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"""Compute AMI, ARI, NMI for all (network, method) pairs.
Uses the official network_evaluation scripts from
https://github.com/illinois-or-research-analytics/network_evaluation
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
# Add network_evaluation to path
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, os.path.join(BASE_DIR, "network_evaluation", "commdet_acc"))
import pandas as pd
from compute_cd_accuracy import clustering_accuracy
from config import NETWORKS, METHODS, RESULTS_DIR
def compute_single_accuracy(network_name, method_name):
"""Compute accuracy for a single (network, method) pair using official scripts."""
net = NETWORKS[network_name]
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
out_prefix = os.path.join(RESULTS_DIR, "accuracy", f"{network_name}_{method_name}")
clustering_accuracy(
input_edgelist=net["edge_tsv"],
groundtruth_clustering=net["com_gt_tsv"],
estimated_clustering=est_path,
output_prefix=out_prefix,
num_processors=1,
local=False,
overwrite=True,
)
# Read back the results
result = {}
for metric in ["ami", "ari", "nmi", "node_coverage"]:
fpath = f"{out_prefix}.{metric}"
if os.path.exists(fpath):
with open(fpath) as f:
result[metric] = float(f.read().strip())
return result
def compute_all_accuracy():
"""Compute accuracy for all (network, method) pairs and save CSV."""
out_dir = os.path.join(RESULTS_DIR, "accuracy")
os.makedirs(out_dir, exist_ok=True)
rows = []
for net_name in NETWORKS:
for method in METHODS:
m_name = method["name"]
print(f"Computing accuracy: {net_name} / {m_name}")
result = compute_single_accuracy(net_name, m_name)
if result is not None:
rows.append({
"network": net_name,
"method": m_name,
**result,
})
df = pd.DataFrame(rows)
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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