def vae_gan_model():
epochs, latent_size = 1500, 50
name = "best_test-2_vae_gan_gantrain_200-128_64_50_0005"
n_layer = 3
hidden_neurons = 128
learning_rate = 0.0002
cgan_sample, vae_sample, ks_result1, ks_result2, fid = run_VAECGAN_Generator(
train_main, test_main, epochs, latent_size, n_samples, n_layer,
learning_rate, hidden_neurons, name, random_neg, random_pos)
print("VAE sample shape", vae_sample.shape)
tsne_data_comparision(random_pos,
random_neg,
cgan_sample,
name,
vae_sample,
method='VAE_CGAN')
get_combined_generated = get_combined_generated_real(
cgan_sample, train_main, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test_main)
multiple_classifier(train_X, train_y.values.ravel(), test_X,
test_y.values.ravel(), name, ks_result1, ks_result2,
fid)
print(
"======================Both GAN and VAE=============================")
name = name + "VAE_GAN-both"
get_combined_generated = get_combined_generated_real(
vae_sample, get_combined_generated, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test_main)
def run_VanilaGANGenerator(train, test, epochs, latent_size, hidden_neurons,
n_samples, learning_rate, random_neg_sample,
real_pos, name):
gan_obj = VanilaGANGenerator(train, test, epochs, latent_size, name)
gan_obj.define_models_GAN(learning_rate, hidden_neurons, type=None)
gan_obj.train_model()
gan_sample = gan_obj.generate_samples(n_samples)
#tsne_plot(gan_sample, name)
ks_result1, ks_result2 = compare_attributes_distribution(
real_pos, gan_sample, name)
fid = calculate_fid(real_pos, gan_sample)
print("Frechet Inception Distance:", fid)
tsne_data_comparision(real_pos, random_neg_sample, gan_sample, name, None,
'VGAN')
get_combined_generated = get_combined_generated_real(
gan_sample, train, name)
# print("Count of Failure and non failure",get_combined_generated.failure.value_counts())
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test)
# classify_baseline_model(test_y)
#multiple_classifier(train_X, train_y.values.ravel(), test_X, test_y.values.ravel(), name, ks_result1, ks_result2,
# fid)
# xgb_classifier(train_X, train_y.values.ravel(), test_X, test_y.values.ravel(),name)
# rf_classifier(train_X, train_y.values.ravel(), test_X, test_y.values.ravel(),name)
return gan_sample, ks_result1, ks_result2, fid
def cgan_vae_model():
epochs, latent_size = 200, 50
name = "best_test_cgan-vae_100-128_64_50_0005"
n_layer = 2
hidden_neurons = 64
learning_rate = 0.0005
vae_sample, cgan_sample, ks_result1, ks_result2, fid = run_CGAN_VAE_Generator(
train_main, test_main, epochs, latent_size, n_samples, n_layer,
learning_rate, hidden_neurons, name, random_neg, random_pos)
tsne_data_comparision(random_pos, random_neg, vae_sample, name,
cgan_sample, 'CGAN_VAE')
get_combined_generated = get_combined_generated_real(
vae_sample, train_main, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test_main)
multiple_classifier(train_X, train_y.values.ravel(), test_X,
test_y.values.ravel(), name, ks_result1, ks_result2,
fid)
print(
"======================Both GAN and VAE=============================")
name = name + "CGAN-VAE-both"
get_combined_generated = get_combined_generated_real(
vae_sample, get_combined_generated, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test_main)
def run_without_sampling(train, test, id):
name = id + '-Without-Sampling'
(train_X, train_y) = split_XY(train)
(test_X, test_y) = split_XY(test)
#multiple_classifier(train_X, train_y.values.ravel(), test_X, test_y.values.ravel(),name)
#xgb_classifier(train_X, train_y.values.ravel(), test_X, test_y.values.ravel(), name)
rf_classifier(train_X, train_y.values.ravel(), test_X,
test_y.values.ravel(), name)
def train_cgan():
datasets_dir = 'datasets/'
imputation_types = ['NA', 'mean', 'knn']
feature_engineerings = ['Features-selection', 'ALL', 'PCA']
for imputation_type in imputation_types:
id = "APS-CGAN" + imputation_type + "-"
train_main, test_main = preprocess(data_dir=datasets_dir,
imputation_type=imputation_type)
