Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/12577#discussion_r60655544
--- Diff: python/pyspark/ml/evaluation.py ---
@@ -187,6 +187,13 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol,
HasPredictionCol):
0.993...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"})
2.649...
+ >>> scoreAndLabels = [(4.0, 5.0), (4.0, 1.0), (float('nan'), 2.0),
+ ... (1.0, 3.0), (float('nan'), 4.0)]
+ >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw",
"label"])
+ ...
+ >>> evaluator =
RegressionEvaluator(predictionCol="raw").setDropNaN(True)
+ >>> evaluator.evaluate(dataset)
--- End diff --
This doesn't quite mirror the Scala test, since the scala test first checks
that the result is `NaN` when `dropNaN` is true. Shall we do the same check
here?
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