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"""Quick experiment run on real datasets — 50 pairs, key datasets only."""
import os, sys, json, csv, time, hashlib, random, threading, re
sys.path.insert(0, '/root/autodl-tmp/AdaCoAgentEA')
CONFIG = {
'api_base': os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1"),
'api_key': "",
'model': "gpt-3.5-turbo-1106",
'max_workers': 5,
'max_pairs': 50,
'data_dir': '/root/autodl-tmp/AdaCoAgentEA/data',
'results_dir': '/root/autodl-tmp/AdaCoAgentEA/results',
'evorm_persistence': '/tmp/evorm_quick',
}
os.makedirs(CONFIG['results_dir'], exist_ok=True)
# ===== DATA LOADERS =====
def load_dm_dataset(ds_path, max_pairs=50):
table_a, table_b = {}, {}
for tn, td in [('tableA.csv', table_a), ('tableB.csv', table_b)]:
tp = os.path.join(ds_path, tn)
if not os.path.exists(tp): tp = tp.replace('.csv', '_tok.csv')
if os.path.exists(tp):
with open(tp, 'r', encoding='utf-8', errors='replace') as f:
for row in csv.DictReader(f): td[row.get('id', '')] = dict(row)
pairs = []
for split in ['test.csv', 'valid.csv']:
sp = os.path.join(ds_path, split)
if not os.path.exists(sp): continue
with open(sp, 'r', encoding='utf-8', errors='replace') as f:
for row in csv.DictReader(f):
pairs.append({
'record_a': table_a.get(row.get('ltable_id', ''), {'id': row.get('ltable_id', '')}),
'record_b': table_b.get(row.get('rtable_id', ''), {'id': row.get('rtable_id', '')}),
'label': int(row.get('label', 0)),
})
if pairs: break
if len(pairs) > max_pairs:
random.seed(42)
pos = [p for p in pairs if p['label'] == 1]
neg = [p for p in pairs if p['label'] == 0]
n_pos = min(len(pos), max_pairs // 2)
pairs = random.sample(pos, n_pos) + random.sample(neg, max_pairs - n_pos)
random.shuffle(pairs)
return pairs
def load_dbp15k(data_dir, lang_pair, max_pairs=50):
pair_dir = os.path.join(data_dir, lang_pair)
if not os.path.exists(pair_dir): return []
def load_labels(p):
labels = {}
with open(p, 'r', encoding='utf-8') as f:
for line in f:
parts = line.strip().split('\t')
if len(parts) >= 2: labels[parts[0]] = parts[1]
return labels
s_labels = load_labels(os.path.join(pair_dir, 's_labels'))
t_labels = load_labels(os.path.join(pair_dir, 't_labels'))
pairs = []
ill_path = os.path.join(pair_dir, 'ent_ILLs')
ill_set = set()
with open(ill_path, 'r', encoding='utf-8') as f:
for line in f:
parts = line.strip().split('\t')
if len(parts) >= 2:
ill_set.add((parts[0], parts[1]))
pairs.append({
'source': {'name': s_labels.get(parts[0], parts[0].split('/')[-1]), 'uri': parts[0]},
'target': {'name': t_labels.get(parts[1], parts[1].split('/')[-1]), 'uri': parts[1]},
'label': 1,
})
random.seed(42)
s_uris, t_uris = list(s_labels.keys()), list(t_labels.keys())
neg_pairs = []
for _ in range(len(pairs)):
su, tu = random.choice(s_uris), random.choice(t_uris)
if (su, tu) not in ill_set:
neg_pairs.append({
'source': {'name': s_labels.get(su, su.split('/')[-1]), 'uri': su},
'target': {'name': t_labels.get(tu, tu.split('/')[-1]), 'uri': tu},
'label': 0,
})
if len(neg_pairs) >= len(pairs): break
all_pairs = pairs + neg_pairs[:len(pairs)]
random.shuffle(all_pairs)
if len(all_pairs) > max_pairs: all_pairs = all_pairs[:max_pairs]
return all_pairs
def load_json(path, max_items=50):
if not os.path.exists(path): return []
with open(path, 'r', encoding='utf-8') as f:
try:
data = json.load(f)
if isinstance(data, list):
return data[:max_items] if len(data) > max_items else data
except: pass
return []
def metrics(predictions, labels):
tp = sum(1 for p, l in zip(predictions, labels) if p == 1 and l == 1)
fp = sum(1 for p, l in zip(predictions, labels) if p == 1 and l == 0)
fn = sum(1 for p, l in zip(predictions, labels) if p == 0 and l == 1)
precision = tp / max(1, tp + fp)
recall = tp / max(1, tp + fn)
f1 = 2 * precision * recall / max(0.001, precision + recall)
return {'F1': round(f1, 4), 'Precision': round(precision, 4), 'Recall': round(recall, 4),
'TP': tp, 'FP': fp, 'FN': fn, 'Total': len(predictions)}
# ===== EXPERIMENT RUNNERS =====
def run_er():
"""Run ER experiments on key datasets."""
