Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7005#discussion_r33998321
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala ---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+package org.apache.spark.mllib.fpm
+
+import java.lang.{Iterable => JavaIterable}
+
+import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
+
+import scala.collection.JavaConverters._
+import scala.reflect.ClassTag
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.fpm.AssociationRules.Rule
+import org.apache.spark.rdd.RDD
+
+/**
+ * :: Experimental ::
+ *
+ * Generates association rules from a [[RDD[FreqItemset[Item]]]. This
method only generates
+ * association rules which have a single item as the consequent.
+ */
+@Experimental
+class AssociationRules private (
+ private var minConfidence: Double) extends Logging with Serializable {
+
+ /**
+ * Constructs a default instance with default parameters {minConfidence
= 0.5}.
+ */
+ def this() = this(0.5)
+
+ /**
+ * Sets the minimal confidence (default: `0.5`).
+ */
+ def setMinConfidence(minConfidence: Double): this.type = {
+ this.minConfidence = minConfidence
+ this
+ }
+
+ /**
+ * Computes the association rules with confidence above
[[minConfidence]].
+ * @param model frequent itemset model obtained from [[FPGrowth]]
+ * @return a [[Set[Rule[Item]]] containing the assocation rules.
+ */
+ def run[Item: ClassTag](model: FPGrowthModel[Item]): RDD[Rule[Item]] = {
+ val freqItemsets = model.freqItemsets
+ val numTransactions = model.numTransactions
+
+ freqItemsets.flatMap { itemset =>
+ val items = itemset.items
+ items.map { item =>
+ // Key using List[Item] because cannot use Array[Item] for
map-side combining
+ items.partition(_ == item) match {
+ case (consequent, antecedent) => (antecedent.toList,
(consequent.toList, itemset.freq))
+ }
+ } :+ (items.toList, (Nil, itemset.freq))
+ }.aggregateByKey(Map[List[Item], Long]().empty)(
+ { case (acc, (consequent, freq)) => acc + (consequent -> freq) },
+ { (acc1, acc2) => acc1 ++ acc2 }
+ ).flatMap { case (antecedent, consequentToFreq) =>
+ consequentToFreq.flatMap { case (consequent, freqUnion) =>
+ val freqAntecedent = if (antecedent == Nil) numTransactions else
consequentToFreq(Nil)
+ val confidence = freqUnion.toDouble / freqAntecedent.toDouble
--- End diff --
the second `toDouble` is not necessary
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