Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17086#discussion_r184584878
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala
---
@@ -55,44 +60,128 @@ class MulticlassMetricsSuite extends SparkFunSuite
with MLlibTestSparkContext {
val f2measure1 = (1 + 2 * 2) * precision1 * recall1 / (2 * 2 *
precision1 + recall1)
val f2measure2 = (1 + 2 * 2) * precision2 * recall2 / (2 * 2 *
precision2 + recall2)
-
assert(metrics.confusionMatrix.toArray.sameElements(confusionMatrix.toArray))
- assert(math.abs(metrics.truePositiveRate(0.0) - tpRate0) < delta)
- assert(math.abs(metrics.truePositiveRate(1.0) - tpRate1) < delta)
- assert(math.abs(metrics.truePositiveRate(2.0) - tpRate2) < delta)
- assert(math.abs(metrics.falsePositiveRate(0.0) - fpRate0) < delta)
- assert(math.abs(metrics.falsePositiveRate(1.0) - fpRate1) < delta)
- assert(math.abs(metrics.falsePositiveRate(2.0) - fpRate2) < delta)
- assert(math.abs(metrics.precision(0.0) - precision0) < delta)
- assert(math.abs(metrics.precision(1.0) - precision1) < delta)
- assert(math.abs(metrics.precision(2.0) - precision2) < delta)
- assert(math.abs(metrics.recall(0.0) - recall0) < delta)
- assert(math.abs(metrics.recall(1.0) - recall1) < delta)
- assert(math.abs(metrics.recall(2.0) - recall2) < delta)
- assert(math.abs(metrics.fMeasure(0.0) - f1measure0) < delta)
- assert(math.abs(metrics.fMeasure(1.0) - f1measure1) < delta)
- assert(math.abs(metrics.fMeasure(2.0) - f1measure2) < delta)
- assert(math.abs(metrics.fMeasure(0.0, 2.0) - f2measure0) < delta)
- assert(math.abs(metrics.fMeasure(1.0, 2.0) - f2measure1) < delta)
- assert(math.abs(metrics.fMeasure(2.0, 2.0) - f2measure2) < delta)
+ assert(metrics.confusionMatrix.asML ~== confusionMatrix.asML relTol
delta)
+ assert(metrics.truePositiveRate(0.0) ~== tpRate0 absTol delta)
+ assert(metrics.truePositiveRate(1.0) ~== tpRate1 absTol delta)
+ assert(metrics.truePositiveRate(2.0) ~== tpRate2 absTol delta)
+ assert(metrics.falsePositiveRate(0.0) ~== fpRate0 absTol delta)
+ assert(metrics.falsePositiveRate(1.0) ~== fpRate1 absTol delta)
+ assert(metrics.falsePositiveRate(2.0) ~== fpRate2 absTol delta)
+ assert(metrics.precision(0.0) ~== precision0 absTol delta)
+ assert(metrics.precision(1.0) ~== precision1 absTol delta)
+ assert(metrics.precision(2.0) ~== precision2 absTol delta)
+ assert(metrics.recall(0.0) ~== recall0 absTol delta)
+ assert(metrics.recall(1.0) ~== recall1 absTol delta)
+ assert(metrics.recall(2.0) ~== recall2 absTol delta)
+ assert(metrics.fMeasure(0.0) ~== f1measure0 absTol delta)
+ assert(metrics.fMeasure(1.0) ~== f1measure1 absTol delta)
+ assert(metrics.fMeasure(2.0) ~== f1measure2 absTol delta)
+ assert(metrics.fMeasure(0.0, 2.0) ~== f2measure0 absTol delta)
+ assert(metrics.fMeasure(1.0, 2.0) ~== f2measure1 absTol delta)
+ assert(metrics.fMeasure(2.0, 2.0) ~== f2measure2 absTol delta)
+
+ assert(metrics.accuracy ~==
+ (2.0 + 3.0 + 1.0) / ((2 + 3 + 1) + (1 + 1 + 1)) absTol delta)
+ assert(metrics.accuracy ~== metrics.precision absTol delta)
+ assert(metrics.accuracy ~== metrics.recall absTol delta)
+ assert(metrics.accuracy ~== metrics.fMeasure absTol delta)
+ assert(metrics.accuracy ~== metrics.weightedRecall absTol delta)
+ val weight0 = 4.0 / 9
+ val weight1 = 4.0 / 9
+ val weight2 = 1.0 / 9
