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_backend
None
orjoblib
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.
Last update:
Oct 29, 2024