Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7005#discussion_r34007084
--- 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 scala.reflect.ClassTag
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
+import org.apache.spark.mllib.fpm.AssociationRules.Rule
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
+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
= 0.8}.
+ */
+ def this() = this(0.8)
+
+ /**
+ * Sets the minimal confidence (default: `0.8`).
+ */
+ def setMinConfidence(minConfidence: Double): this.type = {
+ this.minConfidence = minConfidence
+ this
+ }
+
+ /**
+ * Computes the association rules with confidence above
[[minConfidence]].
+ * @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
+ * @return a [[Set[Rule[Item]]] containing the assocation rules.
+ */
+ def run[Item: ClassTag](freqItemsets: RDD[FreqItemset[Item]]):
RDD[Rule[Item]] = {
+ freqItemsets.flatMap { itemset =>
+ val items = itemset.items
+ items.flatMap { item =>
+ items.partition(_ == item) match {
+ // Itemsets and items in itemsets are unique, so every
(antecedent, consequent) is unique
+ case (consequent, antecedent) if !antecedent.isEmpty =>
+ Some((antecedent.toSeq, (consequent.toSeq, itemset.freq)))
+ case _ => None
+ }
+ } :+ (items.toSeq, (Nil, itemset.freq))
+ }.aggregateByKey(Map[Seq[Item], Long]().empty)(
+ // Since every (antecedent, consequent) is unique, there are no
collisions in the Map
+ seqOp = { case (acc, (consequent, freq)) => acc + (consequent ->
freq) },
+ combOp = _ ++ _
+ ).flatMap { case (antecedent, consequentToFreq) =>
+ consequentToFreq.flatMap { case (consequent, freqUnion) =>
+ val freqAntecedent = consequentToFreq(Nil)
+ val confidence = 1.0 * freqUnion / freqAntecedent
+ if (!consequent.isEmpty && confidence >= minConfidence) {
+ Some(new Rule[Item](antecedent.toArray, consequent.toArray,
freqUnion, freqAntecedent))
+ } else {
+ None
+ }
+ }
+ }
+ }
+
+ def run[Item](freqItemsets: JavaRDD[FreqItemset[Item]]):
JavaRDD[Rule[Item]] = {
+ val tag = fakeClassTag[Item]
+ run(freqItemsets.rdd)(tag)
+ }
+}
+
+object AssociationRules {
+
+ /**
+ * :: Experimental ::
+ *
+ * An association rule between sets of items.
+ * @param antecedent hypotheses of the rule
+ * @param consequent conclusion of the rule
+ * @param freqUnion the frequency (num. occurrences) of the union of the
antecedent and consequent
+ * as subsets of transactions in the transaction data
+ * @param freqAntecedent the frequency of the antecedent in the
transaction data
+ * @tparam Item item type
+ */
+ @Experimental
+ class Rule[Item] private[mllib] (
+ antecedent: Array[Item],
+ consequent: Array[Item],
+ freqUnion: Double,
+ freqAntecedent: Double) extends Serializable {
+
+ def confidence: Double = 1.0 * freqUnion / freqAntecedent
+
+ require(antecedent.toSet.intersect(consequent.toSet).isEmpty, {
--- End diff --
minor: This could be quite expensive to check. It generates many temporary
sets when we have many rules. Given the fact that `Rule` can be only created by
`AssociationRules`, we could move this to unit test if it hurts the
performance. But let's keep it here for now.
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