Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15721#discussion_r93762341
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala ---
@@ -224,4 +208,59 @@ object MLTestingUtils extends SparkFunSuite {
}.toDF()
(overSampledData, weightedData)
}
+
+ /**
+ * 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]](
+ df: DataFrame,
+ estimator: E with HasWeightCol with HasLabelCol with HasFeaturesCol,
+ modelEquals: (M, M) => Unit,
+ seed: Long): Unit = {
+ 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]](
+ ds: Dataset[Instance],
--- End diff --
I'd prefer to change this to ```data: Dataset[LabeledPoint]```(pass in
dataset w/o weight), and move ```.withColumn("weight", lit(1.0))```(which are
duplicated in test cases of each algorithms currently) inside this function.
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