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[SPARK-6080] [PySpark] correct LogisticRegressionWithLBFGS regType parameter for pyspark #4831

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[SPARK-6080] [PySpark] correct LogisticRegressionWithLBFGS regType parameter for pyspark #4831

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2 changes: 1 addition & 1 deletion python/pyspark/mllib/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,7 +207,7 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
float(regParam), str(regType), bool(intercept), int(corrections),
float(regParam), regType, bool(intercept), int(corrections),

@mengxr mengxr Mar 1, 2015

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Shall we use str(regType) if regType else None?

@yanboliang yanboliang Mar 2, 2015

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I think directly use regType is enough, because py4j can translate "l1","l2",None in Python to "l1","l2",null in Java/Scala smoothly.
I found the peer functions LogisticRegressionWithSGD and SVMWithSGD also directly use regType and it can work well.

@mengxr mengxr Mar 2, 2015

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The difference is the error message. If we use str(...), the Java function call would be successful and inside the Java function, we say the input regType is not recognized.

If we don't use str(..), if users input something like 1, 2. Py4j will throw an error saying there is no Java function matching the input signature, which usually confuses users.

Anyway, this is a minor issue for regType.

float(tolerance))

return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
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