File size: 2,869 Bytes
4e2940e | 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 | #!/usr/bin/env python
"""CI coverage breakdown: diagnose why 95% CI has only 27% coverage."""
from _common import *
from scptr.deep.synthetic import generate_kinetic_data
OUT = output_dir("06_ci_coverage")
def main():
set_figure_style()
print("Generating synthetic data and fitting DeepPTR...")
adata, truth = generate_kinetic_data(n_cells=1500, n_genes=100, seed=0)
torch.set_num_threads(4)
scptr.deep.fit_deepptr(adata, verbose=False, **DEEP_HP)
gt = truth["gamma"]
mu = adata.layers["gamma"]
var = adata.layers["gamma_var"]
z = 1.96
std = np.sqrt(np.clip(var, 1e-10, None))
inside = (gt >= mu - z * std) & (gt <= mu + z * std)
overall = float(inside.mean())
per_gene = inside.mean(axis=0)
per_cell = inside.mean(axis=1)
# Diagnosis
ci_width = np.median(2 * z * std)
true_range = np.median(np.ptp(gt, axis=0))
rel_error = np.median(np.abs(mu - gt) / (gt + 1e-8))
print(f"\n Overall 95% CI coverage: {overall:.4f} (target: 0.95)")
print(f" Median CI width: {ci_width:.4f}")
print(f" Median true range: {true_range:.4f}")
print(f" CI/range ratio: {ci_width / true_range:.4f}")
print(f" Median relative error: {rel_error:.4f}")
print(f" Diagnosis: {'Overconfident' if overall < 0.5 else 'Moderate'} "
f"(posterior {ci_width/true_range:.1%} of true range)")
# What would coverage be at different CI levels?
levels = [0.50, 0.80, 0.90, 0.95, 0.99]
coverages = []
for lev in levels:
zl = stats.norm.ppf(0.5 + lev / 2)
ins = (gt >= mu - zl * std) & (gt <= mu + zl * std)
coverages.append(float(ins.mean()))
print(f" {lev*100:.0f}% CI → actual coverage: {ins.mean():.4f}")
results = {
"overall_coverage": overall,
"ci_width": float(ci_width), "true_range": float(true_range),
"rel_error": float(rel_error),
"calibration": [{"nominal": l, "actual": c} for l, c in zip(levels, coverages)],
}
save_json(results, "ci_coverage", OUT)
# Calibration plot
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
axes[0].plot(levels, coverages, "o-", color="darkorange", label="Actual")
axes[0].plot([0, 1], [0, 1], "k--", alpha=0.3, label="Perfect calibration")
axes[0].set_xlabel("Nominal coverage")
axes[0].set_ylabel("Actual coverage")
axes[0].set_title("CI Calibration")
axes[0].legend()
axes[1].hist(per_gene, bins=20, color="steelblue", alpha=0.7)
axes[1].axvline(0.95, color="red", ls="--", label="Target")
axes[1].set_xlabel("Per-gene 95% CI coverage")
axes[1].set_ylabel("Count")
axes[1].set_title(f"Coverage distribution (median={np.median(per_gene):.3f})")
axes[1].legend()
fig.tight_layout()
save_fig(fig, "ci_calibration", OUT)
if __name__ == "__main__":
main()
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