sdv.constraints.Constraint

class sdv.constraints.Constraint(handling_strategy, fit_columns_model=False)[source]

Constraint base class.

This class is not intended to be used directly and should rather be subclassed to create different types of constraints.

If handling_strategy is passed with the value transform or reject_sampling, the filter_valid or transform and reverse_transform methods will be replaced respectively by a simple identity function.

constraint_columns

The names of the columns used by this constraint.

Type

tuple[str]

rebuild_columns

The names of the columns that this constraint will rebuild during reverse_transform.

Type

typle[str]

Parameters
  • handling_strategy (str) – How this Constraint should be handled, which can be transform, reject_sampling or all.

  • fit_columns_model (bool) – If False, reject sampling will be used to handle conditional sampling. Otherwise, a model will be trained and used to sample other columns based on the conditioned column.

__init__(handling_strategy, fit_columns_model=False)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(handling_strategy[, fit_columns_model])

Initialize self.

filter_valid(table_data)

Get only the rows that are valid.

fit(table_data)

Fit Constraint class to data.

fit_transform(table_data)

Fit this Constraint to the data and then transform it.

from_dict(constraint_dict)

Build a Constraint object from a dict.

is_valid(table_data)

Say whether the given table rows are valid.

reverse_transform(table_data)

Identity method for completion.

to_dict()

Return a dict representation of this Constraint.

transform(table_data)

Perform necessary transformations needed by constraint.

Attributes

constraint_columns

rebuild_columns