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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()
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