class CTGANSynthesizer(object):
"""Conditional Table GAN Synthesizer.
This is the core class of the CTGAN project, where the different components
are orchestrated together.
For more details about the process, please check the [Modeling Tabular data using
Conditional GAN](https://arxiv.org/abs/1907.00503) paper.
Args:
embedding_dim (int):
Size of the random sample passed to the Generator. Defaults to 128.
gen_dim (tuple or list of ints):
Size of the output samples for each one of the Residuals. A Residual Layer
will be created for each one of the values provided. Defaults to (256, 256).
dis_dim (tuple or list of ints):
Size of the output samples for each one of the Discriminator Layers. A Linear Layer
will be created for each one of the values provided. Defaults to (256, 256).
l2scale (float):
Weight Decay for the Adam Optimizer. Defaults to 1e-6.
batch_size (int):
Number of data samples to process in each step.
discriminator_steps (int):
Number of discriminator updates to do for each generator update.
From the WGAN paper: https://arxiv.org/abs/1701.07875. WGAN paper
default is 5. Default used is 1 to match original CTGAN implementation.
log_frequency (boolean):
Whether to use log frequency of categorical levels in conditional
sampling. Defaults to ``True``.
blackbox_model:
Model that implements fit, predict, predict_proba
"""
def __init__(
self,
embedding_dim=128,
gen_dim=(256, 256),
dis_dim=(256, 256),
l2scale=1e-6,
batch_size=500,
discriminator_steps=1,
log_frequency=True,
blackbox_model=None,
preprocessing_pipeline=None,
bb_loss="logloss",
confidence_levels=[], #default value if no confidence given
):
self.embedding_dim = embedding_dim
self.gen_dim = gen_dim
self.dis_dim = dis_dim
self.l2scale = l2scale
self.batch_size = batch_size
self.log_frequency = log_frequency
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.trained_epoches = 0
self.discriminator_steps = discriminator_steps
self.blackbox_model = blackbox_model
self.preprocessing_pipeline = preprocessing_pipeline
self.confidence_levels = confidence_levels #set here not in fit
self.bb_loss = bb_loss
#print("self.confidence_levels")
@staticmethod
def _gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1):
"""Deals with the instability of the gumbel_softmax for older versions of torch.
For more details about the issue:
https://drive.google.com/file/d/1AA5wPfZ1kquaRtVruCd6BiYZGcDeNxyP/view?usp=sharing
Args:
logits:
[…, num_features] unnormalized log probabilities
tau:
non-negative scalar temperature
hard:
if True, the returned samples will be discretized as one-hot vectors,
but will be differentiated as if it is the soft sample in autograd
dim (int):
a dimension along which softmax will be computed. Default: -1.
Returns:
Sampled tensor of same shape as logits from the Gumbel-Softmax distribution.
"""
if version.parse(torch.__version__) < version.parse("1.2.0"):
for i in range(10):
transformed = functional.gumbel_softmax(logits,
tau=tau,
hard=hard,
eps=eps,
dim=dim)
if not torch.isnan(transformed).any():
return transformed
raise ValueError("gumbel_softmax returning NaN.")
return functional.gumbel_softmax(logits,
tau=tau,
hard=hard,
eps=eps,
dim=dim)
def _apply_activate(self, data):
data_t = []
st = 0
for item in self.transformer.output_info:
if item[1] == "tanh":
ed = st + item[0]
data_t.append(torch.tanh(data[:, st:ed]))
st = ed
elif item[1] == "softmax":
ed = st + item[0]
transformed = self._gumbel_softmax(data[:, st:ed], tau=0.2)
data_t.append(transformed)
st = ed
else:
assert 0
return torch.cat(data_t, dim=1)
def _cond_loss(self, data, c, m):
loss = []
st = 0
st_c = 0
skip = False
for item in self.transformer.output_info:
if item[1] == "tanh":
st += item[0]
skip = True
elif item[1] == "softmax":
if skip:
skip = False
st += item[0]
continue
ed = st + item[0]
ed_c = st_c + item[0]
tmp = functional.cross_entropy(
data[:, st:ed],
torch.argmax(c[:, st_c:ed_c], dim=1),
reduction="none",
)
loss.append(tmp)
st = ed
st_c = ed_c
else:
assert 0
loss = torch.stack(loss, dim=1)
return (loss * m).sum() / data.size()[0]
def fit(
self,
train_data,
discrete_columns=tuple(),
epochs=300,
verbose=True,
gen_lr=2e-4,
):
"""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.
