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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.partial
import java.util.{HashMap => JHashMap}
import scala.collection.JavaConversions.mapAsScalaMap
import scala.collection.Map
import scala.collection.mutable.HashMap
import scala.reflect.ClassTag
import org.apache.commons.math3.distribution.NormalDistribution
import org.apache.spark.util.collection.OpenHashMap
/**
* An ApproximateEvaluator for counts by key. Returns a map of key to confidence interval.
*/
private[spark] class GroupedCountEvaluator[T : ClassTag](totalOutputs: Int, confidence: Double)
extends ApproximateEvaluator[OpenHashMap[T,Long], Map[T, BoundedDouble]] {
var outputsMerged = 0
var sums = new OpenHashMap[T,Long]() // Sum of counts for each key
override def merge(outputId: Int, taskResult: OpenHashMap[T,Long]) {
outputsMerged += 1
taskResult.foreach { case (key, value) =>
sums.changeValue(key, value, _ + value)
}
}
override def currentResult(): Map[T, BoundedDouble] = {
if (outputsMerged == totalOutputs) {
val result = new JHashMap[T, BoundedDouble](sums.size)
sums.foreach { case (key, sum) =>
result(key) = new BoundedDouble(sum, 1.0, sum, sum)
}
result
} else if (outputsMerged == 0) {
new HashMap[T, BoundedDouble]
} else {
val p = outputsMerged.toDouble / totalOutputs
val confFactor = new NormalDistribution().
inverseCumulativeProbability(1 - (1 - confidence) / 2)
val result = new JHashMap[T, BoundedDouble](sums.size)
sums.foreach { case (key, sum) =>
val mean = (sum + 1 - p) / p
val variance = (sum + 1) * (1 - p) / (p * p)
val stdev = math.sqrt(variance)
val low = mean - confFactor * stdev
val high = mean + confFactor * stdev
result(key) = new BoundedDouble(mean, confidence, low, high)
}
result
}
}
}
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