Github user rubenjanssen 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 --
    That is the case in ml, but not in mllib unfortunately. As said before, the 
best way that would be aligned with the other examples in mllib doesn't seem to 
be possible as its not supported at the moment.
    Given that this is an issue specific to mllib and not to ml, and that ml is 
preferred over mllib, makes me wonder if its worth the effort to attempt 
resolving this.

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