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I am having trouble using a UDF on a column of Vectors in PySpark which can be illustrated here:

from pyspark import SparkContext
from pyspark.sql import Row
from pyspark.sql.types import DoubleType
from pyspark.sql.functions import udf
from pyspark.mllib.linalg import Vectors

FeatureRow = Row('id', 'features')
data = sc.parallelize([(0, Vectors.dense([9.7, 1.0, -3.2])),
                       (1, Vectors.dense([2.25, -11.1, 123.2])),
                       (2, Vectors.dense([-7.2, 1.0, -3.2]))])
df = data.map(lambda r: FeatureRow(*r)).toDF()

vector_udf = udf(lambda vector: sum(vector), DoubleType())

df.withColumn('feature_sums', vector_udf(df.features)).first()

This fails with the following stack trace:

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 31.0 failed 1 times, most recent failure: Lost task 5.0 in stage 31.0 (TID 95, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/Users/colin/src/spark/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main
    process()
  File "/Users/colin/src/spark/python/lib/pyspark.zip/pyspark/worker.py", line 106, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
x1  File "/Users/colin/src/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "/Users/colin/src/spark/python/pyspark/sql/functions.py", line 469, in <lambda>
    func = lambda _, it: map(lambda x: f(*x), it)
  File "/Users/colin/pokitdok/spark_mapper/spark_mapper/filters.py", line 143, in <lambda>
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

Looking at what gets passed to the UDF, there seems to be something strange. The argument passed should be a Vector, but instead it gets passed a Python tuple like this:

(1, None, None, [9.7, 1.0, -3.2])

Is it not possible to use UDFs on DataFrame columns of Vectors?

EDIT

So it was pointed out on the mailing list that this is a known issue. Going to accept the answer from @hyim since it does provider a temporary workaround for dense vectors.

Christian Alis
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calstad
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1 Answers1

5

In spark-sql, vectors are treated (type, size, indices, value) tuple.

You can use udf on vectors with pyspark. Just modify some code to work with values in vector type.

vector_udf = udf(lambda vector: sum(vector[3]), DoubleType())

df.withColumn('feature_sums', vector_udf(df.features)).first()

https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala

hyim
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  • So the object in vector[3] is a python list and not a pyspark.mllib.linalg.DenseVector. I guess its up to me to convert it to the Vector type? – calstad Jun 18 '15 at 15:00