Github user hhbyyh commented on a diff in the pull request: https://github.com/apache/spark/pull/15415#discussion_r102860331 --- Diff: mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala --- @@ -0,0 +1,346 @@ +/* + * 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.functions._ +import org.apache.spark.sql.types._ + +/** + * Common params for FPGrowth and FPGrowthModel + */ +private[fpm] trait FPGrowthParams extends Params with HasFeaturesCol with HasPredictionCol { + + /** + * 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 a frequent pattern", + ParamValidators.inRange(0.0, 1.0)) + setDefault(minSupport -> 0.3) + + /** @group getParam */ + @Since("2.2.0") + def getMinSupport: Double = $(minSupport) + + /** + * Number of partitions (>=1) used by parallel FP-growth. By default the param is not set, and + * partition number of the input dataset is used. + * @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. + * Note that minConfidence has no effect during fitting. + * Default: 0.8 + * @group param + */ + @Since("2.2.0") + val minConfidence: DoubleParam = new DoubleParam(this, "minConfidence", + "minimal confidence for generating Association Rule", + ParamValidators.inRange(0.0, 1.0)) + setDefault(minConfidence -> 0.8) + + /** @group getParam */ + @Since("2.2.0") + def getMinConfidence: Double = $(minConfidence) + + /** + * Validates and transforms the input schema. + * @param schema input schema + * @return output schema + */ + @Since("2.2.0") + protected def validateAndTransformSchema(schema: StructType): StructType = { + val inputType = schema($(featuresCol)).dataType + require(inputType.isInstanceOf[ArrayType], + s"The input column must be ArrayType, but got $inputType.") + SchemaUtils.appendColumn(schema, $(predictionCol), schema($(featuresCol)).dataType) + } +} + +/** + * :: Experimental :: + * A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in + * <a href="http://dx.doi.org/10.1145/1454008.1454027">Li et al., PFP: Parallel FP-Growth for Query + * Recommendation</a>. PFP distributes computation in such a way that each worker executes an + * independent group of mining tasks. The FP-Growth algorithm is described in + * <a href="http://dx.doi.org/10.1145/335191.335372">Han et al., Mining frequent patterns without + * candidate generation</a>. + * + * @see <a href="http://en.wikipedia.org/wiki/Association_rule_learning"> + * Association rule learning (Wikipedia)</a> + */ +@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 */ + @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) + + @Since("2.2.0") + override def fit(dataset: Dataset[_]): FPGrowthModel = { + transformSchema(dataset.schema, logging = true) + genericFit(dataset) + } + + private def genericFit[T: ClassTag](dataset: Dataset[_]): FPGrowthModel = { + val data = dataset.select($(featuresCol)) + val items = data.where(col($(featuresCol)).isNotNull).rdd.map(r => r.getSeq[T](0).toArray) + val mllibFP = new MLlibFPGrowth().setMinSupport($(minSupport)) + if (isSet(numPartitions)) { + mllibFP.setNumPartitions($(numPartitions)) + } + val parentModel = mllibFP.run(items) + val rows = parentModel.freqItemsets.map(f => Row(f.items, f.freq)) + + val schema = StructType(Seq( + StructField("items", dataset.schema($(featuresCol)).dataType, nullable = false), + StructField("freq", LongType, nullable = false))) + val frequentItems = dataset.sparkSession.createDataFrame(rows, schema) + copyValues(new FPGrowthModel(uid, frequentItems)).setParent(this) + } + + @Since("2.2.0") + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) + } + + @Since("2.2.0") + 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"[Seq], "freq"[Long]) + */ +@Since("2.2.0") +@Experimental +class FPGrowthModel private[ml] ( + @Since("2.2.0") override val uid: String, + @transient 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[T] and "confidence" is Double. + */ + @Since("2.2.0") + @transient lazy val associationRules: DataFrame = { + val freqItems = freqItemsets + AssociationRules.getAssociationRulesFromFP(freqItems, "items", "freq", $(minConfidence)) + } + + /** + * The transform method first generates the association rules according to the frequent itemsets. + * Then for each association rule, it will examine the input items against antecedents and + * summarize the consequents as prediction. The prediction column has the same data type as the + * input column. (Array[T]) + * Note that internally it uses Cartesian join and may exhaust memory for large datasets. + */ + @Since("2.2.0") + override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) + genericTransform(dataset) + } + + private def genericTransform[T](dataset: Dataset[_]): DataFrame = { + // use index to perform the join and aggregateByKey, and keep the original order after join. + val indexToItems = dataset.select($(featuresCol)).rdd.map(r => r.getSeq[T](0)) + .zipWithIndex().map(_.swap) + val rulesRDD = associationRules.select("antecedent", "consequent").rdd + .map(r => (r.getSeq[T](0), r.getSeq[T](1))) + + val indexToConsequents = indexToItems.cartesian(rulesRDD).map { + case ((id, items), (antecedent, consequent)) => + val consequents = if (items != null) { + val itemSet = items.toSet + if (antecedent.forall(itemSet.contains)) { + consequent.filterNot(itemSet.contains) + } else { + Seq.empty + } + } else { + Seq.empty + } + (id, consequents) + }.aggregateByKey(new ArrayBuffer[T])((ar, seq) => ar ++= seq, (ar, seq) => ar ++= seq) + .map { case (index, cons) => (index, cons.distinct) } + + val rowAndConsequents = dataset.toDF().rdd.zipWithIndex().map(_.swap) + .join(indexToConsequents).sortByKey(ascending = true, dataset.rdd.getNumPartitions) --- End diff -- Checked again and the current implementation is as quick. I will just remove the sort.
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