rpotter6298
137 lines
5.5 KiB
Python
137 lines
5.5 KiB
Python
#!/usr/bin/env python3
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"""siamese_similarity_tsne.py — t-SNE of the siamese's OWN similarity space.
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Unlike manifest_tsne.py (which recolors the VGG16 feature layout), this builds
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the layout directly from the siamese pairwise distance 1 - P(same-patient), so
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proximity reflects how the siamese model itself relates images. Points are
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colored by the siamese edge-rank groups.
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This is the diagnostic view for the over-merge: if the siamese collapses several
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patients together (over-confidence on IQ-OTH), the largest group forms one dense
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mass in its own space; coherent patients form tight, separated islands.
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Usage:
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conda activate fundus_imaging
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python scripts/visualizations/siamese_similarity_tsne.py
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"""
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import os
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import sys
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import re
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import csv
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import argparse
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import numpy as np
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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.manifold import TSNE
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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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DEFAULT_DATASET = os.path.join(os.path.dirname(ROOT),
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"The IQ-OTHNCCD lung cancer dataset")
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from classes import SiamesePatientMatcher
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CLASS_DIRS = {"Bengin cases": "Benign", "Malignant cases": "Malignant",
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"Normal cases": "Normal"}
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VALID_EXT = (".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp")
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RANDOM_STATE = 42
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def f2n(fname):
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m = re.search(r"\((\d+)\)", fname)
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num = int(m.group(1)) if m else None
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for cls_key, prefix in [("Bengin cases", "B"), ("Malignant cases", "M"),
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("Normal cases", "N")]:
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if fname.startswith(cls_key.rstrip("s")):
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return f"{prefix}_{num:03d}" if num else fname
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return fname
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def load_manifest(path):
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mapping = {}
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with open(path, newline="") as f:
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for row in csv.DictReader(f):
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for img in row["images"].split(";"):
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if img:
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mapping[img] = row["patient_id"]
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return mapping
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--model", default=os.path.join(ROOT, "models", "siamese_resnet18.pt"))
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ap.add_argument("--backbone", default="resnet18")
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ap.add_argument("--dataset", default=DEFAULT_DATASET)
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ap.add_argument("--manifest", default=os.path.join(ROOT, "results", "siamese_manifest.csv"))
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ap.add_argument("--name", default="siamese_sim")
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ap.add_argument("--highlight-largest", type=int, default=0,
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help="Grey all points and bold only the N largest groups.")
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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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matcher = SiamesePatientMatcher(args.model, backbone=args.backbone, input_size=224)
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groups = load_manifest(args.manifest)
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for class_dir, cls in CLASS_DIRS.items():
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cpath = os.path.join(args.dataset, class_dir)
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files = sorted(f for f in os.listdir(cpath) if f.lower().endswith(VALID_EXT))
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paths = [os.path.join(cpath, f) for f in files]
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ids = [f2n(f) for f in files]
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print(f"\n{cls}: {len(paths)} images")
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# Siamese pairwise distance -> t-SNE on precomputed distances.
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emb = matcher.embed_images(paths)
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P = matcher._dense_prob_matrix(emb)
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dist = np.clip(1.0 - P, 0.0, None)
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np.fill_diagonal(dist, 0.0)
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perp = max(5, min(30, (len(paths) - 1) // 3))
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X = TSNE(n_components=2, metric="precomputed", init="random",
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perplexity=perp, random_state=RANDOM_STATE).fit_transform(dist)
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img_group = np.array([groups.get(i, "unassigned") for i in ids])
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g_order = sorted(set(img_group), key=lambda g: -(img_group == g).sum())
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n = len(g_order)
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fig, ax = plt.subplots(figsize=(14, 10))
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if args.highlight_largest > 0:
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# Grey everything, then bold only the N largest groups.
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ax.scatter(X[:, 0], X[:, 1], c="lightgray", s=12, alpha=0.5)
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hl = g_order[:args.highlight_largest]
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hl_cmap = plt.cm.tab10
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for gi, g in enumerate(hl):
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gm = img_group == g
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ax.scatter(X[gm, 0], X[gm, 1], c=[hl_cmap(gi)], s=28, alpha=0.9,
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edgecolors="black", linewidths=0.3,
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label=f"{g} ({gm.sum()} imgs)")
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ax.legend(loc="lower right", title="Largest siamese groups")
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ax.set_title(f"{cls} — siamese similarity t-SNE "
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f"(largest {len(hl)} of {n} groups highlighted)")
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else:
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cmap = plt.cm.tab20 if n <= 20 else plt.cm.gist_ncar
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for gi, g in enumerate(g_order):
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color = cmap(gi % 20) if n <= 20 else cmap(gi / max(n - 1, 1))
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gm = img_group == g
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ax.scatter(X[gm, 0], X[gm, 1], c=[color], s=18, alpha=0.8)
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biggest = (img_group == g_order[0]).sum()
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ax.set_title(f"{cls} — siamese similarity t-SNE "
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f"({n} groups; largest={biggest} imgs)")
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ax.set_xlabel("t-SNE dim 1 (siamese distance)")
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ax.set_ylabel("t-SNE dim 2 (siamese distance)")
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plt.tight_layout()
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suffix = "_highlight" if args.highlight_largest > 0 else ""
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out = os.path.join(PLOTS_DIR, "tsne", f"tsne_{args.name}_{cls}{suffix}{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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print("DONE")
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if __name__ == "__main__":
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main()
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