#
# 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.fpm import FPGrowth
# $example off$
from pyspark.sql import SparkSession

"""
An example demonstrating FPGrowth.
Run with:
  bin/spark-submit examples/src/main/python/ml/fpgrowth_example.py
"""

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()