Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15415#discussion_r101698335
--- Diff: mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala ---
@@ -0,0 +1,327 @@
+/*
+ * 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.ml.fpm
+
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol}
+import org.apache.spark.ml.util._
+import org.apache.spark.mllib.fpm.{AssociationRules =>
MLlibAssociationRules,
+FPGrowth => MLlibFPGrowth}
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
+import org.apache.spark.sql._
+import org.apache.spark.sql.types._
+
+/**
+ * Common params for FPGrowth and FPGrowthModel
+ */
+private[fpm] trait FPGrowthParams extends Params with HasFeaturesCol with
HasPredictionCol {
+
+ /**
+ * Validates and transforms the input schema.
+ * @param schema input schema
+ * @return output schema
+ */
+ protected def validateAndTransformSchema(schema: StructType): StructType
= {
+ val inputType = schema($(featuresCol)).dataType
+ require(inputType.isInstanceOf[ArrayType],
+ s"The input column must be ArrayType, but got $inputType.")
+ SchemaUtils.appendColumn(schema, $(predictionCol),
schema($(featuresCol)).dataType)
+ }
+
+ /**
+ * Minimal support level of the frequent pattern. [0.0, 1.0]. Any
pattern that appears
+ * more than (minSupport * size-of-the-dataset) times will be output
+ * Default: 0.3
+ * @group param
+ */
+ @Since("2.2.0")
+ val minSupport: DoubleParam = new DoubleParam(this, "minSupport",
+ "the minimal support level of the frequent pattern (Default: 0.3)",
+ ParamValidators.inRange(0.0, 1.0))
+ setDefault(minSupport -> 0.3)
+
+ /** @group getParam */
+ @Since("2.2.0")
+ def getMinSupport: Double = $(minSupport)
+
+ /**
+ * Number of partitions used by parallel FP-growth
+ * @group expertParam
+ */
+ @Since("2.2.0")
+ val numPartitions: IntParam = new IntParam(this, "numPartitions",
+ "Number of partitions used by parallel FP-growth",
ParamValidators.gtEq[Int](1))
+
+ /** @group expertGetParam */
+ @Since("2.2.0")
+ def getNumPartitions: Int = $(numPartitions)
+
+ /**
+ * minimal confidence for generating Association Rule
+ * Default: 0.8
+ * @group param
+ */
+ @Since("2.2.0")
+ val minConfidence: DoubleParam = new DoubleParam(this, "minConfidence",
+ "minimal confidence for generating Association Rule (Default: 0.8)",
+ ParamValidators.inRange(0.0, 1.0))
+ setDefault(minConfidence -> 0.8)
+
+ /** @group getParam */
+ @Since("2.2.0")
+ def getMinConfidence: Double = $(minConfidence)
+
+}
+
+/**
+ * :: Experimental ::
+ * A parallel FP-growth algorithm to mine frequent itemsets.
+ *
+ * @see [[http://dx.doi.org/10.1145/1454008.1454027 Li et al., PFP:
Parallel FP-Growth for Query
+ * Recommendation]]
+ */
+@Since("2.2.0")
+@Experimental
+class FPGrowth @Since("2.2.0") (
+ @Since("2.2.0") override val uid: String)
+ extends Estimator[FPGrowthModel] with FPGrowthParams with
DefaultParamsWritable {
+
+ @Since("2.2.0")
+ def this() = this(Identifiable.randomUID("fpgrowth"))
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setMinSupport(value: Double): this.type = set(minSupport, value)
+
+ /** @group expertSetParam */
+ @Since("2.2.0")
+ def setNumPartitions(value: Int): this.type = set(numPartitions, value)
+
+ /** @group setParam
+ * minConfidence has not effect during fitting.