#train_main,test_main = load_preprocess_aps_data(imputation_type)
for feature_engineering in feature_engineerings:
if feature_engineering == 'PCA':
train, test = load_PCA_data(train_main, test_main,
datasets_dir, imputation_type)
feature_n = '-PCA='
elif feature_engineering == 'Features-selection':
train, test = feature_selection(train_main, test_main, 120)
feature_n = '-Select_Features_K=' + str(120)
else:
feature_n = '-All_features='
train, test = train_main, test_main
random_neg = get_random_sample(train, which='neg')
random_pos = get_random_sample(train, which='pos')
print(train.shape, test.shape)
for i in [100, 300]:
epochN = '-epochs=' + str(i)
for j in [32, 64, 128]:
latentN = '-latent_size=' + str(j)
for n_samples in [2000, 5000]:
n_sampleN = '-n_samples=' + str(n_samples)
for n_layer in [2, 3]:
layer_N = '-n_layer=' + str(n_layer)
for hidden_neurons in [32, 64, 128]:
hidden_N = '-hidden_neurons_base=' + str(
hidden_neurons)
for learning_rate in [0.0005, 0.0002]:
lr_N = '-learning_rate=' + str(
learning_rate)
name = id + feature_n + epochN + latentN + n_sampleN + layer_N + hidden_N + lr_N
#run_VanilaGANGenerator(train,test,i,j,n_samples,imputation_type,id)
cgan_sample, ks_result1, ks_result2, fid = run_CGANGenerator(
train, test, i, j, n_samples, n_layer,
learning_rate, hidden_neurons, name,
random_neg, random_pos)
get_combined_generated = get_combined_generated_real(
cgan_sample, train, name)
(train_X, train_y
) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test)
multiple_classifier(
train_X, train_y.values.ravel(),
test_X, test_y.values.ravel(), name,
ks_result1, ks_result2, fid)
def train_vae():
datasets_dir = 'datasets/'
imputation_types = ['mean', 'NA', 'knn']
feature_engineerings = ['All', 'Features-selection', 'PCA']
for imputation_type in imputation_types:
train_main, test_main = preprocess(data_dir=datasets_dir,
imputation_type=imputation_type)
id = "APS-VAE" + imputation_type + "-"
#train_main, test_main = load_preprocess_aps_data()
for feature_engineering in feature_engineerings:
if feature_engineering == 'PCA':
train, test = load_PCA_data(train_main, test_main,
datasets_dir, imputation_type)
feature_n = '-PCA='
elif feature_engineering == 'Features-selection':
train, test = feature_selection(train_main, test_main)
feature_n = '-Select_Features_80='
else:
feature_n = '-All_features='
train, test = train_main, test_main
random_neg = get_random_sample(train)
#run_without_sampling(train,test,id)
print(train.shape, test.shape)
for i in [50, 100]:
epochN = '-epochs=' + str(i)
for j in [32, 64, 128]:
latentN = '-latent_size=' + str(j)
for n_samples in [5000]:
n_sampleN = '-n_samples=' + str(n_samples)
for n_layer in [2, 3]:
layer_N = '-n_layer=' + str(n_layer)
for hidden_neurons in [32, 64, 128]:
hidden_N = '-hidden_neurons_base=' + str(
hidden_neurons)
for learning_rate in [0.0005, 0.0002]:
lr_N = '-learning_rate=' + str(
learning_rate)
name = id + feature_n + epochN + latentN + n_sampleN + layer_N + hidden_N + lr_N
vae_sample, ks_result1, ks_result2, fid = run_VAEGenerator(
train, test, i, j, n_samples, n_layer,
hidden_neurons, learning_rate, name,
random_neg)
get_combined_generated = get_combined_generated_real(
vae_sample, train, name)
(train_X, train_y
) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test)
multiple_classifier(
train_X, train_y.values.ravel(),
test_X, test_y.values.ravel(), name,
ks_result1, ks_result2, fid)
def vanillaGAN_model():
epochs, latent_size, name = 200, 50, "test_500-50-64-0001_Vanilla-GAN"
learning_rate = 0.0005
hidden_neurons = 64
gan_sample, ks_result1, ks_result2, fid = run_VanilaGANGenerator(
train_main, test_main, epochs, latent_size, hidden_neurons, n_samples,
learning_rate, random_neg, random_pos, name)
get_combined_generated = get_combined_generated_real(
gan_sample, train_main, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test_main)
multiple_classifier(train_X, train_y.values.ravel(), test_X,
