def extend_gan_train(x_train, y_train, x_test, cat_cols, gen_x_times=1.2, epochs=300):
"""
Extends train by generating new data by GAN
:param x_train: train dataframe
:param y_train: target for train dataframe
:param x_test: dataframe
:param cat_cols: List of categorical columns
:param gen_x_times: Factor for which initial dataframe should be increased
:param cat_cols: List of categorical columns
:param epochs: Number of epoch max to train the GAN
:return: extended train with target
"""
if gen_x_times == 0:
raise ValueError("Passed gen_x_times with value 0!")
x_train["target"] = y_train
x_test_bigger = int(1.1 * x_test.shape[0] / x_train.shape[0])
ctgan = CTGANSynthesizer()
ctgan.fit(x_train, cat_cols, epochs=epochs)
generated_df = ctgan.sample((x_test_bigger) * x_train.shape[0])
data_dtype = x_train.dtypes.values
for i in range(len(generated_df.columns)):
generated_df[generated_df.columns[i]] = generated_df[
generated_df.columns[i]
].astype(data_dtype[i])
generated_df = pd.concat(
[
x_train.sample(frac=(x_test_bigger), replace=True, random_state=42),
generated_df,
]
).reset_index(drop=True)
num_cols = []
for col in x_train.columns:
if "num" in col:
num_cols.append(col)
for num_col in num_cols:
min_val = x_test[num_col].quantile(0.02)
max_val = x_test[num_col].quantile(0.98)
generated_df = generated_df.loc[
(generated_df[num_col] >= min_val) & (generated_df[num_col] <= max_val)
]
generated_df = generated_df.reset_index(drop=True)
ad_model = adversarial_test(x_test, generated_df.drop("target", axis=1), cat_cols)
generated_df["test_similarity"] = ad_model.predict(
generated_df.drop("target", axis=1), return_shape=False
)
generated_df.sort_values("test_similarity", ascending=False, inplace=True)
generated_df = generated_df.head(int(gen_x_times * x_train.shape[0]))
x_train = pd.concat(
[x_train, generated_df.drop("test_similarity", axis=1)], axis=0
).reset_index(drop=True)
del generated_df
gc.collect()
return x_train.drop("target", axis=1), x_train["target"]
def baseline_ctgan(args, df_naive):
from ctgan import CTGANSynthesizer
ctgan = CTGANSynthesizer()
ctgan.fit(df_naive)
ctgan_samples = ctgan.sample(args.n_gen_samples)
# print(ctgan_samples)
return ctgan_samples
def augment_ctgan_classification(csvfile):
data = pd.read_csv(csvfile)
ctgan = CTGANSynthesizer()
ctgan.fit(data, epochs=10) #15
percent_generated = 1
df_gen = ctgan.sample(int(len(data) * percent_generated))
df_gen['class_'] = df_gen['class_'].apply(np.floor)
values = list(set(list(data['class_'])))
newclass = df_gen['class_']
newclass2 = list()
for i in range(len(newclass)):
if newclass[i] not in values:
newvalue = find_nearestval(newclass[i], values)
newclass2.append(newvalue)
else:
newclass2.append(newclass[i])
df_gen['class_'] = newclass2
# now count each value and balance
classcol = list(df_gen['class_'])
unique_classes = list(set(df_gen['class_']))
counts = list()
for i in range(len(unique_classes)):
counts.append(classcol.count(unique_classes[i]))
minval = min(counts)
print(minval)
# now balance out the classes by removing all to minimum value
for i in range(len(unique_classes)):
print(unique_classes[i])
index_pos_list = get_index_positions(classcol, unique_classes[i])
while len(index_pos_list) >= minval:
index_pos_list = get_index_positions(classcol, unique_classes[i])
random_ind = random.choice(index_pos_list)
df_gen = df_gen.drop(df_gen.index[random_ind])
classcol = list(df_gen['class_'])
print('augmented with %s samples' % (str(len(unique_classes) * minval)))
print(df_gen)
# now add both togrther to make new .CSV file
newfile1 = 'augmented_' + csvfile
df_gen.to_csv(newfile1, index=0)
# now combine augmented and regular dataset
data2 = pd.read_csv('augmented_' + csvfile)
frames = [data, data2]
result = pd.concat(frames)
newfile = 'augmented_combined_' + csvfile
result.to_csv(newfile, index=0)
return [csvfile, newfile1, newfile2]
class CTGAN(GenerativeModel):
"""
A generative adversarial network for tabular data
"""
def __init__(self,
metadata,
embedding_dim=128,
gen_dim=(256, 256),
dis_dim=(256, 256),
l2scale=1e-6,
batch_size=500,
epochs=300):
self.synthesiser = CTGANSynthesizer(embedding_dim, gen_dim, dis_dim,
l2scale, batch_size, epochs)
self.metadata = metadata
self.datatype = DataFrame
self.trained = False
self.__name__ = 'CTGAN'
def fit(self, rawTrain):
"""
Fit a generative model of the training data distribution.
