❖ BootstrapSynthesizer

❖ SDV Enterprise Bundle. This feature is available as part of the XSynthesizers Bundle, an optional add-on to SDV Enterprise. For more information, please visit the XSynthesizers Bundle page.

The BootstrapSynthesizer is a synthesizer specifically designed to work when you only have a few rows of data — or if your data is "short and wide", containing more columns than rows. This synthesizer internally bootstraps your real data, and then uses the bootstrapped data to build a model. The modeling part is compatible with any other single-table synthesizer.

from sdv.single_table import BootstrapSynthesizer

synthesizer = BootstrapSynthesizer(metadata)
synthesizer.fit(data)

synthetic_data = synthesizer.sample(num_rows=10)

Creating a synthesizer

When creating your synthesizer, you are required to pass in a Metadata object as the first argument. All other parameters are optional. You can include them to customize the synthesizer.

synthesizer = BootstrapSynthesizer(
    metadata, # required
    num_rows_bootstrap=1000,
    bootstrap_noise_amt=1.5,
    data_synthesizer='GaussianCopulaSynthesizer',
    enforce_min_max_values=True,
    synthesize_missing_values=False
)

Parameter Reference

num_rows_bootstrap : Specify the number of additional rows to bootstrap before modeling the data.

(default) 1000	Bootstrap the original data by creating 1000 rows of additional data
<integer>	Create the desired number of bootstrapped rows before building the model

bootstrap_noise_amount : The amount of noise to add when bootstrapping the data. Some noise is necessary to provide a greater diversity of data points for modeling.

(default) 1.5	When bootstrapping the data, add noise that is equal to 1.5x the standard deviation of each row.
<float>	Add the desired amount of noise to the bootstrapped data. This is the multiplier to the standard deviation, so 1.5 means 1.5x the standard deviation, 2 means 2x the standard deviation, etc.

data_synthesizer : The single-table synthesizer to use when modeling the bootstrapped data.

(default) 'GaussianCopulaSynthesizer'	Use the GaussianCopulaSynthesizer to build a model of the bootstrapped data
<synthesizer_name>	Supply a synthesizer name from the list of single table synthesizers. For example 'XGCSynthesizer' or 'CTGANSynthesizer'.

data_synthesizer_params : A dictionary of parameters to use for the synthesizer

(default) None	Use the default parameters for the synthesizer
<dictionary>	Update the default parameters for the synthesizer you've chosen by providing a dictionary of key/values pairs for each parameter. Refer to the docs for your synthesizer for possible parameters. For example, for GaussianCopulaSynthesizer you can supply: {'default_distribution': 'norm'}.

enforce_min_max_values: Control whether the synthetic data should adhere to the same min/max boundaries set by the real data

(default) True	The synthetic data will contain numerical values that are within the ranges of the real data.
False	The synthetic data may contain numerical values that are less than or greater than the real data.

synthesize_missing_values: Control whether the synthetic data should include missing values.

(default) True	The synthetic data will contain missing values in roughly the same proportion as the original data
False	The synthetic data may should not contain any missing values for numerical and datetime columns.

get_parameters

Use this function to access the all parameters your synthesizer uses -- those you have provided as well as the default ones.

Parameters None

Output A dictionary with the parameter names and the values

synthesizer.get_parameters()

{
    'num_rows_bootstrap': 1000,
    'bootstrap_noise_amt': 1.5,
    'data_synthesizer': 'GaussianCopulaSynthesizer',
    'enforce_min_max_bounds': True,
    'synthesize_missing_values': False
}

The returned parameters are a copy. Changing them will not affect the synthesizer.

get_metadata

Use this function to access the metadata object that you have included for the synthesizer

Parameters None

Output A Metadata object

metadata = synthesizer.get_metadata()

The returned metadata is a copy. Changing it will not affect the synthesizer.

Learning from your data

To learn a machine learning model based on your real data, use the fit method.

fit

Parameters

• (required) data: A pandas DataFrame object containing the real data that the machine learning model will learn from

Output (None)

synthesizer.fit(data)

Technical Details: This synthesizer internally bootstraps your real data by adding noise, and then uses the bootstrapped data to build a model. The modeling part is compatible with any other single-table synthesizer.

Saving your synthesizer

Save your trained synthesizer for future use.

save

Use this function to save your trained synthesizer as a Python pickle file.

Parameters

• (required) filepath: A string describing the filepath where you want to save your synthesizer. Make sure this ends in .pkl

Output (None) The file will be saved at the desired location

synthesizer.save(
    filepath='my_synthesizer.pkl'
)

BootstrapSynthesizer.load

Use this function to load a trained synthesizer from a Python pickle file

Parameters

• (required) filepath: A string describing the filepath of your saved synthesizer

Output Your synthesizer, as an XGCSynthesizer object

from sdv.single_table import BootstrapSynthesizer

synthesizer = BootstrapSynthesizer.load(
    filepath='my_synthesizer.pkl'
)

What's next?

After training your synthesizer, you can now sample synthetic data. See the Sampling section for more details.

Want to improve your synthesizer? Input logical rules in the form of constraints, and customize the transformations used for pre- and post-processing the data.

For more details, see Customizations.

FAQ

What does it mean to bootstrap data?

Bootstrapping data means creating more examples of training data using your original rows. Bootstrapping involves duplicating the original rows, and then adding some noise to the values in order to create a larger, more varied dataset.

This is necessary because the AI-based models expect a larger number of data points for accurate learning.