol=\"dst\")\n+        kwargs = self._input_kwargs\n+        self.setParams(**kwargs)\n+\n+    @keyword_only\n+    @since(\"2.4.0\")\n+    def setParams(self, k=2, maxIter=20, initMode=\"random\", srcCol=\"src\", dstCol=\"dst\",\n+                  weightCol=None):\n+        \"\"\"\n+        setParams(self, k=2, maxIter=20, initMode=\"random\", srcCol=\"src\", dstCol=\"dst\",\\\n+                  weightCol=None)\n+        Sets params for PowerIterationClustering.\n+        \"\"\"\n+        kwargs = self._input_kwargs\n+        return self._set(**kwargs)\n+\n+    @since(\"2.4.0\")\n+    def setK(self, value):\n+        \"\"\"\n+        Sets the value of :py:attr:`k`.\n+        \"\"\"\n+        return self._set(k=value)\n+\n+    @since(\"2.4.0\")\n+    def getK(self):\n+        \"\"\"\n+        Gets the value of :py:attr:`k`.\n+        \"\"\"\n+        return self.getOrDefault(self.k)\n+\n+    @since(\"2.4.0\")\n+    def setInitMode(self, value):\n+        \"\"\"\n+        Sets the value of :py:attr:`initMode`.\n+        \"\"\"\n+        return self._set(initMode=value)\n+\n+    @since(\"2.4.0\")\n+    def getInitMode(self):\n+        \"\"\"\n+        Gets the value of `initMode`\n+        \"\"\"\n+        return self.getOrDefault(self.initMode)\n+\n+    @since(\"2.4.0\")\n+    def setSrcCol(self, value):\n+        \"\"\"\n+        Sets the value of :py:attr:`srcCol`.\n+        \"\"\"\n+        return self._set(srcCol=value)\n+\n+    @since(\"2.4.0\")\n+    def getSrcCol(self):\n+        \"\"\"\n+        Gets the value of :py:attr:`srcCol`.\n+        \"\"\"\n+        return self.getOrDefault(self.srcCol)\n+\n+    @since(\"2.4.0\")\n+    def setDstCol(self, value):\n+        \"\"\"\n+        Sets the value of :py:attr:`dstCol`.\n+        \"\"\"\n+        return self._set(dstCol=value)\n+\n+    @since(\"2.4.0\")\n+    def getDstCol(self):\n+        \"\"\"\n+        Gets the value of :py:attr:`dstCol`.\n+        \"\"\"\n+        return self.getOrDefault(self.dstCol)\n+\n+    @since(\"2.4.0\")\n+    def assignClusters(self, dataset):\n+        \"\"\"\n+        Run the PIC algorithm and returns a cluster assignment for each input vertex.\n+\n+        :param dataset:\n+          A dataset with columns src, dst, weight representing the affinity matrix,\n+          which is the matrix A in the PIC paper. Suppose the src column value is i,\n+          the dst column value is j, the weight column value is similarity s,,ij,,\n+          which must be nonnegative. This is a symmetric matrix and hence\n+          s,,ij,, = s,,ji,,. For any (i, j) with nonzero similarity, there should be\n+          either (i, j, s,,ij,,) or (j, i, s,,ji,,) in the input. Rows with i = j are\n+          ignored, because we assume s,,ij,, = 0.0.\n+\n+        :return: A dataset that contains columns of vertex id and the corresponding cluster for\n+                 the id. The schema of it will be:\n+                   - id: Long\n+                   - cluster: Int\n+        \"\"\"\n+        weightCol = None\n+        w = None\n+        if (self.isDefined(self.weightCol)):\n+            weightCol = self.getWeightCol()\n+        if (weightCol is None or len(weightCol) == 0):\n+            w = lit(1.0)\n+        else:\n+            w = col(weightCol).cast(DoubleType())\n+        srcCol = self.getSrcCol()\n+        dstCol = self.getDstCol()\n+        df = dataset.select(col(srcCol).cast(LongType()), col(dstCol).cast(LongType()), w)\n+        data = df.rdd.map(lambda x: (x[0], x[1], x[2]))\n+        algorithm = MLlibPowerIterationClustering()","path":"python/pyspark/ml/clustering.py","position":255,"original_position":255,"commit_id":"cdc97c103ea3f3da60e5e93482a7994f94e790c8","original_commit_id":"cdc97c103ea3f3da60e5e93482a7994f94e790c8","user":{"login":"huaxingao","id":13592258,"node_id":"MDQ6VXNlcjEzNTkyMjU4","avatar_url":"https://avatars3.githubusercontent.com/u/13592258?v=4","gravatar_id":"","url":"https://api.github.com/users/huaxingao","html_url":"https://github.com/huaxingao","followers_url":"https://api.github.com/users/huaxingao/followers","following_url":"https://api.github.com/users/huaxingao/following{/other_user}","gists_url":"https://api.github.com/users/huaxingao/gists{/gist_id}","starred_url":"https://api.github.com/users/huaxingao/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/huaxingao/subscriptions","organizations_url":"https://api.github.com/users/huaxingao/orgs","repos_url":"https://api.github.com/users/huaxingao/repos","events_url":"https://api.github.com/users/huaxingao/events{/privacy}","received_events_url":"https://api.github.com/users/huaxingao/received_events","type":"User","site_admin":false},"body":"@WeichenXu123 Thank you very much for your comment. Will change