贵港做网站建设价格费用,张家港建网站,使用iframe做网站,wordpress用户邮箱验证码DeepAnt论文如下#xff0c;其主要是用于时间序列的无监督粗差探测。 其提出的模型架构如下#xff1a; 该文提出了一个无监督的时间序列粗差探测模型#xff0c;其主要有预测模块和探测模块组成#xff0c;其中预测模块的网络结构如下。 预测结构是将时间序列数据组…DeepAnt论文如下其主要是用于时间序列的无监督粗差探测。 其提出的模型架构如下 该文提出了一个无监督的时间序列粗差探测模型其主要有预测模块和探测模块组成其中预测模块的网络结构如下。 预测结构是将时间序列数据组织成数据集之后经过两次的卷积和最大池化最后将卷积结果通过一个全连接层转换为一个输出数据若是单步预测则输出单元个数为1 探测模块是将模型的时序预测结果与该时刻的观测数据相比来计算欧氏距离以此来作为当前时间点距离的异常分数。以此来作为数据粗差探测的标准。 本博客主要是分享复现代码论文中的细节原理可自行下载学习 复现代码数据不便分享
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset,DataLoader,TensorDataset
from sklearn.preprocessing import MinMaxScaler,StandardScalerdef MSE(arr1,arr2):arr1,arr2 np.array(arr1).flatten(),np.array(arr2).flatten()assert arr1.shape[0] arr2.shape[0]return np.sum(np.power(arr1-arr2,2)) / arr1.shape[0]def MAE(arr1,arr2):arr1,arr2 np.array(arr1).flatten(),np.array(arr2).flatten()assert arr1.shape[0] arr2.shape[0]return np.sum(np.abs(arr1-arr2)) / arr1.shape[0]class MyData(Dataset):def __init__(self,arr,history_window,predict_len) - None:self.length arr.flatten().shape[0]self.history_window history_windowself.dataset_x,self.dataset_y self.get_dataset(arr,history_window,predict_len)def get_dataset(self,arr,history_window,predict_len):arr np.array(arr).flatten()N history_windowM predict_lendataset_x np.zeros((arr.shape[0] - N,N))dataset_y np.zeros((arr.shape[0] - N,M))for i in range(arr.shape[0] - N):dataset_x[i] arr[i:iN]dataset_y[i] arr[iN:iNM]dataset_x torch.from_numpy(dataset_x).to(torch.float)dataset_y torch.from_numpy(dataset_y).to(torch.float)return (dataset_x,dataset_y)def __getitem__(self, index): # 定义方法 data[i] 的返回值return (self.dataset_x[index,:],self.dataset_y[index,:])def __len__(self): # 获取数据集样本个数return self.length - self.history_windowclass DeepAnt(nn.Module):def __init__(self,lag,p_w):super().__init__()self.convblock1 nn.Sequential(nn.Conv1d(in_channels1, out_channels32, kernel_size3, paddingvalid),nn.ReLU(inplaceTrue),nn.MaxPool1d(kernel_size2))self.convblock2 nn.Sequential(nn.Conv1d(in_channels32, out_channels32, kernel_size3, paddingvalid),nn.ReLU(inplaceTrue),nn.MaxPool1d(kernel_size2))self.flatten nn.Flatten()self.denseblock nn.Sequential(nn.Linear(32, 40), # for lag 10#nn.Linear(96, 40), # for lag 20#nn.Linear(192, 40), # for lag 30nn.ReLU(inplaceTrue),nn.Dropout(p0.25),)self.out nn.Linear(40, p_w)def forward(self, x):x x.view(-1,1,lag)x self.convblock1(x)x self.convblock2(x)x self.flatten(x)x self.denseblock(x)x self.out(x)return xdef Train(model,data_set,EPOCH,task_id):if torch.cuda.is_available():device torch.device(cuda)print(cuda is used...)else:torch.device(cpu)print(cpu is used...)scale StandardScaler()loss_fn nn.MSELoss()model.to(device)loss_fn.to(device)train_x,train_y data_set.dataset_x,data_set.dataset_ytrain_x scale.fit_transform(train_x)train_x torch.from_numpy(train_x).to(torch.float).to(device)train_y train_y.to(device).to(torch.float)torch_dataset TensorDataset(train_x,train_y)optimizer torch.optim.Adam(model.parameters())BATCH_SIZE 100model model.train()train_loss []print(Start training...)print(fEpoch is {EPOCH}\ntrain_x shape is {train_x.shape}\nBATCH_SIZE is {BATCH_SIZE})for i in range(EPOCH):loader DataLoader(datasettorch_dataset,batch_sizeBATCH_SIZE,shuffleTrue)temp_1 []for step,(batch_x,batch_y) in enumerate(loader):out model(batch_x)optimizer.zero_grad()loss loss_fn(out,batch_y)temp_1.append(loss.item())loss.backward()optimizer.step()torch.cuda.empty_cache()train_loss.append(np.mean(np.array(temp_1)))if i % 10 0:print(fThe {i}/{EPOCH} is end, loss is {np.round(np.mean(np.array(temp_1)),6)}.)print(Training end...)model model.eval()plt.plot(train_loss)pred model(train_x).cpu().data.numpy()print(fpred shape {pred.shape})plt.figure()y train_y.cpu().data.numpy().flatten()print(fy shape {y.shape})plt.plot(y,cb,labelTrue)plt.plot(pred,r,labelpred)plt.legend()plt.title(Train_result)plt.show()return predif __name__ __main__:data_f pd.read_csv(HF05_processed.csv)data np.array(pd.DataFrame(data_f)[OT])lag 10dataset MyData(data,lag,1)model DeepAnt(lag,1)res Train(model,dataset,200,1)data data[lag:].flatten() plt.plot(data)plt.plot(res,cr)err data - res.flatten()anomaly_score np.sqrt(np.power(err,2))plt.figure()plt.plot(anomaly_score)error_list []threshold 0.04for i in range(anomaly_score.shape[0]):if anomaly_score[i] threshold:error_list.append(i)print(len(error_list))plt.figure()plt.plot(data)plt.plot(error_list,[data[i] for i in error_list],ls,markerx,cr,markersize4)plt.show()
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