Github user wangmiao1981 commented on a diff in the pull request:
https://github.com/apache/spark/pull/15770#discussion_r143078744
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala
---
@@ -0,0 +1,216 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.linalg.Vector
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util._
+import org.apache.spark.mllib.clustering.{PowerIterationClustering =>
MLlibPowerIterationClustering}
+import
org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.types.{IntegerType, LongType, StructField,
StructType}
+
+/**
+ * Common params for PowerIterationClustering
+ */
+private[clustering] trait PowerIterationClusteringParams extends Params
with HasMaxIter
+ with HasFeaturesCol with HasPredictionCol with HasWeightCol {
+
+ /**
+ * The number of clusters to create (k). Must be > 1. Default: 2.
+ * @group param
+ */
+ @Since("2.3.0")
+ final val k = new IntParam(this, "k", "The number of clusters to create.
" +
+ "Must be > 1.", ParamValidators.gt(1))
+
+ /** @group getParam */
+ @Since("2.3.0")
+ def getK: Int = $(k)
+
+ /**
+ * Param for the initialization algorithm. This can be either "random"
to use a random vector
+ * as vertex properties, or "degree" to use normalized sum similarities.
Default: random.
+ */
+ @Since("2.3.0")
+ final val initMode = {
+ val allowedParams = ParamValidators.inArray(Array("random", "degree"))
+ new Param[String](this, "initMode", "The initialization algorithm. " +
+ "Supported options: 'random' and 'degree'.", allowedParams)
+ }
+
+ /** @group expertGetParam */
+ @Since("2.3.0")
+ def getInitMode: String = $(initMode)
+
+ /**
+ * Param for the column name for ids returned by
PowerIterationClustering.transform().
+ * Default: "id"
+ * @group param
+ */
+ @Since("2.3.0")
+ val idCol = new Param[String](this, "id", "column name for ids.")
+
+ /** @group getParam */
+ @Since("2.3.0")
+ def getIdCol: String = $(idCol)
+
+ /**
+ * Param for the column name for neighbors required by
PowerIterationClustering.transform().
+ * Default: "neighbor"
+ * @group param
+ */
+ @Since("2.3.0")
+ val neighborCol = new Param[String](this, "neighbor", "column name for
neighbors.")
+
+ /** @group getParam */
+ @Since("2.3.0")
+ def getNeighborCol: String = $(neighborCol)
+
+ /**
+ * Validates the input schema
+ * @param schema input schema
+ */
+ protected def validateSchema(schema: StructType): Unit = {
+ SchemaUtils.checkColumnType(schema, $(idCol), LongType)
+ SchemaUtils.checkColumnType(schema, $(predictionCol), IntegerType)
+ }
+}
+
+/**
+ * :: 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.
+ *
+ * Note that we implement [[PowerIterationClustering]] as a transformer.
The [[transform]] is an
+ * expensive operation, because it uses PIC algorithm to cluster the whole
input dataset.
+ *
+ * @see <a href=http://en.wikipedia.org/wiki/Spectral_clustering>
+ * Spectral clustering (Wikipedia)</a>
+ */
+@Since("2.3.0")
+@Experimental
+class PowerIterationClustering private[clustering] (
+ @Since("2.3.0") override val uid: String)
+ extends Transformer with PowerIterationClusteringParams with
DefaultParamsWritable {
+
+ setDefault(
+ k -> 2,
+ maxIter -> 20,
+ initMode -> "random",
+ idCol -> "id",
+ weightCol -> "weight",
+ neighborCol -> "neighbor")
+
+ @Since("2.3.0")
+ override def copy(extra: ParamMap): PowerIterationClustering =
defaultCopy(extra)
+
+ @Since("2.3.0")
+ def this() = this(Identifiable.randomUID("PowerIterationClustering"))
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setK(value: Int): this.type = set(k, value)
+
+ /** @group expertSetParam */
+ @Since("2.3.0")
+ def setInitMode(value: String): this.type = set(initMode, value)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setIdCol(value: String): this.type = set(idCol, value)
+
+ /**
+ * Sets the value of param [[weightCol]].
+ * Default is "weight"
+ *
+ * @group setParam
+ */
+ @Since("2.3.0")
+ def setWeightCol(value: String): this.type = set(weightCol, value)
+
+ /**
+ * Sets the value of param [[neighborCol]].
+ * Default is "neighbor"
+ *
+ * @group setParam
+ */
+ @Since("2.3.0")
+ def setNeighborCol(value: String): this.type = set(neighborCol, value)
+
+ @Since("2.3.0")
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ val sparkSession = dataset.sparkSession
+ val rdd: RDD[(Long, Long, Double)] =
+ dataset.select(col($(idCol)), col($(neighborCol)),
col($(weightCol))).rdd.flatMap {
+ case Row(id: Long, nbr: Vector, weight: Vector) =>
+ require(nbr.size == weight.size,
+ "The length of neighbor list must be equal to the the length of
the weight list.")
+ val ids = Array.fill(nbr.size)(id)
+ for (i <- 0 until ids.size) yield (ids(i), nbr(i).toLong,
weight(i))}
--- End diff --
I remember last time you mentioned not using zip. I might get it wrong. By
the way,` it seems we need a helper function`, do you want a helper function
takes GraphFrame and returns the RDD used by PIC?
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