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use crate::stats::univariate::Sample;
use crate::stats::univariate::{self, mixed};
use crate::stats::Distribution;
use crate::benchmark::BenchmarkConfig;
use crate::error::Result;
use crate::estimate::{
build_change_estimates, ChangeDistributions, ChangeEstimates, ChangePointEstimates, Estimates,
};
use crate::measurement::Measurement;
use crate::report::BenchmarkId;
use crate::{fs, Criterion, SavedSample};
// Common comparison procedure
#[cfg_attr(feature = "cargo-clippy", allow(clippy::type_complexity))]
pub(crate) fn common<M: Measurement>(
id: &BenchmarkId,
avg_times: &Sample<f64>,
config: &BenchmarkConfig,
criterion: &Criterion<M>,
) -> Result<(
f64,
Distribution<f64>,
ChangeEstimates,
ChangeDistributions,
Vec<f64>,
Vec<f64>,
Vec<f64>,
Estimates,
)> {
let mut sample_file = criterion.output_directory.clone();
sample_file.push(id.as_directory_name());
sample_file.push(&criterion.baseline_directory);
sample_file.push("sample.json");
let sample: SavedSample = fs::load(&sample_file)?;
let SavedSample { iters, times, .. } = sample;
let mut estimates_file = criterion.output_directory.clone();
estimates_file.push(id.as_directory_name());
estimates_file.push(&criterion.baseline_directory);
estimates_file.push("estimates.json");
let base_estimates: Estimates = fs::load(&estimates_file)?;
let base_avg_times: Vec<f64> = iters
.iter()
.zip(times.iter())
.map(|(iters, elapsed)| elapsed / iters)
.collect();
let base_avg_time_sample = Sample::new(&base_avg_times);
let mut change_dir = criterion.output_directory.clone();
change_dir.push(id.as_directory_name());
change_dir.push("change");
fs::mkdirp(&change_dir)?;
let (t_statistic, t_distribution) = t_test(avg_times, base_avg_time_sample, config);
let (estimates, relative_distributions) =
estimates(id, avg_times, base_avg_time_sample, config, criterion);
Ok((
t_statistic,
t_distribution,
estimates,
relative_distributions,
iters,
times,
base_avg_times.clone(),
base_estimates,
))
}
// Performs a two sample t-test
fn t_test(
avg_times: &Sample<f64>,
base_avg_times: &Sample<f64>,
config: &BenchmarkConfig,
) -> (f64, Distribution<f64>) {
let nresamples = config.nresamples;
let t_statistic = avg_times.t(base_avg_times);
let t_distribution = elapsed!(
"Bootstrapping the T distribution",
mixed::bootstrap(avg_times, base_avg_times, nresamples, |a, b| (a.t(b),))
)
.0;
// HACK: Filter out non-finite numbers, which can happen sometimes when sample size is very small.
// Downstream code doesn't like non-finite values here.
let t_distribution = Distribution::from(
t_distribution
.iter()
.filter(|a| a.is_finite())
.cloned()
.collect::<Vec<_>>()
.into_boxed_slice(),
);
(t_statistic, t_distribution)
}
// Estimates the relative change in the statistics of the population
fn estimates<M: Measurement>(
id: &BenchmarkId,
avg_times: &Sample<f64>,
base_avg_times: &Sample<f64>,
config: &BenchmarkConfig,
criterion: &Criterion<M>,
) -> (ChangeEstimates, ChangeDistributions) {
fn stats(a: &Sample<f64>, b: &Sample<f64>) -> (f64, f64) {
(
a.mean() / b.mean() - 1.,
a.percentiles().median() / b.percentiles().median() - 1.,
)
}
let cl = config.confidence_level;
let nresamples = config.nresamples;
let (dist_mean, dist_median) = elapsed!(
"Bootstrapping the relative statistics",
univariate::bootstrap(avg_times, base_avg_times, nresamples, stats)
);
let distributions = ChangeDistributions {
mean: dist_mean,
median: dist_median,
};
let (mean, median) = stats(avg_times, base_avg_times);
let points = ChangePointEstimates { mean, median };
let estimates = build_change_estimates(&distributions, &points, cl);
{
log_if_err!({
let mut estimates_path = criterion.output_directory.clone();
estimates_path.push(id.as_directory_name());
estimates_path.push("change");
estimates_path.push("estimates.json");
fs::save(&estimates, &estimates_path)
});
}
(estimates, distributions)
}
|
