diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala index 2c30a1d9aa947..71c0ccf2c65f1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala @@ -97,13 +97,15 @@ private[clustering] trait PowerIterationClusteringParams extends Params with Has def getNeighborsCol: String = $(neighborsCol) /** - * Param for the name of the input column for neighbors in the adjacency list representation. + * Param for the name of the input column for non-negative weights (similarities) of edges + * between the vertex in `idCol` and each neighbor in `neighborsCol`. * Default: "similarities" * @group param */ @Since("2.4.0") val similaritiesCol = new Param[String](this, "similaritiesCol", - "Name of the input column for neighbors in the adjacency list representation.", + "Name of the input column for non-negative weights (similarities) of edges between the " + + "vertex in `idCol` and each neighbor in `neighborsCol`.", (value: String) => value.nonEmpty) setDefault(similaritiesCol, "similarities") diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py index b3d5fb17f6b81..317f24d4be81f 100644 --- a/python/pyspark/ml/clustering.py +++ b/python/pyspark/ml/clustering.py @@ -19,14 +19,14 @@ from pyspark import since, keyword_only from pyspark.ml.util import * -from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaWrapper +from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams, JavaTransformer, JavaWrapper from pyspark.ml.param.shared import * from pyspark.ml.common import inherit_doc __all__ = ['BisectingKMeans', 'BisectingKMeansModel', 'BisectingKMeansSummary', 'KMeans', 'KMeansModel', 'GaussianMixture', 'GaussianMixtureModel', 'GaussianMixtureSummary', - 'LDA', 'LDAModel', 'LocalLDAModel', 'DistributedLDAModel'] + 'LDA', 'LDAModel', 'LocalLDAModel', 'DistributedLDAModel', 'PowerIterationClustering'] class ClusteringSummary(JavaWrapper): @@ -836,7 +836,7 @@ class LDA(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed, HasCheckpointInter Terminology: - - "term" = "word": an el + - "term" = "word": an element of the vocabulary - "token": instance of a term appearing in a document - "topic": multinomial distribution over terms representing some concept - "document": one piece of text, corresponding to one row in the input data @@ -938,7 +938,7 @@ def __init__(self, featuresCol="features", maxIter=20, seed=None, checkpointInte k=10, optimizer="online", learningOffset=1024.0, learningDecay=0.51,\ subsamplingRate=0.05, optimizeDocConcentration=True,\ docConcentration=None, topicConcentration=None,\ - topicDistributionCol="topicDistribution", keepLastCheckpoint=True): + topicDistributionCol="topicDistribution", keepLastCheckpoint=True) """ super(LDA, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.LDA", self.uid) @@ -967,7 +967,7 @@ def setParams(self, featuresCol="features", maxIter=20, seed=None, checkpointInt k=10, optimizer="online", learningOffset=1024.0, learningDecay=0.51,\ subsamplingRate=0.05, optimizeDocConcentration=True,\ docConcentration=None, topicConcentration=None,\ - topicDistributionCol="topicDistribution", keepLastCheckpoint=True): + topicDistributionCol="topicDistribution", keepLastCheckpoint=True) Sets params for LDA. """ @@ -1156,6 +1156,205 @@ def getKeepLastCheckpoint(self): return self.getOrDefault(self.keepLastCheckpoint) +@inherit_doc +class PowerIterationClustering(HasMaxIter, HasPredictionCol, JavaTransformer, JavaParams, + JavaMLReadable, JavaMLWritable): + """ + .. note:: Experimental + + Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by + Lin and Cohen. From the abstract: + PIC finds a very low-dimensional embedding of a dataset using truncated power + iteration on a normalized pair-wise similarity matrix of the data. + + PIC takes an affinity matrix between items (or vertices) as input. An affinity matrix + is a symmetric matrix whose entries are non-negative similarities between items. + PIC takes this