Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/11419#discussion_r57983012
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala ---
@@ -0,0 +1,291 @@
+/*
+ * 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.hadoop.fs.Path
+
+import org.apache.spark.SparkException
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.param.{IntParam, ParamMap, Params}
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util._
+import org.apache.spark.mllib.clustering.{GaussianMixture => MLlibGM,
GaussianMixtureModel => MLlibGMModel}
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{IntegerType, StructType}
+
+
+/**
+ * Common params for GaussianMixture and GaussianMixtureModel
+ */
+private[clustering] trait GaussianMixtureParams extends Params with
HasMaxIter with HasFeaturesCol
+ with HasSeed with HasPredictionCol with HasProbabilityCol with HasTol {
+
+ /**
+ * Set the number of clusters to create (k). Must be > 1. Default: 2.
+ * @group param
+ */
+ @Since("2.0.0")
+ final val k = new IntParam(this, "k", "number of clusters to create",
(x: Int) => x > 1)
+
+ /** @group getParam */
+ @Since("2.0.0")
+ def getK: Int = $(k)
+
+ /**
+ * Validates and transforms the input schema.
+ * @param schema input schema
+ * @return output schema
+ */
+ protected def validateAndTransformSchema(schema: StructType): StructType
= {
+ validateParams()
+ SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
+ SchemaUtils.appendColumn(schema, $(predictionCol), IntegerType)
+ SchemaUtils.appendColumn(schema, $(probabilityCol), new VectorUDT)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by GaussianMixture.
+ * @param parentModel a model trained by
spark.mllib.clustering.GaussianMixture.
+ */
+@Since("2.0.0")
+@Experimental
+class GaussianMixtureModel private[ml] (
+ @Since("2.0.0") override val uid: String,
+ private val parentModel: MLlibGMModel)
+ extends Model[GaussianMixtureModel] with GaussianMixtureParams with
MLWritable {
+
+ @Since("2.0.0")
+ override def copy(extra: ParamMap): GaussianMixtureModel = {
+ val copied = new GaussianMixtureModel(uid, parentModel)
+ copyValues(copied, extra)
+ }
+
+ @Since("2.0.0")
+ override def transform(dataset: DataFrame): DataFrame = {
+ val predUDF = udf((vector: Vector) => predict(vector))
+ val probUDF = udf((vector: Vector) => predictProbability(vector))
+ dataset.withColumn($(predictionCol), predUDF(col($(featuresCol))))
+ .withColumn($(probabilityCol), probUDF(col($(featuresCol))))
+ }
+
+ @Since("2.0.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ private[clustering] def predict(features: Vector): Int =
parentModel.predict(features)
+
+ private[clustering] def predictProbability(features: Vector): Vector = {
+ Vectors.dense(parentModel.predictSoft(features))
+ }
+
+ @Since("2.0.0")
+ def weights: Array[Double] = parentModel.weights
+
+ @Since("2.0.0")
+ def gaussians: Array[MultivariateGaussian] = parentModel.gaussians
+
+ @Since("2.0.0")
+ override def write: MLWriter = new
GaussianMixtureModel.GaussianMixtureModelWriter(this)
+
+ private var trainingSummary: Option[GaussianMixtureSummary] = None
+
+ private[clustering] def setSummary(summary: GaussianMixtureSummary):
this.type = {
+ this.trainingSummary = Some(summary)
+ this
+ }
+
+ /**
+ * Gets summary of model on training set. An exception is
+ * thrown if `trainingSummary == None`.
