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Handle empty 'pred' lists gracefully in evaluate function #13

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Handle empty 'pred' lists gracefully in evaluate function #13

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29 changes: 19 additions & 10 deletions evaluation/evaluate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,27 +55,33 @@ def evaluate(data_name, prompt_type, samples: list=None, file_path: str=None, ma
idx = 0
score_mat = []
for sample in samples:
if not sample['pred']: # Skip samples with empty 'pred' list
continue
sample['score'] = scores[idx: idx+len(sample['pred'])]
assert len(sample['score']) == len(sample['pred'])
score_mat.append(sample['score'])
idx += len(sample['pred'])

max_len = max([len(s) for s in score_mat])
if not score_mat: # Handle case where score_mat might be empty
col_means = np.array([])
mean_score = []
else:
max_len = max([len(s) for s in score_mat])

for i, s in enumerate(score_mat):
if len(s) < max_len:
score_mat[i] = s + [s[-1]] * (max_len - len(s)) # pad
for i, s in enumerate(score_mat):
if len(s) < max_len:
score_mat[i] = s + [s[-1]] * (max_len - len(s)) # pad

# output mean of each column of scores
col_means= np.array(score_mat).mean(axis=0)
mean_score = list(np.round(col_means * 100, decimals=1))
# output mean of each column of scores
col_means= np.array(score_mat).mean(axis=0)
mean_score = list(np.round(col_means * 100, decimals=1))

result_json = {
"num_samples": len(samples),
"num_scores": len(scores),
"timeout_samples": timeout_cnt,
"empty_samples": len([s for s in samples if not s['pred'][-1]]),
"acc": mean_score[0]
"empty_samples": len([s for s in samples if not s['pred']]),
"acc": mean_score[0] if mean_score else 0
}

# each type score
Expand All @@ -84,7 +90,8 @@ def evaluate(data_name, prompt_type, samples: list=None, file_path: str=None, ma
for sample in samples:
if sample['type'] not in type_scores:
type_scores[sample['type']] = []
type_scores[sample['type']].append(sample['score'][-1])
if sample['pred']: # Add only if 'pred' is not empty
type_scores[sample['type']].append(sample['score'][-1])
type_scores = {k: np.round(np.array(v).mean() * 100, decimals=1) for k, v in type_scores.items()}
type_scores = {k: v for k, v in sorted(type_scores.items(), key=lambda item: item[0])}
result_json['type_acc'] = type_scores
Expand All @@ -107,3 +114,5 @@ def parse_args():
args = parse_args()
evaluate(data_name=args.data_name, prompt_type=args.prompt_type, file_path=args.file_path,
max_num_samples=args.max_num_samples, execute=args.execute)