def test(args, model, eval_examples, chunk_size=1000):
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
retr_res_path = os.path.join(args.output_dir, "raw_result.csv")
cache_file = "cached_single_test.dat"
if args.overwrite or not os.path.isfile(cache_file):
chunked_retrivial_examples = eval_examples.get_chunked_retrivial_task_examples(
chunk_query_num=args.chunk_query_num, chunk_size=chunk_size)
torch.save(chunked_retrivial_examples, cache_file)
else:
chunked_retrivial_examples = torch.load(cache_file)
retrival_dataloader = DataLoader(chunked_retrivial_examples,
batch_size=args.per_gpu_eval_batch_size)
res = []
for batch in tqdm(retrival_dataloader, desc="retrival evaluation"):
nl_ids = batch[0]
pl_ids = batch[1]
labels = batch[2]
with torch.no_grad():
model.eval()
inputs = format_batch_input_for_single_bert(
batch, eval_examples, model)
sim_score = model.get_sim_score(**inputs)
for n, p, prd, lb in zip(nl_ids.tolist(), pl_ids.tolist(),
sim_score, labels.tolist()):
res.append((n, p, prd, lb))
df = results_to_df(res)
df.to_csv(retr_res_path)
m = metrics(df, output_dir=args.output_dir)
return m
def test(args, model, eval_examples, cache_file, batch_size=1000):
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
retr_res_path = os.path.join(args.output_dir, "raw_result.csv")
if args.overwrite or not os.path.isfile(cache_file):
chunked_retrivial_examples = eval_examples.get_chunked_retrivial_task_examples(
chunk_query_num=args.chunk_query_num, chunk_size=batch_size)
torch.save(chunked_retrivial_examples, cache_file)
else:
chunked_retrivial_examples = torch.load(cache_file)
retrival_dataloader = DataLoader(chunked_retrivial_examples,
batch_size=args.per_gpu_eval_batch_size)
res = []
for batch in tqdm(retrival_dataloader, desc="retrival evaluation"):
nl_ids = batch[0]
pl_ids = batch[1]
labels = batch[2]
nl_embd, pl_embd = eval_examples.id_pair_to_embd_pair(nl_ids, pl_ids)
with torch.no_grad():
model.eval()
nl_embd = nl_embd.to(model.device)
pl_embd = pl_embd.to(model.device)
sim_score = model.get_sim_score(text_hidden=nl_embd,
code_hidden=pl_embd)
for n, p, prd, lb in zip(nl_ids.tolist(), pl_ids.tolist(),
sim_score, labels.tolist()):
res.append((n, p, prd, lb))
df = results_to_df(res)
df.to_csv(retr_res_path)
m = metrics(df, output_dir=args.output_dir)
return m
def evalute_retrivial_for_single_bert(model, eval_examples, batch_size,
res_dir):
if not os.path.isdir(res_dir):
os.makedirs(res_dir)
retr_res_path = os.path.join(res_dir, "raw_result.csv")
summary_path = os.path.join(res_dir, "summary.txt")
retrival_dataloader = eval_examples.get_retrivial_task_dataloader(
batch_size)
res = []
for batch in tqdm(retrival_dataloader, desc="retrival evaluation"):
nl_ids = batch[0]
pl_ids = batch[1]
labels = batch[2]
inputs = format_batch_input_for_single_bert(batch, eval_examples,
model)
sim_score = model.get_sim_score(**inputs)
for n, p, prd, lb in zip(nl_ids.tolist(), pl_ids.tolist(), sim_score,
labels.tolist()):
res.append((n, p, prd, lb))
df = results_to_df(res)
df.to_csv(retr_res_path)
m = metrics(df, output_dir=res_dir)
pk = m.precision_at_K(3)
best_f1, best_f2, details, _ = m.precision_recall_curve("pr_curve.png")
map = m.MAP_at_K(3)
summary = "\nprecision@3={}, best_f1 = {}, best_f2={}, MAP={}\n".format(
pk, best_f1, best_f2, map)
with open(summary_path, 'w') as fout:
fout.write(summary)
fout.write(str(details))
return pk, best_f1, map
def evaluate_retrival(model, eval_examples, batch_size, res_dir):
