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[SPARK-21306][ML] OneVsRest should support setWeightCol #18554

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facaiy wants to merge 16 commits into from
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[SPARK-21306][ML] OneVsRest should support setWeightCol #18554

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f178595
BUG: cache weightCol if necessary
facaiy Jul 6, 2017
a3e6e96
TST: add unit test
facaiy Jul 6, 2017
931d02d
TST: concise comment
facaiy Jul 6, 2017
25d681f
ENH: python, cache weightCol
facaiy Jul 7, 2017
c380ba7
BUG: check weightCol defined
facaiy Jul 7, 2017
e511b90
TST: model is not None
facaiy Jul 7, 2017
1c215f3
CLN: use if-indent style
facaiy Jul 7, 2017
2d3ce90
ENH: add weightCol
facaiy Jul 13, 2017
ea92228
TST: modify test case
facaiy Jul 13, 2017
57fc4b3
ENH: add weightCol for python part
facaiy Jul 13, 2017
00a7ed8
CLN: revise comment
facaiy Jul 13, 2017
a57f096
TST: python test for setWeightCol
facaiy Jul 13, 2017
54e0fca
DOC: add description
facaiy Jul 13, 2017
9ba0e2b
TST: classifier doesn't have weightCol
facaiy Jul 19, 2017
db303a0
CLN: mv weightCol to the end
facaiy Jul 26, 2017
8c0beba
TST: remove ovr2 nad ovr4
facaiy Jul 26, 2017
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ENH: python, cache weightCol
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@facaiy
facaiy committed Jul 7, 2017
commit 25d681f38ff670c8209dd1ae5c874e10ac89b402
5 changes: 4 additions & 1 deletion python/pyspark/ml/classification.py
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Expand Up @@ -1546,7 +1546,10 @@ def _fit(self, dataset):

numClasses = int(dataset.agg({labelCol: "max"}).head()["max("+labelCol+")"]) + 1

multiclassLabeled = dataset.select(labelCol, featuresCol)
if isinstance(classifier, HasWeightCol) and classifier.getWeightCol():
multiclassLabeled = dataset.select(labelCol, featuresCol, classifier.getWeightCol())
else:
multiclassLabeled = dataset.select(labelCol, featuresCol)

# persist if underlying dataset is not persistent.
handlePersistence = \
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10 changes: 10 additions & 0 deletions python/pyspark/ml/tests.py
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Expand Up @@ -1255,6 +1255,16 @@ def test_output_columns(self):
output = model.transform(df)
self.assertEqual(output.columns, ["label", "features", "prediction"])

def test_cache_weightCol_if_necessary(self):
df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8), 1.0),
(1.0, Vectors.sparse(2, [], []), 1.0),
(2.0, Vectors.dense(0.5, 0.5), 1.0)],
["label", "features", "weight"])
lr = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight")
ovr = OneVsRest(classifier=lr)
model = ovr.fit(df)
self.assertIsNone(model)


class HashingTFTest(SparkSessionTestCase):

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