Add scripts for patient classification and visualization

- Created `classification.py` for comparing image-level and patient-level classification results using various CNN models.
- Implemented `create_patient_groups.py` to extract features, generate PCA/t-SNE plots, and identify patient groups via K-means clustering.
- Added `figure6.py` to generate boxplots for test accuracy across multiple seeds.
- Developed `simple_patient_tsne.py` to perform t-SNE visualization of patient groups and save results in a manifest file.
- Introduced `simple_patient_manifest.csv` to store patient IDs, classes, image counts, and associated images.

scripts/figure6.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""
+figure6.py — test accuracy boxplots across 20 seeds (Figure 6).
+
+Usage:
+    conda activate fundus_imaging
+    python scripts/figure6.py
+"""
+
+import os, sys, json
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+from tqdm import tqdm
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from classes import PatientLeakageClassifier
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MODELS = ["VGG16", "DenseNet121", "EfficientNetB1", "MobileNetV2", "ResNet50"]
+SEEDS = list(range(1, 21))
+
+clf = PatientLeakageClassifier(
+    os.path.join(ROOT, "results", "simple_patient_manifest.csv"),
+    os.path.join(ROOT, "features"),
+    n_jobs=8)
+
+# ---------------------------------------------------------------------------
+# Run all seeds
+# ---------------------------------------------------------------------------
+
+all_data = []  # flat list of per-run dicts for raw data file
+
+for name in tqdm(MODELS, desc="Models"):
+    for seed in tqdm(SEEDS, desc=f"  {name} seeds", leave=False):
+        for stype in ["image", "patient"]:
+            r = clf.run(name, seed, stype)
+            all_data.append(r)
+
+# Save raw data
+with open(os.path.join(ROOT, "results", "figure6_data.json"), "w") as f:
+    json.dump(all_data, f, indent=2)
+
+# ---------------------------------------------------------------------------
+# Build per-model accuracy lists
+# ---------------------------------------------------------------------------
+
+accs = {}  # model -> {image: [accs], patient: [accs]}
+for name in MODELS:
+    accs[name] = {"image": [], "patient": []}
+for r in all_data:
+    accs[r["model"]][r["split_type"]].append(r["test"])
+
+# ---------------------------------------------------------------------------
+# Plot
+# ---------------------------------------------------------------------------
+
+fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+
+for i, name in enumerate(MODELS):
+    pos_img = i * 2 + 0.7
+    pos_pat = i * 2 + 1.3
+
+    for pos, stype, color in [(pos_img, "image", '#4C9BD4'),
+                               (pos_pat, "patient", '#6DBF6D')]:
+        data = accs[name][stype]
+        bp = ax.boxplot(data, positions=[pos], widths=0.5,
+                        patch_artist=True, showfliers=True,
+                        flierprops=dict(marker='o', markersize=3))
+        bp['boxes'][0].set_facecolor(color)
+        med = np.median(data)
+        ax.annotate(f"{med:.3f}", (pos, med), fontsize=6,
+                   ha='center', va='bottom')
+
+ax.legend(handles=[
+    Patch(facecolor='#4C9BD4', label='Image-level split'),
+    Patch(facecolor='#6DBF6D', label='Patient-level split'),
+], loc='lower right')
+
+ax.set_xticks([p + 1 for p in range(0, len(MODELS) * 2, 2)])
+ax.set_xticklabels(MODELS)
+ax.set_ylabel("Test accuracy")
+ax.set_title("Figure 6 — Image-level vs Patient-level test accuracy (20 seeds)")
+ax.set_ylim(0.7, 1.02)
+ax.grid(axis='y', alpha=0.3)
+
+plt.tight_layout()
+out = os.path.join(ROOT, "plots", "figure6.png")
+plt.savefig(out, dpi=150)
+plt.close()
+
+print(f"\nSaved → {out}")
+print(f"Saved → {ROOT}/results/figure6_data.json")
+print("DONE")