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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17508) Setting weightCol to None in ML library causes an error
Date Mon, 12 Sep 2016 17:28:20 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17508?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15484704#comment-15484704
] 

Sean Owen commented on SPARK-17508:
-----------------------------------

This looks a lot like the problem solved in SPARK-14931 / https://github.com/apache/spark/pull/12816
 -- you sure it's an issue in 2.0.0?

> Setting weightCol to None in ML library causes an error
> -------------------------------------------------------
>
>                 Key: SPARK-17508
>                 URL: https://issues.apache.org/jira/browse/SPARK-17508
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.0.0
>            Reporter: Evan Zamir
>
> The following code runs without error:
> {code}
> spark = SparkSession.builder.appName('WeightBug').getOrCreate()
> df = spark.createDataFrame(
>     [
>         (1.0, 1.0, Vectors.dense(1.0)),
>         (0.0, 1.0, Vectors.dense(-1.0))
>     ],
>     ["label", "weight", "features"])
> lr = LogisticRegression(maxIter=5, regParam=0.0, weightCol="weight")
> model = lr.fit(df)
> {code}
> My expectation from reading the documentation is that setting weightCol=None should treat
all weights as 1.0 (regardless of whether a column exists). However, the same code with weightCol
set to None causes the following errors:
> Traceback (most recent call last):
>   File "/Users/evanzamir/ams/px-seed-model/scripts/bug.py", line 32, in <module>
>     model = lr.fit(df)
>   File "/usr/local/spark-2.0.0-bin-hadoop2.7/python/pyspark/ml/base.py", line 64, in
fit
>     return self._fit(dataset)
>   File "/usr/local/spark-2.0.0-bin-hadoop2.7/python/pyspark/ml/wrapper.py", line 213,
in _fit
>     java_model = self._fit_java(dataset)
>   File "/usr/local/spark-2.0.0-bin-hadoop2.7/python/pyspark/ml/wrapper.py", line 210,
in _fit_java
>     return self._java_obj.fit(dataset._jdf)
>   File "/usr/local/spark-2.0.0-bin-hadoop2.7/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py",
line 933, in __call__
>   File "/usr/local/spark-2.0.0-bin-hadoop2.7/python/pyspark/sql/utils.py", line 63, in
deco
>     return f(*a, **kw)
>   File "/usr/local/spark-2.0.0-bin-hadoop2.7/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py",
line 312, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling o38.fit.
> : java.lang.NullPointerException
> 	at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:264)
> 	at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:259)
> 	at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:159)
> 	at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
> 	at org.apache.spark.ml.Predictor.fit(Predictor.scala:71)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> 	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> 	at java.lang.reflect.Method.invoke(Method.java:498)
> 	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
> 	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> 	at py4j.Gateway.invoke(Gateway.java:280)
> 	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
> 	at py4j.commands.CallCommand.execute(CallCommand.java:79)
> 	at py4j.GatewayConnection.run(GatewayConnection.java:211)
> 	at java.lang.Thread.run(Thread.java:745)
> Process finished with exit code 1



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