Github user viirya commented on a diff in the pull request:

    https://github.com/apache/spark/pull/20285#discussion_r162223829
  
    --- Diff: examples/src/main/python/ml/vector_size_hint_example.py ---
    @@ -0,0 +1,57 @@
    +#
    +# 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
    +#
    +#    http://www.apache.org/licenses/LICENSE-2.0
    +#
    +# 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
    +
    +# $example on$
    +from pyspark.ml.linalg import Vectors
    +from pyspark.ml.feature import (VectorSizeHint, VectorAssembler)
    +# $example off$
    +from pyspark.sql import SparkSession
    +
    +if __name__ == "__main__":
    +    spark = SparkSession\
    +        .builder\
    +        .appName("VectorSizeHintExample")\
    +        .getOrCreate()
    +
    +    # $example on$
    +    dataset = spark.createDataFrame(
    +        [(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0),
    +         (0, 18, 1.0, Vectors.dense([0.0, 10.0]), 0.0)],
    +        ["id", "hour", "mobile", "userFeatures", "clicked"])
    +
    +    sizeHint = VectorSizeHint(
    +        inputCol="userFeatures",
    +        handleInvalid="skip",
    +        size=3)
    +
    +    datasetWithSize = sizeHint.transform(dataset)
    +    print("Rows where 'userFeatures' is not the right size are filtered 
out")
    +    datasetWithSize.show(truncate=False)
    +
    +    assembler = VectorAssembler(
    +        inputCols=["hour", "mobile", "userFeatures"],
    +        outputCol="features")
    +
    +    # This dataframe can be used by used by downstream transformers as 
before
    --- End diff --
    
    I think there is some typos here.


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