#!/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")