wasm-bindgen-test 0.3.66

Internal testing crate for wasm-bindgen
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158

use super::stats::bivariate::regression::Slope;
use super::stats::bivariate::Data;
use super::stats::univariate::Sample;
use super::stats::{Distribution, Tails};

use super::benchmark::BenchmarkConfig;
use super::estimate::{
    build_estimates, ConfidenceInterval, Distributions, Estimate, Estimates, PointEstimates,
};
use super::measurement::Measurement;
use super::report::{BenchmarkId, Report};
use super::routine::Routine;
use super::{baseline, compare, Criterion, SavedSample};

use alloc::vec::Vec;

// Common analysis procedure
pub(crate) async fn common<M: Measurement>(
    id: &BenchmarkId,
    routine: &mut dyn Routine<M>,
    config: &BenchmarkConfig,
    criterion: &Criterion<M>,
) {
    criterion.report.benchmark_start(id);

    let (sampling_mode, iters, times);
    let sample = routine
        .sample(&criterion.measurement, id, config, criterion)
        .await;
    sampling_mode = sample.0;
    iters = sample.1;
    times = sample.2;

    criterion.report.analysis(id);

    if times.contains(&0.0) {
        return;
    }

    let avg_times = iters
        .iter()
        .zip(times.iter())
        .map(|(&iters, &elapsed)| elapsed / iters)
        .collect::<Vec<f64>>();
    let avg_times = Sample::new(&avg_times);
    let labeled_sample = super::stats::univariate::outliers::tukey::classify(avg_times);

    let data = Data::new(&iters, &times);
    let (mut distributions, mut estimates) = estimates(avg_times, config);
    if sampling_mode.is_linear() {
        let (distribution, slope) = regression(&data, config);

        estimates.slope = Some(slope);
        distributions.slope = Some(distribution);
    }

    let comparison = compare::common(id, avg_times, config).map(
        |(t_value, t_distribution, relative_estimates, ..)| {
            let p_value = t_distribution.p_value(t_value, &Tails::Two);
            super::report::ComparisonData {
                p_value,
                relative_estimates,
                significance_threshold: config.significance_level,
                noise_threshold: config.noise_threshold,
            }
        },
    );

    let measurement_data = super::report::MeasurementData {
        avg_times: labeled_sample,
        absolute_estimates: estimates.clone(),
        comparison,
    };

    criterion
        .report
        .measurement_complete(id, &measurement_data, criterion.measurement.formatter());

    baseline::write(
        id.desc(),
        baseline::BenchmarkBaseline {
            file: criterion.location.as_ref().map(|l| l.file.clone()),
            module_path: criterion.location.as_ref().map(|l| l.module_path.clone()),
            iters: data.x().as_ref().to_vec(),
            times: data.y().as_ref().to_vec(),
            sample: SavedSample {
                sampling_mode,
                iters: data.x().as_ref().to_vec(),
                times: data.y().as_ref().to_vec(),
            },
            estimates,
        },
    );
}

// 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 = 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,
        },
    )
}

// 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 points = PointEstimates {
        mean,
        median,
        std_dev,
        median_abs_dev: mad,
    };

    let (dist_mean, dist_stddev, dist_median, dist_mad) = avg_times.bootstrap(nresamples, stats);

    let distributions = Distributions {
        mean: dist_mean,
        slope: None,
        median: dist_median,
        median_abs_dev: dist_mad,
        std_dev: dist_stddev,
    };

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

    (distributions, estimates)
}