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Benchmark SpikeCascade against an offline baseline and emit the
calibration contract required by the paper.
Usage:
python benchmark.py --data path/to/recording.npy \
--baseline path/to/kilosort_output.npz
The output is a single JSON object with the four calibration fields:
activity_fraction, stage3_fraction, latency_ms, correlation
plus a 'speedup_vs_full' field computed by the theorem
S = (1 + alpha) / (1/k + alpha), alpha = (1 - T_s/T) / (T_s/T).
"""
import argparse
import json
import time
import numpy as np
from modeling_spikecascade import SpikeCascadeForSorting
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data", required=True, help="(n_channels, n_samples) .npy")
parser.add_argument("--baseline", required=True, help=".npz with 'unit_ids' and 'spike_times_ms'")
parser.add_argument("--model", default="your-org/SpikeCascade")
args = parser.parse_args()
voltage = np.load(args.data).astype(np.float32)
baseline = np.load(args.baseline)
baseline_counts = {}
for uid in baseline["unit_ids"]:
baseline_counts[int(uid)] = baseline_counts.get(int(uid), 0) + 1
model = SpikeCascadeForSorting.from_pretrained(args.model)
# Warm up.
_ = model.infer(voltage[:, :3000])
t0 = time.perf_counter()
report = model.infer(voltage, baseline_spike_counts=baseline_counts)
wall_ms = (time.perf_counter() - t0) * 1000
# The theorem: S = (1 + alpha) / (1/k + alpha), with
# alpha = (1 - f) / f and f = stage3_fraction.
f = report.stage3_fraction
alpha = (1.0 - f) / f if f > 0 else float("inf")
k = 20.0 # conservative cost ratio (full clustering / template match)
speedup = (1.0 + alpha) / (1.0 / k + alpha) if f > 0 else 1.0
out = {
"activity_fraction": report.activity_fraction,
"stage3_fraction": report.stage3_fraction,
"latency_ms": report.latency_ms,
"wall_ms": wall_ms,
"correlation": report.correlation,
"speedup_vs_full": speedup,
"warning": report.warning,
}
print(json.dumps(out, indent=2))
if __name__ == "__main__":
main() |