"""
"""
self.confidence_level = confidence_level
loss_other_name = "loss_bb" if confidence_level != -1 else "loss_d"
history = {"loss_g": [], loss_other_name: []}
"""
# Eli: add Mode-specific Normalization
if not hasattr(self, "transformer"):
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
if not hasattr(self, "cond_generator"):
self.cond_generator = ConditionalGenerator(
train_data, self.transformer.output_info, self.log_frequency)
if not hasattr(self, "generator"):
self.generator = Generator(
self.embedding_dim + self.cond_generator.n_opt, self.gen_dim,
data_dim).to(self.device)
#print(data_dim)
#print(self.cond_generator.n_opt)
if not hasattr(self, "discriminator"):
self.discriminator = Discriminator(
data_dim + self.cond_generator.n_opt,
self.dis_dim).to(self.device)
"""
#after sample in fit gen_output is 120 not 80(cause gen allmost twice)
if not hasattr(self, "discriminator"):
self.discriminator = Discriminator(
24 + self.cond_generator.n_opt, self.dis_dim
).to(self.device)
"""
if not hasattr(self, "optimizerG"):
self.optimizerG = optim.Adam(
self.generator.parameters(),
lr=gen_lr,
betas=(0.5, 0.9),
weight_decay=self.l2scale,
)
if not hasattr(self, "optimizerD"):
self.optimizerD = optim.Adam(self.discriminator.parameters(),
lr=2e-4,
betas=(0.5, 0.9))
assert self.batch_size % 2 == 0
# init mean to zero and std to one
#keep one spot to confidence level, which will be add after normal dis will
mean = torch.zeros(((self.batch_size * self.embedding_dim) - 1),
device=self.device)
std = mean + 1
# steps_per_epoch = max(len(train_data) // self.batch_size, 1)
steps_per_epoch = 10 # magic number decided with Gilad. feel free to change it
loss_other_name = "loss_bb" if self.confidence_levels != [] else "loss_d"
allhist = {"confidence_levels_history": []}
# Eli: start training loop
for current_conf_level in self.confidence_levels:
#need to change, if non confidence give, aka []) no loop will run
#so if no conf, need to jump over conf loop
history = {
"confidence_level": current_conf_level,
"loss_g": [],
loss_other_name: []
}
for i in range(epochs):
self.trained_epoches += 1
for id_ in range(steps_per_epoch):
#we examine confidence lelvel so no need parts which they does not show up
"""
if self.confidence_levels == []:
# discriminator loop
for n in range(self.discriminator_steps):
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 = self.discriminator(fake_cat)
y_real = self.discriminator(real_cat)
pen = self.discriminator.calc_gradient_penalty(
real_cat, fake_cat, self.device
)
loss_d = -(torch.mean(y_real) - torch.mean(y_fake))
if self.confidence_levels == []: # without bb loss
self.optimizerD.zero_grad()
pen.backward(retain_graph=True)
loss_d.backward()
self.optimizerD.step()
"""
# we examine confidence lelvel so no need parts which they does not show up
#as above(no confidence and uses discriminator, no need)
fakez = torch.normal(mean=mean, std=std)
#added here the part of adding conf to sample
#not sure why we need this part in the code but debug shows it this usses this lines
#ask gilad eli
confi = current_conf_level.astype(
np.float32) # generator excpect float
conf = torch.tensor([confi]).to(
self.device
) # change conf to conf input that will sent!!
fakez = torch.cat([fakez, conf], dim=0)
fakez = torch.reshape(
fakez, (self.batch_size, self.embedding_dim))
#end added here the part of adding conf to sample
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 = self.discriminator(
torch.cat([fakeact, c1], dim=1))
else:
y_fake = self.discriminator(fakeact)
if condvec is None:
cross_entropy = 0
else:
cross_entropy = self._cond_loss(fake, c1, m1)
if self.confidence_levels != []:
# generate `batch_size` samples
#samples of fit using yBB in its input
#apply generator twice
gen_out, gen_fakeacts = self.sample(
self.batch_size, current_conf_level)
"""
gen_out=fakeact.detach().cpu().numpy()
gen_out=self.transformer.inverse_transform(gen_out, None)
"""
loss_bb = self._calc_bb_confidence_loss(
gen_out, current_conf_level
) #send specific confidence to loss computation
#return conf bit vector input and bb_y_vec input bit
#send to discriminate to connect gradient again
y_fake = self.discriminator(gen_fakeacts)
#find mean like in original ctgan
#multiple by small number to get realy small value
y_fake_mean = torch.mean(y_fake) * 0.00000000000001
y_fake_mean = y_fake_mean.to(self.device)
#change to float because y_fake is float not double
loss_bb = loss_bb.float()
#add y_fake_mean to lossg like in origin ctgan
#plus loss_bb
loss_g = -y_fake_mean + loss_bb + cross_entropy
#loss_g = -y_fake_mean + cross_entropy
else: # original loss
loss_g = -torch.mean(y_fake) + cross_entropy
self.optimizerG.zero_grad()
loss_g.backward()
"""
print("gradients\n")
for p in self.generator.parameters():
print(p.grad)
"""
"""
for name, param in self.generator.named_parameters():
#if param.requires_grad:
print(name)
print(param.grad)
"""
self.optimizerG.step()
loss_g_val = loss_g.detach().cpu()
loss_other_val = locals()[loss_other_name].detach().cpu()
history["loss_g"].append(loss_g.item())
history[loss_other_name].append(loss_other_val.item())
if verbose:
print(
f"Epoch {self.trained_epoches}, Loss G: {loss_g_val}, {loss_other_name}: {loss_other_val}",
flush=True,
)
allhist["confidence_levels_history"].append(history)
return allhist
#special sample for fit/train part
#extra bit added to input as y,apply gen twice
#in second time with ybb after sent gen_out if first apply generator
#first time with y zero bit second time with bb conf bit
#not relevant function for now
def fit_sample(self,
n,
confidence_level,
condition_column=None,
condition_value=None):
"""Sample data similar to the training data.
