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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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import scopt.OptionParser
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.fpm.FPGrowth
/**
* Example for mining frequent itemsets using FP-growth.
* Example usage: ./bin/run-example mllib.FPGrowthExample \
* --minSupport 0.8 --numPartition 2 ./data/mllib/sample_fpgrowth.txt
*/
object FPGrowthExample {
case class Params(
input: String = null,
minSupport: Double = 0.3,
numPartition: Int = -1) extends AbstractParams[Params]
def main(args: Array[String]) {
val defaultParams = Params()
val parser = new OptionParser[Params]("FPGrowthExample") {
head("FPGrowth: an example FP-growth app.")
opt[Double]("minSupport")
.text(s"minimal support level, default: ${defaultParams.minSupport}")
.action((x, c) => c.copy(minSupport = x))
opt[Int]("numPartition")
.text(s"number of partition, default: ${defaultParams.numPartition}")
.action((x, c) => c.copy(numPartition = x))
arg[String]("<input>")
.text("input paths to input data set, whose file format is that each line " +
"contains a transaction with each item in String and separated by a space")
.required()
.action((x, c) => c.copy(input = x))
}
parser.parse(args, defaultParams) match {
case Some(params) => run(params)
case _ => sys.exit(1)
}
}
def run(params: Params): Unit = {
val conf = new SparkConf().setAppName(s"FPGrowthExample with $params")
val sc = new SparkContext(conf)
val transactions = sc.textFile(params.input).map(_.split(" ")).cache()
println(s"Number of transactions: ${transactions.count()}")
val model = new FPGrowth()
.setMinSupport(params.minSupport)
.setNumPartitions(params.numPartition)
.run(transactions)
println(s"Number of frequent itemsets: ${model.freqItemsets.count()}")
model.freqItemsets.collect().foreach { itemset =>
println(s"${itemset.items.mkString("[", ",", "]")}, ${itemset.freq}")
}
sc.stop()
}
}
// scalastyle:on println