/*
 * 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 org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.fpm.AssociationRules
import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
// $example off$

object AssociationRulesExample {

  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("AssociationRulesExample")
    val sc = new SparkContext(conf)

    // $example on$
    val freqItemsets = sc.parallelize(Seq(
      new FreqItemset(Array("a"), 15L),
      new FreqItemset(Array("b"), 35L),
      new FreqItemset(Array("a", "b"), 12L)
    ))

    val ar = new AssociationRules()
      .setMinConfidence(0.8)
    val results = ar.run(freqItemsets)

    results.collect().foreach { rule =>
      println("[" + rule.antecedent.mkString(",")
        + "=>"
        + rule.consequent.mkString(",") + "]," + rule.confidence)
    }
    // $example off$

    sc.stop()
  }

}
// scalastyle:on println
