statsmodels.base.distributed_estimation.DistributedModel.fit_joblib¶

DistributedModel.fit_joblib(data_generator, fit_kwds, parallel_backend, init_kwds_generator=None)[source]¶

Performs the distributed estimation in parallel using joblib

Parameters:¶

data_generatorgenerator

A generator that produces a sequence of tuples where the first element in the tuple corresponds to an endog array and the element corresponds to an exog array.

fit_kwdsdict-like

Keywords needed for the model fitting.

parallel_backendNone or joblib parallel_backend object

used to allow support for more complicated backends, ex: dask.distributed

init_kwds_generatorgenerator or None

Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS

Returns:¶

join_method result. For the default, _join_debiased, it returns a

p length array.