#

# 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,

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"""

An example demonstrating FPGrowth.

Run with:

bin/spark-submit examples/src/main/python/ml/fpgrowth_example.py

"""

# $example on$

from pyspark.ml.fpm import FPGrowth

# $example off$

from pyspark.sql import SparkSession

if __name__ == "__main__":

spark = SparkSession\

.builder\

.appName("FPGrowthExample")\

.getOrCreate()

# $example on$

df = spark.createDataFrame([

(0, [1, 2, 5]),

(1, [1, 2, 3, 5]),

(2, [1, 2])

], ["id", "items"])

fpGrowth = FPGrowth(itemsCol="items", minSupport=0.5, minConfidence=0.6)

model = fpGrowth.fit(df)

# Display frequent itemsets.

model.freqItemsets.show()

# Display generated association rules.

model.associationRules.show()

# transform examines the input items against all the association rules and summarize the

# consequents as prediction

model.transform(df).show()

# $example off$

spark.stop()