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Posted to commits@spark.apache.org by me...@apache.org on 2015/03/02 19:17:28 UTC
spark git commit: [SPARK-6080] [PySpark] correct
LogisticRegressionWithLBFGS regType parameter for pyspark
Repository: spark
Updated Branches:
refs/heads/master e7d8ae444 -> af2effdd7
[SPARK-6080] [PySpark] correct LogisticRegressionWithLBFGS regType parameter for pyspark
Currently LogisticRegressionWithLBFGS in python/pyspark/mllib/classification.py will invoke callMLlibFunc with a wrong "regType" parameter.
It was assigned to "str(regType)" which translate None(Python) to "None"(Java/Scala). The right way should be translate None(Python) to null(Java/Scala) just as what we did at LogisticRegressionWithSGD.
Author: Yanbo Liang <yb...@gmail.com>
Closes #4831 from yanboliang/pyspark_classification and squashes the following commits:
12db65a [Yanbo Liang] correct LogisticRegressionWithLBFGS regType parameter for pyspark
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/af2effdd
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/af2effdd
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/af2effdd
Branch: refs/heads/master
Commit: af2effdd7b54316af0c02e781911acfb148b962b
Parents: e7d8ae4
Author: Yanbo Liang <yb...@gmail.com>
Authored: Mon Mar 2 10:17:24 2015 -0800
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Mon Mar 2 10:17:24 2015 -0800
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python/pyspark/mllib/classification.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/af2effdd/python/pyspark/mllib/classification.py
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diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 00e2e76..e476517 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -207,7 +207,7 @@ class LogisticRegressionWithLBFGS(object):
"""
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),
float(tolerance))
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
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