python/ml/elementwise_product_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.feature import ElementwiseProduct
0022 from pyspark.ml.linalg import Vectors
0023 # $example off$
0024 from pyspark.sql import SparkSession
0025
0026 if __name__ == "__main__":
0027     spark = SparkSession\
0028         .builder\
0029         .appName("ElementwiseProductExample")\
0030         .getOrCreate()
0031
0032     # $example on$
0033     # Create some vector data; also works for sparse vectors
0034     data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)]
0035     df = spark.createDataFrame(data, ["vector"])
0036     transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]),
0037                                      inputCol="vector", outputCol="transformedVector")
0038     # Batch transform the vectors to create new column:
0039     transformer.transform(df).show()
0040     # $example off$
0041
0042     spark.stop()