Implement the KMeans API for spark.ml Pipelines in Scala

mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala

@@ -0,0 +1,197 @@
+/*
+ * 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
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed}
+import org.apache.spark.ml.param.{Param, ParamMap, Params}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib
+import org.apache.spark.mllib.clustering.KMeans
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.util.Utils
+
+
+/**
+ * Common params for KMeans and KMeansModel
+ */
+private[clustering] trait KMeansParams
+    extends Params with HasMaxIter with HasFeaturesCol with HasSeed with HasPredictionCol {
+  /**
+   * Param for the column name for the number of clusters to create.
+   * @group param
+   */
+  val k = new Param[Int](this, "k", "number of clusters to create")
+
+  /** @group getParam */
+  def getK: Int = $(k)
+
+  /**
+   * Param for the column name for the number of runs of the algorithm to execute in parallel.
+   * @group param
+   */
+  val runs = new Param[Int](this, "runs", "number of runs of the algorithm to execute in parallel")
+
+  /** @group getParam */
+  def getRuns: Int = $(runs)
+
+  /**
+   * Param for the column name for the distance threshold
+   * within which we've consider centers to have converged.
+   * @group param
+   */
+  val epsilon = new Param[Double](this, "epsilon", "distance threshold")
+
+  /** @group getParam */
+  def getEpsilon: Double = $(epsilon)
+
+  /**
+   * Param for the initialization algorithm.
+   * @group param
+   */
+  val initializationMode = new Param[String](this, "initializationMode", "initialization algorithm")
+
+  /** @group getParam */
+  def getInitializationMode: String = $(initializationMode)
+
+  /**
+   * Param for the number of steps for k-means initialization mode.
+   * @group param
+   */
+  val initializationSteps =
+    new Param[Int](this, "initializationSteps", "number of steps for k-means||")
+
+  /** @group getParam */
+  def getInitializationSteps: Int = $(initializationSteps)
+
+  /**
+   * Validates and transforms the input schema.
+   * @param schema input schema
+   * @return output schema
+   */
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
+    SchemaUtils.appendColumn(schema, $(predictionCol), IntegerType)
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by KMeans.
+ *
+ * @param paramMap a parameter map for fitting.
+ * @param parentModel a model trained by spark.mllib.clustering.KMeans.
+ */
+@Experimental
+class KMeansModel private[ml] (
+  override val uid: String,
+  val paramMap: ParamMap,
+  val parentModel: mllib.clustering.KMeansModel
+) extends Model[KMeansModel] with KMeansParams {
+  /**
+   * Transforms the input dataset.
+   */
+  override def transform(dataset: DataFrame): DataFrame = {
+    dataset.select(
+          dataset("*"),
+          callUDF(predict _, IntegerType, col($(featuresCol))).as($(predictionCol))
+        )
+  }
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * Derives the output schema from the input schema.
+   */
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  def predict(features: Vector): Int = parentModel.predict(features)
+
+  def clusterCenters: Array[Vector] = parentModel.clusterCenters
+}
+
+/**
+ * :: Experimental ::
+ * KMeans API for spark.ml Pipeline.
+ */
+@Experimental
+class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams {
+  setK(2)
+  setMaxIter(20)
+  setRuns(1)
+  setInitializationMode(KMeans.K_MEANS_PARALLEL)
+  setInitializationSteps(5)
+  setEpsilon(1e-4)
+  setSeed(Utils.random.nextLong())
+
+  def this() = this(Identifiable.randomUID("kmeans"))
+
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group setParam */
+  def setK(value: Int): this.type = set(k, value)
+
+  /** @group setParam */
+  def setInitializationMode(value: String): this.type = {
+    mllib.clustering.KMeans.validateInitializationMode(value)
+    set(initializationMode, value)
+  }
+
+  /** @group setParam */
+  def setInitializationSteps(value: Int): this.type = {
+    require(value > 0, "Number of initialization steps must be positive")
+    set(initializationSteps, value)
+  }
+
+  /** @group setParam */
+  def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+  /** @group setParam */
+  def setRuns(value: Int): this.type = set(runs, value)
+
+  /** @group setParam */
+  def setEpsilon(value: Double): this.type = set(epsilon, value)
+
+  /** @group setParam */
+  def setSeed(value: Long): this.type = set(seed, value)
+
+
+  override def fit(dataset: DataFrame): KMeansModel = {
+    val map = this.extractParamMap()
+    val rdd = dataset.select(col(map(featuresCol))).map { case Row(point: Vector) => point}
+
+    val algo = new mllib.clustering.KMeans()
+        .setK(map(k))
+        .setMaxIterations(map(maxIter))
+        .setSeed(map(seed))
+    val parentModel = algo.run(rdd)
+    new KMeansModel(uid, map, parentModel)
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+}
+

mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala

@@ -85,9 +85,7 @@ class KMeans private (
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
    */
   def setInitializationMode(initializationMode: String): this.type = {
-    if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) {
-      throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode)
-    }
+    KMeans.validateInitializationMode(initializationMode)
     this.initializationMode = initializationMode
     this
   }
@@ -521,6 +519,13 @@ object KMeans {
       v2: VectorWithNorm): Double = {
     MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
   }
+
+  private[spark] def validateInitializationMode(initializationMode: String): Boolean = {
+    if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) {
+      throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode)
+    }
+    true
+  }
 }
 
 /**

mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala

@@ -0,0 +1,71 @@
+/*
+ * 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.SparkFunSuite
+import org.apache.spark.mllib.clustering.KMeans
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, SQLContext}
+
+private[clustering] case class TestRow(features: Vector)
+
+object KMeansSuite {
+  def generateKMeansData(sql: SQLContext, rows: Int, dim: Int, k: Int): DataFrame = {
+    val sc = sql.sparkContext
+    val rdd = sc.parallelize(1 to rows).map(i => Vectors.dense(Array.fill(dim)((i % k).toDouble)))
+        .map(v => new TestRow(v))
+    sql.createDataFrame(rdd)
+  }
+}
+
+class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  val k = 5
+  @transient var dataset: DataFrame = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+
+    dataset = KMeansSuite.generateKMeansData(sqlContext, 1000, 3, k)
+  }
+
+  test("default parameters") {
+    val kmeans = new KMeans()
+
+    assert(kmeans.getK === 2)
+    assert(kmeans.getFeaturesCol === "features")
+    assert(kmeans.getMaxIter === 20)
+    assert(kmeans.getRuns === 1)
+    assert(kmeans.getInitializationMode === KMeans.K_MEANS_PARALLEL)
+    assert(kmeans.getInitializationSteps === 5)
+    assert(kmeans.getEpsilon === 1e-4)
+  }
+
+  test("fit & transform") {
+    val kmeans = new KMeans().setK(k)
+    val model = kmeans.fit(dataset)
+    assert(model.clusterCenters.length === k)
+
+    val transformed = model.transform(dataset)
+    assert(transformed.columns === Array("features", "prediction"))
+    val clusters = transformed.select("prediction")
+        .map(row => row.apply(0)).distinct().collect().toSet
+    assert(clusters === Set(0, 1, 2, 3, 4))
+  }
+}