update LocalPrefixSpan impl

Author: mengxr

mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala

@@ -30,34 +30,25 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
    * Calculate all patterns of a projected database.
    * @param minCount minimum count
    * @param maxPatternLength maximum pattern length
-   * @param prefix prefix
-   * @param database the projected dabase
+   * @param prefixes prefixes in reversed order
+   * @param database the projected database
    * @return a set of sequential pattern pairs,
-   *         the key of pair is sequential pattern (a list of items),
+   *         the key of pair is sequential pattern (a list of items in reversed order),
    *         the value of pair is the pattern's count.
    */
   def run(
       minCount: Long,
       maxPatternLength: Int,
-      prefix: List[Int],
+      prefixes: List[Int],
       database: Array[Array[Int]]): Iterator[(List[Int], Long)] = {
-
-    if (database.isEmpty) return Iterator.empty
-
+    if (prefixes.length == maxPatternLength || database.isEmpty) return Iterator.empty
     val frequentItemAndCounts = getFreqItemAndCounts(minCount, database)
-    val frequentItems = frequentItemAndCounts.map(_._1).toSet
-    val frequentPatternAndCounts = frequentItemAndCounts
-      .map { case (item, count) => ((item :: prefix), count) }
-
-
-    if (prefix.length + 1 < maxPatternLength) {
-      val filteredProjectedDatabase = database.map(x => x.filter(frequentItems.contains(_)))
-      frequentPatternAndCounts.iterator ++ frequentItems.flatMap { item =>
-        val nextProjected = project(filteredProjectedDatabase, item)
-        run(minCount, maxPatternLength, item :: prefix, nextProjected)
-      }
-    } else {
-      frequentPatternAndCounts.iterator
+    val filteredDatabase = database.map(x => x.filter(frequentItemAndCounts.contains))
+    frequentItemAndCounts.iterator.flatMap { case (item, count) =>
+      val newPrefixes = item :: prefixes
+      val newProjected = project(filteredDatabase, item)
+      Iterator.single((newPrefixes, count)) ++
+        run(minCount, maxPatternLength, newPrefixes, newProjected)
     }
   }
 
@@ -78,24 +69,26 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
 
   def project(database: Array[Array[Int]], prefix: Int): Array[Array[Int]] = {
     database
-      .map(candidateSeq => getSuffix(prefix, candidateSeq))
+      .map(getSuffix(prefix, _))
       .filter(_.nonEmpty)
   }
 
   /**
    * Generates frequent items by filtering the input data using minimal count level.
    * @param minCount the minimum count for an item to be frequent
    * @param database database of sequences
-   * @return item and count pairs
+   * @return freq item to count map
    */
   private def getFreqItemAndCounts(
       minCount: Long,
-      database: Array[Array[Int]]): Iterable[(Int, Long)] = {
-    database.flatMap(_.distinct)
-      .foldRight(mutable.Map[Int, Long]().withDefaultValue(0L)) { case (item, ctr) =>
-        ctr(item) += 1
-        ctr
+      database: Array[Array[Int]]): mutable.Map[Int, Long] = {
+    // TODO: use PrimitiveKeyOpenHashMap
+    val counts = mutable.Map[Int, Long]().withDefaultValue(0L)
+    database.foreach { sequence =>
+      sequence.distinct.foreach { item =>
+        counts(item) += 1L
       }
-      .filter(_._2 >= minCount)
+    }
+    counts.filter(_._2 >= minCount)
   }
 }

mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala

@@ -47,8 +47,8 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     def compareResult(
         expectedValue: Array[(Array[Int], Long)],
         actualValue: Array[(Array[Int], Long)]): Boolean = {
-      expectedValue.map(x => (x._1.toList, x._2)).toSet ==
-        actualValue.map(x => (x._1.toList, x._2)).toSet
+      expectedValue.map(x => (x._1.toSeq, x._2)).toSet ==
+        actualValue.map(x => (x._1.toSeq, x._2)).toSet
     }
 
     val prefixspan = new PrefixSpan()