Repository: spark Updated Branches: refs/heads/master 3e5b4ae63 -> a99d284c1
[SPARK-19826][ML][PYTHON] add spark.ml Python API for PIC ## What changes were proposed in this pull request? add spark.ml Python API for PIC ## How was this patch tested? add doctest Author: Huaxin Gao <huax...@us.ibm.com> Closes #21513 from huaxingao/spark--19826. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/a99d284c Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/a99d284c Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/a99d284c Branch: refs/heads/master Commit: a99d284c16cc4e00ce7c83ecdc3db6facd467552 Parents: 3e5b4ae Author: Huaxin Gao <huax...@us.ibm.com> Authored: Mon Jun 11 12:15:14 2018 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Mon Jun 11 12:15:14 2018 -0700 ---------------------------------------------------------------------- python/pyspark/ml/clustering.py | 184 ++++++++++++++++++++++++++++++++++- 1 file changed, 179 insertions(+), 5 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/a99d284c/python/pyspark/ml/clustering.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py index b3d5fb1..4aa1cf8 100644 --- a/python/pyspark/ml/clustering.py +++ b/python/pyspark/ml/clustering.py @@ -19,14 +19,15 @@ import sys 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, JavaWrapper from pyspark.ml.param.shared import * from pyspark.ml.common import inherit_doc +from pyspark.sql import DataFrame __all__ = ['BisectingKMeans', 'BisectingKMeansModel', 'BisectingKMeansSummary', 'KMeans', 'KMeansModel', 'GaussianMixture', 'GaussianMixtureModel', 'GaussianMixtureSummary', - 'LDA', 'LDAModel', 'LocalLDAModel', 'DistributedLDAModel'] + 'LDA', 'LDAModel', 'LocalLDAModel', 'DistributedLDAModel', 'PowerIterationClustering'] class ClusteringSummary(JavaWrapper): @@ -836,7 +837,7 @@ 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 +939,7 @@ class LDA(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed, HasCheckpointInter 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 +968,7 @@ class LDA(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed, HasCheckpointInter 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 +1157,179 @@ class LDA(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed, HasCheckpointInter return self.getOrDefault(self.keepLastCheckpoint) +@inherit_doc +class PowerIterationClustering(HasMaxIter, HasWeightCol, JavaParams, JavaMLReadable, + JavaMLWritable): + """ + .. note:: Experimental + + Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by + <a href=http://www.icml2010.org/papers/387.pdf>Lin and Cohen</a>. 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. + + This class is not yet an Estimator/Transformer, use :py:func:`assignClusters` method + to run the PowerIterationClustering algorithm. + + .. seealso:: `Wikipedia on Spectral clustering \ + <http://en.wikipedia.org/wiki/Spectral_clustering>`_ + + >>> data = [(1, 0, 0.5), \ + (2, 0, 0.5), (2, 1, 0.7), \ + (3, 0, 0.5), (3, 1, 0.7), (3, 2, 0.9), \ + (4, 0, 0.5), (4, 1, 0.7), (4, 2, 0.9), (4, 3, 1.1), \ + (5, 0, 0.5), (5, 1, 0.7), (5, 2, 0.9), (5, 3, 1.1), (5, 4, 1.3)] + >>> df = spark.createDataFrame(data).toDF("src", "dst", "weight") + >>> pic = PowerIterationClustering(k=2, maxIter=40, weightCol="weight") + >>> assignments = pic.assignClusters(df) + >>> assignments.sort(assignments.id).show(truncate=False) + +---+-------+ + |id |cluster| + +---+-------+ + |0 |1 | + |1 |1 | + |2 |1 | + |3 |1 | + |4 |1 | + |5 |0 | + +---+-------+ + ... + >>> pic_path = temp_path + "/pic" + >>> pic.save(pic_path) + >>> pic2 = PowerIterationClustering.load(pic_path) + >>> pic2.getK() + 2 + >>> pic2.getMaxIter() + 40 + + .. 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) + srcCol = Param(Params._dummy(), "srcCol", + "Name of the input column for source vertex IDs.", + typeConverter=TypeConverters.toString) + dstCol = Param(Params._dummy(), "dstCol", + "Name of the input column for destination vertex IDs.", + typeConverter=TypeConverters.toString) + + @keyword_only + def __init__(self, k=2, maxIter=20, initMode="random", srcCol="src", dstCol="dst", + weightCol=None): + """ + __init__(self, k=2, maxIter=20, initMode="random", srcCol="src", dstCol="dst",\ + weightCol=None) + """ + 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", srcCol="src", dstCol="dst") + kwargs = self._input_kwargs + self.setParams(**kwargs) + + @keyword_only + @since("2.4.0") + def setParams(self, k=2, maxIter=20, initMode="random", srcCol="src", dstCol="dst", + weightCol=None): + """ + setParams(self, k=2, maxIter=20, initMode="random", srcCol="src", dstCol="dst",\ + weightCol=None) + 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` or its default value. + """ + 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 :py:attr:`initMode` or its default value. + """ + return self.getOrDefault(self.initMode) + + @since("2.4.0") + def setSrcCol(self, value): + """ + Sets the value of :py:attr:`srcCol`. + """ + return self._set(srcCol=value) + + @since("2.4.0") + def getSrcCol(self): + """ + Gets the value of :py:attr:`srcCol` or its default value. + """ + return self.getOrDefault(self.srcCol) + + @since("2.4.0") + def setDstCol(self, value): + """ + Sets the value of :py:attr:`dstCol`. + """ + return self._set(dstCol=value) + + @since("2.4.0") + def getDstCol(self): + """ + Gets the value of :py:attr:`dstCol` or its default value. + """ + return self.getOrDefault(self.dstCol) + + @since("2.4.0") + def assignClusters(self, dataset): + """ + Run the PIC algorithm and returns a cluster assignment for each input vertex. + + :param dataset: + A dataset with columns src, dst, weight representing the affinity matrix, + which is the matrix A in the PIC paper. Suppose the src column value is i, + the dst column value is j, the weight column value is similarity s,,ij,, + which must be nonnegative. This is a symmetric matrix and hence + s,,ij,, = s,,ji,,. For any (i, j) with nonzero similarity, there should be + either (i, j, s,,ij,,) or (j, i, s,,ji,,) in the input. Rows with i = j are + ignored, because we assume s,,ij,, = 0.0. + + :return: + A dataset that contains columns of vertex id and the corresponding cluster for + the id. The schema of it will be: + - id: Long + - cluster: Int + + .. versionadded:: 2.4.0 + """ + self._transfer_params_to_java() + jdf = self._java_obj.assignClusters(dataset._jdf) + return DataFrame(jdf, dataset.sql_ctx) + + if __name__ == "__main__": import doctest import pyspark.ml.clustering --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
