修改完善下列代码，得到十折交叉验证三分类的平均每一折的分类报告，平均每一折的混淆矩阵，平均每一折的auc值和roc曲线。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) # convert to numpy arrays X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) # train gcForest config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1)

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
from sklearn.model_selection import StratifiedKFold
import numpy as np
import matplotlib.pyplot as plt

# define function to get gcForest configuration
def get_config():
    config = {}
    config["random_state"] = 0
    config["num_classes"] = 3
    config["num_estimators"] = [10, 10, 10]
    config["criterion"] = ["gini", "gini", "gini"]
    config["max_depth"] = [None, None, None]
    config["min_samples_split"] = [2, 2, 2]
    config["min_samples_leaf"] = [1, 1, 1]
    config["max_features"] = ["auto", "auto", "auto"]
    config["max_leaf_nodes"] = [None, None, None]
    return config

# define function to plot ROC curve
def plot_roc_curve(fpr, tpr):
    plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve')
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()

# define function to train and evaluate gcForest
def train_and_evaluate(X_train, y_train, X_test, y_test):
    # train gcForest
    config = get_config()
    tree = gcForest(config)
    tree.fit(X_train, y_train)
    
    # evaluate gcForest
    y_pred = tree.predict(X_test)
    report = classification_report(y_test, y_pred)
    matrix = confusion_matrix(y_test, y_pred)
    auc = roc_auc_score(y_test, y_pred, multi_class='ovr')
    fpr, tpr, _ = roc_curve(y_test, y_pred, pos_label=None)
    return report, matrix, auc, fpr, tpr

# load data
x = ...
y = ...

# scale data
min_max_scaler = MinMaxScaler()
x = min_max_scaler.fit_transform(x)

# define k-fold cross-validation
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)

# initialize lists to store results
reports = []
matrices = []
aucs = []
fprs = []
tprs = []

# loop over folds
for train_id, test_id in kfold.split(x, y):
    X_train, X_test = x[train_id], x[test_id]
    y_train, y_test = y[train_id], y[test_id]
    report, matrix, auc, fpr, tpr = train_and_evaluate(X_train, y_train, X_test, y_test)
    reports.append(report)
    matrices.append(matrix)
    aucs.append(auc)
    fprs.append(fpr)
    tprs.append(tpr)

# calculate average results
average_report = np.mean(np.array(reports), axis=0)
average_matrix = np.mean(np.array(matrices), axis=0)
average_auc = np.mean(np.array(aucs))
mean_fpr = np.mean(fprs, axis=0)
mean_tpr = np.mean(tprs, axis=0)

# print average results
print("Average classification report: \n", average_report)
print("Average confusion matrix: \n", average_matrix)
print("Average AUC: ", average_auc)

# plot average ROC curve
plot_roc_curve(mean_fpr, mean_tpr)

注意：需要将数据集x和y替换为实际的数据集，并且需要安装matplotlib库才能成功绘制ROC曲线。