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[SPARK-16831] [Python] Fixed bug in CrossValidator.avgMetrics #14456

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[SPARK-16831] [Python] Fixed bug in CrossValidator.avgMetrics #14456

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e253be5
fixed CrossValidator.avgMetric calculation
pkch Aug 2, 2016
0a4b0a9
added regression test in doc string
pkch Aug 3, 2016
c426a98
Update tuning.py
pkch Aug 3, 2016
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4 changes: 3 additions & 1 deletion python/pyspark/ml/tuning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,6 +166,8 @@ class CrossValidator(Estimator, ValidatorParams):
>>> evaluator = BinaryClassificationEvaluator()
>>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
>>> cvModel = cv.fit(dataset)
>>> cvModel.avgMetrics[0]
0.5
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@srowen srowen Aug 3, 2016

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Nice, I assume this would have printed 1.5 before

>>> evaluator.evaluate(cvModel.transform(dataset))
0.8333...

Expand Down Expand Up @@ -234,7 +236,7 @@ def _fit(self, dataset):
model = est.fit(train, epm[j])
# TODO: duplicate evaluator to take extra params from input
metric = eva.evaluate(model.transform(validation, epm[j]))
metrics[j] += metric
metrics[j] += metric/nFolds
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@srowen srowen Aug 2, 2016

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This may fail style checks but we'll see

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@srowen srowen Aug 2, 2016

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OK looks fine actually. Would it be possible to add a little bit to the test above this to demonstrate that the result is correct now? just testing the value of the first element of metrics for example.

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@pkch pkch Aug 3, 2016 •
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Done (2 commits because I made a typo.)


if eva.isLargerBetter():
bestIndex = np.argmax(metrics)
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