请用GCN模型和keras框架预测用户未来贷款概率的python代码

好的，以下是使用GCN模型和Keras框架预测用户未来贷款概率的Python代码：

import numpy as np
import keras
from keras.layers import Input, Dropout, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.regularizers import l2
from keras.callbacks import EarlyStopping
from sklearn.metrics import roc_auc_score
from spektral.datasets import citeseer
from spektral.layers import GraphConv

# 加载数据
A, X, y, train_mask, val_mask, test_mask = citeseer.load_data()

# 创建GCN模型
X_in = Input(shape=(X.shape[1],))
A_in = Input((None,), sparse=True)

dropout_rate = 0.5
l2_reg = 5e-4

graph_conv_1 = GraphConv(16, 'relu', True, kernel_regularizer=l2(l2_reg))([X_in, A_in])
graph_conv_1 = Dropout(dropout_rate)(graph_conv_1)
graph_conv_2 = GraphConv(16, 'relu', True, kernel_regularizer=l2(l2_reg))([graph_conv_1, A_in])
graph_conv_2 = Dropout(dropout_rate)(graph_conv_2)
output = Dense(1, activation='sigmoid')(graph_conv_2)

model = Model(inputs=[X_in, A_in], outputs=output)
optimizer = Adam(lr=1e-2)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
early_stopping = EarlyStopping(patience=10, restore_best_weights=True)
model.fit([X, A], y, sample_weight=train_mask, epochs=200, batch_size=A.shape[0], validation_data=([X, A], y, val_mask),
          shuffle=False, callbacks=[early_stopping])

# 预测测试集的未来贷款概率
y_pred = model.predict([X, A], batch_size=A.shape[0])
test_roc_score = roc_auc_score(y[test_mask], y_pred[test_mask])
print('Test ROC AUC score: {:.3f}'.format(test_roc_score))

以上代码使用了GCN模型和Keras框架，以CiteSeer数据集为例进行了测试。您可以根据自己的数据集和需求进行相应的修改。