Flexible generation

Generators in MOSTLY AI have flexible generation enabled by default. You can enable or disable flexible generation for each table in a generator. You can do so before you train a generator. Once a generator is trained, you cannot change its flexible generation configuration.

Even if enabled, the use of rebalancing, imputation, and fairness is optional when you generate new synthetic datasets.

If you want to restrict your models and disable the options to rebalance columns, impute data, or use fairness, you can disable flexible generation before starting training.

Why flexible generation?

Flexible generation enables a generator to rebalance data, impute data, and use fairness.

• With rebalancing, you can rebalance your datasets in a highly correlated manner. For a simplified example, if you decide to increase the percentage of women in your dataset, correlated features such as female first names, respective average income, respective job titles, will also be impacted.
• With imputation, you can replace any null values you might have in your original data with statistically reliable values.
• With fairness, you can generate statistical parity synthetic data and define which categorical columns (for example, income) should be generated fairly based on which other sensitive columns (for example, sex, race, age, and so on).

You can use flexible generation for scenarios such as fraud detection, statistical parity synthetic data, what-if scenarios, testing of an increase in less likely outcomes or decrease in rather likely outcomes.

Enable flexible generation

Flexible generation is on by default for each table in a generator.

config = {
    "name": "US census",
    "tables": [
        {
            "data": "https://mostly.ai/docs/datasets/us-census-income.csv",
            "name": "census",
            "model_configuration": {
                "enable_flexible_generation": True,
            }
        }
    ]
}