RegressionMetricsSuite.scala

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 * 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
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 *
 *    http://www.apache.org/licenses/LICENSE-2.0
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 * distributed under the License is distributed on an "AS IS" BASIS,
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package org.apache.spark.mllib.evaluation

import org.apache.spark.SparkFunSuite
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.mllib.util.TestingUtils._

class RegressionMetricsSuite extends SparkFunSuite with MLlibTestSparkContext {

  test("regression metrics for unbiased (includes intercept term) predictor") {
    /* Verify results in R:
       preds = c(2.25, -0.25, 1.75, 7.75)
       obs = c(3.0, -0.5, 2.0, 7.0)

       SStot = sum((obs - mean(obs))^2)
       SSreg = sum((preds - mean(obs))^2)
       SSerr = sum((obs - preds)^2)

       explainedVariance = SSreg / length(obs)
       explainedVariance
       > [1] 8.796875
       meanAbsoluteError = mean(abs(preds - obs))
       meanAbsoluteError
       > [1] 0.5
       meanSquaredError = mean((preds - obs)^2)
       meanSquaredError
       > [1] 0.3125
       rmse = sqrt(meanSquaredError)
       rmse
       > [1] 0.559017
       r2 = 1 - SSerr / SStot
       r2
       > [1] 0.9571734
     */
    val predictionAndObservations = sc.parallelize(
      Seq((2.25, 3.0), (-0.25, -0.5), (1.75, 2.0), (7.75, 7.0)), 2)
    val metrics = new RegressionMetrics(predictionAndObservations)
    assert(metrics.explainedVariance ~== 8.79687 absTol 1E-5,
      "explained variance regression score mismatch")
    assert(metrics.meanAbsoluteError ~== 0.5 absTol 1E-5, "mean absolute error mismatch")
    assert(metrics.meanSquaredError ~== 0.3125 absTol 1E-5, "mean squared error mismatch")
    assert(metrics.rootMeanSquaredError ~== 0.55901 absTol 1E-5,
      "root mean squared error mismatch")
    assert(metrics.r2 ~== 0.95717 absTol 1E-5, "r2 score mismatch")
  }

  test("regression metrics for biased (no intercept term) predictor") {
    /* Verify results in R:
       preds = c(2.5, 0.0, 2.0, 8.0)
       obs = c(3.0, -0.5, 2.0, 7.0)

       SStot = sum((obs - mean(obs))^2)
       SSreg = sum((preds - mean(obs))^2)
       SSerr = sum((obs - preds)^2)

       explainedVariance = SSreg / length(obs)
       explainedVariance
       > [1] 8.859375
       meanAbsoluteError = mean(abs(preds - obs))
       meanAbsoluteError
       > [1] 0.5
       meanSquaredError = mean((preds - obs)^2)
       meanSquaredError
       > [1] 0.375
       rmse = sqrt(meanSquaredError)
       rmse
       > [1] 0.6123724
       r2 = 1 - SSerr / SStot
       r2
       > [1] 0.9486081
     */
    val predictionAndObservations = sc.parallelize(
      Seq((2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)), 2)
    val metrics = new RegressionMetrics(predictionAndObservations)
    assert(metrics.explainedVariance ~== 8.85937 absTol 1E-5,
      "explained variance regression score mismatch")
    assert(metrics.meanAbsoluteError ~== 0.5 absTol 1E-5, "mean absolute error mismatch")
    assert(metrics.meanSquaredError ~== 0.375 absTol 1E-5, "mean squared error mismatch")
    assert(metrics.rootMeanSquaredError ~== 0.61237 absTol 1E-5,
      "root mean squared error mismatch")
    assert(metrics.r2 ~== 0.94860 absTol 1E-5, "r2 score mismatch")
  }

  test("regression metrics with complete fitting") {
    val predictionAndObservations = sc.parallelize(
      Seq((3.0, 3.0), (0.0, 0.0), (2.0, 2.0), (8.0, 8.0)), 2)
    val metrics = new RegressionMetrics(predictionAndObservations)
    assert(metrics.explainedVariance ~== 8.6875 absTol 1E-5,
      "explained variance regression score mismatch")
    assert(metrics.meanAbsoluteError ~== 0.0 absTol 1E-5, "mean absolute error mismatch")
    assert(metrics.meanSquaredError ~== 0.0 absTol 1E-5, "mean squared error mismatch")
    assert(metrics.rootMeanSquaredError ~== 0.0 absTol 1E-5,
      "root mean squared error mismatch")
    assert(metrics.r2 ~== 1.0 absTol 1E-5, "r2 score mismatch")
  }
}