2026001

2026-07-01 17:35:58 +02:00
parent 9bfcc0243b
commit 35cbd9ac3c
84 changed files with 8500 additions and 423 deletions
@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+"""classification_siamese.py — Run classification with siamese patient manifest."""
+
+import os, sys, json
+import numpy as np
+
+os.environ["OMP_NUM_THREADS"] = "1"
+os.environ["OPENBLAS_NUM_THREADS"] = "1"
+os.environ["MKL_NUM_THREADS"] = "1"
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(
+    os.path.abspath(__file__)))))
+from classes import PatientLeakageClassifier
+
+ROOT = os.path.dirname(os.path.dirname(os.path.dirname(
+    os.path.abspath(__file__))))
+MANIFEST = os.path.join(ROOT, "results", "siamese_manifest.csv")
+FEATURES_DIR = os.path.join(ROOT, "features")
+RESULTS_DIR = os.path.join(ROOT, "results")
+SEED = 20
+MODELS = ["VGG16", "DenseNet121", "EfficientNetB1", "MobileNetV2", "ResNet50"]
+
+clf = PatientLeakageClassifier(MANIFEST, FEATURES_DIR, n_jobs=6)
+
+print("=" * 60)
+print("Siamese-based patient classification")
+print("=" * 60)
+
+results = []
+for model_name in MODELS:
+    print(f"\n  {model_name} ...", flush=True)
+    img = clf.run(model_name, SEED, "image")
+    pat = clf.run(model_name, SEED, "patient")
+    drop = img["test"] - pat["test"]
+    results.append({
+        "model": model_name, "manifest": "siamese",
+        "image_cv": img["cv"], "image_test": img["test"],
+        "patient_cv": pat["cv"], "patient_test": pat["test"],
+        "drop": drop,
+    })
+    print(f"    Image:   CV={img['cv']:.4f}  Test={img['test']:.4f}")
+    print(f"    Patient: CV={pat['cv']:.4f}  Test={pat['test']:.4f}  "
+          f"Drop={drop:.4f}")
+
+# Save
+out = os.path.join(RESULTS_DIR, "classification_siamese.json")
+with open(out, "w") as f:
+    json.dump(results, f, indent=2)
+
+# Comparison table
+print(f"\n{'='*80}")
+print("COMPARISON — All three patient-clustering methods (seed=20)")
+print(f"{'='*80}")
+
+def safe_load(path):
+    if os.path.exists(path):
+        with open(path) as f: return {r["model"]: r for r in json.load(f)}
+    return None
+
+pca50 = safe_load(os.path.join(RESULTS_DIR, "classification_pca50.json"))
+thumbnail = safe_load(os.path.join(RESULTS_DIR, "classification_thumbnail.json"))
+siamese = {r["model"]: r for r in results}
+
+print(f"\n{'Model':<18s} {'PCA50 Pat':>10s} {'Thumb Pat':>11s} {'Siam Pat':>10s} "
+      f"{'PCA50 Drop':>11s} {'Thumb Drop':>11s} {'Siam Drop':>10s}")
+print("-" * 82)
+for m in MODELS:
+    f_pat = f"{pca50[m]['patient_test']:>10.4f}" if pca50 else "       N/A"
+    t_pat = f"{thumbnail[m]['patient_test']:>11.4f}" if thumbnail else "        N/A"
+    f_drop = f"{pca50[m]['drop']:>11.4f}" if pca50 else "        N/A"
+    t_drop = f"{thumbnail[m]['drop']:>11.4f}" if thumbnail else "        N/A"
+    print(f"{m:<18s} {f_pat} {t_pat} "
+          f"{siamese[m]['patient_test']:>10.4f} {f_drop} {t_drop} "
+          f"{siamese[m]['drop']:>10.4f}")
+
+print(f"\nSaved → {out}")
+print("DONE")