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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, Experimental, Since}
/**
* :: Experimental ::
* Class for calculating variance during regression
*/
@Since("1.0.0")
@Experimental
object Variance extends Impurity {
/**
* :: 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
override def calculate(counts: Array[Double], totalCount: Double): Double =
throw new UnsupportedOperationException("Variance.calculate")
/**
* :: DeveloperApi ::
* variance calculation
* @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
override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
if (count == 0) {
return 0
}
val squaredLoss = sumSquares - (sum * sum) / count
squaredLoss / count
}
/**
* Get this impurity instance.
* This is useful for passing impurity parameters to a Strategy in Java.
*/
@Since("1.0.0")
def instance: this.type = this
}
/**
* Class 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.
*/
private[tree] class VarianceAggregator()
extends ImpurityAggregator(statsSize = 3) with Serializable {
/**
* 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 = {
allStats(offset) += instanceWeight
allStats(offset + 1) += instanceWeight * label
allStats(offset + 2) += instanceWeight * label * label
}
/**
* 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): VarianceCalculator = {
new VarianceCalculator(allStats.view(offset, offset + statsSize).toArray)
}
}
/**
* Stores statistics for one (node, feature, bin) for calculating impurity.
* Unlike [[GiniAggregator]], 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] class VarianceCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
require(stats.length == 3,
s"VarianceCalculator requires sufficient statistics array stats to be of length 3," +
s" but was given array of length ${stats.length}.")
/**
* Make a deep copy of this [[ImpurityCalculator]].
*/
def copy: VarianceCalculator = new VarianceCalculator(stats.clone())
/**
* Calculate the impurity from the stored sufficient statistics.
*/
def calculate(): Double = Variance.calculate(stats(0), stats(1), stats(2))
/**
* Number of data points accounted for in the sufficient statistics.
*/
def count: Long = stats(0).toLong
/**
* Prediction which should be made based on the sufficient statistics.
*/
def predict: Double = if (count == 0) {
0
} else {
stats(1) / count
}
override def toString: String = {
s"VarianceAggregator(cnt = ${stats(0)}, sum = ${stats(1)}, sum2 = ${stats(2)})"
}
}
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