Revert "[SPARK-7212] [MLLIB] Add sequence learning flag"

This reverts commit 25f574eb. After speaking to some users and developers, we realized that FP-growth doesn't meet the requirement for frequent sequence mining. PrefixSpan (SPARK-6487) would be the correct algorithm for it. feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #7240 from mengxr/SPARK-7212.revert and squashes the following commits:

2b3d66b [Xiangrui Meng] Revert "[SPARK-7212] [MLLIB] Add sequence learning flag"

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

@@ -36,7 +36,7 @@ import org.apache.spark.storage.StorageLevel
  * :: Experimental ::
  *
  * Model trained by [[FPGrowth]], which holds frequent itemsets.
- * @param freqItemsets frequent itemsets, which is an RDD of [[FreqItemset]]
+ * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
  */
 @Experimental
@@ -62,14 +62,13 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
 @Experimental
 class FPGrowth private (
     private var minSupport: Double,
-    private var numPartitions: Int,
-    private var ordered: Boolean) extends Logging with Serializable {
+    private var numPartitions: Int) extends Logging with Serializable {
 
   /**
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
-   * as the input data, ordered: `false`}.
+   * as the input data}.
    */
-  def this() = this(0.3, -1, false)
+  def this() = this(0.3, -1)
 
   /**
    * Sets the minimal support level (default: `0.3`).
@@ -87,15 +86,6 @@ class FPGrowth private (
     this
   }
 
-  /**
-   * Indicates whether to mine itemsets (unordered) or sequences (ordered) (default: false, mine
-   * itemsets).
-   */
-  def setOrdered(ordered: Boolean): this.type = {
-    this.ordered = ordered
-    this
-  }
-
   /**
    * Computes an FP-Growth model that contains frequent itemsets.
    * @param data input data set, each element contains a transaction
@@ -165,7 +155,7 @@ class FPGrowth private (
     .flatMap { case (part, tree) =>
       tree.extract(minCount, x => partitioner.getPartition(x) == part)
     }.map { case (ranks, count) =>
-      new FreqItemset(ranks.map(i => freqItems(i)).reverse.toArray, count, ordered)
+      new FreqItemset(ranks.map(i => freqItems(i)).toArray, count)
     }
   }
 
@@ -181,12 +171,9 @@ class FPGrowth private (
       itemToRank: Map[Item, Int],
       partitioner: Partitioner): mutable.Map[Int, Array[Int]] = {
     val output = mutable.Map.empty[Int, Array[Int]]
-    // Filter the basket by frequent items pattern
+    // Filter the basket by frequent items pattern and sort their ranks.
     val filtered = transaction.flatMap(itemToRank.get)
-    if (!this.ordered) {
-      ju.Arrays.sort(filtered)
-    }
-    // Generate conditional transactions
+    ju.Arrays.sort(filtered)
     val n = filtered.length
     var i = n - 1
     while (i >= 0) {
@@ -211,18 +198,9 @@ object FPGrowth {
    * Frequent itemset.
    * @param items items in this itemset. Java users should call [[FreqItemset#javaItems]] instead.
    * @param freq frequency
-   * @param ordered indicates if items represents an itemset (false) or sequence (true)
    * @tparam Item item type
    */
-  class FreqItemset[Item](val items: Array[Item], val freq: Long, val ordered: Boolean)
-    extends Serializable {
-
-    /**
-     * Auxillary constructor, assumes unordered by default.
-     */
-    def this(items: Array[Item], freq: Long) {
-      this(items, freq, false)
-    }
+  class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.

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

@@ -22,7 +22,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
 
 
-  test("FP-Growth frequent itemsets using String type") {
+  test("FP-Growth using String type") {
     val transactions = Seq(
       "r z h k p",
       "z y x w v u t s",
@@ -38,14 +38,12 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
-      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .setOrdered(false)
       .run(rdd)
     val freqItemsets3 = model3.freqItemsets.collect().map { itemset =>
       (itemset.items.toSet, itemset.freq)
@@ -63,59 +61,17 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
-      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 54)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
-      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 625)
   }
 
-  test("FP-Growth frequent sequences using String type"){
-    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 model1 = fpg
-      .setMinSupport(0.5)
-      .setNumPartitions(2)
-      .setOrdered(true)
-      .run(rdd)
-
-    /*
-      Use the following R code to verify association rules using arulesSequences package.
-
-      data = read_baskets("path", info = c("sequenceID","eventID","SIZE"))
-      freqItemSeq = cspade(data, parameter = list(support = 0.5))
-      resSeq = as(freqItemSeq, "data.frame")
-      resSeq$support = resSeq$support * length(transactions)
-      names(resSeq)[names(resSeq) == "support"] = "freq"
-      resSeq
-     */
-    val expected = Set(
-      (Seq("r"), 3L), (Seq("s"), 3L), (Seq("t"), 3L), (Seq("x"), 4L), (Seq("y"), 3L),
-      (Seq("z"), 5L), (Seq("z", "y"), 3L), (Seq("x", "t"), 3L), (Seq("y", "t"), 3L),
-      (Seq("z", "t"), 3L), (Seq("z", "y", "t"), 3L)
-    )
-    val freqItemseqs1 = model1.freqItemsets.collect().map { itemset =>
-      (itemset.items.toSeq, itemset.freq)
-    }.toSet
-    assert(freqItemseqs1 == expected)
-  }
-
-  test("FP-Growth frequent itemsets using Int type") {
+  test("FP-Growth using Int type") {
     val transactions = Seq(
       "1 2 3",
       "1 2 3 4",
@@ -132,14 +88,12 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
-      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .setOrdered(false)
       .run(rdd)
     assert(model3.freqItemsets.first().items.getClass === Array(1).getClass,
       "frequent itemsets should use primitive arrays")
@@ -155,14 +109,12 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
-      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 15)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
-      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 65)
   }

python/pyspark/mllib/fpm.py

@@ -39,8 +39,8 @@ class FPGrowthModel(JavaModelWrapper):
     >>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]]
     >>> rdd = sc.parallelize(data, 2)
     >>> model = FPGrowth.train(rdd, 0.6, 2)
-    >>> sorted(model.freqItemsets().collect(), key=lambda x: x.items)
-    [FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'a', u'c'], freq=3), ...
+    >>> sorted(model.freqItemsets().collect())
+    [FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'c'], freq=3), ...
     """
 
     def freqItemsets(self):