spark MeanEvaluator 源码
spark MeanEvaluator 代码
文件路径：/core/src/main/scala/org/apache/spark/partial/MeanEvaluator.scala
/*
* 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.partial
import org.apache.commons.math3.distribution.{NormalDistribution, TDistribution}
import org.apache.spark.util.StatCounter
/**
* An ApproximateEvaluator for means.
*/
private[spark] class MeanEvaluator(totalOutputs: Int, confidence: Double)
extends ApproximateEvaluator[StatCounter, BoundedDouble] {
private var outputsMerged = 0
private val counter = new StatCounter()
override def merge(outputId: Int, taskResult: StatCounter): Unit = {
outputsMerged += 1
counter.merge(taskResult)
}
override def currentResult(): BoundedDouble = {
if (outputsMerged == totalOutputs) {
new BoundedDouble(counter.mean, 1.0, counter.mean, counter.mean)
} else if (outputsMerged == 0 || counter.count == 0) {
new BoundedDouble(0, 0.0, Double.NegativeInfinity, Double.PositiveInfinity)
} else if (counter.count == 1) {
new BoundedDouble(counter.mean, confidence, Double.NegativeInfinity, Double.PositiveInfinity)
} else {
val mean = counter.mean
val stdev = math.sqrt(counter.sampleVariance / counter.count)
val confFactor = if (counter.count > 100) {
// For large n, the normal distribution is a good approximation to t-distribution
new NormalDistribution().inverseCumulativeProbability((1 + confidence) / 2)
} else {
// t-distribution describes distribution of actual population mean
// note that if this goes to 0, TDistribution will throw an exception.
// Hence special casing 1 above.
val degreesOfFreedom = (counter.count - 1).toInt
new TDistribution(degreesOfFreedom).inverseCumulativeProbability((1 + confidence) / 2)
}
// Symmetric, so confidence interval is symmetric about mean of distribution
val low = mean - confFactor * stdev
val high = mean + confFactor * stdev
new BoundedDouble(mean, confidence, low, high)
}
}
}
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