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new b3d30a8 [SPARK-27577][MLLIB] Correct thresholds downsampled in
BinaryClassificationMetrics
b3d30a8 is described below
commit b3d30a8660de4b8d1a0d704ce18f37703b2f5cc2
Author: Shaochen Shi <shishaoc...@bytedance.com>
AuthorDate: Tue May 7 08:41:58 2019 -0500
[SPARK-27577][MLLIB] Correct thresholds downsampled in
BinaryClassificationMetrics
## What changes were proposed in this pull request?
Choose the last record in chunks when calculating metrics with downsampling
in `BinaryClassificationMetrics`.
## How was this patch tested?
A new unit test is added to verify thresholds from downsampled records.
Closes #24470 from shishaochen/spark-mllib-binary-metrics.
Authored-by: Shaochen Shi <shishaoc...@bytedance.com>
Signed-off-by: Sean Owen <sean.o...@databricks.com>
(cherry picked from commit d5308cd86fff1e4bf9c24e0dd73d8d2c92737c4f)
Signed-off-by: Sean Owen <sean.o...@databricks.com>
---
.../spark/mllib/evaluation/BinaryClassificationMetrics.scala | 11 +++++++----
.../mllib/evaluation/BinaryClassificationMetricsSuite.scala | 11 +++++++++++
2 files changed, 18 insertions(+), 4 deletions(-)
diff --git
a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index 2cfcf38..764806b 100644
---
a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++
b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -175,12 +175,15 @@ class BinaryClassificationMetrics @Since("1.3.0") (
grouping = Int.MaxValue
}
counts.mapPartitions(_.grouped(grouping.toInt).map { pairs =>
- // The score of the combined point will be just the first one's
score
- val firstScore = pairs.head._1
- // The point will contain all counts in this chunk
+ // The score of the combined point will be just the last one's
score, which is also
+ // the minimal in each chunk since all scores are already sorted
in descending.
+ val lastScore = pairs.last._1
+ // The combined point will contain all counts in this chunk. Thus,
calculated
+ // metrics (like precision, recall, etc.) on its score (or
so-called threshold) are
+ // the same as those without sampling.
val agg = new BinaryLabelCounter()
pairs.foreach(pair => agg += pair._2)
- (firstScore, agg)
+ (lastScore, agg)
})
}
}
diff --git
a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
index a08917a..4cc9ee5 100644
---
a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
+++
b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
@@ -155,6 +155,17 @@ class BinaryClassificationMetricsSuite extends
SparkFunSuite with MLlibTestSpark
(1.0, 1.0), (1.0, 1.0)
) ==
downsampledROC)
+
+ val downsampledRecall =
downsampled.recallByThreshold().collect().sorted.toList
+ assert(
+ // May have to add 1 if the sample factor didn't divide evenly
+ numBins + (if (scoreAndLabels.size % numBins == 0) 0 else 1) ==
+ downsampledRecall.size)
+ assert(
+ List(
+ (0.1, 1.0), (0.2, 1.0), (0.4, 0.75), (0.6, 0.75), (0.8, 0.25)
+ ) ==
+ downsampledRecall)
}
}
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