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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}
import org.apache.spark.mllib.evaluation.RegressionMetrics
import org.apache.spark.sql.{Dataset, Row}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{DoubleType, FloatType}
/**
* :: Experimental ::
* Evaluator for regression, which expects two input columns: prediction and label.
*/
@Since("1.4.0")
@Experimental
final class RegressionEvaluator @Since("1.4.0") (@Since("1.4.0") override val uid: String)
extends Evaluator with HasPredictionCol with HasLabelCol with DefaultParamsWritable {
@Since("1.4.0")
def this() = this(Identifiable.randomUID("regEval"))
/**
* Param for metric name in evaluation. Supports:
* - `"rmse"` (default): root mean squared error
* - `"mse"`: mean squared error
* - `"r2"`: R^2^ metric
* - `"mae"`: mean absolute error
*
* @group param
*/
@Since("1.4.0")
val metricName: Param[String] = {
val allowedParams = ParamValidators.inArray(Array("mse", "rmse", "r2", "mae"))
new Param(this, "metricName", "metric name in evaluation (mse|rmse|r2|mae)", allowedParams)
}
/** @group getParam */
@Since("1.4.0")
def getMetricName: String = $(metricName)
/** @group setParam */
@Since("1.4.0")
def setMetricName(value: String): this.type = set(metricName, value)
/** @group setParam */
@Since("1.4.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)
/** @group setParam */
@Since("1.4.0")
def setLabelCol(value: String): this.type = set(labelCol, value)
setDefault(metricName -> "rmse")
@Since("2.0.0")
override def evaluate(dataset: Dataset[_]): Double = {
val schema = dataset.schema
val predictionColName = $(predictionCol)
val predictionType = schema($(predictionCol)).dataType
require(predictionType == FloatType || predictionType == DoubleType,
s"Prediction column $predictionColName must be of type float or double, " +
s" but not $predictionType")
val labelColName = $(labelCol)
val labelType = schema($(labelCol)).dataType
require(labelType == FloatType || labelType == DoubleType,
s"Label column $labelColName must be of type float or double, but not $labelType")
val predictionAndLabels = dataset
.select(col($(predictionCol)).cast(DoubleType), col($(labelCol)).cast(DoubleType))
.rdd.
map { case Row(prediction: Double, label: Double) =>
(prediction, label)
}
val metrics = new RegressionMetrics(predictionAndLabels)
val metric = $(metricName) match {
case "rmse" => metrics.rootMeanSquaredError
case "mse" => metrics.meanSquaredError
case "r2" => metrics.r2
case "mae" => metrics.meanAbsoluteError
}
metric
}
@Since("1.4.0")
override def isLargerBetter: Boolean = $(metricName) match {
case "rmse" => false
case "mse" => false
case "r2" => true
case "mae" => false
}
@Since("1.5.0")
override def copy(extra: ParamMap): RegressionEvaluator = defaultCopy(extra)
}
@Since("1.6.0")
object RegressionEvaluator extends DefaultParamsReadable[RegressionEvaluator] {
@Since("1.6.0")
override def load(path: String): RegressionEvaluator = super.load(path)
}
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