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c4586dd | 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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | #!/usr/bin/env python3
"""
example.py — Usage demonstrations for hv_falsification_os.
NumPy only.
"""
import numpy as np
from hv_falsification_os import (
FalsificationOS,
make_synthetic_workload,
make_two_workloads,
make_amortized_workload,
)
def demo_cv():
print("=" * 72)
print("Demo 1 — measure the CV of a metric")
print("=" * 72)
print()
os_ = FalsificationOS()
fn = make_synthetic_workload(base=1.0, cv=0.05, seed_offset=0)
r = os_.measure_cv(fn, n_trials=500, seed=0)
print(f" mean: {r['mean']:.4f}")
print(f" std: {r['std']:.4f}")
print(f" CV: {r['cv']:.4f}")
print(f" 95% CI: +/- {r['ci95_halfwidth']:.4f}")
print()
print(" Interpretation: a delta smaller than "
f"{2*r['cv']:.2%} is indistinguishable from noise.")
print()
def demo_same_code():
print("=" * 72)
print("Demo 2 — is this delta real?")
print("=" * 72)
print()
os_ = FalsificationOS()
for delta in [0.01, 0.05, 0.10, 0.15, 0.50]:
fn_a, fn_b = make_two_workloads(delta, cv=0.05, seed_offset=0)
r = os_.same_code_test(fn_a, fn_b, n_trials=500, seed=0)
print(f" delta={delta:>5.2f} ratio={r['ratio_to_cv']:>5.2f} "
f"verdict={r['verdict']:<10s} "
f"(design CV = 5%)")
print()
def demo_amortization():
print("=" * 72)
print("Demo 3 — amortization sweep")
print("=" * 72)
print()
os_ = FalsificationOS()
fn = make_amortized_workload(peak_param=1000.0, peak_speedup=0.78)
params = [1, 10, 100, 500, 1000, 2000, 5000, 10000, 50000]
r = os_.amortization_sweep(fn, params, n_trials=20, seed=0)
print(f" {'param':>8s} {'value':>10s} {'bar'}")
print(" " + "-" * 50)
for p, m in zip(r['param_values'], r['means']):
bar = "#" * int(m * 40)
print(f" {p:>8.0f} {m:>10.4f} {bar}")
print()
print(f" Peak: param = {r['peak_param']:.0f}, "
f"value = {r['peak_value']:.3f}")
if r['peak_value'] < 1.0:
print(" Verdict: peak loses to baseline.")
print()
def demo_precondition():
print("=" * 72)
print("Demo 4 — precondition testing")
print("=" * 72)
print()
os_ = FalsificationOS()
def clustered(seed):
rng = np.random.default_rng(seed)
K, d, N = 5, 64, 500
centers = rng.standard_normal((K, d))
centers /= np.linalg.norm(centers, axis=1, keepdims=True)
a = rng.integers(0, K, size=N)
items = centers[a] + 0.1 * rng.standard_normal((N, d))
items /= np.linalg.norm(items, axis=1, keepdims=True)
i = rng.integers(0, N, size=2000)
j = rng.integers(0, N, size=2000)
return float((np.einsum('ij,ij->i', items[i], items[j]) > 0.5).mean()) > 0.05
def uniform(seed):
rng = np.random.default_rng(seed)
N, d = 500, 64
items = rng.standard_normal((N, d))
items /= np.linalg.norm(items, axis=1, keepdims=True)
i = rng.integers(0, N, size=2000)
j = rng.integers(0, N, size=2000)
return float((np.einsum('ij,ij->i', items[i], items[j]) > 0.5).mean()) > 0.05
for name, fn in [('clustered', clustered), ('uniform', uniform)]:
r = os_.precondition_test(fn, n_trials=30, seed=0)
print(f" {name:<10s} pass_rate = {r['pass_rate']:.1%} "
f"holds = {r['holds']}")
print()
print(" The same test passes on clustered data and fails on uniform.")
print(" That is the precondition check.")
print()
def demo_four_question():
print("=" * 72)
print("Demo 5 — four-question probe")
print("=" * 72)
print()
os_ = FalsificationOS()
rng = np.random.default_rng(42)
N, d = 1000, 64
print(f" Case A: low-rank task in random H")
H = rng.standard_normal((N, d))
Y = H[:, :3].sum(axis=1) + 0.5 * rng.standard_normal(N)
r = os_.four_question_probe(H, Y)
print(f" effective_rank_H = {r['effective_rank_H']:.2f}")
print(f" cross_cov_rank = {r['cross_cov_rank']:.2f}")
print(f" fresh_probe_r2 = {r['fresh_probe_r2']:.4f}")
print()
print(f" Case B: task unrelated to H")
Y = rng.standard_normal(N)
r = os_.four_question_probe(H, Y)
print(f" effective_rank_H = {r['effective_rank_H']:.2f}")
print(f" cross_cov_rank = {r['cross_cov_rank']:.2f}")
print(f" fresh_probe_r2 = {r['fresh_probe_r2']:.4f}")
print()
print(" In case A the task signal is recoverable (R² > 0.9).")
print(" In case B it is not, even though H is high-rank.")
print()
def demo_reversal():
print("=" * 72)
print("Demo 6 — reversal detection")
print("=" * 72)
print()
os_ = FalsificationOS()
cases = [
('clear winner', (1.00, 1.10), (1.02, 1.11)),
('flipping winner', (1.00, 0.98), (0.99, 1.01)),
('tiny gap', (1.000, 1.001), (1.002, 1.000)),
]
for name, r1, r2 in cases:
r = os_.reversal_detection(r1, r2)
status = "REVERSED" if r['reversal'] else "stable"
print(f" {name:<18s} run1={r['winner_1']} run2={r['winner_2']} "
f"→ {status}")
print()
def main():
demo_cv()
demo_same_code()
demo_amortization()
demo_precondition()
demo_four_question()
demo_reversal()
if __name__ == "__main__":
main() |