Distributed EstimationΒΆ
This notebook goes through a couple of examples to show how to use distributed_estimation. We import the DistributedModel class and make the exog and endog generators.
[1]:
import numpy as np
from scipy.stats.distributions import norm
from statsmodels.base.distributed_estimation import DistributedModel
def _exog_gen(exog, partitions):
"""partitions exog data"""
n_exog = exog.shape[0]
n_part = np.ceil(n_exog / partitions)
ii = 0
while ii < n_exog:
jj = int(min(ii + n_part, n_exog))
yield exog[ii:jj, :]
ii += int(n_part)
def _endog_gen(endog, partitions):
"""partitions endog data"""
n_endog = endog.shape[0]
n_part = np.ceil(n_endog / partitions)
ii = 0
while ii < n_endog:
jj = int(min(ii + n_part, n_endog))
yield endog[ii:jj]
ii += int(n_part)
Next we generate some random data to serve as an example.
[2]:
X = np.random.normal(size=(1000, 25))
beta = np.random.normal(size=25)
beta *= np.random.randint(0, 2, size=25)
y = norm.rvs(loc=X.dot(beta))
m = 5
This is the most basic fit, showing all of the defaults, which are to use OLS as the model class, and the debiasing procedure.
[3]:
debiased_OLS_mod = DistributedModel(m)
debiased_OLS_fit = debiased_OLS_mod.fit(
zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={"alpha": 0.2}
)
Then we run through a slightly more complicated example which uses the GLM model class.
[4]:
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.families import Gaussian
debiased_GLM_mod = DistributedModel(
m, model_class=GLM, init_kwds={"family": Gaussian()}
)
debiased_GLM_fit = debiased_GLM_mod.fit(
zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={"alpha": 0.2}
)
We can also change the estimation_method and the join_method. The below example show how this works for the standard OLS case. Here we using a naive averaging approach instead of the debiasing procedure.
[5]:
from statsmodels.base.distributed_estimation import _est_regularized_naive, _join_naive
naive_OLS_reg_mod = DistributedModel(
m, estimation_method=_est_regularized_naive, join_method=_join_naive
)
naive_OLS_reg_params = naive_OLS_reg_mod.fit(
zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={"alpha": 0.2}
)
Finally, we can also change the results_class used. The following example shows how this work for a simple case with an unregularized model and naive averaging.
[6]:
from statsmodels.base.distributed_estimation import (
_est_unregularized_naive,
DistributedResults,
)
naive_OLS_unreg_mod = DistributedModel(
m,
estimation_method=_est_unregularized_naive,
join_method=_join_naive,
results_class=DistributedResults,
)
naive_OLS_unreg_params = naive_OLS_unreg_mod.fit(
zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={"alpha": 0.2}
)