zero323 commented on a change in pull request #30413:
URL: https://github.com/apache/spark/pull/30413#discussion_r529829185
##########
File path: python/pyspark/mllib/util.py
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@@ -273,24 +317,30 @@ def convertVectorColumnsFromML(dataset, *cols):
return callMLlibFunc("convertVectorColumnsFromML", dataset, list(cols))
@staticmethod
- @since("2.0.0")
def convertMatrixColumnsToML(dataset, *cols):
"""
Converts matrix columns in an input DataFrame from the
:py:class:`pyspark.mllib.linalg.Matrix` type to the new
:py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml`
package.
- :param dataset:
- input dataset
- :param cols:
- a list of matrix columns to be converted.
- New matrix columns will be ignored. If unspecified, all old
- matrix columns will be converted excepted nested ones.
- :return:
- the input dataset with old matrix columns converted to the
- new matrix type
+ .. versionadded:: 2.0.0
+ dataset : :py:class:`pyspark.sql.DataFrame`
+ input dataset
+ cols : str
Review comment:
That's a good question. Looking at
[`numpy.ndarray.item`](https://numpy.org/devdocs/reference/generated/numpy.ndarray.item.html)
we should make varargs explicit
*cols : ...
As of type I was thinking `str` as we support variable number of `str`
values, but looking at the code, a single `List[str]` would do as well.
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