Choosing a condition_column and condition_value will increase the probability of the
discrete condition_value happening in the condition_column.
Args:
n (int):
Number of rows to sample.
condition_column (string):
Name of a discrete column.
condition_value (string):
Name of the category in the condition_column which we wish to increase the
probability of happening.
Returns:
numpy.ndarray or pandas.DataFrame
"""
if condition_column is not None and condition_value is not None:
condition_info = self.transformer.covert_column_name_value_to_id(
condition_column, condition_value)
global_condition_vec = (
self.cond_generator.generate_cond_from_condition_column_info(
condition_info, self.batch_size))
else:
global_condition_vec = None
steps = n // self.batch_size + 1
data = []
gen_input_layer = self.generator.seq[0].fc #get linearinpputlayer
#now first sample part need to be
#with out not gradients updates
#so for all first part we do:
with torch.no_grad():
for i in range(steps):
#check if it is okay decrease noise by one for adding conf
#add conf ass input to sample function, as number vector
#give one vector place to adding y to gen noise
mean = torch.zeros(self.batch_size, (self.embedding_dim - 2))
std = mean + 1
fakez = torch.normal(mean=mean, std=std).to(self.device)
#fakez = torch.reshape(fakez,(-1,))
confidence_level = confidence_level.astype(
np.float32) #generator excpect float
#create C column vector of confidence level C
conf = torch.zeros(self.batch_size, 1) + confidence_level
conf = conf.to(
self.device) #change conf to conf input that will sent!!
#first sample will geet zero as y column vector
yzero = torch.zeros(self.batch_size, 1).to(self.device)
#adding y bit to fakez gen noise,generator input
fakez = torch.cat([fakez, conf, yzero], dim=1)
fakez = torch.reshape(fakez,
(self.batch_size, self.embedding_dim))
if global_condition_vec is not None:
condvec = global_condition_vec.copy()
else:
condvec = self.cond_generator.sample_zero(self.batch_size)
if condvec is None:
pass
else:
c1 = condvec
c1 = torch.from_numpy(c1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
#conftens = torch.tensor([0.5]);
#fakez = torch.cat([fakez, conftens], dim=1)#check to delelte!!
#reset to zero weights of y_zero and his bias
gen_input_layer.weight[-1] = 0
#-1 is last line, where I considered yzero weights to be
#which contains all weights of last bit of input
gen_input_layer.bias[-1] = 0
#-1 is last bias,where I considered yzero weights to be
#in biases vector which contain the bias of last bit
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
data.append(fakeact.detach().cpu().numpy())
data = np.concatenate(data, axis=0)
data = data[:n]
# instead of return data
# we will use it here to give it to BB and get new conf y
gen_out = self.transformer.inverse_transform(data, None)
y_prob_for_y_zero = self.blackbox_model.predict_proba(gen_out)
y_conf_gen_for_y_zero = y_prob_for_y_zero[:, 0].astype(np.float32)
#now we use this y conf and put it as y bit instead 0 and sample normally
#######
#second part of samples with BB y conf bit
#######
if condition_column is not None and condition_value is not None:
condition_info = self.transformer.covert_column_name_value_to_id(
condition_column, condition_value)
global_condition_vec = (
self.cond_generator.generate_cond_from_condition_column_info(
condition_info, self.batch_size))
else:
global_condition_vec = None
steps = n // self.batch_size + 1
data = []
for i in range(steps):
#check if it is okay decrease noise by one for adding conf
#add conf ass input to sample function, as vector cloumn
#decrease by one more for y vec (-2 for y bit a conf level in input)
mean = torch.zeros(self.batch_size, (self.embedding_dim - 2))
std = mean + 1
fakez = torch.normal(mean=mean, std=std).to(self.device)
#fakez = torch.reshape(fakez,(-1,))
confidence_level = confidence_level.astype(
np.float32) #generator excpect float
#conf = torch.tensor([confidence_level],requires_grad=True).to(self.device)#change conf to conf input that will sent!!
