解释代码：y_multiclass = torch.from_numpy(y_multiclass_np).view(-1,1) if not torch.is_tensor(y_multiclass_np) else y_multiclass_np y_multiclass=y_multiclass.view(-1) self.y_matrix = torch.stack([self.cast(y_multiclass, k) for k in range(self.n_svm)],0).to(self.device) self.kernel = kernel a = self.a b = self.b

解释以下代码:def cv_model(clf, train_x, train_y, test_x, clf_name): folds = 5 seed = 2021 kf = KFold(n_splits=folds, shuffle=True, random_state=seed) test = np.zeros((test_x.shape[0],4)) cv_scores = [] onehot_encoder = OneHotEncoder(sparse=False) for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)): print('** {} '.format(str(i+1))) trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index] if clf_name == "lgb": train_matrix = clf.Dataset(trn_x, label=trn_y) valid_matrix = clf.Dataset(val_x, label=val_y) params = { 'boosting_type': 'gbdt', 'objective': 'multiclass', 'num_class': 4, 'num_leaves': 2 5, 'feature_fraction': 0.8, 'bagging_fraction': 0.8, 'bagging_freq': 4, 'learning_rate': 0.1, 'seed': seed, 'nthread': 28, 'n_jobs':24, 'verbose': -1, } model = clf.train(params, train_set=train_matrix, valid_sets=valid_matrix, num_boost_round=2000, verbose_eval=100, early_stopping_rounds=200) val_pred = model.predict(val_x, num_iteration=model.best_iteration) test_pred = model.predict(test_x, num_iteration=model.best_iteration) val_y=np.array(val_y).reshape(-1, 1) val_y = onehot_encoder.fit_transform(val_y) print('预测的概率矩阵为：') print(test_pred) test += test_pred score=abs_sum(val_y, val_pred) cv_scores.append(score) print(cv_scores) print("%s_scotrainre_list:" % clf_name, cv_scores) print("%s_score_mean:" % clf_name, np.mean(cv_scores)) print("%s_score_std:" % clf_name, np.std(cv_scores)) test=test/kf.n_splits return test

这段代码定义了一个函数cv_model，用于进行交叉验证和模型训练。函数的输入包括分类器clf，训练数据train_x和train_y，测试数据test_x，以及分类器名称clf_name。函数的输出为测试数据的预测结果。具体...

from sklearn.linear_model import LogisticRegression from sklearn import metrics import numpy as np import pandas as pd data = pd.read_csv( 'final_data1.csv') Y = data.y X = data.drop('y', axis=1)#归一化 xmin = X.min(axis=0) xmax = X.max(axis=0) X_norm = (X-xmin)/(xmax-xmin) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_norm, Y, test_size=0.2, random_state=42) clf = LogisticRegression(random_state=0,multi_class='multinomial') clf.fit(X_norm,Y)y_pred= clf.predict(X_test) y_pred= np.round(y_pred) print(metrics.confusion_matrix(y_test, y_pred)) from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score print('逻辑回归分类模型准确率分值: {0:0.4f}'.format(accuracy_score(y_test, y_pred))) print("逻辑回归分类模型查准率 :", round(precision_score(y_test, y_pred), 4), "\n") print("逻辑回归分类模型召回率 :", round(recall_score(y_test, y_pred), 4), "\n") print("逻辑回归分类模型F1分值:", round(f1_score(y_test, y_pred), 4), "\n") 运行上述代码报错ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].如何解决

例如，你可以将以下代码段： python print("逻辑回归分类模型查准率 :", round(precision_score(y_test, y_pred), 4), "\n") 修改为： python print("逻辑回归分类模型查准率 :", round(precision_...

使用这段代码时候报错，该怎么处理 import numpy as np from sklearn.metrics import ndcg_score a1=np.array([1,2,3,4,5]) a2=np.array([1,2,3,4,5]) ndcg_result=ndcg_score(y_true=a1, y_score=a2) 报错 ValueError: Only ('multilabel-indicator', 'continuous-multioutput', 'multiclass-multioutput') formats are supported. Got multiclass instead

这个函数只支持 multilabel-indicator, continuous-multioutput, multiclass-multioutput 这三种格式的标签，而您传入的是一个一维数组，因此无法计算 NDCG 值。可以尝试将标签转换为上述支持的格式，或者...

优化代码from sklearn.metrics import classification_report, roc_curve, auc # 评估分类模型性能 fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1]) roc_auc = auc(fpr, tpr)出错ValueError: multiclass format is not supported

fpr, tpr, thresholds = roc_curve(y_test[:, 1], y_score[:, 1]) roc_auc = auc(fpr, tpr) 注意，这里的y_test[:, 1]表示取出one-hot编码后的第二列，即正例的概率，y_score[:, 1]表示取出分类器预测的正例...

