Example Sweep 3 Float 0 Cat No Repeats

"""Auto-generated example: 3 Float, 0 Categorical (no repeats)."""

import math

import bencher as bn

class HashBenchmark(bn.ParametrizedSweep):
    """Hash throughput across key size, payload size, and iterations."""

    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")

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

    def benchmark(self):
        self.throughput = 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)


def example_sweep_3_float_0_cat_no_repeats(run_cfg: bn.BenchRunCfg | None = None) -> bn.Bench:
    """3 Float, 0 Categorical (no repeats)."""
    bench = HashBenchmark().to_bench(run_cfg)
    bench.plot_sweep(input_vars=['key_size', 'payload_size', 'iterations'], result_vars=['throughput'], description='A 3 float + 0 categorical parameter sweep with a single sample per combination. Bencher calculates the Cartesian product of all input variables and evaluates the benchmark function at each point. With no repeats, each combination appears exactly once -- useful for deterministic functions or quick exploration before committing to longer runs. A 3D float sweep produces a volumetric representation. This is useful for visualising scalar fields in 3D parameter spaces.', post_description='Each tab shows a different view of the same data: interactive plots, tabular summaries, and raw data. Use the tabs to explore the sweep results from different angles.')

    return bench


if __name__ == "__main__":
    bn.run(example_sweep_3_float_0_cat_no_repeats, subsampling_divisions=4)