帮我用python实现通过KNN模型对红酒问题分类，将原始数据集分层划分为训练集和测试集，通过交叉验证方法选出合适的K值，再计算F1_score，精确度和召回率

在Python中，你可以使用sklearn库中的KNeighborsClassifier来实现基于KNN（K-Nearest Neighbors）的红酒分类。以下是一个步骤指南：

1. 导入所需库：

import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import f1_score, precision_score, recall_score

2. 加载数据集：

data = pd.read_csv('wine_data.csv')

3. 数据预处理：

X = data.drop('target', axis=1)  # 特征
y = data['target']               # 类别

# 将数据集划分为训练集和测试集（通常70%训练，30%测试）
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

4. 使用交叉验证选择K值：

param_grid = {'n_neighbors': list(range(1, 31))}
knn = KNeighborsClassifier()
grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='f1_macro')  # 'f1_macro'适用于多分类任务
grid_search.fit(X_train, y_train)

best_k = grid_search.best_params_['n_neighbors']
print(f"Best k value: {best_k}")

5. 训练模型并计算性能指标：

knn_best = KNeighborsClassifier(n_neighbors=best_k)
knn_best.fit(X_train, y_train)

y_pred = knn_best.predict(X_test)

# F1_score, 精确度, 召回率
f1 = f1_score(y_test, y_pred, average='weighted')
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')

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