Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15721#discussion_r93891859
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala ---
@@ -182,34 +182,18 @@ object MLTestingUtils extends SparkFunSuite {
.toMap
}
- def genClassificationInstancesWithWeightedOutliers(
- spark: SparkSession,
- numClasses: Int,
- numInstances: Int): DataFrame = {
- val data = Array.tabulate[Instance](numInstances) { i =>
- val feature = i % numClasses
- if (i < numInstances / 3) {
- // give large weights to minority of data with 1 to 1 mapping
feature to label
- Instance(feature, 1.0, Vectors.dense(feature))
- } else {
- // give small weights to majority of data points with reverse
mapping
- Instance(numClasses - feature - 1, 0.01, Vectors.dense(feature))
- }
- }
- val labelMeta =
-
NominalAttribute.defaultAttr.withName("label").withNumValues(numClasses).toMetadata()
- spark.createDataFrame(data).select(col("label").as("label",
labelMeta), col("weight"),
- col("features"))
- }
-
+ /**
+ * Given a dataframe, generate two output dataframes: one having the
original rows oversampled
+ * an integer number of times, and one having the original rows but with
a column of weights
+ * proportional to the number of oversampled instances in the
oversampled dataframe.
+ */
def genEquivalentOversampledAndWeightedInstances(
--- End diff --
I made them all take `Dataset[LabeledPoint]`. Good suggestion.
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