print('Finish Synthetic Data Generation')
#%% Performance Metrics
# 1. Discriminative Score
Acc = list()
for tt in range(Sub_Iteration):
Temp_Disc = discriminative_score_metrics(dataX, dataX_hat)
Acc.append(Temp_Disc)
Discriminative_Score.append(np.mean(Acc))
# 2. Predictive Performance
MAE_All = list()
for tt in range(Sub_Iteration):
MAE_All.append(predictive_score_metrics(dataX, dataX_hat))
Predictive_Score.append(np.mean(MAE_All))
# Print Results
print('Discriminative Score - Mean: ' +
str(np.round(np.mean(Discriminative_Score), 4)) + ', Std: ' +
str(np.round(np.std(Discriminative_Score), 4)))
print('Predictive Score - Mean: ' +
str(np.round(np.mean(Predictive_Score), 4)) + ', Std: ' +
str(np.round(np.std(Predictive_Score), 4)))
#%% 3. Visualization
PCA_Analysis(dataX, dataX_hat, 'original_pca.png')
tSNE_Analysis(dataX, dataX_hat, 'original_tsne.png')
Discriminative_Score.append(np.mean(Acc))
print('generated discriminative score')
# 2. Predictive Performance
MAE_All = list()
for tt in range(Sub_Iteration):
MAE_All.append(predictive_score_metrics (dataX, dataX_hat))
Predictive_Score.append(np.mean(MAE_All))
print('generated predictive score')
pyplot.scatter(np.linspace(1,24,24),dataX_hat[1][:,2], color='red')
pyplot.scatter(np.linspace(1,24,24),dataX[1][:,2], color='blue')
#%% 3. Visualization
PCA_Analysis (dataX, dataX_hat)
tSNE_Analysis (dataX, dataX_hat)
# Print Results
print('Discriminative Score - Mean: ' + str(np.round(np.mean(Discriminative_Score),4)) + ', Std: ' + str(np.round(np.std(Discriminative_Score),4)))
print('Predictive Score - Mean: ' + str(np.round(np.mean(Predictive_Score),4)) + ', Std: ' + str(np.round(np.std(Predictive_Score),4)))
f = open('/home/awasthi/shrutarv/timegan/score.txt','w')
f.write('Discriminative Score - Mean: ' + str(np.round(np.mean(Discriminative_Score),4)) + ', Std: ' + str(np.round(np.std(Discriminative_Score),4)) + '\n' + 'Predictive Score - Mean: ' + str(np.round(np.mean(Predictive_Score),4)) + ', Std: ' + str(np.round(np.std(Predictive_Score),4)))
f.close()
data_unshuffle = unshuffle_data(dataX_hat,idx)
data_reorder = reorder_data(data_unshuffle)
data_denormal = denormalize(data_reorder, min, max)
data_to_excel(data_denormal)
print('Data Saved')
Discriminative_Score.append(np.mean(Acc))
# 2. Predictive Performance
MAE_All = list()
for tt in range(Sub_Iteration):
MAE_All.append(predictive_score_metrics (dataXNorm, dataX_hatNorm))
Predictive_Score.append(np.mean(MAE_All))
print('Finish TGAN iterations')
#%% 3. Visualization
PCA_Analysis (dataXNorm, dataX_hatNorm)
tSNE_Analysis (dataXNorm, dataX_hatNorm)
# Print Results
print('Discriminative Score - Mean: ' + str(np.round(np.mean(Discriminative_Score),4)) + ', Std: ' + str(np.round(np.std(Discriminative_Score),4)))
print('Predictive Score - Mean: ' + str(np.round(np.mean(Predictive_Score),4)) + ', Std: ' + str(np.round(np.std(Predictive_Score),4)))
#Rounding off the device ids and their values
dataX_hat[:,:,1:] = np.round(dataX_hat[:,:,1:])
#Combining all samples
allSamples = [dataX_hat[i,j,:] for i in range(dataX_hat.shape[0]) for j in range(dataX_hat.shape[1])]
allSamples = np.array(allSamples)
#For sorting allSamples on the basis of time
sortedIndex = allSamples[:,0].argsort()
Discriminative_Score.append(np.mean(Acc))
# 2. Predictive Performance
MAE_All = list()
for tt in range(Sub_Iteration):
MAE_All.append(predictive_score_metrics(dataX, dataX_hat))
Predictive_Score.append(np.mean(MAE_All))
print("Finished Computing Performance Metrics")
print('Finish TGAN iterations')
#%%
# tSNE AND PCA
tSNE_Analysis(
dataX, dataX_hat,
os.path.join("hyperparameter_tests/lstm_sl_24", data_name + "_tSNE.png"))
PCA_Analysis(
dataX, dataX_hat,
os.path.join("hyperparameter_tests/lstm_sl_24", data_name + "_PCA.png"))
print("Finished tSNE and PCA analysis")
#%%
# invert scaling
dataX_hat = reverse_soro_data_loading(dataX_hat, seq_length)
dataX_hat = scaler.inverse_transform(dataX_hat)
dataX = reverse_soro_data_loading(dataX, seq_length)
dataX = scaler.inverse_transform(dataX)
print("Finished Data Inversion")
def tSNE(self, output_file='tSNE.png'):
tSNE_Analysis(self.dataX, self.dataX_hat, output_file)
print('Finish Synthetic Data Generation')
#%% Performance Metrics
# 1. Discriminative Score
Acc = list()
for tt in range(Sub_Iteration):
Temp_Disc = discriminative_score_metrics (dataX, dataX_hat)
Acc.append(Temp_Disc)
Discriminative_Score.append(np.mean(Acc))
# 2. Predictive Performance
MAE_All = list()
for tt in range(Sub_Iteration):
MAE_All.append(predictive_score_metrics (dataX, dataX_hat))
Predictive_Score.append(np.mean(MAE_All))
with open(parameters['output_dir'] + "generated.pck", "wb") as gen:
pickle.dump(dataX_hat, gen)
#%% 3. Visualization
# Print Results
print('Discriminative Score - Mean: ' + str(np.round(np.mean(Discriminative_Score),4)) + ', Std: ' + str(np.round(np.std(Discriminative_Score),4)))
print('Predictive Score - Mean: ' + str(np.round(np.mean(Predictive_Score),4)) + ', Std: ' + str(np.round(np.std(Predictive_Score),4)))
PCA_Analysis (dataX, dataX_hat,parameters)
tSNE_Analysis (dataX, dataX_hat,parameters)
