rpotter6298
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
#!/usr/bin/env python3
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"""classification_siamese.py — Run classification with siamese patient manifest."""
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import os, sys, json
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import numpy as np
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os.environ["OMP_NUM_THREADS"] = "1"
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os.environ["OPENBLAS_NUM_THREADS"] = "1"
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os.environ["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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from classes import PatientLeakageClassifier
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ROOT = os.path.dirname(os.path.dirname(os.path.dirname(
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os.path.abspath(__file__))))
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MANIFEST = os.path.join(ROOT, "results", "siamese_manifest.csv")
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FEATURES_DIR = os.path.join(ROOT, "features")
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RESULTS_DIR = os.path.join(ROOT, "results")
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SEED = 20
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MODELS = ["VGG16", "DenseNet121", "EfficientNetB1", "MobileNetV2", "ResNet50"]
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clf = PatientLeakageClassifier(MANIFEST, FEATURES_DIR, n_jobs=6)
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print("=" * 60)
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print("Siamese-based patient classification")
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print("=" * 60)
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results = []
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for model_name in MODELS:
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print(f"\n {model_name} ...", flush=True)
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img = clf.run(model_name, SEED, "image")
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pat = clf.run(model_name, SEED, "patient")
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drop = img["test"] - pat["test"]
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results.append({
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"model": model_name, "manifest": "siamese",
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"image_cv": img["cv"], "image_test": img["test"],
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"patient_cv": pat["cv"], "patient_test": pat["test"],
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"drop": drop,
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})
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print(f" Image: CV={img['cv']:.4f} Test={img['test']:.4f}")
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print(f" Patient: CV={pat['cv']:.4f} Test={pat['test']:.4f} "
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f"Drop={drop:.4f}")
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# Save
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out = os.path.join(RESULTS_DIR, "classification_siamese.json")
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with open(out, "w") as f:
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json.dump(results, f, indent=2)
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# Comparison table
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print(f"\n{'='*80}")
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print("COMPARISON — All three patient-clustering methods (seed=20)")
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print(f"{'='*80}")
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def safe_load(path):
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if os.path.exists(path):
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with open(path) as f: return {r["model"]: r for r in json.load(f)}
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return None
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pca50 = safe_load(os.path.join(RESULTS_DIR, "classification_pca50.json"))
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thumbnail = safe_load(os.path.join(RESULTS_DIR, "classification_thumbnail.json"))
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siamese = {r["model"]: r for r in results}
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print(f"\n{'Model':<18s} {'PCA50 Pat':>10s} {'Thumb Pat':>11s} {'Siam Pat':>10s} "
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f"{'PCA50 Drop':>11s} {'Thumb Drop':>11s} {'Siam Drop':>10s}")
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print("-" * 82)
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for m in MODELS:
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f_pat = f"{pca50[m]['patient_test']:>10.4f}" if pca50 else " N/A"
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t_pat = f"{thumbnail[m]['patient_test']:>11.4f}" if thumbnail else " N/A"
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f_drop = f"{pca50[m]['drop']:>11.4f}" if pca50 else " N/A"
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t_drop = f"{thumbnail[m]['drop']:>11.4f}" if thumbnail else " N/A"
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print(f"{m:<18s} {f_pat} {t_pat} "
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f"{siamese[m]['patient_test']:>10.4f} {f_drop} {t_drop} "
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f"{siamese[m]['drop']:>10.4f}")
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print(f"\nSaved → {out}")
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print("DONE")
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