Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/9267#discussion_r48585214
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala ---
@@ -53,6 +65,96 @@ class FPGrowthModel[Item: ClassTag] @Since("1.3.0") (
val associationRules = new AssociationRules(confidence)
associationRules.run(freqItemsets)
}
+
+ override def save(sc: SparkContext, path: String): Unit = {
+ FPGrowthModel.SaveLoadV1_0.save(this, path)
+ }
+
+ override protected val formatVersion: String = "1.0"
+}
+
+object FPGrowthModel extends Loader[FPGrowthModel[_]] {
+
+ override def load(sc: SparkContext, path: String): FPGrowthModel[_] = {
+ val inferredItemset = FPGrowthModel.SaveLoadV1_0.inferItemType(sc,
path)
+ FPGrowthModel.SaveLoadV1_0.load(sc, path, inferredItemset)
+ }
+
+ private[fpm] object SaveLoadV1_0 {
+
+ private val thisFormatVersion = "1.0"
+
+ private[fpm] val thisClassName =
"org.apache.spark.mllib.fpm.FPGrowthModel"
+
+ def save[Item: ClassTag: TypeTag](model: FPGrowthModel[Item], path:
String): Unit = {
+ val sc = model.freqItemsets.sparkContext
+ val sqlContext = new SQLContext(sc)
+
+ val metadata = compact(render(
+ ("class" -> thisClassName) ~ ("version" -> thisFormatVersion)))
+ sc.parallelize(Seq(metadata),
1).saveAsTextFile(Loader.metadataPath(path))
+
+ val itemType = ScalaReflection.schemaFor[Item].dataType
+ val fields = Array(StructField("items", ArrayType(itemType)),
+ StructField("freq", LongType))
+ val schema = StructType(fields)
+ val rowDataRDD = model.freqItemsets.map { x =>
+ Row(x.items, x.freq)
+ }
+ sqlContext.createDataFrame(rowDataRDD,
schema).write.parquet(Loader.dataPath(path))
+ }
+
+ def inferItemType(sc: SparkContext, path: String): FreqItemset[_] = {
+ val sqlContext = new SQLContext(sc)
+ val freqItemsets = sqlContext.read.parquet(Loader.dataPath(path))
+ val itemsetType = freqItemsets.schema(0).dataType
+ val freqType = freqItemsets.schema(1).dataType
+ require(itemsetType.isInstanceOf[ArrayType],
+ s"items should be ArrayType, but get $itemsetType")
+ require(freqType.isInstanceOf[LongType], s"freq should be LongType,
but get $freqType")
+ val itemType = itemsetType.asInstanceOf[ArrayType].elementType
+ val result = itemType match {
+ case BooleanType => new FreqItemset(Array[Boolean](), 0L)
+ case BinaryType => new FreqItemset(Array(Array[Byte]()), 0L)
+ case StringType => new FreqItemset(Array[String](), 0L)
+ case ByteType => new FreqItemset(Array[Byte](), 0L)
+ case ShortType => new FreqItemset(Array[Short](), 0L)
+ case IntegerType => new FreqItemset(Array[Int](), 0L)
+ case LongType => new FreqItemset(Array[Long](), 0L)
+ case FloatType => new FreqItemset(Array[Float](), 0L)
+ case DoubleType => new FreqItemset(Array[Double](), 0L)
+ case DateType => new FreqItemset(Array[java.sql.Date](), 0L)
+ case DecimalType.SYSTEM_DEFAULT => new
FreqItemset(Array[java.math.BigDecimal](), 0L)
+ case TimestampType => new FreqItemset(Array[java.sql.Timestamp](),
0L)
+ case _: ArrayType => new FreqItemset(Array[Seq[_]](), 0L)
+ case _: MapType => new FreqItemset(Array[Map[_, _]](), 0L)
+ case _: StructType => new FreqItemset(Array[Row](), 0L)
+ case other =>
+ throw new UnsupportedOperationException(s"Schema for type $other
is not supported")
+ }
+ result
+ }
+
+ def load[Item: ClassTag: TypeTag](
+ sc: SparkContext,
+ path: String,
+ inferredItemset: FreqItemset[Item]): FPGrowthModel[Item] = {
+ implicit val formats = DefaultFormats
+ val sqlContext = new SQLContext(sc)
+
+ val (className, formatVersion, metadata) = loadMetadata(sc, path)
+ assert(className == thisClassName)
+ assert(formatVersion == thisFormatVersion)
+
+ val freqItemsets = sqlContext.read.parquet(Loader.dataPath(path))
+ val freqItemsetsRDD = freqItemsets.map { x =>
+ val items = x.getAs[Seq[Item]](0).toArray
--- End diff --
Are you able to do ```getSeq[_]``` here? I'm wondering if we can eliminate
```inferItemType```.
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