+ assert(metrics.weightedTruePositiveRate ~==
+ (weight0 * tpRate0 + weight1 * tpRate1 + weight2 * tpRate2) absTol
delta)
+ assert(metrics.weightedFalsePositiveRate ~==
+ (weight0 * fpRate0 + weight1 * fpRate1 + weight2 * fpRate2) absTol
delta)
+ assert(metrics.weightedPrecision ~==
+ (weight0 * precision0 + weight1 * precision1 + weight2 * precision2)
absTol delta)
+ assert(metrics.weightedRecall ~==
+ (weight0 * recall0 + weight1 * recall1 + weight2 * recall2) absTol
delta)
+ assert(metrics.weightedFMeasure ~==
+ (weight0 * f1measure0 + weight1 * f1measure1 + weight2 * f1measure2)
absTol delta)
+ assert(metrics.weightedFMeasure(2.0) ~==
+ (weight0 * f2measure0 + weight1 * f2measure1 + weight2 * f2measure2)
absTol delta)
+ assert(metrics.labels === labels)
+ }
+
+ test("Multiclass evaluation metrics with weights") {
+ /*
+ * Confusion matrix for 3-class classification with total 9 instances
with 2 weights:
+ * |2 * w1|1 * w2 |1 * w1| true class0 (4 instances)
+ * |1 * w2|2 * w1 + 1 * w2|0 | true class1 (4 instances)
+ * |0 |0 |1 * w2| true class2 (1 instance)
+ */
+ val w1 = 2.2
+ val w2 = 1.5
+ val tw = 2.0 * w1 + 1.0 * w2 + 1.0 * w1 + 1.0 * w2 + 2.0 * w1 + 1.0 *
w2 + 1.0 * w2
+ val confusionMatrix = Matrices.dense(3, 3,
+ Array(2 * w1, 1 * w2, 0, 1 * w2, 2 * w1 + 1 * w2, 0, 1 * w1, 0, 1 *
w2))
+ val labels = Array(0.0, 1.0, 2.0)
+ val predictionAndLabelsWithWeights = sc.parallelize(
+ Seq((0.0, 0.0, w1), (0.0, 1.0, w2), (0.0, 0.0, w1), (1.0, 0.0, w2),
+ (1.0, 1.0, w1), (1.0, 1.0, w2), (1.0, 1.0, w1), (2.0, 2.0, w2),
+ (2.0, 0.0, w1)), 2)
+ val metrics = new MulticlassMetrics(predictionAndLabelsWithWeights)
+ val tpRate0 = (2.0 * w1) / (2.0 * w1 + 1.0 * w2 + 1.0 * w1)
+ val tpRate1 = (2.0 * w1 + 1.0 * w2) / (2.0 * w1 + 1.0 * w2 + 1.0 * w2)
+ val tpRate2 = (1.0 * w2) / (1.0 * w2 + 0)
+ val fpRate0 = (1.0 * w2) / (tw - (2.0 * w1 + 1.0 * w2 + 1.0 * w1))
+ val fpRate1 = (1.0 * w2) / (tw - (1.0 * w2 + 2.0 * w1 + 1.0 * w2))
+ val fpRate2 = (1.0 * w1) / (tw - (1.0 * w2))
+ val precision0 = (2.0 * w1) / (2 * w1 + 1 * w2)
+ val precision1 = (2.0 * w1 + 1.0 * w2) / (2.0 * w1 + 1.0 * w2 + 1.0 *
w2)
+ val precision2 = (1.0 * w2) / (1 * w1 + 1 * w2)
+ val recall0 = (2.0 * w1) / (2.0 * w1 + 1.0 * w2 + 1.0 * w1)
+ val recall1 = (2.0 * w1 + 1.0 * w2) / (2.0 * w1 + 1.0 * w2 + 1.0 * w2)
+ val recall2 = (1.0 * w2) / (1.0 * w2 + 0)
+ val f1measure0 = 2 * precision0 * recall0 / (precision0 + recall0)
+ val f1measure1 = 2 * precision1 * recall1 / (precision1 + recall1)
+ val f1measure2 = 2 * precision2 * recall2 / (precision2 + recall2)
+ val f2measure0 = (1 + 2 * 2) * precision0 * recall0 / (2 * 2 *
precision0 + recall0)
+ val f2measure1 = (1 + 2 * 2) * precision1 * recall1 / (2 * 2 *
precision1 + recall1)
+ val f2measure2 = (1 + 2 * 2) * precision2 * recall2 / (2 * 2 *
precision2 + recall2)
+
+ assert(metrics.confusionMatrix.asML ~== confusionMatrix.asML relTol
delta)
+ assert(metrics.truePositiveRate(0.0) ~== tpRate0 absTol delta)
+ assert(metrics.truePositiveRate(1.0) ~== tpRate1 absTol delta)
+ assert(metrics.truePositiveRate(2.0) ~== tpRate2 absTol delta)
+ assert(metrics.falsePositiveRate(0.0) ~== fpRate0 absTol delta)
+ assert(metrics.falsePositiveRate(1.0) ~== fpRate1 absTol delta)
+ assert(metrics.falsePositiveRate(2.0) ~== fpRate2 absTol delta)
