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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-1303] [MLLIB] Added discretization capa...
Date Tue, 25 Mar 2014 19:03:31 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/216#discussion_r10947517
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/discretization/EntropyMinimizationDiscretizer.scala
---
    @@ -0,0 +1,402 @@
    +/*
    + * 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.discretization
    +
    +import scala.collection.mutable.Stack
    +import org.apache.spark.SparkContext._
    +import org.apache.spark.mllib.util.InfoTheory
    +import org.apache.spark.rdd.RDD
    +import org.apache.spark.storage.StorageLevel
    +import org.apache.spark.mllib.regression.LabeledPoint
    +import scala.collection.mutable
    +
    +
    +/**
    + * This class contains methods to discretize continuous values with the method proposed
in
    + * [Fayyad and Irani, Multi-Interval Discretization of Continuous-Valued Attributes,
1993]
    + */
    +class EntropyMinimizationDiscretizer private (
    +    @transient var data: RDD[LabeledPoint],
    +    @transient var continousFeatures: Seq[Int],
    +    var elementsPerPartition: Int = 18000,
    +    var maxBins: Int = Int.MaxValue)
    +  extends DiscretizerModel {
    +
    +  private var thresholds = Map.empty[Int, Seq[Double]]
    +  private val partitions = { x : Long => math.ceil(x.toDouble / elementsPerPartition).toInt
}
    +
    +  def this() = this(null, null)
    +
    +  /**
    +   * Sets the RDD[LabeledPoint] to be discretized
    +   *
    +   * @param data RDD[LabeledPoint] to be discretized
    +   */
    +  def setData(data: RDD[LabeledPoint]): EntropyMinimizationDiscretizer = {
    +    this.data = data
    +    this
    +  }
    +
    +  /**
    +   * Sets the indexes of the features to be discretized
    +   *
    +   * @param continuousFeatures Indexes of features to be discretized
    +   */
    +  def setContinuousFeatures(continuousFeatures: Seq[Int]): EntropyMinimizationDiscretizer
= {
    +    this.continousFeatures = continuousFeatures
    +    this
    +  }
    +
    +  /**
    +   * Sets the maximum number of elements that a partition should have
    +   *
    +   * @param ratio Maximum number of elements for a partition
    +   * @return The Discretizer with the parameter changed
    +   */
    +  def setElementsPerPartition(ratio: Int): EntropyMinimizationDiscretizer = {
    +    this.elementsPerPartition = ratio
    +    this
    +  }
    +
    +  /**
    +   * Sets the maximum number of discrete values
    +   *
    +   * @param maxBins Maximum number of discrete values
    +   * @return The Discretizer with the parameter changed
    +   */
    +  def setMaxBins(maxBins: Int): EntropyMinimizationDiscretizer = {
    +    this.maxBins = maxBins
    +    this
    +  }
    +  
    +  /**
    +   * Returns the thresholds used to discretized the given feature
    +   *
    +   * @param feature The number of the feature to discretize
    +   */
    +  def getThresholdsForFeature(feature: Int): Seq[Double] = {
    +    thresholds.get(feature) match {
    +      case Some(a) => a
    +      case None =>
    +        val featureValues = data.map({
    +          case LabeledPoint(label, values) => (values(feature), label.toString.trim)
    +        })
    +        val sortedValues = featureValues.sortByKey()
    +        val initial_candidates = initialThresholds(sortedValues)
    +        val thresholdsForFeature = this.getThresholds(initial_candidates)
    +        this.thresholds += ((feature, thresholdsForFeature))
    +        thresholdsForFeature
    +    }
    +  }
    +
    +  /**
    +   * Returns the thresholds used to discretized the given features
    +   *
    +   * @param features The number of the feature to discretize
    +   */
    +  def getThresholdsForFeature(features: Seq[Int]): Map[Int, Seq[Double]] = {
    +    for (feature <- features diff this.thresholds.keys.toSeq) {
    +      getThresholdsForFeature(feature)
    +    }
    +
    +    this.thresholds.filter({ features.contains(_) })
    +  }
    +
    +  /**
    +   * Returns the thresholds used to discretized the continuous features
    +   */
    +  def getThresholdsForContinuousFeatures: Map[Int, Seq[Double]] = {
    +    for (feature <- continousFeatures diff this.thresholds.keys.toSeq) {
    +      getThresholdsForFeature(feature)
    +    }
    +
    +    this.thresholds
    +  }
    +
    +  /**
    +   * Calculates the initial candidate treholds for a feature
    +   * @param data RDD of (value, label) pairs
    +   * @return RDD of (candidate, class frequencies between last and current candidate)
pairs
    +   */
    +  private def initialThresholds(data: RDD[(Double, String)]): RDD[(Double, Map[String,Int])]
= {
    +    data.mapPartitions({ it =>
    +      var lastX = Double.NegativeInfinity
    +      var lastY = ""
    +      var result = Seq.empty[(Double, Map[String, Int])]
    +      var freqs = Map.empty[String, Int]
    +
    +      for ((x, y) <- it) {
    +        if (x != lastX && y != lastY && lastX != Double.NegativeInfinity)
{
    +          // new candidate and interval
    +          result = ((x + lastX) / 2, freqs) +: result
    +          freqs = freqs.empty + ((y, 1))
    +        } else {
    +          // we continue on the same interval
    +          freqs = freqs.updated(y, freqs.getOrElse(y, 0) + 1)
    +        }
    +        lastX = x
    +        lastY = y
    +      }
    +
    +      // we add last element as a candidate threshold for convenience 
    +      result = (lastX, freqs) +: result
    +
    +      result.reverse.toIterator
    +    }).persist(StorageLevel.MEMORY_AND_DISK)
    +  }
    +  
    +  /**
    +   * Returns a sequence of doubles that define the intervals to make the discretization.