See <https://github.com/sdv-dev/CTGAN> for details.
:param rawTrain: DataFrame or ndarray: Training set
"""
assert isinstance(
rawTrain, self.datatype
), f'{self.__class__.__name__} expects {self.datatype} as input data but got {type(rawTrain)}'
logger.debug(
f'Start fitting {self.__class__.__name__} to data of shape {rawTrain.shape}...'
)
self.synthesiser.fit(rawTrain, self.metadata)
logger.debug(f'Finished fitting')
self.trained = True
def generate_samples(self, nsamples):
"""
Samples synthetic data records from the fitted generative distribution
:param nsamples: int: Number of synthetic records to generate
:return: synData: DataFrame: A synthetic dataset
"""
assert self.trained, "Model must first be fitted to some data."
logger.debug(f'Generate synthetic dataset of size {nsamples}')
synData = self.synthesiser.sample(nsamples)
return synData
def run_cgan(X_S, x_T, n_samples):
X_train = np.vstack([X_S, x_T])
ctgan = CTGANSynthesizer(embedding_dim=128,
gen_dim=(256, 256),
dis_dim=(256, 256),
l2scale=1e-6,
batch_size=500)
ts = time.time()
ctgan.fit(X_train, epochs=300)
Z = ctgan.sample(n_samples)
# print(Z[0].tolist())
# return None
run_train = time.time() - ts
return Z, run_train
def augment_ctgan_regression(csvfile):
data=pd.read_csv(csvfile)
ctgan = CTGANSynthesizer()
ctgan.fit(data,epochs=10) #15
percent_generated=1
df_gen = ctgan.sample(int(len(data)*percent_generated))
print('augmented with %s samples'%(str(len(df_gen))))
print(df_gen)
# now add both togrther to make new .CSV file
newfile1='augmented_'+csvfile
df_gen.to_csv(newfile1, index=0)
# now combine augmented and regular dataset
data2=pd.read_csv('augmented_'+csvfile)
frames = [data, data2]
result = pd.concat(frames)
newfile2='augmented_combined_'+csvfile
result.to_csv(newfile2, index=0)
return [csvfile,newfile1,newfile2]
class CTGAN(GenerativeModel):
"""A conditional generative adversarial network for tabular data"""
def __init__(self, metadata,
embedding_dim=128, gen_dim=(256, 256),
dis_dim=(256, 256), l2scale=1e-6,
batch_size=500, epochs=300,
multiprocess=False):
self.synthesiser = CTGANSynthesizer(embedding_dim, gen_dim, dis_dim,
l2scale, batch_size, epochs)
self.metadata = metadata
self.datatype = DataFrame
self.multiprocess = bool(multiprocess)
self.infer_ranges = True
self.trained = False
self.__name__ = 'CTGAN'
def fit(self, data):
"""Train a generative adversarial network on tabular data.
Input data is assumed to be of shape (n_samples, n_features)
See https://github.com/DAI-Lab/SDGym for details"""
assert isinstance(data, self.datatype), f'{self.__class__.__name__} expects {self.datatype} as input data but got {type(data)}'
LOGGER.debug(f'Start fitting {self.__class__.__name__} to data of shape {data.shape}...')
self.synthesiser.fit(data, self.metadata)
LOGGER.debug(f'Finished fitting')
self.trained = True
def generate_samples(self, nsamples):
"""Generate random samples from the fitted Gaussian distribution"""
assert self.trained, "Model must first be fitted to some data."
