1 files changed, 92 insertions, 0 deletions
diff --git a/scripts/compute_accuracy.py b/scripts/compute_accuracy.py
new file mode 100644
index 0000000..4aeb6a2
--- /dev/null
+++ b/scripts/compute_accuracy.py
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+"""Compute AMI, ARI, NMI for all (network, method) pairs."""
+
+import argparse
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import adjusted_mutual_info_score, adjusted_rand_score, normalized_mutual_info_score
+
+from config import NETWORKS, METHODS, RESULTS_DIR
+from load_data import load_edge_list, load_communities
+
+
+def align_labels(gt_com, est_com, edge_path):
+    """Align ground truth and estimated labels over the full node set from edges.
+    Nodes missing from a clustering get unique singleton community IDs."""
+    edge_df = load_edge_list(edge_path)
+    all_nodes = sorted(set(
+        pd.unique(edge_df[["src", "tgt"]].values.ravel("K"))
+    ))
+
+    gt_labels = []
+    est_labels = []
+    # For nodes not in GT or EST, assign unique singleton IDs
+    gt_next = max((int(v) for v in gt_com.values() if v.lstrip('-').isdigit()), default=0) + 1
+    est_next = max((int(v) for v in est_com.values() if v.lstrip('-').isdigit()), default=0) + 1
+
+    for node in all_nodes:
+        if node in gt_com:
+            gt_labels.append(gt_com[node])
+        else:
+            gt_labels.append(f"gt_singleton_{gt_next}")
+            gt_next += 1
+
+        if node in est_com:
+            est_labels.append(est_com[node])
+        else:
+            est_labels.append(f"est_singleton_{est_next}")
+            est_next += 1
+
+    return gt_labels, est_labels
+
+
+def compute_accuracy(network_name, method_name):
+    """Compute AMI, ARI, NMI for a single (network, method) pair."""
+    net = NETWORKS[network_name]
+    gt_com = load_communities(net["com_gt_tsv"])
+
+    est_path = os.path.join(RESULTS_DIR, network_name, method_name, "com.tsv")
+    if not os.path.exists(est_path):
+        print(f"  WARNING: {est_path} not found, skipping")
+        return None
+
+    est_com = load_communities(est_path)
+    gt_labels, est_labels = align_labels(gt_com, est_com, net["edge_tsv"])
+
+    ami = adjusted_mutual_info_score(gt_labels, est_labels, average_method="arithmetic")
+    ari = adjusted_rand_score(gt_labels, est_labels)
+    nmi = normalized_mutual_info_score(gt_labels, est_labels, average_method="arithmetic")
+
+    return {"ami": ami, "ari": ari, "nmi": nmi}
+
+
+def compute_all_accuracy():
+    """Compute accuracy for all (network, method) pairs and save CSV."""
+    rows = []
+    for net_name in NETWORKS:
+        for method in METHODS:
+            m_name = method["name"]
+            print(f"Computing accuracy: {net_name} / {m_name}")
+            result = compute_accuracy(net_name, m_name)
+            if result is not None:
+                rows.append({
+                    "network": net_name,
+                    "method": m_name,
+                    **result,
+                })
+
+    df = pd.DataFrame(rows)
+    out_dir = os.path.join(RESULTS_DIR, "accuracy")
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = os.path.join(out_dir, "accuracy_table.csv")
+    df.to_csv(out_path, index=False)
+    print(f"\nAccuracy table saved to {out_path}")
+    print(df.to_string(index=False))
+    return df
+
+
+if __name__ == "__main__":
+    compute_all_accuracy()