code/_compute_n24_spearman.py

"""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}%")