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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-4001][MLlib] adding parallel FP-Growth ...
Date Wed, 21 Jan 2015 02:00:26 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/2847#discussion_r23273446
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala ---
    @@ -0,0 +1,208 @@
    +/*
    + * 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.mllib.fpm
    +
    +import org.apache.spark.Logging
    +import org.apache.spark.SparkContext._
    +import org.apache.spark.broadcast._
    +import org.apache.spark.rdd.RDD
    +
    +import scala.collection.mutable.{ArrayBuffer, Map}
    +
    +/**
    + * This class implements Parallel FPGrowth algorithm to do frequent pattern matching
on input data.
    + * Parallel FPGrowth (PFP) partitions computation in such a way that each machine executes
an
    + * independent group of mining tasks. More detail of this algorithm can be found at
    + * http://infolab.stanford.edu/~echang/recsys08-69.pdf
    + */
    +class FPGrowth private(private var minSupport: Double) extends Logging with Serializable
{
    +
    +  /**
    +   * Constructs a FPGrowth instance with default parameters:
    +   * {minSupport: 0.5}
    +   */
    +  def this() = this(0.5)
    +
    +  /**
    +   * set the minimal support level, default is 0.5
    +   * @param minSupport minimal support level
    +   */
    +  def setMinSupport(minSupport: Double): this.type = {
    +    this.minSupport = minSupport
    +    this
    +  }
    +
    +  /**
    +   * Compute a FPGrowth Model that contains frequent pattern result.
    +   * @param data input data set
    +   * @return FPGrowth Model
    +   */
    +  def run(data: RDD[Array[String]]): FPGrowthModel = {
    +    val model = runAlgorithm(data)
    +    model
    +  }
    +
    +  /**
    +   * Implementation of PFP.
    +   */
    +  private def runAlgorithm(data: RDD[Array[String]]): FPGrowthModel = {
    +    val count = data.count()
    +    val minCount = minSupport * count
    +    val single = generateSingleItem(data, minCount)
    +    val combinations = generateCombinations(data, minCount, single)
    +    new FPGrowthModel(single ++ combinations)
    +  }
    +
    +  /**
    +   * Generate single item pattern by filtering the input data using minimal support level
    +   */
    +  private def generateSingleItem(
    +      data: RDD[Array[String]],
    +      minCount: Double): Array[(String, Int)] = {
    +    data.flatMap(v => v)
    +      .map(v => (v, 1))
    +      .reduceByKey(_ + _)
    +      .filter(_._2 >= minCount)
    +      .collect()
    +      .distinct
    +      .sortWith(_._2 > _._2)
    +  }
    +
    +  /**
    +   * Generate combination of items by computing on FPTree,
    +   * the computation is done on each FPTree partitions.
    +   */
    +  private def generateCombinations(
    +      data: RDD[Array[String]],
    +      minCount: Double,
    +      singleItem: Array[(String, Int)]): Array[(String, Int)] = {
    +    val single = data.context.broadcast(singleItem)
    +    data.flatMap(basket => createFPTree(basket, single))
    +      .groupByKey()
    +      .flatMap(partition => runFPTree(partition, minCount))
    +      .collect()
    +  }
    +
    +  /**
    +   * Create FP-Tree partition for the giving basket
    +   */
    +  private def createFPTree(
    +      basket: Array[String],
    +      singleItem: Broadcast[Array[(String, Int)]]): Array[(String, Array[String])] =
{
    --- End diff --
    
    You can call `single.value` in `generateCombinations` and remove `Broadcast[..]` for this
function. This produces better code separation.


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