rmse = calculate_rmse(y_test_np, y_pred_np) print(f"RMSE: {rmse:.4f}") 出现name 'calculate_rmse' is not defined

这段代码试图计算预测值（y_pred_np）与测试值（y_test_np）之间的均方根误差（RMSE），然后打印结果。然而，出现了 "name 'calculate_rmse' is not defined" 的错误，说明Python环境中并没有定义名为 `calculate_rmse` 的函数。

解决这个问题的方法是确保你已经定义了一个名为 `calculate_rmse` 的函数来计算 RMSE，该函数接收两个数组作为参数（通常应该是预测值和真实值）。这个函数可能会包含如下的计算公式：

import numpy as np
from math import sqrt

def calculate_rmse(true_values, predicted_values):
    squared_errors = (true_values - predicted_values) ** 2
    mean_squared_error = np.mean(squared_errors)
    rmse = sqrt(mean_squared_error)
    return rmse

# 现在你可以正常调用函数
rmse = calculate_rmse(y_test_np, y_pred_np)
print(f"RMSE: {rmse:.4f}")

如果没有看到类似上面的函数定义，那么你需要添加它或者确保你从其他地方导入了已定义好的 `calculate_rmse` 函数。

相关问题

将以下语句定义成函数使其能重复调用 r2 = r2_score(y_test_scaled, y_pred) mse = mean_squared_error(y_test_scaled, y_pred) rmse = np.sqrt(mse)

当然，你可以把这些计算指标封装到一个函数中，以便于多次调用。这里是一个简单的例子：

import sklearn.metrics as metrics

def calculate_metrics(y_test_scaled, y_pred):
    r2 = metrics.r2_score(y_test_scaled, y_pred)
    mse = metrics.mean_squared_error(y_test_scaled, y_pred)
    rmse = np.sqrt(mse)
    
    return r2, mse, rmse

# 使用时只需传入实际的测试数据和预测结果
y_test_scaled = ... # 填写测试数据标准化后的值
y_pred = ... # 填写模型的预测结果

r2, mse, rmse = calculate_metrics(y_test_scaled, y_pred)

这个`calculate_metrics`函数接受两个参数：`y_test_scaled` 和 `y_pred`，并返回三个指标的值。每次你需要计算这些指标时，只需要调用这个函数即可。

修改和补充下列代码得到十折交叉验证的平均auc值和平均aoc曲线，平均分类报告以及平均混淆矩阵 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) print('F?深度森林RMSE:', rmse) print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))

以下是修改和补充后的代码，实现了十折交叉验证的平均auc值和平均aoc曲线，平均分类报告以及平均混淆矩阵：

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc
from sklearn.model_selection import StratifiedKFold

min_max_scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = [], [], [], []
X_train_fuzzy, X_test_fuzzy, y_train_fuzzy, y_test_fuzzy = [], [], [], []
y_pred, y_pred1 = [], []
y_pred_proba, y_pred_proba1 = [], []

config = get_config()
tree = gcForest(config)

skf = StratifiedKFold(n_splits=10)

for train_id, test_id in skf.split(x, y):
    # split data and normalize
    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)

    # train gcForest
    tree.fit(X_train1, y_train1)

    # predict on test set
    y_pred11 = tree.predict(X_test1)
    y_pred_proba11 = tree.predict_proba(X_test1)

    # append predictions and test data
    y_pred1.append(y_pred11)
    y_pred_proba1.append(y_pred_proba11)
    X_train.append(X_train1)
    X_test.append(X_test1)
    y_test.append(y_test1)
    y_train.append(y_train1)

    # split fuzzy data and normalize
    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)

    # train gcForest on fuzzy data
    tree.fit(X_train_fuzzy1, y_train_fuzzy1)

    # predict on fuzzy test set
    y_predd = tree.predict(X_test_fuzzy1)
    y_predd_proba = tree.predict_proba(X_test_fuzzy1)

    # append predictions and test data
    y_pred.append(y_predd)
    y_pred_proba.append(y_predd_proba)
    X_test_fuzzy.append(X_test_fuzzy1)
    y_test_fuzzy.append(y_test_fuzzy1)

# concatenate and convert to categorical
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)

# calculate and print average accuracy and RMSE
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)
print('F?深度森林RMSE:', rmse)
print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))

# calculate and print average classification report
report1 = classification_report(y_test, y_pred1)
print("DF", report1)

report = classification_report(y_test_fuzzy, y_pred)
print("DF-F", report)

# calculate and print average confusion matrix
cm1 = confusion_matrix(y_test.argmax(axis=1), y_pred1.argmax(axis=1))
cm = confusion_matrix(y_test_fuzzy.argmax(axis=1), y_pred.argmax(axis=1))
print('DF Confusion Matrix:')
print(cm1)
print('DF-F Confusion Matrix:')
print(cm)

# calculate and print average ROC curve and AUC value
fpr1, tpr1, threshold1 = roc_curve(y_test.ravel(), y_pred_proba1.ravel())
fpr, tpr, threshold = roc_curve(y_test_fuzzy.ravel(), y_pred_proba.ravel())
roc_auc1 = auc(fpr1, tpr1)
roc_auc = auc(fpr, tpr)
print('DF ROC AUC:', roc_auc1)
print('DF-F ROC AUC:', roc_auc)

# plot average ROC curve
plt.title('Receiver Operating Characteristic')
plt.plot(fpr1, tpr1, 'b', label = 'DF AUC = %0.2f' % roc_auc1)
plt.plot(fpr, tpr, 'g', label = 'DF-F AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()