Enable simplification rules

Signed-off-by: Keith Battocchi <[email protected]>

econml/_tree_exporter.py

@@ -694,7 +694,7 @@ def export_graphviz(self, out_file=None, feature_names=None, treatment_names=Non
         own_file = False
         try:
             if isinstance(out_file, str):
-                out_file = open(out_file, "w", encoding="utf-8")
+                out_file = open(out_file, "w", encoding="utf-8")  # noqa: SIM115, we close explicitly by design
                 own_file = True
 
             return_string = out_file is None

econml/dml/causal_forest.py

@@ -763,7 +763,7 @@ def tune(self, Y, T, *, X=None, W=None,
         else:
             # If custom param grid, check that only estimator parameters are being altered
             estimator_param_names = self.tunable_params
-            for key in params.keys():
+            for key in params:
                 if key not in estimator_param_names:
                     raise ValueError(f"Parameter `{key}` is not an tunable causal forest parameter.")
 

econml/orf/_ortho_forest.py

@@ -721,9 +721,9 @@ def __init__(self, model_T, model_Y, random_state=None, second_stage=True,
     def __call__(self, Y, T, X, W, sample_weight=None, split_indices=None):
         if self.global_residualization:
             return 0
-        if self.discrete_treatment:
-            # Check that all discrete treatments are represented
-            if len(np.unique(T @ np.arange(1, T.shape[1] + 1))) < T.shape[1] + 1:
+        # Check that all discrete treatments are represented
+        if (self.discrete_treatment and
+            len(np.unique(T @ np.arange(1, T.shape[1] + 1))) < T.shape[1] + 1):
                 return None
         # Nuissance estimates evaluated with cross-fitting
         this_random_state = check_random_state(self.random_state)

econml/sklearn_extensions/model_selection.py

@@ -525,10 +525,10 @@ def _convert_model(model, args, kwargs):
             best_model, score = SklearnCVSelector._convert_model(inner_model, args, kwargs)
             return Pipeline(steps=[*model.steps[:-1], (name, best_model)]), score
 
-        if isinstance(model, GridSearchCV) or isinstance(model, RandomizedSearchCV):
+        if isinstance(model, (GridSearchCV, RandomizedSearchCV)):
             return model.best_estimator_, model.best_score_
 
-        for known_type in SklearnCVSelector._model_mapping().keys():
+        for known_type in SklearnCVSelector._model_mapping():
             if isinstance(model, known_type):
                 converter = SklearnCVSelector._model_mapping()[known_type]
                 return converter(model, args, kwargs)

econml/tests/test_drlearner.py

@@ -669,7 +669,7 @@ def _test_drlearner_with_inference_all_attributes(self, use_ray):
                                                                       [2, 3, len(feature_names) +
                                                                        (W.shape[1] if W is not None else 0)])
 
-                                    if isinstance(est, LinearDRLearner) or isinstance(est, SparseLinearDRLearner):
+                                    if isinstance(est, (LinearDRLearner, SparseLinearDRLearner)):
                                         if X is not None:
                                             for t in [1, 2]:
                                                 true_coef = np.zeros(

econml/tests/test_policy_forest.py

@@ -117,7 +117,7 @@ def _test_policy_honesty(self, trainer, dr=False):
                 for sample_weight in [None, 'rand']:
                     for n_outcomes in n_outcome_list:
                         config = self._get_base_config()
-                        config['honest'] = True if not dr else False
+                        config['honest'] = not dr
                         config['criterion'] = criterion
                         config['max_depth'] = 2
                         config['min_samples_leaf'] = 5

pyproject.toml

@@ -150,14 +150,18 @@ ignore = [
     "D103", # Missing docstring in public function
     "D104", # Missing docstring in public package
     "D105", # Missing docstring in magic method
-    "D301", # Use r""" if any backslashes in a docstring
-          ]
-extend-select = [
+    "D301", # Use r""" if any backslashes in a docstring,
+    "SIM108", # Use ternary instead of if-else (looks ugly for some of our long expressions)
+    "SIM300", # Yoda condition detected (these are often easier to understand in array expressions) 
+]
+select = [
     "D", # Docstring
-    "E501", # Line too long
     "W", # Pycodestyle warnings
+    "E", # All Pycodestyle erros, not just the default ones
+    "F", # All pyflakes rules
+    "SIM", # Simplifification 
 ]
 extend-per-file-ignores = { "econml/tests" = ["D"] } # ignore docstring rules for tests
 
 [tool.ruff.lint.pydocstyle]
-convention = "numpy"
+convention = "numpy"