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sdv.metrics.tabular.NumericalMLP.compute

classmethod NumericalMLP.compute(real_data, synthetic_data, metadata=None, key_fields=None, sensitive_fields=None, model_kwargs=None, loss_function=None, loss_function_kwargs=None)

Compute this metric.

This fits an adversial attacker model on the synthetic data and then evaluates it making predictions on the real data.

A key_fields column(s) name must be given, either directly or as a first level entry in the metadata dict, which will be used as the key column(s) for the attack.

A sensitive_fields column(s) name must be given, either directly or as a first level entry in the metadata dict, which will be used as the sensitive column(s) for the attack.

Parameters

  • real_data (Union[numpy.ndarray, pandas.DataFrame]) – The values from the real dataset.

  • synthetic_data (Union[numpy.ndarray, pandas.DataFrame]) – The values from the synthetic dataset.

  • metadata (dict) – Table metadata dict. If not passed, it is build based on the real_data fields and dtypes.

  • key_fields (list(str)) – Name of the column(s) to use as the key attributes.

  • sensitive_fields (list(str)) – Name of the column(s) to use as the sensitive attributes.

  • model_kwargs (dict) – Key word arguments of the attacker model. cls.MODEL_KWARGS will be used if none is provided.

  • loss_function (Class) – The loss function to use. cls.LOSS_FUNCTION will be used if none is provided.

  • loss_function_kwargs (dict) – Key word arguments of the loss function. cls.LOSS_FUNCTION_KWARGS will be used if none is provided.

Returns

Scores obtained by the attackers when evaluated on the real data.

Return type

union[float, tuple[float]]

sdv.metrics.tabular.NumericalMLP.get_subclasses sdv.metrics.tabular.NumericalLR