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https://issues.apache.org/jira/browse/MATH-1441?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16341802#comment-16341802
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Gilles commented on MATH-1441:
------------------------------
I reverted the change in commit 60bcdd450d6950da981d70b8b08379bdd744c82d
because I couldn't notice any performance improvement.
Please post the result of your benchmark on the current code ("master" branch).
Thanks.
> SimpleRegression#getSlopeConfidenceInterval recalculates t distribution on
> every call
> -------------------------------------------------------------------------------------
>
> Key: MATH-1441
> URL: https://issues.apache.org/jira/browse/MATH-1441
> Project: Commons Math
> Issue Type: Improvement
> Affects Versions: 3.6.1
> Environment: Java 8, Linux x64.
> Reporter: Max Aller
> Priority: Minor
> Labels: performance
>
> SimpleRegression#getSlopeConfidenceInterval, when called a lot (on the other
> of 100k or 1M times), is surprisingly slow - 3M calls, on my 3rd gen i7
> machine, takes *75 seconds*. This is primarily because recalculating the
> inverse cumulative probability, and reconstructing the TDistribution object
> itself, is somewhat expensive, entailing a lot of `log` and `sqrt` and
> iteration and all that stuff.
> The confidence interval is based on two values - *n* and *alpha*. I'd posit
> that alpha will _usually_ be one of a very small set of values, and n, well,
> at least in my case, I'm always calling this method with the same number of
> data points - a moving window of time-series data. But I recognize n might be
> all over the place for some users.
> In any event, I strongly believe some level of caching would greatly benefit
> the users of Commons Math. I've forked my own version of
> getSlopeConfidenceInterval that uses caching. Here's a test case
> demonstrating that:
> {code:java}
> class SlowRegressionsTest {
> @Test
> void slow() {
> SimpleRegression simpleRegression = new SimpleRegression();
> simpleRegression.addData(0.0, 0.0);
> simpleRegression.addData(1.0, 1.0);
> simpleRegression.addData(2.0, 2.0);
> long start = System.currentTimeMillis();
> for (int i = 0; i < 3_000_000; i++) {
> simpleRegression.getSlopeConfidenceInterval();
> }
> long duration = System.currentTimeMillis() - start;
> System.out.printf("`slow` took %dms%n", duration);
> }
> @Test
> void fast() {
> SimpleRegression simpleRegression = new SimpleRegression();
> simpleRegression.addData(0.0, 0.0);
> simpleRegression.addData(1.0, 1.0);
> simpleRegression.addData(2.0, 2.0);
> long start = System.currentTimeMillis();
> for (int i = 0; i < 3_000_000; i++) {
>
> SimpleRegressionUtilsKt.fastGetSlopeConfidenceInterval(simpleRegression);
> }
> long duration = System.currentTimeMillis() - start;
> System.out.printf("`fast` took %dms%n", duration);
> }
> }{code}
> which prints out
> {noformat}
> `fast` took 159ms
> `slow` took 75304ms{noformat}
> Nearly 500x faster - 53ns/call. My particular solution is written in Kotlin
> for Java 8, so not of direct relevance to you, but here it is:
> {code:java}
> package math
> import org.apache.commons.math3.distribution.TDistribution
> import org.apache.commons.math3.exception.OutOfRangeException
> import org.apache.commons.math3.exception.util.LocalizedFormats
> import org.apache.commons.math3.stat.regression.SimpleRegression
> import java.util.concurrent.ConcurrentHashMap
> @JvmOverloads
> fun SimpleRegression.fastGetSlopeConfidenceInterval(alpha: Double = 0.05):
> Double {
> if (n < 3) {
> return Double.NaN
> }
> if (alpha >= 1 || alpha <= 0) {
> throw OutOfRangeException(
> LocalizedFormats.SIGNIFICANCE_LEVEL,
> alpha, 0, 1
> )
> }
> // No advertised NotStrictlyPositiveException here - will return NaN above
> // PATCH: use cached inverse cumulative probability
> return slopeStdErr * getInverseCumulativeProbability(n, alpha)
> }
> private val cache = ConcurrentHashMap<Key, Double>()
> private data class Key(val n: Long, val alpha: Double)
> private fun getInverseCumulativeProbability(n: Long, alpha: Double): Double =
> cache.getOrPut(Key(n, alpha)) {
> TDistribution((n - 2).toDouble()).inverseCumulativeProbability(1.0 -
> alpha / 2.0)
> }
> {code}
> Limitations: 1. Kotlin, 2. ConcurrentHashMap is unbounded here.
> I don't know how/if Commons Math does caching elsewhere, but it'd sure be
> handy here, I believe. What are your thoughts?
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