#

# 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.

#

# $example on$

from pyspark.ml.feature import OneHotEncoder

# $example off$

from pyspark.sql import SparkSession

if __name__ == "__main__":

spark = SparkSession\

.builder\

.appName("OneHotEncoderExample")\

.getOrCreate()

# Note: categorical features are usually first encoded with StringIndexer

# $example on$

df = spark.createDataFrame([

(0.0, 1.0),

(1.0, 0.0),

(2.0, 1.0),

(0.0, 2.0),

(0.0, 1.0),

(2.0, 0.0)

], ["categoryIndex1", "categoryIndex2"])

encoder = OneHotEncoder(inputCols=["categoryIndex1", "categoryIndex2"],

outputCols=["categoryVec1", "categoryVec2"])

model = encoder.fit(df)

encoded = model.transform(df)

encoded.show()

# $example off$

spark.stop()