/* * 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.stages.impl.feature import com.salesforce.op._ import com.salesforce.op.features.types._ import com.salesforce.op.features.{Feature, FeatureLike} import com.salesforce.op.stages.impl.regression.IsotonicRegressionCalibrator import com.salesforce.op.stages.sparkwrappers.specific.OpBinaryEstimatorWrapper import com.salesforce.op.test.{TestFeatureBuilder, TestSparkContext} import org.apache.spark.ml.Transformer import org.apache.spark.ml.regression.{IsotonicRegression, IsotonicRegressionModel} import org.apache.spark.sql._ import org.junit.runner.RunWith import org.scalatest.junit.JUnitRunner import org.scalatest.{FlatSpec, Matchers} @RunWith(classOf[JUnitRunner]) class IsotonicRegressionCalibratorTest extends FlatSpec with TestSparkContext { val isoExpectedPredictions = Array(1, 2, 2, 2, 6, 16.5, 16.5, 17, 18) val isoExpectedModelBoundaries = Array(0, 1, 3, 4, 5, 6, 7, 8) val isoExpectedModelPredictions = Array(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0) val isoDataLabels = Seq(1, 2, 3, 1, 6, 17, 16, 17, 18) val isoTestData = isoDataLabels.zipWithIndex.map { case (label, i) => label.toRealNN -> i.toRealNN } val (isoScoresDF, isoLabels, isoScores): (DataFrame, Feature[RealNN], Feature[RealNN]) = TestFeatureBuilder(isoTestData) val antiExpectedPredictions = Array(7.0, 5.0, 4.0, 4.0, 1.0) val antiExpectedModelBoundaries = Array(0, 1, 2, 3, 4) val antiExpectedModelPredictions = Array(7.0, 5.0, 4.0, 4.0, 1.0) val antiDataLabels = Seq(7, 5, 3, 5, 1) val antiTestData = antiDataLabels.zipWithIndex.map { case (label, i) => label.toRealNN -> i.toRealNN } val (antiScoresDF, antiLabels, antiScores): (DataFrame, Feature[RealNN], Feature[RealNN]) = TestFeatureBuilder(antiTestData) Spec[IsotonicRegressionCalibrator] should "isotonically calibrate scores using shortcut" in { val calibratedScores = isoScores.toIsotonicCalibrated(isoLabels) val estimator = calibratedScores.originStage .asInstanceOf[OpBinaryEstimatorWrapper[RealNN, RealNN, RealNN, IsotonicRegression, IsotonicRegressionModel]] val model = estimator.fit(isoScoresDF).getSparkMlStage().get val predictionsDF = model.asInstanceOf[Transformer] .transform(isoScoresDF) validateOutput(calibratedScores, model, predictionsDF, true, isoExpectedPredictions, isoExpectedModelBoundaries, isoExpectedModelPredictions) } it should "isotonically calibrate scores" in { val isotonicCalibrator = new IsotonicRegressionCalibrator().setInput(isoLabels, isoScores) val calibratedScores = isotonicCalibrator.getOutput() val model = isotonicCalibrator.fit(isoScoresDF).getSparkMlStage().get val predictionsDF = model.asInstanceOf[Transformer] .transform(isoScoresDF) validateOutput(calibratedScores, model, predictionsDF, true, isoExpectedPredictions, isoExpectedModelBoundaries, isoExpectedModelPredictions) } it should "antitonically calibrate scores" in { val isIsotonic: Boolean = false val isotonicCalibrator = new IsotonicRegressionCalibrator().setIsotonic(isIsotonic).setInput(isoLabels, isoScores) val calibratedScores = isotonicCalibrator.getOutput() val model = isotonicCalibrator.fit(antiScoresDF).getSparkMlStage().get val predictionsDF = model.asInstanceOf[Transformer] .transform(antiScoresDF) validateOutput(calibratedScores, model, predictionsDF, isIsotonic, antiExpectedPredictions, antiExpectedModelBoundaries, antiExpectedModelPredictions) } def validateOutput(calibratedScores: FeatureLike[RealNN], model: IsotonicRegressionModel, predictionsDF: DataFrame, expectedIsIsotonic: Boolean, expectedPredictions: Array[Double], expectedModelBoundaries: Array[Int], expectedModelPredictions: Array[Double]): Unit = { val predictions = predictionsDF.select(calibratedScores.name).rdd.map { case Row(pred) => pred }.collect() val isIsotonic = model.getIsotonic isIsotonic should be(expectedIsIsotonic) predictions should contain theSameElementsInOrderAs expectedPredictions model.boundaries.toArray should contain theSameElementsInOrderAs expectedModelBoundaries model.predictions.toArray should contain theSameElementsInOrderAs expectedModelPredictions } }