import networkx as nxfrom sklearn.model_selection import train_test_splitfrom networkx.algorithms.link_prediction import katz_similarityfrom sklearn.metrics import roc_auc_score, average_precision_score# 加载数据集edges = []with open("email-Eu-core.txt") as f: for line in f: if line.startswith("#"): continue edge = line.strip().split() edges.append((int(edge[0]), int(edge[1])))# 划分训练集和测试集train_edges, test_edges = train_test_split(edges, test_size=0.1, random_state=42)# 构建无向图graph = nx.Graph()graph.add_edges_from(train_edges)# 计算相似度katz_scores = katz_similarity(graph, max_l=200, beta=0.01)# 计算AUC值y_true = [1 if edge in test_edges else 0 for edge in graph.edges()]y_scores = [katz_scores[edge] for edge in graph.edges()]auc = roc_auc_score(y_true, y_scores)# 计算Precisionk = 100top_k_edges = sorted(graph.edges(), key=lambda x: katz_scores[x], reverse=True)[:k]y_true = [1 if edge in test_edges else 0 for edge in top_k_edges]y_scores = [katz_scores[edge] for edge in top_k_edges]precision = average_precision_score(y_true, y_scores)print("Katz Algorithm")print("AUC: {:.4f}".format(auc))print("Precision@{}: {:.4f}".format(k, precision))有什么问题

这段代码中有一个语法错误，即第一行的 import 语句中没有正确地使用换行符。应该在 `import networkx as nx` 后添加一个换行符。

另外，代码中的 katz_similarity() 函数调用中使用了一个 max_l 参数，这个参数是不必要的，可以直接去掉，因为该函数不支持传递该参数。

最后，代码未定义变量 k，所以在计算 Precision 时会出错。您应该在代码开头定义一个 k 变量，或者将 k 的值硬编码到代码中。

下面是修改后的代码：

import networkx as nx
from sklearn.model_selection import train_test_split
from networkx.algorithms.link_prediction import katz_similarity
from sklearn.metrics import roc_auc_score, average_precision_score

# 加载数据集
edges = []
with open("email-Eu-core.txt") as f:
    for line in f:
        if line.startswith("#"):
            continue
        edge = line.strip().split()
        edges.append((int(edge[0]), int(edge[1])))

# 划分训练集和测试集
train_edges, test_edges = train_test_split(edges, test_size=0.1, random_state=42)

# 构建无向图
graph = nx.Graph()
graph.add_edges_from(train_edges)

# 计算相似度
katz_scores = katz_similarity(graph, beta=0.01)

# 计算AUC值
y_true = [1 if edge in test_edges else 0 for edge in graph.edges()]
y_scores = [katz_scores[edge] for edge in graph.edges()]
auc = roc_auc_score(y_true, y_scores)

# 计算Precision
k = 100
top_k_edges = sorted(graph.edges(), key=lambda x: katz_scores[x], reverse=True)[:k]
y_true = [1 if edge in test_edges else 0 for edge in top_k_edges]
y_scores = [katz_scores[edge] for edge in top_k_edges]
precision = average_precision_score(y_true, y_scores)

print("Katz Algorithm")
print("AUC: {:.4f}".format(auc))
print("Precision@{}: {:.4f}".format(k, precision))

请注意，这段代码依赖于 `email-Eu-core.txt` 数据集文件，您需要确保该文件存在并且与代码在同一目录下。