rpotter6298
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
#!/usr/bin/env python3
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"""figure_s5.py — Confusion matrix for VGG16 patient-level classification.
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Reuses PatientLeakageClassifier.run(return_predictions=True) so the RF ranking,
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gamma grid, and final fit are not duplicated here — the same code path that
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produces the Figure 4/6 numbers also produces these predictions.
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Usage: python scripts/visualizations/figure_s5.py [--tag TAG]
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"""
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import os
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import sys
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import argparse
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
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os.environ.setdefault("OMP_NUM_THREADS", "1")
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os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
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os.environ.setdefault("MKL_NUM_THREADS", "1")
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(
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os.path.abspath(__file__)))))
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ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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PLOTS_DIR = os.path.join(ROOT, "plots")
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from classes import PatientLeakageClassifier
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SEED = 20
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N_JOBS = 6
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CLASS_NAMES = ["Benign", "Malignant", "Normal"]
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def display_name(raw):
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"""Map a raw dataset label ('Bengin cases', ...) to a display class name."""
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if raw.startswith("Bengin"):
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return "Benign"
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if raw.startswith("Malignant"):
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return "Malignant"
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return "Normal"
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ap = argparse.ArgumentParser()
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ap.add_argument("--tag", default="")
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args = ap.parse_args()
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tag = f"_{args.tag}" if args.tag else ""
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clf = PatientLeakageClassifier(
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os.path.join(ROOT, "results", "simple_patient_manifest.csv"),
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os.path.join(ROOT, "features"), n_jobs=N_JOBS)
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r = clf.run("VGG16", SEED, "patient", return_predictions=True)
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print(f"Best: n={r['nfeat']}, gamma={r['gamma']:.6e}, "
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f"CV={r['cv']:.4f}, Test={r['test']:.4f}")
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y_true = [display_name(c) for c in r["y_true"]]
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y_pred = [display_name(c) for c in r["y_pred"]]
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cm = confusion_matrix(y_true, y_pred, labels=CLASS_NAMES, normalize="true")
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fig, ax = plt.subplots(figsize=(6, 5))
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ConfusionMatrixDisplay(cm, display_labels=CLASS_NAMES).plot(
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cmap="Blues", ax=ax, colorbar=True, values_format=".2f")
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ax.set_title("Figure S5 — Patient-level Confusion Matrix (VGG16)", fontsize=12)
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plt.tight_layout()
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out = os.path.join(PLOTS_DIR, "figure_s5", f"figure_s5{tag}.png")
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os.makedirs(os.path.dirname(out), exist_ok=True)
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plt.savefig(out, dpi=150)
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plt.close()
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print(f"Saved → {out}")
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