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
partitions : scalar
The number of partitions that the data will be split into.
model_class : statsmodels model class
The model class which will be used for estimation. If None this defaults to OLS.
init_kwds : dict-like or None
Keywords needed for initializing the model, in addition to endog and exog.
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
estimation_method : function or None
The method that performs the estimation for each partition. If None this defaults to _est_regularized_debiased.
estimation_kwds : dict-like or None
Keywords to be passed to estimation_method.
join_method : function or None
The method used to recombine the results from each partition. If None this defaults to _join_debiased.
join_kwds : dict-like or None
Keywords to be passed to join_method.
results_class : results class or None
The class of results that should be returned. If None this defaults to RegularizedResults.
results_kwds : dict-like or None
Keywords to be passed to results class.
Attributes
partitions
(scalar) See Parameters.
model_class
(statsmodels model class) See Parameters.
init_kwds
(dict-like) See Parameters.
init_kwds_generator
(generator or None) See Parameters.
estimation_method
(function) See Parameters.
estimation_kwds
(dict-like) See Parameters.
join_method
(function) See Parameters.
join_kwds
(dict-like) See Parameters.
results_class
(results class) See Parameters.
results_kwds
(dict-like) See Parameters.
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
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