Github user srowen commented on a diff in the pull request: https://github.com/apache/spark/pull/2978#discussion_r19465130 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala --- @@ -0,0 +1,83 @@ +/* + * 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.mllib.evaluation + +import org.apache.spark.annotation.Experimental +import org.apache.spark.rdd.RDD +import org.apache.spark.Logging +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.mllib.rdd.RDDFunctions._ + +/** + * :: Experimental :: + * Evaluator for regression. + * + * @param valuesAndPreds an RDD of (value, pred) pairs. + */ +@Experimental +class RegressionMetrics(valuesAndPreds: RDD[(Double, Double)]) extends Logging { + + /** + * Use MultivariateOnlineSummarizer to calculate mean and variance of different combination. + * MultivariateOnlineSummarizer is a numerically stable algorithm to compute mean and variance + * in a online fashion. + */ + private lazy val summarizer: MultivariateOnlineSummarizer = { + val summarizer: MultivariateOnlineSummarizer = valuesAndPreds.map{ + case (value,pred) => Vectors.dense( + Array(value, pred, value - pred, math.abs(value - pred), math.pow(value - pred, 2.0)) + ) + }.treeAggregate(new MultivariateOnlineSummarizer())( + (summary, v) => summary.add(v), + (sum1,sum2) => sum1.merge(sum2) + ) + summarizer + } + + /** + * Computes the explained variance regression score + */ + def explainedVarianceScore(): Double = { + 1 - summarizer.variance(2) / summarizer.variance(0) + } + + /** + * Computes the mean absolute error, which is a risk function corresponding to the + * expected value of the absolute error loss or l1-norm loss. + */ + def mae(): Double = { + summarizer.mean(3) + } + + /** + * Computes the mean square error, which is a risk function corresponding to the + * expected value of the squared error loss or quadratic loss. + */ + def mse(): Double = { + summarizer.mean(4) + } + + /** + * Computes R^2^, the coefficient of determination. + * @return + */ + def r2_socre(): Double = { --- End diff -- Typo in name
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