import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix

# 加载数据集
data = pd.read_csv('电力系统故障数据.csv')

# 数据预处理
X = data.drop('故障类型', axis=1)
y = data['故障类型']

# 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)

# 特征缩放
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 创建模型
model = SVC(kernel='linear', C=1)

# 训练模型
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 评估模型
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))