File size: 4,448 Bytes
189f45b | 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 | """Compute Spearman correlations + bootstrap 95% CIs on the combined 24-config sweep
(18 original + 6 gap-fill)."""
import numpy as np
from scipy import stats
# All 24 configurations: (name, type, topsim, posdis, causal, cross16, cross192)
rows = [
# Original 12 single-property configs
("disc_L2_V5", "discrete", 0.88, 0.20, 0.02, 41.7, 43.9),
("disc_L2_V10", "discrete", 0.84, 0.25, 0.05, 46.1, 41.7),
("disc_L3_V5", "discrete", 0.84, 0.13, 0.02, 43.3, 42.8),
("disc_L3_V10", "discrete", 0.84, 0.12, 0.01, 43.3, 45.6),
("disc_L4_V5", "discrete", 0.90, 0.10, 0.01, 41.1, 42.2),
("disc_L4_V10", "discrete", 0.82, 0.08, 0.02, 45.0, 45.0),
("disc_L5_V5", "discrete", 0.89, 0.07, 0.02, 40.0, 43.9),
("cont_dim2", "continuous", 0.92, 0.15, 0.20, 48.9, 54.4),
("cont_dim3", "continuous", 0.91, 0.15, 0.02, 40.6, 41.1),
("cont_dim5", "continuous", 0.89, 0.06, 0.03, 47.2, 43.9),
("cont_dim10", "continuous", 0.88, 0.04, 0.01, 47.8, 48.3),
("cont_dim20", "continuous", 0.90, 0.02, 0.00, 48.9, 55.0),
# Original 3 multi-property 3-class
("disc_multi_L3_V5", "disc_multi", 0.59, 0.51, 0.06, 40.0, 46.1),
("disc_multi_L4_V10", "disc_multi", 0.68, 0.48, 0.01, 45.6, 50.6),
("cont_multi_dim3", "cont_multi", 0.72, 0.40, 0.10, 50.6, 55.0),
# Original 3 multi-property 5-class
("disc_multi5_L2_V5", "disc_multi", 0.78, 0.82, 0.07, 47.2, 52.2),
("disc_multi5_L3_V5", "disc_multi", 0.69, 0.83, 0.29, 45.0, 46.1),
("disc_multi5_L4_V5", "disc_multi", 0.68, 0.70, 0.06, 43.9, 47.8),
# NEW gap-fill (6 configs)
("disc_multi5_L2_V10_e250", "disc_multi", 0.66, 0.70, 0.12, 48.9, 55.6),
("disc_multi5_L3_V10_e250", "disc_multi", 0.60, 0.81, 0.03, 41.7, 43.3),
("disc_multi5_L4_V10_e250", "disc_multi", 0.65, 0.70, 0.07, 41.7, 41.7),
("disc_multi5_L2_V5_e200", "disc_multi", 0.75, 0.83, 0.13, 47.2, 51.1),
("disc_multi5_L4_V5_e250", "disc_multi", 0.79, 0.91, 0.03, 42.2, 46.7),
("disc_multi_L5_V5_3cls", "disc_multi", 0.72, 0.73, 0.02, 39.4, 42.2),
]
def boot_ci(x, y, n_resamples=5000, seed=42):
rng = np.random.default_rng(seed)
idx = np.arange(len(x))
rhos = []
for _ in range(n_resamples):
s = rng.choice(idx, size=len(idx), replace=True)
rho, _ = stats.spearmanr(x[s], y[s])
if not np.isnan(rho):
rhos.append(rho)
return float(np.percentile(rhos, 2.5)), float(np.percentile(rhos, 97.5))
topsim = np.array([r[2] for r in rows])
posdis = np.array([r[3] for r in rows])
causal = np.array([r[4] for r in rows])
cross16 = np.array([r[5] for r in rows])
cross192 = np.array([r[6] for r in rows])
n = len(rows)
print(f"=== n={n} configs (18 original + 6 gap-fill) ===\n")
print(f"PosDis range: {posdis.min():.2f} -- {posdis.max():.2f}")
print(f"Cross-scen N=192 range: {cross192.min():.1f}% -- {cross192.max():.1f}%")
print(f"Cross-scen N=16 range: {cross16.min():.1f}% -- {cross16.max():.1f}%")
print()
for x, xname in [(topsim, "TopSim"), (posdis, "PosDis"), (causal, "CausalSpec")]:
for y, yname in [(cross16, "Cross16"), (cross192, "Cross192")]:
rho, p = stats.spearmanr(x, y)
lo, hi = boot_ci(x, y)
print(f" {xname} vs {yname}: rho={rho:+.3f} p={p:.3f} CI=[{lo:+.2f}, {hi:+.2f}]")
# Also recompute for original n=18 (for paper consistency)
print(f"\n=== Original n=18 (for comparison) ===")
top18 = topsim[:18]; pd18 = posdis[:18]; ca18 = causal[:18]
c16_18 = cross16[:18]; c192_18 = cross192[:18]
for x, xname in [(top18, "TopSim"), (pd18, "PosDis"), (ca18, "CausalSpec")]:
for y, yname in [(c16_18, "Cross16"), (c192_18, "Cross192")]:
rho, p = stats.spearmanr(x, y)
lo, hi = boot_ci(x, y)
print(f" {xname} vs {yname}: rho={rho:+.3f} p={p:.3f} CI=[{lo:+.2f}, {hi:+.2f}]")
# Sufficiency observation: highest-PosDis configs vs lowest-PosDis
print(f"\n=== Sufficiency observation ===")
# Top 5 PosDis configs
top5_pd_idx = np.argsort(posdis)[-5:]
top5_pd = posdis[top5_pd_idx]
top5_cross = cross192[top5_pd_idx]
print(f"Top 5 PosDis: {top5_pd.tolist()} Cross192: {top5_cross.tolist()} range: {top5_cross.min():.1f}-{top5_cross.max():.1f}%")
bot5_pd_idx = np.argsort(posdis)[:5]
bot5_pd = posdis[bot5_pd_idx]
bot5_cross = cross192[bot5_pd_idx]
print(f"Bot 5 PosDis: {bot5_pd.tolist()} Cross192: {bot5_cross.tolist()} range: {bot5_cross.min():.1f}-{bot5_cross.max():.1f}%")
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