"""Auto-generated example: 1 Float, 3 Categorical (no repeats)."""
import math
import bencher as bn
class SortFullMatrix(bn.ParametrizedSweep):
"""Full sort matrix: size, algorithm, distribution, and order."""
array_size = bn.FloatSweep(default=100, bounds=[10, 10000], doc="Array length")
algorithm = bn.StringSweep(["quicksort", "mergesort", "heapsort"], doc="Sort algorithm")
distribution = bn.StringSweep(["uniform", "sorted", "reversed"], doc="Data distribution")
stability = bn.StringSweep(["stable", "unstable"], doc="Sort stability")
time = bn.ResultFloat(units="ms", doc="Sort duration")
def benchmark(self):
algo_factor = {"quicksort": 1.0, "mergesort": 1.2, "heapsort": 1.5}[self.algorithm]
dist_factor = {"uniform": 1.0, "sorted": 0.6, "reversed": 1.8}[self.distribution]
stab_factor = {"stable": 1.1, "unstable": 1.0}[self.stability]
self.time = algo_factor * dist_factor * stab_factor * self.array_size * math.log2(self.array_size + 1) * 0.001
def example_sweep_1_float_3_cat_no_repeats(run_cfg: bn.BenchRunCfg | None = None) -> bn.Bench:
"""1 Float, 3 Categorical (no repeats)."""
bench = SortFullMatrix().to_bench(run_cfg)
bench.plot_sweep(input_vars=['array_size', 'algorithm', 'distribution', 'stability'], result_vars=['time'], description='A 1 float + 3 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. Adding categorical variables to a float sweep creates faceted line plots -- one curve per category, making it easy to compare how each setting modifies the continuous relationship.', 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_1_float_3_cat_no_repeats, subsampling_divisions=4)