Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15721#discussion_r93172654
--- 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,
+ estimator: E with HasWeightCol with HasLabelCol with
HasFeaturesCol,
+ categoricalFeaturesInfo: Map[Int, Int],
+ numPoints: Int,
+ numClasses: Int,
+ numFeatures: Int,
+ modelEquals: (M, M) => Unit,
+ seed: Long): Unit = {
+ import spark.implicits._
+ val df = generateNoisyData(numPoints, numClasses, numFeatures,
categoricalFeaturesInfo,
+ seed).toDF()
+ val (overSampledData, weightedData) =
genEquivalentOversampledAndWeightedInstances(
+ df, estimator.getLabelCol, estimator.getFeaturesCol, seed)
+ val weightedModel = estimator.set(estimator.weightCol,
"weight").fit(weightedData)
+ val overSampledModel = estimator.set(estimator.weightCol,
"").fit(overSampledData)
+ modelEquals(weightedModel, overSampledModel)
+ }
+
+ /**
+ * Helper function for testing sample weights. Tests that injecting a
large number of outliers
+ * with very small sample weights does not affect fitting. The predictor
should learn the true
+ * model despite the outliers.
+ */
+ def testOutliersWithSmallWeights[M <: Model[M], E <: Estimator[M]](
+ spark: SparkSession,
+ estimator: E with HasWeightCol with HasLabelCol with
HasFeaturesCol,
+ categoricalFeaturesInfo: Map[Int, Int],
+ numPoints: Int,
+ numClasses: Int,
+ numFeatures: Int,
+ modelEquals: (M, M) => Unit,
+ seed: Long): Unit = {
+ import spark.implicits._
+ val df = generateNoisyData(numPoints, numClasses, numFeatures,
categoricalFeaturesInfo,
+ seed).toDF()
+ val outlierFunction = getRandomLinearPredictionFunction(numFeatures,
numClasses, seed - 1)
--- End diff --
I'm more prefer to implement ```outlierFunction``` as a simple function
such as:
* ```class -> numClass - class - 1``` for classification.
* ```label -> -label``` for regression.
which should be more intuitional and easy to be understand by
developers/contributors.
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