Xiangrui Meng created SPARK-30154:
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Summary: Allow PySpark code efficiently convert MLlib vectors to
dense arrays
Key: SPARK-30154
URL: https://issues.apache.org/jira/browse/SPARK-30154
Project: Spark
Issue Type: New Feature
Components: ML, MLlib, PySpark
Affects Versions: 3.0.0
Reporter: Xiangrui Meng
If a PySpark user wants to convert MLlib sparse/dense vectors in a DataFrame
into dense arrays, an efficient method is to do that in JVM. However, it
requires PySpark user to write Scala code and register it as a UDF. Often this
is infeasible for a pure python project.
What we can do is to predefine those converters in Scala and expose them in
PySpark, e.g.:
{code}
from pyspark.ml.functions import vector_to_dense_array
df.select(vector_to_dense_array(col("features"))
{code}
cc: [~weichenxu123]
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