基于pytorch的cic-ids2017数据集使用代价敏感决策树平衡数据集完整代码

以下是基于PyTorch的CIC-IDS2017数据集使用代价敏感决策树平衡数据集的完整代码：

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from imblearn.datasets import fetch_datasets
from imblearn.tree import CostSensitiveDecisionTreeClassifier

# Load the CIC-IDS2017 dataset
dataset = fetch_datasets()['cic_ids_2017']

# Convert the dataset to a Pandas DataFrame
data = pd.DataFrame(dataset.data, columns=dataset.feature_names)
target = dataset.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)

# Use a cost-sensitive decision tree to balance the training set
clf = CostSensitiveDecisionTreeClassifier(random_state=0, min_samples_leaf=10, cost_matrix={
    0: {0: 0, 1: 1},
    1: {0: 5, 1: 0}
})
clf.fit(X_train, y_train)

# Measure the performance of the decision tree on the testing set
y_pred = clf.predict(X_test)
tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
print(f"True Negatives: {tn}")
print(f"False Positives: {fp}")
print(f"False Negatives: {fn}")
print(f"True Positives: {tp}")

这段代码使用了PyTorch和scikit-learn库来加载CIC-IDS2017数据集，并使用代价敏感决策树来平衡训练集。最后，代码输出了决策树在测试集上的性能评估结果。请注意，这里使用的代价矩阵可能不适用于你的具体问题，需要根据实际情况进行调整。