def train(self,
data,
categorical_columns=None,
ordinal_columns=None,
update_epsilon=None):
if update_epsilon:
self.epsilon = update_epsilon
self.transformer = DataTransformer()
self.transformer.fit(data, discrete_columns=categorical_columns)
train_data = self.transformer.transform(data)
data_sampler = Sampler(train_data, self.transformer.output_info)
data_dim = self.transformer.output_dimensions
self.cond_generator = ConditionalGenerator(
train_data, self.transformer.output_info, self.log_frequency)
self.generator = Generator(
self.embedding_dim + self.cond_generator.n_opt, self.gen_dim,
data_dim).to(self.device)
discriminator = Discriminator(data_dim + self.cond_generator.n_opt,
self.dis_dim, self.loss,
self.pack).to(self.device)
optimizer_g = optim.Adam(self.generator.parameters(),
lr=2e-4,
betas=(0.5, 0.9),
weight_decay=self.l2scale)
optimizer_d = optim.Adam(discriminator.parameters(),
lr=2e-4,
betas=(0.5, 0.9))
privacy_engine = opacus.PrivacyEngine(
discriminator,
batch_size=self.batch_size,
sample_size=train_data.shape[0],
alphas=[1 + x / 10.0 for x in range(1, 100)] + list(range(12, 64)),
noise_multiplier=self.sigma,
max_grad_norm=self.max_per_sample_grad_norm,
clip_per_layer=True,
)
if not self.disabled_dp:
privacy_engine.attach(optimizer_d)
one = torch.tensor(1, dtype=torch.float).to(self.device)
mone = one * -1
real_label = 1
fake_label = 0
criterion = nn.BCELoss()
assert self.batch_size % 2 == 0
mean = torch.zeros(self.batch_size,
self.embedding_dim,
device=self.device)
std = mean + 1
steps_per_epoch = len(train_data) // self.batch_size
for i in range(self.epochs):
for id_ in range(steps_per_epoch):
fakez = torch.normal(mean=mean, std=std)
condvec = self.cond_generator.sample(self.batch_size)
if condvec is None:
c1, m1, col, opt = None, None, None, None
real = data_sampler.sample(self.batch_size, col, opt)
else:
c1, m1, col, opt = condvec
c1 = torch.from_numpy(c1).to(self.device)
m1 = torch.from_numpy(m1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
perm = np.arange(self.batch_size)
np.random.shuffle(perm)
real = data_sampler.sample(self.batch_size, col[perm],
opt[perm])
c2 = c1[perm]
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
real = torch.from_numpy(real.astype("float32")).to(self.device)
if c1 is not None:
fake_cat = torch.cat([fakeact, c1], dim=1)
real_cat = torch.cat([real, c2], dim=1)
else:
real_cat = real
fake_cat = fake
optimizer_d.zero_grad()
if self.loss == "cross_entropy":
y_fake = discriminator(fake_cat)
# print ('y_fake is {}'.format(y_fake))
label_fake = torch.full(
(int(self.batch_size / self.pack), ),
fake_label,
dtype=torch.float,
device=self.device,
)
# print ('label_fake is {}'.format(label_fake))
error_d_fake = criterion(y_fake, label_fake)
error_d_fake.backward()
optimizer_d.step()
# train with real
label_true = torch.full(
(int(self.batch_size / self.pack), ),
real_label,
dtype=torch.float,
device=self.device,
)
y_real = discriminator(real_cat)
error_d_real = criterion(y_real, label_true)
error_d_real.backward()
optimizer_d.step()
loss_d = error_d_real + error_d_fake
else:
y_fake = discriminator(fake_cat)
mean_fake = torch.mean(y_fake)
mean_fake.backward(one)
y_real = discriminator(real_cat)
mean_real = torch.mean(y_real)
mean_real.backward(mone)
optimizer_d.step()
loss_d = -(mean_real - mean_fake)
max_grad_norm = []
for p in discriminator.parameters():
param_norm = p.grad.data.norm(2).item()
max_grad_norm.append(param_norm)
# pen = calc_gradient_penalty(discriminator, real_cat, fake_cat, self.device)
# pen.backward(retain_graph=True)
# loss_d.backward()
# optimizer_d.step()
fakez = torch.normal(mean=mean, std=std)
condvec = self.cond_generator.sample(self.batch_size)
if condvec is None:
c1, m1, col, opt = None, None, None, None
else:
c1, m1, col, opt = condvec
c1 = torch.from_numpy(c1).to(self.device)
