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)

"""
Explanation: Distributed Estimation
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.
End of explanation
"""


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

"""
Explanation: Next we generate some random data to serve as an example.
End of explanation
"""


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})

"""
Explanation: This is the most basic fit, showing all of the defaults, which are to use OLS as the model class, and the debiasing procedure.
End of explanation
"""


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})

"""
Explanation: Then we run through a slightly more complicated example which uses the GLM model class.
End of explanation
"""


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})

"""
Explanation: 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.
End of explanation
"""


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})

"""
Explanation: 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.
End of explanation
"""


