"""Auto-generated example: 0 Float, 3 Categorical (over time)."""
import random
import bencher as bn
from datetime import datetime, timedelta
class DeploymentConfig(bn.ParametrizedSweep):
"""Full config matrix: protocol, region, and log level."""
protocol = bn.StringSweep(["http", "grpc"], doc="Network protocol")
region = bn.StringSweep(["us-east", "eu-west", "ap-south"], doc="Deployment region")
log_level = bn.StringSweep(["debug", "info", "warn"], doc="Logging level")
throughput = bn.ResultFloat(units="req/s", doc="Request throughput")
_time_offset = 0.0
def benchmark(self):
proto_factor = {"http": 1.0, "grpc": 1.8}[self.protocol]
region_base = {"us-east": 500, "eu-west": 420, "ap-south": 350}[self.region]
log_penalty = {"debug": 0.7, "info": 1.0, "warn": 1.0}[self.log_level]
self.throughput = region_base * proto_factor * log_penalty + random.gauss(0, 0.1 * 50)
self.throughput += self._time_offset * 10
def example_sweep_0_float_3_cat_over_time(run_cfg: bn.BenchRunCfg | None = None) -> bn.Bench:
"""0 Float, 3 Categorical (over time)."""
if run_cfg is None:
run_cfg = bn.BenchRunCfg()
benchable = DeploymentConfig()
bench = benchable.to_bench(run_cfg)
_base_time = datetime(2000, 1, 1)
for i, offset in enumerate([0.0, 0.5, 1.0]):
benchable._time_offset = offset
run_cfg.clear_cache = True
run_cfg.clear_history = i == 0
res = bench.plot_sweep(
"over_time",
input_vars=['protocol', 'region', 'log_level'],
result_vars=['throughput'],
description='A 0 float + 3 categorical parameter sweep tracked over time. Setting over_time=True records multiple time snapshots that can be scrubbed via a slider. Each call to plot_sweep with a new time_src appends a snapshot to the history. This is designed for nightly benchmarks or CI pipelines where you want to track how metrics evolve across commits, releases, or environmental changes. Use clear_history=True on the first snapshot to reset, and clear_cache=True to force re-evaluation. Categorical-only sweeps produce bar/swarm plots comparing discrete settings.',
post_description="The time slider lets you scrub through snapshots. The 'All Time Points (aggregated)' tab pools all snapshots into one view, smoothing out per-snapshot noise to reveal long-term trends.",
run_cfg=run_cfg,
time_src=_base_time + timedelta(seconds=i),
)
return bench
if __name__ == "__main__":
bn.run(example_sweep_0_float_3_cat_over_time, subsampling_divisions=4, over_time=True)