python实现用SVM模型对sklearn数据库中的红酒数据集进行分类，通过五折交叉验证和网格搜索法确定最佳参数，再计算准确率、精度、F1_score和召回率；最后在同一张图上绘制三类分别的ROC和整体的macro ROC

在Python中，我们可以使用scikit-learn库来实现这个任务。以下是一步步的步骤：

1. 导入必要的模块：

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score, roc_curve, auc, plot_roc_curve
import matplotlib.pyplot as plt

2. 加载红酒数据集并预处理：

wine = datasets.load_wine()
X = wine.data
y = wine.target

# 数据标准化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

3. 划分训练集和测试集（例如70%训练，30%测试）：

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)

4. 使用GridSearchCV确定SVM的最佳参数：

param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf'], 'gamma': ['scale', 'auto']}
svc = SVC()
grid_search = GridSearchCV(svc, param_grid, cv=5)
grid_search.fit(X_train, y_train)

best_params = grid_search.best_params_
print(f"Best parameters: {best_params}")

5. 训练模型并评估性能：

model = SVC(**best_params)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')

print(f"Accuracy: {accuracy:.2f}, Precision: {precision:.2f}, F1 Score: {f1:.2f}, Recall: {recall:.2f}")

# 可能需要导入classification_report以获得详细的报告

6. 计算并绘制ROC曲线：

y_scores = model.predict_proba(X_test)[:, 1] # 获取概率值
fpr = dict()
tpr = dict()
roc_auc = dict()

for i in range(3): # 对于红酒数据集的三类
    fpr[i], tpr[i], _ = roc_curve(y_test == i, y_scores[y_test == i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# 绘制平均和每类的ROC曲线
mean_fpr = np.linspace(0, 1, 100)
all_tpr = np.zeros_like(mean_fpr)
for i, (fpr_, tpr_) in enumerate(zip(fpr.values(), tpr.values())):
    all_tpr += interp(mean_fpr, fpr_, tpr_)
    roc_auc['micro'] = auc(mean_fpr, all_tpr / 3.)

plot_roc_curve(model, X_test, y_test)
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
         label='Chance', alpha=.8)

plt.plot(mean_fpr, all_tpr / 3., label=f'Micro-average ROC curve (area = {roc_auc["micro"] :.2f})',
         color='b', linewidth=2, alpha=.8)

plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(loc="lower right")
plt.show()

这将显示三类的ROC曲线以及总体的macro ROC曲线，以及相应的AUC分数。