Merge pull request #148 from DoubleML/m-pep8

Fix some pep8 issues

Author: MalteKurz

doubleml/double_ml_pliv.py

@@ -270,14 +270,14 @@ def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_
                          search_mode, n_iter_randomized_search):
         if self.partialX & (not self.partialZ):
             res = self._nuisance_tuning_partial_x(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
-                                                     search_mode, n_iter_randomized_search)
+                                                  search_mode, n_iter_randomized_search)
         elif (not self.partialX) & self.partialZ:
             res = self._nuisance_tuning_partial_z(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
-                                                     search_mode, n_iter_randomized_search)
+                                                  search_mode, n_iter_randomized_search)
         else:
             assert (self.partialX & self.partialZ)
             res = self._nuisance_tuning_partial_xz(smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
-                                                      search_mode, n_iter_randomized_search)
+                                                   search_mode, n_iter_randomized_search)
 
         return res
 
@@ -424,7 +424,7 @@ def _nuisance_est_partial_xz(self, smpls, n_jobs_cv):
         return psi_a, psi_b, preds
 
     def _nuisance_tuning_partial_x(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
-                                      search_mode, n_iter_randomized_search):
+                                   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,
@@ -487,7 +487,7 @@ def _nuisance_tuning_partial_x(self, smpls, param_grids, scoring_methods, n_fold
         return res
 
     def _nuisance_tuning_partial_z(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
-                                      search_mode, n_iter_randomized_search):
+                                   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,
                           force_all_finite=False)
@@ -512,7 +512,7 @@ def _nuisance_tuning_partial_z(self, smpls, param_grids, scoring_methods, n_fold
         return res
 
     def _nuisance_tuning_partial_xz(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
-                                       search_mode, n_iter_randomized_search):
+                                    search_mode, n_iter_randomized_search):
         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)),

doubleml/tests/_utils_iivm_manual.py

@@ -61,10 +61,10 @@ def fit_nuisance_iivm(y, x, d, z, learner_g, learner_m, learner_r, smpls,
     train_cond0 = np.where(z == 0)[0]
     if is_classifier(learner_g):
         g_hat0_list = fit_predict_proba(y, x, ml_g0, g0_params, smpls,
-                              train_cond=train_cond0)
+                                        train_cond=train_cond0)
     else:
         g_hat0_list = fit_predict(y, x, ml_g0, g0_params, smpls,
-                              train_cond=train_cond0)
+                                  train_cond=train_cond0)
 
     ml_g1 = clone(learner_g)
     train_cond1 = np.where(z == 1)[0]

doubleml/tests/_utils_irm_manual.py

@@ -59,19 +59,19 @@ def fit_nuisance_irm(y, x, d, learner_g, learner_m, smpls, score,
     train_cond0 = np.where(d == 0)[0]
     if is_classifier(learner_g):
         g_hat0_list = fit_predict_proba(y, x, ml_g0, g0_params, smpls,
-                              train_cond=train_cond0)
+                                        train_cond=train_cond0)
     else:
         g_hat0_list = fit_predict(y, x, ml_g0, g0_params, smpls,
-                              train_cond=train_cond0)
+                                  train_cond=train_cond0)
 
     if score == 'ATE':
         train_cond1 = np.where(d == 1)[0]
         if is_classifier(learner_g):
             g_hat1_list = fit_predict_proba(y, x, ml_g1, g1_params, smpls,
-                                  train_cond=train_cond1)
+                                            train_cond=train_cond1)
         else:
             g_hat1_list = fit_predict(y, x, ml_g1, g1_params, smpls,
-                                  train_cond=train_cond1)
+                                      train_cond=train_cond1)
     else:
         assert score == 'ATTE'
         g_hat1_list = list()

doubleml/tests/conftest.py

@@ -214,7 +214,8 @@ def generate_data_iivm_binary(request):
     prz = 1 / (1 + np.exp((-1) * (x[:, 0] * (-1) * b[4] + x[:, 1] * b[2] + np.random.standard_normal(size=[n, ]))))
     z = np.random.binomial(p=prz, n=1, size=[n, ])
     u = np.random.standard_normal(size=[n, ])
-    pr = 1 / (1 + np.exp((-1) * (0.5 * z + x[:, 0] * (-0.5) + x[:, 1] * 0.25 - 0.5 * u + np.random.standard_normal(size=[n, ]))))
+    pr = 1 / (1 + np.exp((-1) * (0.5 * z + x[:, 0] * (-0.5) + x[:, 1] * 0.25 - 0.5 * u
+                                 + np.random.standard_normal(size=[n, ]))))
     d = np.random.binomial(p=pr, n=1, size=[n, ])
     err = np.random.standard_normal(n)
 

doubleml/tests/test_dml_data.py

@@ -398,7 +398,7 @@ def test_disjoint_sets():
     msg = (r'At least one variable/column is set as instrumental variable \(``z_cols``\) and cluster variable in '
            '``cluster_cols``.')
     with pytest.raises(ValueError, match=msg):
-        _ = DoubleMLClusterData(df, y_col='yy', d_cols=['dd1'], x_cols=['xx1'], z_cols = ['xx2'], cluster_cols='xx2')
+        _ = DoubleMLClusterData(df, y_col='yy', d_cols=['dd1'], x_cols=['xx1'], z_cols=['xx2'], cluster_cols='xx2')
 
 
 @pytest.mark.ci

doubleml/tests/test_iivm_classifier.py

@@ -55,7 +55,7 @@ def dml_iivm_classifier_fixture(generate_data_iivm_binary, learner, score, dml_p
     ml_r = clone(learner[1])
 
     np.random.seed(3141)
-    obj_dml_data =  dml.DoubleMLData.from_arrays(x, y, d, z)
+    obj_dml_data = dml.DoubleMLData.from_arrays(x, y, d, z)
     dml_iivm_obj = dml.DoubleMLIIVM(obj_dml_data,
                                     ml_g, ml_m, ml_r,
                                     n_folds,

doubleml/tests/test_irm_classifier.py

@@ -13,6 +13,7 @@
 from ._utils import draw_smpls
 from ._utils_irm_manual import fit_irm, boot_irm
 
+
 @pytest.fixture(scope='module',
                 params=[[LogisticRegression(solver='lbfgs', max_iter=250),
                          LogisticRegression(solver='lbfgs', max_iter=250)],