[GitHub] spark pull request #16618: [SPARK-14409][ML][WIP] Add RankingEvaluator
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[GitHub] spark pull request #16618: [SPARK-14409][ML][WIP] Add RankingEvaluator
Github user daniloascione commented on a diff in the pull request:
https://github.com/apache/spark/pull/16618#discussion_r113721386
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/RankingMetrics.scala ---
@@ -0,0 +1,202 @@
+/*
+ * 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.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.{Column, DataFrame}
+import org.apache.spark.sql.functions.{mean, sum}
+import org.apache.spark.sql.functions.udf
+import org.apache.spark.sql.types.DoubleType
+
+@Since("2.2.0")
+class RankingMetrics(
+ predictionAndObservations: DataFrame, predictionCol: String, labelCol:
String)
+ extends Logging with Serializable {
+
+ /**
+ * Compute the Mean Percentile Rank (MPR) of all the queries.
+ *
+ * See the following paper for detail ("Expected percentile rank" in the
paper):
+ * Hu, Y., Y. Koren, and C. Volinsky. âCollaborative Filtering for
Implicit Feedback Datasets.â
+ * In 2008 Eighth IEEE International Conference on Data Mining,
263â72, 2008.
+ * doi:10.1109/ICDM.2008.22.
+ *
+ * @return the mean percentile rank
+ */
+ lazy val meanPercentileRank: Double = {
+
+def rank = udf((predicted: Seq[Any], actual: Any) => {
+ val l_i = predicted.indexOf(actual)
+
+ if (l_i == -1) {
+1
+ } else {
+l_i.toDouble / predicted.size
+ }
+}, DoubleType)
+
+val R_prime = predictionAndObservations.count()
+val predictionColumn: Column =
predictionAndObservations.col(predictionCol)
+val labelColumn: Column = predictionAndObservations.col(labelCol)
+
+val rankSum: Double = predictionAndObservations
+ .withColumn("rank", rank(predictionColumn, labelColumn))
+ .agg(sum("rank")).first().getDouble(0)
+
+rankSum / R_prime
+ }
+
+ /**
+ * Compute the average precision of all the queries, truncated at
ranking position k.
+ *
+ * If for a query, the ranking algorithm returns n (n is less than k)
results, the precision
+ * value will be computed as #(relevant items retrieved) / k. This
formula also applies when
+ * the size of the ground truth set is less than k.
+ *
+ * If a query has an empty ground truth set, zero will be used as
precision together with
+ * a log warning.
+ *
+ * See the following paper for detail:
+ *
+ * IR evaluation methods for retrieving highly relevant documents. K.
Jarvelin and J. Kekalainen
+ *
+ * @param k the position to compute the truncated precision, must be
positive
+ * @return the average precision at the first k ranking positions
+ */
+ @Since("2.2.0")
+ def precisionAt(k: Int): Double = {
+require(k > 0, "ranking position k should be positive")
+
+def precisionAtK = udf((predicted: Seq[Any], actual: Seq[Any]) => {
+ val actualSet = actual.toSet
+ if (actualSet.nonEmpty) {
+val n = math.min(predicted.length, k)
+var i = 0
+var cnt = 0
+while (i < n) {
+ if (actualSet.contains(predicted(i))) {
+cnt += 1
+ }
+ i += 1
+}
+cnt.toDouble / k
+ } else {
+logWarning("Empty ground truth set, check input data")
+0.0
+ }
+}, DoubleType)
+
+val predictionColumn: Column =
predictionAndObservations.col(predictionCol)
+val labelColumn: Column = predictionAndObservations.col(labelCol)
+
+predictionAndObservations
+ .withColumn("predictionAtK", precisionAtK(predictionColumn,
labelColumn))
+ .agg(mean("predictionAtK")).first().getDouble(0)
+ }
+
+ /**
+ * Returns the mean average precision (MAP) of all the queries.
