initial framework; to be extended

scripts/compare_embeddings.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""
+Compares two latent embedding matrices (e.g., CTRL vs EED-i),
+computes similarity metrics (cosine, Euclidean, L1),
+and saves both a CSV and an optional line plot.
+
+Usage:
+  python scripts/compare_embeddings_general.py \
+      --emb1 results/emb.npy \
+      --emb2 results/emb_eedi.npy \
+      --label1 CTRL --label2 EEDi \
+      --prefix results/chr21
+"""
+
+import argparse
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import cosine, euclidean, cityblock
+import os
+
+
+def compute_metrics(emb1, emb2):
+    """Compute cosine similarity, cosine distance, L2, and L1 per row."""
+    cos_sims, cos_dists, l2_dists, l1_dists = [], [], [], []
+    for a, b in zip(emb1, emb2):
+        cos_sim = 1 - cosine(a, b)
+        cos_dist = 1 - cos_sim
+        l2 = euclidean(a, b)
+        l1 = cityblock(a, b)
+        cos_sims.append(cos_sim)
+        cos_dists.append(cos_dist)
+        l2_dists.append(l2)
+        l1_dists.append(l1)
+    return np.array(cos_sims), np.array(cos_dists), np.array(l2_dists), np.array(l1_dists)
+
+
+def main():
+    p = argparse.ArgumentParser(description="Compare two embedding matrices")
+    p.add_argument("--emb1", required=True, help="Path to first embedding .npy")
+    p.add_argument("--emb2", required=True, help="Path to second embedding .npy")
+    p.add_argument("--label1", default="A", help="Label for first embedding")
+    p.add_argument("--label2", default="B", help="Label for second embedding")
+    p.add_argument("--prefix", default="results/compare", help="Prefix for output files")
+    p.add_argument("--no-plot", action="store_true", help="Skip generating the plot")
+    args = p.parse_args()
+
+    # ---- Load ----
+    emb1 = np.load(args.emb1)
+    emb2 = np.load(args.emb2)
+    if emb1.shape != emb2.shape:
+        raise ValueError(f"Shape mismatch: {emb1.shape} vs {emb2.shape}")
+
+    os.makedirs(os.path.dirname(args.prefix), exist_ok=True)
+    n_bins, n_dim = emb1.shape
+    print(f"Loaded embeddings: {n_bins} bins × {n_dim} dims")
+
+    # ---- Compute metrics ----
+    cos_sims, cos_dists, l2_dists, l1_dists = compute_metrics(emb1, emb2)
+
+    df = pd.DataFrame({
+        "bin_id": np.arange(n_bins),
+        "cosine_similarity": cos_sims,
+        "cosine_distance": cos_dists,
+        "euclidean": l2_dists,
+        "manhattan": l1_dists
+    })
+    csv_path = f"{args.prefix}_delta.csv"
+    df.to_csv(csv_path, index=False)
+    print(f"Saved metrics → {csv_path}")
+
+    # ---- Plot ----
+    if not args.no_plot:
+        plt.figure(figsize=(12, 4))
+        plt.plot(df["bin_id"], df["cosine_distance"], lw=0.8, color="steelblue")
+        plt.title(f"Δ-Embedding ({args.label1} vs {args.label2})")
+        plt.xlabel("Bin index")
+        plt.ylabel("Cosine distance (1 – similarity)")
+        plt.tight_layout()
+        fig_path = f"{args.prefix}_delta.png"
+        plt.savefig(fig_path, dpi=300)
+        print(f"Saved plot → {fig_path}")
+
+
+if __name__ == "__main__":
+    main()