+ */
+ @Since("2.0.0")
+ def summary: GaussianMixtureSummary = trainingSummary match {
+ case Some(summ) => summ
+ case None =>
+ throw new SparkException(
+ s"No training summary available for the
${this.getClass.getSimpleName}",
+ new NullPointerException())
+ }
+}
+
+@Since("2.0.0")
+object GaussianMixtureModel extends MLReadable[GaussianMixtureModel] {
+
+ @Since("2.0.0")
+ override def read: MLReader[GaussianMixtureModel] = new
GaussianMixtureModelReader
+
+ @Since("2.0.0")
+ override def load(path: String): GaussianMixtureModel = super.load(path)
+
+ /** [[MLWriter]] instance for [[GaussianMixtureModel]] */
+ private[GaussianMixtureModel] class GaussianMixtureModelWriter(
+ instance: GaussianMixtureModel) extends MLWriter {
+
+ private case class Data(weights: Array[Double], mus: Array[Vector],
sigmas: Array[Matrix])
+
+ override protected def saveImpl(path: String): Unit = {
+ // Save metadata and Params
+ DefaultParamsWriter.saveMetadata(instance, path, sc)
+ // Save model data: weights and gaussians
+ val weights = instance.weights
+ val gaussians = instance.gaussians
+ val mus = gaussians.map(_.mu)
+ val sigmas = gaussians.map(_.sigma)
+ val data = Data(weights, mus, sigmas)
+ val dataPath = new Path(path, "data").toString
+
sqlContext.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
+ }
+ }
+
+ private class GaussianMixtureModelReader extends
MLReader[GaussianMixtureModel] {
+
+ /** Checked against metadata when loading model */
+ private val className = classOf[GaussianMixtureModel].getName
+
+ override def load(path: String): GaussianMixtureModel = {
+ val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
+
+ val dataPath = new Path(path, "data").toString
+ val df = sqlContext.read.parquet(dataPath)
+ val weights =
df.select("weights").head().get(0).asInstanceOf[Seq[Double]].toArray
+ val mus =
df.select("mus").head().get(0).asInstanceOf[Seq[Vector]].toArray
+ val sigmas =
df.select("sigmas").head().get(0).asInstanceOf[Seq[Matrix]].toArray
+ require(mus.length == sigmas.length)
+ val gaussians = (mus zip sigmas).map {
+ case (mu, sigma) =>
+ new MultivariateGaussian(mu, sigma)
+ }
+ val model = new GaussianMixtureModel(metadata.uid, new
MLlibGMModel(weights, gaussians))
+
+ DefaultParamsReader.getAndSetParams(model, metadata)
+ model
+ }
+ }
+}
+
+/**
+ * :: Experimental ::
+ * GaussianMixture clustering.
+ */
+@Since("2.0.0")
+@Experimental
+class GaussianMixture @Since("2.0.0") (
+ @Since("2.0.0") override val uid: String)
+ extends Estimator[GaussianMixtureModel] with GaussianMixtureParams with
DefaultParamsWritable {
+
+ setDefault(
+ k -> 2,
+ maxIter -> 100,
+ tol -> 0.01)
+
+ @Since("2.0.0")
+ override def copy(extra: ParamMap): GaussianMixture = defaultCopy(extra)
+
+ @Since("2.0.0")
+ def this() = this(Identifiable.randomUID("GaussianMixture"))
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setProbabilityCol(value: String): this.type = set(probabilityCol,
value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setK(value: Int): this.type = set(k, value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setTol(value: Double): this.type = set(tol, value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setSeed(value: Long): this.type = set(seed, value)
+
+ @Since("2.0.0")
+ override def fit(dataset: DataFrame): GaussianMixtureModel = {
+ val rdd = dataset.select(col($(featuresCol))).rdd.map { case
Row(point: Vector) => point }
+
+ val algo = new MLlibGM()
+ .setK($(k))
+ .setMaxIterations($(maxIter))
+ .setSeed($(seed))
+ .setConvergenceTol($(tol))
+ val parentModel = algo.run(rdd)
+ val model = copyValues(new GaussianMixtureModel(uid,
parentModel).setParent(this))
+ val summary = new GaussianMixtureSummary(model.transform(dataset),
+ $(predictionCol), $(probabilityCol), $(featuresCol))
+ model.setSummary(summary)
+ }
+
+ @Since("2.0.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+}
+
+@Since("2.0.0")
+object GaussianMixture extends DefaultParamsReadable[GaussianMixture] {
+
+ @Since("2.0.0")
+ override def load(path: String): GaussianMixture = super.load(path)
+}
+
+class GaussianMixtureSummary private[clustering] (
+ @Since("2.0.0") @transient val predictions: DataFrame,
+ @Since("2.0.0") val predictionCol: String,
+ @Since("2.0.0") val probabilityCol: String,
+ @Since("2.0.0") val featuresCol: String) extends Serializable {
+
+ /**
+ * Cluster centers of the transformed data.
+ */
+ @Since("2.0.0")
+ @transient lazy val cluster: DataFrame =
predictions.select(predictionCol)
+
+ /**
+ * Probability of eacho cluster.
+ */
+ @Since("2.0.0")
+ @transient lazy val probability: DataFrame =
predictions.select(probabilityCol)
+
+ /**
+ * Size of each cluster.
+ */
+ @Since("2.0.0")
+ lazy val size: Array[Int] = cluster.rdd.map {
+ case Row(clusterIdx: Int) => (clusterIdx, 1)
--- End diff --
Easier to do this within DataFrames:
```
cluster.groupBy(predictionCol).count().select(predictionCol,
"count").collect().map { case Row(cluster: Int, count: Long) => cluster ->
count }.toMap
```
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