python实现将neo4j的知识图谱利用TransH嵌入方法转换成嵌入向量

以下是基于 PyTorch 实现的 TransH 算法，可以将 Neo4j 的知识图谱转换成嵌入向量：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
from tqdm import tqdm
from py2neo import Graph

# 定义 TransH 模型
class TransH(nn.Module):
    def __init__(self, entity_num, relation_num, dim, margin=1.0):
        super(TransH, self).__init__()
        self.entity_num = entity_num
        self.relation_num = relation_num
        self.dim = dim
        self.margin = margin

        # 定义实体、关系、映射矩阵
        self.entity_embeddings = nn.Embedding(entity_num, dim)
        self.relation_embeddings = nn.Embedding(relation_num, dim)
        self.projection_matrix = nn.Embedding(relation_num, dim * dim)

    def forward(self, head, relation, tail):
        # 获取实体、关系、映射矩阵的向量表示
        head_emb = self.entity_embeddings(head)
        relation_emb = self.relation_embeddings(relation)
        tail_emb = self.entity_embeddings(tail)
        proj_mat = self.projection_matrix(relation)

        # 将向量表示转换成矩阵表示
        head_mat = head_emb.view(-1, 1, self.dim)
        tail_mat = tail_emb.view(-1, 1, self.dim)
        proj_mat = proj_mat.view(-1, self.dim, self.dim)

        # 计算 TransH 中的映射向量
        head_proj_mat = torch.matmul(head_mat, proj_mat)
        tail_proj_mat = torch.matmul(tail_mat, proj_mat)
        head_proj_vec = head_proj_mat.view(-1, self.dim)
        tail_proj_vec = tail_proj_mat.view(-1, self.dim)

        # 计算 TransH 中的距离函数
        dist = torch.norm(head_proj_vec + relation_emb - tail_proj_vec, p=2, dim=1)
        return dist

    # 定义 TransH 中的 margin loss
    def margin_loss(self, pos_dist, neg_dist):
        loss = torch.sum(torch.max(pos_dist - neg_dist + self.margin, torch.zeros_like(pos_dist)))
        return loss

# 定义训练函数
def train(model, train_data, optimizer, batch_size, margin):
    # 将数据集分成若干个 batch
    batch_num = (len(train_data) - 1) // batch_size + 1
    np.random.shuffle(train_data)
    total_loss = 0.0
    for i in tqdm(range(batch_num)):
        start_idx = i * batch_size
        end_idx = min((i + 1) * batch_size, len(train_data))
        batch_data = train_data[start_idx:end_idx]
        head = torch.LongTensor(batch_data[:, 0])
        relation = torch.LongTensor(batch_data[:, 1])
        tail = torch.LongTensor(batch_data[:, 2])
        neg_head = torch.LongTensor(batch_data[:, 3])
        neg_tail = torch.LongTensor(batch_data[:, 4])

        # 将数据转移到 GPU 上
        if torch.cuda.is_available():
            model.cuda()
            head = head.cuda()
            relation = relation.cuda()
            tail = tail.cuda()
            neg_head = neg_head.cuda()
            neg_tail = neg_tail.cuda()

        # 计算正样本和负样本的距离
        pos_dist = model(head, relation, tail)
        neg_dist = model(neg_head, relation, neg_tail)

        # 计算 margin loss 并进行反向传播
        loss = model.margin_loss(pos_dist, neg_dist)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.data.cpu().numpy()

    return total_loss / batch_num

# 定义 TransH 算法的训练过程
def transh_train(entity_list, relation_list, triple_list, dim, lr=0.001, margin=1.0, batch_size=1024, epoch=100):
    # 初始化模型和优化器
    entity2id = {entity: idx for idx, entity in enumerate(entity_list)}
    relation2id = {relation: idx for idx, relation in enumerate(relation_list)}
    model = TransH(len(entity2id), len(relation2id), dim, margin=margin)
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)

    # 将三元组转换成训练数据
    train_data = []
    for head, relation, tail in triple_list:
        if head not in entity2id or tail not in entity2id or relation not in relation2id:
            continue
        head_id = entity2id[head]
        tail_id = entity2id[tail]
        relation_id = relation2id[relation]
        train_data.append([head_id, relation_id, tail_id])

    # 开始训练
    for i in range(epoch):
        loss = train(model, train_data, optimizer, batch_size, margin)
        print("Epoch %d: loss=%.4f" % (i + 1, loss))

    # 返回实体的嵌入向量
    entity_embeddings = model.entity_embeddings.weight.data.cpu().numpy()
    return entity_embeddings

# 连接 Neo4j 数据库并查询数据
graph = Graph(host="localhost", http_port=7474, user="neo4j", password="password")
result = graph.run("MATCH (n)-[r]->(m) RETURN n.name, r.name, m.name").data()

# 提取实体、关系和三元组列表
entity_list = list(set([item['n.name'] for item in result] + [item['m.name'] for item in result]))
relation_list = list(set([item['r.name'] for item in result]))
triple_list = [[item['n.name'], item['r.name'], item['m.name']] for item in result]

# 使用 TransH 算法将知识图谱转换成嵌入向量
entity_embeddings = transh_train(entity_list, relation_list, triple_list, dim=50, lr=0.01, margin=1.0, batch_size=1024, epoch=100)

# 保存实体嵌入向量
np.savetxt("entity_embeddings.txt", entity_embeddings, delimiter=",")

其中，`TransH` 类定义了 TransH 模型，包括实体嵌入矩阵、关系嵌入矩阵和映射矩阵，并实现了前向传播和 margin loss 函数。`train` 函数定义了模型的训练过程，包括将数据集分成若干个 batch，计算正负样本的距离和 margin loss，并进行反向传播。`transh_train` 函数定义了 TransH 算法的训练过程，包括将三元组转换成训练数据，初始化模型和优化器，并开始训练。最后将实体嵌入矩阵保存到文件中。

你需要根据自己的数据集和需求，修改代码中的参数和超参数，例如嵌入维度、学习率、margin、batch_size 和 epoch 等。