我正在尝试在Python中构建一个自定义的ANN模型。我构建模型的方法如下：
def binary_class(x_train,nodes,activation,n): #Creating customized ANN Model model=Sequential() for i in range(len(nodes)): if(i==0): if(activation=='sigmoid'): model.add(Dense(units = nodes[i], kernel_initializer = 'glorot_uniform',activation='sigmoid',input_dim = len(x_train[1]))) if(activation=='relu'): model.add(Dense(units = nodes[i], kernel_initializer = 'he_uniform',activation='relu',input_dim = len(x_train[1]))) if(activation=='tanh'): model.add(Dense(units = nodes[i], kernel_initializer = 'glorot_normal',activation='tanh',input_dim = len(x_train[1]))) if(activation=='softmax'): model.add(Dense(units = nodes[i], kernel_initializer = 'glorot_normal',activation='softmax',input_dim = len(x_train[1]))) if(activation== 'elu'): model.add(Dense(units = nodes[i], kernel_initializer = 'he_normal',activation='elu',input_dim = len(x_train[1]))) if(activation=='softplus'): model.add(Dense(units = nodes[i], kernel_initializer = 'he_normal',activation='softplus',input_dim = len(x_train[1]))) else: if(activation=='sigmoid'): model.add(Dense(units = nodes[i], kernel_initializer = 'glorot_uniform',activation='sigmoid')) if(activation=='relu'): model.add(Dense(units = nodes[i], kernel_initializer = 'he_uniform',activation='relu')) if(activation=='tanh'): model.add(Dense(units = nodes[i], kernel_initializer = 'glorot_normal',activation='tanh')) if(activation=='softmax'): model.add(Dense(units = nodes[i], kernel_initializer = 'glorot_uniform',activation='softmax')) if(activation=='elu'): model.add(Dense(units = nodes[i], kernel_initializer = 'he_normal',activation='elu')) if(activation=='softplus'): model.add(Dense(units = nodes[i], kernel_initializer = 'he_normal',activation='softplus')) model.add(Dropout(n)) #Adding output layer model.add(Dense(units=1, kernel_initializer = 'glorot_uniform',activation='sigmoid')) return model我的优化器函数如下：
def optibin(model,opt,x_train,y_train,spl,bs,epochs,x_test,y_test): #Choosing the proper optimizer to use if(opt=='sgd'): print("Enter Momentum:") mom=float(input()) lr=float(input("Enter value of Learning rate:")) opti=keras.optimizers.SGD(learning_rate=lr, momentum=mom, nesterov=False) if(opt=='Adam'): lr=float(input("Enter value of Learning rate:")) opti=keras.optimizers.Adam(learning_rate=lr) if(opt=='Adamax'): lr=float(input("Enter value of Learning rate:")) beta_1=float(input("Enter value of beta 1 (Generally close to 1)")) beta_2=float(input("Enter value of beta 2 (Generally close to 1)")) opti=keras.optimizers.Adamax(learning_rate=lr, beta_1=beta_1, beta_2=beta_2) if(opt=='Nadam'): lr=float(input("Enter value of Learning rate:")) beta_1=float(input("Enter value of beta 1 (Generally close to 1)")) beta_2=float(input("Enter value of beta 2 (Generally close to 1)")) opti=keras.optimizers.Nadam(learning_rate=lr, beta_1=beta_1, beta_2=beta_2) if(opt=='RMSprop'): lr=float(input("Enter value of Learning rate:")) opti=keras.optimizers.RMSprop(learning_rate=lr) if(opt=='Adagrad'): lr=float(input("Enter value of Learning rate:")) opti=keras.optimizers.Adagrad(learning_rate=lr) model.compile(optimizer = opti, loss = 'binary_crossentropy', metrics = ['accuracy']) model_history=model.fit(x_train, y_train,validation_split=spl, batch_size = bs,epochs = epochs) return model_history, model我需要尝试创建模型的性能指标，其中之一是构建ROC和AUC。我使用sklearn来制作混淆矩阵、特异性和敏感性。但我还需要制作一个ROC曲线。如何从这个模型中构建ROC曲线呢？
回答：
像这样的代码应该可以解决问题：
from sklearn import metricsfpr, tpr, thresholds = metrics.roc_curve(true_values, predicted_values, pos_label=1)roc_auc = metrics.auc(fpr, tpr)lw = 2plt.figure()plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--', alpha=0.15)plt.plot(fpr, tpr, lw=lw, label=f'ROC curve (area = {roc_auc: 0.2f})')plt.xlabel('(1–Specificity) - False Positive Rate')plt.ylabel('Sensitivity - True Positive Rate')plt.title(f'Receiver Operating Characteristic')plt.legend(loc="lower right")plt.show()