python/ml/vector_size_hint_example.py

0001 #
0002 # Licensed to the Apache Software Foundation (ASF) under one or more
0003 # contributor license agreements.  See the NOTICE file distributed with
0004 # this work for additional information regarding copyright ownership.
0005 # The ASF licenses this file to You under the Apache License, Version 2.0
0006 # (the "License"); you may not use this file except in compliance with
0007 # the License.  You may obtain a copy of the License at
0008 #
0009 #    http://www.apache.org/licenses/LICENSE-2.0
0010 #
0011 # Unless required by applicable law or agreed to in writing, software
0012 # distributed under the License is distributed on an "AS IS" BASIS,
0013 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014 # See the License for the specific language governing permissions and
0015 # limitations under the License.
0016 #
0017
0018 from __future__ import print_function
0019
0020 # $example on$
0021 from pyspark.ml.linalg import Vectors
0022 from pyspark.ml.feature import (VectorSizeHint, VectorAssembler)
0023 # $example off$
0024 from pyspark.sql import SparkSession
0025
0026 if __name__ == "__main__":
0027     spark = SparkSession\
0028         .builder\
0029         .appName("VectorSizeHintExample")\
0030         .getOrCreate()
0031
0032     # $example on$
0033     dataset = spark.createDataFrame(
0034         [(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0),
0035          (0, 18, 1.0, Vectors.dense([0.0, 10.0]), 0.0)],
0036         ["id", "hour", "mobile", "userFeatures", "clicked"])
0037
0038     sizeHint = VectorSizeHint(
0039         inputCol="userFeatures",
0040         handleInvalid="skip",
0041         size=3)
0042
0043     datasetWithSize = sizeHint.transform(dataset)
0044     print("Rows where 'userFeatures' is not the right size are filtered out")
0045     datasetWithSize.show(truncate=False)
0046
0047     assembler = VectorAssembler(
0048         inputCols=["hour", "mobile", "userFeatures"],
0049         outputCol="features")
0050
0051     # This dataframe can be used by downstream transformers as before
0052     output = assembler.transform(datasetWithSize)
0053     print("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'")
0054     output.select("features", "clicked").show(truncate=False)
0055     # $example off$
0056
0057     spark.stop()