Github user daniloascione commented on a diff in the pull request:
https://github.com/apache/spark/pull/16618#discussion_r113702013
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/RankingEvaluator.scala ---
@@ -0,0 +1,138 @@
+/*
+ * 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.evaluation
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.param.{IntParam, Param, ParamMap,
ParamValidators}
+import org.apache.spark.ml.param.shared.{HasLabelCol, HasPredictionCol}
+import org.apache.spark.ml.util.{DefaultParamsReadable,
DefaultParamsWritable, Identifiable, SchemaUtils}
+import org.apache.spark.sql.{DataFrame, Dataset}
+import org.apache.spark.sql.expressions.Window
+import org.apache.spark.sql.functions.{coalesce, col, collect_list,
row_number, udf}
+import org.apache.spark.sql.types.LongType
+
+/**
+ * Evaluator for ranking.
+ */
+@Since("2.2.0")
+@Experimental
+final class RankingEvaluator @Since("2.2.0")(@Since("2.2.0") override val
uid: String)
+ extends Evaluator with HasPredictionCol with HasLabelCol with
DefaultParamsWritable {
+
+ @Since("2.2.0")
+ def this() = this(Identifiable.randomUID("rankingEval"))
+
+ @Since("2.2.0")
+ val k = new IntParam(this, "k", "Top-K cutoff", (x: Int) => x > 0)
+
+ /** @group getParam */
+ @Since("2.2.0")
+ def getK: Int = $(k)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setK(value: Int): this.type = set(k, value)
+
+ setDefault(k -> 1)
+
+ @Since("2.2.0")
+ val metricName: Param[String] = {
+ val allowedParams = ParamValidators.inArray(Array("mpr"))
+ new Param(this, "metricName", "metric name in evaluation (mpr)",
allowedParams)
+ }
+
+ /** @group getParam */
+ @Since("2.2.0")
+ def getMetricName: String = $(metricName)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setMetricName(value: String): this.type = set(metricName, value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setLabelCol(value: String): this.type = set(labelCol, value)
+
+ /**
+ * Param for query column name.
+ * @group param
+ */
+ val queryCol: Param[String] = new Param[String](this, "queryCol", "query
column name")
+
+ setDefault(queryCol, "query")
+
+ /** @group getParam */
+ @Since("2.2.0")
+ def getQueryCol: String = $(queryCol)
+
+ /** @group setParam */
+ @Since("2.2.0")
+ def setQueryCol(value: String): this.type = set(queryCol, value)
+
+ setDefault(metricName -> "mpr")
+
+ @Since("2.2.0")
+ override def evaluate(dataset: Dataset[_]): Double = {
+ val schema = dataset.schema
+ SchemaUtils.checkNumericType(schema, $(predictionCol))
+ SchemaUtils.checkNumericType(schema, $(labelCol))
+ SchemaUtils.checkNumericType(schema, $(queryCol))
+
+ val w =
Window.partitionBy(col($(queryCol))).orderBy(col($(predictionCol)).desc)
+
+ val topAtk: DataFrame = dataset
+ .na.drop("all", Seq($(predictionCol)))
+ .select(col($(predictionCol)), col($(labelCol)).cast(LongType),
col($(queryCol)))
+ .withColumn("rn", row_number().over(w)).where(col("rn") <= $(k))
+ .drop("rn")
+ .groupBy(col($(queryCol)))
+ .agg(collect_list($(labelCol)).as("topAtk"))
+
+ val mapToEmptyArray_ = udf(() => Array.empty[Long])
+
+ val predictionAndLabels: DataFrame = dataset
+ .join(topAtk, Seq($(queryCol)), "outer")
+ .withColumn("topAtk", coalesce(col("topAtk"), mapToEmptyArray_()))
+ .select($(labelCol), "topAtk")
--- End diff --
Yes, I agree. This is currently done in the previous step, when the topAtk
Dataframe is calculated ([line
101](https://github.com/apache/spark/pull/16618/files/fa2155af8947347a2fc1e565cf05a19529022266#diff-0345c4cb1878d3bb0d84297202fdc95fR101)).
Unfortunately this is not compatible with RankingMetrics, which expects the
format of predictionAndLabels as input. I didn't want to change RankingMetrics
in this same PR.
So the predictionAndLabels DataFrame is calculated to use the same
RankingMetrics from mllib (well, it is now UDFs based, but I didn't touched its
logic).
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