请帮我编写一段利用LSTM方法进行财务风险预警分析的代码,需要用到K折为10进行交叉验证输出平均预测的准确率。原始数据中有13个因子,包含原始38个变量的信息。random_state=20,需要拟合的是dataX_train,dataY_train
时间: 2024-05-01 10:18:02 浏览: 18
好的,我会帮您编写代码。根据您的要求,以下是使用LSTM方法进行财务风险预警分析的Python代码:
```python
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import KFold
# 设置随机数种子
np.random.seed(20)
tf.random.set_seed(20)
# 读取原始数据,包含13个因子和1个风险标签
data = pd.read_csv('data.csv')
# 对原始数据进行预处理,包括数据清洗、缺失值处理、特征选择等步骤
# ...
# 将数据集划分为训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]
# 定义LSTM模型
def build_model():
model = tf.keras.Sequential([
tf.keras.layers.LSTM(64, return_sequences=True, input_shape=(train_data.shape[1]-1, 1)),
tf.keras.layers.LSTM(32),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# 进行K折交叉验证
k = 10
kf = KFold(n_splits=k, shuffle=True)
scores = []
for train_index, test_index in kf.split(train_data):
# 划分训练集和验证集
train_fold = train_data.iloc[train_index]
valid_fold = train_data.iloc[test_index]
# 提取特征和标签数据
train_features = train_fold.drop('risk', axis=1).values
train_labels = train_fold['risk'].values
valid_features = valid_fold.drop('risk', axis=1).values
valid_labels = valid_fold['risk'].values
# 将输入数据reshape为适合LSTM模型的三维格式
train_features = np.reshape(train_features, (train_features.shape[0], train_features.shape[1], 1))
valid_features = np.reshape(valid_features, (valid_features.shape[0], valid_features.shape[1], 1))
# 建立模型并训练
model = build_model()
model.fit(train_features, train_labels, epochs=10, batch_size=32, validation_data=(valid_features, valid_labels))
# 使用测试集数据评估模型,获取分数
test_features = test_data.drop('risk', axis=1).values
test_labels = test_data['risk'].values
test_features = np.reshape(test_features, (test_features.shape[0], test_features.shape[1], 1))
score = model.evaluate(test_features, test_labels, verbose=0)
scores.append(score[1])
# 输出K折交叉验证的平均准确率
print('Average Accuracy:', np.mean(scores))
```
以上是我根据您的要求编写的代码,其中使用了Keras API建立LSTM模型,并使用sklearn进行K折交叉验证。如果您有其他需要,请随时告诉我。