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Dec 15, 2021
Merged

rename abstract methods #131

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00406cf
rename abstract methods
MalteKurz Dec 3, 2021
f7f2a72
remove accidentally commited template file
MalteKurz Dec 3, 2021
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1,129 changes: 492 additions & 637 deletions doc/oop.svg
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28 changes: 14 additions & 14 deletions doubleml/double_ml.py
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Original file line number Diff line number Diff line change
Expand Up @@ -474,7 +474,7 @@ def fit(self, n_jobs_cv=None, keep_scores=True, store_predictions=False):

# ml estimation of nuisance models and computation of score elements
self._psi_a[:, self._i_rep, self._i_treat], self._psi_b[:, self._i_rep, self._i_treat], preds =\
self._ml_nuisance_and_score_elements(self.__smpls, n_jobs_cv)
self._nuisance_est(self.__smpls, n_jobs_cv)

if store_predictions:
self._store_predictions(preds)
Expand Down Expand Up @@ -799,11 +799,11 @@ def tune(self,
self._i_rep = i_rep

# tune hyperparameters
res = self._ml_nuisance_tuning(self.__smpls,
param_grids, scoring_methods,
n_folds_tune,
n_jobs_cv,
search_mode, n_iter_randomized_search)
res = self._nuisance_tuning(self.__smpls,
param_grids, scoring_methods,
n_folds_tune,
n_jobs_cv,
search_mode, n_iter_randomized_search)

tuning_res[i_rep][i_d] = res
nuisance_params.append(res['params'])
Expand All @@ -816,11 +816,11 @@ def tune(self,
else:
smpls = [(np.arange(self._dml_data.n_obs), np.arange(self._dml_data.n_obs))]
# tune hyperparameters
res = self._ml_nuisance_tuning(smpls,
param_grids, scoring_methods,
n_folds_tune,
n_jobs_cv,
search_mode, n_iter_randomized_search)
res = self._nuisance_tuning(smpls,
param_grids, scoring_methods,
n_folds_tune,
n_jobs_cv,
search_mode, n_iter_randomized_search)
tuning_res[i_d] = res

if set_as_params:
Expand Down Expand Up @@ -887,12 +887,12 @@ def _initialize_ml_nuisance_params(self):
pass

@abstractmethod
def _ml_nuisance_and_score_elements(self, smpls, n_jobs_cv):
def _nuisance_est(self, smpls, n_jobs_cv):
pass

@abstractmethod
def _ml_nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
pass

@staticmethod
Expand Down
6 changes: 3 additions & 3 deletions doubleml/double_ml_iivm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,7 +210,7 @@ def _check_data(self, obj_dml_data):
raise ValueError(err_msg)
return

def _ml_nuisance_and_score_elements(self, smpls, n_jobs_cv):
def _nuisance_est(self, smpls, n_jobs_cv):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
x, z = check_X_y(x, np.ravel(self._dml_data.z),
Expand Down Expand Up @@ -283,8 +283,8 @@ def _score_elements(self, y, z, d, g_hat0, g_hat1, m_hat, r_hat0, r_hat1, smpls)

return psi_a, psi_b

def _ml_nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
x, z = check_X_y(x, np.ravel(self._dml_data.z),
Expand Down
6 changes: 3 additions & 3 deletions doubleml/double_ml_irm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ def _check_data(self, obj_dml_data):
'needs to be specified as treatment variable.')
return

def _ml_nuisance_and_score_elements(self, smpls, n_jobs_cv):
def _nuisance_est(self, smpls, n_jobs_cv):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
x, d = check_X_y(x, self._dml_data.d,
Expand Down Expand Up @@ -230,8 +230,8 @@ def _score_elements(self, y, d, g_hat0, g_hat1, m_hat, smpls):

return psi_a, psi_b

def _ml_nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
x, d = check_X_y(x, self._dml_data.d,
Expand Down
30 changes: 15 additions & 15 deletions doubleml/double_ml_pliv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -255,33 +255,33 @@ def _check_data(self, obj_dml_data):
'use DoubleMLPLR instead of DoubleMLPLIV.')
return

