imatiach-msft commented on a change in pull request #23157:
[SPARK-26185][PYTHON]add weightCol in python MulticlassClassificationEvaluator
URL: https://github.com/apache/spark/pull/23157#discussion_r251622289
##########
File path: python/pyspark/mllib/evaluation.py
##########
@@ -192,16 +192,53 @@ class MulticlassMetrics(JavaModelWrapper):
0.66...
>>> metrics.weightedFMeasure(2.0)
0.65...
+ >>> predAndLabelsWithOptWeight = sc.parallelize([(0.0, 0.0, 1.0), (0.0,
1.0, 1.0),
+ ... (0.0, 0.0, 1.0), (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0,
1.0), (1.0, 1.0, 1.0),
+ ... (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)])
+ >>> metrics = MulticlassMetrics(predAndLabelsWithOptWeight)
+ >>> metrics.confusionMatrix().toArray()
+ array([[ 2., 1., 1.],
+ [ 1., 3., 0.],
+ [ 0., 0., 1.]])
+ >>> metrics.falsePositiveRate(0.0)
+ 0.2...
+ >>> metrics.precision(1.0)
+ 0.75...
+ >>> metrics.recall(2.0)
+ 1.0...
+ >>> metrics.fMeasure(0.0, 2.0)
+ 0.52...
+ >>> metrics.accuracy
+ 0.66...
+ >>> metrics.weightedFalsePositiveRate
+ 0.19...
+ >>> metrics.weightedPrecision
+ 0.68...
+ >>> metrics.weightedRecall
+ 0.66...
+ >>> metrics.weightedFMeasure()
+ 0.66...
+ >>> metrics.weightedFMeasure(2.0)
+ 0.65...
.. versionadded:: 1.4.0
"""
- def __init__(self, predictionAndLabels):
- sc = predictionAndLabels.ctx
+ def __init__(self, predAndLabelsWithOptWeight):
+ sc = predAndLabelsWithOptWeight.ctx
sql_ctx = SQLContext.getOrCreate(sc)
- df = sql_ctx.createDataFrame(predictionAndLabels, schema=StructType([
- StructField("prediction", DoubleType(), nullable=False),
- StructField("label", DoubleType(), nullable=False)]))
+ numCol = len(predAndLabelsWithOptWeight.first())
+ schema = None
+ if (numCol == 2):
Review comment:
minor: you can refactor out the StructField's below to variables
(prediction, label) or create an initial list and append the weight to it (to
limit duplicate code, as first two StructFields are exactly the same)
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