python/ml/dct_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 DCT
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("DCTExample")\
0030         .getOrCreate()
0031
0032     # $example on$
0033     df = spark.createDataFrame([
0034         (Vectors.dense([0.0, 1.0, -2.0, 3.0]),),
0035         (Vectors.dense([-1.0, 2.0, 4.0, -7.0]),),
0036         (Vectors.dense([14.0, -2.0, -5.0, 1.0]),)], ["features"])
0037
0038     dct = DCT(inverse=False, inputCol="features", outputCol="featuresDCT")
0039
0040     dctDf = dct.transform(df)
0041
0042     dctDf.select("featuresDCT").show(truncate=False)
0043     # $example off$
0044
0045     spark.stop()