+ * If a query has an empty ground truth se
[GitHub] spark pull request #16618: [SPARK-14409][ML][WIP] Add RankingEvaluator
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/spa
[GitHub] spark pull request #16618: [SPARK-14409][ML][WIP] Add RankingEvaluator
Github user ebernhardson commented on a diff in the pull request:
https://github.com/apache/spark/pull/16618#discussion_r113360277
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/RankingMetrics.scala ---
@@ -0,0 +1,202 @@
+/*
+ * 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.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.{Column, DataFrame}
+import org.apache.spark.sql.functions.{mean, sum}
+import org.apache.spark.sql.functions.udf
+import org.apache.spark.sql.types.DoubleType
+
+@Since("2.2.0")
+class RankingMetrics(
+ predictionAndObservations: DataFrame, predictionCol: String, labelCol:
String)
+ extends Logging with Serializable {
+
+ /**
+ * Compute the Mean Percentile Rank (MPR) of all the queries.
+ *
+ * See the following paper for detail ("Expected percentile rank" in the
paper):
+ * Hu, Y., Y. Koren, and C. Volinsky. âCollaborative Filtering for
Implicit Feedback Datasets.â
+ * In 2008 Eighth IEEE International Conference on Data Mining,
263â72, 2008.
+ * doi:10.1109/ICDM.2008.22.
+ *
+ * @return the mean percentile rank
+ */
+ lazy val meanPercentileRank: Double = {
+
+def rank = udf((predicted: Seq[Any], actual: Any) => {
+ val l_i = predicted.indexOf(actual)
+
+ if (l_i == -1) {
+1
+ } else {
+l_i.toDouble / predicted.size
+ }
+}, DoubleType)
+
+val R_prime = predictionAndObservations.count()
+val predictionColumn: Column =
predictionAndObservations.col(predictionCol)
+val labelColumn: Column = predictionAndObservations.col(labelCol)
+
+val rankSum: Double = predictionAndObservations
+ .withColumn("rank", rank(predictionColumn, labelColumn))
+ .agg(sum("rank")).first().getDouble(0)
+
+rankSum / R_prime
+ }
+
+ /**
+ * Compute the average precision of all the queries, truncated at
ranking position k.
+ *
+ * If for a query, the ranking algorithm returns n (n is less than k)
results, the precision
+ * value will be computed as #(relevant items retrieved) / k. This
formula also applies when
+ * the size of the ground truth set is less than k.
+ *
+ * If a query has an empty ground truth set, zero will be used as
precision together with
+ * a log warning.
+ *
+ * See the following paper for detail:
+ *
+ * IR evaluation methods for retrieving highly relevant documents. K.
Jarvelin and J. Kekalainen
+ *
+ * @param k the position to compute the truncated precision, must be
positive
+ * @return the average precision at the first k ranking positions
+ */
+ @Since("2.2.0")
+ def precisionAt(k: Int): Double = {
+require(k > 0, "ranking position k should be positive")
+
+def precisionAtK = udf((predicted: Seq[Any], actual: Seq[Any]) => {
+ val actualSet = actual.toSet
+ if (actualSet.nonEmpty) {
+val n = math.min(predicted.length, k)
+var i = 0
+var cnt = 0
+while (i < n) {
+ if (actualSet.contains(predicted(i))) {
+cnt += 1
+ }
+ i += 1
+}
+cnt.toDouble / k
+ } else {
+logWarning("Empty ground truth set, check input data")
+0.0
+ }
+}, DoubleType)
+
+val predictionColumn: Column =
predictionAndObservations.col(predictionCol)
+val labelColumn: Column = predictionAndObservations.col(labelCol)
+
+predictionAndObservations
+ .withColumn("predictionAtK", precisionAtK(predictionColumn,
labelColumn))
+ .agg(mean("predictionAtK")).first().getDouble(0)
+ }
+
+ /**
+ * Returns the mean average precision (MAP) of all the queries.
