def test_calc_nodewise_weight():
np.random.seed(435265)
X = np.random.normal(size=(50, 3))
ghat = np.random.normal(size=2)
that = _calc_nodewise_weight(X, ghat, 0, 0.01)
assert_(isinstance(that, float))
def test_calc_approx_inv_cov():
np.random.seed(435265)
X = np.random.normal(size=(50, 3))
ghat_l = []
that_l = []
for i in range(3):
ghat = _calc_nodewise_row(X, i, 0.01)
that = _calc_nodewise_weight(X, ghat, i, 0.01)
ghat_l.append(ghat)
that_l.append(that)
theta_hat = _calc_approx_inv_cov(np.array(ghat_l), np.array(that_l))
assert_equal(theta_hat.shape, (3, 3))
def _est_regularized_debiased(mod, mnum, partitions, fit_kwds=None,
score_kwds=None, hess_kwds=None):
"""estimates the regularized fitted parameters, is the default
estimation_method for class DistributedModel.
Parameters
----------
mod : statsmodels model class instance
The model for the current partition.
mnum : scalar
Index of current partition.
partitions : scalar
Total number of partitions.
fit_kwds : dict-like or None
Keyword arguments to be given to fit_regularized
score_kwds : dict-like or None
Keyword arguments for the score function.
hess_kwds : dict-like or None
Keyword arguments for the Hessian function.
Returns
-------
A tuple of parameters for regularized fit
An array-like object of the fitted parameters, params
An array-like object for the gradient
A list of array like objects for nodewise_row
A list of array like objects for nodewise_weight
"""
score_kwds = {} if score_kwds is None else score_kwds
hess_kwds = {} if hess_kwds is None else hess_kwds
if fit_kwds is None:
raise ValueError("_est_regularized_debiased currently " +
"requires that fit_kwds not be None.")
else:
alpha = fit_kwds["alpha"]
if "L1_wt" in fit_kwds:
L1_wt = fit_kwds["L1_wt"]
else:
L1_wt = 1
nobs, p = mod.exog.shape
p_part = int(np.ceil((1. * p) / partitions))
params = mod.fit_regularized(**fit_kwds).params
grad = _calc_grad(mod, params, alpha, L1_wt, score_kwds) / nobs
wexog = _calc_wdesign_mat(mod, params, hess_kwds)
nodewise_row_l = []
nodewise_weight_l = []
for idx in range(mnum * p_part, min((mnum + 1) * p_part, p)):
nodewise_row = _calc_nodewise_row(wexog, idx, alpha)
nodewise_row_l.append(nodewise_row)
nodewise_weight = _calc_nodewise_weight(wexog, nodewise_row, idx,
alpha)
nodewise_weight_l.append(nodewise_weight)
return params, grad, nodewise_row_l, nodewise_weight_l
def _est_regularized_debiased(mod,
mnum,
partitions,
fit_kwds=None,
score_kwds=None,
hess_kwds=None):
"""estimates the regularized fitted parameters, is the default
estimation_method for class DistributedModel.
Parameters
----------
mod : statsmodels model class instance
The model for the current partition.
mnum : scalar
Index of current partition.
partitions : scalar
Total number of partitions.
fit_kwds : dict-like or None
Keyword arguments to be given to fit_regularized
score_kwds : dict-like or None
Keyword arguments for the score function.
hess_kwds : dict-like or None
Keyword arguments for the Hessian function.
Returns
-------
A tuple of parameters for regularized fit
An array-like object of the fitted parameters, params
An array-like object for the gradient
A list of array like objects for nodewise_row
A list of array like objects for nodewise_weight
"""
score_kwds = {} if score_kwds is None else score_kwds
hess_kwds = {} if hess_kwds is None else hess_kwds
if fit_kwds is None:
raise ValueError("_est_regularized_debiased currently " +
"requires that fit_kwds not be None.")
else:
alpha = fit_kwds["alpha"]
if "L1_wt" in fit_kwds:
L1_wt = fit_kwds["L1_wt"]
else:
L1_wt = 1
nobs, p = mod.exog.shape
p_part = int(np.ceil((1. * p) / partitions))
params = mod.fit_regularized(**fit_kwds).params
grad = _calc_grad(mod, params, alpha, L1_wt, score_kwds) / nobs
wexog = _calc_wdesign_mat(mod, params, hess_kwds)
nodewise_row_l = []
nodewise_weight_l = []
for idx in range(mnum * p_part, min((mnum + 1) * p_part, p)):
nodewise_row = _calc_nodewise_row(wexog, idx, alpha)
nodewise_row_l.append(nodewise_row)
nodewise_weight = _calc_nodewise_weight(wexog, nodewise_row, idx,
alpha)
nodewise_weight_l.append(nodewise_weight)
return params, grad, nodewise_row_l, nodewise_weight_l