now. ","created_at":"2018-06-08T21:53:49Z","updated_at":"2018-06-08T21:53:49Z","html_url":"https://github.com/apache/spark/pull/21513#discussion_r194190113","pull_request_url":"https://api.github.com/repos/apache/spark/pulls/21513","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/apache/spark/pulls/comments/194190113"},"html":{"href":"https://github.com/apache/spark/pull/21513#discussion_r194190113"},"pull_request":{"href":"https://api.github.com/repos/apache/spark/pulls/21513"}},"in_reply_to_id":194167552},"pull_request":{"url":"https://api.github.com/repos/apache/spark/pulls/21513","id":193708774,"node_id":"MDExOlB1bGxSZXF1ZXN0MTkzNzA4Nzc0","html_url":"https://github.com/apache/spark/pull/21513","diff_url":"https://github.com/apache/spark/pull/21513.diff","patch_url":"https://github.com/apache/spark/pull/21513.patch","issue_url":"https://api.github.com/repos/apache/spark/issues/21513","number":21513,"state":"open","locked":false,"title":"[SPARK-19826][ML][PYTHON]add spark.ml Python API for PIC","user":{"login":"huaxingao","id":13592258,"node_id":"MDQ6VXNlcjEzNTkyMjU4","avatar_url":"https://avatars3.githubusercontent.com/u/13592258?v=4","gravatar_id":"","url":"https://api.github.com/users/huaxingao","html_url":"https://github.com/huaxingao","followers_url":"https://api.github.com/users/huaxingao/followers","following_url":"https://api.github.com/users/huaxingao/following{/other_user}","gists_url":"https://api.github.com/users/huaxingao/gists{/gist_id}","starred_url":"https://api.github.com/users/huaxingao/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/huaxingao/subscriptions","organizations_url":"https://api.github.com/users/huaxingao/orgs","repos_url":"https://api.github.com/users/huaxingao/repos","events_url":"https://api.github.com/users/huaxingao/events{/privacy}","received_events_url":"https://api.github.com/users/huaxingao/received_events","type":"User","site_admin":false},"body":"## What changes were proposed in this pull request?\r\n\r\nadd spark.ml Python API for PIC\r\n\r\n## How was this patch tested?\r\n\r\nadd doctest\r\n","created_at":"2018-06-08T19:54:17Z","updated_at":"2018-06-08T21:53:49Z","closed_at":null,"merged_at":null,"merge_commit_sha":"ce04ad7c24aec9f126fd9e15e850aee95bb01326","assignee":null,"assignees":[],"requested_reviewers":[],"requested_teams":[],"labels":[],"milestone":null,"commits_url":"https://api.github.com/repos/apache/spark/pulls/21513/commits","review_comments_url":"https://api.github.com/repos/apache/spark/pulls/21513/comments","review_comment_url":"https://api.github.com/repos/apache/spark/pulls/comments{/number}","comments_url":"https://api.github.com/repos/apache/spark/issues/21513/comments","statuses_url":"https://api.github.com/repos/apache/spark/statuses/cdc97c103ea3f3da60e5e93482a7994f94e790c8","head":{"label":"huaxingao:spark--19826","ref":"spark--19826","sha":"cdc97c103ea3f3da60e5e93482a7994f94e790c8","user":{"login":"huaxingao","id":13592258,"node_id":"MDQ6VXNlcjEzNTkyMjU4","avatar_url":"https://avatars3.githubusercontent.com/u/13592258?v=4","gravatar_id":"","url":"https://api.github.com/users/huaxingao","html_url":"https://github.com/huaxingao","followers_url":"https://api.github.com/users/huaxingao/followers","following_url":"https://api.github.com/users/huaxingao/following{/other_user}","gists_url":"https://api.github.com/users/huaxingao/gists{/gist_id}","starred_url":"https://api.github.com/users/huaxingao/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/huaxingao/subscriptions","organizations_url":"https://api.github.com/users/huaxingao/orgs","repos_url":"https://api.github.com/users/huaxingao/repos","events_url":"https://api.github.com/users/huaxingao/events{/privacy}","received_events_url":"https://api.github.com/users/huaxingao/received_events","type":"User","site_admin":false},"repo":{"id":50813048,"node_id":"MDEwOlJlcG9zaXRvcnk1MDgxMzA0OA==","name":"spark","full_name":"huaxingao/spark","owner":{"login":"huaxingao","id":13592258,"node_id":"MDQ6VXNlcjEzNTkyMjU4","avatar_url":"https://avatars3.githubusercontent.com/u/13592258?v=4","gravatar_id":"","url":"https://api.github.com/users/huaxingao","html_url":"https://github.com/huaxingao","followers_url":"https://api.github.com/users/huaxingao/followers","following_url":"https://api.github.com/users/huaxingao/following{/other_user}","gists_url":"https://api.github.com/users/huaxingao/gists{/gist_id}","starred_url":"https://api.github.com/users/huaxingao/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/huaxingao/subscriptions","organizations_url":"https://api.github.com/users/huaxingao/orgs","repos_url":"https://api.github.com/users/huaxingao/repos","events_url":"https://api.github.com/users/huaxingao/events{/privacy}","received_events_url":"https://api.github.com/users/huaxingao/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/huaxingao/spark","description":"Mirror 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