m1 = torch.from_numpy(m1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
if c1 is not None:
y_fake = discriminator(torch.cat([fakeact, c1], dim=1))
else:
y_fake = discriminator(fakeact)
# if condvec is None:
cross_entropy = 0
# else:
# cross_entropy = self._cond_loss(fake, c1, m1)
if self.loss == "cross_entropy":
label_g = torch.full(
(int(self.batch_size / self.pack), ),
real_label,
dtype=torch.float,
device=self.device,
)
# label_g = torch.full(int(self.batch_size/self.pack,),1,device=self.device)
loss_g = criterion(y_fake, label_g)
loss_g = loss_g + cross_entropy
else:
loss_g = -torch.mean(y_fake) + cross_entropy
optimizer_g.zero_grad()
loss_g.backward()
optimizer_g.step()
if not self.disabled_dp:
# if self.loss == 'cross_entropy':
# autograd_grad_sample.clear_backprops(discriminator)
# else:
for p in discriminator.parameters():
if hasattr(p, "grad_sample"):
del p.grad_sample
if self.target_delta is None:
self.target_delta = 1 / train_data.shape[0]
epsilon, best_alpha = optimizer_d.privacy_engine.get_privacy_spent(
self.target_delta)
self.epsilon_list.append(epsilon)
self.alpha_list.append(best_alpha)
# if self.verbose:
if not self.disabled_dp:
if self.epsilon < epsilon:
break
self.loss_d_list.append(loss_d)
self.loss_g_list.append(loss_g)
if self.verbose:
print(
"Epoch %d, Loss G: %.4f, Loss D: %.4f" %
(i + 1, loss_g.detach().cpu(), loss_d.detach().cpu()),
flush=True,
)
print("epsilon is {e}, alpha is {a}".format(e=epsilon,
a=best_alpha))
return self.loss_d_list, self.loss_g_list, self.epsilon_list, self.alpha_list
def fit(self, train_data, discrete_columns=tuple(), epochs=300, log_frequency=True):
"""Fit the CTGAN Synthesizer models to the training data.
Args:
train_data (numpy.ndarray or pandas.DataFrame):
Training Data. It must be a 2-dimensional numpy array or a
pandas.DataFrame.
discrete_columns (list-like):
List of discrete columns to be used to generate the Conditional
Vector. If ``train_data`` is a Numpy array, this list should
contain the integer indices of the columns. Otherwise, if it is
a ``pandas.DataFrame``, this list should contain the column names.
epochs (int):
Number of training epochs. Defaults to 300.
log_frequency (boolean):
Whether to use log frequency of categorical levels in conditional
sampling. Defaults to ``True``.
"""
self.transformer = DataTransformer()
self.transformer.fit(train_data, discrete_columns)
train_data = self.transformer.transform(train_data)
data_sampler = Sampler(train_data, self.transformer.output_info)
data_dim = self.transformer.output_dimensions
self.cond_generator = ConditionalGenerator(
train_data, self.transformer.output_info, log_frequency
)
self.generator = Generator(
self.embedding_dim + self.cond_generator.n_opt, self.gen_dim, data_dim
).to(self.device)
discriminator = Discriminator(
data_dim + self.cond_generator.n_opt, self.dis_dim
).to(self.device)
optimizerG = optim.Adam(
self.generator.parameters(),
lr=2e-4,
betas=(0.5, 0.9),
weight_decay=self.l2scale,
)
optimizerD = optim.Adam(discriminator.parameters(), lr=2e-4, betas=(0.5, 0.9))
assert self.batch_size % 2 == 0
mean = torch.zeros(self.batch_size, self.embedding_dim, device=self.device)
std = mean + 1
train_losses = []
early_stopping = EarlyStopping(patience=self.patience, verbose=False)
steps_per_epoch = max(len(train_data) // self.batch_size, 1)
for i in range(epochs):
for id_ in range(steps_per_epoch):
fakez = torch.normal(mean=mean, std=std)
condvec = self.cond_generator.sample(self.batch_size)
if condvec is None:
c1, m1, col, opt = None, None, None, None
real = data_sampler.sample(self.batch_size, col, opt)
else:
c1, m1, col, opt = condvec
c1 = torch.from_numpy(c1).to(self.device)
m1 = torch.from_numpy(m1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
perm = np.arange(self.batch_size)
np.random.shuffle(perm)
real = data_sampler.sample(self.batch_size, col[perm], opt[perm])
