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


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