API Overview

Using twinify as a library, you retain full control over data loading, pre- and postprocessing, in contrast to the command line tool. The main actors in the twinify APIs are twinify.InferenceModel and twinify.InferenceResult.

InferenceModel

InferenceModel fully encapsulates a model and algorithm to fit it to the data. It defines a single function

fit(data: pd.DataFrame, rng: d3p.random.PRNGState, epsilon: float, delta: float, **kwargs) -> InferenceResult

which takes an input data set given as a pandas DataFrame as well as privacy parameters and a randomness state. It returns a representation of the model fitted to the data in the form of a InferenceResult object.

Currently twinify provides twinify.dpvi.DPVIModel and twinify.napsu_mq.NapsuMQModel as concrete implementations, with the following initializers:

• DPVIModel(model: NumPyroModelFunction, guide: Optional[NumPyroGuideFunction] = None, clipping_threshold: float = 1., num_epochs: int = 1000, subsample_ratio: float = 0.01)

• NapsuMQModel(column_feature_set: Iterable[FrozenSet[str]], use_laplace_approximation: bool = True)

InferenceResult

InferenceResult represents a learned model from which synthetic data can be generated. To that end it defines the method

generate(
        rng: d3p.random.PRNGState,
        num_parameter_samples: int,
        num_data_per_parameter_sample: int = 1,
        single_dataframe: bool = True
    ) -> Union[Iterable[pd.DataFrame], pd.DataFrame]

This method first draws num_parameter_samples parameter samples from the model posterior represented by the InferenceResult object and then samples num_data_per_parameter_sample data points for each parameter sample from the model, and returns them as either one large combined DataFrame or an iterable over one DataFrame per parameter sample.

InferenceResult classes also allow saving and loading of learned models via the save and static load methods respectively.

Note that DPVIResult.load requires the same NumPyro model as used for inference to be provided during model loading.