用Python实现通过网格搜索＋五折交叉验证方法选择多项逻辑回归的方法，从multinomial，ovo,ovr几种方式中选择

在Python中，我们可以使用scikit-learn库来实现网格搜索（Grid Search）结合五折交叉验证（5-Fold Cross Validation）来优化多项逻辑回归模型的选择。这里我们通常会关注`MultinomialNB`、`OneVsRestClassifier`（ovo）以及`OneVsOneClassifier`（ovr）这几种分类方式。以下是简化的步骤：

1. **导入所需的库**：

   from sklearn.datasets import load_iris
   from sklearn.model_selection import GridSearchCV, train_test_split
   from sklearn.linear_model import LogisticRegression
   from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier

2. **加载数据并预处理**：

   iris = load_iris()
   X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

3. **定义逻辑回归分类器和策略组合**：

   lr_base = LogisticRegression(multi_class='auto')  # 'auto'让sklearn自动选择
   strategies = {'multi_class': ['multinomial', 'ova', 'ovr']}
   ovr_lr = OneVsRestClassifier(lr_base)
   ovo_lr = OneVsOneClassifier(lr_base)

4. **创建网格搜索对象**：

   param_grid = {
       'ovr_lr': [{'estimator__multi_class': strategies}],
       'ovo_lr': [{'estimator__multi_class': strategies}]
   }
   grid_searches = {'ovr': GridSearchCV(ovr_lr, param_grid, cv=5),
                    'ovo': GridSearchCV(ovo_lr, param_grid, cv=5)}

5. **运行网格搜索并评估模型**：

   for name, search in grid_searches.items():
       search.fit(X_train, y_train)
       best_params = search.best_params_
       print(f"{name} Best parameters found: {best_params}")
       best_clf = search.best_estimator_

6. **查看最佳模型的性能**：

   scores = search.cv_results_
   print("Cross-validation scores:")
   for name, score in zip(['ovr', 'ovo'], [scores['mean_test_score'][search.best_index_], scores['mean_test_score'][search.best_index_]]):
       print(f"{name}: {score}")

7.