LongArticleAlgServer/applications/textSimilarity.py

"""
@author: luojunhui
"""
import torch
import numpy as np


def score_to_attention(score, symbol=1):
    """

    :param score:
    :param symbol:
    :return:
    """
    score_pred = torch.FloatTensor(score).unsqueeze(0)
    score_norm = symbol * torch.nn.functional.normalize(score_pred, p=2)
    score_attn = torch.nn.functional.softmax(score_norm, dim=1)
    return score_attn, score_norm, score_pred

def compare_tensor(tensor1, tensor2):
    if tensor1.shape != tensor2.shape:
        print(f"[EmbeddingManager]shape error: {tensor1.shape} vs {tensor2.shape}")
        return
    if not torch.allclose(tensor1, tensor2):
        print("[EmbeddingManager]value error: tensor1 not close to tensor2")

class NLPFunction(object):
    """
    NLP Task
    """

    def __init__(self, model, embedding_manager):
        self.model = model
        self.embedding_manager = embedding_manager

    def base_string_similarity(self, text_dict):
        """
        基础功能，计算两个字符串的相似度
        :param text_dict:
        :return:
        """
        score_tensor = self.model.similarity(
            text_dict['text_a'],
            text_dict['text_b']
        )
        # test embedding manager functions
        text_emb1 = self.embedding_manager.get_embedding(text_dict['text_a'])
        text_emb2 = self.embedding_manager.get_embedding(text_dict['text_b'])
        score_function = self.model.score_functions['cos_sim']
        score_tensor_new = score_function(text_emb1, text_emb2)
        compare_tensor(score_tensor, score_tensor_new)

        response = {
            "score": score_tensor.squeeze().tolist()
        }
        return response

    def base_list_similarity(self, pair_list_dict):
        """
        计算两个list的相似度
        :return:
        """
        score_tensor = self.model.similarity(
            pair_list_dict['text_list_a'],
            pair_list_dict['text_list_b']
        )
        response = {
            "score_list_list": score_tensor.tolist()
        }
        return response

    def max_cross_similarity(self, data):
        """
        max
        :param data:
        :return:
        """
        score_list_max = []
        text_list_max = []
        score_array = self.base_list_similarity(data)['score_list_list']
        text_list_a, text_list_b = data['text_list_a'], data['text_list_b']
        for i, row in enumerate(score_array):
            max_index = np.argmax(row)
            max_value = row[max_index]
            score_list_max.append(max_value)
            text_list_max.append(text_list_b[max_index])
        response = {
            'score_list_max': score_list_max,
            'text_list_max': text_list_max,
            'score_list_list': score_array,
        }
        return response

    def mean_cross_similarity(self, data):
        """
        :param data:
        :return:
        """
        resp = self.max_cross_similarity(data)
        score_list_max, text_list_max, score_array = resp['score_list_max'], resp['text_list_max'], resp['score_list_list']
        score_tensor = torch.tensor(score_array)
        score_res = torch.mean(score_tensor, dim=1)
        score_list = score_res.tolist()
        response = {
            'score_list_mean': score_list,
            'text_list_max': text_list_max,
            'score_list_list': score_array,
        }
        return response

    def avg_cross_similarity(self, data):
        """
        :param data:
        :return:
        """
        score_list_b = data['score_list_b']
        symbol = data['symbol']
        # score_list_max, text_list_max, score_array = self.max_cross_similarity(data)
        resp = self.max_cross_similarity(data)
        score_list_max, text_list_max, score_array = resp['score_list_max'], resp['text_list_max'], resp[
            'score_list_list']
        score_attn, score_norm, score_pred = score_to_attention(score_list_b, symbol=symbol)
        score_tensor = torch.tensor(score_array)
        score_res = torch.matmul(score_tensor, score_attn.transpose(0, 1))
        score_list = score_res.squeeze(-1).tolist()
        response = {
            'score_list_avg': score_list,
            'text_list_max': text_list_max,
            'score_list_list': score_array,
        }
        return response