| |
| from sentence_transformers import SentenceTransformer |
| import pandas as pd |
| from collections import defaultdict |
| import torch |
| from tqdm import tqdm |
| from new.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\H2Retrieval\new\data_sample1k') |
|
|
|
|
| randEmbed = True |
| 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(5) |
| print_ndcgs(10) |
| print_ndcgs(15) |
| print_ndcgs(20) |
| print_ndcgs(30) |
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