n_classes = y_bin.shape[1] for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) roc_auc[i] = auc(fpr[i], tpr[i])怎么改
时间: 2023-11-30 21:13:46 浏览: 33
在这段代码中,`y_test` 和 `y_score` 数组的使用方式是在多分类问题中计算 ROC 曲线和 AUC 值的常见方式。如果你在这段代码中遇到了之前提到的错误,请尝试修改为以下代码:
```
n_classes = y_bin.shape[1]
fpr = {}
tpr = {}
roc_auc = {}
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
```
在这里,我们使用了字典来存储每个类别的 FPR、TPR 和 ROC AUC 值。这样,就可以避免出现之前提到的错误。
相关问题
修改和补充下列代码得到十折交叉验证的平均每一折auc值和平均每一折aoc曲线,平均每一折分类报告以及平均每一折混淆矩阵 min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1) y_pred11 = tree.predict(X_test1) y_pred1.append(y_pred11 X_train.append(X_train1) X_test.append(X_test1) y_test.append(y_test1) y_train.append(y_train1) X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id] y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id] X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1) X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1) X_train_fuzzy1 = np.array(X_train_fuzzy1) X_test_fuzzy1 = np.array(X_test_fuzzy1) config = get_config() tree = gcForest(config) tree.fit(X_train_fuzzy1, y_train_fuzzy1) y_predd = tree.predict(X_test_fuzzy1) y_pred.append(y_predd) X_test_fuzzy.append(X_test_fuzzy1) y_test_fuzzy.append(y_test_fuzzy1)y_pred = to_categorical(np.concatenate(y_pred), num_classes=3) y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3) y_test = to_categorical(np.concatenate(y_test), num_classes=3) y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3) print(y_pred.shape) print(y_pred1.shape) print(y_test.shape) print(y_test_fuzzy.shape) # 深度森林 report1 = classification_report(y_test, y_prprint("DF",report1) report = classification_report(y_test_fuzzy, y_pred) print("DF-F",report) mse = mean_squared_error(y_test, y_pred1) rmse = math.sqrt(mse) print('深度森林RMSE:', rmse) print('深度森林Accuracy:', accuracy_score(y_test, y_pred1)) mse = mean_squared_error(y_test_fuzzy, y_pred) rmse = math.sqrt(mse) print('F深度森林RMSE:', rmse) print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred)) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse)
首先,需要将代码放入循环中进行十折交叉验证。每一折都需要记录相应的分类报告、混淆矩阵、auc值和aoc曲线。以下是修改后的代码:
```
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve, auc
from sklearn.model_selection import StratifiedKFold
from gcforest.gcforest import GCForest
import numpy as np
import math
min_max_scaler = MinMaxScaler()
config = get_config()
tree = gcForest(config)
X_train = []
X_test = []
y_train = []
y_test = []
X_test_fuzzy = []
y_test_fuzzy = []
y_pred = []
y_pred1 = []
auc_scores = []
aoc_fprs = []
aoc_tprs = []
skf = StratifiedKFold(n_splits=10)
for train_id, test_id in skf.split(x, y):
X_train1, X_test1 = x[train_id], x[test_id]
y_train1, y_test1 = y[train_id], y[test_id]
X_train1 = min_max_scaler.fit_transform(X_train1)
X_test1 = min_max_scaler.transform(X_test1)
X_train1 = np.array(X_train1)
X_test1 = np.array(X_test1)
tree.fit(X_train1, y_train1)
y_pred11 = tree.predict(X_test1)
y_pred1.append(y_pred11)
X_train.append(X_train1)
X_test.append(X_test1)
y_test.append(y_test1)
y_train.append(y_train1)
X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id]
y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id]
X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1)
X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1)
X_train_fuzzy1 = np.array(X_train_fuzzy1)
X_test_fuzzy1 = np.array(X_test_fuzzy1)
tree.fit(X_train_fuzzy1, y_train_fuzzy1)
y_predd = tree.predict(X_test_fuzzy1)
y_pred.append(y_predd)
X_test_fuzzy.append(X_test_fuzzy1)
y_test_fuzzy.append(y_test_fuzzy1)
y_pred_proba = tree.predict_proba(X_test1)
auc_score = roc_auc_score(y_test1, y_pred_proba, multi_class='ovr')
auc_scores.append(auc_score)
fpr, tpr, _ = roc_curve(y_test1, y_pred_proba[:, 1])
aoc_fprs.append(fpr)
aoc_tprs.append(tpr)
y_pred = to_categorical(np.concatenate(y_pred), num_classes=3)
y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3)
y_test = to_categorical(np.concatenate(y_test), num_classes=3)
y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3)
print(y_pred.shape)
print(y_pred1.shape)
print(y_test.shape)
print(y_test_fuzzy.shape)
# 深度森林
report1 = classification_report(y_test, y_pred1)
print("DF Classification Report (10-Fold CV): \n", report1)
report = classification_report(y_test_fuzzy, y_pred)
print("DF-F Classification Report (10-Fold CV): \n", report)
mse = mean_squared_error(y_test, y_pred1)
rmse = math.sqrt(mse)
print('DF RMSE (10-Fold CV):', rmse)
print('DF Accuracy (10-Fold CV):', accuracy_score(y_test, y_pred1))
mse = mean_squared_error(y_test_fuzzy, y_pred)
rmse = math.sqrt(mse)
print('DF-F RMSE (10-Fold CV):', rmse)
print('DF-F Accuracy (10-Fold CV):', accuracy_score(y_test_fuzzy, y_pred))
mean_auc = np.mean(auc_scores)
print("Mean AUC (10-Fold CV): ", mean_auc)
mean_fpr = np.mean(aoc_fprs, axis=0)
mean_tpr = np.mean(aoc_tprs, axis=0)
mean_auc = auc(mean_fpr, mean_tpr)
print("Mean AOC (10-Fold CV): ", mean_auc)
```
在修改后的代码中,`StratifiedKFold`函数被用来进行十折交叉验证。每一折的训练数据和测试数据都是通过`train_id`和`test_id`来确定的。在每一折的训练和测试之后,需要记录相应的分类报告、混淆矩阵、auc值和aoc曲线。最后,需要计算平均每一折的auc值和aoc曲线。
y_score = np.array(y_score).T # 计算并绘制 AUC 曲线 fpr = dict() tpr = dict() roc_auc = dict() for i in range(y_test.shape[1]): fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[0, :, i])怎么修正
根据你提供的代码,我看到 y_score 是一个三维数组,它的第一维是样本数,第二维是类别数,第三维是阈值。而在计算 ROC 曲线时,只需要使用 y_score 的第二维和第三维,即每个类别在不同阈值下的得分。因此,你需要修改代码如下:
```python
y_score = np.array(y_score) # 去掉转置操作
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(y_test.shape[1]):
fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i, :].ravel()) # 传入每个类别的得分
roc_auc[i] = auc(fpr[i], tpr[i]) # 计算 AUC
```
这里,我们将 y_score 的第二维作为循环变量 i,即每次计算一个类别的 ROC 曲线和 AUC 值。在计算 ROC 曲线时,我们需要将 y_score 的第一维和第三维展开成一维数组,即使用 ravel() 方法。最后,我们将每个类别的 AUC 值存储在字典 roc_auc 中。
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