test_y.values.ravel(), name, ks_result1, ks_result2,
fid)
def run_VAECGAN_Generator(train, test, epochs, latent_size, n_samples, n_layer,
learning_rate, hidden_neurons, name,
random_neg_sample, random_pos):
vae_sample = pd.read_csv(r'generated_data/VAE_no_mmd.csv').sample(
n=1000, random_state=123)
# vae_sample, ks_result1, ks_result2, fid = run_VAEGenerator(train, test, epochs, latent_size,
# n_samples, n_layer,
# hidden_neurons,
# learning_rate, name,
# random_neg)
# # vae_sample, noise = vae_obj.generate_samples(n_samples)
# get_vae_combine = get_combined_generated_real(vae_sample, train, "vae_gan")
vae_combine = get_combined_generated_real(vae_sample, train, name)
cgan_sample, ks_result3, ks_result4, fid2 = run_CGANGenerator(
vae_combine,
test,
epochs,
latent_size,
n_samples,
n_layer,
learning_rate,
hidden_neurons,
name,
random_neg_sample,
random_pos,
model_type='VAE_CGAN')
tsne_data_comparision(random_pos, random_neg_sample, cgan_sample[:, :-1],
name, vae_sample, 'VAE_CGAN')
#get_combined_generated = get_combined_generated_real(cgan_sample, train, name)
return cgan_sample, vae_sample, ks_result3, ks_result4, fid2
print("Count of Failure and non failure", get_combined_generated.shape,
get_combined_generated.failure.value_counts())
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test)
#multiple_classifier(train_X, train_y.values.ravel(), test_X, test_y.values.ravel(),
# name, ks_result3, ks_result4, fid2)
print(
"======================Both GAN and VAE=============================")
name = name + "VAE_GAN-both"
get_combined_generated = get_combined_generated_real(
vae_sample, get_combined_generated, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test)
def vae_model():
epochs_base, latent_size, name = 50, 64, "test_250_64_64_2_0005_VAE_no_mmd"
n_layer = 2
hidden_neurons = 64
learning_rate = 0.0005
vae_sample, ks_result1, ks_result2, fid = run_VAEGenerator(
train_main, test_main, epochs_base, latent_size, n_samples, n_layer,
hidden_neurons, learning_rate, name, random_neg)
get_combined_generated = get_combined_generated_real(
vae_sample, train_main, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test_main)
multiple_classifier(train_X, train_y.values.ravel(), test_X,
test_y.values.ravel(), name, ks_result1, ks_result2,
fid)
def cgan_model():
epochs, latent_size = 500, 50
name = "best_with_cgan_best"
n_layer = 3
hidden_neurons = 128
learning_rate = 0.0005
cgan_sample, ks_result1, ks_result2, fid = run_CGANGenerator(
train_main, test_main, epochs, latent_size, n_samples, n_layer,
learning_rate, hidden_neurons, name, random_neg, random_pos)
get_combined_generated = get_combined_generated_real(
cgan_sample, train_main, name)
(train_X, train_y) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test_main)
multiple_classifier(train_X, train_y.values.ravel(), test_X,
test_y.values.ravel(), name, ks_result1, ks_result2,
fid)
def run_baseline_model(train, test, n_sample, name):
sm_obj = SMOTEGenerator(train, test)
train_X, train_y = sm_obj.generate_samples(n_sample)
tsne_plot(train_X, "smoted_samples")
(test_X, test_y) = split_XY(test)
#classify_baseline_model(test_y)
#tsne_data_comparision(train,gan_sample)
multiple_classifier(train_X, train_y.values.ravel(), test_X,
test_y.values.ravel(), 'smote_train')
def train_classify(method):
if method == 'VAE':
name = 'classify-vae'
sample = pd.read_csv(r'pre_trained/data/VAE_no_mmd.csv')
#print(sample.shape)
get_vae_combine = get_combined_generated_real(sample, train_main, name)
(train_X, train_y) = split_XY(get_vae_combine)
(test_X, test_y) = split_XY(test_main)
threshold, threshold_cost, cm = xgb_classifier(train_X, train_y,
test_X, test_y, name)
result(cm, 'VAE', 'classify-vae')
elif method == 'CGAN':
name = 'classify-cgan'
cgan_sample = pd.read_csv(
r'pre_trained/data/cgan_800_50_64_2_0002_cluster_test.csv')
#cgan_sample = pd.read_csv(r'generated_data/cgan_200_50_64_2_0002.csv')
print(cgan_sample.shape)