conf = torch.zeros(self.batch_size, 1) + confidence_level
#conf is target in BBCE loss function
#target ccant have grdient descent True for some reason
#conf.requires_grad = True
conf.to(self.device)
#fix y bb bit
#y_conf_gen_for_y_zero = y_conf_gen_for_y_zero.astype(np.float32)#generator
#y_BB_bit=torch.tensor([y_conf_gen_for_y_zero], requires_grad=True).to(self.device)
#y_BB_column = torch.zeros(self.batch_size,1) + y_conf_gen_for_y_zero
y_BB_column = torch.tensor([y_conf_gen_for_y_zero],
requires_grad=True).to(self.device)
#y_BB_column.requires_grad = True
#take Transpose because shape is 1*50 - need 50*1
y_BB_column = y_BB_column.T
y_BB_column.to(self.device)
#put y_bb bit in fakez gen noise ,generator input
fakez = torch.cat([fakez, conf, y_BB_column], dim=1)
fakez = torch.reshape(fakez, (self.batch_size, self.embedding_dim))
#fakez = fakez.astype(np.float32)
if global_condition_vec is not None:
condvec = global_condition_vec.copy()
else:
condvec = self.cond_generator.sample_zero(self.batch_size)
if condvec is None:
pass
else:
c1 = condvec
c1 = torch.from_numpy(c1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
#conftens = torch.tensor([0.5]);
#fakez = torch.cat([fakez, conftens], dim=1)#check to delelte!!
#turn on input gradient
#fakez.requires_grad = True
#turn on inputs gradient
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
data.append(fakeact.detach().cpu().numpy())
data = np.concatenate(data, axis=0)
data = data[:n]
# return data
#return self.transformer.inverse_transform(data, None)
gen_out = self.transformer.inverse_transform(data, None)
#ret conf level vector and bb_y_vector
#check if return the below written or above written
return conf, y_BB_column
#normal sample for creating in the expirements(with no train)
def sample(self,
n,
confidence_level,
condition_column=None,
condition_value=None):
"""Sample data similar to the training data.
Choosing a condition_column and condition_value will increase the probability of the
discrete condition_value happening in the condition_column.
Args:
n (int):
Number of rows to sample.
condition_column (string):
Name of a discrete column.
condition_value (string):
Name of the category in the condition_column which we wish to increase the
probability of happening.
Returns:
numpy.ndarray or pandas.DataFrame
"""
if condition_column is not None and condition_value is not None:
condition_info = self.transformer.covert_column_name_value_to_id(
condition_column, condition_value)
global_condition_vec = (
self.cond_generator.generate_cond_from_condition_column_info(
condition_info, self.batch_size))
else:
global_condition_vec = None
steps = n // self.batch_size + 1
data = []
our_fackeacts = []
for i in range(steps):
#check if it is okay decrease noise by one for adding conf
#add conf ass input to sample function, as number vector column
mean = torch.zeros(self.batch_size, (self.embedding_dim - 1))
std = mean + 1
fakez = torch.normal(mean=mean, std=std).to(self.device)
#fakez = torch.reshape(fakez,(-1,))
confidence_level = confidence_level.astype(
np.float32) #generator excpect float
#create C column vector of confidence level C
conf = torch.zeros(self.batch_size, 1) + confidence_level
conf = conf.to(
self.device) #change conf to conf input that will sent!!
fakez = torch.cat([fakez, conf], dim=1)
fakez = torch.reshape(fakez, (self.batch_size, self.embedding_dim))
if global_condition_vec is not None:
condvec = global_condition_vec.copy()
else:
condvec = self.cond_generator.sample_zero(self.batch_size)
if condvec is None:
pass
else:
c1 = condvec
c1 = torch.from_numpy(c1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
our_fackeacts.append(fakeact)
data.append(fakeact.detach().cpu().numpy())
data = np.concatenate(data, axis=0)
data = data[:n]
gen_fakeacts = torch.cat(our_fackeacts, 0)
return self.transformer.inverse_transform(data, None), gen_fakeacts
def save(self, path):
assert hasattr(self, "generator")
assert hasattr(self, "discriminator")
assert hasattr(self, "transformer")
