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Running on Zero
Running on Zero
File size: 1,907 Bytes
749bffa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | """Deterministic seeding.
WHY: "Seed everything" is a stated engineering convention, and it is not
cosmetic here. With n ~ 124-169 cells, the difference between two models is
often smaller than the difference between two random 5-fold partitions of the
same data. Unless the partitions are reproducible, a benchmark table cannot be
regenerated and a reported improvement cannot be distinguished from a lucky
split.
"""
from __future__ import annotations
import os
import random
DEFAULT_SEED = 42
def set_global_seed(seed: int = DEFAULT_SEED) -> int:
"""Seed Python's ``random``, NumPy, and hash randomisation.
``PYTHONHASHSEED`` is set for completeness but only affects interpreters
started afterwards; it is recorded so that the provenance stamp is honest
about what was and was not controlled.
Args:
seed: The seed to apply.
Returns:
The seed applied, for convenient logging.
"""
os.environ["PYTHONHASHSEED"] = str(seed)
random.seed(seed)
try:
import numpy as np
np.random.seed(seed)
except ImportError: # NumPy is a hard dependency in practice; tolerated here
pass # so the scaffold validates before deps are installed.
return seed
def seed_for_repeat(base_seeds: list[int], repeat_index: int) -> int:
"""Return the fixed seed for a given outer-CV repeat.
WHY explicit per-repeat seeds rather than ``base + i``: the seeds are
recorded in ``configs/models.yaml``, so a re-run reproduces the same ten
partitions even if the number of repeats is later changed.
"""
if not 0 <= repeat_index < len(base_seeds):
raise IndexError(
f"Repeat index {repeat_index} is outside the {len(base_seeds)} configured repeat seeds."
)
return base_seeds[repeat_index]
if __name__ == "__main__":
print(f"Global seed set to {set_global_seed()}")
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