[SPARK-4001][MLlib] adding parallel FP-Growth algorithm for frequent …

…pattern mining in MLlib Apriori is the classic algorithm for frequent item set mining in a transactional data set. It will be useful if Apriori algorithm is added to MLLib in Spark. This PR add an implementation for it. There is a point I am not sure wether it is most efficient. In order to filter out the eligible frequent item set, currently I am using a cartesian operation on two RDDs to calculate the degree of support of each item set, not sure wether it is better to use broadcast variable to achieve the same. I will add an example to use this algorithm if requires Author: Jacky Li <[email protected]> Author: Jacky Li <[email protected]> Author: Xiangrui Meng <[email protected]> Closes apache#2847 from jackylk/apriori and squashes the following commits: bee3093 [Jacky Li] Merge pull request #1 from mengxr/SPARK-4001 7e69725 [Xiangrui Meng] simplify FPTree and update FPGrowth ec21f7d [Jacky Li] fix scalastyle 93f3280 [Jacky Li] create FPTree class d110ab2 [Jacky Li] change test case to use MLlibTestSparkContext a6c5081 [Jacky Li] Add Parallel FPGrowth algorithm eb3e4ca [Jacky Li] add FPGrowth 03df2b6 [Jacky Li] refactory according to comments 7b77ad7 [Jacky Li] fix scalastyle check f68a0bd [Jacky Li] add 2 apriori implemenation and fp-growth implementation 889b33f [Jacky Li] modify per scalastyle check da2cba7 [Jacky Li] adding apriori algorithm for frequent item set mining in Spark
ThalesGroup · Feb 2, 2015 · 859f724 · 859f724
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diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
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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.
+ */
+
+package org.apache.spark.mllib.fpm
+
+import java.{util => ju}
+
+import scala.collection.mutable
+
+import org.apache.spark.{SparkException, HashPartitioner, Logging, Partitioner}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+class FPGrowthModel(val freqItemsets: RDD[(Array[String], Long)]) extends Serializable
+
+/**
+ * This class implements Parallel FP-growth algorithm to do frequent pattern matching on input data.
+ * Parallel FPGrowth (PFP) partitions computation in such a way that each machine executes an
+ * independent group of mining tasks. More detail of this algorithm can be found at
+ * [[http://dx.doi.org/10.1145/1454008.1454027, PFP]], and the original FP-growth paper can be
+ * found at [[http://dx.doi.org/10.1145/335191.335372, FP-growth]]
+ *
+ * @param minSupport the minimal support level of the frequent pattern, any pattern appears
+ *                   more than (minSupport * size-of-the-dataset) times will be output
+ * @param numPartitions number of partitions used by parallel FP-growth
+ */
+class FPGrowth private (
+    private var minSupport: Double,
+    private var numPartitions: Int) extends Logging with Serializable {
+
+  /**
+   * Constructs a FPGrowth instance with default parameters:
+   * {minSupport: 0.3, numPartitions: auto}
+   */
+  def this() = this(0.3, -1)
+
+  /**
+   * Sets the minimal support level (default: 0.3).
+   */
+  def setMinSupport(minSupport: Double): this.type = {
+    this.minSupport = minSupport
+    this
+  }
+
+  /**
+   * Sets the number of partitions used by parallel FP-growth (default: same as input data).
+   */
+  def setNumPartitions(numPartitions: Int): this.type = {
+    this.numPartitions = numPartitions
+    this
+  }
+
+  /**
+   * Computes an FP-Growth model that contains frequent itemsets.
+   * @param data input data set, each element contains a transaction
+   * @return an [[FPGrowthModel]]
+   */
+  def run(data: RDD[Array[String]]): FPGrowthModel = {
+    if (data.getStorageLevel == StorageLevel.NONE) {
+      logWarning("Input data is not cached.")
+    }
+    val count = data.count()
+    val minCount = math.ceil(minSupport * count).toLong
+    val numParts = if (numPartitions > 0) numPartitions else data.partitions.length
+    val partitioner = new HashPartitioner(numParts)
+    val freqItems = genFreqItems(data, minCount, partitioner)
+    val freqItemsets = genFreqItemsets(data, minCount, freqItems, partitioner)
+    new FPGrowthModel(freqItemsets)
+  }
+
+  /**
+   * Generates frequent items by filtering the input data using minimal support level.