from baselines.evorm_wrappers.matchgpt import run_matchgpt, run_anymatch
datasets = [
("Amazon-Google", "Structured"),
("Beer", "Structured"),
("DBLP-ACM", "Structured"),
("Fodors-Zagats", "Structured"),
("iTunes-Amazon", "Structured"),
("Abt-Buy", "Textual"),
]
results = []
print(f"\n{'='*60}\nER Experiments: {len(datasets)} datasets\n{'='*60}")
for ds_name, category in datasets:
ds_path = os.path.join(CONFIG['data_dir'], 'er', category, ds_name)
if not os.path.exists(ds_path):
print(f" {ds_name}: not found, skipping")
continue
pairs = load_dm_dataset(ds_path, CONFIG['max_pairs'])
if not pairs:
print(f" {ds_name}: no data, skipping")
continue
tmp_path = f'/tmp/er_{hashlib.md5(ds_name.encode()).hexdigest()[:8]}.json'
with open(tmp_path, 'w') as f: json.dump(pairs, f)
pos_count = sum(1 for p in pairs if p['label'] == 1)
print(f"\n--- {ds_name} ({category}): {len(pairs)} pairs (pos={pos_count}) ---")
for backend in ['default', 'mixtral', 'solar', 'beluga2']:
for use_evorm in [False, True]:
try:
r = run_matchgpt(tmp_path, backend=backend, use_evorm=use_evorm, max_pairs=CONFIG['max_pairs'])
r['dataset'] = ds_name; r['task'] = 'ER'
results.append(r)
ev = '+EvoRM' if use_evorm else ''
print(f" MatchGPT[{backend}]{ev}: F1={r.get('F1', 'N/A')}")
except Exception as e:
print(f" MatchGPT[{backend}]{'+EvoRM' if use_evorm else ''}: ERROR - {e}")
for use_evorm in [False, True]:
try:
r = run_anymatch(tmp_path, use_evorm=use_evorm, max_pairs=CONFIG['max_pairs'])
r['dataset'] = ds_name; r['task'] = 'ER'
results.append(r)
ev = '+EvoRM' if use_evorm else ''
print(f" AnyMatch{ev}: F1={r.get('F1', 'N/A')}")
except Exception as e:
print(f" AnyMatch{'+EvoRM' if use_evorm else ''}: ERROR - {e}")
os.remove(tmp_path)
return results
def run_el():
"""Run EL experiments."""
