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Clean up iterator usage in benchmarks

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Specifically we turned `mean`, `classify_outliers`, `jackknife`,
into concrete functions that take only `const_iterator` from vecs,
instead of generic iterators over anything.

I also changed `resample` to take `const_iterator` instead of
plain `iterator`, and similar for `standard_deviation`, and
`analyse_samples`.
This commit is contained in:
Martin Hořeňovský 2023-04-20 22:47:21 +02:00
parent f3c678c0ab
commit 7d07efc92b
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GPG Key ID: DE48307B8B0D381A
2 changed files with 75 additions and 40 deletions
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62
src/catch2/benchmark/detail/catch_stats.cpp
View File

@ -26,7 +26,10 @@ namespace {
using Catch::Benchmark::Detail::sample;
template <typename URng, typename Estimator>
sample resample(URng& rng, unsigned int resamples, std::vector<double>::iterator first, std::vector<double>::iterator last, Estimator& estimator) {
static sample resample(URng& rng, unsigned int resamples,
std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last,
Estimator& estimator) {
auto n = static_cast<size_t>(last - first);
std::uniform_int_distribution<decltype(n)> dist(0, n - 1);
@ -118,7 +121,8 @@ using Catch::Benchmark::Detail::sample;
return p * x;
}
double standard_deviation(std::vector<double>::iterator first, std::vector<double>::iterator last) {
double standard_deviation(std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last) {
auto m = Catch::Benchmark::Detail::mean(first, last);
double variance = std::accumulate( first,
last,
@ -161,6 +165,47 @@ namespace Catch {
return xj + g * (xj1 - xj);
}
OutlierClassification
classify_outliers( std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last ) {
std::vector<double> copy( first, last );
auto q1 = weighted_average_quantile( 1, 4, copy.begin(), copy.end() );
auto q3 = weighted_average_quantile( 3, 4, copy.begin(), copy.end() );
auto iqr = q3 - q1;
auto los = q1 - ( iqr * 3. );
auto lom = q1 - ( iqr * 1.5 );
auto him = q3 + ( iqr * 1.5 );
auto his = q3 + ( iqr * 3. );
OutlierClassification o;
for ( ; first != last; ++first ) {
const double t = *first;
if ( t < los ) {
++o.low_severe;
} else if ( t < lom ) {
++o.low_mild;
} else if ( t > his ) {
++o.high_severe;
} else if ( t > him ) {
++o.high_mild;
}
++o.samples_seen;
}
return o;
}
double mean( std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last ) {
auto count = last - first;
double sum = 0.;
while (first != last) {
sum += *first;
++first;
}
return sum / static_cast<double>(count);
}
double erfc_inv(double x) {
return erf_inv(1.0 - x);
@ -210,7 +255,10 @@ namespace Catch {
}
bootstrap_analysis analyse_samples(double confidence_level, unsigned int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last) {
bootstrap_analysis analyse_samples(double confidence_level,
unsigned int n_resamples,
std::vector<double>::iterator first,
std::vector<double>::iterator last) {
CATCH_INTERNAL_START_WARNINGS_SUPPRESSION
CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
static std::random_device entropy;
@ -218,11 +266,12 @@ namespace Catch {
auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++
auto mean = &Detail::mean<std::vector<double>::iterator>;
auto mean = &Detail::mean;
auto stddev = &standard_deviation;
#if defined(CATCH_CONFIG_USE_ASYNC)
auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
auto Estimate = [=](double(*f)(std::vector<double>::const_iterator,
std::vector<double>::const_iterator)) {
auto seed = entropy();
return std::async(std::launch::async, [=] {
std::mt19937 rng(seed);
@ -237,7 +286,8 @@ namespace Catch {
auto mean_estimate = mean_future.get();
auto stddev_estimate = stddev_future.get();
#else
auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
auto Estimate = [=](double(*f)(std::vector<double>::const_iterator,
std::vector<double>::const_iterator)) {
auto seed = entropy();
std::mt19937 rng(seed);
auto resampled = resample(rng, n_resamples, first, last, f);

53
src/catch2/benchmark/detail/catch_stats.hpp
View File

@ -30,39 +30,17 @@ namespace Catch {
double weighted_average_quantile(int k, int q, std::vector<double>::iterator first, std::vector<double>::iterator last);
template <typename Iterator>
OutlierClassification classify_outliers(Iterator first, Iterator last) {
std::vector<double> copy(first, last);
OutlierClassification
classify_outliers( std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last );
auto q1 = weighted_average_quantile(1, 4, copy.begin(), copy.end());
auto q3 = weighted_average_quantile(3, 4, copy.begin(), copy.end());
auto iqr = q3 - q1;
auto los = q1 - (iqr * 3.);
auto lom = q1 - (iqr * 1.5);
auto him = q3 + (iqr * 1.5);
auto his = q3 + (iqr * 3.);
double mean( std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last );
OutlierClassification o;
for (; first != last; ++first) {
auto&& t = *first;
if (t < los) ++o.low_severe;
else if (t < lom) ++o.low_mild;
else if (t > his) ++o.high_severe;
else if (t > him) ++o.high_mild;
++o.samples_seen;
}
return o;
}
template <typename Iterator>
double mean(Iterator first, Iterator last) {
auto count = last - first;
double sum = std::accumulate(first, last, 0.);
return sum / static_cast<double>(count);
}
template <typename Estimator, typename Iterator>
sample jackknife(Estimator&& estimator, Iterator first, Iterator last) {
template <typename Estimator>
sample jackknife(Estimator&& estimator,
std::vector<double>::iterator first,
std::vector<double>::iterator last) {
auto n = static_cast<size_t>(last - first);
auto second = first;
++second;
@ -85,8 +63,12 @@ namespace Catch {
double normal_quantile(double p);
template <typename Iterator, typename Estimator>
Estimate<double> bootstrap(double confidence_level, Iterator first, Iterator last, sample const& resample, Estimator&& estimator) {
template <typename Estimator>
Estimate<double> bootstrap( double confidence_level,
std::vector<double>::iterator first,
std::vector<double>::iterator last,
sample const& resample,
Estimator&& estimator ) {
auto n_samples = last - first;
double point = estimator(first, last);
@ -136,7 +118,10 @@ namespace Catch {
double outlier_variance;
};
bootstrap_analysis analyse_samples(double confidence_level, unsigned int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last);
bootstrap_analysis analyse_samples(double confidence_level,
unsigned int n_resamples,
std::vector<double>::iterator first,
std::vector<double>::iterator last);
} // namespace Detail
} // namespace Benchmark
} // namespace Catch