def error_compoute(y_pred_upper, y_pred_lower, y_tes, alpha): # Ravel for ease of computation y_pred_upper = y_pred_upper.ravel() y_pred_lower = y_pred_lower.ravel() y_tes = y_tes.ravel() # Find out of bound indices for WS idx_oobl = np.where((y_tes < y_pred_lower))[0] # 低于下限 # idx_oolu = np.where((y_tes >= y_pred_lower & y_tes <= y_pred_upper))[0] # 介于上下限之间 idx_oobu = np.where((y_tes > y_pred_upper))[0] # 高于上限 picp = np.sum((y_tes > y_pred_lower) & (y_tes <= y_pred_upper)) / len(y_tes) * 100 ace = picp - (1 - alpha) * 100 pinaw = np.sum(y_pred_upper - y_pred_lower) / (np.max(y_tes) - np.min(y_tes)) / len(y_tes) ws = np.sum(np.sum(-2 * alpha * (y_pred_upper - y_pred_lower)) + np.sum(-4 * (y_pred_lower[idx_oobl] - y_tes[idx_oobl])) + np.sum(-4 * (y_tes[idx_oobu] - y_pred_upper[idx_oobu]))) / len(y_tes) print("PICP: {:.4f}%".format(picp)) print("ACE: {:.4f}".format(ace)) print("PINAW: {:.4f}".format(pinaw)) print("WS: {:.4f}".format(ws))

import numpy as np def calc_JC(y_true, y_pred): ''' 计算并返回JC系数 :param y_true: 参考模型给出的簇，类型为ndarray :param y_pred: 聚类模型给出的簇，类型为ndarray :return: JC系数 ''' #**** Begin *# #** End *# def calc_FM(y_true, y_pred): ''' 计算并返回FM指数 :param y_true: 参考模型给出的簇，类型为ndarray :param y_pred: 聚类模型给出的簇，类型为ndarray :return: FM指数 ''' #** Begin *# #** End *# def calc_Rand(y_true, y_pred): ''' 计算并返回Rand指数 :param y_true: 参考模型给出的簇，类型为ndarray :param y_pred: 聚类模型给出的簇，类型为ndarray :return: Rand指数 ''' #** Begin # # End #

def calc_JC(true_labels, pred_labels): """ 计算 Jaccard Coefficient :param true_labels: 真实标签，类型为ndarray :param pred_labels: 预测标签，类型为ndarray :return: JC 值，类型为float """ n = ...

def dice_coef_fun(smooth=1): def dice_coef(y_true, y_pred): #求得每个sample的每个类的dice intersection = K.sum(y_true * y_pred, axis=(1,2,3)) union = K.sum(y_true, axis=(1,2,3)) + K.sum(y_pred, axis=(1,2,3)) sample_dices=(2. * intersection + smooth) / (union + smooth) #一维数组为各个类别的dice #求得每个类的dice dices=K.mean(sample_dices,axis=0) return K.mean(dices) #所有类别dice求平均的dice return dice_coef def dice_coef_loss_fun(smooth=0): def dice_coef_loss(y_true,y_pred): return 1-1-dice_coef_fun(smooth=smooth)(y_true=y_true,y_pred=y_pred) return dice_coef_loss

在函数内部，首先计算了预测值 y_pred 和真实标签 y_true 的交集，并对其进行求和操作，轴为 (1,2,3)。然后，计算了真实标签和预测值的总和，并同样进行求和操作。最后，根据 Dice Coefficient 的公式，将交集...

import tensorflow as tfdef cross_entropy_loss(y_true, y_pred): # 计算交叉熵损失 cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred) return tf.reduce_mean(cross_entropy)def boundary_loss(y_true, y_pred): # 计算边界损失 boundary_filter = tf.constant([[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=tf.float32) y_true_boundary = tf.nn.conv2d(y_true, boundary_filter, strides=[1, 1, 1, 1], padding='SAME') y_pred_boundary = tf.nn.conv2d(y_pred, boundary_filter, strides=[1, 1, 1, 1], padding='SAME') boundary_loss = tf.reduce_mean(tf.square(y_true_boundary - y_pred_boundary)) return boundary_lossdef total_loss(y_true, y_pred): # 总损失函数 = 交叉熵损失 + 边界损失 return cross_entropy_loss(y_true, y_pred) + 0.5 * boundary_loss(y_true, y_pred)# 构建模型model = ...# 编译模型model.compile(optimizer='adam', loss=total_loss, metrics=['accuracy'])

return cross_entropy_loss(y_true, y_pred) + 0.5 * boundary_loss(y_true, y_pred) # 构建模型 class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.conv1 = nn.Conv2d(in_...