+ assert(metrics.precision(0.0) ~== precision0 absTol delta)
+ assert(metrics.precision(1.0) ~== precision1 absTol delta)
+ assert(metrics.precision(2.0) ~== precision2 absTol delta)
+ assert(metrics.recall(0.0) ~== recall0 absTol delta)
+ assert(metrics.recall(1.0) ~== recall1 absTol delta)
+ assert(metrics.recall(2.0) ~== recall2 absTol delta)
+ assert(metrics.fMeasure(0.0) ~== f1measure0 absTol delta)
+ assert(metrics.fMeasure(1.0) ~== f1measure1 absTol delta)
+ assert(metrics.fMeasure(2.0) ~== f1measure2 absTol delta)
+ assert(metrics.fMeasure(0.0, 2.0) ~== f2measure0 absTol delta)
+ assert(metrics.fMeasure(1.0, 2.0) ~== f2measure1 absTol delta)
+ assert(metrics.fMeasure(2.0, 2.0) ~== f2measure2 absTol delta)
- assert(math.abs(metrics.accuracy -
- (2.0 + 3.0 + 1.0) / ((2 + 3 + 1) + (1 + 1 + 1))) < delta)
- assert(math.abs(metrics.accuracy - metrics.precision) < delta)
- assert(math.abs(metrics.accuracy - metrics.recall) < delta)
- assert(math.abs(metrics.accuracy - metrics.fMeasure) < delta)
- assert(math.abs(metrics.accuracy - metrics.weightedRecall) < delta)
- assert(math.abs(metrics.weightedTruePositiveRate -
- ((4.0 / 9) * tpRate0 + (4.0 / 9) * tpRate1 + (1.0 / 9) * tpRate2)) <
delta)
- assert(math.abs(metrics.weightedFalsePositiveRate -
- ((4.0 / 9) * fpRate0 + (4.0 / 9) * fpRate1 + (1.0 / 9) * fpRate2)) <
delta)
- assert(math.abs(metrics.weightedPrecision -
- ((4.0 / 9) * precision0 + (4.0 / 9) * precision1 + (1.0 / 9) *
precision2)) < delta)
- assert(math.abs(metrics.weightedRecall -
- ((4.0 / 9) * recall0 + (4.0 / 9) * recall1 + (1.0 / 9) * recall2)) <
delta)
- assert(math.abs(metrics.weightedFMeasure -
- ((4.0 / 9) * f1measure0 + (4.0 / 9) * f1measure1 + (1.0 / 9) *
f1measure2)) < delta)
- assert(math.abs(metrics.weightedFMeasure(2.0) -
- ((4.0 / 9) * f2measure0 + (4.0 / 9) * f2measure1 + (1.0 / 9) *
f2measure2)) < delta)
- assert(metrics.labels.sameElements(labels))
+ assert(metrics.accuracy ~==
+ (2.0 * w1 + 2.0 * w1 + 1.0 * w2 + 1.0 * w2) / tw absTol delta)
+ assert(metrics.accuracy ~== metrics.precision absTol delta)
+ assert(metrics.accuracy ~== metrics.recall absTol delta)
+ assert(metrics.accuracy ~== metrics.fMeasure absTol delta)
+ assert(metrics.accuracy ~== metrics.weightedRecall absTol delta)
+ val weight0 = (2 * w1 + 1 * w2 + 1 * w1) / tw
+ val weight1 = (1 * w2 + 2 * w1 + 1 * w2) / tw
+ val weight2 = 1 * w2 / tw
+ assert(metrics.weightedTruePositiveRate ~==
+ (weight0 * tpRate0 + weight1 * tpRate1 + weight2 * tpRate2) absTol
delta)
+ assert(metrics.weightedFalsePositiveRate ~==
+ (weight0 * fpRate0 + weight1 * fpRate1 + weight2 * fpRate2) absTol
delta)
+ assert(metrics.weightedPrecision ~==
+ (weight0 * precision0 + weight1 * precision1 + weight2 * precision2)
absTol delta)
+ assert(metrics.weightedRecall ~==
+ (weight0 * recall0 + weight1 * recall1 + weight2 * recall2) absTol
delta)
+ assert(metrics.weightedFMeasure ~==
+ (weight0 * f1measure0 + weight1 * f1measure1 + weight2 * f1measure2)
absTol delta)
+ assert(metrics.weightedFMeasure(2.0) ~==
+ (weight0 * f2measure0 + weight1 * f2measure1 + weight2 * f2measure2)
absTol delta)
--- End diff --
I think maybe `relTol` will be better than `absTol`, except the cases that
one side is zero. What do you think of it ?
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