    +   *
    +   * @param candidates RDD of (value, label) pairs
    +   */
    +  private def getThresholds(candidates: RDD[(Double, Map[String,Int])]): Seq[Double]
= {
    +
    +    //Create queue
    +    val stack = new mutable.Queue[((Double, Double), Option[Double])]
    +
    +    //Insert first in the stack
    +    stack.enqueue(((Double.NegativeInfinity, Double.PositiveInfinity), None))
    +    var result = Seq(Double.NegativeInfinity)
    +
    +    // While more elements to eval, continue
    +    while(stack.length > 0 && result.size < this.maxBins){
    +
    +      val (bounds, lastThresh) = stack.dequeue
    +
    +      var cands = candidates.filter({ case (th, _) => th > bounds._1 &&
th <= bounds._2 })
    +      val nCands = cands.count
    +      if (nCands > 0) {
    +        cands = cands.coalesce(partitions(nCands))
    +
    +        evalThresholds(cands, lastThresh) match {
    +          case Some(th) =>
    +            result = th +: result
    +            stack.enqueue(((bounds._1, th), Some(th)))
    +            stack.enqueue(((th, bounds._2), Some(th)))
    +          case None => /* criteria not fulfilled, finish */
    +        }
    +      }
    +    }
    +    (Double.PositiveInfinity +: result).sorted
    +  }
    +
    +  /**
    +   * Selects one final thresholds among the candidates and returns two partitions to
recurse
    +   *
    +   * @param candidates RDD of (candidate, class frequencies between last and current
candidate)
    +   * @param lastSelected last selected threshold to avoid selecting it again
    +   */
    +  private def evalThresholds(
    +      candidates: RDD[(Double, Map[String, Int])],
    +      lastSelected : Option[Double]) = {
    +
    +    var result = candidates.map({
    +      case (cand, freqs) =>
    +        (cand, freqs, Seq.empty[Int], Seq.empty[Int])
    +    }).cache
    +
    +    val numPartitions = candidates.partitions.size
    +    val bc_numPartitions = candidates.context.broadcast(numPartitions)
    +
    +    // stores accumulated freqs from left to right
    +    val l_total = candidates.context.accumulator(Map.empty[String, Int])(MapAccumulator)
    +    // stores accumulated freqs from right to left
    +    val r_total = candidates.context.accumulator(Map.empty[String, Int])(MapAccumulator)
    +
    +    // calculates accumulated frequencies for each candidate
    +    (0 until numPartitions) foreach { l2r_i =>
    +
    +      val bc_l_total = l_total.value
    +      val bc_r_total = r_total.value
    +
    +      result =
    +        result.mapPartitionsWithIndex({ (slice, it) =>
    +
    +          val l2r = slice == l2r_i
    +          val r2l = slice == bc_numPartitions.value - 1 - l2r_i
    +
    +          if (l2r && r2l) {
    +
    +            // accumulate both from left to right and right to left
    +            var partialResult = Seq.empty[(Double, Map[String, Int], Seq[Int], Seq[Int])]
    +            var accum = Map.empty[String, Int]
    +
    +            for ((cand, freqs, _, r_freqs) <- it) {
    +              accum = Utils.sumFreqMaps(accum, freqs)
    +              val l_freqs = Utils.sumFreqMaps(accum, bc_l_total).values.toList
    +              partialResult = (cand, freqs, l_freqs, r_freqs) +: partialResult
    +            }
    +
    +            l_total += accum
    +
    +            val r2lIt = partialResult.iterator
    +            partialResult = Seq.empty[(Double, Map[String, Int], Seq[Int], Seq[Int])]
    +            accum = Map.empty[String, Int]
    +            for ((cand, freqs, l_freqs, _) <- r2lIt) {
    +              val r_freqs = Utils.sumFreqMaps(accum, bc_r_total).values.toList
    +              accum = Utils.sumFreqMaps(accum, freqs)
    +              partialResult = (cand, freqs, l_freqs, r_freqs) +: partialResult
    +            }
    +            r_total += accum
    +
    +            partialResult.iterator
    +
    +          } else if (l2r) {
    +
    +            // accumulate freqs from left to right
    +            var partialResult = Seq.empty[(Double, Map[String, Int], Seq[Int], Seq[Int])]
    +            var accum = Map.empty[String, Int]
    +
    +            for ((cand, freqs, _, r_freqs) <- it) {
