You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@spark.apache.org by jk...@apache.org on 2015/07/15 22:32:29 UTC
spark git commit: [SPARK-9005] [MLLIB] Fix RegressionMetrics
computation of explainedVariance
Repository: spark
Updated Branches:
refs/heads/master ec9b62164 -> 536533cad
[SPARK-9005] [MLLIB] Fix RegressionMetrics computation of explainedVariance
Fixes implementation of `explainedVariance` and `r2` to be consistent with their definitions as described in [SPARK-9005](https://issues.apache.org/jira/browse/SPARK-9005).
Author: Feynman Liang <fl...@databricks.com>
Closes #7361 from feynmanliang/SPARK-9005-RegressionMetrics-bugs and squashes the following commits:
f1112fc [Feynman Liang] Add explainedVariance formula
1a3d098 [Feynman Liang] SROwen code review comments
08a0e1b [Feynman Liang] Fix pyspark tests
db8605a [Feynman Liang] Style fix
bde9761 [Feynman Liang] Fix RegressionMetrics tests, relax assumption predictor is unbiased
c235de0 [Feynman Liang] Fix RegressionMetrics tests
4c4e56f [Feynman Liang] Fix RegressionMetrics computation of explainedVariance and r2
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/536533ca
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/536533ca
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/536533ca
Branch: refs/heads/master
Commit: 536533cad83a26f8fa7c60042904a31057ab56c2
Parents: ec9b621
Author: Feynman Liang <fl...@databricks.com>
Authored: Wed Jul 15 13:32:25 2015 -0700
Committer: Joseph K. Bradley <jo...@databricks.com>
Committed: Wed Jul 15 13:32:25 2015 -0700
----------------------------------------------------------------------
.../mllib/evaluation/RegressionMetrics.scala | 27 +++++---
.../evaluation/RegressionMetricsSuite.scala | 69 ++++++++++++++++++--
python/pyspark/mllib/evaluation.py | 2 +-
3 files changed, 83 insertions(+), 15 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/536533ca/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index e577bf8..408847a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -53,14 +53,22 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
)
summary
}
+ private lazy val SSerr = math.pow(summary.normL2(1), 2)
+ private lazy val SStot = summary.variance(0) * (summary.count - 1)
+ private lazy val SSreg = {
+ val yMean = summary.mean(0)
+ predictionAndObservations.map {
+ case (prediction, _) => math.pow(prediction - yMean, 2)
+ }.sum()
+ }
/**
- * Returns the explained variance regression score.
- * explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
- * Reference: [[http://en.wikipedia.org/wiki/Explained_variation]]
+ * Returns the variance explained by regression.
+ * explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n
+ * @see [[https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained]]
*/
def explainedVariance: Double = {
- 1 - summary.variance(1) / summary.variance(0)
+ SSreg / summary.count
}
/**
@@ -76,8 +84,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
* expected value of the squared error loss or quadratic loss.
*/
def meanSquaredError: Double = {
- val rmse = summary.normL2(1) / math.sqrt(summary.count)
- rmse * rmse
+ SSerr / summary.count
}
/**
@@ -85,14 +92,14 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
* the mean squared error.
*/
def rootMeanSquaredError: Double = {
- summary.normL2(1) / math.sqrt(summary.count)
+ math.sqrt(this.meanSquaredError)
}
/**
- * Returns R^2^, the coefficient of determination.
- * Reference: [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
+ * Returns R^2^, the unadjusted coefficient of determination.
