pre-refactor 041426

2026-04-14 19:42:16 +02:00
parent eb9eafe715
commit 13290575d5
75 changed files with 8900 additions and 77 deletions
@@ -0,0 +1,119 @@
+"""
+Phase 3 modality ablation — Image-only vs Clinical-only vs HyperTower (fused).
+
+Pools all rep×fold predictions from the phase3/baseline run and plots
+per-fold AUC for each modality as a box plot with jittered points.
+
+Output: v3/figures/phase3_modality_ablation.png
+
+Usage:
+    python -m v3.scripts.output_analysis.plot_phase3_modality_ablation
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from sklearn.metrics import roc_auc_score
+
+REPO_ROOT    = Path(__file__).resolve().parents[3]
+RESULTS_DIR  = REPO_ROOT / "v3" / "results" / "phase3" / "baseline"
+FIGURES_DIR  = REPO_ROOT / "v3" / "figures"
+OUT_PNG      = FIGURES_DIR / "phase3_modality_ablation.png"
+
+C_BASELINE = "#dd8452"
+C_OTHER    = "#4c72b0"
+C_MEDIAN   = "#c44e52"
+FSIZE      = 10
+
+MODALITIES = [
+    ("prob_img_c1",   "Image only",    C_OTHER),
+    ("prob_md_c1",    "Clinical only", C_OTHER),
+    ("prob_fused_c1", "HyperTower\n(fused)", C_BASELINE),
+]
+
+
+def load_fold_aucs() -> dict[str, list[float]]:
+    aucs: dict[str, list[float]] = {col: [] for col, _, _ in MODALITIES}
+
+    for rep_dir in sorted(RESULTS_DIR.glob("rep*")):
+        fold_root = rep_dir / "binary" / "single"
+        if not fold_root.exists():
+            continue
+        for fold_dir in sorted(fold_root.glob("fold[0-9]")):
+            csv = fold_dir / "predictions_test.csv"
+            if not csv.exists():
+                continue
+            df = pd.read_csv(csv)
+            if df["y_true"].nunique() < 2:
+                continue
+            for col, _, _ in MODALITIES:
+                if col in df.columns:
+                    try:
+                        aucs[col].append(roc_auc_score(df["y_true"], df[col]))
+                    except Exception:
+                        pass
+
+    return aucs
+
+
+def main():
+    print("Loading fold AUCs ...")
+    aucs = load_fold_aucs()
+
+    n_folds = len(next(iter(aucs.values())))
+    print(f"  {n_folds} folds found")
+    for col, label, _ in MODALITIES:
+        vals = aucs[col]
+        print(f"  {label.replace(chr(10), ' '):<30}  "
+              f"mean={np.mean(vals):.4f}  std={np.std(vals):.4f}  n={len(vals)}")
+
+    # ── Plot ──────────────────────────────────────────────────────────────────
+    fig, ax = plt.subplots(figsize=(6, 4.5))
+
+    data_list = [np.array(aucs[col]) for col, _, _ in MODALITIES]
+    colors     = [color for _, _, color in MODALITIES]
+    labels     = [lbl   for _, lbl, _ in MODALITIES]
+    x          = np.arange(len(MODALITIES))
+
+    bp = ax.boxplot(
+        data_list,
+        vert=True,
+        patch_artist=True,
+        positions=x,
+        widths=0.3,
+        showfliers=True,
+        flierprops=dict(marker="o", markersize=3, alpha=0.5),
+        medianprops=dict(color=C_MEDIAN, linewidth=2),
+    )
+    for patch, color in zip(bp["boxes"], colors):
+        patch.set_facecolor(color)
+        patch.set_alpha(0.8)
+
+    ax.set_xlim(-0.5, len(MODALITIES) - 0.5)
+    tick_labels = [
+        f"{lbl}\nAUC={np.mean(np.array(aucs[col])):.3f}"
+        for col, lbl, _ in MODALITIES
+    ]
+    ax.set_xticks(x)
+    ax.set_xticklabels(tick_labels, fontsize=FSIZE)
+    ax.set_ylabel("AUC (ROC)", fontsize=FSIZE + 1)
+    fig.suptitle(
+        f"Phase 3 — Modality Ablation: Image / Clinical / Fused  ({n_folds} folds)",
+        fontsize=FSIZE + 3, fontweight="bold",
+    )
+    ax.grid(axis="y", alpha=0.3)
+
+    fig.tight_layout()
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    fig.savefig(OUT_PNG, dpi=180, bbox_inches="tight")
+    plt.close(fig)
+    print(f"Saved: {OUT_PNG}")
+
+
+if __name__ == "__main__":
+    main()