def mcp_loss(y_true, y_pred): """ 计算单调性约束损失函数在所有样本和时刻上的平均值 """ batch_size=64 num_timesteps=6 delta_p = tf.random.uniform(shape=(batch_size, num_timesteps, 15), minval=0, maxval=1) p = inputs[:, :, -15:] # 取原始输入数据的最后15列作为 p p_new = p + delta_p newinputs = tf.concat([inputs[:, :, 0:1], p_new], axis=-1) y_pred_ = model(newinputs) y_true_ = model(inputs) return tf.keras.losses.mean_squared_error(y_true_, y_pred_) def combined_loss(y_true, y_pred): """ 组合MSE损失函数和单调性约束损失函数的新损失函数 """ alpha = 0.5 # 可以根据需要进行调整 mse_loss = mean_squared_error(y_true, y_pred) mcp_loss_val = mcp_loss(y_true, y_pred) return alpha * mse_loss + (1 - alpha) * mcp_loss_val将以上代码修改为kera能识别的代码

def mcp_loss(y_true, y_pred): """ 计算单调性约束损失函数在所有样本和时刻上的平均值 """ batch_size=64 num_timesteps=6 delta_p = tf.random.uniform(shape=(batch_size, num_timesteps, 15), ...

def median_absolute_percentage_error(y_true,y_pred):

def median_absolute_percentage_error(y_true, y_pred): """ Computes median absolute percentage error between two arrays. """ y_true = np.array(y_true) y_pred = np.array(y_pred) return np.median...

def quantile_loss(q, y_true, y_pred): error = y_true - y_pred return K.mean(K.maximum(q * error, (q - 1) * error), axis=-1)

对于给定的分位数 q，y_true表示真实值，y_pred表示预测值。在这个定义的分位数损失函数中，首先计算了真实值与预测值之间的差异 error = y_true - y_pred。然后，使用Keras的backend模块进行计算。K....

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=80#测试集梳理为总数180-100 model=SVC(kernel="linear",random_state=123)#使用线性核（rbf） model.fit(x_train_s,y_train)#模型估计 model.score(x_test_s,y_test)#计算预测准确率 model_rmse = np.sqrt(mean_squared_error(y_test,linear_pred)) #RMSE，计算训练误差 model_mae = mean_absolute_error(y_test,linear_pred) #MAE，计算平均绝对误差 model_r2 = r2_score(y_test, linear_pred) # R2，准确率 print("The RMSE of RBF_SVR: ", model_rmse) print("The MAE of RBF_SVR: ",model_mae) print("R^2 of RBF_SVR: ",model_r2)NameError Traceback (most recent call last) Input In [38], in <cell line: 1>() ----> 1 model_rmse = np.sqrt(mean_squared_error(y_test,linear_pred)) #RMSE，计算训练误差 2 model_mae = mean_absolute_error(y_test,linear_pred) #MAE，计算平均绝对误差 3 model_r2 = r2_score(y_test, linear_pred) # R2，准确率 NameError: name 'linear_pred' is not defined ,stratify=y,random_state=0)#

解释如下代码：def lr_model(train_pic_sample,feature_list): Y = train_pic_sample['value'].values.ravel() X = train_pic_sample[feature_list].values scaler = preprocessing.MinMaxScaler().fit(X) X_scaled = scaler.transform(X) model = linear_model.LinearRegression() model.fit(X_scaled, Y) y_true = Y y_pred = model.predict(X_scaled) y_pred[y_pred<0] = 1.0 print('train mean:' + str(np.mean(y_true)) ) print('train mean pred:' + str(np.mean(y_pred)) ) print('train mse:' + str(np.std(y_true - y_pred) ) )

首先，将训练样本的标签和特征分别存储在Y和X中。然后，使用preprocessing.MinMaxScaler()对特征进行归一化。归一化后的特征存储在X_scaled中。接下来，使用linear_model.LinearRegression()来初始化一个线性回归...

# 使用模型进行预测 y_pred = model.predict(X) # 构建带有概率的预测结果 y_pred_proba = model.predict_proba(X) # 过滤掉和值超过6或小于6的预测值，并去除重复的行 results = pd.DataFrame({'y_pred': y_pred, 'y_pred_proba': y_pred_proba[:, 1]}) results = results[(results['y_pred'] >= 0) & (results['y_pred'] <= 6)] results.drop_duplicates(inplace=True) # 将预测结果打印出来 print(y_pred)这个怎么改使用predict方法来获取分类的预测结果

y_pred = np.argmax(model.predict(X), axis=-1) 这里使用argmax函数来获取模型预测结果中概率最大的类别对应的索引，即分类的预测结果。同时，代码中也不再需要使用predict_proba方法来获取概率值了，因为...

def lgb_metric(y_true, y_pred): return 'balanced_log_loss', balanced_log_loss(y_true, y_pred), False

