class GAN_Manager:
def __init__(self, discriminator_in_nodes, generator_out_nodes, ps_model,
ps_model_type, device):
self.discriminator = Discriminator(
in_nodes=discriminator_in_nodes).to(device)
self.discriminator.apply(self.__weights_init)
self.generator = Generator(out_nodes=generator_out_nodes).to(device)
self.generator.apply(self.__weights_init)
self.loss = nn.BCELoss()
self.ps_model = ps_model
self.ps_model_type = ps_model_type
def get_generator(self):
return self.generator
def train_GAN(self, train_parameters, device):
epochs = train_parameters["epochs"]
train_set = train_parameters["train_set"]
lr = train_parameters["lr"]
shuffle = train_parameters["shuffle"]
batch_size = train_parameters["batch_size"]
BETA = train_parameters["BETA"]
data_loader_train = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
shuffle=shuffle)
g_optimizer = optim.Adam(self.generator.parameters(), lr=lr)
d_optimizer = optim.Adam(self.discriminator.parameters(), lr=lr)
for epoch in range(epochs):
epoch += 1
total_G_loss = 0
total_D_loss = 0
total_prop_loss = 0
total_d_pred_real = 0
total_d_pred_fake = 0
for batch in data_loader_train:
covariates_X_control, ps_score_control, y_f = batch
covariates_X_control = covariates_X_control.to(device)
covariates_X_control_size = covariates_X_control.size(0)
ps_score_control = ps_score_control.squeeze().to(device)
# 1. Train Discriminator
real_data = covariates_X_control
# Generate fake data
fake_data = self.generator(
self.__noise(covariates_X_control_size)).detach()
# Train D
d_error, d_pred_real, d_pred_fake = self.__train_discriminator(
d_optimizer, real_data, fake_data)
total_D_loss += d_error
total_d_pred_real += d_pred_real
total_d_pred_fake += d_pred_fake
# 2. Train Generator
# Generate fake data
fake_data = self.generator(
self.__noise(covariates_X_control_size))
# Train G
error_g, prop_loss = self.__train_generator(
g_optimizer, fake_data, BETA, ps_score_control, device)
total_G_loss += error_g
total_prop_loss += prop_loss
if epoch % 1000 == 0:
print(
"Epoch: {0}, D_loss: {1}, D_score_real: {2}, D_score_Fake: {3}, G_loss: {4}, "
"Prop_loss: {5}".format(epoch, total_D_loss,
total_d_pred_real,
total_d_pred_fake, total_G_loss,
total_prop_loss))
def eval_GAN(self, eval_size, device):
treated_g = self.generator(self.__noise(eval_size))
ps_score_list_treated = self.__get_propensity_score(treated_g, device)
return treated_g, ps_score_list_treated
def __cal_propensity_loss(self, ps_score_control, gen_treated, device):
ps_score_list_treated = self.__get_propensity_score(
gen_treated, device)
ps_score_treated = torch.tensor(ps_score_list_treated).to(device)
ps_score_control = ps_score_control.to(device)
prop_loss = torch.sum((torch.sub(ps_score_treated.float(),
ps_score_control.float()))**2)
return prop_loss
def __get_propensity_score(self, gen_treated, device):
if self.ps_model_type == Constants.PS_MODEL_NN:
return self.__get_propensity_score_NN(gen_treated, device)
else:
return self.__get_propensity_score_LR(gen_treated)
def __get_propensity_score_LR(self, gen_treated):
ps_score_list_treated = self.ps_model.predict_proba(
gen_treated.cpu().detach().numpy())[:, -1].tolist()
return ps_score_list_treated
def __get_propensity_score_NN(self, gen_treated, device):
# Assign Treated
Y = np.ones(gen_treated.size(0))
eval_set = Utils.convert_to_tensor(gen_treated.cpu().detach().numpy(),
Y)
ps_eval_parameters_NN = {"eval_set": eval_set}
ps_score_list_treated = self.ps_model.eval(ps_eval_parameters_NN,
device,
eval_from_GAN=True)
return ps_score_list_treated
@staticmethod
def __noise(_size):
n = Variable(
torch.normal(mean=0,
std=1,
size=(_size, Constants.GAN_GENERATOR_IN_NODES)))
# print(n.size())
if torch.cuda.is_available(): return n.cuda()
return n
@staticmethod
def __weights_init(m):
if type(m) == nn.Linear:
nn.init.xavier_uniform_(m.weight)
torch.nn.init.zeros_(m.bias)
@staticmethod
def __real_data_target(size):
data = Variable(torch.ones(size, 1))
if torch.cuda.is_available(): return data.cuda()
return data
@staticmethod
def __fake_data_target(size):
data = Variable(torch.zeros(size, 1))
if torch.cuda.is_available(): return data.cuda()
return data
def __train_discriminator(self, optimizer, real_data, fake_data):
# Reset gradients
optimizer.zero_grad()
# 1.1 Train on Real Data
prediction_real = self.discriminator(real_data)
real_score = torch.mean(prediction_real).item()
# Calculate error and back propagate
error_real = self.loss(prediction_real,
self.__real_data_target(real_data.size(0)))
error_real.backward()
# 1.2 Train on Fake Data
prediction_fake = self.discriminator(fake_data)
fake_score = torch.mean(prediction_fake).item()
# Calculate error and backpropagate
error_fake = self.loss(prediction_fake,
self.__fake_data_target(real_data.size(0)))
error_fake.backward()
# 1.3 Update weights with gradients
optimizer.step()
loss_D = error_real + error_fake
# Return error
return loss_D.item(), real_score, fake_score
def __train_generator(self, optimizer, fake_data, BETA, ps_score_control,
device):
# 2. Train Generator
# Reset gradients
optimizer.zero_grad()
# Sample noise and generate fake data
predicted_D = self.discriminator(fake_data)
# Calculate error and back propagate
ps_score_control = ps_score_control.to(device)
fake_data = fake_data.to(device)
error_g = self.loss(predicted_D,
self.__real_data_target(predicted_D.size(0)))
prop_loss = self.__cal_propensity_loss(ps_score_control, fake_data,
device)
error = error_g + (BETA * prop_loss)
error.backward()
# Update weights with gradients
optimizer.step()
# Return error
return error_g.item(), prop_loss.item()
# Load state dicts
netG_A2B.load_state_dict(torch.load(opt.generator_A2B))
netG_B2A.load_state_dict(torch.load(opt.generator_B2A))
netD_A.load_state_dict(torch.load(opt.discriminator_A))
netD_B.load_state_dict(torch.load(opt.discriminator_B))
# Set model's test mode
netG_A2B.eval()
netG_B2A.eval()
netD_A.eval()
netD_B.eval()
else:
netG_A2B.apply(weights_init_normal)
netG_B2A.apply(weights_init_normal)
netD_A.apply(weights_init_normal)
netD_B.apply(weights_init_normal)
# Lossess
criterion_GAN = torch.nn.MSELoss()
criterion_cycle = torch.nn.L1Loss()
criterion_identity = torch.nn.L1Loss()
criterion_BCE = torch.nn.BCEWithLogitsLoss()
# Optimizers & LR schedulers
optimizer_G = torch.optim.Adam(itertools.chain(netG_A2B.parameters(),
netG_B2A.parameters()),
lr=opt.lr,
betas=(0.5, 0.999))
optimizer_D_A = torch.optim.Adam(netD_A.parameters(),
lr=opt.lr,