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From feynmanliang <...@git.apache.org>
Subject [GitHub] spark pull request: [Spark-7879][MLlib] KMeans API for spark.ml Pi...
Date Thu, 02 Jul 2015 17:48:08 GMT
Github user feynmanliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/6756#discussion_r33803659
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala ---
    @@ -0,0 +1,201 @@
    +/*
    + * 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.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
    +   */
    +  val k = new Param[Int](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
    +   */
    +  val runs = new Param[Int](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
    +   */
    +  val epsilon = new Param[Double](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
    +   */
    +  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
    +   */
    +  val initSteps = new Param[Int](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)
    +  setDefault(maxIter, 20)
    +  setDefault(runs, 1)
    +  setDefault(initMode, MLlibKMeans.K_MEANS_PARALLEL)
    +  setDefault(initSteps, 5)
    +  setDefault(epsilon, 1e-4)
    +  setDefault(seed, Utils.random.nextLong())
    --- End diff --
    
    Can do all this in one call e.g. `setDefault(k -> 2, maxIter -> 20, ...)`


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