2026001

scripts/visualizations/figure5.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+"""figure5.py — PCA and t-SNE of VGG16 features, colored by class.
+
+Usage:
+    python scripts/visualizations/figure5.py
+    python scripts/visualizations/figure5.py --tag v2
+"""
+
+import os, sys, argparse
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(
+    os.path.abspath(__file__)))))
+ROOT = os.path.dirname(os.path.dirname(os.path.dirname(
+    os.path.abspath(__file__))))
+FEATURES_DIR = os.path.join(ROOT, "features")
+PLOTS_DIR = os.path.join(ROOT, "plots")
+SEED = 42
+CLASS_COLORS = {"Bengin cases": "#2CA02C", "Malignant cases": "#D62728",
+                "Normal cases": "#1F77B4"}
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--tag", default="", help="Append tag to filename")
+    args = ap.parse_args()
+    tag = f"_{args.tag}" if args.tag else ""
+
+    data = np.load(os.path.join(FEATURES_DIR, "VGG16_features.npz"),
+                   allow_pickle=True)
+    X, Y = data["X"], data["Y"]
+    pca50 = PCA(n_components=50, random_state=SEED).fit_transform(X)
+    pca2 = PCA(n_components=2, random_state=SEED).fit_transform(pca50)
+    tsne = TSNE(n_components=2, random_state=SEED, perplexity=30,
+                max_iter=1000).fit_transform(pca50)
+
+    fig, axes = plt.subplots(1, 2, figsize=(12, 5))
+    for ax, coords, title in [(axes[0], pca2, "(a) PCA"),
+                               (axes[1], tsne, "(b) t-SNE")]:
+        for cls in sorted(np.unique(Y)):
+            mask = Y == cls
+            label = cls.replace("Bengin cases", "Benign")
+            ax.scatter(coords[mask, 0], coords[mask, 1],
+                       c=CLASS_COLORS[cls], label=label,
+                       alpha=0.6, s=15, edgecolors="none")
+        ax.set_title(title, fontsize=12)
+        ax.set_xlabel("Component 1")
+        ax.set_ylabel("Component 2")
+        ax.legend(markerscale=2, fontsize=9)
+        ax.grid(alpha=0.2)
+
+    fig.suptitle("Figure 5 — PCA and t-SNE of VGG16 Features (by class)",
+                 fontsize=13)
+    plt.tight_layout()
+    out = os.path.join(PLOTS_DIR, "figure5", f"figure5{tag}.png")
+    os.makedirs(os.path.dirname(out), exist_ok=True)
+    plt.savefig(out, dpi=150)
+    plt.close()
+    print(f"Saved → {out}")
+
+
+if __name__ == "__main__":
+    main()