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from sentence_transformers import SentenceTransformer |
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import pandas as pd |
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from collections import defaultdict |
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import torch |
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from tqdm import tqdm |
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from new.test_pytrec_eval import ndcg_in_all |
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if torch.cuda.is_available(): |
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device = torch.device('cuda') |
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else: |
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device = torch.device('cpu') |
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def load_dataset(path): |
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df = pd.read_parquet(path, engine="pyarrow") |
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return df |
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def load_all_dataset(path, convert=False): |
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qrels_pd = load_dataset(path + r'\qrels.parquet') |
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corpus = load_dataset(path + r'\corpus.parquet') |
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queries = load_dataset(path + r'\queries.parquet') |
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if convert: |
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qrels = defaultdict(dict) |
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for i, e in tqdm(qrels_pd.iterrows(), desc="load_all_dataset: Converting"): |
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qrels[e['qid']][e['cid']] = e['score'] |
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else: |
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qrels = qrels_pd |
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return corpus, queries, qrels |
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corpus, queries, qrels = load_all_dataset(r'D:\datasets\H2Retrieval\new\data_sample1k') |
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randEmbed = True |
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if randEmbed: |
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corpusEmbeds = torch.rand((1, len(corpus))) |
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queriesEmbeds = torch.rand((len(queries), 1)) |
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else: |
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with torch.no_grad(): |
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path = r'D:\models\bce' |
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model = SentenceTransformer(path, device='cuda:0') |
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corpusEmbeds = model.encode(corpus['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=32) |
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queriesEmbeds = model.encode(queries['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=32) |
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queriesEmbeds = torch.tensor(queriesEmbeds, device=device) |
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corpusEmbeds = corpusEmbeds.T |
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corpusEmbeds = torch.tensor(corpusEmbeds, device=device) |
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@torch.no_grad() |
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def getTopK(corpusEmbeds, qEmbeds, qid, k=200): |
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scores = qEmbeds @ corpusEmbeds |
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top_k_indices = torch.argsort(scores, descending=True)[:k] |
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scores = scores.cpu() |
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top_k_indices = top_k_indices.cpu() |
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retn = [] |
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for x in top_k_indices: |
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x = int(x) |
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retn.append((qid, corpus['cid'][x], float(scores[x]))) |
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return retn |
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def print_ndcgs(k): |
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with torch.no_grad(): |
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results = [] |
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for i in tqdm(range(len(queries)), desc="Converting"): |
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results.extend(getTopK(corpusEmbeds, queriesEmbeds[i], queries['qid'][i], k=k)) |
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results = pd.DataFrame(results, columns=['qid', 'cid', 'score']) |
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results['score'] = results['score'].astype(float) |
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tmp = ndcg_in_all(qrels, results) |
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ndcgs = torch.tensor([x for x in tmp.values()], device=device) |
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mean = torch.mean(ndcgs) |
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std = torch.std(ndcgs) |
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print(f'NDCG@{k}: {mean*100:.2f}±{std*100:.2f}') |
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print_ndcgs(5) |
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print_ndcgs(10) |
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print_ndcgs(15) |
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print_ndcgs(20) |
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print_ndcgs(30) |
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