+ * If a query has an empty ground truth set
[GitHub] spark pull request #16618: [SPARK-14409][ML][WIP] Add RankingEvaluator
Github user ebernhardson commented on a diff in the pull request:
https://github.com/apache/spark/pull/16618#discussion_r113358325
--- 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 --
Don't we also need to run an aggregation on the label column, roughly the
same as the previous aggregation but using labelCol as the sort inst
[GitHub] spark pull request #16618: [SPARK-14409][ML][WIP] Add RankingEvaluator
Github user ebernhardson commented on a diff in the pull request:
https://github.com/apache/spark/pull/16618#discussion_r113355473
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/RankingMetrics.scala ---
@@ -0,0 +1,202 @@
+/*
+ * 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.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.{Column, DataFrame}
+import org.apache.spark.sql.functions.{mean, sum}
+import org.apache.spark.sql.functions.udf
+import org.apache.spark.sql.types.DoubleType
+
+@Since("2.2.0")
+class RankingMetrics(
+ predictionAndObservations: DataFrame, predictionCol: String, labelCol:
String)
+ extends Logging with Serializable {
+
+ /**
+ * Compute the Mean Percentile Rank (MPR) of all the queries.
+ *
+ * See the following paper for detail ("Expected percentile rank" in the
paper):
+ * Hu, Y., Y. Koren, and C. Volinsky. âCollaborative Filtering for
Implicit Feedback Datasets.â
+ * In 2008 Eighth IEEE International Conference on Data Mining,
263â72, 2008.
+ * doi:10.1109/ICDM.2008.22.
+ *
+ * @return the mean percentile rank
+ */
+ lazy val meanPercentileRank: Double = {
+
+def rank = udf((predicted: Seq[Any], actual: Any) => {
+ val l_i = predicted.indexOf(actual)
+
+ if (l_i == -1) {
+1
+ } else {
+l_i.toDouble / predicted.size
+ }
+}, DoubleType)
+
+val R_prime = predictionAndObservations.count()
+val predictionColumn: Column =
predictionAndObservations.col(predictionCol)
+val labelColumn: Column = predictionAndObservations.col(labelCol)
+
+val rankSum: Double = predictionAndObservations
+ .withColumn("rank", rank(predictionColumn, labelColumn))
+ .agg(sum("rank")).first().getDouble(0)
+
+rankSum / R_prime
+ }
+
+ /**
+ * Compute the average precision of all the queries, truncated at
ranking position k.
+ *
+ * If for a query, the ranking algorithm returns n (n is less than k)
results, the precision
+ * value will be computed as #(relevant items retrieved) / k. This
formula also applies when
+ * the size of the ground truth set is less than k.
+ *
+ * If a query has an empty ground truth set, zero will be used as
precision together with
+ * a log warning.
+ *
+ * See the following paper for detail:
+ *
+ * IR evaluation methods for retrieving highly relevant documents. K.
Jarvelin and J. Kekalainen
+ *
+ * @param k the position to compute the truncated precision, must be
positive
+ * @return the average precision at the first k ranking positions
+ */
+ @Since("2.2.0")
+ def precisionAt(k: Int): Double = {
+require(k > 0, "ranking position k should be positive")
+
+def precisionAtK = udf((predicted: Seq[Any], actual: Seq[Any]) => {
+ val actualSet = actual.toSet
+ if (actualSet.nonEmpty) {
+val n = math.min(predicted.length, k)
+var i = 0
+var cnt = 0
+while (i < n) {
+ if (actualSet.contains(predicted(i))) {
+cnt += 1
+ }
+ i += 1
+}
+cnt.toDouble / k
+ } else {
+logWarning("Empty ground truth set, check input data")
+0.0
+ }
+}, DoubleType)
+
+val predictionColumn: Column =
predictionAndObservations.col(predictionCol)
+val labelColumn: Column = predictionAndObservations.col(labelCol)
+
+predictionAndObservations
+ .withColumn("predictionAtK", precisionAtK(predictionColumn,
labelColumn))
+ .agg(mean("predictionAtK")).first().getDouble(0)
+ }
+
+ /**
+ * Returns the mean average precision (MAP) of all the queries.
+ * If a query has an empty ground truth set
