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"""
This function is adapted from [FITS] by [VEWOXIC]
Original source: [https://github.com/VEWOXIC/FITS]
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from typing import Dict
import torchinfo
import tqdm
import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import math
from ..utils.torch_utility import EarlyStoppingTorch, get_gpu
from ..utils.dataset import ReconstructDataset
class Model(nn.Module):
# FITS: Frequency Interpolation Time Series Forecasting
def __init__(self, seq_len, pred_len, individual, enc_in, cut_freq):
super(Model, self).__init__()
self.seq_len = seq_len
self.pred_len = pred_len
self.individual = individual
self.channels = enc_in
self.dominance_freq = cut_freq # 720/24
self.length_ratio = (self.seq_len + self.pred_len)/self.seq_len
if self.individual:
self.freq_upsampler = nn.ModuleList()
for i in range(self.channels):
self.freq_upsampler.append(nn.Linear(self.dominance_freq, int(self.dominance_freq*self.length_ratio)).to(torch.cfloat))
else:
self.freq_upsampler = nn.Linear(self.dominance_freq, int(self.dominance_freq*self.length_ratio)).to(torch.cfloat) # complex layer for frequency upcampling]
# configs.pred_len=configs.seq_len+configs.pred_len
# #self.Dlinear=DLinear.Model(configs)
# configs.pred_len=self.pred_len
def forward(self, x):
# RIN
x_mean = torch.mean(x, dim=1, keepdim=True)
x = x - x_mean
x_var=torch.var(x, dim=1, keepdim=True)+ 1e-5
# print(x_var)
x = x / torch.sqrt(x_var)
low_specx = torch.fft.rfft(x, dim=1)
low_specx[:,self.dominance_freq:]=0 # LPF
low_specx = low_specx[:,0:self.dominance_freq,:] # LPF
# print(low_specx.permute(0,2,1))
if self.individual:
low_specxy_ = torch.zeros([low_specx.size(0),int(self.dominance_freq*self.length_ratio),low_specx.size(2)],dtype=low_specx.dtype).to(low_specx.device)
for i in range(self.channels):
low_specxy_[:,:,i]=self.freq_upsampler[i](low_specx[:,:,i].permute(0,1)).permute(0,1)
else:
low_specxy_ = self.freq_upsampler(low_specx.permute(0,2,1)).permute(0,2,1)
# print(low_specxy_)
low_specxy = torch.zeros([low_specxy_.size(0),int((self.seq_len+self.pred_len)/2+1),low_specxy_.size(2)],dtype=low_specxy_.dtype).to(low_specxy_.device)
low_specxy[:,0:low_specxy_.size(1),:]=low_specxy_ # zero padding
low_xy=torch.fft.irfft(low_specxy, dim=1)
low_xy=low_xy * self.length_ratio # energy compemsation for the length change
# dom_x=x-low_x
# dom_xy=self.Dlinear(dom_x)
# xy=(low_xy+dom_xy) * torch.sqrt(x_var) +x_mean # REVERSE RIN
xy=(low_xy) * torch.sqrt(x_var) +x_mean
return xy, low_xy* torch.sqrt(x_var)
class FITS():
def __init__(self,
win_size=100,
DSR=4,
individual=True,
input_c=1,
batch_size=128,
cut_freq=12,
epochs=50,
lr=1e-3,
validation_size=0.2
):
super().__init__()
self.__anomaly_score = None
self.cuda = True
self.device = get_gpu(self.cuda)
self.win_size = win_size
self.DSR = DSR
self.individual = individual
self.input_c = input_c
self.batch_size = batch_size
self.cut_freq = cut_freq
self.validation_size = validation_size
self.model = Model(seq_len=self.win_size//self.DSR, pred_len=self.win_size-self.win_size//self.DSR, individual=self.individual, enc_in=self.input_c, cut_freq=self.cut_freq).to(self.device)
self.epochs = epochs
self.learning_rate = lr
self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, step_size=5, gamma=0.75)
self.loss = nn.MSELoss()
self.anomaly_criterion = nn.MSELoss(reduce=False)
self.save_path = None
self.early_stopping = EarlyStoppingTorch(save_path=self.save_path, patience=3)
def fit(self, data):
tsTrain = data[:int((1-self.validation_size)*len(data))]
tsValid = data[int((1-self.validation_size)*len(data)):]
train_loader = DataLoader(
dataset=ReconstructDataset(tsTrain, window_size=self.win_size),
batch_size=self.batch_size,
shuffle=True
)
valid_loader = DataLoader(
dataset=ReconstructDataset(tsValid, window_size=self.win_size),
batch_size=self.batch_size,
shuffle=False
)
for epoch in range(1, self.epochs + 1):
self.model.train(mode=True)
avg_loss = 0
loop = tqdm.tqdm(enumerate(train_loader),total=len(train_loader),leave=True)
for idx, (x, target) in loop:
x = x[:, ::self.DSR, :]
x, target = x.to(self.device), target.to(self.device)
self.optimizer.zero_grad()
output, _ = self.model(x)
# print('x: ', x.shape)
# print('target: ', target.shape)
loss = self.loss(output, target)
loss.backward()
self.optimizer.step()
avg_loss += loss.cpu().item()
loop.set_description(f'Training Epoch [{epoch}/{self.epochs}]')
loop.set_postfix(loss=loss.item(), avg_loss=avg_loss/(idx+1))
self.model.eval()
avg_loss = 0
loop = tqdm.tqdm(enumerate(valid_loader),total=len(valid_loader),leave=True)
with torch.no_grad():
for idx, (x, target) in loop:
x = x[:, ::self.DSR, :]
x, target = x.to(self.device), target.to(self.device)
output, _ = self.model(x)
loss = self.loss(output, target)
avg_loss += loss.cpu().item()
loop.set_description(f'Validation Epoch [{epoch}/{self.epochs}]')
loop.set_postfix(loss=loss.item(), avg_loss=avg_loss/(idx+1))
valid_loss = avg_loss/max(len(valid_loader), 1)
self.scheduler.step()
self.early_stopping(valid_loss, self.model)
if self.early_stopping.early_stop:
print(" Early stopping<<<")
break
def decision_function(self, data):
test_loader = DataLoader(
dataset=ReconstructDataset(data, window_size=self.win_size),
batch_size=self.batch_size,
shuffle=False
)
self.model.eval()
scores = []
loop = tqdm.tqdm(enumerate(test_loader),total=len(test_loader),leave=True)
with torch.no_grad():
for idx, (x, target) in loop:
x = x[:, ::self.DSR, :]
x, target = x.to(self.device), target.to(self.device)
output, _ = self.model(x)
# loss = self.loss(output, target)
score = torch.mean(self.anomaly_criterion(output, target), dim=-1)
scores.append(score.cpu()[:,-1])
loop.set_description(f'Testing: ')
scores = torch.cat(scores, dim=0)
scores = scores.numpy().flatten()
assert scores.ndim == 1
self.__anomaly_score = scores
if self.__anomaly_score.shape[0] < len(data):
self.__anomaly_score = np.array([self.__anomaly_score[0]]*math.ceil((self.win_size-1)/2) +
list(self.__anomaly_score) + [self.__anomaly_score[-1]]*((self.win_size-1)//2))
return self.__anomaly_score
def anomaly_score(self) -> np.ndarray:
return self.__anomaly_score
def param_statistic(self, save_file):
model_stats = torchinfo.summary(self.model, (self.batch_size, self.input_len), verbose=0)
with open(save_file, 'w') as f:
f.write(str(model_stats)) |