Danger

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.ScalarRange

class sdv.constraints.ScalarRange(column_name, low_value, high_value, strict_boundaries=True)

Ensure that the column_name is between the range of low and high.

The transformation strategy works by replacing the column_name with a scaled version and then applying a logit function. The reverse transform applies a sigmoid to the data and then scales it back to the original space.

Parameters

• column_name (str) – Name of the column that has to be between the lower bound and upper bound.

• low_value (int or float) – Lower bound on the values of the column_name.

• high_value (int or float) – Higher bound on the values of the column_name.

• strict_boundaries (bool) – Whether the comparison of the values should be strict >= or not > when comparing them.

__init__(column_name, low_value, high_value, strict_boundaries=True)

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

Methods

__init__(column_name, low_value, high_value)	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 column_name is between the low and high values.
reverse_transform(table_data)	Handle logic around reverse transforming constraints.
to_dict()	Return a dict representation of this Constraint.
transform(table_data)	Perform necessary transformations needed by constraint.

Attributes

constraint_columns

sdv.constraints.Range.to_dict sdv.constraints.ScalarRange.fit