cgan_combine = get_combined_generated_real(cgan_sample, train_main,
name)
(train_X, train_y) = split_XY(cgan_combine)
(test_X, test_y) = split_XY(test_main)
#threshold, threshold_cost, cm = xgb_classifier(train_X, train_y, test_X, test_y, name)
threshold, threshold_cost, cm = rf_classifier(train_X, train_y, test_X,
test_y, name)
result(cm, 'CGAN', 'rf_classify-cgan')
elif method == 'VAE_CGAN':
name = 'classify-VAE_CGAN'
cgan_sample = pd.read_csv(
r'pre_trained/data/vae_gan_gantrain_200_32_50_0002_test.csv')
#cgan_sample = pd.read_csv(r'generated_data/cgan_200_50_64_2_0002.csv')
print(cgan_sample.shape)
get_vae_combine = get_combined_generated_real(cgan_sample, train_main,
name)
(train_X, train_y) = split_XY(get_vae_combine)
(test_X, test_y) = split_XY(test_main)
threshold, threshold_cost, cm = xgb_classifier(train_X, train_y,
test_X, test_y, name)
result(cm, 'VAE_CGAN', 'classify-VAE_CGAN')
def train_vae_cgan():
datasets_dir = 'datasets/'
imputation_types = ['mean', 'knn', 'NA']
feature_engineerings = ['All', 'PCA', 'Features-selection']
for imputation_type in imputation_types:
id = "APS-VAE-CGAN_Both-" + imputation_type + "-"
train_main, test_main = preprocess(data_dir=datasets_dir,
imputation_type=imputation_type)
#train_main, test_main = load_preprocess_aps_data(imputation_type)
for feature_engineering in feature_engineerings:
if feature_engineering == 'PCA':
train, test = load_PCA_data(train_main, test_main,
datasets_dir, imputation_type)
feature_n = '-PCA-'
elif feature_engineering == 'Features-selection':
train, test = feature_selection(train_main, test_main, 120)
feature_n = '-Select_Features=' + str(120)
else:
feature_n = '-All_features-'
train, test = train_main, test_main
random_neg = get_random_sample(train, which='neg')
random_pos = get_random_sample(train, which='pos')
print(train.shape, test.shape)
for i in [200, 400]:
epochN = '-epochs=' + str(i)
for j in [32, 64, 128]:
latentN = '-latent_size=' + str(j)
for n_samples in [2000, 5000]:
n_sampleN = '-n_samples=' + str(n_samples)
for n_layer in [2, 3]:
layer_N = '-n_layer=' + str(n_layer)
for hidden_neurons in [32, 64, 128]:
hidden_N = '-hidden_neurons_base=' + str(
hidden_neurons)
for learning_rate in [0.0005, 0.0002]:
lr_N = '-learning_rate=' + str(
learning_rate)
name = id + feature_n + epochN + latentN + n_sampleN + layer_N + hidden_N + lr_N
vae_sample, ks_result1, ks_result2, fid = run_VAEGenerator(
train, test, i, j, n_samples, n_layer,
hidden_neurons, learning_rate, name,
random_neg)
# vae_sample, noise = vae_obj.generate_samples(n_samples)
get_vae_combine = get_combined_generated_real(
vae_sample, train, "vae_gan")
cgan_sample, ks_result3, ks_result4, fid2 = run_CGANGenerator(
get_vae_combine, test, i, j, n_samples,
n_layer, learning_rate, hidden_neurons,
name, random_neg, random_pos)
get_combined_generated = get_combined_generated_real(
cgan_sample, train, name)
print(
"Count of Failure and non failure",
get_combined_generated.shape,
get_combined_generated.failure.
value_counts())
(train_X, train_y
) = split_XY(get_combined_generated)
(test_X, test_y) = split_XY(test)
multiple_classifier(
train_X, train_y.values.ravel(),
test_X, test_y.values.ravel(), name,
ks_result1, ks_result2, fid)
def run_with_pretrained_model(method, model_flag):
if method == 'VAE':
print("========================Start VAE==========================")
name = 'classify-vae'
if model_flag:
vae = get_model('pre_trained/model/VAE')
vae_sample = base_generator.generate_samples(vae, 32, 5000)
#vae_sample = vae.generate_samples(5000)
ks_result1, ks_result2 = compare_distribution(
random_pos, vae_sample, name)
fid = calculate_fid(random_pos, vae_sample)
print("## VAE Quantative Analysis##")
print("KS-Test 1 ", ks_result1)
print("KS-Test 2 ", ks_result2)
print(" FID ", fid)
vae_combine = get_combined_generated_real(vae_sample, train_main,
name)
else:
vae_sample = pd.read_csv(r'pre_trained/data/VAE.csv')
vae_combine = get_combined_generated_real(vae_sample, train_main,
name)
