2026001

scripts/visualizations/figure_s5.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""figure_s5.py — Confusion matrix for VGG16 patient-level classification.
+
+Reuses PatientLeakageClassifier.run(return_predictions=True) so the RF ranking,
+gamma grid, and final fit are not duplicated here — the same code path that
+produces the Figure 4/6 numbers also produces these predictions.
+
+Usage: python scripts/visualizations/figure_s5.py [--tag TAG]
+"""
+
+import os
+import sys
+import argparse
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+
+os.environ.setdefault("OMP_NUM_THREADS", "1")
+os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
+os.environ.setdefault("MKL_NUM_THREADS", "1")
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(
+    os.path.abspath(__file__)))))
+ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+PLOTS_DIR = os.path.join(ROOT, "plots")
+
+from classes import PatientLeakageClassifier
+
+SEED = 20
+N_JOBS = 6
+CLASS_NAMES = ["Benign", "Malignant", "Normal"]
+
+
+def display_name(raw):
+    """Map a raw dataset label ('Bengin cases', ...) to a display class name."""
+    if raw.startswith("Bengin"):
+        return "Benign"
+    if raw.startswith("Malignant"):
+        return "Malignant"
+    return "Normal"
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("--tag", default="")
+args = ap.parse_args()
+tag = f"_{args.tag}" if args.tag else ""
+
+clf = PatientLeakageClassifier(
+    os.path.join(ROOT, "results", "simple_patient_manifest.csv"),
+    os.path.join(ROOT, "features"), n_jobs=N_JOBS)
+r = clf.run("VGG16", SEED, "patient", return_predictions=True)
+print(f"Best: n={r['nfeat']}, gamma={r['gamma']:.6e}, "
+      f"CV={r['cv']:.4f}, Test={r['test']:.4f}")
+
+y_true = [display_name(c) for c in r["y_true"]]
+y_pred = [display_name(c) for c in r["y_pred"]]
+cm = confusion_matrix(y_true, y_pred, labels=CLASS_NAMES, normalize="true")
+
+fig, ax = plt.subplots(figsize=(6, 5))
+ConfusionMatrixDisplay(cm, display_labels=CLASS_NAMES).plot(
+    cmap="Blues", ax=ax, colorbar=True, values_format=".2f")
+ax.set_title("Figure S5 — Patient-level Confusion Matrix (VGG16)", fontsize=12)
+plt.tight_layout()
+out = os.path.join(PLOTS_DIR, "figure_s5", f"figure_s5{tag}.png")
+os.makedirs(os.path.dirname(out), exist_ok=True)
+plt.savefig(out, dpi=150)
+plt.close()
+print(f"Saved → {out}")