from baselines.evorm_wrappers.lela_el import run_lela
el_dir = os.path.join(CONFIG['data_dir'], 'el')
results = []
datasets = []
for fname in ['AIDA-CoNLL_real.json', 'WNED-CWEB_real.json']:
fp = os.path.join(el_dir, fname)
if os.path.exists(fp):
datasets.append((fname.replace('_real.json', ''), fp))
if not datasets:
print("No EL datasets found, skipping")
return results
print(f"\n{'='*60}\nEL Experiments: {len(datasets)} datasets\n{'='*60}")
for ds_name, ds_path in datasets:
items = load_json(ds_path, CONFIG['max_pairs'])
print(f"\n--- {ds_name}: {len(items)} items ---")
for use_evorm in [False, True]:
try:
r = run_lela(ds_path, use_evorm=use_evorm, max_items=CONFIG['max_pairs'])
r['dataset'] = ds_name; r['task'] = 'EL'
results.append(r)
ev = '+EvoRM' if use_evorm else ''
print(f" LELA{ev}: Acc={r.get('Accuracy', 'N/A')}")
except Exception as e:
print(f" LELA{'+EvoRM' if use_evorm else ''}: ERROR - {e}")
return results
def run_sm():
"""Run SM experiments."""
from baselines.evorm_wrappers.lela_el import run_schema_matching
sm_dir = os.path.join(CONFIG['data_dir'], 'sm')
results = []
datasets = []
for fname in ['T2Dv2_schema.json']:
fp = os.path.join(sm_dir, fname)
if os.path.exists(fp):
datasets.append(('T2Dv2', fp))
if not datasets:
print("No SM datasets found, skipping")
return results
print(f"\n{'='*60}\nSM Experiments: {len(datasets)} datasets\n{'='*60}")
for ds_name, ds_path in datasets:
items = load_json(ds_path, CONFIG['max_pairs'])
print(f"\n--- {ds_name}: {len(items)} items ---")
for method in ['llm_dp', 'rematch', 'matchmaker']:
for use_evorm in [False, True]:
try:
r = run_schema_matching(ds_path, method=method, use_evorm=use_evorm, max_items=CONFIG['max_pairs'])
r['dataset'] = ds_name; r['task'] = 'SM'
results.append(r)
ev = '+EvoRM' if use_evorm else ''
print(f" {method}{ev}: F1={r.get('F1', 'N/A')}")
except Exception as e:
print(f" {method}{'+EvoRM' if use_evorm else ''}: ERROR - {e}")
return results
def run_ea():
"""Run EA experiments."""
from openai import OpenAI
from evorm_plugin import EvoRMPlugin
from ThreadPoolExecutor import ThreadPoolExecutor
from tqdm import tqdm
import tokens_cal
ea_dir = os.path.join(CONFIG['data_dir'], 'ea', 'dbp15k')
results = []
if not os.path.exists(ea_dir):
print("No DBP15K data found")
return results
lang_pairs = [d for d in sorted(os.listdir(ea_dir)) if os.path.isdir(os.path.join(ea_dir, d))]
lang_pairs = [lp for lp in lang_pairs if lp in ['fr_en', 'ja_en', 'zh_en']]
if not lang_pairs:
print("No DBP15K language pairs found")
return results
print(f"\n{'='*60}\nEA Experiments: {len(lang_pairs)} language pairs\n{'='*60}")
client = OpenAI(base_url=CONFIG['api_base'], api_key=CONFIG['api_key'])
for lang_pair in lang_pairs:
pairs = load_dbp15k(ea_dir, lang_pair, CONFIG['max_pairs'])
if not pairs:
print(f" {lang_pair}: no data, skipping")
continue
pos_count = sum(1 for p in pairs if p['label'] == 1)
print(f"\n--- DBP15K {lang_pair}: {len(pairs)} pairs (pos={pos_count}) ---")
for use_evorm in [False, True]:
evorm = EvoRMPlugin(client=client, persistence_dir=f'{CONFIG["evorm_persistence"]}/ea_{lang_pair}') if use_evorm else None
predictions, labels_list = [], []
lock = threading.Lock()
tokens_cal.global_tokens = 0
t0 = time.time()
def process_ea(item):
s, t, label = item['source'], item['target'], item['label']
prompt = f"""Entity Alignment: Determine if these two entities refer to the same real-world entity.
