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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.
#
# To run this example use
# ./bin/spark-submit examples/src/main/r/ml/fpm.R
# Load SparkR library into your R session
library(SparkR)
# Initialize SparkSession
sparkR.session(appName = "SparkR-ML-fpm-example")
# $example on$
# Load training data
df <- selectExpr(createDataFrame(data.frame(rawItems = c(
"1,2,5", "1,2,3,5", "1,2"
))), "split(rawItems, ',') AS items")
fpm <- spark.fpGrowth(df, itemsCol="items", minSupport=0.5, minConfidence=0.6)
# Extracting frequent itemsets
spark.freqItemsets(fpm)
# Extracting association rules
spark.associationRules(fpm)
# Predict uses association rules to and combines possible consequents
predict(fpm, df)
# $example off$
sparkR.session.stop()
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