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GRUD.py
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GRUD.py
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# -*- coding: utf-8 -*-
"""
Created on Sat May 12 16:48:54 2018
@author: Zhiyong
"""
import torch.utils.data as utils
import torch.nn.functional as F
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math
import numpy as np
import pandas as pd
import time
class FilterLinear(nn.Module):
def __init__(self, in_features, out_features, filter_square_matrix, bias=True):
'''
filter_square_matrix : filter square matrix, whose each elements is 0 or 1.
'''
super(FilterLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
use_gpu = torch.cuda.is_available()
self.filter_square_matrix = None
if use_gpu:
self.filter_square_matrix = Variable(filter_square_matrix.cuda(), requires_grad=False)
else:
self.filter_square_matrix = Variable(filter_square_matrix, requires_grad=False)
self.weight = Parameter(torch.Tensor(out_features, in_features))
if bias:
self.bias = Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
# print(self.weight.data)
# print(self.bias.data)
def forward(self, input):
# print(self.filter_square_matrix.mul(self.weight))
return F.linear(input, self.filter_square_matrix.mul(self.weight), self.bias)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'in_features=' + str(self.in_features) \
+ ', out_features=' + str(self.out_features) \
+ ', bias=' + str(self.bias is not None) + ')'
class GRUD(nn.Module):
def __init__(self, input_size, cell_size, hidden_size, X_mean, output_last = False):
"""
Recurrent Neural Networks for Multivariate Times Series with Missing Values
GRU-D: GRU exploit two representations of informative missingness patterns, i.e., masking and time interval.
cell_size is the size of cell_state.
Implemented based on the paper:
@article{che2018recurrent,
title={Recurrent neural networks for multivariate time series with missing values},
author={Che, Zhengping and Purushotham, Sanjay and Cho, Kyunghyun and Sontag, David and Liu, Yan},
journal={Scientific reports},
volume={8},
number={1},
pages={6085},
year={2018},
publisher={Nature Publishing Group}
}
GRU-D:
input_size: variable dimension of each time
hidden_size: dimension of hidden_state
mask_size: dimension of masking vector
X_mean: the mean of the historical input data
"""
super(GRUD, self).__init__()
self.hidden_size = hidden_size
self.delta_size = input_size
self.mask_size = input_size
use_gpu = torch.cuda.is_available()
if use_gpu:
self.identity = torch.eye(input_size).cuda()
self.zeros = Variable(torch.zeros(input_size).cuda())
self.X_mean = Variable(torch.Tensor(X_mean).cuda())
else:
self.identity = torch.eye(input_size)
self.zeros = Variable(torch.zeros(input_size))
self.X_mean = Variable(torch.Tensor(X_mean))
self.zl = nn.Linear(input_size + hidden_size + self.mask_size, hidden_size)
self.rl = nn.Linear(input_size + hidden_size + self.mask_size, hidden_size)
self.hl = nn.Linear(input_size + hidden_size + self.mask_size, hidden_size)
self.gamma_x_l = FilterLinear(self.delta_size, self.delta_size, self.identity)
self.gamma_h_l = nn.Linear(self.delta_size, self.delta_size)
self.output_last = output_last
def step(self, x, x_last_obsv, x_mean, h, mask, delta):
batch_size = x.shape[0]
dim_size = x.shape[1]
delta_x = torch.exp(-torch.max(self.zeros, self.gamma_x_l(delta)))
delta_h = torch.exp(-torch.max(self.zeros, self.gamma_h_l(delta)))
x = mask * x + (1 - mask) * (delta_x * x_last_obsv + (1 - delta_x) * x_mean)
h = delta_h * h
combined = torch.cat((x, h, mask), 1)
z = F.sigmoid(self.zl(combined))
r = F.sigmoid(self.rl(combined))
combined_r = torch.cat((x, r * h, mask), 1)
h_tilde = F.tanh(self.hl(combined_r))
h = (1 - z) * h + z * h_tilde
return h
def forward(self, input):
batch_size = input.size(0)
type_size = input.size(1)
step_size = input.size(2)
spatial_size = input.size(3)
Hidden_State = self.initHidden(batch_size)
X = torch.squeeze(input[:,0,:,:])
X_last_obsv = torch.squeeze(input[:,1,:,:])
Mask = torch.squeeze(input[:,2,:,:])
Delta = torch.squeeze(input[:,3,:,:])
outputs = None
for i in range(step_size):
Hidden_State = self.step(torch.squeeze(X[:,i:i+1,:])\
, torch.squeeze(X_last_obsv[:,i:i+1,:])\
, torch.squeeze(self.X_mean[:,i:i+1,:])\
, Hidden_State\
, torch.squeeze(Mask[:,i:i+1,:])\
, torch.squeeze(Delta[:,i:i+1,:]))
if outputs is None:
outputs = Hidden_State.unsqueeze(1)
else:
outputs = torch.cat((outputs, Hidden_State.unsqueeze(1)), 1)
if self.output_last:
return outputs[:,-1,:]
else:
return outputs
def initHidden(self, batch_size):
use_gpu = torch.cuda.is_available()
if use_gpu:
Hidden_State = Variable(torch.zeros(batch_size, self.hidden_size).cuda())
return Hidden_State
else:
Hidden_State = Variable(torch.zeros(batch_size, self.hidden_size))
return Hidden_State