# always save a cpu model.
device_bak = self.device
self.device = torch.device("cpu")
self.generator.to(self.device)
self.discriminator.to(self.device)
torch.save(self, path)
self.device = device_bak
self.generator.to(self.device)
self.discriminator.to(self.device)
@classmethod
def load(cls, path):
model = torch.load(path)
model.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
model.generator.to(model.device)
model.discriminator.to(model.device)
return model
def _calc_bb_confidence_loss(self, gen_out, conf_level):
#added specific conf level as input arguement
y_prob = self.blackbox_model.predict_proba(gen_out)
y_conf_gen = y_prob[:, 0] # confidence scores
# create vector with the same size of y_confidence filled with `confidence_level` values
if isinstance(conf_level, list):
conf = np.random.choice(conf_level)
else:
conf = conf_level
y_conf_wanted = np.full(len(y_conf_gen), conf)
# to tensor
y_conf_gen = torch.tensor(y_conf_gen,
requires_grad=True).to(self.device)
y_conf_wanted = torch.tensor(y_conf_wanted).to(self.device)
# loss
bb_loss = self._get_loss_by_name(self.bb_loss)
bb_loss_val = bb_loss(y_conf_gen, y_conf_wanted)
return bb_loss_val
@staticmethod
def _get_loss_by_name(loss_name):
if loss_name == "log":
return torch.nn.BCELoss()
elif loss_name == "l1":
return torch.nn.L1Loss()
elif loss_name == "l2":
return torch.nn.L1Loss()
elif loss_name == "focal":
return WeightedFocalLoss()
else:
raise ValueError(f"Unknown loss name '{loss_name}'")
class CTGAN(object):
"""Conditional Table GAN Synthesizer.
This is the core class of the CTGAN project, where the different components
are orchestrated together.
For more details about the process, please check the [Modeling Tabular data using
Conditional GAN](https://arxiv.org/abs/1907.00503) paper.
Args:
embedding_dim (int):
Size of the random sample passed to the Generator. Defaults to 128.
gen_dim (tuple or list of ints):
Size of the output samples for each one of the Residuals. A Residual Layer
will be created for each one of the values provided. Defaults to (256, 256).
dis_dim (tuple or list of ints):
Size of the output samples for each one of the Discriminator Layers. A Linear Layer
will be created for each one of the values provided. Defaults to (256, 256).
l2scale (float):
Wheight Decay for the Adam Optimizer. Defaults to 1e-6.
batch_size (int):
Number of data samples to process in each step.
"""
def __init__(self,
embedding_dim=128,
gen_dim=(256, 256),
dis_dim=(256, 256),
l2scale=1e-6,
batch_size=500,
epochs=100):
self.embedding_dim = embedding_dim
self.gen_dim = gen_dim
self.dis_dim = dis_dim
self.l2scale = l2scale
self.batch_size = batch_size
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.trained_epoches = 0
self.epochs = epochs
def _apply_activate(self, data):
data_t = []
st = 0
for item in self.transformer.output_info:
if item[1] == 'tanh':
ed = st + item[0]
data_t.append(torch.tanh(data[:, st:ed]))
st = ed
elif item[1] == 'softmax':
ed = st + item[0]
data_t.append(
functional.gumbel_softmax(data[:, st:ed], tau=0.2))
st = ed
else:
assert 0
return torch.cat(data_t, dim=1)
def _cond_loss(self, data, c, m):
loss = []
st = 0
st_c = 0
skip = False
for item in self.transformer.output_info:
if item[1] == 'tanh':
st += item[0]
skip = True
elif item[1] == 'softmax':
if skip:
skip = False
st += item[0]
continue
ed = st + item[0]
ed_c = st_c + item[0]
tmp = functional.cross_entropy(data[:, st:ed],
torch.argmax(c[:, st_c:ed_c],
dim=1),
reduction='none')
loss.append(tmp)
st = ed
st_c = ed_c
else:
assert 0
loss = torch.stack(loss, dim=1)
return (loss * m).sum() / data.size()[0]
def train(self,
train_data,
categorical_columns=tuple(),
epochs=-1,
log_frequency=True,
ordinal_columns=None,
update_epsilon=None,
verbose=False,
mlflow=False):
"""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 init param.
log_frequency (boolean):
Whether to use log frequency of categorical levels in conditional
sampling. Defaults to ``True``.
