[SPARK-30938][ML][MLLIB] BinaryClassificationMetrics optimization #27682
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testCode: import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import scala.util.Random
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import scala.util.Random
val scoreAndLabels = sc.range(0, 40000000L, 1, 4).mapPartitionsWithIndex{ case (pid, iter) => val rng=new Random(pid); iter.map{_ => (rng.nextDouble, rng.nextInt(2).toDouble)} }
scoreAndLabels.count
val metrics = new BinaryClassificationMetrics(scoreAndLabels, 1)
val start = System.currentTimeMillis; val auc = metrics.areaUnderROC; val end = System.currentTimeMillis; end - start
result:
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zhengruifeng
commented
Feb 24, 2020
| if (grouping < 2) { | ||
| // numBins was more than half of the size; no real point in down-sampling to bins | ||
| logInfo(s"Curve is too small ($countsSize) for $numBins bins to be useful") | ||
| counts | ||
| } else { | ||
| if (grouping >= Int.MaxValue) { |
There was a problem hiding this comment.
Iterator.grouped(size: Int) does not support grouping larger than Int.MaxValue
After this change, BinaryClassificationMetrics can deal with grouping larger than Int.MaxValue
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Test build #118863 has finished for PR 27682 at commit
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Merged to master |
sjincho
pushed a commit
to sjincho/spark
that referenced
this pull request
Apr 15, 2020
### What changes were proposed in this pull request? 1, avoid `Iterator.grouped(size: Int)`, which need to maintain an arraybuffer of `size` 2, keep the number of partitions in curve computation ### Why are the changes needed? 1, `BinaryClassificationMetrics` tend to fail (OOM) when `grouping=count/numBins` is too large, due to `Iterator.grouped(size: Int)` need to maintain an arraybuffer with `size` entries, however, in `BinaryClassificationMetrics` we do not need to maintain such a big array; 2, make sizes of partitions more even; This PR computes metrics more stable and a littler faster; ### Does this PR introduce any user-facing change? No ### How was this patch tested? existing testsuites Closes apache#27682 from zhengruifeng/grouped_opt. Authored-by: zhengruifeng <ruifengz@foxmail.com> Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
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What changes were proposed in this pull request?
1, avoid
Iterator.grouped(size: Int), which need to maintain an arraybuffer ofsize2, keep the number of partitions in curve computation
Why are the changes needed?
1,
BinaryClassificationMetricstend to fail (OOM) whengrouping=count/numBinsis too large, due toIterator.grouped(size: Int)need to maintain an arraybuffer withsizeentries, however, inBinaryClassificationMetricswe do not need to maintain such a big array;2, make sizes of partitions more even;
This PR computes metrics more stable and a littler faster;
Does this PR introduce any user-facing change?
No
How was this patch tested?
existing testsuites