matrix (or graph) as an adjacency matrix. Specifically, each input row + includes: + + - :py:attr:`idCol`: vertex ID + - :py:attr:`neighborsCol`: neighbors of vertex in :py:attr:`idCol` + - :py:attr:`similaritiesCol`: non-negative weights (similarities) of edges between the + vertex in :py:attr:`idCol` and each neighbor in :py:attr:`neighborsCol` + + PIC returns a cluster assignment for each input vertex. It appends a new column + :py:attr:`predictionCol` containing the cluster assignment in :py:attr:`[0,k)` for + each row (vertex). + + .. note:: + + - [[PowerIterationClustering]] is a transformer with an expensive [[transform]] operation. + Transform runs the iterative PIC algorithm to cluster the whole input dataset. + - Input validation: This validates that similarities are non-negative but does NOT validate + that the input matrix is symmetric. + + .. seealso:: `Wikipedia on Spectral clustering \ + `_ + + >>> from pyspark.sql.types import ArrayType, DoubleType, LongType, StructField, StructType + >>> similarities = [((long)(1), [0], [0.5]), ((long)(2), [0, 1], [0.7,0.5]), \ + ((long)(3), [0, 1, 2], [0.9, 0.7, 0.5]), \ + ((long)(4), [0, 1, 2, 3], [1.1, 0.9, 0.7,0.5]), \ + ((long)(5), [0, 1, 2, 3, 4], [1.3, 1.1, 0.9, 0.7,0.5])] + >>> rdd = sc.parallelize(similarities, 2) + >>> schema = StructType([StructField("id", LongType(), False), \ + StructField("neighbors", ArrayType(LongType(), False), True), \ + StructField("similarities", ArrayType(DoubleType(), False), True)]) + >>> df = spark.createDataFrame(rdd, schema) + >>> pic = PowerIterationClustering() + >>> result = pic.setK(2).setMaxIter(10).transform(df) + >>> predictions = sorted(set([(i[0], i[1]) for i in result.select(result.id, result.prediction) + ... .collect()]), key=lambda x: x[0]) + >>> predictions[0] + (1, 1) + >>> predictions[1] + (2, 1) + >>> predictions[2] + (3, 0) + >>> predictions[3] + (4, 0) + >>> predictions[4] + (5, 0) + >>> pic_path = temp_path + "/pic" + >>> pic.save(pic_path) + >>> pic2 = PowerIterationClustering.load(pic_path) + >>> pic2.getK() + 2 + >>> pic2.getMaxIter() + 10 + >>> pic3 = PowerIterationClustering(k=4, initMode="degree") + >>> pic3.getIdCol() + 'id' + >>> pic3.getK() + 4 + >>> pic3.getMaxIter() + 20 + >>> pic3.getInitMode() + 'degree' + + .. versionadded:: 2.4.0 + """ + + k = Param(Params._dummy(), "k", + "The number of clusters to create. Must be > 1.", + typeConverter=TypeConverters.toInt) + initMode = Param(Params._dummy(), "initMode", + "The initialization algorithm. This can be either " + + "'random' to use a random vector as vertex properties, or 'degree' to use " + + "a normalized sum of similarities with other vertices. Supported options: " + + "'random' and 'degree'.", + typeConverter=TypeConverters.toString) + idCol = Param(Params._dummy(), "idCol", + "Name of the input column for vertex IDs.", + typeConverter=TypeConverters.toString) + neighborsCol = Param(Params._dummy(), "neighborsCol", + "Name of the input column for neighbors in the adjacency list " + + "representation.", + typeConverter=TypeConverters.toString) + similaritiesCol = Param(Params._dummy(), "similaritiesCol", + "Name of the input column for non-negative weights (similarities) " + + "of edges between the vertex in `idCol` and each neighbor in " + + "`neighborsCol`", + typeConverter=TypeConverters.toString) + + @keyword_only + def __init__(self, predictionCol="prediction", k=2, maxIter=20, initMode="random", + idCol="id", neighborsCol="neighbors", similaritiesCol="similarities"): + """ + __init__(self, predictionCol="prediction", k=2, maxIter=20, initMode="random",\ + idCol="id", neighborsCol="neighbors", similaritiesCol="similarities") + """ + super(PowerIterationClustering, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.clustering.PowerIterationClustering", self.uid) + self._setDefault(k=2, maxIter=20, initMode="random", idCol="id", neighborsCol="neighbors", + similaritiesCol="similarities") + kwargs = self._input_kwargs + self.setParams(**kwargs) + + @keyword_only + @since("2.4.0") + def setParams(self, predictionCol="prediction", k=2, maxIter=20, initMode="random", + idCol="id", neighborsCol="neighbors", similaritiesCol="similarities"): + """ + setParams(self, predictionCol="prediction", k=2, maxIter=20, initMode="random",\ + idCol="id", neighborsCol="neighbors", similaritiesCol="similarities") + Sets params for PowerIterationClustering. + """ + kwargs = self._input_kwargs + return self._set(**kwargs) + + @since("2.4.0") + def setK(self, value): + """ + Sets the value of :py:attr:`k`. + """ + return self._set(k=value) + + @since("2.4.0") + def getK(self): + """ + Gets the value of :py:attr:`k`. + """ + return self.getOrDefault(self.k) + + @since("2.4.0") + def setInitMode(self, value): + """ + Sets the value of :py:attr:`initMode`. + """ + return self._set(initMode=value) + + @since("2.4.0") + def getInitMode(self): + """ + Gets the value of `initMode` + """ + return self.getOrDefault(self.initMode) + + @since("2.4.0") + def setIdCol(self, value): + """ + Sets the value of :py:attr:`idCol`. + """ + return self._set(idCol=value) + + @since("2.4.0") + def getIdCol(self): + """ + Gets the value of :py:attr:`idCol`. + """ + return self.getOrDefault(self.idCol) + + @since("2.4.0") + def setNeighborsCol(self, value): + """ + Sets the value of :py:attr:`neighborsCol`. + """ + return self._set(neighborsCol=value) + + @since("2.4.0") + def getNeighborsCol(self): + """ + Gets the value of :py:attr:`neighborsCol`. + """ + return self.getOrDefault(self.neighborsCol) + + @since("2.4.0") + def setSimilaritiesCol(self, value): + """ + Sets the value of :py:attr:`similaritiesCol`. + """ + return self._set(similaritiesCol=value) + + @since("2.4.0") + def getSimilaritiesCol(self): + """ + Gets the value of :py:attr:`similaritiesCol`. + """ + return self.getOrDefault(self.similaritiesCol) + + if __name__ == "__main__": import doctest import pyspark.ml.clustering diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py index 2ec0be60e9fa9..0a5a7e2592ac1 100755 --- a/python/pyspark/ml/tests.py +++ b/python/pyspark/ml/tests.py @@ -1873,6 +1873,53 @@ def test_kmeans_cosine_distance(self): self.assertTrue(result[4].prediction == result[5].prediction) +class PowerIterationClustering(SparkSessionTestCase): + + def test_power_iteration_clustering(self): + from pyspark.sql.types import ArrayType, DoubleType, LongType, StructField, StructType + from pyspark.ml.clustering import PowerIterationClustering + import math + + def genCircle(r, n): + points = [] + for i in range(0, n): + theta = 2.0 * math.pi * i / n + points.append((r * math.cos(theta), r * math.sin(theta))) + return points + + def sim(x, y): + dist = (x[0] - y[0]) * (x[0] - y[0]) + (x[1] - y[1]) * (x[1] - y[1]) + return math.exp(-dist / 2.0) + + r1 = 1.0 + n1 = 10 + r2 = 4.0 + n2 = 40 + n = n1 + n2 + points = genCircle(r1, n1) + genCircle(r2, n2) + similarities = [] + for i in range(1, n): + neighbor = [] + weight = [] + for j in range(i): + neighbor.append((long)(j)) + weight.append(sim(points[i], points[j])) + similarities.append([(long)(i), neighbor, weight]) + rdd = self.sc.parallelize(similarities, 2) + schema = StructType([StructField("id", LongType(), False), + StructField("neighbors", ArrayType(LongType(), False), True), + StructField("similarities", ArrayType(DoubleType(), False), True)]) + df = self.spark.createDataFrame(rdd, schema) + pic = PowerIterationClustering() + result = pic.setK(2).setMaxIter(40).transform(df) + predictions = sorted(set([(i[0], i[1]) for i in result.select(result.id, + result.prediction).collect()]), key=lambda x: x[0]) + for i in range(0, 8): + self.assertEqual(predictions[i], (i+1, 1)) + for i in range(9, 48): + self.assertEqual(predictions[i], (i+1, 0)) + + class OneVsRestTests(SparkSessionTestCase): def test_copy(self):