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#
# 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.feature import VectorSlicer
from pyspark.mllib.linalg import Vectors
from pyspark.sql.types import Row
# $example off$
from pyspark.sql import SparkSession
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("VectorSlicerExample")\
.getOrCreate()
# $example on$
df = spark.createDataFrame([
Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3}),),
Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]),)])
slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=[1])
output = slicer.transform(df)
output.select("userFeatures", "features").show()
# $example off$
spark.stop()
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