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
index abac08022ea473f0f7c4cf030f6e73dcc54f10c4..efa8459d3cdba2ac6452d80c1b3c8272615bbd3d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/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.
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
index 1a8a1e79f2810abe756fbd781c6f9d81db570916..66ae3543ecc4e231218e5a5149561eabec7ac571 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
+++ b/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)
}
diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py
index b7f00d60069e651a9e4f01a30c11041c88230d58..bdc4a132b1b182a898830112984963c17360b7a3 100644
--- a/python/pyspark/mllib/fpm.py
+++ b/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):