Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8061#discussion_r36855786
--- Diff: python/pyspark/ml/feature.py ---
@@ -166,6 +167,62 @@ def getSplits(self):
@inherit_doc
+class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol):
+ """
+ Outputs the Hadamard product (i.e., the element-wise product) of each
input vector
+ with a provided "weight" vector. In other words, it scales each column
of the dataset
+ by a scalar multiplier.
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sqlContext.createDataFrame([(Vectors.dense([2.0, 1.0,
3.0]),)], ["values"])
+ >>> ep = ElementwiseProduct(scalingVec=Vectors.dense([1.0, 2.0, 3.0]),
+ ... inputCol="values", outputCol="eprod")
+ >>> ep.transform(df).head().eprod
+ DenseVector([2.0, 2.0, 9.0])
+ >>> ep.setParams(scalingVec=Vectors.dense([2.0, 3.0, 5.0]),
--- End diff --
Here I think setting output columns is necessary because it's not
recommended that the downstream transformers to reuse the output columns of the
upstream transformers in ML pipeline, so a new output column is better.
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