third_party/rust_crates/vendor/criterion/src/analysis/mod.rs

use std::collections::BTreeMap;
use std::path::Path;

use stats::bivariate::regression::Slope;
use stats::bivariate::Data;
use stats::univariate::outliers::tukey::{self, LabeledSample};
use stats::univariate::Sample;
use stats::{Distribution, Tails};

use benchmark::BenchmarkConfig;
use estimate::{Distributions, Estimates, Statistic};
use report::{BenchmarkId, ReportContext};
use routine::Routine;
use {build_estimates, Baseline, ConfidenceInterval, Criterion, Estimate, Throughput};
use {format, fs};

macro_rules! elapsed {
    ($msg:expr, $block:expr) => {{
        let start = ::std::time::Instant::now();
        let out = $block;
        let elapsed = &start.elapsed();

        info!(
            "{} took {}",
            $msg,
            format::time(::DurationExt::to_nanos(elapsed) as f64)
        );

        out
    }};
}

mod compare;

// Common analysis procedure
pub(crate) fn common<T>(
    id: &BenchmarkId,
    routine: &mut Routine<T>,
    config: &BenchmarkConfig,
    criterion: &Criterion,
    report_context: &ReportContext,
    parameter: &T,
    throughput: Option<Throughput>,
) {
    if criterion.list_mode {
        println!("{}: bench", id);
        return;
    }
    criterion.report.benchmark_start(id, report_context);

    // In test mode, run the benchmark exactly once, then exit.
    if criterion.test_mode {
        routine.test(parameter);
        criterion.report.terminated(id, report_context);
        return;
    }

    if let Baseline::Compare = criterion.baseline {
        if !base_dir_exists(
            id,
            &criterion.baseline_directory,
            &criterion.output_directory,
        ) {
            panic!(format!(
                "Baseline '{base}' must exist before comparison is allowed; try --save-baseline {base}",
                base=criterion.baseline_directory,
            ));
        }
    }

    // In profiling mode, skip all of the analysis.
    if let Some(time) = criterion.profile_time {
        routine.profile(id, criterion, report_context, time, parameter);
        return;
    }

    let (iters, times) = routine.sample(id, config, criterion, report_context, parameter);

    criterion.report.analysis(id, report_context);

    let avg_times = iters
        .iter()
        .zip(times.iter())
        .map(|(&iters, &elapsed)| elapsed / iters)
        .collect::<Vec<f64>>();
    let avg_times = Sample::new(&avg_times);

    log_if_err!(fs::mkdirp(&format!(
        "{}/{}/new",
        criterion.output_directory,
        id.as_directory_name()
    )));

    let data = Data::new(&iters, &times);
    let labeled_sample = outliers(id, &criterion.output_directory, avg_times);
    let (distribution, slope) = regression(&data, config);
    let (mut distributions, mut estimates) = estimates(avg_times, config);

    estimates.insert(Statistic::Slope, slope);
    distributions.insert(Statistic::Slope, distribution);

    log_if_err!(fs::save(
        &(data.x().as_ref(), data.y().as_ref()),
        &format!(
            "{}/{}/new/sample.json",
            criterion.output_directory,
            id.as_directory_name()
        ),
    ));
    log_if_err!(fs::save(
        &estimates,
        &format!(
            "{}/{}/new/estimates.json",
            criterion.output_directory,
            id.as_directory_name()
        )
    ));

    let compare_data = if base_dir_exists(
        id,
        &criterion.baseline_directory,
        &criterion.output_directory,
    ) {
        let result = compare::common(id, avg_times, config, criterion);
        match result {
            Ok((
                t_value,
                t_distribution,
                relative_estimates,
                relative_distributions,
                base_iter_counts,
                base_sample_times,
                base_avg_times,
                base_estimates,
            )) => {
                let p_value = t_distribution.p_value(t_value, &Tails::Two);
                Some(::report::ComparisonData {
                    p_value,
                    t_distribution,
                    t_value,
                    relative_estimates,
                    relative_distributions,
                    significance_threshold: config.significance_level,
                    noise_threshold: config.noise_threshold,
                    base_iter_counts,
                    base_sample_times,
                    base_avg_times,
                    base_estimates,
                })
            }
            Err(e) => {
                ::error::log_error(&e);
                None
            }
        }
    } else {
        None
    };

    let measurement_data = ::report::MeasurementData {
        data: Data::new(&*iters, &*times),
        avg_times: labeled_sample,
        absolute_estimates: estimates.clone(),
        distributions,
        comparison: compare_data,
        throughput,
    };

