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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.
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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()