Synthetic Model
In AryaXAI, generating synthetic data begins with training a 'Synthetic model.' This model generates initial data based on the uploaded training data. After assessing and approving the quality of this generated data, users can proceed to produce additional synthetic data.
Following model training, the 'Synthetic Models' displays the trained models along with their respective status. This comprehensive analysis provides key information such as the model's Name, creator, creation date, overall quality score, Column shapes, and Column pair trends.
Train Model
Train synthetic model:
project.train_synthetic_model()
project.train_synthetic_model(
model_name='CTGAN', # CTGAN / GPT2 , models are avaialable
data_config=data_config,
hyper_params=hyper_params,
# instance_type = "2xlargeT4" #pass instance_type for dedicated reesources , defaults to shared resources
)
Help function to train the synthetic model:
help(project.train_synthetic_model)
Define parameters for your synthetic model:
data_config = {
"tags": ["Training"],
"feature_include": feature_include # data used for training/generating synthetic data
}
hyper_params = {
"epochs": 2, # epochs are no of iteration of data into model (more the better, but longer) # Max 100 supported
"test_ratio": 0.2 # Data used for training/generating synthetic data. how much to keep aside for testing
}
project.train_synthetic_model(
model_name='CTGAN', # CTGAN / GPT2 , models are avaialable
data_config=data_config,
hyper_params=hyper_params,
# instance_type = "2xlargeT4" #pass instance_type for dedicated reesources , defaults to shared resources
)
Fetch trained models:
project.synthetic_models()
Synthetic Model
To generate data and analyze the synthetic model quality via SDK:
# select model you want
project.synthetic_model(model_name='CTGAN_v14')
model = project.synthetic_model(model_name='CTGAN_v1')
model.get_data_quality()
Select compute
Retrieve available synthetic custom servers provided by AryaXAI library:
aryaxai.available_synthetic_custom_servers()
Synthetic Model
In AryaXAI, generating synthetic data begins with training a 'Synthetic model.' This model generates initial data based on the uploaded training data. After assessing and approving the quality of this generated data, users can proceed to produce additional synthetic data.
Following model training, the 'Synthetic Models' displays the trained models along with their respective status. This comprehensive analysis provides key information such as the model's Name, creator, creation date, overall quality score, Column shapes, and Column pair trends.
Train Model
Train synthetic model:
project.train_synthetic_model()
project.train_synthetic_model(
model_name='CTGAN', # CTGAN / GPT2 , models are avaialable
data_config=data_config,
hyper_params=hyper_params,
# instance_type = "2xlargeT4" #pass instance_type for dedicated reesources , defaults to shared resources
)
Help function to train the synthetic model:
help(project.train_synthetic_model)
Define parameters for your synthetic model:
data_config = {
"tags": ["Training"],
"feature_include": feature_include # data used for training/generating synthetic data
}
hyper_params = {
"epochs": 2, # epochs are no of iteration of data into model (more the better, but longer) # Max 100 supported
"test_ratio": 0.2 # Data used for training/generating synthetic data. how much to keep aside for testing
}
project.train_synthetic_model(
model_name='CTGAN', # CTGAN / GPT2 , models are avaialable
data_config=data_config,
hyper_params=hyper_params,
# instance_type = "2xlargeT4" #pass instance_type for dedicated reesources , defaults to shared resources
)
Fetch trained models:
project.synthetic_models()
Synthetic Model
To generate data and analyze the synthetic model quality via SDK:
# select model you want
project.synthetic_model(model_name='CTGAN_v14')
model = project.synthetic_model(model_name='CTGAN_v1')
model.get_data_quality()
Select compute
Retrieve available synthetic custom servers provided by AryaXAI library:
aryaxai.available_synthetic_custom_servers()