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Commit afd471d4 authored by 蒙律师's avatar 蒙律师
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revised Mian, Model, Ogata

parent fdb5d302
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Showing with 137 additions and 36 deletions
Main.py
View file @ afd471d4
from Model import CustomizedLSTM
from Model2 import CustomizedLSTM
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import torch
# from import_files import CustomDataset
from import_RMTPP_dataset import import_Dataset
# from RNN import NaiveCustomRNN
from tqdm import tqdm
from argparse import ArgumentParser
......@@ -12,19 +12,28 @@ import torch.nn.utils.rnn as rnn_utils
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
if __name__ == "__main__":
coef = 10
EPOCHS = 100
LR = 1e-2
# = pd.read_csv('result_B=100.csv', header=None).to_numpy()
# Data = pd.read_csv('SHP lambda=1, length=34 random seed=0.csv', header=None).to_numpy()
# X = (Data[:, :-2].T)[np.newaxis, :, :]
# x_size = X.shape[1]
Data = pd.read_csv('result_B=100.csv', header=None).to_numpy()
# event = Data[:, -1][np.newaxis, :]-1
# time = Data[:, -2][np.newaxis, :]
X = (Data[:, :-2].T)[np.newaxis, :, :]
x_size = X.shape[1]
time, event = import_Dataset('exp')
event = event[np.newaxis, :] - 1
time = time[np.newaxis, :]
event = Data[:, -1][np.newaxis, :]
time = Data[:, -2][np.newaxis, :]
seq_len = time.shape[1]
X = np.zeros((1, 5, seq_len))
x_size = 5
hidden_size = 5
......@@ -32,7 +41,7 @@ if __name__ == "__main__":
emb_dim = 5
model = CustomizedLSTM(x_size, hidden_size, event_class, emb_dim)
model.set_optimizer(lr=1e-3, weight_decay=1e-3)
model.set_optimizer(lr=LR, weight_decay=1e-3)
train_X = X[:, :, :-1]
train_event = event[:, :-1]
......@@ -41,11 +50,41 @@ if __name__ == "__main__":
epoch = 0
train_loss_list = []
test_loss_list = []
while epoch < 30:
train_loss = model.train_batch_all([train_X, train_event, train_time])
model.init_Parameter()
while epoch < EPOCHS:
print("At epoch: {}".format(epoch))
train_loss, tr_log_ft_lst, tr_log_lambda_lst = model.train_batch_all([train_X, train_event, train_time])
# train_loss = model.train_batch([train_X, train_event, train_time])
test_loss, log_f_t, predicted_time_list, event_acc = model.predict_batch([X, event, time])
test_loss, log_f_t, predicted_time_list, event_acc, pred_inter_event_time_lst, gt_inter_event_time_lst = model.predict_batch(
[X, event, time])
'''
plt.figure()
plt.title('DeltaLSTM lambda vs time index')
plt.plot(np.exp(tr_log_lambda_lst))# label='Training loss')
plt.xlabel('Time index')
plt.ylabel('lambda')
plt.savefig('./lambda/epoch {}, lr {} lambda.png'.format(epoch, LR))
plt.figure()
plt.title('DeltaLSTM ft vs time index')
plt.plot(np.exp(tr_log_ft_lst))# label='Training loss')
plt.xlabel('Time index')
plt.ylabel('ft')
plt.savefig('./ft/epoch {}, lr {} lambda.png'.format(epoch, LR))
'''
plt.figure()
plt.title('DeltaLSTM inter event time')
plt.plot(pred_inter_event_time_lst, label='DeltaLSTM', color='red')
plt.plot(gt_inter_event_time_lst, label='Data', color='grey')
plt.legend()
plt.xlabel('Time index')
plt.ylabel('Inter-event Time')
plt.savefig('./Inter-event Time/epoch {}, lr {} Inter-event Time.png'.format(epoch, LR))
epoch += 1
train_loss_c, test_loss_c = train_loss.clone().detach().numpy(), test_loss.clone().detach().numpy()
