cv_scores.index(max(cv_scores))
时间: 2024-04-01 10:31:40 浏览: 24
这段代码用于找到交叉验证的评分(cv_scores)中最高分数的索引。具体来说,`cv_scores.index()` 函数用于返回某个元素在列表中第一次出现的索引,而 `max()` 函数则用于返回列表中的最大值。因此,`cv_scores.index(max(cv_scores))` 将返回 `cv_scores` 列表中最大值的索引。这在选择最佳模型时非常有用,因为通常我们会根据交叉验证的评分来选择最佳模型。
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把这段代码的PCA换成LDA:LR_grid = LogisticRegression(max_iter=1000, random_state=42) LR_grid_search = GridSearchCV(LR_grid, param_grid=param_grid, cv=cvx ,scoring=scoring,n_jobs=10,verbose=0) LR_grid_search.fit(pca_X_train, train_y) estimators = [ ('lr', LR_grid_search.best_estimator_), ('svc', svc_grid_search.best_estimator_), ] clf = StackingClassifier(estimators=estimators, final_estimator=LinearSVC(C=5, random_state=42),n_jobs=10,verbose=1) clf.fit(pca_X_train, train_y) estimators = [ ('lr', LR_grid_search.best_estimator_), ('svc', svc_grid_search.best_estimator_), ] param_grid = {'final_estimator':[LogisticRegression(C=0.00001),LogisticRegression(C=0.0001), LogisticRegression(C=0.001),LogisticRegression(C=0.01), LogisticRegression(C=0.1),LogisticRegression(C=1), LogisticRegression(C=10),LogisticRegression(C=100), LogisticRegression(C=1000)]} Stacking_grid =StackingClassifier(estimators=estimators,) Stacking_grid_search = GridSearchCV(Stacking_grid, param_grid=param_grid, cv=cvx, scoring=scoring,n_jobs=10,verbose=0) Stacking_grid_search.fit(pca_X_train, train_y) Stacking_grid_search.best_estimator_ train_pre_y = cross_val_predict(Stacking_grid_search.best_estimator_, pca_X_train,train_y, cv=cvx) train_res1=get_measures_gridloo(train_y,train_pre_y) test_pre_y = Stacking_grid_search.predict(pca_X_test) test_res1=get_measures_gridloo(test_y,test_pre_y) best_pca_train_aucs.append(train_res1.loc[:,"AUC"]) best_pca_test_aucs.append(test_res1.loc[:,"AUC"]) best_pca_train_scores.append(train_res1) best_pca_test_scores.append(test_res1) train_aucs.append(np.max(best_pca_train_aucs)) test_aucs.append(best_pca_test_aucs[np.argmax(best_pca_train_aucs)].item()) train_scores.append(best_pca_train_scores[np.argmax(best_pca_train_aucs)]) test_scores.append(best_pca_test_scores[np.argmax(best_pca_train_aucs)]) pca_comp.append(n_components[np.argmax(best_pca_train_aucs)]) print("n_components:") print(n_components[np.argmax(best_pca_train_aucs)])
如果要将代码中的PCA替换为LDA,可以按照以下步骤进行修改:
```python
LR_grid = LogisticRegression(max_iter=1000, random_state=42)
LR_grid_search = GridSearchCV(LR_grid, param_grid=param_grid, cv=cvx ,scoring=scoring,n_jobs=10,verbose=0)
LR_grid_search.fit(lda_X_train, train_y)
estimators = [
('lr', LR_grid_search.best_estimator_),
('svc', svc_grid_search.best_estimator_),
]
clf = StackingClassifier(estimators=estimators, final_estimator=LinearSVC(C=5, random_state=42),n_jobs=10,verbose=1)
clf.fit(lda_X_train, train_y)