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 将字符标签转换为数值标签 le = LabelEncoder() y = le.fit_transform(y) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 构建多分类模型 model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=42) model.fit(X_train, y_train) # 在测试集上预测每个标签的概率 y_pred = model.predict(X_test) # # 计算micro-averaging的ROC曲线数据 fpr, tpr, _ = roc_curve(y_test, y_pred) roc_auc = auc(fpr, tpr)将此段代码按上述修改

根据上述修改，将代码修改为： python from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.multiclass import OneVsRestClassifier from ...

import lightgbm as lgb from lightgbm import LGBMClassifier from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score from sklearn.model_selection import GridSearchCV # 定义xgboost模型 lgb_model = lgb.LGBMClassifier() # 设置参数空间 params = { 'boosting_type': 'gbdt', #'boosting_type': 'dart', 'objective': 'multiclass', 'metric': 'multi_logloss', 'min_child_weight': 1.5, 'num_leaves': 25, 'lambda_l2': 10, 'subsample': 0.7, 'colsample_bytree': 0.7, 'learning_rate': 0.03, 'seed': 2017, "num_class": 2, 'silent': True, } # 输出最优参数 print('Best Parameter:', params) # 使用最优参数构建新的xgboost模型 lgb_model = lgb.LGBMClassifier(params) # 使用训练集对新模型进行拟合 # X_train = np.array(X_train) # pdb.set_trace() lgb_model.fit(X_train, y_train) # 使用新模型进行预测 y_pred = lgb_model.predict(X_test)改为svm模型

好的，那么你需要将导入的库改为sklearn中的SVM库，代码如下： python from sklearn.svm import SVC from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score from ...

k = 5 error = 0 color_cate = {1: 'b', 2: 'r', 3: 'g', 4: 'y'} plt.figure(figsize=(10, 6)) for index1, item1 in enumerate(zip(raw_data, pca_data)): raw_points, pca_points = item1[0], item1[1] x_train, y_train = [], [] for index2, item2 in enumerate(raw_data): if index1 != index2: x_train.append(item2[1:]) y_train.append(item2[0]) knn = KNeighborsClassifier(n_neighbors=k, weights='distance') knn.fit(x_train, y_train) x_test = [raw_points[1:]] predict = knn.predict(x_test) if predict[0] != raw_points[0]: error += 1 print('预测：', predict[0], '真实：', raw_points[:1])报错Traceback (most recent call last): File "E:\好好学习\数据分析\test\analysis\PCA.py", line 55, in <module> knn.fit(x_train, y_train) File "D:\Anoconda\1\lib\site-packages\sklearn\neighbors\_classification.py", line 198, in fit return self._fit(X, y) File "D:\Anoconda\1\lib\site-packages\sklearn\neighbors\_base.py", line 420, in _fit check_classification_targets(y) File "D:\Anoconda\1\lib\site-packages\sklearn\utils\multiclass.py", line 197, in check_classification_targets raise ValueError("Unknown label type: %r" % y_type) ValueError: Unknown label type: 'continuous'

if not isinstance(y_train[0], int): y_train = np.array(y_train) # 将 y_train 转换为 numpy 数组类型 y_train = y_train.astype('int') # 将 y_train 转换为整数类型 knn = KNeighborsClassifier(n_neighbors=...

importance_scores = one_vs_rest.estimators_[class_label].feature_importances_ AttributeError: '_ConstantPredictor' object has no attribute 'feature_importances_'

from sklearn.multiclass import OneVsRestClassifier # 加载数据 data = pd.read_csv('genotype_data.csv') # 提取特征和标签 features = data.iloc[:, :1750] labels = data['group'] # 初始化随机深林分类器 ...

优化代码# 打印分类报告 print(classification_report(y_test, y_pred)) from sklearn.metrics import classification_report, roc_curve, auc # 评估分类模型性能 fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1]) roc_auc = auc(fpr, tpr)出错ValueError: multiclass format is not supported

cm = confusion_matrix(y_test, y_pred) print(cm) 你也可以使用 classification_report 函数来打印准确率、精确率、召回率和 F1-score。例如： python from sklearn.metrics import classification_...

pandas中fpr,tpr,thresholds=roc_curve(y_test.values,y_pred)出现错误：multiclass format is not supported怎么解决

roc_curve() 函数只支持二分类问题的ROC曲线绘制，而你的数据集是多分类问题，因此出现了 multiclass format is not supported 的错误。如果你想绘制多分类问题的ROC曲线，可以使用 roc_auc_score() 函数...