+ * @see [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
*/
def r2: Double = {
- 1 - math.pow(summary.normL2(1), 2) / (summary.variance(0) * (summary.count - 1))
+ 1 - SSerr / SStot
}
}
http://git-wip-us.apache.org/repos/asf/spark/blob/536533ca/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
----------------------------------------------------------------------
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
index 9de2bdb..4b7f1be 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
@@ -23,24 +23,85 @@ import org.apache.spark.mllib.util.TestingUtils._
class RegressionMetricsSuite extends SparkFunSuite with MLlibTestSparkContext {
- test("regression metrics") {
+ test("regression metrics for unbiased (includes intercept term) predictor") {
+ /* Verify results in R:
+ preds = c(2.25, -0.25, 1.75, 7.75)
+ obs = c(3.0, -0.5, 2.0, 7.0)
+
+ SStot = sum((obs - mean(obs))^2)
+ SSreg = sum((preds - mean(obs))^2)
+ SSerr = sum((obs - preds)^2)
+
+ explainedVariance = SSreg / length(obs)
+ explainedVariance
+ > [1] 8.796875
+ meanAbsoluteError = mean(abs(preds - obs))
+ meanAbsoluteError
+ > [1] 0.5
+ meanSquaredError = mean((preds - obs)^2)
+ meanSquaredError
+ > [1] 0.3125
+ rmse = sqrt(meanSquaredError)
+ rmse
+ > [1] 0.559017
+ r2 = 1 - SSerr / SStot
+ r2
+ > [1] 0.9571734
+ */
+ val predictionAndObservations = sc.parallelize(
+ Seq((2.25, 3.0), (-0.25, -0.5), (1.75, 2.0), (7.75, 7.0)), 2)
+ val metrics = new RegressionMetrics(predictionAndObservations)
+ assert(metrics.explainedVariance ~== 8.79687 absTol 1E-5,
+ "explained variance regression score mismatch")
+ assert(metrics.meanAbsoluteError ~== 0.5 absTol 1E-5, "mean absolute error mismatch")
+ assert(metrics.meanSquaredError ~== 0.3125 absTol 1E-5, "mean squared error mismatch")
+ assert(metrics.rootMeanSquaredError ~== 0.55901 absTol 1E-5,
+ "root mean squared error mismatch")
+ assert(metrics.r2 ~== 0.95717 absTol 1E-5, "r2 score mismatch")
+ }
+
+ test("regression metrics for biased (no intercept term) predictor") {
+ /* Verify results in R:
+ preds = c(2.5, 0.0, 2.0, 8.0)
+ obs = c(3.0, -0.5, 2.0, 7.0)
+
+ SStot = sum((obs - mean(obs))^2)
+ SSreg = sum((preds - mean(obs))^2)
+ SSerr = sum((obs - preds)^2)
+
+ explainedVariance = SSreg / length(obs)
+ explainedVariance
+ > [1] 8.859375
+ meanAbsoluteError = mean(abs(preds - obs))
+ meanAbsoluteError
+ > [1] 0.5
+ meanSquaredError = mean((preds - obs)^2)
+ meanSquaredError
+ > [1] 0.375
+ rmse = sqrt(meanSquaredError)
+ rmse
+ > [1] 0.6123724
+ r2 = 1 - SSerr / SStot
+ r2
+ > [1] 0.9486081
+ */
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.explainedVariance ~== 0.95717 absTol 1E-5,
+ assert(metrics.explainedVariance ~== 8.85937 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.r2 ~== 0.94861 absTol 1E-5, "r2 score mismatch")
+ assert(metrics.r2 ~== 0.94860 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.explainedVariance ~== 1.0 absTol 1E-5,
+ assert(metrics.explainedVariance ~== 8.6875 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")
http://git-wip-us.apache.org/repos/asf/spark/blob/536533ca/python/pyspark/mllib/evaluation.py
----------------------------------------------------------------------
diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py
index f214037..4398ca8 100644
--- a/python/pyspark/mllib/evaluation.py
+++ b/python/pyspark/mllib/evaluation.py
@@ -82,7 +82,7 @@ class RegressionMetrics(JavaModelWrapper):
... (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)])
>>> metrics = RegressionMetrics(predictionAndObservations)
>>> metrics.explainedVariance
- 0.95...
+ 8.859...
>>> metrics.meanAbsoluteError
0.5...
>>> metrics.meanSquaredError
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@spark.apache.org
For additional commands, e-mail: commits-help@spark.apache.org