该函数接受两个参数，y_true 和 y_pred，分别表示真实标签和模型预测的概率。函数返回一个元组，包含三个值：评估指标的名称 'balanced_log_loss'，计算得到的平衡对数损失值，以及一个布尔值表示是否要最小化这个...

def show_matrix(y_test, y_pred): # 定义一个函数显示混淆矩阵 y_test=y_test.detach().numpy() y_pred=y_pred.detach().numpy() print(y_test) cm = confusion_matrix(y_test,y_pred) # 调用混淆矩阵 plt.title("ANN Confusion Matrix") # 标题 sns.heatmap(cm,annot=True,cmap="Blues",fmt="d",cbar=False) # 热力图设定 plt.show() # 显示混淆矩阵

def show_matrix(y_test, y_pred): y_test = y_test.detach().numpy() y_pred = y_pred.detach().numpy() print(y_test) cm = confusion_matrix(y_test, y_pred) plt.title("ANN Confusion Matrix") sns....

from sklearn import metrics print('Mean Absolute Error:', metrics.mean_absolute_error(Y_validation,y_pred)) print('Mean Squared Error:', metrics.mean_squared_error(Y_validation,y_pred)) print('Root Mean Squared Error:',np.sqrt(metrics.mean_squared_error(Y_validation,y_pred))) print('R2 =',metrics.r2_score(Y_validation,y_pred))请优化

rmse = np.sqrt(metrics.mean_squared_error(Y_validation, y_pred)) print('Root Mean Squared Error:', rmse) del rmse r2 = metrics.r2_score(Y_validation, y_pred) print('R2 =', r2) del r2 通过这些...

def tilted_loss(y_true, y_pred, quantiles): y_true = backend.cast(y_true, "float32") y_pred = backend.cast(y_pred, "float32") e = y_true - y_pred # find the sum of average loss of each quantile return backend.sum(backend.mean(backend.maximum(quantiles * e, (quantiles - 1) * e), axis=0))

在这个函数中，y_true 和 y_pred 分别表示真实值和预测值。quantiles 是一个包含要计算的分位数的列表。首先，使用 Keras 的后端模块将 y_true 和 y_pred 转换为 float32 类型。然后，计算 e = y_true ...

@staticmethod def calc_generator_moments_loss(y_true, y_pred): y_true_mean, y_true_var = nn.moments(x=y_true, axes=[0]) y_pred_mean, y_pred_var = nn.moments(x=y_pred, axes=[0]) g_loss_mean = reduce_mean(abs(y_true_mean - y_pred_mean)) g_loss_var = reduce_mean(abs(sqrt(y_true_var + 1e-6) - sqrt(y_pred_var + 1e-6))) return g_loss_mean + g_loss_var

该函数接受两个输入，y_true y_pred，分别表示真实样本和生成样本。首先，使用 nn.moments 函数计算真实样本和生成样本的均值和方差。其中 axes=[0] 表示在第0个维度（通常是样本维度）上进行计算。接...

import keras.backend as K smooth = 1. def dice_coef(y_true, y_pred): y_true_f = K.flatten(y_true>0.5) y_pred_f = K.flatten(y_pred>0.5) intersection = K.sum(y_true_f * y_pred_f) return 1 - (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

这是一个使用Keras后端实现的Dice Coefficient损失函数。Dice Coefficient是一种衡量两个集合相似度的指标，通常用于图像分割任务中。这个函数返回的是1减去Dice Coefficient值，因为在Keras中通常是最小化损失函数...

def mean_absolute_percentage_error(y_true, y_pred): y_true,y_pred = np.array(y_true).ravel(),np.array(y_pred).ravel() return 1-np.mean(np.abs((y_true - y_pred) / y_true)) from sklearn.metrics import mean_absolute_error

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def mean_absolute_percentage_error(y_true, y_pred): y_true,y_pred = np.array(y_true).ravel(),np.array(y_pred).ravel() return 1-np.mean(np.abs((y_true - y_pred) / y_true)) from sklearn.metrics import mean_absolute_error

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def median_absolute_percentage_error(y_true,y_pred):

def quantile_loss(q, y_true, y_pred): error = y_true - y_pred return K.mean(K.maximum(q * error, (q - 1) * error), axis=-1)

def lgb_metric(y_true, y_pred): return 'balanced_log_loss', balanced_log_loss(y_true, y_pred), False

def tilted_loss(y_true, y_pred, quantiles): y_true = backend.cast(y_true, "float32") y_pred = backend.cast(y_pred, "float32") e = y_true - y_pred # find the sum of average loss of each quantile return backend.sum(backend.mean(backend.maximum(quantiles * e, (quantiles - 1) * e), axis=0))

import keras.backend as K smooth = 1. def dice_coef(y_true, y_pred): y_true_f = K.flatten(y_true>0.5) y_pred_f = K.flatten(y_pred>0.5) intersection = K.sum(y_true_f * y_pred_f) return 1 - (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

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