test_2 / func.py
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import pandas as pd
import numpy as np
import torch
from transformers import BertModel, BertTokenizer
from sklearn.metrics.pairwise import cosine_similarity
tokenizer = BertTokenizer.from_pretrained("DeepPavlov/rubert-base-cased-sentence")
model = BertModel.from_pretrained("DeepPavlov/rubert-base-cased-sentence", output_hidden_states = True)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
def filter_by_ganre(df: pd.DataFrame, ganre_list: list):
filtered_df = df[df['ganres'].apply(lambda x: any(g in ganre_list for g in(x)))]
filt_ind = filtered_df.index.to_list()
return filt_ind
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output['last_hidden_state']
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return sum_embeddings / sum_mask
def recommendation(filt_ind: list, embeddings: np.array, user_text: str, n=10):
token_user_text = tokenizer(user_text, return_tensors='pt', padding='max_length', truncation=True, max_length=512)
user_embeddings = torch.Tensor().to(device)
model.to(device)
model.eval()
with torch.no_grad():
batch = {k: v.to(device) for k, v in token_user_text.items()}
outputs = model(**batch)
user_embeddings = torch.cat([user_embeddings, mean_pooling(outputs, batch['attention_mask'])])
user_embeddings = user_embeddings.cpu().numpy()
cosine_similarities = cosine_similarity(embeddings[filt_ind], user_embeddings.reshape(1, -1))
df_res = pd.DataFrame(cosine_similarities.ravel(), columns=['cos_sim']).sort_values('cos_sim', ascending=False)
dict_topn = df_res.iloc[:n, :].cos_sim.to_dict()
return dict_topn