org.apache.spark.util.Distribution.scala Maven / Gradle / Ivy
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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,
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* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.util
import java.io.PrintStream
import scala.collection.immutable.IndexedSeq
/**
* Util for getting some stats from a small sample of numeric values, with some handy
* summary functions.
*
* Entirely in memory, not intended as a good way to compute stats over large data sets.
*
* Assumes you are giving it a non-empty set of data
*/
private[spark] class Distribution(val data: Array[Double], val startIdx: Int, val endIdx: Int) {
require(startIdx < endIdx)
def this(data: Traversable[Double]) = this(data.toArray, 0, data.size)
java.util.Arrays.sort(data, startIdx, endIdx)
val length = endIdx - startIdx
val defaultProbabilities = Array(0, 0.25, 0.5, 0.75, 1.0)
/**
* Get the value of the distribution at the given probabilities. Probabilities should be
* given from 0 to 1
* @param probabilities
*/
def getQuantiles(probabilities: Traversable[Double] = defaultProbabilities)
: IndexedSeq[Double] = {
probabilities.toIndexedSeq.map { p: Double => data(closestIndex(p)) }
}
private def closestIndex(p: Double) = {
math.min((p * length).toInt + startIdx, endIdx - 1)
}
def showQuantiles(out: PrintStream = System.out): Unit = {
// scalastyle:off println
out.println("min\t25%\t50%\t75%\tmax")
getQuantiles(defaultProbabilities).foreach{q => out.print(q + "\t")}
out.println
// scalastyle:on println
}
def statCounter: StatCounter = StatCounter(data.slice(startIdx, endIdx))
/**
* print a summary of this distribution to the given PrintStream.
* @param out
*/
def summary(out: PrintStream = System.out) {
// scalastyle:off println
out.println(statCounter)
showQuantiles(out)
// scalastyle:on println
}
}
private[spark] object Distribution {
def apply(data: Traversable[Double]): Option[Distribution] = {
if (data.size > 0) {
Some(new Distribution(data))
} else {
None
}
}
def showQuantiles(out: PrintStream = System.out, quantiles: Traversable[Double]) {
// scalastyle:off println
out.println("min\t25%\t50%\t75%\tmax")
quantiles.foreach{q => out.print(q + "\t")}
out.println
// scalastyle:on println
}
}