c2 = c1[perm]
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
real = torch.from_numpy(real.astype("float32")).to(self.device)
if c1 is not None:
fake_cat = torch.cat([fakeact, c1], dim=1)
real_cat = torch.cat([real, c2], dim=1)
else:
real_cat = real
fake_cat = fake
y_fake = discriminator(fake_cat)
y_real = discriminator(real_cat)
pen = discriminator.calc_gradient_penalty(
real_cat, fake_cat, self.device
)
loss_d = -(torch.mean(y_real) - torch.mean(y_fake))
train_losses.append(loss_d.item())
optimizerD.zero_grad()
pen.backward(retain_graph=True)
loss_d.backward()
optimizerD.step()
fakez = torch.normal(mean=mean, std=std)
condvec = self.cond_generator.sample(self.batch_size)
if condvec is None:
c1, m1, col, opt = None, None, None, None
else:
c1, m1, col, opt = condvec
c1 = torch.from_numpy(c1).to(self.device)
m1 = torch.from_numpy(m1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
if c1 is not None:
y_fake = discriminator(torch.cat([fakeact, c1], dim=1))
else:
y_fake = discriminator(fakeact)
if condvec is None:
cross_entropy = 0
else:
cross_entropy = self._cond_loss(fake, c1, m1)
loss_g = -torch.mean(y_fake) + cross_entropy
train_losses.append(loss_g.item())
optimizerG.zero_grad()
loss_g.backward()
optimizerG.step()
early_stopping(np.average(train_losses))
if early_stopping.early_stop:
print("GAN: Early stopping after epochs {}".format(i))
break
train_losses = []
def train(self,
data,
categorical_columns=None,
ordinal_columns=None,
update_epsilon=None):
if update_epsilon:
self.epsilon = update_epsilon
sample_per_teacher = self.sample_per_teacher if self.sample_per_teacher < len(
data) else 1000
self.num_teachers = int(len(data) / sample_per_teacher) + 1
self.transformer = DataTransformer()
self.transformer.fit(data, discrete_columns=categorical_columns)
data = self.transformer.transform(data)
data_partitions = np.array_split(data, self.num_teachers)
data_dim = self.transformer.output_dimensions
self.cond_generator = ConditionalGenerator(
data, self.transformer.output_info, self.log_frequency)
# create conditional generator for each teacher model
cond_generator = [
ConditionalGenerator(d, self.transformer.output_info,
self.log_frequency) for d in data_partitions
]
self.generator = Generator(
self.embedding_dim + self.cond_generator.n_opt, self.gen_dim,
data_dim).to(self.device)
discriminator = Discriminator(data_dim + self.cond_generator.n_opt,
self.dis_dim, self.loss,
self.pack).to(self.device)
student_disc = discriminator
student_disc.apply(weights_init)
teacher_disc = [discriminator for i in range(self.num_teachers)]
for i in range(self.num_teachers):
teacher_disc[i].apply(weights_init)
optimizerG = optim.Adam(self.generator.parameters(),
lr=2e-4,
betas=(0.5, 0.9),
weight_decay=self.l2scale)
optimizerS = optim.Adam(student_disc.parameters(),
lr=2e-4,
betas=(0.5, 0.9))
optimizerT = [
optim.Adam(teacher_disc[i].parameters(), lr=2e-4, betas=(0.5, 0.9))
for i in range(self.num_teachers)
]
noise_multiplier = self.noise_multiplier
alphas = torch.tensor([0.0 for i in range(self.moments_order)],
device=self.device)
l_list = 1 + torch.tensor(range(self.moments_order),
device=self.device)
eps = 0
mean = torch.zeros(self.batch_size,
self.embedding_dim,
device=self.device)
std = mean + 1
REAL_LABEL = 1
FAKE_LABEL = 0
criterion = nn.BCELoss() if (self.loss
== "cross_entropy") else self.WLoss
if (self.verbose):
print("using loss {} and regularization {}".format(
self.loss, self.regularization))
while eps < self.epsilon:
# train teacher discriminators
for t_2 in range(self.teacher_iters):
for i in range(self.num_teachers):
partition_data = data_partitions[i]
data_sampler = Sampler(partition_data,
self.transformer.output_info)
fakez = torch.normal(mean, std=std).to(self.device)
condvec = cond_generator[i].sample(self.batch_size)
if condvec is None:
c1, m1, col, opt = None, None, None, None
