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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 spark.partial
import java.util.{HashMap => JHashMap}
import java.util.{Map => JMap}
import scala.collection.mutable.HashMap
import scala.collection.Map
import scala.collection.JavaConversions.mapAsScalaMap
import spark.util.StatCounter
/**
* An ApproximateEvaluator for sums by key. Returns a map of key to confidence interval.
*/
private[spark] class GroupedSumEvaluator[T](totalOutputs: Int, confidence: Double)
extends ApproximateEvaluator[JHashMap[T, StatCounter], Map[T, BoundedDouble]] {
var outputsMerged = 0
var sums = new JHashMap[T, StatCounter] // Sum of counts for each key
override def merge(outputId: Int, taskResult: JHashMap[T, StatCounter]) {
outputsMerged += 1
val iter = taskResult.entrySet.iterator()
while (iter.hasNext) {
val entry = iter.next()
val old = sums.get(entry.getKey)
if (old != null) {
old.merge(entry.getValue)
} else {
sums.put(entry.getKey, entry.getValue)
}
}
}
override def currentResult(): Map[T, BoundedDouble] = {
if (outputsMerged == totalOutputs) {
val result = new JHashMap[T, BoundedDouble](sums.size)
val iter = sums.entrySet.iterator()
while (iter.hasNext) {
val entry = iter.next()
val sum = entry.getValue.sum
result(entry.getKey) = new BoundedDouble(sum, 1.0, sum, sum)
}
result
} else if (outputsMerged == 0) {
new HashMap[T, BoundedDouble]
} else {
val p = outputsMerged.toDouble / totalOutputs
val studentTCacher = new StudentTCacher(confidence)
val result = new JHashMap[T, BoundedDouble](sums.size)
val iter = sums.entrySet.iterator()
while (iter.hasNext) {
val entry = iter.next()
val counter = entry.getValue
val meanEstimate = counter.mean
val meanVar = counter.sampleVariance / counter.count
val countEstimate = (counter.count + 1 - p) / p
val countVar = (counter.count + 1) * (1 - p) / (p * p)
val sumEstimate = meanEstimate * countEstimate
val sumVar = (meanEstimate * meanEstimate * countVar) +
(countEstimate * countEstimate * meanVar) +
(meanVar * countVar)
val sumStdev = math.sqrt(sumVar)
val confFactor = studentTCacher.get(counter.count)
val low = sumEstimate - confFactor * sumStdev
val high = sumEstimate + confFactor * sumStdev
result(entry.getKey) = new BoundedDouble(sumEstimate, confidence, low, high)
}
result
}
}
}
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