github.com/jgbaldwinbrown/perf@v0.1.1/benchmath/anone.go

// Copyright 2022 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package benchmath

import (
	"fmt"
	"math"
	"sync"

	"github.com/aclements/go-moremath/stats"
)

// AssumeNothing is a non-parametric Assumption (that is, it makes no
// distributional assumptions). The summary statistic is the sample
// median and comparisons are done using the Mann-Whitney U test.
//
// This is a good default assumption for benchmarks.
// There's substantial evidence that benchmark results are non-normal.
// The disadvantage (of any non-parametric methods) is that this is
// less statistically powerful than parametric methods.
var AssumeNothing = assumeNothing{}

type assumeNothing struct{}

var _ Assumption = assumeNothing{}

// medianCache maps from ciKey to stats.QuantileCIResult for median
// confidence intervals.
var medianCache sync.Map

func medianCI(n int, confidence float64) stats.QuantileCIResult {
	type ciKey struct {
		n          int
		confidence float64
	}
	key := ciKey{n, confidence}
	if ciX, ok := medianCache.Load(key); ok {
		return ciX.(stats.QuantileCIResult)
	}
	ci := stats.QuantileCI(n, 0.5, confidence)
	medianCache.Store(key, ci)
	return ci
}

// medianSamples returns the minimum number of samples required to get
// a finite confidence interval at the given confidence level.
func medianSamples(confidence float64) (op string, n int) {
	const limit = 50
	// We need at least two samples to have an interval.
	for n = 2; n <= limit; n++ {
		ci := medianCI(n, confidence)
		if 0 < ci.LoOrder && ci.HiOrder <= n {
			return ">=", n
		}
	}
	return ">", limit
}

func (assumeNothing) SummaryLabel() string {
	return "median"
}

func (assumeNothing) Summary(s *Sample, confidence float64) Summary {
	ci := medianCI(len(s.Values), confidence)
	median, lo, hi := ci.SampleCI(s.sample())

	var warnings []error
	if math.IsInf(lo, 0) || math.IsInf(hi, 0) {
		// Explain to the user why there's a ±∞
		op, need := medianSamples(confidence)
		msg := fmt.Errorf("need %s %d samples for confidence interval at level %v", op, need, confidence)
		warnings = append(warnings, msg)
	}

	return Summary{median, lo, hi, ci.Confidence, warnings}
}

// uTestMinP[n] is the minimum possible P value for the U-test with
// two samples of size n.
//
// Generated by go run mktables.go.
var uTestMinP = []float64{
	1: 1,
	2: 0.3333333333333333,
	3: 0.1,
	4: 0.02857142857142857,
	5: 0.007936507936507936,
	6: 0.0021645021645021645,
	7: 0.0005827505827505828,
	8: 0.0001554001554001554,
	9: 4.113533525298231e-05,
}

// uTestSamples returns the minimum number of samples required for the
// U-test to achieve statistical significance at the given alpha
// level.
func uTestSamples(alpha float64) (op string, n int) {
	for n, minP := range uTestMinP {
		if n == 0 {
			continue
		}
		if minP <= alpha {
			return ">=", n
		}
	}
	return ">", len(uTestMinP)
}

func (assumeNothing) Compare(s1, s2 *Sample) Comparison {
	res, err := stats.MannWhitneyUTest(s1.Values, s2.Values, stats.LocationDiffers)
	if err != nil {
		// The U-test failed. Report as if there's no
		// significant difference, along with the error.
		return Comparison{P: 1, N1: len(s1.Values), N2: len(s2.Values), Alpha: s1.Thresholds.CompareAlpha, Warnings: []error{err}}
	}
	cmp := Comparison{P: res.P, N1: res.N1, N2: res.N2, Alpha: s1.Thresholds.CompareAlpha}
	// Warn if there aren't enough samples to report a difference
	// even if they were maximally diverged.
	if cmp.P > cmp.Alpha {
		op, n := uTestSamples(cmp.Alpha)
		if cmp.N1 < n && cmp.N2 < n {
			// We could deal with asymmetric sample sizes
			// by first ramping up the smaller sample
			// until the minimum P value is sufficient or
			// the sample sizes are equal. But it doesn't
			// seem worth the complexity.
			msg := fmt.Errorf("need %s %d samples to detect a difference at alpha level %v", op, n, cmp.Alpha)
			cmp.Warnings = append(cmp.Warnings, msg)
		}
	}
	return cmp
}