Example Sweep 3 Float 2 Cat With Repeats

"""Auto-generated example: 3 Float, 2 Categorical (with repeats)."""

import random
import math

import bencher as bn

class HashAnalysis(bn.ParametrizedSweep):
    """Hash analysis: key size, payload, iterations, algorithm, and mode."""

    key_size = bn.FloatSweep(default=32, bounds=[8, 256], doc="Key size in bytes")
    payload_size = bn.FloatSweep(default=1024, bounds=[64, 65536], doc="Payload size in bytes")
    iterations = bn.FloatSweep(default=100, bounds=[10, 1000], doc="Hash iterations")
    algorithm = bn.StringSweep(["sha256", "blake2", "md5"], doc="Hash algorithm")
    mode = bn.StringSweep(["stream", "block"], doc="Processing mode")

    throughput = bn.ResultFloat(units="MB/s", doc="Hash throughput")

    def benchmark(self):
        algo_speed = {"sha256": 1.0, "blake2": 1.4, "md5": 1.8}[self.algorithm]
        mode_factor = {"stream": 1.0, "block": 0.85}[self.mode]
        self.throughput = algo_speed * mode_factor * 500.0 / (1.0 + 0.5 * math.log2(self.key_size / 8)) / (1.0 + 0.3 * math.log2(self.payload_size / 64)) * (self.iterations / 100)
        self.throughput += random.gauss(0, 0.15 * 30)


def example_sweep_3_float_2_cat_with_repeats(run_cfg: bn.BenchRunCfg | None = None) -> bn.Bench:
    """3 Float, 2 Categorical (with repeats)."""
    bench = HashAnalysis().to_bench(run_cfg)
    bench.plot_sweep(input_vars=['key_size', 'payload_size', 'iterations', 'algorithm', 'mode'], result_vars=['throughput'], description='A 3 float + 2 categorical parameter sweep with multiple repeats per combination. Repeating measurements reveals the noise structure of your benchmark. If your function is deterministic, all repeats will be identical; if it has stochastic components, repeats let you estimate confidence intervals and distinguish signal from noise. The benchmark function must be pure -- if past calls affect future calls through side effects, the statistics will be invalid. A 3D float sweep produces a volumetric representation. This is useful for visualising scalar fields in 3D parameter spaces.', post_description='Swarm/violin plots show the distribution of repeated measurements. If repeat has high variance, it suggests either measurement noise or unintended side effects in the benchmark function.')

    return bench


if __name__ == "__main__":
    bn.run(example_sweep_3_float_2_cat_with_repeats, subsampling_divisions=4, repeats=3)