LOGGER.debug(f'Generate synthetic dataset of size {nsamples}')
synthetic_data = self.synthesiser.sample(nsamples)
return synthetic_data
def main():
dataset = 'diabetes'
epochs = 300
train_size = 0.7
print(datetime.datetime.now(), 'Dataset: %s' % dataset)
D = get_dataset(dataset, path_dataset, normalize=None)
X_train, y_train, X_test, y_test = D['X_train'], D['y_train'], D[
'X_test'], D['y_test']
# n_classes = D['n_classes']
n_features = D['n_features']
feature_names = D['feature_names']
class_name = D['class_name']
le = LabelEncoder()
le.fit(y_train)
y_train = le.transform(y_train)
y_test = le.transform(y_test)
Xy_train = np.hstack((X_train, y_train.reshape(-1, 1)))
print(datetime.datetime.now(), 'Training CTGAN')
ctgan = CTGANSynthesizer(embedding_dim=128,
gen_dim=(256, 256),
dis_dim=(256, 256),
l2scale=1e-6,
batch_size=500)
ts = time.time()
ctgan.fit(Xy_train, epochs=epochs, discrete_columns=[n_features + 1])
cgan_fit_time = time.time() - ts
n_fake_instances = len(Xy_train)
print(datetime.datetime.now(), 'Generating synthetic data')
ts = time.time()
Xy_fake = ctgan.sample(n_fake_instances)
cgan_gen_time = time.time() - ts
# print('F 0', np.mean(Xy_fake[:, 0]), np.min(Xy_fake[:,0]), np.max(Xy_fake[:,0]))
# print('F 1', np.mean(Xy_fake[:, 1]), np.min(Xy_fake[:, 1]), np.max(Xy_fake[:, 1]))
#
# print('R 0', np.mean(X_train[:, 0]), np.min(X_train[:, 0]), np.max(X_train[:, 0]))
# print('R 1', np.mean(X_train[:, 1]), np.min(X_train[:, 1]), np.max(X_train[:, 1]))
# return -1
print(datetime.datetime.now(), 'Storing synthetic data')
df = pd.DataFrame(data=Xy_fake, columns=feature_names + [class_name])
df.to_csv(path_syht_dataset + '%s.csv' % dataset, index=False)
X_fake = Xy_fake[:, :-1]
X_real = X_train
y_real = np.ones(len(X_real))
y_fake = np.zeros(len(X_fake))
X_rf = np.concatenate([X_real, X_fake])
y_rf = np.concatenate([y_real, y_fake])
X_rf_train, X_rf_test, y_rf_train, y_rf_test = train_test_split(
X_rf, y_rf, train_size=train_size, stratify=y_rf)
res_dict = dict()
for clf_name, clf in clf_list.items():
print(datetime.datetime.now(), 'Training %s' % clf_name)
ts = time.time()
clf.fit(X_rf_train, y_rf_train)
disc_fit_time = time.time() - ts
pickle.dump(
clf, open(path_discr + '%s_%s.pickle' % (dataset, clf_name), 'wb'))
y_pred_train = clf.predict(X_rf_train)
y_pred_test = clf.predict(X_rf_test)
acc_train = accuracy_score(y_rf_train, y_pred_train)
acc_test = accuracy_score(y_rf_test, y_pred_test)
res_dict['%s_acc_train' % clf_name] = acc_train
res_dict['%s_acc_test' % clf_name] = acc_test
res_dict['%s_disc_fit_time' % clf_name] = disc_fit_time
print(datetime.datetime.now(),
'\taccuracy %.3f, %.3f' % (acc_train, acc_test))
res_dict['dataset'] = dataset
res_dict['cgan_fit_time'] = cgan_fit_time
res_dict['cgan_gen_time'] = cgan_gen_time
print(datetime.datetime.now(), 'Storing evaluation')
store_result(res_dict, path_ctgan_eval + 'tabular.json')
def main(_):
data, meta = read_data(FLAGS.data, FLAGS.meta)
model = CTGANSynthesizer(epochs=FLAGS.max_epoch)
model.fit(data, meta['discrete_columns'], tuple())
data_syn = model.sample(FLAGS.sample)
write_data(data_syn, meta, FLAGS.output)
data_cancer = pd.read_csv("../../data/diabetes/raw.csv")
data_diab = data_cancer[50000:100000]
good_data_cancer = data_cancer.dropna()
good_data_cancer.profile_report().to_file("data_diab.html")