    +              accum = Utils.sumFreqMaps(accum, freqs)
    +              val l_freqs = Utils.sumFreqMaps(accum, bc_l_total).values.toList
    +              partialResult = (cand, freqs, l_freqs, r_freqs) +: partialResult
    +            }
    +
    +            l_total += accum
    +            partialResult.reverseIterator
    +
    +          } else if (r2l) {
    +
    +            // accumulate freqs from right to left
    +            var partialResult = Seq.empty[(Double, Map[String, Int], Seq[Int], Seq[Int])]
    +            var accum = Map.empty[String, Int]
    +            val r2lIt = it.toSeq.reverseIterator
    +
    +            for ((cand, freqs, l_freqs, _) <- r2lIt) {
    +              val r_freqs = Utils.sumFreqMaps(accum, bc_r_total).values.toList
    +              accum = Utils.sumFreqMaps(accum, freqs)
    +              partialResult = (cand, freqs, l_freqs, r_freqs) +: partialResult
    +            }
    +
    +            r_total += accum
    +
    +            partialResult.iterator
    +
    +          } else {
    +            // do nothing in this iteration
    +            it
    +          }
    +        }, true) // important to maintain partitions within the loop
    +        .persist(StorageLevel.MEMORY_AND_DISK) // needed, otherwise spark repeats calculations
    +
    +      result.foreachPartition({ _ => }) // Forces the iteration to be calculated
    +    }
    +
    +    // calculate h(S)
    +    // s: number of elements
    +    // k: number of distinct classes
    +    // hs: entropy
    +
    +    val s  = l_total.value.values.reduce(_ + _)
    +    val hs = InfoTheory.entropy(l_total.value.values.toSeq, s)
    +    val k  = l_total.value.values.size
    +
    +    // select best threshold according to the criteria
    +    val final_candidates =
    +      result.flatMap({
    +        case (cand, _, l_freqs, r_freqs) =>
    +
    +          val k1  = l_freqs.size
    +          val s1  = if (k1 > 0) l_freqs.reduce(_ + _) else 0
    +          val hs1 = InfoTheory.entropy(l_freqs, s1)
    +
    +          val k2  = r_freqs.size
    +          val s2  = if (k2 > 0) r_freqs.reduce(_ + _) else 0
    +          val hs2 = InfoTheory.entropy(r_freqs, s2)
    +
    +          val weighted_hs = (s1 * hs1 + s2 * hs2) / s
    +          val gain        = hs - weighted_hs
    +          val delta       = Utils.log2(3 ^ k - 2) - (k * hs - k1 * hs1 - k2 * hs2)
    +          var criterion   = (gain - (Utils.log2(s - 1) + delta) / s) > -1e-5
    +
    +          lastSelected match {
    +              case None =>
    +              case Some(last) => criterion = criterion && (cand != last)
    +          }
    +
    +          if (criterion) {
    +            Seq((weighted_hs, cand))
    +          } else {
    +            Seq.empty[(Double, Double)]
    +          }
    +      })
    +
    +    // choose best candidates and partition data to make recursive calls
    +    if (final_candidates.count > 0) {
    +      val selected_threshold = final_candidates.reduce({ case ((whs1, cand1), (whs2,
cand2)) =>
    +        if (whs1 < whs2) (whs1, cand1) else (whs2, cand2)
    +      })._2;
    +      Some(selected_threshold)
    +    } else {
    +      None
    +    }
    +
    +  }
    +
    +  /**
    +   * Discretizes a value with a set of intervals.
    +   *
    +   * @param value The value to be discretized
    +   * @param thresholds Thresholds used to asign a discrete value
    +   */
    +  private def assignDiscreteValue(value: Double, thresholds: Seq[Double]) = {
    +    var aux = thresholds.zipWithIndex
    +    while (value > aux.head._1) aux = aux.tail
    +    aux.head._2
    +  }
    +
    +  /**
    +   * Discretizes an RDD of (label, array of values) pairs.
    +   */
    +  def discretize: RDD[LabeledPoint] = {
    +    // calculate thresholds that aren't already calculated
    +    getThresholdsForContinuousFeatures
    +
    +    val bc_thresholds = this.data.context.broadcast(this.thresholds)
    +
    +    // applies thresholds to discretize every continuous feature
    +    data.map {
    +      case LabeledPoint(label, values) =>
    --- End diff --
    
    move this line to the one above


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