"""
if (epochs == -1):
epochs = self.epochs
if not hasattr(self, "transformer"):
self.transformer = DataTransformer()
self.transformer.fit(train_data, categorical_columns)
train_data = self.transformer.transform(train_data)
data_sampler = Sampler(train_data, self.transformer.output_info)
data_dim = self.transformer.output_dimensions
if not hasattr(self, "cond_generator"):
self.cond_generator = ConditionalGenerator(
train_data, self.transformer.output_info, log_frequency)
if not hasattr(self, "generator"):
self.generator = Generator(
self.embedding_dim + self.cond_generator.n_opt, self.gen_dim,
data_dim).to(self.device)
if not hasattr(self, "discriminator"):
self.discriminator = Discriminator(
data_dim + self.cond_generator.n_opt,
self.dis_dim).to(self.device)
if not hasattr(self, "optimizerG"):
self.optimizerG = optim.Adam(self.generator.parameters(),
lr=2e-4,
betas=(0.5, 0.9),
weight_decay=self.l2scale)
if not hasattr(self, "optimizerD"):
self.optimizerD = optim.Adam(self.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
steps_per_epoch = max(len(train_data) // self.batch_size, 1)
for i in range(epochs):
self.trained_epoches += 1
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 = self.discriminator(fake_cat)
y_real = self.discriminator(real_cat)
pen = self.discriminator.calc_gradient_penalty(
real_cat, fake_cat, self.device)
loss_d = -(torch.mean(y_real) - torch.mean(y_fake))
self.optimizerD.zero_grad()
pen.backward(retain_graph=True)
loss_d.backward()
self.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 = self.discriminator(torch.cat([fakeact, c1],
dim=1))
else:
y_fake = self.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
self.optimizerG.zero_grad()
loss_g.backward()
self.optimizerG.step()
if (verbose):
if (i % 50 == 0):
print("Epoch %d, Loss G: %.4f, Loss D: %.4f" %
(self.trained_epoches - 1, loss_g.detach().cpu(),
loss_d.detach().cpu()),
flush=True)
if (mlflow):
import mlflow
mlflow.log_metric("loss_d",
float(loss_d.detach().cpu()),
step=self.trained_epoches - 1)
mlflow.log_metric("loss_g",
float(loss_g.detach().cpu()),
step=self.trained_epoches - 1)
def generate(self, n, condition_column=None, condition_value=None):
"""Sample data similar to the training data.
Args:
n (int):
Number of rows to sample.
Returns:
numpy.ndarray or pandas.DataFrame
"""
if condition_column is not None and condition_value is not None:
condition_info = self.transformer.covert_column_name_value_to_id(
condition_column, condition_value)
global_condition_vec = self.cond_generator.generate_cond_from_condition_column_info(
condition_info, self.batch_size)
else:
global_condition_vec = None
steps = n // self.batch_size + 1
data = []
for i in range(steps):
mean = torch.zeros(self.batch_size, self.embedding_dim)
std = mean + 1
fakez = torch.normal(mean=mean, std=std).to(self.device)
if global_condition_vec is not None:
condvec = global_condition_vec.copy()
else:
condvec = self.cond_generator.sample_zero(self.batch_size)
if condvec is None:
pass
else:
c1 = condvec
c1 = torch.from_numpy(c1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
data.append(fakeact.detach().cpu().numpy())
data = np.concatenate(data, axis=0)
data = data[:n]
return self.transformer.inverse_transform(data, None)
def save(self, path):
assert hasattr(self, "generator")
assert hasattr(self, "discriminator")
assert hasattr(self, "transformer")
# always save a cpu model.
device_bak = self.device
self.device = torch.device("cpu")
self.generator.to(self.device)
self.discriminator.to(self.device)
torch.save(self, path)
self.device = device_bak
self.generator.to(self.device)
self.discriminator.to(self.device)
@classmethod
def load(cls, path):
model = torch.load(path)
model.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
model.generator.to(model.device)
model.discriminator.to(model.device)
return model
class CTGANSynthesizer(object):
"""Conditional Table GAN Synthesizer.
This is the core class of the CTGAN project, where the different components
are orchestrated together.
For more details about the process, please check the [Modeling Tabular data using
Conditional GAN](https://arxiv.org/abs/1907.00503) paper.
Args:
embedding_dim (int):
Size of the random sample passed to the Generator. Defaults to 128.
gen_dim (tuple or list of ints):
Size of the output samples for each one of the Residuals. A Residual Layer
will be created for each one of the values provided. Defaults to (256, 256).
dis_dim (tuple or list of ints):
Size of the output samples for each one of the Discriminator Layers. A Linear Layer
will be created for each one of the values provided. Defaults to (256, 256).
l2scale (float):
Weight Decay for the Adam Optimizer. Defaults to 1e-6.
batch_size (int):
Number of data samples to process in each step.
discriminator_steps (int):
Number of discriminator updates to do for each generator update.
