信用卡欺诈数据自动编码器异常检测召回率代码

以下是使用自动编码器进行信用卡欺诈数据异常检测并计算召回率的代码示例[^2]：

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, recall_score

# 加载数据
data = pd.read_csv('credit_card_data.csv')

# 划分特征和标签
X = data.drop('Class', axis=1)
y = data['Class']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 构建自动编码器模型
input_dim = X_train.shape[1]
encoding_dim = 14

input_layer = tf.keras.layers.Input(shape=(input_dim,))
encoder = tf.keras.layers.Dense(encoding_dim, activation='relu')(input_layer)
decoder = tf.keras.layers.Dense(input_dim, activation='relu')(encoder)
autoencoder = tf.keras.models.Model(inputs=input_layer, outputs=decoder)

# 编译和训练模型
autoencoder.compile(optimizer='adam', loss='mean_squared_error')
autoencoder.fit(X_train, X_train, epochs=10, batch_size=32, shuffle=True, validation_data=(X_test, X_test))

# 使用训练好的自动编码器进行预测
X_pred = autoencoder.predict(X_test)

# 计算召回率
y_pred = np.argmax(X_pred, axis=1)
recall = recall_score(y_test, y_pred)

print("召回率：", recall)

请注意，上述代码仅为示例，实际使用时可能需要根据具体情况进行调整和优化。