def _ml_nuisance_and_score_elements(self, smpls, n_jobs_cv):
def _nuisance_est(self, smpls, n_jobs_cv):
if self.partialX & (not self.partialZ):
psi_a, psi_b, preds = self._ml_nuisance_and_score_elements_partial_x(smpls, n_jobs_cv)
psi_a, psi_b, preds = self._nuisance_est_partial_x(smpls, n_jobs_cv)
elif (not self.partialX) & self.partialZ:
psi_a, psi_b, preds = self._ml_nuisance_and_score_elements_partial_z(smpls, n_jobs_cv)
psi_a, psi_b, preds = self._nuisance_est_partial_z(smpls, n_jobs_cv)
else:
assert (self.partialX & self.partialZ)
psi_a, psi_b, preds = self._ml_nuisance_and_score_elements_partial_xz(smpls, n_jobs_cv)
psi_a, psi_b, preds = self._nuisance_est_partial_xz(smpls, n_jobs_cv)

return psi_a, psi_b, preds

def _ml_nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
if self.partialX & (not self.partialZ):
res = self._ml_nuisance_tuning_partial_x(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
res = self._nuisance_tuning_partial_x(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search)
elif (not self.partialX) & self.partialZ:
res = self._ml_nuisance_tuning_partial_z(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
res = self._nuisance_tuning_partial_z(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search)
else:
assert (self.partialX & self.partialZ)
res = self._ml_nuisance_tuning_partial_xz(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
res = self._nuisance_tuning_partial_xz(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search)

return res

def _ml_nuisance_and_score_elements_partial_x(self, smpls, n_jobs_cv):
def _nuisance_est_partial_x(self, smpls, n_jobs_cv):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
x, d = check_X_y(x, self._dml_data.d,
Expand Down Expand Up @@ -357,7 +357,7 @@ def _score_elements(self, y, z, d, g_hat, m_hat, r_hat, smpls):

return psi_a, psi_b

def _ml_nuisance_and_score_elements_partial_z(self, smpls, n_jobs_cv):
def _nuisance_est_partial_z(self, smpls, n_jobs_cv):
y = self._dml_data.y
xz, d = check_X_y(np.hstack((self._dml_data.x, self._dml_data.z)),
self._dml_data.d,
Expand All @@ -380,7 +380,7 @@ def _ml_nuisance_and_score_elements_partial_z(self, smpls, n_jobs_cv):

return psi_a, psi_b, preds

def _ml_nuisance_and_score_elements_partial_xz(self, smpls, n_jobs_cv):
def _nuisance_est_partial_xz(self, smpls, n_jobs_cv):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
xz, d = check_X_y(np.hstack((self._dml_data.x, self._dml_data.z)),
Expand Down Expand Up @@ -423,7 +423,7 @@ def _ml_nuisance_and_score_elements_partial_xz(self, smpls, n_jobs_cv):

return psi_a, psi_b, preds

def _ml_nuisance_tuning_partial_x(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
def _nuisance_tuning_partial_x(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
Expand Down Expand Up @@ -486,7 +486,7 @@ def _ml_nuisance_tuning_partial_x(self, smpls, param_grids, scoring_methods, n_f

return res

def _ml_nuisance_tuning_partial_z(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
def _nuisance_tuning_partial_z(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
xz, d = check_X_y(np.hstack((self._dml_data.x, self._dml_data.z)),
self._dml_data.d,
Expand All @@ -511,7 +511,7 @@ def _ml_nuisance_tuning_partial_z(self, smpls, param_grids, scoring_methods, n_f

return res

def _ml_nuisance_tuning_partial_xz(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
def _nuisance_tuning_partial_xz(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
Expand Down
6 changes: 3 additions & 3 deletions doubleml/double_ml_plr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,7 @@ def _check_data(self, obj_dml_data):
'To fit a partially linear IV regression model use DoubleMLPLIV instead of DoubleMLPLR.')
return

def _ml_nuisance_and_score_elements(self, smpls, n_jobs_cv):
def _nuisance_est(self, smpls, n_jobs_cv):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
x, d = check_X_y(x, self._dml_data.d,
Expand Down Expand Up @@ -188,8 +188,8 @@ def _score_elements(self, y, d, g_hat, m_hat, smpls):

return psi_a, psi_b

def _ml_nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
search_mode, n_iter_randomized_search):
x, y = check_X_y(self._dml_data.x, self._dml_data.y,
force_all_finite=False)
x, d = check_X_y(x, self._dml_data.d,
Expand Down