+   * @param minCount minimum count for frequent itemsets
+   * @param partitioner partitioner used to distribute items
+   * @return array of frequent pattern ordered by their frequencies
+   */
+  private def genFreqItems(
+      data: RDD[Array[String]],
+      minCount: Long,
+      partitioner: Partitioner): Array[String] = {
+    data.flatMap { t =>
+      val uniq = t.toSet
+      if (t.length != uniq.size) {
+        throw new SparkException(s"Items in a transaction must be unique but got ${t.toSeq}.")
+      }
+      t
+    }.map(v => (v, 1L))
+      .reduceByKey(partitioner, _ + _)
+      .filter(_._2 >= minCount)
+      .collect()
+      .sortBy(-_._2)
+      .map(_._1)
+  }
+
+  /**
+   * Generate frequent itemsets by building FP-Trees, the extraction is done on each partition.
+   * @param data transactions
+   * @param minCount minimum count for frequent itemsets
+   * @param freqItems frequent items
+   * @param partitioner partitioner used to distribute transactions
+   * @return an RDD of (frequent itemset, count)
+   */
+  private def genFreqItemsets(
+      data: RDD[Array[String]],
+      minCount: Long,
+      freqItems: Array[String],
+      partitioner: Partitioner): RDD[(Array[String], Long)] = {
+    val itemToRank = freqItems.zipWithIndex.toMap
+    data.flatMap { transaction =>
+      genCondTransactions(transaction, itemToRank, partitioner)
+    }.aggregateByKey(new FPTree[Int], partitioner.numPartitions)(
+      (tree, transaction) => tree.add(transaction, 1L),
+      (tree1, tree2) => tree1.merge(tree2))
+    .flatMap { case (part, tree) =>
+      tree.extract(minCount, x => partitioner.getPartition(x) == part)
+    }.map { case (ranks, count) =>
+      (ranks.map(i => freqItems(i)).toArray, count)
+    }
+  }
+
+  /**
+   * Generates conditional transactions.
+   * @param transaction a transaction
+   * @param itemToRank map from item to their rank
+   * @param partitioner partitioner used to distribute transactions
+   * @return a map of (target partition, conditional transaction)
+   */
+  private def genCondTransactions(
+      transaction: Array[String],
+      itemToRank: Map[String, Int],
+      partitioner: Partitioner): mutable.Map[Int, Array[Int]] = {
+    val output = mutable.Map.empty[Int, Array[Int]]
+    // Filter the basket by frequent items pattern and sort their ranks.
+    val filtered = transaction.flatMap(itemToRank.get)
+    ju.Arrays.sort(filtered)
+    val n = filtered.length
+    var i = n - 1
+    while (i >= 0) {
+      val item = filtered(i)
+      val part = partitioner.getPartition(item)
+      if (!output.contains(part)) {
+        output(part) = filtered.slice(0, i + 1)
+      }
+      i -= 1
+    }
+    output
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala
@@ -0,0 +1,134 @@
+/*
+ * 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.collection.mutable
+import scala.collection.mutable.ListBuffer
+
+/**
+ * FP-Tree data structure used in FP-Growth.
