Datasets:
Tasks:
Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
DOI:
License:
| import argparse | |
| import json | |
| import csv | |
| import math | |
| import os | |
| def compute_dcg(pred_docs, gold_docs): | |
| dcg_score = 0.0 | |
| for i, doc in enumerate(pred_docs): | |
| position = i + 1 | |
| discount = 1.0 / math.log2(position + 1) | |
| relevance = 0.0 | |
| if doc in gold_docs: | |
| # If predicted image is present in gold list, set relevance to 1.0 | |
| relevance = 1.0 | |
| else: | |
| for gdoc in gold_docs: | |
| # If predicted image is a sub-image or parent image of an image in gold list, | |
| # we set relevance to 0.5 to provide partial credit | |
| if doc in gdoc or gdoc in doc: | |
| relevance = 0.5 | |
| break | |
| dcg_score += (discount * relevance) | |
| return dcg_score | |
| def compute_idcg(relevance_ranking, rank): | |
| sorted_relevance_ranking = list(sorted(relevance_ranking.items(), key=lambda x: x[1], reverse=True)) | |
| # Only consider top k relevant items for IDCG@k | |
| sorted_relevance_ranking = sorted_relevance_ranking[:min(len(sorted_relevance_ranking), rank)] | |
| idcg_score = sum([ (1.0 / (math.log2(i + 2))) * x[1] for i, x in enumerate(sorted_relevance_ranking)]) | |
| return idcg_score | |
| def run_eval(pred_labels, gold_labels, parse_folder, claim_citekeys, debug): | |
| ranks_to_eval = [5, 10] | |
| ndcg_scores = {n: {} for n in ranks_to_eval} | |
| non_empty_samples = 0 | |
| for claim_id in pred_labels: | |
| if claim_id not in gold_labels: | |
| print(f"Warning: Claim ID {claim_id} not found in gold data - skipping!") | |
| continue | |
| if not gold_labels[claim_id]: | |
| print(f"Warning: Claim ID {claim_id} has no associated evidence figures/tables - skipping!") | |
| continue | |
| non_empty_samples += 1 | |
| for rank in ranks_to_eval: | |
| # If #predictions < rank in predicted ranking, include all for evaluation | |
| pred_images = pred_labels[claim_id][:min(len(pred_labels[claim_id]), rank)] | |
| gold_images = gold_labels[claim_id] | |
| # Compute DCG score | |
| dcg_score = compute_dcg(pred_images, gold_images) | |
| # Compute ideal DCG score | |
| # First need to get relevance scores for all possible images | |
| # Images in gold list get relevance score of 1.0 | |
| relevance_ranking = {x: 1.0 for x in gold_images} | |
| for file in os.listdir(os.path.join(parse_folder, claim_citekeys[claim_id])): | |
| if 'CAPTION' in file: | |
| continue | |
| image_id = file.split('.png')[0] | |
| if image_id not in gold_images: | |
| relevance_ranking[image_id] = 0.0 | |
| # All images that are parent/sub-images of a gold image get relevance of 0.5 | |
| for gold_image in gold_images: | |
| if image_id in gold_image or gold_image in image_id: | |
| relevance_ranking[image_id] = 0.5 | |
| break | |
| idcg_score = compute_idcg(relevance_ranking, rank) | |
| # Finally compute and store NDCG score@k | |
| ndcg_score = dcg_score / idcg_score | |
| ndcg_scores[rank][claim_id] = ndcg_score | |
| # Display final evaluation scores | |
| for rank in ranks_to_eval: | |
| final_ndcg = sum(list(ndcg_scores[rank].values())) / len(gold_labels) | |
| print(f'NDCG@{rank}: {final_ndcg}') | |
| if debug: | |
| json.dump(ndcg_scores, open("task1_scores.json", "w")) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--pred_file", type=str, required=True, help="Path to prediction file") | |
| parser.add_argument("--gold_file", type=str, required=True, help="Path to gold data file") | |
| parser.add_argument("--parse_folder", type=str, required=True, help="Path to folder containing parsed images/tables") | |
| parser.add_argument("--debug", type=bool, default=False, help="Dump per-prediction scores for debuggin/analysis") | |
| args = parser.parse_args() | |
| gold_data = json.loads(open(args.gold_file).read()) | |
| gold_labels = {x["id"]: x["findings"] for x in gold_data} | |
| claim_citekeys = {x["id"]: x["citekey"] for x in gold_data} | |
| reader = csv.reader(open(args.pred_file)) | |
| next(reader, None) | |
| pred_labels = {} | |
| for row in reader: | |
| pred_labels[row[0]] = row[1].split(',') | |
| run_eval(pred_labels, gold_labels, args.parse_folder, claim_citekeys, args.debug) | |