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EpiDeepCN.py
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EpiDeepCN.py
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# -*- coding: utf-8 -*-
"""
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
from rnnAttention import RNN
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from sklearn.cluster import KMeans
import numpy as np
from utils import EarlyStopping
dtype = torch.float
STOP_INIT_EPIDEEP=350
def target_distribution(q):
weight = q**2 / q.sum(0)
return (weight.t() / weight.sum(1)).t()
def mse_loss(inp, target):
return torch.sum((inp - target)**2) / inp.data.nelement()
def buildNetwork(layers, activation="relu", dropout=0):
net = []
for i in range(1, len(layers)):
net.append(nn.Linear(layers[i-1], layers[i]))
if activation=="relu":
net.append(nn.ReLU())
elif activation=="sigmoid":
net.append(nn.Sigmoid())
elif activation=="leakyReLU":
net.append(nn.LeakyReLU())
if dropout > 0:
net.append(nn.Dropout(dropout))
return nn.Sequential(*net)
class EpiDeep(nn.Module):
'''
'''
def __init__(self, input1_dim, embed1_dim, input2_dim, embed2_dim, n_centroids, encode_layers=[500, 200], decode_layers=[ 200, 500], mapping_layers=[100,200, 100],\
device=torch.device("cpu"),encod_out_dim=20):
super(EpiDeep, self).__init__()
self.device = device
self.input1_dim = input1_dim
self.embed1_dim = embed1_dim
self.n_centroids = n_centroids
self.input2_dim = input2_dim
self.embed2_dim = embed2_dim
self.first_encoder = buildNetwork([input1_dim]+encode_layers+[embed1_dim]).to(self.device)
self.first_decoder = buildNetwork([embed1_dim]+encode_layers+[input1_dim]).to(self.device)
self.first_cluster_layer = Parameter(torch.Tensor(n_centroids, embed1_dim)).to(self.device)
torch.nn.init.xavier_normal_(self.first_cluster_layer.data)
self.second_encoder = buildNetwork([input2_dim]+encode_layers+[embed2_dim]).to(self.device)
self.second_decoder = buildNetwork([embed2_dim]+encode_layers+[input2_dim]).to(self.device)
self.second_cluster_layer = Parameter(torch.Tensor(n_centroids, embed2_dim)).to(self.device)
torch.nn.init.xavier_normal_(self.second_cluster_layer.data)
self.mapper = buildNetwork([embed1_dim] + mapping_layers+[embed2_dim], activation="LeakyReLu").to(self.device)
self.encoder = RNN(1, encod_out_dim, 2, device=device).to(self.device)
deco_layers = [self.encoder.hidden_size + embed2_dim, 20, 20]
self.decoder = buildNetwork(deco_layers, activation="LeakyReLu").to(self.device)
self.regressor_layers = [20, 20, 20, 1]
self.regressor = buildNetwork(self.regressor_layers, activation="LeakyReLu").to(self.device)
self.alpha = 1
def pre_train(self, qdata, fdata, feature_data, pre_train_epochs=1000):
x1 = qdata
x2 = fdata
optimizer = torch.optim.Adam(self.parameters())
for _ in range(pre_train_epochs):
z1 = self.first_encoder(x1)
x1_bar = self.first_decoder(z1)
optimizer.zero_grad()
loss = F.mse_loss(x1_bar, x1)
loss.backward()
optimizer.step()
for _ in range(pre_train_epochs):
z2 = self.second_encoder(x2)
x2_bar = self.second_decoder(z2)
optimizer.zero_grad()
loss = F.mse_loss(x2_bar, x2)
loss.backward()
optimizer.step()
def forward_clustering_first(self, x1):
z1 = self.first_encoder(x1)
x1_bar = self.first_decoder(z1)
q = 1.0 / (1.0 + torch.sum(
torch.pow(z1.unsqueeze(1) - self.first_cluster_layer, 2), 2) / self.alpha)
q = q.pow((self.alpha + 1.0) / 2.0)
q = (q.t() / torch.sum(q, 1)).t()
return x1_bar, q ,z1
def forward_clustering_second(self, x2):
z2 = self.second_encoder(x2)
x2_bar = self.second_decoder(z2)
q = 1.0 / (1.0 + torch.sum(
torch.pow(z2.unsqueeze(1) - self.second_cluster_layer, 2), 2) / self.alpha)
q = q.pow((self.alpha + 1.0) / 2.0)
q = (q.t() / torch.sum(q, 1)).t()
return x2_bar, q , z2
def loss_function(self, x1, recon_x1, x2, recon_x2, rnn_data, rnn_labels, q1, q2, emb1, emb2, k, epoch,init_epideep, feature_data, cost_vectors):
p1 = target_distribution(q1)
loss_val1 = F.kl_div(q1.log(), p1.detach())
p2 = target_distribution(q2)
loss_val2 = F.kl_div(q2.log(), p2.detach())
translated_emb = self.mapper(emb1)
rnn_out = self.encoder(rnn_data)
out = self.decoder(torch.cat((rnn_out, translated_emb),1))
# NOTE: the following will be skipped if there are no added modules
# forward pass on each module and concat with previous embedding
feat_mod_rloss = torch.tensor([0.], dtype=dtype, device=self.device)