......@@ -53,15 +92,20 @@ if __name__ == "__main__":
train_loss_list.append(train_loss_c)
test_loss_list.append(test_loss_c)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(np.arange(len(train_loss_list)), np.log(train_loss_list), np.log(test_loss_list))
plt.show()
plt.title('DeltaLSTM_RMTPP')
plt.plot(np.arange(len(train_loss_list)), train_loss_list, label='Training loss')
plt.plot(np.arange(len(train_loss_list)), test_loss_list, label='Testing loss')
plt.legend()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.savefig('epoch:{}, lr:{}.png'.format(EPOCHS, LR))
print(train_loss_list)
print(test_loss_list)
print(predicted_time_list)
print(event_acc)
# predicted_time_list = np.array(predicted_time_list)
# np.savetxt('noting really matters.txt',predicted_time_list)
Model.py
View file @ afd471d4
......@@ -153,7 +153,7 @@ class CustomizedLSTM(nn.Module):
return h_seq, c_seq
def set_optimizer(self, lr=1e-3, weight_decay=1e-5):
def set_optimizer(self, lr=1e-3, weight_decay=1e-3):
# from torch.optim import Adam
# self.optimizer = Adam(self.parameters(), lr=lr, weight_decay=weight_decay)
......@@ -174,8 +174,9 @@ class CustomizedLSTM(nn.Module):
weight.data.uniform_(-stdv, stdv)
def init_Parameter(self):
stdv = 1.0 / math.sqrt(self.hidden_size)
for weight in self.parameters():
torch.nn.init.normal_(weight, mean=0, std=1)
weight.data.uniform_(-stdv, stdv)
def softplus(self, x):
return torch.log(1 + torch.exp(x))
......@@ -207,12 +208,16 @@ class CustomizedLSTM(nn.Module):
self.w_t = nn.Parameter(self.softplus(self.w_t))
S = torch.mm(combine, self.v_t)
# S = torch.mm(h_j, self.v_t)
log_lambda = S + self.w_t * (t-t_j)
print('The lambda is: {}'.format(torch.exp(log_lambda)))
log_f_t = S + self.w_t * (t - t_j) + (1 / self.w_t) * (torch.exp(S) - torch.exp(S + self.w_t * (t - t_j)))
time_loss = (-1) * log_f_t
loss = torch.mean(time_loss) + torch.mean(event_loss)
loss = torch.mean(time_loss) #+ torch.mean(event_loss)
return loss, log_f_t
......@@ -243,8 +248,12 @@ class CustomizedLSTM(nn.Module):
loss, log_f_t = self.my_loss(target_event, target_t, t_j, h_j, c_j)
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
......@@ -287,9 +296,17 @@ class CustomizedLSTM(nn.Module):
LOSS += loss
LOSS /= seq_len
# print(self.parameters())
# print("grad before clip:" + str(self.linear.weight.grad))
LOSS.backward()
self.optimizer.step()
# print('what is the v_t grad?', self.v_t.grad)
# print('what is the w_t?', self.w_t)
# print('what is the w_t grad?', self.w_t.grad)
# for weight in self.parameters():
# print('the weight is {}, and the grad is {}'.format(weight, weight.grad))
self.optimizer.zero_grad()
return LOSS
......@@ -299,6 +316,7 @@ class CustomizedLSTM(nn.Module):
w_t = self.softplus(self.w_t).detach().item()
combine = np.concatenate((h_j, c_j), axis=0)[np.newaxis, :]
S = np.dot(combine, v_t)
log_f_t = S + w_t * (t - t_j) + 1 / w_t * (np.exp(S) - np.exp(S + w_t * (t - t_j)))
......@@ -322,16 +340,38 @@ class CustomizedLSTM(nn.Module):
v_t = self.v_t.clone().detach().numpy()
w_t = self.softplus(self.w_t.clone()).detach().item()
combine = np.concatenate((h_j, c_j), axis=0)[np.newaxis, :]
S = np.dot(combine, v_t)
log_f_t = S + w_t * (t - t_j) + 1 / w_t * (np.exp(S) - np.exp(S + w_t * (t - t_j)))
f_t = np.exp(log_f_t)
return (t * f_t)
