statsmodels.base.distributed_estimation.DistributedModel.fit

DistributedModel.fit(data_generator, fit_kwds=None, parallel_method='sequential', parallel_backend=None, init_kwds_generator=None)[source]

Performs the distributed estimation using the corresponding DistributedModel

Parameters:

  • data_generator (generator) – 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_kwds (dict-like or None) – Keywords needed for the model fitting.

  • parallel_method (str) – type of distributed estimation to be used, currently “sequential”, “joblib” and “dask” are supported.

  • parallel_backend (None or joblib parallel_backend object) – used to allow support for more complicated backends, ex: dask.distributed

  • init_kwds_generator (generator 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.