+ */
+ @Since("2.2.0")
+ def setMinConfidence(value: Double): this.type = set(minConfidence,
value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ override def fit(dataset: Dataset[_]): FPGrowthModel = {
+ genericFit(dataset)
+ }
+
+ private def genericFit[T: ClassTag](dataset: Dataset[_]): FPGrowthModel
= {
+ val data = dataset.select($(featuresCol)).rdd.map(r =>
r.getSeq[T](0).toArray)
+ val parentModel = new
MLlibFPGrowth().setMinSupport($(minSupport)).run(data)
+ val rows = parentModel.freqItemsets
+ .map(f => (f.items, f.freq))
+ .map(cols => Row(cols._1, cols._2))
+
+ val dt = dataset.schema($(featuresCol)).dataType
+ val fields = Array(StructField("items", dt, nullable = false),
+ StructField("freq", LongType, nullable = false))
+ val schema = StructType(fields)
+ val frequentItems = dataset.sparkSession.createDataFrame(rows,
schema).toDF("items", "freq")
+ copyValues(new FPGrowthModel(uid, frequentItems)).setParent(this)
+ }
+
+ @Since("2.2.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ override def copy(extra: ParamMap): FPGrowth = defaultCopy(extra)
+}
+
+
+@Since("2.2.0")
+object FPGrowth extends DefaultParamsReadable[FPGrowth] {
+
+ @Since("2.2.0")
+ override def load(path: String): FPGrowth = super.load(path)
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by FPGrowth.
+ *
+ * @param freqItemsets frequent items in the format of DataFrame("items",
"freq")
+ */
+@Since("2.2.0")
+@Experimental
+class FPGrowthModel private[ml] (
+ @Since("2.2.0") override val uid: String,
+ val freqItemsets: DataFrame)
+ extends Model[FPGrowthModel] with FPGrowthParams with MLWritable {
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setMinConfidence(value: Double): this.type = set(minConfidence,
value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /**
+ * Get association rules fitted by AssociationRules using the
minConfidence. Returns a dataframe
+ * with three fields, "antecedent", "consequent" and "confidence", where
"antecedent" and
+ * "consequent" are Array[String] and "confidence" is Double.
+ */
+ @Since("2.2.0")
+ @transient lazy val getAssociationRules: DataFrame = {
+ val freqItems = getFreqItemsets
+ AssocaitionRules.getAssocationRulesFromFP(freqItems, "items", "freq",
$(minConfidence))
+ }
+
+ /**
+ * Get frequent items fitted by FPGrowth, in the format of
DataFrame("items", "freq")
+ */
+ @Since("2.2.0")
+ @transient val getFreqItemsets: DataFrame = freqItemsets
+
+ @Since("2.2.0")
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ genericTransform(dataset, getAssociationRules)
+ }
+
+ private def genericTransform[T](dataset: Dataset[_], associationRules:
DataFrame): DataFrame = {
+ // use unique id to perform the join and aggregateByKey
+ val itemsRDD = dataset.select($(featuresCol)).rdd.map(r =>
r.getSeq[T](0))
+ .distinct().zipWithUniqueId().map(_.swap).cache()
+ val rulesRDD = associationRules.rdd.map(r => (r.getSeq[T](0),
r.getSeq[T](1)))
+
+ val itemsWithConsequents = itemsRDD.cartesian(rulesRDD).map {
+ case ((id, items), (antecedent, consequent)) =>
+ val itemSet = items.toSet
+ val consequents = if (antecedent.forall(itemSet.contains(_)))
consequent else Seq.empty
+ (id, consequents)
+ }.aggregateByKey(new ArrayBuffer[T])(
+ (ar, seq) => ar ++= seq, (ar, seq) => ar ++= seq)
+
+ val mappingRDD = itemsRDD.join(itemsWithConsequents)
+ .map { case (id, (items, consequent)) => (items, consequent) }
+ .map (cols => Row(cols._1, cols._2))
+ val dt = dataset.schema($(featuresCol)).dataType
+ val fields = Array($(featuresCol), $(predictionCol))
+ .map(fieldName => StructField(fieldName, dt, nullable = true))
+ val schema = StructType(fields)
+ val mapping = dataset.sparkSession.createDataFrame(mappingRDD, schema)
+
+ dataset.join(mapping, $(featuresCol))
+ }
+
+ @Since("2.2.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ @Since("2.2.0")
+ override def copy(extra: ParamMap): FPGrowthModel = {
+ val copied = new FPGrowthModel(uid, freqItemsets)
+ copyValues(copied, extra).setParent(this.parent)
+ }
+
+ @Since("2.2.0")
+ override def write: MLWriter = new
FPGrowthModel.FPGrowthModelWriter(this)
+}
+
+object FPGrowthModel extends MLReadable[FPGrowthModel] {
--- End diff --
Add Since annotation to object
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