real = data_sampler.sample(self.batch_size, col, opt)
else:
c1, m1, col, opt = condvec
c1 = torch.from_numpy(c1).to(self.device)
m1 = torch.from_numpy(m1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
perm = np.arange(self.batch_size)
np.random.shuffle(perm)
real = data_sampler.sample(self.batch_size, col[perm],
opt[perm])
c2 = c1[perm]
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
real = torch.from_numpy(real.astype('float32')).to(
self.device)
if c1 is not None:
fake_cat = torch.cat([fakeact, c1], dim=1)
real_cat = torch.cat([real, c2], dim=1)
else:
real_cat = real
fake_cat = fake
optimizerT[i].zero_grad()
y_all = torch.cat(
[teacher_disc[i](fake_cat), teacher_disc[i](real_cat)])
label_fake = torch.full(
(int(self.batch_size / self.pack), 1),
FAKE_LABEL,
dtype=torch.float,
device=self.device)
label_true = torch.full(
(int(self.batch_size / self.pack), 1),
REAL_LABEL,
dtype=torch.float,
device=self.device)
labels = torch.cat([label_fake, label_true])
errD = criterion(y_all, labels)
errD.backward()
if (self.regularization == 'dragan'):
pen = teacher_disc[i].dragan_penalty(
real_cat, device=self.device)
pen.backward(retain_graph=True)
optimizerT[i].step()
# train student discriminator
for t_3 in range(self.student_iters):
data_sampler = Sampler(data, self.transformer.output_info)
fakez = torch.normal(mean, std=std)
condvec = self.cond_generator.sample(self.batch_size)
if condvec is None:
c1, m1, col, opt = None, None, None, None
real = data_sampler.sample(self.batch_size, col, opt)
else:
c1, m1, col, opt = condvec
c1 = torch.from_numpy(c1).to(self.device)
m1 = torch.from_numpy(m1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
perm = np.arange(self.batch_size)
np.random.shuffle(perm)
real = data_sampler.sample(self.batch_size, col[perm],
opt[perm])
c2 = c1[perm]
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
if c1 is not None:
fake_cat = torch.cat([fakeact, c1], dim=1)
else:
fake_cat = fake
fake_data = fake_cat
predictions, votes = pate(fake_data,
teacher_disc,
noise_multiplier,
device=self.device)
output = student_disc(fake_data.detach())
# update moments accountant
alphas = alphas + moments_acc(self.num_teachers,
votes,
noise_multiplier,
l_list,
device=self.device)
loss_s = criterion(output, predictions.float().to(self.device))
optimizerS.zero_grad()
loss_s.backward()
if (self.regularization == 'dragan'):
vals = torch.cat([predictions, fake_data], axis=1)
ordered = vals[vals[:, 0].sort()[1]]
data_list = torch.split(
ordered,
predictions.shape[0] - int(predictions.sum().item()))
synth_cat = torch.cat(data_list[1:], axis=0)[:, 1:]
pen = student_disc.dragan_penalty(synth_cat,
device=self.device)
pen.backward(retain_graph=True)
optimizerS.step()
# print ('iterator {i}, student discriminator loss is {j}'.format(i=t_3, j=loss_s))
#train generator
fakez = torch.normal(mean=mean, std=std)
condvec = self.cond_generator.sample(self.batch_size)
if condvec is None:
c1, m1, col, opt = None, None, None, None
else:
c1, m1, col, opt = condvec
c1 = torch.from_numpy(c1).to(self.device)
m1 = torch.from_numpy(m1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
if c1 is not None:
y_fake = student_disc(torch.cat([fakeact, c1], dim=1))
else:
y_fake = student_disc(fakeact)
if condvec is None:
cross_entropy = 0
else:
cross_entropy = self._cond_loss(fake, c1, m1)
if self.loss == 'cross_entropy':
label_g = torch.full((int(self.batch_size / self.pack), 1),
REAL_LABEL,
dtype=torch.float,
device=self.device)
loss_g = criterion(y_fake, label_g.float())
loss_g = loss_g + cross_entropy
else:
loss_g = -torch.mean(y_fake) + cross_entropy
optimizerG.zero_grad()
loss_g.backward()
optimizerG.step()
eps = min((alphas - math.log(self.delta)) / l_list)
if (self.verbose):
print('eps: {:f} \t G: {:f} \t D: {:f}'.format(
eps,
loss_g.detach().cpu(),
loss_s.detach().cpu()))