print(y_test.shape, y_pred4.shape)

这段代码是在输出两个变量的形状（shape），分别是 y_test 和 y_pred4。一般来说，这两个变量都是数组或矩阵，它们的形状表示了它们各维度的大小。例如，一个形状为 (100, 3) 的数组，表示它有 100 行和 3 列。在这里，我们无法确定 y_test 和 y_pred4 的具体形状，因此无法输出它们的值。你需要在使用这段代码之前先定义这两个变量。

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。

from BP import BPNeuralNetwork import numpy as np import pandas as pd # 读取 Excel 文件并划分数据集 train_input=pd.read_excel('test.xlsx', nrows=100, usecols=[0,1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15, 16,17,18,19,20,21, 22, 23, 24, 25, 26,27,28,29,30,31, 32, 33, 34]) test_input=pd.read_excel('test.xlsx', skiprows=99, nrows=50, usecols=[0,1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15, 16,17,18,19,20,21, 22, 23, 24, 25, 26,27,28,29,30,31, 32, 33, 34]) train_output=pd.read_excel('result.xlsx', nrows=100, usecols=[0]) test_output=pd.read_excel('result.xlsx', skiprows=99, nrows=50, usecols=[0]) print(train_input.shape, test_input.shape) print(train_output.shape, test_output.shape) # 构建神经网络模型并训练 nn = BPNeuralNetwork(num_input=train_input.shape[1], num_hidden=3, num_output=train_output.shape[1], learning_rate=0.1) nn.train(train_input, train_output, num_epochs=500) # 在测试集上进行预测并计算准确率 y_pred = nn.predict(test_input) #print(test_output.shape, y_pred.shape)02 right = 0 if not test_output.empty: # 进行您的操作 for i in range(test_output.shape[0]): if test_output[i] == y_pred[i]: right += 1 accuracy = right / test_output.shape[0] print("Accuracy: {:.2f}%".format(accuracy * 100)) else: # DataFrame 为空的处理逻辑 print("test_output DataFrame为空")

根据您提供的代码，我注意到您正在使用BPNeuralNetwork类构建一个神经网络模型，并使用train_input和train_output来训练模型。然后，您使用test_input来进行预测，并将预测结果与test_output进行比较以计算准确率。

在代码中，我还看到您在循环中使用索引来访问test_output和y_pred的元素。但是，请注意，pandas DataFrame中的列需要使用列名或loc方法来进行访问，而不是使用索引。

修改您的代码如下：

right = 0
if not test_output.empty:
    # 进行您的操作
    for i in range(test_output.shape[0]):
        if test_output.loc[i, 0] == y_pred[i, 0]:
            right += 1

    accuracy = right / test_output.shape[0]
    print("Accuracy: {:.2f}%".format(accuracy * 100))
else:
    # DataFrame 为空的处理逻辑
    print("test_output DataFrame为空")

这样，您将能够通过使用loc方法来访问test_output DataFrame中的元素，并计算准确率。

请确保在使用索引访问DataFrame时使用正确的列名或loc方法，并确保DataFrame的形状和索引与预期一致。如果问题仍然存在，请提供更多的代码和数据细节，以便我能够更好地帮助您解决问题。