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from typing import Dict
import torchinfo
import tqdm, math
import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from ..utils.utility import get_activation_by_name
from ..utils.torch_utility import EarlyStoppingTorch, get_gpu
from ..utils.dataset import ForecastDataset
class AdaptiveConcatPool1d(nn.Module):
def __init__(self):
super().__init__()
self.ap = torch.nn.AdaptiveAvgPool1d(1)
self.mp = torch.nn.AdaptiveAvgPool1d(1)
def forward(self, x):
return torch.cat([self.ap(x), self.mp(x)], 1)
class CNNModel(nn.Module):
def __init__(self,
n_features,
num_channel=[32, 32, 40],
kernel_size=3,
stride=1,
predict_time_steps=1,
dropout_rate=0.25,
hidden_activation='relu',
device='cpu'):
# initialize the super class
super(CNNModel, self).__init__()
# save the default values
self.n_features = n_features
self.dropout_rate = dropout_rate
self.hidden_activation = hidden_activation
self.kernel_size = kernel_size
self.stride = stride
self.predict_time_steps = predict_time_steps
self.num_channel = num_channel
self.device = device
# get the object for the activations functions
self.activation = get_activation_by_name(hidden_activation)
# initialize encoder and decoder as a sequential
self.conv_layers = nn.Sequential()
prev_channels = self.n_features
for idx, out_channels in enumerate(self.num_channel[:-1]):
self.conv_layers.add_module(
"conv" + str(idx),
torch.nn.Conv1d(prev_channels, self.num_channel[idx + 1],
self.kernel_size, self.stride))
self.conv_layers.add_module(self.hidden_activation + str(idx),
self.activation)
self.conv_layers.add_module("pool" + str(idx), nn.MaxPool1d(kernel_size=2))
prev_channels = out_channels
self.fc = nn.Sequential(
AdaptiveConcatPool1d(),
torch.nn.Flatten(),
torch.nn.Linear(2*self.num_channel[-1], self.num_channel[-1]),
torch.nn.ReLU(),
torch.nn.Dropout(dropout_rate),
torch.nn.Linear(self.num_channel[-1], self.n_features)
)
def forward(self, x):
b, l, c = x.shape
x = x.view(b, c, l)
x = self.conv_layers(x) # [128, feature, 23]
outputs = torch.zeros(self.predict_time_steps, b, self.n_features).to(self.device)
for t in range(self.predict_time_steps):
decoder_input = self.fc(x)
outputs[t] = torch.squeeze(decoder_input, dim=-2)
return outputs
class CNN():
def __init__(self,
window_size=100,
pred_len=1,
batch_size=128,
epochs=50,
lr=0.0008,
feats=1,
num_channel=[32, 32, 40],
validation_size=0.2):
super().__init__()
self.__anomaly_score = None
cuda = True
self.y_hats = None
self.cuda = cuda
self.device = get_gpu(self.cuda)
self.window_size = window_size
self.pred_len = pred_len
self.batch_size = batch_size
self.epochs = epochs
self.feats = feats
self.num_channel = num_channel
self.lr = lr
self.validation_size = validation_size
self.model = CNNModel(n_features=feats, num_channel=num_channel, predict_time_steps=self.pred_len, device=self.device).to(self.device)
self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, step_size=5, gamma=0.75)
self.loss = nn.MSELoss()
self.save_path = None
self.early_stopping = EarlyStoppingTorch(save_path=self.save_path, patience=3)
self.mu = None
self.sigma = None
self.eps = 1e-10
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(
ForecastDataset(tsTrain, window_size=self.window_size, pred_len=self.pred_len),
batch_size=self.batch_size,
shuffle=True)
valid_loader = DataLoader(
ForecastDataset(tsValid, window_size=self.window_size, pred_len=self.pred_len),
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, target = x.to(self.device), target.to(self.device)
# print('x: ', x.shape) # (bs, win, feat)
# print('target: ', target.shape) # # (bs, pred_len, feat)
# print('len(tsTrain): ', len(tsTrain))
# print('len(train_loader): ', len(train_loader))
self.optimizer.zero_grad()
output = self.model(x)
output = output.view(-1, self.feats*self.pred_len)
target = target.view(-1, self.feats*self.pred_len)
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()
scores = []
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, target = x.to(self.device), target.to(self.device)
output = self.model(x)
output = output.view(-1, self.feats*self.pred_len)
target = target.view(-1, self.feats*self.pred_len)
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))
mse = torch.sub(output, target).pow(2)
scores.append(mse.cpu())
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 or epoch == self.epochs - 1:
# fitting Gaussian Distribution
if len(scores) > 0:
scores = torch.cat(scores, dim=0)
self.mu = torch.mean(scores)
self.sigma = torch.var(scores)
print(self.mu.size(), self.sigma.size())
if self.early_stopping.early_stop:
print(" Early stopping<<<")
break
def decision_function(self, data):
test_loader = DataLoader(
ForecastDataset(data, window_size=self.window_size, pred_len=self.pred_len),
batch_size=self.batch_size,
shuffle=False
)
self.model.eval()
scores = []
y_hats = []
loop = tqdm.tqdm(enumerate(test_loader),total=len(test_loader),leave=True)
with torch.no_grad():
for idx, (x, target) in loop:
x, target = x.to(self.device), target.to(self.device)
output = self.model(x)
output = output.view(-1, self.feats*self.pred_len)
target = target.view(-1, self.feats*self.pred_len)
mse = torch.sub(output, target).pow(2)
y_hats.append(output.cpu())
scores.append(mse.cpu())
loop.set_description(f'Testing: ')
scores = torch.cat(scores, dim=0)
# scores = 0.5 * (torch.log(self.sigma + self.eps) + (scores - self.mu)**2 / (self.sigma+self.eps))
scores = scores.numpy()
scores = np.mean(scores, axis=1)
y_hats = torch.cat(y_hats, dim=0)
y_hats = y_hats.numpy()
l, w = y_hats.shape
# new_scores = np.zeros((l - self.pred_len, w))
# for i in range(w):
# new_scores[:, i] = scores[self.pred_len - i:l-i, i]
# scores = np.mean(new_scores, axis=1)
# scores = np.pad(scores, (0, self.pred_len - 1), 'constant', constant_values=(0,0))
# new_y_hats = np.zeros((l - self.pred_len, w))
# for i in range(w):
# new_y_hats[:, i] = y_hats[self.pred_len - i:l-i, i]
# y_hats = np.mean(new_y_hats, axis=1)
# y_hats = np.pad(y_hats, (0, self.pred_len - 1), 'constant',constant_values=(0,0))
assert scores.ndim == 1
# self.y_hats = y_hats
print('scores: ', scores.shape)
if scores.shape[0] < len(data):
padded_decision_scores_ = np.zeros(len(data))
padded_decision_scores_[: self.window_size+self.pred_len-1] = scores[0]
padded_decision_scores_[self.window_size+self.pred_len-1 : ] = scores
self.__anomaly_score = padded_decision_scores_
return padded_decision_scores_
def anomaly_score(self) -> np.ndarray:
return self.__anomaly_score
def get_y_hat(self) -> np.ndarray:
return self.y_hats
def param_statistic(self, save_file):
model_stats = torchinfo.summary(self.model, (self.batch_size, self.window_size), verbose=0)
with open(save_file, 'w') as f:
f.write(str(model_stats))
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