tcn集成方法进行信用卡欺诈检测代码
时间: 2024-01-03 22:05:27 浏览: 34
以下是一个使用TCN(Temporal Convolutional Network)进行信用卡欺诈检测的Python代码示例:
首先,我们导入所需的库和模块:
```python
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, Conv1D, MaxPooling1D, Flatten, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
from tcn import TCN
```
接下来,我们加载数据集并进行预处理:
```python
# 加载数据集
data = pd.read_csv('creditcard.csv')
# 将Time列归一化到0-1之间
data['Time'] = data['Time'].apply(lambda x: x / 3600 % 24)
# 将Amount列归一化到0-1之间
data['Amount'] = np.log(data['Amount'] + 1)
data['Amount'] = (data['Amount'] - data['Amount'].mean()) / data['Amount'].std()
# 将Class列改名为label
data.rename(columns={'Class': 'label'}, inplace=True)
# 将数据集分为训练集和测试集
train_data = data.sample(frac=0.8, random_state=2022)
test_data = data.drop(train_data.index)
# 将训练集和测试集分为特征和标签
train_features = train_data.iloc[:, :-1].values
train_labels = train_data.iloc[:, -1].values
test_features = test_data.iloc[:, :-1].values
test_labels = test_data.iloc[:, -1].values
# 将特征和标签转换为张量
train_features = tf.convert_to_tensor(train_features, dtype=tf.float32)
train_labels = tf.convert_to_tensor(train_labels, dtype=tf.float32)
test_features = tf.convert_to_tensor(test_features, dtype=tf.float32)
test_labels = tf.convert_to_tensor(test_labels, dtype=tf.float32)
```
然后,我们定义一个TCN模型:
```python
def create_tcn_model(input_shape):
# 定义输入层
input_layer = Input(shape=input_shape)
# 定义TCN层
tcn_layer = TCN(64, kernel_size=2, dilation_rate=2, activation='relu', use_skip_connections=True)(input_layer)
# 定义全连接层
fc_layer = Dense(64, activation='relu')(tcn_layer)
# 定义输出层
output_layer = Dense(1, activation='sigmoid')(fc_layer)
# 定义模型
model = Model(inputs=input_layer, outputs=output_layer)
# 编译模型
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
return model
```
接下来,我们训练模型并进行评估:
```python
# 创建TCN模型
model = create_tcn_model((train_features.shape[1], 1))
# 定义早停回调
early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=2)
# 训练模型
history = model.fit(train_features, train_labels, epochs=50, batch_size=128, validation_split=0.2, callbacks=[early_stopping])
# 评估模型
test_loss, test_acc = model.evaluate(test_features, test_labels)
print('Test loss:', test_loss)
print('Test accuracy:', test_acc)
```
最后,我们可以使用模型进行预测:
```python
# 进行预测
predictions = model.predict(test_features)
# 将预测结果转换为0和1
predictions = [1 if x > 0.5 else 0 for x in predictions]
# 计算准确率和召回率
tp = sum([1 for i in range(len(predictions)) if predictions[i] == 1 and test_labels[i] == 1])
tn = sum([1 for i in range(len(predictions)) if predictions[i] == 0 and test_labels[i] == 0])
fp = sum([1 for i in range(len(predictions)) if predictions[i] == 1 and test_labels[i] == 0])
fn = sum([1 for i in range(len(predictions)) if predictions[i] == 0 and test_labels[i] == 1])
accuracy = (tp + tn) / len(predictions)
recall = tp / (tp + fn)
print('Accuracy:', accuracy)
print('Recall:', recall)
```
这就是一个使用TCN进行信用卡欺诈检测的Python代码示例。