Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15770#discussion_r178984276
--- 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(
--- End diff --
nit: It'd be nice to put these defaults right next the Param definitions in
PowerIterationClusteringParams so that the default specified in the docstring
is close to the default specified by setDefault (to make sure they stay in
sync).
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