Source Entity: {s.get('name', '')}
Target Entity: {t.get('name', '')}
Are these the same entity? Answer ONLY "MATCH" or "NON-MATCH".
Your answer:"""
if evorm:
es_ctx = {'entity_name': s.get('name', ''), 'uri': s.get('uri', '')}
et_ctx = {'entity_name': t.get('name', ''), 'uri': t.get('uri', '')}
eid = s.get('name', '')[:20]; tid = t.get('name', '')[:20]
decision, triggered, cands = evorm.stage1(eid, tid, es_ctx, et_ctx)
if decision is not None:
pred = decision
else:
result = evorm.stage2(eid, tid, es_ctx, et_ctx, triggered, cands, prompt)
raw = result.get('raw_response', '')
pred = 1 if raw and 'MATCH' in raw.upper() and 'NON' not in raw.upper() else (result.get('decision', 0) or 0)
evorm.record_trajectory(eid, tid, es_ctx, et_ctx, decision=pred,
rationale=result.get('rationale', ''),
triggered_rules=triggered,
rule_feedback=result.get('rule_feedback', {}))
else:
response = client.chat.completions.create(
model=CONFIG['model'], messages=[{'role': 'user', 'content': prompt}], temperature=0.1)
answer = response.choices[0].message.content.strip().upper()
tokens_cal.update_add_var(response.usage.total_tokens)
pred = 1 if 'MATCH' in answer and 'NON' not in answer else 0
with lock:
predictions.append(pred)
labels_list.append(label)
executor = ThreadPoolExecutor(max_workers=CONFIG['max_workers'])
for item in tqdm(pairs, desc=f"EA {lang_pair}{' +EvoRM' if use_evorm else ''}"):
executor.submit(process_ea, item)
executor.shutdown(wait=True)
elapsed = time.time() - t0
m = metrics(predictions, labels_list)
m['dataset'] = f'DBP15K {lang_pair}'; m['task'] = 'EA'
m['method'] = f"EA{' + EvoRM' if use_evorm else ''}"
m['time_seconds'] = round(elapsed, 1); m['tokens'] = tokens_cal.global_tokens
if evorm: m['evorm_stats'] = evorm.get_stats()
results.append(m)
ev = '+EvoRM' if use_evorm else ''
print(f" EA{ev}: F1={m.get('F1', 'N/A')}, Prec={m.get('Precision', 'N/A')}, Rec={m.get('Recall', 'N/A')}")
return results
def main():
print("=" * 70)
print("EvoRM Quick Experiment Suite — REAL Datasets")
print("=" * 70)
print(f"Model: {CONFIG['model']}, Max pairs: {CONFIG['max_pairs']}")
all_results = []
for name, func in [('ER', run_er), ('EA', run_ea), ('EL', run_el), ('SM', run_sm)]:
print(f"\n{'='*70}\nPART: {name}\n{'='*70}")
try:
r = func()
all_results.extend(r)
print(f"\n{name} completed: {len(r)} experiments")
except Exception as e:
print(f"{name} failed: {e}")
import traceback; traceback.print_exc()
timestamp = time.strftime('%Y%m%d_%H%M%S')
results_path = os.path.join(CONFIG['results_dir'], f'quick_results_{timestamp}.json')
with open(results_path, 'w') as f:
json.dump(all_results, f, indent=2, ensure_ascii=False)
print(f"\n{'='*70}")
print(f"ALL DONE: {len(all_results)} experiments")
print(f"Results: {results_path}")
print(f"{'='*70}")
for task in ['ER', 'EA', 'EL', 'SM']:
tr = [r for r in all_results if r.get('task') == task]
if not tr: continue
print(f"\n{task} ({len(tr)} experiments):")
for r in tr:
method = r.get('method', '?')
ds = r.get('dataset', '?')
f1 = r.get('F1', r.get('Accuracy', 'N/A'))
print(f" {ds} | {method}: {f1}")
return all_results
if __name__ == '__main__':
main()
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