Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/6756#discussion_r34301648
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
---
@@ -0,0 +1,202 @@
+/*
+ * 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.{Param, Params, IntParam, DoubleParam,
ParamMap}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasMaxIter,
HasPredictionCol, HasSeed}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans,
KMeansModel => MLlibKMeansModel}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{IntegerType, StructType}
+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 {
+
+ /**
+ * Set the number of clusters to create (k). Default: 2.
+ * @group param
+ */
+ final val k = new IntParam(this, "k", "number of clusters to create",
(x: Int) => x > 1)
+
+ /** @group getParam */
+ def getK: Int = $(k)
+
+ /**
+ * Param the number of runs of the algorithm to execute in parallel. We
initialize the algorithm
+ * this many times with random starting conditions (configured by the
initialization mode), then
+ * return the best clustering found over any run. Default: 1.
+ * @group param
+ */
+ final val runs = new IntParam(this, "runs",
+ "number of runs of the algorithm to execute in parallel", (value: Int)
=> value >= 1)
+
+ /** @group getParam */
+ def getRuns: Int = $(runs)
+
+ /**
+ * Param the distance threshold within which we've consider centers to
have converged.
+ * If all centers move less than this Euclidean distance, we stop
iterating one run.
+ * @group param
+ */
+ final val epsilon = new DoubleParam(this, "epsilon", "distance
threshold")
+
+ /** @group getParam */
+ def getEpsilon: Double = $(epsilon)
+
+ /**
+ * Param for the initialization algorithm. This can be either "random"
to choose random points as
+ * initial cluster centers, or "k-means||" to use a parallel variant of
k-means++
+ * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
+ * @group param
+ */
+ final val initMode = new Param[String](this, "initMode", "initialization
algorithm",
+ (value: String) => MLlibKMeans.validateInitializationMode(value))
+
+ /** @group getParam */
+ def getInitializationMode: String = $(initMode)
+
+ /**
+ * Param for the number of steps for the k-means|| initialization mode.
This is an advanced
+ * setting -- the default of 5 is almost always enough. Default: 5.
+ * @group param
+ */
+ final val initSteps = new IntParam(this, "initSteps", "number of steps
for k-means||",
+ (value: Int) => value > 0)
+
+ /** @group getParam */
+ def getInitializationSteps: Int = $(initSteps)
+
+ /**
+ * 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, $(predictionCol), IntegerType)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by KMeans.
+ *
+ * @param parentModel a model trained by spark.mllib.clustering.KMeans.
+ */
+@Experimental
+class KMeansModel private[ml] (
+ override val uid: String,
+ private val parentModel: MLlibKMeansModel) extends Model[KMeansModel]
with KMeansParams {
+
+ override def copy(extra: ParamMap): KMeansModel = {
+ val copied = new KMeansModel(uid, parentModel)
+ copyValues(copied, extra)
+ }
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ val predictUDF = udf((vector: Vector) => predict(vector))
+ dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ private[clustering]
+ 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 {
+
+ setDefault(
+ k -> 2,
+ maxIter -> 20,
+ runs -> 1,
+ initMode -> MLlibKMeans.K_MEANS_PARALLEL,
+ initSteps -> 5,
+ epsilon -> 1e-4,
+ seed -> Utils.random.nextLong())
+
+ override def copy(extra: ParamMap): Estimator[KMeansModel] =
defaultCopy(extra)
+
+ def this() = this(Identifiable.randomUID("kmeans"))
+
+ /** @group setParam */
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ def setK(value: Int): this.type = set(k, value)
+
+ /** @group setParam */
+ def setInitializationMode(value: String): this.type = set(initMode,
value)
+
+ /** @group setParam */
+ def setInitializationSteps(value: Int): this.type = set(initSteps, value)
--- End diff --
I think you meant `setInitSteps`
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