From the WGAN paper: https://arxiv.org/abs/1701.07875. WGAN paper
default is 5. Default used is 1 to match original CTGAN implementation.
log_frequency (boolean):
Whether to use log frequency of categorical levels in conditional
sampling. Defaults to ``True``.
blackbox_model:
Model that implements fit, predict, predict_proba
"""
def __init__(
self,
embedding_dim=128,
gen_dim=(256, 256),
dis_dim=(256, 256),
l2scale=1e-6,
batch_size=500,
discriminator_steps=1,
log_frequency=True,
blackbox_model=None,
preprocessing_pipeline=None,
bb_loss="logloss",
):
self.embedding_dim = embedding_dim
self.gen_dim = gen_dim
self.dis_dim = dis_dim
self.l2scale = l2scale
self.batch_size = batch_size
self.log_frequency = log_frequency
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.trained_epoches = 0
self.discriminator_steps = discriminator_steps
self.blackbox_model = blackbox_model
self.preprocessing_pipeline = preprocessing_pipeline
self.confidence_level = -1 # will set in fit
self.bb_loss = bb_loss
@staticmethod
def _gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1):
"""Deals with the instability of the gumbel_softmax for older versions of torch.
For more details about the issue:
https://drive.google.com/file/d/1AA5wPfZ1kquaRtVruCd6BiYZGcDeNxyP/view?usp=sharing
Args:
logits:
[…, num_features] unnormalized log probabilities
tau:
non-negative scalar temperature
hard:
if True, the returned samples will be discretized as one-hot vectors,
but will be differentiated as if it is the soft sample in autograd
dim (int):
a dimension along which softmax will be computed. Default: -1.
Returns:
Sampled tensor of same shape as logits from the Gumbel-Softmax distribution.
"""
if version.parse(torch.__version__) < version.parse("1.2.0"):
for i in range(10):
transformed = functional.gumbel_softmax(logits,
tau=tau,
hard=hard,
eps=eps,
dim=dim)
if not torch.isnan(transformed).any():
return transformed
raise ValueError("gumbel_softmax returning NaN.")
return functional.gumbel_softmax(logits,
tau=tau,
hard=hard,
eps=eps,
dim=dim)
def _apply_activate(self, data):
data_t = []
st = 0
for item in self.transformer.output_info:
if item[1] == "tanh":
ed = st + item[0]
data_t.append(torch.tanh(data[:, st:ed]))
st = ed
elif item[1] == "softmax":
ed = st + item[0]
transformed = self._gumbel_softmax(data[:, st:ed], tau=0.2)
data_t.append(transformed)
st = ed
else:
assert 0
return torch.cat(data_t, dim=1)
def _cond_loss(self, data, c, m):
loss = []
st = 0
st_c = 0
skip = False
for item in self.transformer.output_info:
if item[1] == "tanh":
st += item[0]
skip = True
elif item[1] == "softmax":
if skip:
skip = False
st += item[0]
continue
ed = st + item[0]
ed_c = st_c + item[0]
tmp = functional.cross_entropy(
data[:, st:ed],
torch.argmax(c[:, st_c:ed_c], dim=1),
reduction="none",
)
loss.append(tmp)
st = ed
st_c = ed_c
else:
assert 0
loss = torch.stack(loss, dim=1)
return (loss * m).sum() / data.size()[0]
def fit(
self,
train_data,
discrete_columns=tuple(),
epochs=300,
confidence_level=-1,
verbose=True,
gen_lr=2e-4,
):
"""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.
"""
self.confidence_level = confidence_level
loss_other_name = "loss_bb" if confidence_level != -1 else "loss_d"
history = {"loss_g": [], loss_other_name: []}
# Eli: add Mode-specific Normalization
if not hasattr(self, "transformer"):
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
if not hasattr(self, "cond_generator"):
self.cond_generator = ConditionalGenerator(
train_data, self.transformer.output_info, self.log_frequency)
if not hasattr(self, "generator"):
self.generator = Generator(
self.embedding_dim + self.cond_generator.n_opt, self.gen_dim,
data_dim).to(self.device)
if not hasattr(self, "discriminator"):
self.discriminator = Discriminator(
data_dim + self.cond_generator.n_opt,
self.dis_dim).to(self.device)
if not hasattr(self, "optimizerG"):
self.optimizerG = optim.Adam(
self.generator.parameters(),
lr=gen_lr,
betas=(0.5, 0.9),
weight_decay=self.l2scale,
)
if not hasattr(self, "optimizerD"):
self.optimizerD = optim.Adam(self.discriminator.parameters(),
lr=2e-4,
betas=(0.5, 0.9))
assert self.batch_size % 2 == 0
# init mean to zero and std to one
mean = torch.zeros(self.batch_size,
self.embedding_dim,
device=self.device)
std = mean + 1
# steps_per_epoch = max(len(train_data) // self.batch_size, 1)
steps_per_epoch = 10 # magic number decided with Gilad. feel free to change it
# Eli: start training loop
for i in range(epochs):
self.trained_epoches += 1
for id_ in range(steps_per_epoch):