if not os.path.isdir(res_dir):
os.makedirs(res_dir)
retr_res_path = os.path.join(res_dir, "raw_result.csv")
summary_path = os.path.join(res_dir, "summary.txt")
retrival_dataloader = eval_examples.get_retrivial_task_dataloader(
batch_size)
res = []
for batch in tqdm(retrival_dataloader, desc="retrival evaluation"):
nl_ids = batch[0]
pl_ids = batch[1]
labels = batch[2]
nl_embd, pl_embd = eval_examples.id_pair_to_embd_pair(nl_ids, pl_ids)
with torch.no_grad():
model.eval()
nl_embd = nl_embd.to(model.device)
pl_embd = pl_embd.to(model.device)
sim_score = model.get_sim_score(text_hidden=nl_embd,
code_hidden=pl_embd)
for n, p, prd, lb in zip(nl_ids.tolist(), pl_ids.tolist(),
sim_score, labels.tolist()):
res.append((n, p, prd, lb))
df = results_to_df(res)
df.to_csv(retr_res_path)
m = metrics(df, output_dir=res_dir)
pk = m.precision_at_K(3)
best_f1, best_f2, details, _ = m.precision_recall_curve("pr_curve.png")
map = m.MAP_at_K(3)
summary = "\nprecision@3={}, best_f1 = {}, best_f2={}， MAP={}\n".format(
pk, best_f1, best_f2, map)
with open(summary_path, 'w') as fout:
fout.write(summary)
fout.write(str(details))
return pk, best_f1, map
def evaluate_rnn_retrival(model: RNNTracer, eval_examples, batch_size,
res_dir):
if not os.path.isdir(res_dir):
os.makedirs(res_dir)
retr_res_path = os.path.join(res_dir, "raw_result.csv")
summary_path = os.path.join(res_dir, "summary.txt")
# chunk size is 1000 as default
retrival_dataloader = eval_examples.get_retrivial_task_dataloader(
batch_size)
res = []
for batch in tqdm(retrival_dataloader, desc="retrival evaluation"):
nl_ids = batch[0]
pl_ids = batch[1]
labels = batch[2]
nl_embd, pl_embd = _id_to_embd(nl_ids,
eval_examples.NL_index), _id_to_embd(
pl_ids, eval_examples.PL_index)
with torch.no_grad():
model.eval()
nl_embd = nl_embd.to(model.device)
pl_embd = pl_embd.to(model.device)
sim_score = model.get_sim_score(text_hidden=nl_embd,
code_hidden=pl_embd)
for n, p, prd, lb in zip(nl_ids.tolist(), pl_ids.tolist(),
sim_score, labels.tolist()):
res.append((n, p, prd, lb))
df = results_to_df(res)
df.to_csv(retr_res_path)
m = metrics(df, output_dir=res_dir)
m.write_summary(0)
pk = m.precision_at_K(3)
best_f1, best_f2, details, threshold = m.precision_recall_curve(
"pr_curve.png")
map = m.MAP_at_K(3)
return pk, best_f1, map
elif args.model_path == "LSI":
model = LSI()
model.build_model(doc_tokens, num_topics=200)
else:
raise Exception("Model not found...")
cache_file = os.path.join(out_root, "cached_test.dat")
if args.overwrite or not os.path.isfile(cache_file):
chunked_retrivial_examples = test_examples.get_chunked_retrivial_task_examples(chunk_size=1000)
torch.save(chunked_retrivial_examples, cache_file)
else:
chunked_retrivial_examples = torch.load(cache_file)
retrival_dataloader = DataLoader(chunked_retrivial_examples, batch_size=1000)
start_time = time.time()
res = []
for batch in tqdm(retrival_dataloader, desc="retrival evaluation"):
for s_id, t_id, label in zip(batch[0].tolist(), batch[1].tolist(), batch[2].tolist()):
pred = model.get_link_scores(test_examples.NL_index[s_id], test_examples.PL_index[t_id])
res.append((s_id, t_id, pred, label))
df = pd.DataFrame()
df['s_id'] = [x[0] for x in res]
df['t_id'] = [x[1] for x in res]
df['pred'] = [x[2] for x in res]
df['label'] = [x[3] for x in res]
exe_time = time.time() - start_time
raw_res_file = os.path.join(args.output_dir, "raw_res.csv")
df.to_csv(raw_res_file)
m = metrics(df, output_dir=args.output_dir)
m.write_summary(exe_time)