Hi,
I am trying to apply a UDF to a column in a PySpark df containing
SparseVectors (created using pyspark.ml.feature.IDF). Originally, I was
trying to apply a more involved function, but am getting the same error with
any application of a function. So for the sake of an example:
udfSum = udf(lambda x: np.sum(x.values), FloatType())
df = df.withColumn("vec_sum", udfSum(df.idf))
df.take(10)
I am getting this error:
Py4JJavaError: An error occurred while calling
z:org.apache.spark.sql.execution.python.EvaluatePython.takeAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
in stage 55.0 failed 4 times, most recent failure: Lost task 0.3 in stage
55.0 (TID 111, 10.0.11.102): net.razorvine.pickle.PickleException: expected
zero arguments for construction of ClassDict (for numpy.dtype)
If I convert the df to Pandas and apply the function, I can confirm that
FloatType() is the correct response type. This seems related:
https://issues.apache.org/jira/browse/SPARK-7902, but I'm not sure how to
proceed. Any ideas?
Thanks!
Abby
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