| | |
| | from sentence_transformers import SentenceTransformer |
| | import pandas as pd |
| | from collections import defaultdict |
| | import torch |
| | from tqdm import tqdm |
| | from test_pytrec_eval import ndcg_in_all |
| |
|
| | if torch.cuda.is_available(): |
| | device = torch.device('cuda') |
| | else: |
| | device = torch.device('cpu') |
| |
|
| |
|
| | def load_dataset(path): |
| | df = pd.read_parquet(path, engine="pyarrow") |
| | return df |
| |
|
| |
|
| | def load_all_dataset(path, convert=False): |
| | qrels_pd = load_dataset(path + r'\qrels.parquet') |
| | corpus = load_dataset(path + r'\corpus.parquet') |
| | queries = load_dataset(path + r'\queries.parquet') |
| | if convert: |
| | qrels = defaultdict(dict) |
| | for i, e in tqdm(qrels_pd.iterrows(), desc="load_all_dataset: Converting"): |
| | qrels[e['qid']][e['cid']] = e['score'] |
| | else: |
| | qrels = qrels_pd |
| | return corpus, queries, qrels |
| |
|
| |
|
| | corpus, queries, qrels = load_all_dataset(r'D:\datasets\G2Retrieval\data_sample2k') |
| |
|
| |
|
| | randEmbed = False |
| | if randEmbed: |
| | corpusEmbeds = torch.rand((1, len(corpus))) |
| | queriesEmbeds = torch.rand((len(queries), 1)) |
| | else: |
| | with torch.no_grad(): |
| | path = r'D:\models\bce' |
| | model = SentenceTransformer(path, device='cuda:0') |
| |
|
| | corpusEmbeds = model.encode(corpus['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=32) |
| | queriesEmbeds = model.encode(queries['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=32) |
| |
|
| | queriesEmbeds = torch.tensor(queriesEmbeds, device=device) |
| | corpusEmbeds = corpusEmbeds.T |
| | corpusEmbeds = torch.tensor(corpusEmbeds, device=device) |
| |
|
| |
|
| | @torch.no_grad() |
| | def getTopK(corpusEmbeds, qEmbeds, qid, k=200): |
| | scores = qEmbeds @ corpusEmbeds |
| | top_k_indices = torch.argsort(scores, descending=True)[:k] |
| | scores = scores.cpu() |
| | top_k_indices = top_k_indices.cpu() |
| | retn = [] |
| | for x in top_k_indices: |
| | x = int(x) |
| | retn.append((qid, corpus['cid'][x], float(scores[x]))) |
| | return retn |
| |
|
| | def print_ndcgs(k): |
| | with torch.no_grad(): |
| | results = [] |
| | for i in tqdm(range(len(queries)), desc="Converting"): |
| | results.extend(getTopK(corpusEmbeds, queriesEmbeds[i], queries['qid'][i], k=k)) |
| |
|
| | results = pd.DataFrame(results, columns=['qid', 'cid', 'score']) |
| | results['score'] = results['score'].astype(float) |
| | tmp = ndcg_in_all(qrels, results) |
| | ndcgs = torch.tensor([x for x in tmp.values()], device=device) |
| |
|
| | mean = torch.mean(ndcgs) |
| | std = torch.std(ndcgs) |
| |
|
| | print(f'NDCG@{k}: {mean*100:.2f}±{std*100:.2f}') |
| |
|
| | print_ndcgs(3) |
| | print_ndcgs(10) |
| | print_ndcgs(50) |
| | print_ndcgs(100) |
| | print_ndcgs(200) |
| | |
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