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Adding binaryclassification bin score evaluator (#119)
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core/src/main/scala/com/salesforce/op/evaluators/OpBinScoreEvaluator.scala
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| /* | ||
| * Copyright (c) 2017, Salesforce.com, Inc. | ||
| * All rights reserved. | ||
| * | ||
| * Redistribution and use in source and binary forms, with or without | ||
| * modification, are permitted provided that the following conditions are met: | ||
| * | ||
| * * Redistributions of source code must retain the above copyright notice, this | ||
| * list of conditions and the following disclaimer. | ||
| * | ||
| * * Redistributions in binary form must reproduce the above copyright notice, | ||
| * this list of conditions and the following disclaimer in the documentation | ||
| * and/or other materials provided with the distribution. | ||
| * | ||
| * * Neither the name of the copyright holder nor the names of its | ||
| * contributors may be used to endorse or promote products derived from | ||
| * this software without specific prior written permission. | ||
| * | ||
| * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
| * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
| * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | ||
| * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
| * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
| * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
| * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| */ | ||
| package com.salesforce.op.evaluators | ||
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| import com.fasterxml.jackson.databind.annotation.JsonDeserialize | ||
| import com.salesforce.op.UID | ||
| import org.apache.spark.ml.linalg.Vector | ||
| import org.apache.spark.sql.{Dataset, Row} | ||
| import org.slf4j.LoggerFactory | ||
| import org.apache.spark.sql.functions.col | ||
| import org.apache.spark.sql.types.DoubleType | ||
| import com.twitter.algebird.Operators._ | ||
| import com.twitter.algebird.Monoid._ | ||
| import org.apache.spark.rdd.RDD | ||
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| /** | ||
| * | ||
| * Evaluator for Binary Classification which provides statistics about the predicted scores. | ||
| * This evaluator creates the specified number of bins and computes the statistics for each bin | ||
| * and returns BinaryClassificationBinMetrics, which contains | ||
| * | ||
| * Total number of data points per bin | ||
| * Average Score per bin | ||
| * Average Conversion rate per bin | ||
| * Bin Centers for each bin | ||
| * BrierScore for the overall dataset is also computed, which is a default metric as well. | ||
| * | ||
| * @param name name of default metric | ||
| * @param isLargerBetter is metric better if larger | ||
| * @param uid uid for instance | ||
| */ | ||
| private[op] class OpBinScoreEvaluator | ||
| ( | ||
| override val name: EvalMetric = OpEvaluatorNames.BinScore, | ||
| override val isLargerBetter: Boolean = true, | ||
| override val uid: String = UID[OpBinScoreEvaluator], | ||
| val numBins: Int = 100 | ||
| ) extends OpBinaryClassificationEvaluatorBase[BinaryClassificationBinMetrics](uid = uid) { | ||
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| require(numBins > 0, "numBins must be positive") | ||
| @transient private lazy val log = LoggerFactory.getLogger(this.getClass) | ||
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| def getDefaultMetric: BinaryClassificationBinMetrics => Double = _.brierScore | ||
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| override def evaluateAll(data: Dataset[_]): BinaryClassificationBinMetrics = { | ||
| val labelColumnName = getLabelCol | ||
| val dataProcessed = makeDataToUse(data, labelColumnName) | ||
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| val rdd = dataProcessed.select(col(getProbabilityCol), col(labelColumnName).cast(DoubleType)).rdd | ||
| if (rdd.isEmpty()) { | ||
| log.error("The dataset is empty. Returning empty metrics") | ||
| BinaryClassificationBinMetrics(0.0, Seq(), Seq(), Seq(), Seq()) | ||
| } else { | ||
| val scoreAndLabels = rdd.map { | ||
| case Row(prob: Vector, label: Double) => (prob(1), label) | ||
| case Row(prob: Double, label: Double) => (prob, label) | ||
| } | ||
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| val (maxScore, minScore) = scoreAndLabels.map { | ||
| case (score , _) => (score, score) | ||
| }.fold(1.0, 0.0) { | ||
| case((maxVal, minVal), (scoreMax, scoreMin)) => { | ||
| (math.max(maxVal, scoreMax), math.min(minVal, scoreMin)) | ||
| } | ||
| } | ||
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| // Finding stats per bin -> avg score, avg conv rate, | ||
| // total num of data points and overall brier score. | ||
| val stats = scoreAndLabels.map { | ||
| case (score, label) => | ||
| (getBinIndex(score, minScore, maxScore), (score, label, 1L, math.pow((score - label), 2))) | ||
| }.reduceByKey(_ + _).map { | ||
| case (bin, (scoreSum, labelSum, count, squaredError)) => | ||
| (bin, scoreSum / count, labelSum / count, count, squaredError) | ||
| }.collect() | ||
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| val (averageScore, averageConversionRate, numberOfDataPoints, brierScoreSum, numberOfPoints) = | ||
| stats.foldLeft((new Array[Double](numBins), new Array[Double](numBins), new Array[Long](numBins), 0.0, 0L)) { | ||
| case ((score, convRate, dataPoints, brierScoreSum, totalPoints), | ||
| (binIndex, avgScore, avgConvRate, counts, squaredError)) => { | ||
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| score(binIndex) = avgScore | ||
| convRate(binIndex) = avgConvRate | ||
| dataPoints(binIndex) = counts | ||
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| (score, convRate, dataPoints, brierScoreSum + squaredError, totalPoints + counts) | ||
| } | ||
| } | ||
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| // binCenters is the center point in each bin. | ||
| // e.g., for bins [(0.0 - 0.5), (0.5 - 1.0)], bin centers are [0.25, 0.75]. | ||
| val diff = maxScore - minScore | ||
| val binCenters = (for {i <- 0 to numBins-1} yield (minScore + ((diff * i) / numBins) + (diff / (2 * numBins)))) | ||
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| val metrics = BinaryClassificationBinMetrics( | ||
| brierScore = brierScoreSum / numberOfPoints, | ||
| binCenters = binCenters, | ||
| numberOfDataPoints = numberOfDataPoints, | ||
| averageScore = averageScore, | ||
| averageConversionRate = averageConversionRate | ||
| ) | ||
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| log.info("Evaluated metrics: {}", metrics.toString) | ||
| metrics | ||
| } | ||
| } | ||
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| // getBinIndex finds which bin the score associates with. | ||
| private def getBinIndex(score: Double, minScore: Double, maxScore: Double): Int = { | ||
| val binIndex = (numBins * (score - minScore) / (maxScore - minScore)).toInt | ||
| math.min(numBins - 1, binIndex) | ||
| } | ||
| } | ||
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| /** | ||
| * Metrics of BinaryClassificationBinMetrics | ||
| * | ||
| * @param binCenters center of each bin | ||
| * @param numberOfDataPoints total number of data points in each bin | ||
| * @param averageScore average score in each bin | ||
| * @param averageConversionRate average conversion rate in each bin | ||
| * @param brierScore brier score for overall dataset | ||
| */ | ||
| case class BinaryClassificationBinMetrics | ||
| ( | ||
| brierScore: Double, | ||
| @JsonDeserialize(contentAs = classOf[java.lang.Double]) | ||
| binCenters: Seq[Double], | ||
| @JsonDeserialize(contentAs = classOf[java.lang.Long]) | ||
| numberOfDataPoints: Seq[Long], | ||
| @JsonDeserialize(contentAs = classOf[java.lang.Double]) | ||
| averageScore: Seq[Double], | ||
| @JsonDeserialize(contentAs = classOf[java.lang.Double]) | ||
| averageConversionRate: Seq[Double] | ||
| ) extends EvaluationMetrics |
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