Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/7412#discussion_r34649034
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
---
@@ -86,16 +88,69 @@ class PrefixSpan private (
getFreqItemAndCounts(minCount, sequences).collect()
val prefixAndProjectedDatabase = getPrefixAndProjectedDatabase(
lengthOnePatternsAndCounts.map(_._1), sequences)
- val groupedProjectedDatabase = prefixAndProjectedDatabase
- .map(x => (x._1.toSeq, x._2))
- .groupByKey()
- .map(x => (x._1.toArray, x._2.toArray))
- val nextPatterns = getPatternsInLocal(minCount,
groupedProjectedDatabase)
- val lengthOnePatternsAndCountsRdd =
- sequences.sparkContext.parallelize(
- lengthOnePatternsAndCounts.map(x => (Array(x._1), x._2)))
- val allPatterns = lengthOnePatternsAndCountsRdd ++ nextPatterns
- allPatterns
+
+ var patternsCount = lengthOnePatternsAndCounts.length
+ var allPatternAndCounts = sequences.sparkContext.parallelize(
+ lengthOnePatternsAndCounts.map(x => (Array(x._1), x._2)))
+ var currentProjectedDatabase = prefixAndProjectedDatabase
+ while (patternsCount <= minPatternsBeforeShuffle &&
+ currentProjectedDatabase.count() != 0) {
+ val (nextPatternAndCounts, nextProjectedDatabase) =
+ getPatternCountsAndProjectedDatabase(minCount,
currentProjectedDatabase)
+ patternsCount = nextPatternAndCounts.count().toInt
+ currentProjectedDatabase = nextProjectedDatabase
+ allPatternAndCounts = allPatternAndCounts ++ nextPatternAndCounts
+ }
+ if (patternsCount > 0) {
+ val groupedProjectedDatabase = currentProjectedDatabase
+ .map(x => (x._1.toSeq, x._2))
+ .groupByKey()
+ .map(x => (x._1.toArray, x._2.toArray))
+ val nextPatternAndCounts = getPatternsInLocal(minCount,
groupedProjectedDatabase)
+ allPatternAndCounts = allPatternAndCounts ++ nextPatternAndCounts
+ }
+ allPatternAndCounts
+ }
+
+ /**
+ * Get the pattern and counts, and projected database
+ * @param minCount minimum count
+ * @param prefixAndProjectedDatabase prefix and projected database,
+ * @return pattern and counts, and projected database
+ * (Array[pattern, count], RDD[prefix, projected database ])
+ */
+ private def getPatternCountsAndProjectedDatabase(
+ minCount: Long,
+ prefixAndProjectedDatabase: RDD[(Array[Int], Array[Int])]):
+ (RDD[(Array[Int], Long)], RDD[(Array[Int], Array[Int])]) = {
+ val prefixAndFreqentItemAndCounts =
prefixAndProjectedDatabase.flatMap{ x =>
+ x._2.distinct.map(y => ((x._1.toSeq, y), 1L))
+ }.reduceByKey(_ + _)
+ .filter(_._2 >= minCount)
+ val patternAndCounts = prefixAndFreqentItemAndCounts
+ .map(x => (x._1._1.toArray ++ Array(x._1._2), x._2))
+ val prefixlength = prefixAndProjectedDatabase.take(1)(0)._1.length
+ if (prefixlength + 1 >= maxPatternLength) {
+ (patternAndCounts, prefixAndProjectedDatabase.filter(x => false))
+ } else {
+ val frequentItemsMap = prefixAndFreqentItemAndCounts
+ .keys.map(x => (x._1, x._2))
+ .groupByKey()
+ .mapValues(_.toSet)
+ .collect
+ .toMap
+ val nextPrefixAndProjectedDatabase = prefixAndProjectedDatabase
+ .filter(x => frequentItemsMap.contains(x._1))
+ .flatMap { x =>
+ val frequentItemSet = frequentItemsMap(x._1)
+ val filteredSequence = x._2.filter(frequentItemSet.contains(_))
--- End diff --
`x._2` -> `suffix`
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