run pre-commit

Author: JanTeichertKluge

doubleml/tests/test_apo_tune_ml_models.py

@@ -41,4 +41,3 @@ def test_doubleml_apo_optuna_tune(sampler_name, optuna_sampler):
 
         # ensure tuning improved RMSE
         assert tuned_score[learner_name] < untuned_score[learner_name]
-

doubleml/tests/test_did_cs_tune_ml_models.py

@@ -1,7 +1,4 @@
-import logging
-
 import numpy as np
-import optuna
 import pytest
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 
@@ -51,4 +48,3 @@ def test_doubleml_did_cs_optuna_tune(sampler_name, optuna_sampler, score):
 
         # ensure tuning improved RMSE
         assert tuned_score[learner_name] < untuned_score[learner_name]
-

doubleml/tests/test_dml_tune_optuna.py

@@ -38,6 +38,7 @@ def _assert_tree_params(param_dict, depth_range=(2, 10), leaf_range=(2, 100), le
     assert leaf_range[0] <= param_dict["min_samples_leaf"] <= leaf_range[1]
     assert leaf_nodes_range[0] <= param_dict["max_leaf_nodes"] <= leaf_nodes_range[1]
 
+
 def _build_param_space(dml_obj, param_fn):
     """Build parameter grid using the actual params_names from the DML object."""
     param_grid = {learner_name: param_fn for learner_name in dml_obj.params_names}

doubleml/tests/test_irm_tune_ml_models.py

@@ -1,5 +1,4 @@
 import numpy as np
-import optuna
 import pytest
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 

doubleml/tests/test_plr_tune_ml_models.py

@@ -53,4 +53,4 @@ def test_doubleml_plr_optuna_tune(sampler_name, optuna_sampler):
 
     # ensure tuning improved RMSE
     assert tuned_score["ml_l"] < untuned_score["ml_l"]
-    assert tuned_score["ml_m"] < untuned_score["ml_m"]
+    assert tuned_score["ml_m"] < untuned_score["ml_m"]

doubleml/utils/_tune_optuna.py

@@ -148,20 +148,13 @@ def _resolve_optuna_scoring(scoring_method, learner, params_name):
         return scoring_method, message
 
     if is_regressor(learner):
-        message = (
-            "No scoring method provided, using 'neg_root_mean_squared_error' (RMSE) "
-            f"for learner '{params_name}'."
-        )
+        message = "No scoring method provided, using 'neg_root_mean_squared_error' (RMSE) " f"for learner '{params_name}'."
         return "neg_root_mean_squared_error", message
 
     if is_classifier(learner):
-        message = (
-            f"No scoring method provided, using 'neg_log_loss' "
-            f"for learner '{params_name}'."
-        )
+        message = f"No scoring method provided, using 'neg_log_loss' " f"for learner '{params_name}'."
         return "neg_log_loss", message
 
-
     raise RuntimeError(
         f"No scoring method provided and estimator type could not be inferred. Please provide a scoring_method for learner "
         f"'{params_name}'."
@@ -247,8 +240,8 @@ def _check_tuning_inputs(
     if param_grid_func is not None and not callable(param_grid_func):
         raise TypeError(
             "param_grid must be a callable function that takes a trial and returns a dict. "
-            f"Got {type(param_grid_func).__name__} for learner '{params_name}'.")
-
+            f"Got {type(param_grid_func).__name__} for learner '{params_name}'."
+        )
 
     if scoring_method is not None and not callable(scoring_method) and not isinstance(scoring_method, str):
         if not isinstance(scoring_method, Iterable):