Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/5711#discussion_r29217153
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala ---
@@ -967,6 +969,23 @@ class DataFrame private[sql](
}
/**
+ * Randomly splits this DataFrame with the provided weights.
+ *
+ * @param weights weights for splits, will be normalized if they don't
sum to 1
+ * @param seed random seed
+ *
+ * @return split DataFrames in an array
+ */
+ def randomSplit(weights: Array[Double], seed: Long =
Utils.random.nextLong): Array[DataFrame] = {
+ val sum = weights.sum
+ val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _)
+ normalizedCumWeights.sliding(2).map { x =>
+ this.sqlContext.createDataFrame(new PartitionwiseSampledRDD[Row,
Row](
+ rdd, new BernoulliCellSampler[Row](x(0), x(1)), true, seed),
schema)
--- End diff --
this actually breaks the plan -- can we create a logical operator (or
generalizes the existing Sample operator) so the returned DataFrame correctly
preserves the logical plan?
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