Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/15721#discussion_r93172182 --- Diff: mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala --- @@ -224,4 +208,139 @@ object MLTestingUtils extends SparkFunSuite { }.toDF() (overSampledData, weightedData) } + + /** + * Generates a linear prediction function where the coefficients are generated randomly. + * The function produces a continuous (numClasses = 0) or categorical (numClasses > 0) label. + */ + def getRandomLinearPredictionFunction( + numFeatures: Int, + numClasses: Int, + seed: Long): (Vector => Double) = { + val rng = new scala.util.Random(seed) + val trueNumClasses = if (numClasses == 0) 1 else numClasses + val coefArray = Array.fill(numFeatures * trueNumClasses)(rng.nextDouble - 0.5) + (features: Vector) => { + if (numClasses == 0) { + BLAS.dot(features, new DenseVector(coefArray)) + } else { + val margins = new DenseVector(new Array[Double](numClasses)) + val coefMat = new DenseMatrix(numClasses, numFeatures, coefArray) + BLAS.gemv(1.0, coefMat, features, 1.0, margins) + margins.argmax.toDouble + } + } + } + + /** + * A helper function to generate synthetic data. Generates random feature values, + * both categorical and continuous, according to `categoricalFeaturesInfo`. The label is generated + * from a random prediction function, and noise is added to the true label. + * + * @param numPoints The number of data points to generate. + * @param numClasses The number of classes the outcome can take on. 0 for continuous labels. + * @param numFeatures The number of features in the data. + * @param categoricalFeaturesInfo Map of (featureIndex -> numCategories) for categorical features. + * @param seed Random seed. + * @param noiseLevel A number in [0.0, 1.0] indicating how much noise to add to the label. + * @return Generated sequence of noisy instances. + */ + def generateNoisyData( + numPoints: Int, + numClasses: Int, + numFeatures: Int, + categoricalFeaturesInfo: Map[Int, Int], + seed: Long, + noiseLevel: Double = 0.3): Seq[Instance] = { + require(noiseLevel >= 0.0 && noiseLevel <= 1.0, "noiseLevel must be in range [0.0, 1.0]") + val rng = new scala.util.Random(seed) + val predictionFunc = getRandomLinearPredictionFunction(numFeatures, numClasses, seed) + Range(0, numPoints).map { i => + val features = Vectors.dense(Array.tabulate(numFeatures) { j => + val numCategories = categoricalFeaturesInfo.getOrElse(j, 0) + if (numCategories > 0) { + rng.nextInt(numCategories) + } else { + rng.nextDouble() - 0.5 + } + }) + val label = predictionFunc(features) + val noisyLabel = if (numClasses > 0) { + // with probability equal to noiseLevel, select a random class instead of the true class + if (rng.nextDouble < noiseLevel) rng.nextInt(numClasses) else label + } else { + // add noise to the label proportional to the noise level + label + noiseLevel * rng.nextGaussian() + } + Instance(noisyLabel, 1.0, features) + } + } + + /** + * Helper function for testing sample weights. Tests that oversampling each point is equivalent + * to assigning a sample weight proportional to the number of samples for each point. + */ + def testOversamplingVsWeighting[M <: Model[M], E <: Estimator[M]]( + spark: SparkSession, --- End diff -- Indent.
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