cost_vector = np.ones((rnn_out.shape[0], 1)) # default, i.e. all have the same cost
pred = self.regressor(out)
# apply weighted cost to get prediction loss
pred_loss = torch.tensor(cost_vector, dtype=torch.float, device=self.device) * F.mse_loss(pred, rnn_labels, reduction='none')
pred_loss = pred_loss.mean() # now reduce
loss = 0.1*(F.mse_loss(x1, recon_x1)+F.mse_loss(x2, recon_x2)+ F.mse_loss(translated_emb, emb2) + loss_val1 + loss_val2) + 10*pred_loss + feat_mod_rloss
return loss
def fit(self, qdata, fdata, rnn_data, rnn_labels, feature_data=None, cost_vectors=None, lr = 0.001, num_epoch = 10, train_seasons=None, \
first_year=None, pre_train_epochs=1000, init_epideep=False,model_path=None,epiweek=None):
"""
"""
self.train()
in_q_data = Variable(torch.Tensor(qdata).to(self.device), requires_grad= True)
in_f_data = Variable(torch.Tensor(fdata).to(self.device), requires_grad= True)
rnn_data = Variable(torch.Tensor(rnn_data).to(self.device), requires_grad= True)
rnn_labels = Variable(torch.Tensor(rnn_labels).to(self.device))
k=None # default value - will not be used when enters to if
self.pre_train(in_q_data, in_f_data, feature_data, pre_train_epochs)
kmeans = KMeans(n_clusters=self.n_centroids, n_init=10)
z1 = self.first_encoder(in_q_data)
kmeans.fit_predict(z1.cpu().detach().numpy())
self.first_cluster_layer.data = torch.tensor(kmeans.cluster_centers_, device=self.device)
z2 = self.second_encoder(in_f_data)
kmeans.fit_predict(z2.cpu().detach().numpy())
self.second_cluster_layer.data = torch.tensor(kmeans.cluster_centers_, device=self.device)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.parameters()), lr=lr)
import time
start=time.time()
for epoch in range(num_epoch):
x1_bar, q1, z1 = self.forward_clustering_first(in_q_data)
x2_bar, q2, z2 = self.forward_clustering_second(in_f_data)
loss = self.loss_function(in_q_data,x1_bar, in_f_data, x2_bar, rnn_data, rnn_labels, q1, q2, z1, z2, k,epoch, init_epideep, feature_data, cost_vectors)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(epoch, loss.item())
if np.isnan(loss.item()) and epoch>STOP_INIT_EPIDEEP: # break only after normal epideep
break
end=time.time()
print('train time: ',end-start)
# deactivate dropout (if it was set)
self.eval()
def predict(self, data, rnn_data, feature_data=None, in_f_data=None):
'''
Right now guidance is just the full season data, we have to make it be anything
'''
in_data = Variable(torch.Tensor(data).to(self.device))
in_rnn_data = Variable(torch.Tensor(rnn_data).to(self.device))
emd = self.mapper(self.first_encoder(in_data))
rnn_out = self.encoder(in_rnn_data)
out = self.decoder(torch.cat((rnn_out, emd),1))
pred = self.regressor(out)
return pred
class EpiDeepCN(EpiDeep):
'''
'''
def fit_with_dataloader(self, data_loader_hist_train, lr = 0.001, num_epoch = 10, train_seasons=None, \
model_path=None,epiweek=None):
self.train()
# get data from one batch
in_q_data, in_f_data, _, _ = next(iter(data_loader_hist_train))
self.pre_train(in_q_data, in_f_data, [], 10)
kmeans = KMeans(n_clusters=self.n_centroids, n_init=10)
z1 = self.first_encoder(in_q_data)
kmeans.fit_predict(z1.cpu().detach().numpy())
self.first_cluster_layer.data = torch.tensor(kmeans.cluster_centers_, device=self.device)
z2 = self.second_encoder(in_f_data)
kmeans.fit_predict(z2.cpu().detach().numpy())
self.second_cluster_layer.data = torch.tensor(kmeans.cluster_centers_, device=self.device)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.parameters()), lr=lr)
es = EarlyStopping(patience=10, min_delta=0.1)
import time
start=time.time()
feature_data=[]
cost_vectors=[]
for epoch in range(num_epoch):
for bath_query_length,bath_full_length,bath_rnn_data,bath_rnn_label in data_loader_hist_train:
optimizer.zero_grad()
k=None # default value - will not be used when enters to if use_seldonian
x1_bar, q1, z1 = self.forward_clustering_first(bath_query_length)
x2_bar, q2, z2 = self.forward_clustering_second(bath_full_length)
loss = self.loss_function(bath_query_length,x1_bar, bath_full_length, x2_bar, bath_rnn_data, bath_rnn_label, q1, q2, z1, z2, k,epoch, False, feature_data, cost_vectors)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# stopping criteria
if es.step(loss):
break # early stop criterion is met, we can stop now
print(epoch, loss.item())
end=time.time()
print('train time: ',end-start)
# deactivate dropout (if it was set)
self.eval()
if __name__ == "__main__":
pass