Github user aray commented on a diff in the pull request:
https://github.com/apache/spark/pull/15415#discussion_r97168170
--- Diff: mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala ---
@@ -0,0 +1,251 @@
+/*
+ * 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 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.{FPGrowth => MLlibFPGrowth,
FPGrowthModel => MLlibFPGrowthModel}
+import org.apache.spark.sql.{DataFrame, _}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{ArrayType, StringType, StructType}
+
+/**
+ * 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
= {
+ SchemaUtils.checkColumnType(schema, $(featuresCol), new
ArrayType(StringType, false))
+ SchemaUtils.appendColumn(schema, $(predictionCol), new
ArrayType(StringType, false))
+ }
+
+ /**
+ * 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 param
+ */
+ @Since("2.2.0")
+ val numPartitions: IntParam = new IntParam(this, "numPartitions",
+ "Number of partitions used by parallel FP-growth",
ParamValidators.gtEq[Int](1))
+
+ /** @group getParam */
+ @Since("2.2.0")
+ def getNumPartitions: Int = $(numPartitions)
+
+}
+
+/**
+ * :: Experimental ::
+ * A parallel FP-growth algorithm to mine frequent itemsets.
+ *
+ * @see [[http://dx.doi.org/10.1145/1454008.1454027 Li et al., PFP:
Parallel FP-Growth for Query
+ * Recommendation]]
+ */
+@Since("2.2.0")
+@Experimental
+class FPGrowth @Since("2.2.0") (
+ @Since("2.2.0") override val uid: String)
+ extends Estimator[FPGrowthModel] with FPGrowthParams with
DefaultParamsWritable {
+
+ @Since("2.2.0")
+ def this() = this(Identifiable.randomUID("FPGrowth"))
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setMinSupport(value: Double): this.type = set(minSupport, value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setNumPartitions(value: Int): this.type = set(numPartitions, value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ def fit(dataset: Dataset[_]): FPGrowthModel = {
+ val data = dataset.select($(featuresCol)).rdd.map(r =>
r.getSeq[String](0).toArray)
+ val parentModel = new
MLlibFPGrowth().setMinSupport($(minSupport)).run(data)
+ copyValues(new FPGrowthModel(uid, parentModel))
+ }
+
+ @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 parentModel a model trained by spark.mllib.fpm.FPGrowth
+ */
+@Since("2.2.0")
+@Experimental
+class FPGrowthModel private[ml] (
+ @Since("2.2.0") override val uid: String,
+ private val parentModel: MLlibFPGrowthModel[_])
+ extends Model[FPGrowthModel] with FPGrowthParams with MLWritable {
+
+ /**
+ * 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 = $(minConfidence)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setMinConfidence(value: Double): this.type = set(minConfidence,
value)
+
+ @Since("2.2.0")
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ val associationRules = getAssociationRules.rdd.map(r =>
+ (r.getSeq[String](0), r.getSeq[String](1))
+ ).collect()
+
+ // For each rule, examine the input items and summarize the consequents
+ val predictUDF = udf((items: Seq[String]) => associationRules.flatMap(
r =>
+ if (r._1.forall(items.contains(_))) r._2 else Array.empty[String]
+ ).distinct)
+ dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
--- End diff --
Why are we including `featuresCol` as a new column here?
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