print(classification_report(y_train, max_y_pred_train))

这段代码是用来打印训练集的分类报告。分类报告可以帮助我们了解模型在每个类别上的预测表现，包括准确率、召回率、F1值等指标。其中，准确率表示模型对该类别的正确率，召回率表示该类别的真实样本被模型正确预测的比例，F1值是准确率和召回率的调和平均数。

具体来说，y_train是训练集的真实标签，max_y_pred_train是模型在训练集上的预测结果中概率最大的标签。classification_report()函数会根据这两个参数计算出每个类别的指标，并将结果打印出来。

需要注意的是，这段代码是在使用某种分类器进行训练之后才能使用的。如果你没有进行分类器训练，或者使用的分类器不支持分类报告的输出，那么这段代码将无法运行。

相关问题

代码讲解 model = XGBClassifier(gamma=0.01,learning_rate=0.01, max_depth=5, n_estimators=500) model.fit(X_train,y_train) y_pred = model.predict(X_hold_test) print(classification_report(y_hold_test,y_pred)) cm = confusion_matrix(y_hold_test, y_pred) ConfusionMatrixDisplay(cm).plot() plt.show()

这段代码使用了XGBoost分类器（XGBClassifier）来训练和预测模型。其中，gamma参数表示节点分裂所需的最小损失下降值，learning_rate表示每次更新权重时的步长，max_depth表示树的最大深度，n_estimators表示树的数量。

首先，使用fit()函数将训练数据X_train和y_train传入模型中进行训练。然后，使用predict()函数对测试数据进行预测得到y_pred。接着，使用classification_report()函数打印出分类报告，该报告包括准确率、召回率、F1值和支持度。最后，使用confusion_matrix()函数计算混淆矩阵，将其传入ConfusionMatrixDisplay()函数中进行绘制，使用plt.show()函数显示混淆矩阵图像。

该段代码的主要作用是使用XGBoost分类器构建分类模型，并对测试集进行预测和评估，最后绘制混淆矩阵图像，以便更直观地理解模型预测结果。

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()