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/*
* 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.{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.mllib.evaluation.MulticlassMetrics
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.types.DoubleType
/**
* :: Experimental ::
* Evaluator for multiclass classification, which expects two input columns: score and label.
*/
@Since("1.5.0")
@Experimental
class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") override val uid: String)
extends Evaluator with HasPredictionCol with HasLabelCol with DefaultParamsWritable {
@Since("1.5.0")
def this() = this(Identifiable.randomUID("mcEval"))
/**
* param for metric name in evaluation (supports `"f1"` (default), `"precision"`, `"recall"`,
* `"weightedPrecision"`, `"weightedRecall"`)
* @group param
*/
@Since("1.5.0")
val metricName: Param[String] = {
val allowedParams = ParamValidators.inArray(Array("f1", "precision",
"recall", "weightedPrecision", "weightedRecall"))
new Param(this, "metricName", "metric name in evaluation " +
"(f1|precision|recall|weightedPrecision|weightedRecall)", allowedParams)
}
/** @group getParam */
@Since("1.5.0")
def getMetricName: String = $(metricName)
/** @group setParam */
@Since("1.5.0")
def setMetricName(value: String): this.type = set(metricName, value)
/** @group setParam */
@Since("1.5.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)
/** @group setParam */
@Since("1.5.0")
def setLabelCol(value: String): this.type = set(labelCol, value)
setDefault(metricName -> "f1")
@Since("1.5.0")
override def evaluate(dataset: DataFrame): Double = {
val schema = dataset.schema
SchemaUtils.checkColumnType(schema, $(predictionCol), DoubleType)
SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
val predictionAndLabels = dataset.select($(predictionCol), $(labelCol)).rdd.map {
case Row(prediction: Double, label: Double) =>
(prediction, label)
}
val metrics = new MulticlassMetrics(predictionAndLabels)
val metric = $(metricName) match {
case "f1" => metrics.weightedFMeasure
case "precision" => metrics.precision
case "recall" => metrics.recall
case "weightedPrecision" => metrics.weightedPrecision
case "weightedRecall" => metrics.weightedRecall
}
metric
}
@Since("1.5.0")
override def isLargerBetter: Boolean = $(metricName) match {
case "f1" => true
case "precision" => true
case "recall" => true
case "weightedPrecision" => true
case "weightedRecall" => true
}
@Since("1.5.0")
override def copy(extra: ParamMap): MulticlassClassificationEvaluator = defaultCopy(extra)
}
@Since("1.6.0")
object MulticlassClassificationEvaluator
extends DefaultParamsReadable[MulticlassClassificationEvaluator] {
@Since("1.6.0")
override def load(path: String): MulticlassClassificationEvaluator = super.load(path)
}
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