拆分数据集 X_train, X_test, y_train, y_test = train_test_split(heartbeats_image, labels, test_size=0.2, random_state=42) X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42) # 保存数据集 np.save('X_train.npy', X_train) np.save('X_val.npy', X_val) np.save('X_test.npy', X_test) np.save('y_train.npy', y_train) np.save('y_val.npy', y_val) np.save('y_test.npy', y_test) from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout # 定义卷积神经网络 model = Sequential([ Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(255,255,1)), MaxPooling2D(pool_size=(2,2)), Conv2D(filters=64, kernel_size=(3,3), activation='relu'), MaxPooling2D(pool_size=(2,2)), Conv2D(filters=128, kernel_size=(3,3), activation='relu'), MaxPooling2D(pool_size=(2,2)), Flatten(), Dense(units=128, activation='relu'), Dropout(0.5), Dense(units=1, activation='sigmoid') ]) model.add(Dense(20, activation='softmax')) # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val)) # 保存模型 model.save('my_model.h5') from sklearn.metrics import confusion_matrix, roc_curve, auc import matplotlib.pyplot as plt # 对测试集进行预测 y_pred = model.predict(X_test) # 将预测结果转换为标签 y_pred_labels = (y_pred > 0.5).astype(int) from sklearn.metrics import confusion_matrix from sklearn.utils.multiclass import unique_labels # 将多标签指示器转换成标签数组 y_test = unique_labels(y_test) y_pred_labels = unique_labels(y_pred_labels) # 计算混淆矩阵 cm = confusion_matrix(y_test, y_pred_labels) # 绘制混淆矩阵 plt.imshow(cm, cmap=plt.cm.Blues) plt.xlabel("Predicted labels") plt.ylabel("True labels") plt.xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], ['N','L','R','A','a','J','S','V','F','[','!',']','e','j','E','/','f','x','Q','|']) plt.yticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], ['N','L','R','A','a','J','S','V','F','[','!',']','e','j','E','/','f','x','Q','|']) plt.title('Confusion matrix') plt.colorbar() plt.show()之后怎么绘制ROC曲线，let's think step by step

plt.plot([0, 1], [0, 1], color='navy', 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 ...

from sklearn.metrics import multiclass_roc_auc_score显示错误

可以尝试更新 scikit-learn 到最新版本，如果还是无法解决问题，可以尝试使用以下代码替代 multiclass_roc_auc_score 函数： python from sklearn.preprocessing import label_binarize from sklearn.metrics ...

from sklearn.multiclass import OneVsRestClassifier ovr = OneVsRestClassifier(clf) ovr.fit(x_train,y_train) ovr.score(x_test, y_test)

这段代码使用了 OneVsRestClassifier 类来实现多类别分类。它将一个二分类器 clf 包装成一个多类别分类器。首先，通过导入 OneVsRestClassifier 类，创建了一个名为 ovr 的多类别分类器对象，并将 clf ...

import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix,classification_report, roc_curve, auc import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 分割训练集和验证集 train_data = data.sample(frac=0.8, random_state=1) test_data = data.drop(train_data.index) # 定义特征变量和目标变量 features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] target = '交通风险' # 训练随机森林模型 rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(train_data[features], train_data[target]) # 在验证集上进行预测并计算精度、召回率和F1值等指标 pred = rf.predict(test_data[features]) accuracy = accuracy_score(test_data[target], pred) confusion_mat = confusion_matrix(test_data[target], pred) classification_rep = classification_report(test_data[target], pred) print('Accuracy:', accuracy) print('Confusion matrix:') print(confusion_mat) print('Classification report:') print(classification_rep) # 输出混淆矩阵图片 sns.heatmap(confusion_mat, annot=True, cmap="Blues") plt.show() # 计算并绘制ROC曲线和AUC值 fpr, tpr, thresholds = roc_curve(test_data[target], pred) roc_auc = auc(fpr, tpr) print('AUC:', roc_auc) plt.figure() lw = 2 plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', lw=lw, 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() # 读取新数据文件并预测结果 new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') new_pred = rf.predict(new_data[features]) new_data['交通风险预测结果'] = new_pred new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096结果.xlsx', index=False)改进代码使用多元roc曲线

好的，你可以尝试使用以下改进代码，这里使用了 One-vs-Rest 策略来处理多分类任务，并使用 multiclass_roc_auc_score() 函数来计算多类分类的 ROC AUC： import pandas as pd from sklearn.ensemble import ...

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优化代码from sklearn.metrics import classification_report, roc_curve, auc # 评估分类模型性能 fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1]) roc_auc = auc(fpr, tpr)出错ValueError: multiclass format is not supported

importance_scores = one_vs_rest.estimators_[class_label].feature_importances_ AttributeError: '_ConstantPredictor' object has no attribute 'feature_importances_'

pandas中fpr,tpr,thresholds=roc_curve(y_test.values,y_pred)出现错误：multiclass format is not supported怎么解决

from sklearn.metrics import multiclass_roc_auc_score显示错误

from sklearn.multiclass import OneVsRestClassifier ovr = OneVsRestClassifier(clf) ovr.fit(x_train,y_train) ovr.score(x_test, y_test)

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