Github user holdenk commented on a diff in the pull request:
    --- Diff: examples/src/main/python/mllib/ ---
    @@ -0,0 +1,54 @@
    +# Licensed to the Apache Software Foundation (ASF) under one or more
    +# contributor license agreements.  See the NOTICE file distributed with
    +# this work for additional information regarding copyright ownership.
    +# The ASF licenses this file to You under the Apache License, Version 2.0
    +# (the "License"); you may not use this file except in compliance with
    +# the License.  You may obtain a copy of the License at
    +# Unless required by applicable law or agreed to in writing, software
    +# distributed under the License is distributed on an "AS IS" BASIS,
    +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    +# See the License for the specific language governing permissions and
    +# limitations under the License.
    +from __future__ import print_function
    +from pyspark import SparkContext
    +import numpy as np
    +# $example on$
    +from pyspark.mllib.regression import LabeledPoint
    +from pyspark.mllib.feature import ChiSqSelector
    +from pyspark.mllib.util import MLUtils
    +# $example off$
    +if __name__ == "__main__":
    +    sc = SparkContext(appName="ChiSqSelectorExample")
    +    # $example on$
    +    # Load and parse the data file into an RDD of LabeledPoint.
    +    data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
    +    # Discretize data in 16 equal bins since ChiSqSelector requires 
categorical features
    +    def distributeOverBins(lp):
    +        return np.array(map(lambda x: x % 16, lp.features.toArray()))
    +    # Even though features are doubles, the ChiSqSelector treats each 
unique value as a category
    +    discretizedData = lp: LabeledPoint(lp.label, 
    +    # Create ChiSqSelector that will select top 50 of 692 features
    +    selector = ChiSqSelector(numTopFeatures=50)
    +    # Create ChiSqSelector model (selecting features)
    +    transformer =
    +    # Filter the top 50 features from each feature vector
    +    filteredData = transformer.transform( lp: 
    --- End diff --
    Right sorry I assumed this was ml probably because thats where we are doing 
the new feature work, but documenting the old mllib makes sense. Even though 
the PyDoc says "Applies transformation on a vector", looking at the 
JavaVectorTransformer base class it does take RDDs of vectors, so we shouldn't 
need to map it inside - indeed the PyDoc param also says `:param vector: Vector 
or RDD of Vector to be transformed.`
    Have you tried passing in an RDD of Vectors? If that doesn't work its 
certainly a bug we should fix,

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