    criterion
        .report
        .measurement_complete(id, report_context, &measurement_data);

    log_if_err!(fs::save(
        &id,
        &format!(
            "{}/{}/new/benchmark.json",
            criterion.output_directory,
            id.as_directory_name()
        )
    ));

    if let Baseline::Save = criterion.baseline {
        copy_new_dir_to_base(
            id.as_directory_name(),
            &criterion.baseline_directory,
            &criterion.output_directory,
        );
    }
}

fn base_dir_exists(id: &BenchmarkId, baseline: &str, output_directory: &str) -> bool {
    Path::new(&format!(
        "{}/{}/{}",
        output_directory,
        id.as_directory_name(),
        baseline
    ))
    .exists()
}

// Performs a simple linear regression on the sample
fn regression(data: &Data<f64, f64>, config: &BenchmarkConfig) -> (Distribution<f64>, Estimate) {
    let cl = config.confidence_level;

    let distribution = elapsed!(
        "Bootstrapped linear regression",
        data.bootstrap(config.nresamples, |d| (Slope::fit(&d).0,))
    )
    .0;

    let point = Slope::fit(&data);
    let (lb, ub) = distribution.confidence_interval(config.confidence_level);
    let se = distribution.std_dev(None);

    (
        distribution,
        Estimate {
            confidence_interval: ConfidenceInterval {
                confidence_level: cl,
                lower_bound: lb,
                upper_bound: ub,
            },
            point_estimate: point.0,
            standard_error: se,
        },
    )
}

// Classifies the outliers in the sample
fn outliers<'a>(
    id: &BenchmarkId,
    output_directory: &str,
    avg_times: &'a Sample<f64>,
) -> LabeledSample<'a, f64> {
    let sample = tukey::classify(avg_times);
    log_if_err!(fs::save(
        &sample.fences(),
        &format!(
            "{}/{}/new/tukey.json",
            output_directory,
            id.as_directory_name()
        )
    ));
    sample
}

// Estimates the statistics of the population from the sample
fn estimates(avg_times: &Sample<f64>, config: &BenchmarkConfig) -> (Distributions, Estimates) {
    fn stats(sample: &Sample<f64>) -> (f64, f64, f64, f64) {
        let mean = sample.mean();
        let std_dev = sample.std_dev(Some(mean));
        let median = sample.percentiles().median();
        let mad = sample.median_abs_dev(Some(median));

        (mean, std_dev, median, mad)
    }

    let cl = config.confidence_level;
    let nresamples = config.nresamples;

    let (mean, std_dev, median, mad) = stats(avg_times);
    let mut point_estimates = BTreeMap::new();
    point_estimates.insert(Statistic::Mean, mean);
    point_estimates.insert(Statistic::StdDev, std_dev);
    point_estimates.insert(Statistic::Median, median);
    point_estimates.insert(Statistic::MedianAbsDev, mad);

    let (dist_mean, dist_stddev, dist_median, dist_mad) = elapsed!(
        "Bootstrapping the absolute statistics.",
        avg_times.bootstrap(nresamples, stats)
    );

    let mut distributions = Distributions::new();
    distributions.insert(Statistic::Mean, dist_mean);
    distributions.insert(Statistic::StdDev, dist_stddev);
    distributions.insert(Statistic::Median, dist_median);
    distributions.insert(Statistic::MedianAbsDev, dist_mad);

    let estimates = build_estimates(&distributions, &point_estimates, cl);

    (distributions, estimates)
}

fn copy_new_dir_to_base(id: &str, baseline: &str, output_directory: &str) {
    let root_dir = Path::new(output_directory).join(id);
    let base_dir = root_dir.join(baseline);
    let new_dir = root_dir.join("new");

    if !new_dir.exists() {
        return;
    };
    if !base_dir.exists() {
        try_else_return!(fs::mkdirp(&base_dir));
    }

    // TODO: consider using walkdir or similar to generically copy.
    try_else_return!(fs::cp(
        &new_dir.join("estimates.json"),
        &base_dir.join("estimates.json")
    ));
    try_else_return!(fs::cp(
        &new_dir.join("sample.json"),
        &base_dir.join("sample.json")
    ));
    try_else_return!(fs::cp(
        &new_dir.join("tukey.json"),
        &base_dir.join("tukey.json")
    ));
    try_else_return!(fs::cp(
        &new_dir.join("benchmark.json"),
        &base_dir.join("benchmark.json")
    ));
    try_else_return!(fs::cp(&new_dir.join("raw.csv"), &base_dir.join("raw.csv")));
}