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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-3042] [mllib] DecisionTree Filter top-d...
Date Sun, 17 Aug 2014 00:11:25 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/1975#discussion_r16327382
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTMetadata.scala ---
    @@ -0,0 +1,97 @@
    +/*
    + * 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.tree.impl
    +
    +import scala.collection.mutable
    +
    +import org.apache.spark.mllib.regression.LabeledPoint
    +import org.apache.spark.mllib.tree.configuration.Algo._
    +import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
    +import org.apache.spark.mllib.tree.configuration.Strategy
    +import org.apache.spark.mllib.tree.impurity.Impurity
    +import org.apache.spark.rdd.RDD
    +
    +
    +/**
    + * Learning and dataset metadata for DecisionTree.
    + *
    + * @param featureArity  Map: categorical feature index --> arity.
    + *                      I.e., the feature takes values in {0, ..., arity - 1}.
    + */
    +private[tree] class DTMetadata(
    +    val numFeatures: Int,
    +    val numExamples: Long,
    +    val numClasses: Int,
    +    val maxBins: Int,
    +    val featureArity: Map[Int, Int],
    +    val unorderedFeatures: Set[Int],
    +    val impurity: Impurity,
    +    val quantileStrategy: QuantileStrategy) extends Serializable {
    +
    +  def isUnordered(featureIndex: Int): Boolean = unorderedFeatures.contains(featureIndex)
    +
    +  def isClassification: Boolean = numClasses >= 2
    +
    +  def isMulticlass: Boolean = numClasses > 2
    +
    +  def isMulticlassWithCategoricalFeatures: Boolean = isMulticlass && (featureArity.size
> 0)
    +
    +  def isCategorical(featureIndex: Int): Boolean = featureArity.contains(featureIndex)
    +
    +  def isContinuous(featureIndex: Int): Boolean = !featureArity.contains(featureIndex)
    +
    +}
    +
    +private[tree] object DTMetadata {
    +
    +  def buildMetadata(input: RDD[LabeledPoint], strategy: Strategy): DTMetadata = {
    +
    +    val numFeatures = input.take(1)(0).features.size
    +    val numExamples = input.count()
    +    val numClasses = strategy.algo match {
    +      case Classification => strategy.numClassesForClassification
    +      case Regression => 0
    +    }
    +
    +    val maxBins = math.min(strategy.maxBins, numExamples).toInt
    +
    +    val unorderedFeatures = new mutable.HashSet[Int]()
    +    if (numClasses > 2) {
    +      strategy.categoricalFeaturesInfo.foreach { case (f, k) =>
    +        val numUnorderedBins = (1 << k - 1) - 1
    --- End diff --
    
    Need to check the value of `k` first. If `k > 30`, the result will be unexpected. Using
`1L` instead of `1` may help.


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