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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.metrics.tabular.
CategoricalEnsemble
The Categorical Ensemble privacy metric. Scored based on the CategoricalEnsembleAttacker.
When calling cls.compute, please make sure to pass in the argument model_kwargs (dict): {attackers: list[PrivacyAttackerModel]}.
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__()
Initialize self.
compute(real_data, synthetic_data[, …])
compute
Compute this metric.
compute_breakdown(real_data, synthetic_data)
compute_breakdown
Compute this metric breakdown.
get_subclasses([include_parents])
get_subclasses
Recursively find subclasses of this metric.
normalize(raw_score)
normalize
Compute the normalized value of the metric.
Attributes
ACCURACY_BASE
MODEL_KWARGS
goal
max_value
min_value
name