File size: 3,766 Bytes
8604228 | 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 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | #!/usr/bin/env python3
# OB-1b, step 2: SIM PREDICTIONS -- per-decision miss rate for a
# resident-set size K against an ALREADY-BANKED route log, with no model run.
#
# A DECISION is one entry in the router's top-4 selection for one token at
# one layer: the route log has one row per (layer, token) holding all 4
# chosen expert ids, so one row contributes 4 decisions. A decision MISSES
# when its expert id is not in that layer's resident set for K. This is a
# pure lookup against the already-banked route log; it does not run the
# model and does not touch OB1_LEASE/the lease engine. K=0 is not computed by
# lookup -- an empty resident set misses on every decision by definition, so
# the miss rate is stated as exactly 1.0 (100 pct).
#
# Usage: sim_miss.py <route_log> <E> <L> <budget> <resident_sets.json> <K1,K2,...>
import sys, json
import numpy as np
def load_route_log(path, E, L, budget):
# Identical loader to research/ob1/resident_sets.py / resident_sets_knee.py.
data = np.loadtxt(path, delimiter=",", dtype=np.int64)
expected_rows = L * budget
if data.shape != (expected_rows, 6):
raise SystemExit("SHAPE MISMATCH: got %r expected (%d, 6)" % (data.shape, expected_rows))
layer_col = data[:, 0]
token_col = data[:, 1]
expert_ids = data[:, 2:6]
if int((expert_ids < 0).sum()) != 0 or int((expert_ids >= E).sum()) != 0:
raise SystemExit("EXPERT ID OUT OF RANGE")
expected_tok = np.arange(budget)
ids_by_layer = np.empty((L, budget, 4), dtype=np.int64)
for l in range(L):
mask = layer_col == l
n = int(mask.sum())
if n != budget:
raise SystemExit("LAYER %d: got %d rows, expected budget=%d" % (l, n, budget))
tok_l = token_col[mask]
if not np.array_equal(tok_l, expected_tok):
raise SystemExit("ORDER ASSUMPTION VIOLATED: layer %d" % l)
ids_by_layer[l] = expert_ids[mask]
return ids_by_layer
def main():
if len(sys.argv) != 7:
raise SystemExit(
"usage: sim_miss.py <route_log> <E> <L> <budget> <resident_sets.json> <K1,K2,...>"
)
route_log, E, L, budget, sets_json, k_list = sys.argv[1:7]
E = int(E); L = int(L); budget = int(budget)
K_VALUES = sorted({int(x) for x in k_list.split(",")}, reverse=True)
ids_by_layer = load_route_log(route_log, E, L, budget)
total_decisions = L * budget * 4
with open(sets_json) as f:
sets_doc = json.load(f)
resident_sets = sets_doc["resident_sets"]
print("ROUTE_LOG=%s" % route_log)
print("E=%d L=%d budget=%d total_decisions=%d" % (E, L, budget, total_decisions))
results = {}
for K in K_VALUES:
if K == 0:
miss = total_decisions
print("K=0 miss_decisions=%d total_decisions=%d miss_rate=1.000000 "
"(BY DEFINITION: empty resident set, every decision misses)" % (miss, total_decisions))
results["0"] = {"miss_decisions": miss, "total_decisions": total_decisions, "miss_rate": 1.0}
continue
per_layer = resident_sets[str(K)]
# resident mask per layer: E-length boolean
miss_total = 0
for l in range(L):
resident = np.zeros(E, dtype=bool)
resident[np.array(per_layer[str(l)], dtype=np.int64)] = True
sel = ids_by_layer[l] # (budget, 4)
hit = resident[sel]
miss_total += int((~hit).sum())
rate = miss_total / total_decisions
print("K=%d miss_decisions=%d total_decisions=%d miss_rate=%.6f" % (K, miss_total, total_decisions, rate))
results[str(K)] = {"miss_decisions": miss_total, "total_decisions": total_decisions, "miss_rate": rate}
return results
if __name__ == "__main__":
main()
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