exit()
ctgan = CTGANSynthesizer()
ctgan.fit(good_data_cancer, [
'race', 'gender', 'age', 'weight', 'payer_code', 'medical_specialty',
'diag_1', 'diag_2', 'diag_3', 'max_glu_serum', 'A1Cresult', 'metformin',
'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride',
'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide', 'pioglitazone',
'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone', 'tolazamide',
'examide', 'citoglipton', 'insulin', 'glyburide-metformin',
'glipizide-metformin', 'glimepiride-pioglitazone',
'metformin-rosiglitazone', 'metformin-pioglitazone', 'change',
'diabetesMed', 'readmitted'
])
torch.save(ctgan, '../../models/diabetes/ctgan-diabetes-50k-1')
samples = ctgan.sample(300)
print(samples)
print(samples.describe())
#samples.profile_report().to_file("sample_cancer.html")
#print(samples)
def add_synthetic(self, method='random', apply={}):
################# random
if method == 'random':
sys.stdout.write("\r")
print('\n###########\nadding random synthetic samples ..\n###########\n')
print('\n{}\n'.format(apply))
for i in tqdm(apply.keys()):
noise_factor = self.X_train[self.y_train_l == i].mean() * 0.001
totalcount = 0
max_shape = self.X_train[self.y_train_l == i].shape[0] + apply[i]
for xx in range(3):
set_shape = self.X_train[self.y_train_l == i].shape[0]
if set_shape < max_shape:
howManyTimes = round(math.log(max_shape / set_shape)) + 1
for j in range(howManyTimes):
totalcount += 1
rareEventX = self.X_train[self.y_train_l == i].copy()
rareEventY = self.y_train_l[self.y_train_l == i].copy()
noisyRareEvent = rareEventX + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=rareEventX.shape)
if rareEventX.shape[0] + noisyRareEvent.shape[0] > max_shape:
will_be_subtracted = (rareEventX.shape[0] + noisyRareEvent.shape[0]) - max_shape
new_shape = noisyRareEvent.shape[0] - will_be_subtracted
self.X_train = np.concatenate((self.X_train, noisyRareEvent[:new_shape]), axis=0)
self.y_train_l = np.concatenate((self.y_train_l, rareEventY[:new_shape]), axis=0)
else:
self.X_train = np.concatenate((self.X_train, noisyRareEvent), axis=0)
self.y_train_l = np.concatenate((self.y_train_l, rareEventY), axis=0)
self.y_train = tf.keras.utils.to_categorical(self.y_train_l)
print(self.all_labels[i], self.X_train[self.y_train_l == i].shape, " {}.th generation with {} noise".format(totalcount, noise_factor))
noise_factor *= 0.5
################# smoteenn
elif method == 'smoteenn':
print('\n###########\nadding smoteenn synthetic samples ..\n###########\n')
print('\n{}\n'.format(apply))
competitors = apply.keys()
sampling_strategy = {}
for i in competitors:
sampling_strategy[i] = int(self.y_train_l[self.y_train_l == i].shape[0] + apply[i]*1.5)
smote_enn = SMOTEENN(random_state=0, n_jobs=64,
sampling_strategy=sampling_strategy,
enn=imblearn.under_sampling.EditedNearestNeighbours(sampling_strategy='auto', n_neighbors=3)
)
filters = [(self.y_train_l == i) for i in competitors]
X_resampled, y_resampled = smote_enn.fit_resample(self.X_train_l[np.logical_or.reduce(filters)],
self.y_train_l[np.logical_or.reduce(filters)])
# remove selected classes from dataset
filters = [(self.y_train_l != i) for i in apply.keys()]
previous_shapes = {}
for i in apply.keys():
previous_shapes[i] = self.y_train_l[self.y_train_l == i].shape[0]