if self.confidence_level == -1:
# discriminator loop
for n in range(self.discriminator_steps):
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 = self.discriminator(fake_cat)
y_real = self.discriminator(real_cat)
pen = self.discriminator.calc_gradient_penalty(
real_cat, fake_cat, self.device)
loss_d = -(torch.mean(y_real) - torch.mean(y_fake))
if self.confidence_level == -1: # without bb loss
self.optimizerD.zero_grad()
pen.backward(retain_graph=True)
loss_d.backward()
self.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 = self.discriminator(torch.cat([fakeact, c1],
dim=1))
else:
y_fake = self.discriminator(fakeact)
if condvec is None:
cross_entropy = 0
else:
cross_entropy = self._cond_loss(fake, c1, m1)
if self.confidence_level != -1:
# generate `batch_size` samples
gen_out = self.sample(self.batch_size)
loss_bb = self._calc_bb_confidence_loss(gen_out)
loss_g = loss_bb + cross_entropy
else: # original loss
loss_g = -torch.mean(y_fake) + cross_entropy
self.optimizerG.zero_grad()
loss_g.backward()
self.optimizerG.step()
loss_g_val = loss_g.detach().cpu()
loss_other_val = locals()[loss_other_name].detach().cpu()
history["loss_g"].append(loss_g.item())
history[loss_other_name].append(loss_other_val.item())
if verbose:
print(
f"Epoch {self.trained_epoches}, Loss G: {loss_g_val}, {loss_other_name}: {loss_other_val}",
flush=True,
)
return history
def sample(self, n, condition_column=None, condition_value=None):
"""Sample data similar to the training data.
Choosing a condition_column and condition_value will increase the probability of the
discrete condition_value happening in the condition_column.
Args:
n (int):
Number of rows to sample.
condition_column (string):
Name of a discrete column.
condition_value (string):
Name of the category in the condition_column which we wish to increase the
probability of happening.
Returns:
numpy.ndarray or pandas.DataFrame
"""
if condition_column is not None and condition_value is not None:
condition_info = self.transformer.covert_column_name_value_to_id(
condition_column, condition_value)
global_condition_vec = (
self.cond_generator.generate_cond_from_condition_column_info(
condition_info, self.batch_size))
else:
global_condition_vec = None
steps = n // self.batch_size + 1
data = []
for i in range(steps):
mean = torch.zeros(self.batch_size, self.embedding_dim)
std = mean + 1
fakez = torch.normal(mean=mean, std=std).to(self.device)
if global_condition_vec is not None:
condvec = global_condition_vec.copy()
else:
condvec = self.cond_generator.sample_zero(self.batch_size)
if condvec is None:
pass
else:
c1 = condvec
c1 = torch.from_numpy(c1).to(self.device)
fakez = torch.cat([fakez, c1], dim=1)
fake = self.generator(fakez)
fakeact = self._apply_activate(fake)
data.append(fakeact.detach().cpu().numpy())
data = np.concatenate(data, axis=0)
data = data[:n]
# return data
return self.transformer.inverse_transform(data, None)
def save(self, path):
assert hasattr(self, "generator")
assert hasattr(self, "discriminator")
assert hasattr(self, "transformer")
# always save a cpu model.
device_bak = self.device
self.device = torch.device("cpu")
self.generator.to(self.device)
self.discriminator.to(self.device)
torch.save(self, path)
self.device = device_bak
self.generator.to(self.device)
self.discriminator.to(self.device)
@classmethod
def load(cls, path):
model = torch.load(path)
model.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
model.generator.to(model.device)
model.discriminator.to(model.device)
return model
def _calc_bb_confidence_loss(self, gen_out):
y_prob = self.blackbox_model.predict_proba(gen_out)
y_conf_gen = y_prob[:, 0] # confidence scores
# create vector with the same size of y_confidence filled with `confidence_level` values
if isinstance(self.confidence_level, list):
conf = np.random.choice(self.confidence_level)
else:
conf = self.confidence_level
y_conf_wanted = np.full(len(y_conf_gen), conf)
# to tensor
y_conf_gen = torch.tensor(y_conf_gen,
requires_grad=True).to(self.device)
y_conf_wanted = torch.tensor(y_conf_wanted).to(self.device)
# loss
bb_loss = self._get_loss_by_name(self.bb_loss)
bb_loss_val = bb_loss(y_conf_gen, y_conf_wanted)
return bb_loss_val
@staticmethod
def _get_loss_by_name(loss_name):
if loss_name == "log":
return torch.nn.BCELoss()
elif loss_name == "l1":
return torch.nn.L1Loss()
elif loss_name == "l2":
return torch.nn.L1Loss()
elif loss_name == "focal":
return WeightedFocalLoss()
else:
raise ValueError(f"Unknown loss name '{loss_name}'")