def next_time(self, t_j, h_j, c_j):
umax = 100 # self.umax # maximum time
Deltat = umax / 100
dt = torch.linspace(0, umax, 100 + 1)
combine = torch.cat((h_j, c_j), axis=0).unsqueeze(0)
S = torch.mm(combine, self.v_t)
self.w_t = nn.Parameter(self.softplus(self.w_t))
log_f_t = S + self.w_t * dt + 1 / self.w_t * (torch.exp(S) - torch.exp(S + self.w_t * dt))
f_t = torch.exp(log_f_t)
# print('hj+cj: {}, S: {}, v_t: {}'.format(combine, S, self.v_t))
df = dt * f_t
# normalization factor
integrand_ = ((df[1:] + df[:-1]) * 0.5) * Deltat
integral_ = torch.sum(integrand_)
return t_j + integral_
def predict_batch(self, batch):
'''
......@@ -369,16 +409,26 @@ class CustomizedLSTM(nn.Module):
pred_time = []
from scipy import integrate
for idx, t_j in enumerate(current_t):
t_j = t_j.clone().detach().numpy()
h_j = current_h[idx].clone().detach().numpy()
c_j = current_c[idx].clone().detach().numpy()
t_j = t_j # .clone().detach().numpy()
h_j = current_h[idx]# .clone().detach().numpy()
c_j = current_c[idx]# .clone().detach().numpy()
predicted_time = self.next_time(t_j, h_j, c_j)
'''
predicted_time, err = integrate.quad(self.f_t, t_j, np.inf,
args=(t_j, h_j, c_j)) # equation (13), do the integration
args=(t_j, h_j, c_j)) # equation (13), do the integration
print('the current time is, {}, the target time is, {}, the predicted time is {}'
.format(t_j, target_t[idx], predicted_time))
'''
pred_time.append(predicted_time)
......
Ogata thining.py
View file @ afd471d4
......@@ -41,13 +41,14 @@ def Generate_lambda(base, s, X_history, Event_history, Event_time, y, alpha_x, A
return result_lambda
def Thining(base, max_T, Event_class, X_history, X_time, alpha_x, A, B):
def Ogata_Thining(base, max_T, Event_class, X_history, X_time, alpha_x, A, B):
s = 0 # Current Time
Event_list = [] # Event list
T_e = [] # Event time list
while s < max_T:
T_candidate = []
lambda_candidate = []
[latest_x_t_id] = np.argwhere(X_time <= s)[-1]
sub_Xhistory = X_history[:latest_x_t_id + 1]
......@@ -58,14 +59,20 @@ def Thining(base, max_T, Event_class, X_history, X_time, alpha_x, A, B):
w = - np.log(u) / lambda_y_s
s_temp = s + w
D = np.random.uniform(low=0, high=1)
if D * lambda_y_s <= Generate_lambda(base, s_temp, sub_Xhistory, Event_list, T_e, y, alpha_x, A, B):
new_lambda = Generate_lambda(base, s_temp, sub_Xhistory, Event_list, T_e, y, alpha_x, A, B)
if D * lambda_y_s <= new_lambda:
T_candidate.append(s_temp)
lambda_candidate.append(new_lambda) #
else:
T_candidate.append(np.inf)
lambda_candidate.append(0)
if len(T_candidate) == 0:
s = s_temp
continue
min_index = np.argmin(T_candidate)
t_n = np.min(T_candidate)
[[y_star]] = np.argwhere(T_candidate == t_n) + 1
# print('hey this y_star: {}'.format(y_star))
lambda_n = lambda_candidate[min_index]
y_star = min_index + 1
flag = 1
for t_i in X_time:
......@@ -122,7 +129,7 @@ if __name__ == '__main__':
X, X_time = Simulating_X(Dim=X_dim, max_T=max_T,
scale=inverse_lambda) # Generate feature, the time to record feature
Event_list, T_e = Thining(base=0.1, max_T=max_T, Event_class=Event_class, X_history=X, X_time=X_time,
Event_list, T_e = Ogata_Thining(base=0.1, max_T=max_T, Event_class=Event_class, X_history=X, X_time=X_time,
alpha_x=alpha_x, A=A,
B=B) # Generate Event, the time that event occurs
......
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