FixedCombinations

The FixedCombinations constraint enforces that the combinations between a set of columns are fixed. That is, no other permutations or shuffling is allowed other than what's already observed in the data.

Constraint API

Create a FixedCombinations constraint.

Parameters:

• (required) column_names: A list of two or more columns whose combinations are fixed. The columns must be categorical. The SDV will not further shuffle the data between these column names.

• table_name: A string with the name of the table to apply this to. Required if you have a multi-table dataset.

from sdv.cag import FixedCombinations

my_constraint = FixedCombinations(
    column_names=['city', 'country']
)

Usage

Apply the constraint to any SDV synthesizer. Then fit and sample as usual.

synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.add_constraints([my_constraint])

synthesizer.fit(data)
synthetic_data = synthesizer.sample()

For more information about using predefined constraints, please see the Constraint-Augmented Generation tutorial.

❖ Auto-Detection

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

Auto-detection is allowed for this constraint but it is not enabled by default. We recommend auto-detecting the FixedNullCombinations constraint instead, as it covers all the logic of this constraint and more!

If you'd like to auto-detect this constraint specifically, you can supply it in the auto-detection parameters. This will detect all instances of the constraint throughout the dataset.

constraints = synthesizer.detect_constraints(
    data,
    constraint_classes=['FixedCombinations']
)

Detection Parameters: None

FAQs

Why am I only allowed to input categorical columns?

This constraint can only fix the combination of categorical columns because only categorical columns have a fixed set of possible values. For other types of columns such as numerical, datetime, or PII, it's possible to synthesize new values that never appeared in the real data. So it's not possible to fix their permutations.

In some cases, you may not want to fix the actual value of a numerical, datetime, or PII column but rather just fix whether it's null or non-null. In that case, we recommend using the FixedNullCombinations constraint instead.

Why can't I apply this constraint to a single column?

This constraint ensures that the synthetic data only contains combinations that exist in the real data. If there is only one column, there are no combinations.

The SDV already guarantees that the synthetic data contains the same categorical values as the real data for a single column.

The synthetic data has the same combination multiple times. Is this intended?

Yes. This constraint prevents the SDV from creating additional permutations between columns. But the same permutations are allowed to appear multiple times.

For example, it will prevent the SDV from inventing new city, country pairs, but a valid pair such as Boston, USA may appear more than once.

What happens to null values?

This constraint will consider a null value as part of a combination. For example if a null in one column always appears next to a True value in another column, the constraint will learn that.

If you would to only fix the combinations of the null values (and allow synthesizer to continue creating new permutations of the non-null values), please used the FixedNullCombinations constraint instead.