+ * @tparam T item type
+ */
+private[fpm] class FPTree[T] extends Serializable {
+
+  import FPTree._
+
+  val root: Node[T] = new Node(null)
+
+  private val summaries: mutable.Map[T, Summary[T]] = mutable.Map.empty
+
+  /** Adds a transaction with count. */
+  def add(t: Iterable[T], count: Long = 1L): this.type = {
+    require(count > 0)
+    var curr = root
+    curr.count += count
+    t.foreach { item =>
+      val summary = summaries.getOrElseUpdate(item, new Summary)
+      summary.count += count
+      val child = curr.children.getOrElseUpdate(item, {
+        val newNode = new Node(curr)
+        newNode.item = item
+        summary.nodes += newNode
+        newNode
+      })
+      child.count += count
+      curr = child
+    }
+    this
+  }
+
+  /** Merges another FP-Tree. */
+  def merge(other: FPTree[T]): this.type = {
+    other.transactions.foreach { case (t, c) =>
+      add(t, c)
+    }
+    this
+  }
+
+  /** Gets a subtree with the suffix. */
+  private def project(suffix: T): FPTree[T] = {
+    val tree = new FPTree[T]
+    if (summaries.contains(suffix)) {
+      val summary = summaries(suffix)
+      summary.nodes.foreach { node =>
+        var t = List.empty[T]
+        var curr = node.parent
+        while (!curr.isRoot) {
+          t = curr.item :: t
+          curr = curr.parent
+        }
+        tree.add(t, node.count)
+      }
+    }
+    tree
+  }
+
+  /** Returns all transactions in an iterator. */
+  def transactions: Iterator[(List[T], Long)] = getTransactions(root)
+
+  /** Returns all transactions under this node. */
+  private def getTransactions(node: Node[T]): Iterator[(List[T], Long)] = {
+    var count = node.count
+    node.children.iterator.flatMap { case (item, child) =>
+      getTransactions(child).map { case (t, c) =>
+        count -= c
+        (item :: t, c)
+      }
+    } ++ {
+      if (count > 0) {
+        Iterator.single((Nil, count))
+      } else {
+        Iterator.empty
+      }
+    }
+  }
+
+  /** Extracts all patterns with valid suffix and minimum count. */
+  def extract(
+      minCount: Long,
+      validateSuffix: T => Boolean = _ => true): Iterator[(List[T], Long)] = {
+    summaries.iterator.flatMap { case (item, summary) =>
+      if (validateSuffix(item) && summary.count >= minCount) {
+        Iterator.single((item :: Nil, summary.count)) ++
+          project(item).extract(minCount).map { case (t, c) =>
+            (item :: t, c)
+          }
+      } else {
+        Iterator.empty
+      }
+    }
+  }
+}
+
+private[fpm] object FPTree {
+
+  /** Representing a node in an FP-Tree. */
+  class Node[T](val parent: Node[T]) extends Serializable {
+    var item: T = _
+    var count: Long = 0L
+    val children: mutable.Map[T, Node[T]] = mutable.Map.empty
+
+    def isRoot: Boolean = parent == null
+  }
+
+  /** Summary of a item in an FP-Tree. */
+  private class Summary[T] extends Serializable {
+    var count: Long = 0L
+    val nodes: ListBuffer[Node[T]] = ListBuffer.empty
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
@@ -0,0 +1,73 @@
+/*
+ * 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 org.scalatest.FunSuite
+
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class FPGrowthSuite extends FunSuite with MLlibTestSparkContext {
+
+  test("FP-Growth") {
+    val transactions = Seq(
+      "r z h k p",
+      "z y x w v u t s",
+      "s x o n r",
+      "x z y m t s q e",
+      "z",
+      "x z y r q t p")
+      .map(_.split(" "))
+    val rdd = sc.parallelize(transactions, 2).cache()
+
+    val fpg = new FPGrowth()
+
+    val model6 = fpg
+      .setMinSupport(0.9)
+      .setNumPartitions(1)
+      .run(rdd)
+    assert(model6.freqItemsets.count() === 0)
+
+    val model3 = fpg
+      .setMinSupport(0.5)
+      .setNumPartitions(2)
+      .run(rdd)
+    val freqItemsets3 = model3.freqItemsets.collect().map { case (items, count) =>
+      (items.toSet, count)
+    }
+    val expected = Set(
+      (Set("s"), 3L), (Set("z"), 5L), (Set("x"), 4L), (Set("t"), 3L), (Set("y"), 3L),
+      (Set("r"), 3L),
+      (Set("x", "z"), 3L), (Set("t", "y"), 3L), (Set("t", "x"), 3L), (Set("s", "x"), 3L),
+      (Set("y", "x"), 3L), (Set("y", "z"), 3L), (Set("t", "z"), 3L),
+      (Set("y", "x", "z"), 3L), (Set("t", "x", "z"), 3L), (Set("t", "y", "z"), 3L),
+      (Set("t", "y", "x"), 3L),
+      (Set("t", "y", "x", "z"), 3L))
+    assert(freqItemsets3.toSet === expected)
+
+    val model2 = fpg
+      .setMinSupport(0.3)
+      .setNumPartitions(4)
+      .run(rdd)
+    assert(model2.freqItemsets.count() === 54)
+
+    val model1 = fpg
+      .setMinSupport(0.1)
+      .setNumPartitions(8)
+      .run(rdd)
+    assert(model1.freqItemsets.count() === 625)
+  }
+}