self.X_train_l = self.X_train_l[np.logical_and.reduce(filters)]
self.y_train_l = self.y_train_l[np.logical_and.reduce(filters)]
for i in apply.keys():
self.X_train_l = np.concatenate([
self.X_train_l,
X_resampled[y_resampled == i][:previous_shapes[i] + apply[i]]
], axis=0)
self.y_train_l = np.concatenate([
self.y_train_l,
y_resampled[y_resampled == i][:previous_shapes[i] + apply[i]]
], axis=0)
self.X_train = self.X_train_l.reshape(self.X_train_l.shape[0], 1, self.X_train_l.shape[1])
self.y_train = tf.keras.utils.to_categorical(self.y_train_l)
elif method == 'ctgan':
print('\n###########\nadding ctgan synthetic samples ..\n###########\n')
print('\n{}\n'.format(apply))
for i in tqdm(apply.keys()):
gan_batch_size = 10
if self.y_train_l[self.y_train_l == i].shape[0] > 100:
gan_batch_size = 20
elif self.y_train_l[self.y_train_l == i].shape[0] > 300:
gan_batch_size = 50
gan = CTGANSynthesizer(batch_size=gan_batch_size)
gan.fit(self.X_train_l[self.y_train_l == i], epochs=100)
# generate samples
generated = gan.sample(apply[i])
self.X_train_l = np.concatenate([self.X_train_l, generated], axis=0)
self.X_train = self.X_train_l.reshape(self.X_train_l.shape[0], 1, self.X_train_l.shape[1])
self.y_train_l = np.concatenate([self.y_train_l, np.ones(shape=(apply[i],)) * i], axis=0)
self.y_train = tf.keras.utils.to_categorical(self.y_train_l)
# Split combined caste religion into caste and religion
caste_religion = pd.DataFrame(samples.CasteReligion.str.split(' ',
1).tolist(),
columns=['Religion', 'Caste'])
samples = pd.concat([samples, caste_religion], axis=1)
del samples['CasteReligion']
gc.collect()
# Add district name in the generated data
samples['District'] = district
print("ADDING JOB COLUMNS...")
# Generate job columns
job_generator = CTGANSynthesizer()
job_generator = job_generator.load("job.pkl")
jobs = job_generator.sample(population_counts)
# Split Job into JobLabel and JobID
job_label_id = pd.DataFrame(jobs.Job.str.rsplit(' ', 1).tolist(),
columns=['JobLabel', 'JobID'])
samples = pd.concat([samples, job_label_id], axis=1)
gc.collect()
#samples['Job']
# Essential worker columns list
print("Adding essential workers...")
essential_list = [
'Police', 'Sweepers', 'Sales, shop', 'Shopkeepers', 'Boilermen',
'Nursing', 'Journalists', 'Electrical', 'Food', 'Physicians',
'Mail distributors', 'Loaders', 'Village officials', 'Govt officials',
print("Starting DPGAN...")
dpgan = PytorchDPSynthesizer(DPGAN(), GeneralTransformer(), epsilon=1)
dpgan.fit(df, categorical_columns=['sex','educ','race','married'], verbose=True)
synth_data = dpgan.sample(df.size)
s = synth_data.corr()
d = df.corr()
print("Save and reload...")
dpgan.save(os.path.join(git_root_dir, os.path.join("saved_models","dpgan.ckpt")))
newInstance = PytorchDPSynthesizer(DPGAN(), GeneralTransformer(), epsilon=1)
newInstance.load(os.path.join(git_root_dir, os.path.join("saved_models","dpgan.ckpt")))
newInstance.fit(df,categorical_columns=['sex','educ','race','married'], update_epsilon=2, verbose=True)
synth_data = newInstance.sample(df.size)
s = synth_data.corr()
d = df.corr()
a2 = d.subtract(s)
print("Starting CTGAN...")
from ctgan import CTGANSynthesizer
ctgan = CTGANSynthesizer()
ctgan.fit(df, ['sex','educ','race','married'], epochs=10)
synth_data = ctgan.sample(df.size)
s = synth_data.corr()
d = df.corr()
#print(d.subtract(s))