Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3603#discussion_r21582552
--- Diff: python/pyspark/mllib/feature.py ---
@@ -220,12 +220,15 @@ def transform(self, dataset):
the terms which occur in fewer than `minDocFreq`
documents will have an entry of 0.
- :param dataset: an RDD of term frequency vectors
- :return: an RDD of TF-IDF vectors
+ :param data: an RDD of term frequency vectors or a term frequency
vector
+ :return: an RDD of TF-IDF vectors or a TF-IDF vector
"""
- if not isinstance(dataset, RDD):
+ if isinstance(data, RDD):
+ return JavaVectorTransformer.transform(self, data)
+ elif isinstance(data, Vector):
--- End diff --
It might be good to support native Python vector/array types, as in
pyspark's LogisticRegressionModel.predict method in classification.py
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