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You are looking at the documentation for an older version of the SDV! We are no longer supporting or maintaining this version of the software
Click here to go to the new docs pages.
sdv.constraints.
FixedCombinations
Ensure that the combinations across multiple columns are fixed.
One simple example of this constraint can be found in a table that contains the columns country and city, where each country can have multiple cities and the same city name can even be found in multiple countries, but some combinations of country/city would produce invalid results.
This constraint would ensure that the combinations of country/city found in the sampled data always stay within the combinations previously seen during training.
column_names (list[str]) – Names of the columns that need to produce fixed combinations. Must contain at least two columns.
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__(column_names)
Initialize self.
filter_valid(table_data)
filter_valid
Get only the rows that are valid.
fit(table_data)
fit
Fit Constraint class to data.
Constraint
fit_transform(table_data)
fit_transform
Fit this Constraint to the data and then transform it.
from_dict(constraint_dict)
from_dict
Build a Constraint object from a dict.
is_valid(table_data)
is_valid
Say whether the column values are within the original combinations.
reverse_transform(table_data)
reverse_transform
Handle logic around reverse transforming constraints.
to_dict()
to_dict
Return a dict representation of this Constraint.
transform(table_data)
transform
Perform necessary transformations needed by constraint.
Attributes
constraint_columns