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/*
* 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.impurity
import org.apache.spark.annotation.{DeveloperApi, Since}
/**
* Trait for calculating information gain.
* This trait is used for
* (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
* (b) calculating impurity values from sufficient statistics.
*/
@Since("1.0.0")
trait Impurity extends Serializable {
/**
* :: DeveloperApi ::
* information calculation for multiclass classification
* @param counts Array[Double] with counts for each label
* @param totalCount sum of counts for all labels
* @return information value, or 0 if totalCount = 0
*/
@Since("1.1.0")
@DeveloperApi
def calculate(counts: Array[Double], totalCount: Double): Double
/**
* :: DeveloperApi ::
* information calculation for regression
* @param count number of instances
* @param sum sum of labels
* @param sumSquares summation of squares of the labels
* @return information value, or 0 if count = 0
*/
@Since("1.0.0")
@DeveloperApi
def calculate(count: Double, sum: Double, sumSquares: Double): Double
}
/**
* Interface for updating views of a vector of sufficient statistics,
* in order to compute impurity from a sample.
* Note: Instances of this class do not hold the data; they operate on views of the data.
* @param statsSize Length of the vector of sufficient statistics for one bin.
*/
private[spark] abstract class ImpurityAggregator(val statsSize: Int) extends Serializable {
/**
* Merge the stats from one bin into another.
* @param allStats Flat stats array, with stats for this (node, feature, bin) contiguous.
* @param offset Start index of stats for (node, feature, bin) which is modified by the merge.
* @param otherOffset Start index of stats for (node, feature, other bin) which is not modified.
*/
def merge(allStats: Array[Double], offset: Int, otherOffset: Int): Unit = {
var i = 0
while (i < statsSize) {
allStats(offset + i) += allStats(otherOffset + i)
i += 1
}
}
/**
* Update stats for one (node, feature, bin) with the given label.
* @param allStats Flat stats array, with stats for this (node, feature, bin) contiguous.
* @param offset Start index of stats for this (node, feature, bin).
*/
def update(allStats: Array[Double], offset: Int, label: Double, instanceWeight: Double): Unit
/**
* Get an [[ImpurityCalculator]] for a (node, feature, bin).
* @param allStats Flat stats array, with stats for this (node, feature, bin) contiguous.
* @param offset Start index of stats for this (node, feature, bin).
*/
def getCalculator(allStats: Array[Double], offset: Int): ImpurityCalculator
}
/**
* Stores statistics for one (node, feature, bin) for calculating impurity.
* Unlike [[ImpurityAggregator]], this class stores its own data and is for a specific
* (node, feature, bin).
* @param stats Array of sufficient statistics for a (node, feature, bin).
*/
private[spark] abstract class ImpurityCalculator(val stats: Array[Double]) extends Serializable {
/**
* Make a deep copy of this [[ImpurityCalculator]].
*/
def copy: ImpurityCalculator
/**
* Calculate the impurity from the stored sufficient statistics.
*/
def calculate(): Double
/**
* Add the stats from another calculator into this one, modifying and returning this calculator.
*/
def add(other: ImpurityCalculator): ImpurityCalculator = {
require(stats.length == other.stats.length,
s"Two ImpurityCalculator instances cannot be added with different counts sizes." +
s" Sizes are ${stats.length} and ${other.stats.length}.")
var i = 0
val len = other.stats.length
while (i < len) {
stats(i) += other.stats(i)
i += 1
}
this
}
/**
* Subtract the stats from another calculator from this one, modifying and returning this
* calculator.
*/
def subtract(other: ImpurityCalculator): ImpurityCalculator = {
require(stats.length == other.stats.length,
s"Two ImpurityCalculator instances cannot be subtracted with different counts sizes." +
s" Sizes are ${stats.length} and ${other.stats.length}.")
var i = 0
val len = other.stats.length
while (i < len) {
stats(i) -= other.stats(i)
i += 1
}
this
}
/**
* Number of data points accounted for in the sufficient statistics.
*/
def count: Long
/**
* Prediction which should be made based on the sufficient statistics.
*/
def predict: Double
/**
* Probability of the label given by [[predict]], or -1 if no probability is available.
*/
def prob(label: Double): Double = -1
/**
* Return the index of the largest array element.
* Fails if the array is empty.
*/
protected def indexOfLargestArrayElement(array: Array[Double]): Int = {
val result = array.foldLeft(-1, Double.MinValue, 0) {
case ((maxIndex, maxValue, currentIndex), currentValue) =>
if (currentValue > maxValue) {
(currentIndex, currentValue, currentIndex + 1)
} else {
(maxIndex, maxValue, currentIndex + 1)
}
}
if (result._1 < 0) {
throw new RuntimeException("ImpurityCalculator internal error:" +
" indexOfLargestArrayElement failed")
}
result._1
}
}
private[spark] object ImpurityCalculator {
/**
* Create an [[ImpurityCalculator]] instance of the given impurity type and with
* the given stats.
*/
def getCalculator(impurity: String, stats: Array[Double]): ImpurityCalculator = {
impurity match {
case "gini" => new GiniCalculator(stats)
case "entropy" => new EntropyCalculator(stats)
case "variance" => new VarianceCalculator(stats)
case _ =>
throw new IllegalArgumentException(
s"ImpurityCalculator builder did not recognize impurity type: $impurity")
}
}
}
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