Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/7258#discussion_r34408050
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
---
@@ -0,0 +1,179 @@
+/*
+ * 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.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+/**
+ *
+ * :: Experimental ::
+ *
+ * A parallel PrefixSpan algorithm to mine sequential pattern.
+ * The PrefixSpan algorithm is described in
+ * [[http://doi.org/10.1109/ICDE.2001.914830]].
+ *
+ * @param minSupport the minimal support level of the sequential pattern,
any pattern appears
+ * more than (minSupport * size-of-the-dataset) times
will be output
+ * @param maxPatternLength the maximal length of the sequential pattern,
any pattern appears
+ * less than maxPatternLength will be output
+ *
+ * @see [[https://en.wikipedia.org/wiki/Sequential_Pattern_Mining
Sequential Pattern Mining
+ * (Wikipedia)]]
+ */
+@Experimental
+class PrefixSpan private (
+ private var minSupport: Double,
+ private var maxPatternLength: Int) extends Logging with Serializable {
+
+ /**
+ * Constructs a default instance with default parameters
+ * {minSupport: `0.1`, maxPatternLength: `10`}.
+ */
+ def this() = this(0.1, 10)
+
+ /**
+ * Sets the minimal support level (default: `0.1`).
+ */
+ def setMinSupport(minSupport: Double): this.type = {
+ require(minSupport >= 0 && minSupport <= 1,
+ "The minimum support value must be between 0 and 1, including 0 and
1.")
+ this.minSupport = minSupport
+ this
+ }
+
+ /**
+ * Sets maximal pattern length (default: `10`).
+ */
+ def setMaxPatternLength(maxPatternLength: Int): this.type = {
+ require(maxPatternLength >= 1,
+ "The maximum pattern length value must be greater than 0.")
+ this.maxPatternLength = maxPatternLength
+ this
+ }
+
+ /**
+ * Find the complete set of sequential patterns in the input sequences.
+ * @param sequences input data set, contains a set of sequences,
+ * a sequence is an ordered list of elements.
+ * @return a set of sequential pattern pairs,
+ * the key of pair is pattern (a list of elements),
+ * the value of pair is the pattern's count.
+ */
+ def run(sequences: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
+ if (sequences.getStorageLevel == StorageLevel.NONE) {
+ logWarning("Input data is not cached.")
+ }
+ val minCount = getMinCount(sequences)
+ val (lengthOnePatternsAndCounts, prefixAndCandidates) =
+ findLengthOnePatterns(minCount, sequences)
+ val projectedDatabase =
makePrefixProjectedDatabases(prefixAndCandidates)
+ val nextPatterns = getPatternsInLocal(minCount, projectedDatabase)
+ val lengthOnePatternsAndCountsRdd =
+ sequences.sparkContext.parallelize(
+ lengthOnePatternsAndCounts.map(x => (Array(x._1), x._2)))
+ val allPatterns = lengthOnePatternsAndCountsRdd ++ nextPatterns
+ allPatterns
+ }
+
+ /**
+ * Get the minimum count (sequences count * minSupport).
+ * @param sequences input data set, contains a set of sequences,
+ * @return minimum count,
+ */
+ private def getMinCount(sequences: RDD[Array[Int]]): Long = {
+ if (minSupport == 0) 0L else math.ceil(sequences.count() *
minSupport).toLong
+ }
+
+ /**
+ * Generates frequent items by filtering the input data using minimal
count level.
+ * @param minCount the absolute minimum count
+ * @param sequences original sequences data
+ * @return array of item and count pair
+ */
+ private def getFreqItemAndCounts(
+ minCount: Long,
+ sequences: RDD[Array[Int]]): RDD[(Int, Long)] = {
+ sequences.flatMap(_.distinct.map((_, 1L)))
+ .reduceByKey(_ + _)
+ .filter(_._2 >= minCount)
+ }
+
+ /**
+ * Get the frequent prefixes' projected database.
+ * @param frequentPrefixes frequent prefixes
+ * @param sequences sequences data
+ * @return prefixes and projected database
--- End diff --
"(prefix, suffix) pairs where all prefixes come from frequentPrefixes"
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