SPARK-4111 [MLlib] add regression metrics

mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala

@@ -0,0 +1,92 @@
+/*
+ * 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
+
+/**
+ * :: Experimental ::
+ * Evaluator for regression.
+ *
+ * @param predictionAndObservations an RDD of (prediction,observation) pairs.
+ */
+@Experimental
+class RegressionMetrics(predictionAndObservations: 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 = predictionAndObservations.map{
+      case (prediction,observation) => Vectors.dense(
+        Array(observation, observation - prediction)
+      )
+    }.aggregate(new MultivariateOnlineSummarizer())(
+        (summary, v) => summary.add(v),
+        (sum1,sum2) => sum1.merge(sum2)
+      )
+    summarizer
+  }
+
+  /**
+   * Returns the explained variance regression score.
+   * explainedVarianceScore = 1 - variance(y - \hat{y}) / variance(y)
+   * Reference: [[http://en.wikipedia.org/wiki/Explained_variation]]
+   */
+  def explainedVarianceScore: Double = {
+    1 - summarizer.variance(1) / summarizer.variance(0)
+  }
+
+  /**
+   * Returns the mean absolute error, which is a risk function corresponding to the
+   * expected value of the absolute error loss or l1-norm loss.
+   */
+  def meanAbsoluteError: Double = {
+    summarizer.normL1(1) / summarizer.count
+  }
+
+  /**
+   * Returns the mean squared error, which is a risk function corresponding to the
+   * expected value of the squared error loss or quadratic loss.
+   */
+  def meanSquaredError: Double = {
+    math.pow(summarizer.normL2(1),2) / summarizer.count
+  }
+
+  /**
+   * Returns the root mean squared error, which is defined as the square root of
+   * the mean squared error.
+   */
+  def rootMeanSquaredError: Double = {
+    summarizer.normL2(1) / math.sqrt(summarizer.count)
+  }
+
+  /**
+   * Returns R^2^, the coefficient of determination.
+   * Reference: [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
+   */
+  def r2Score: Double = {
+    1 - math.pow(summarizer.normL2(1),2) / (summarizer.variance(0) * (summarizer.count - 1))
+  }
+}

mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala

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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.mllib.evaluation
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+
+class RegressionMetricsSuite extends FunSuite with LocalSparkContext {
+
+  test("regression metrics") {
+    val predictionAndObservations = sc.parallelize(
+      Seq((2.5,3.0),(0.0,-0.5),(2.0,2.0),(8.0,7.0)),2)
+    val metrics = new RegressionMetrics(predictionAndObservations)
+    assert(metrics.explainedVarianceScore ~== 0.95717 absTol 1E-5,
+      "explained variance regression score mismatch")
+    assert(metrics.meanAbsoluteError ~== 0.5 absTol 1E-5, "mean absolute error mismatch")
+    assert(metrics.meanSquaredError ~== 0.375 absTol 1E-5, "mean squared error mismatch")
+    assert(metrics.rootMeanSquaredError ~== 0.61237 absTol 1E-5,
+      "root mean squared error mismatch")
+    assert(metrics.r2Score ~== 0.94861 absTol 1E-5, "r2 score mismatch")
+  }
+
+  test("regression metrics with complete fitting") {
+    val predictionAndObservations = sc.parallelize(
+      Seq((3.0,3.0),(0.0,0.0),(2.0,2.0),(8.0,8.0)),2)
+    val metrics = new RegressionMetrics(predictionAndObservations)
+    assert(metrics.explainedVarianceScore ~== 1.0 absTol 1E-5,
+      "explained variance regression score mismatch")
+    assert(metrics.meanAbsoluteError ~== 0.0 absTol 1E-5, "mean absolute error mismatch")
+    assert(metrics.meanSquaredError ~== 0.0 absTol 1E-5, "mean squared error mismatch")
+    assert(metrics.rootMeanSquaredError ~== 0.0 absTol 1E-5,
+      "root mean squared error mismatch")
+    assert(metrics.r2Score ~== 1.0 absTol 1E-5, "r2 score mismatch")
+  }
+}