estimators = [
('lr', LR_grid_search.best_estimator_),
('svc', svc_grid_search.best_estimator_),
]
param_grid = {'final_estimator': [LogisticRegression(C=0.00001),LogisticRegression(C=0.0001),
LogisticRegression(C=0.001),LogisticRegression(C=0.01),
LogisticRegression(C=0.1),LogisticRegression(C=1),
LogisticRegression(C=10),LogisticRegression(C=100),
LogisticRegression(C=1000)]}
Stacking_grid = StackingClassifier(estimators=estimators,)
Stacking_grid_search = GridSearchCV(Stacking_grid, param_grid=param_grid, cv=cvx,
scoring=scoring, n_jobs=10, verbose=0)
Stacking_grid_search.fit(lda_X_train, train_y)
Stacking_grid_search.best_estimator_
train_pre_y = cross_val_predict(Stacking_grid_search.best_estimator_, lda_X_train, train_y, cv=cvx)
train_res1 = get_measures_gridloo(train_y, train_pre_y)
test_pre_y = Stacking_grid_search.predict(lda_X_test)
test_res1 = get_measures_gridloo(test_y, test_pre_y)
best_lda_train_aucs.append(train_res1.loc[:,"AUC"])
best_lda_test_aucs.append(test_res1.loc[:,"AUC"])
best_lda_train_scores.append(train_res1)
best_lda_test_scores.append(test_res1)
train_aucs.append(np.max(best_lda_train_aucs))
test_aucs.append(best_lda_test_aucs[np.argmax(best_lda_train_aucs)].item())
train_scores.append(best_lda_train_scores[np.argmax(best_lda_train_aucs)])
test_scores.append(best_lda_test_scores[np.argmax(best_lda_train_aucs)])
lda_comp.append(n_components[np.argmax(best_lda_train_aucs)])
print("n_components:")
print(n_components[np.argmax(best_lda_train_aucs)])
```
在这个修改后的代码中,将`pca_X_train`和`pca_X_test`替换为`lda_X_train`和`lda_X_test`,并相应地修改变量和参数的名称。这样就可以使用LDA进行特征降维和模型训练了。
n_components_range = range(2, 10) # 定义交叉验证的折数 n_splits = 5 # 记录每个隐状态数量下的模型性能 cv_scores = [] # 使用K折交叉验证 kf = KFold(n_splits=n_splits) for n_components in n_components_range: # 定义GaussianHMM模型 model = GaussianHMM(n_components=n_components) # 记录每一折交叉验证的评估分数 fold_scores = [] for train_index, test_index in kf.split(X): # 划分训练集和测试集 X_train, X_test = X[train_index], X[test_index] # 在训练集上训练模型 model.fit(X_train) # 在测试集上评估模型性能 score = model.score(X_test) # 记录评估分数 fold_scores.append(score) # 计算平均评估分数作为该隐状态数量下的模型性能 cv_scores.append(sum(fold_scores) / n_splits) # 选取最优隐状态数量 best_n_components = n_components_range[cv_scores.index(max(cv_scores))] print("Best number of hidden states:", best_n_components)
这段代码是一个使用K折交叉验证来选择GaussianHMM隐状态数量的示例代码,具体实现步骤如下:
1. 定义隐状态数量的范围n_components_range和交叉验证的折数n_splits。
2. 定义一个空的列表cv_scores,用于记录每个隐状态数量下的模型性能。
3. 使用KFold方法将数据集X划分为n_splits份,每次使用其中一份作为测试集,其余n_splits-1份作为训练集。
4. 对于每个隐状态数量n_components,定义一个GaussianHMM模型,并在每一折交叉验证中记录评估分数,最后计算平均评估分数作为该隐状态数量下的模型性能,将其添加到cv_scores列表中。
5. 选取最优的隐状态数量,即cv_scores列表中最大值所在的索引,将该索引对应的隐状态数量作为最优隐状态数量。
6. 输出最优隐状态数量。
需要注意的是,上述代码中的X、train_index和test_index变量需要根据实际情况进行修改,以便适应具体的数据集和模型。