import torch from src.model.DKN.KCNN import KCNN from src.model.DKN.attention import Attention from src.model.general.click_predictor.DNN import DNNClickPredictor# 就是把整個模型框架梳理到一塊了！ class DKN(torch.nn.Module):"""Deep knowledge-aware network.Input 1 + K candidate news and a list of user clicked news, produce the click probability."""#純定義，如果看過前面的內容，這里不難理解def __init__(self,config,pretrained_word_embedding=None,pretrained_entity_embedding=None,pretrained_context_embedding=None):super(DKN, self).__init__()self.config = configself.kcnn = KCNN(config, pretrained_word_embedding,pretrained_entity_embedding,pretrained_context_embedding)self.attention = Attention(config)self.click_predictor = DNNClickPredictor(len(self.config.window_sizes) * 2 * self.config.num_filters)def forward(self, candidate_news, clicked_news):"""Args:candidate_news:[{"title": batch_size * num_words_title,"title_entities": batch_size * num_words_title} * (1 + K)]clicked_news:[{"title": batch_size * num_words_title,"title_entities": batch_size * num_words_title} * num_clicked_news_a_user]Returns:click_probability: batch_size"""# batch_size, 1 + K, len(window_sizes) * num_filterscandidate_news_vector = torch.stack( [self.kcnn(x) for x in candidate_news], dim=1)# batch_size, num_clicked_news_a_user, len(window_sizes) * num_filtersclicked_news_vector = torch.stack([self.kcnn(x) for x in clicked_news],dim=1)# batch_size, 1 + K, len(window_sizes) * num_filtersuser_vector = torch.stack([self.attention(x, clicked_news_vector)for x in candidate_news_vector.transpose(0, 1)],dim=1)size = candidate_news_vector.size()# batch_size, 1 + Kclick_probability = self.click_predictor(candidate_news_vector.view(size[0] * size[1], size[2]),user_vector.view(size[0] * size[1],size[2])).view(size[0], size[1])return click_probabilitydef get_news_vector(self, news):"""Args:news:{"title": batch_size * num_words_title,"title_entities": batch_size * num_words_title}Returns:(shape) batch_size, len(window_sizes) * num_filters"""# batch_size, len(window_sizes) * num_filtersreturn self.kcnn(news)def get_user_vector(self, clicked_news_vector):"""Args:clicked_news_vector: batch_size, num_clicked_news_a_user, len(window_sizes) * num_filtersReturns:(shape) batch_size, num_clicked_news_a_user, len(window_sizes) * num_filters"""# batch_size, num_clicked_news_a_user, len(window_sizes) * num_filtersreturn clicked_news_vectordef get_prediction(self, candidate_news_vector, clicked_news_vector):"""Args:candidate_news_vector: candidate_size, len(window_sizes) * num_filtersclicked_news_vector: num_clicked_news_a_user, len(window_sizes) * num_filtersReturns:click_probability: 0-dim tensor"""# candidate_size, len(window_sizes) * num_filtersuser_vector = self.attention(candidate_news_vector,clicked_news_vector.expand(candidate_news_vector.size(0), -1, -1))# candidate_sizeclick_probability = self.click_predictor(candidate_news_vector,user_vector)return click_probability

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