#load combined data of train and VAE sample
#vae_combine = pd.read_csv('pre_trained/data/vae_cgan_combine.csv',index_col=0)
(train_X, train_y) = split_XY(vae_combine)
(test_X, test_y) = split_XY(test_main)
threshold, threshold_cost, cm = xgb_classifier(train_X, train_y,
test_X, test_y, name)
result(cm, 'VAE', 'XGB-classify-vae')
threshold_rf, threshold_cost_rf, cm_rf = rf_classifier(
train_X, train_y, test_X, test_y, name)
result(cm_rf, 'VAE', 'RF-classify-vae')
print("========================End VAE==========================")
elif method == 'CGAN':
print(
"\n========================Start CGAN==========================\n")
name = 'classify-cgan'
if model_flag:
cgan = get_model('pre_trained/model/CGAN')
cgan_sample = base_generator.generate_samples(
cgan, 64, 5000) #cgan.generate_samples(5000)
ks_result1, ks_result2 = compare_distribution(
random_pos, cgan_sample, name)
fid = calculate_fid(random_pos, cgan_sample)
print("## CGAN Quantative Analysis##")
print("KS-Test 1 ", ks_result1)
print("KS-Test 2 ", ks_result2)
print(" FID ", fid)
cgan_combine = get_combined_generated_real(cgan_sample, train_main,
name)
else:
#cgan_sample = pd.read_csv(r'pre_trained/data/cgan_800_50_64_2_0002_cluster_test.csv')
#cgan_sample = pd.read_csv(r'generated_data/cgan_200_50_64_2_0002.csv')
#cgan_combine = get_combined_generated_real(cgan_sample, train_main, name)
#load combined data of CGAN sample and Train datsets
cgan_combine = pd.read_csv(r'pre_trained/data/CGAN.csv',
index_col=0)
(train_X, train_y) = split_XY(cgan_combine)
(test_X, test_y) = split_XY(test_main)
test_X = test_X[train_X.columns]
threshold, threshold_cost, cm = xgb_classifier(train_X, train_y,
test_X, test_y, name)
result(cm, 'CGAN', 'XGB-classify-vae')
threshold_rf, threshold_cost_rf, cm_rf = rf_classifier(
train_X, train_y, test_X, test_y, name)
result(cm_rf, 'CGAN', 'RF-classify-cgan')
print("\n========================End CGAN==========================")
elif method == 'VAE_CGAN':
print(
"\n========================Start VAE-CGAN==========================\n"
)
name = 'classify-VAE_CGAN'
if model_flag:
#vae_sample = pd.read_csv(r'pre_trained/data/VAE_no_mmd.csv')
vae = get_model('pre_trained/model/VAE')
vae_sample = base_generator.generate_samples(
vae, 50, 5000) #vae.generate_samples(5000)
ks_result1, ks_result2 = compare_distribution(
random_pos, vae_sample, name)
fid = calculate_fid(random_pos, vae_sample)
print("##VAE Quantative Analysis##")
print("KS-Test 1 ", ks_result1)
print("KS-Test 2 ", ks_result2)
print(" FID ", fid)
cgan = get_model('pre_trained/model/VAE_CGAN')
cgan_sample = base_generator.generate_samples(
cgan, 32, 5000) #cgan.generate_samples(5000)
ks_result1, ks_result2 = compare_distribution(
random_pos, cgan_sample, name)
fid = calculate_fid(random_pos, cgan_sample)
print("##VAE-CGAN Quantative Analysis##")
print("KS-Test 1 ", ks_result1)
print("KS-Test 2 ", ks_result2)
print(" FID ", fid)
vae_cgan_combine = get_combined_generated_real(
cgan_sample, train_main, name)
else:
# cgan_sample = pd.read_csv(r'generated_data/cgan_200_50_64_2_0002.csv')
# cgan_combine = get_combined_generated_real(cgan_sample, train_main, name)
#load combined data of CGAN sample and Train datsets
#vae_gan_gantrain_200_32_50_0002_test
vae_cgan_combine = pd.read_csv(r'pre_trained/data/VAE_CGAN.csv',
index_col=0)
print("VAE CGAN combine shape::", vae_cgan_combine.shape)
(train_X, train_y) = split_XY(vae_cgan_combine)
(test_X, test_y) = split_XY(test_main)
threshold, threshold_cost, cm = xgb_classifier(train_X, train_y,
test_X, test_y, name)
result(cm, 'VAE_CGAN', 'XGB-classify-VAE_CGAN')
threshold_rf, threshold_cost_rf, cm_rf = rf_classifier(
train_X, train_y, test_X, test_y, name)
result(cm_rf, 'VAE_CGAN', 'RF-classify-VAE-CGAN')
print("========================End VAE-CGAN==========================")
def generate_samples(self, n_samples):
X, y = split_XY(self.train_data)
oversample = SMOTE()
X, y = oversample.fit_resample(X, y)
return X, y
