如何评估知识图谱嵌入模型的性能

import numpy as np
import pandas as pd

# 加载FB15k数据集
def load_data(file_path):
    data = pd.read_csv(file_path, sep='\t', header=None)
    data.columns = ['head', 'relation', 'tail']
    return data

train_data = load_data('FB15k/train.txt')
test_data = load_data('FB15k/test.txt')

print(f'训练集大小: {train_data.shape}')
print(f'测试集大小: {test_data.shape}')

import tensorflow as tf
from tensorflow.keras.layers import Embedding

class TransE(tf.keras.Model):
    def __init__(self, num_entities, num_relations, embedding_dim):
        super(TransE, self).__init__()
        self.entity_embedding = Embedding(input_dim=num_entities, output_dim=embedding_dim)
        self.relation_embedding = Embedding(input_dim=num_relations, output_dim=embedding_dim)

    def call(self, head, relation, tail):
        head_emb = self.entity_embedding(head)
        relation_emb = self.relation_embedding(relation)
        tail_emb = self.entity_embedding(tail)
        
        # TransE 目标函数：h + r ≈ t
        score = tf.norm(head_emb + relation_emb - tail_emb, axis=1)
        return score

# 初始化模型
num_entities = len(set(train_data['head']).union(set(train_data['tail'])))
num_relations = len(set(train_data['relation']))
embedding_dim = 100

transE_model = TransE(num_entities, num_relations, embedding_dim)

optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
margin = 1.0

def loss_fn(pos_score, neg_score):
    return tf.reduce_mean(tf.maximum(0.0, margin + pos_score - neg_score))

@tf.function
def train_step(pos_triplets, neg_triplets):
    with tf.GradientTape() as tape:
        pos_score = transE_model(pos_triplets[:, 0], pos_triplets[:, 1], pos_triplets[:, 2])
        neg_score = transE_model(neg_triplets[:, 0], neg_triplets[:, 1], neg_triplets[:, 2])
        loss = loss_fn(pos_score, neg_score)
    
    gradients = tape.gradient(loss, transE_model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, transE_model.trainable_variables))
    return loss

# 模型训练过程
for epoch in range(100):
    pos_triplets = np.array(...)  # 正确的三元组
    neg_triplets = np.array(...)  # 随机生成的错误三元组
    loss = train_step(pos_triplets, neg_triplets)
    if epoch % 10 == 0:
        print(f'Epoch {epoch}, Loss: {loss.numpy()}')

def evaluate_link_prediction(test_triplets):
    ranks = []
    hits_at_10 = 0
    for triplet in test_triplets:
        head, relation, tail = triplet
        # 计算所有可能的尾实体
        tail_scores = transE_model(head, relation, np.arange(num_entities))
        rank = np.argsort(np.argsort(tail_scores))[tail]
        ranks.append(rank)
        if rank < 10:
            hits_at_10 += 1
    
    mean

_rank = np.mean(ranks)
    hit_at_10_ratio = hits_at_10 / len(test_triplets)
    return mean_rank, hit_at_10_ratio

mean_rank, hit_at_10 = evaluate_link_prediction(np.array(test_data))
print(f'平均排名: {mean_rank}, Hit@10: {hit_at_10}')

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 获取实体的嵌入向量
entity_embeddings = transE_model.entity_embedding(np.arange(num_entities)).numpy()

# 使用逻辑回归进行实体分类
clf = LogisticRegression()
clf.fit(entity_embeddings[train_entities], train_labels)

# 预测并评估准确率
pred_labels = clf.predict(entity_embeddings[test_entities])
accuracy = accuracy_score(test_labels, pred_labels)
print(f'实体分类准确率: {accuracy}')