statsmodels.base.distributed_estimation.DistributedModel¶

class statsmodels.base.distributed_estimation.DistributedModel(partitions, model_class=None, init_kwds=None, estimation_method=None, estimation_kwds=None, join_method=None, join_kwds=None, results_class=None, results_kwds=None)[source]¶

Distributed model class

Parameters:¶

partitionsscalar: The number of partitions that the data will be split into.
model_classstatsmodels model class: The model class which will be used for estimation. If None this defaults to OLS.
init_kwdsdict-like or None: Keywords needed for initializing the model, in addition to endog and exog.
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
estimation_methodfunction or None: The method that performs the estimation for each partition. If None this defaults to _est_regularized_debiased.
estimation_kwdsdict-like or None: Keywords to be passed to estimation_method.
join_methodfunction or None: The method used to recombine the results from each partition. If None this defaults to _join_debiased.
join_kwdsdict-like or None: Keywords to be passed to join_method.
results_classresults class or None: The class of results that should be returned. If None this defaults to RegularizedResults.
results_kwdsdict-like or None: Keywords to be passed to results class.

Attributes:¶

Methods

`fit`(data_generator[, fit_kwds, ...])	Performs the distributed estimation using the corresponding DistributedModel
`fit_joblib`(data_generator, fit_kwds, ...[, ...])	Performs the distributed estimation in parallel using joblib
`fit_sequential`(data_generator, fit_kwds[, ...])	Sequentially performs the distributed estimation using the corresponding DistributedModel

Last update: Dec 16, 2024