imatiach-msft commented on a change in pull request #17084:
[SPARK-24103][ML][MLLIB] ML Evaluators should use weight column - added weight
column for binary classification evaluator
URL: https://github.com/apache/spark/pull/17084#discussion_r259670199
##########
File path: python/pyspark/mllib/evaluation.py
##########
@@ -40,16 +40,28 @@ class BinaryClassificationMetrics(JavaModelWrapper):
>>> metrics.areaUnderPR
0.83...
>>> metrics.unpersist()
+ >>> scoreAndLabelsWithOptWeight = sc.parallelize([
+ ... (0.1, 0.0, 1.0), (0.1, 1.0, 0.4), (0.4, 0.0, 0.2), (0.6, 0.0,
0.6), (0.6, 1.0, 0.9),
+ ... (0.6, 1.0, 0.5), (0.8, 1.0, 0.7)], 2)
+ >>> metrics = BinaryClassificationMetrics(scoreAndLabelsWithOptWeight)
+ >>> metrics.areaUnderROC
+ 0.70...
+ >>> metrics.areaUnderPR
+ 0.83...
.. versionadded:: 1.4.0
"""
- def __init__(self, scoreAndLabels):
- sc = scoreAndLabels.ctx
+ def __init__(self, scoreAndLabelsWithOptWeight):
Review comment:
done, also updated the scala side and the multiclass metrics python and
scala classes. It looks like the python side for regression wasn't updated so
I will do that in a separate PR and undo the scala parameter name change there
too.
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