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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.AlphaComponent
import org.apache.spark.ml.Evaluator
import org.apache.spark.ml.param._
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.types.DoubleType
/**
* :: AlphaComponent ::
*
* Evaluator for binary classification, which expects two input columns: score and label.
*/
@AlphaComponent
class BinaryClassificationEvaluator extends Evaluator with Params
with HasRawPredictionCol with HasLabelCol {
/** param for metric name in evaluation */
val metricName: Param[String] = new Param(this, "metricName",
"metric name in evaluation (areaUnderROC|areaUnderPR)", Some("areaUnderROC"))
def getMetricName: String = get(metricName)
def setMetricName(value: String): this.type = set(metricName, value)
def setScoreCol(value: String): this.type = set(rawPredictionCol, value)
def setLabelCol(value: String): this.type = set(labelCol, value)
override def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = {
val map = this.paramMap ++ paramMap
val schema = dataset.schema
checkInputColumn(schema, map(rawPredictionCol), new VectorUDT)
checkInputColumn(schema, map(labelCol), DoubleType)
// TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2.
val scoreAndLabels = dataset.select(map(rawPredictionCol), map(labelCol))
.map { case Row(rawPrediction: Vector, label: Double) =>
(rawPrediction(1), label)
}
val metrics = new BinaryClassificationMetrics(scoreAndLabels)
val metric = map(metricName) match {
case "areaUnderROC" =>
metrics.areaUnderROC()
case "areaUnderPR" =>
metrics.areaUnderPR()
case other =>
throw new IllegalArgumentException(s"Does not support metric $other.")
}
metrics.unpersist()
metric
}
}
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