Format

Author: Sarah Krebs

deepcave/evaluators/mo_ablation.py

@@ -119,11 +119,11 @@ def predict(self, cfg: list[Any], weighting: np.ndarray) -> Tuple[float, float]:
         return mean, var
 
     def calculate(
-            self,
-            objectives: Optional[Union[Objective, List[Objective]]],  # noqa
-            budget: Optional[Union[int, float]] = None,  # noqa
-            n_trees: int = 50,  # noqa
-            seed: int = 0,  # noqa
+        self,
+        objectives: Optional[Union[Objective, List[Objective]]],  # noqa
+        budget: Optional[Union[int, float]] = None,  # noqa
+        n_trees: int = 50,  # noqa
+        seed: int = 0,  # noqa
     ) -> None:
         """
         Calculate the MO ablation path performances and improvements.
@@ -157,7 +157,7 @@ def calculate(
         for obj in objectives:
             normed = obj.name + "_normed"
             df[normed] = (df[obj.name] - df[obj.name].min()) / (
-                    df[obj.name].max() - df[obj.name].min()
+                df[obj.name].max() - df[obj.name].min()
             )
 
             if obj.optimize == "upper":
@@ -187,12 +187,12 @@ def calculate(
         self.df_importances = self.df_importances.reset_index(drop=True)
 
     def calculate_ablation_path(
-            self,
-            df: pd.DataFrame,
-            objectives_normed: List[str],
-            weighting: np.ndarray,
-            budget: Optional[Union[int, float]],
-        ) -> pd.DataFrame:
+        self,
+        df: pd.DataFrame,
+        objectives_normed: List[str],
+        weighting: np.ndarray,
+        budget: Optional[Union[int, float]],
+    ) -> pd.DataFrame:
         """
         Calculate the ablation path performances.
 
@@ -286,12 +286,12 @@ def calculate_ablation_path(
             return df_abl.reset_index(drop=True)
 
     def ablation(
-            self,
-            budget: Optional[Union[int, float]],
-            incumbent_config: Any,
-            def_cost: Any,
-            hp_it: List[str],
-            weighting: np.ndarray[Any, Any],
+        self,
+        budget: Optional[Union[int, float]],
+        incumbent_config: Any,
+        def_cost: Any,
+        hp_it: List[str],
+        weighting: np.ndarray[Any, Any],
     ) -> Tuple[Any, Any, Any, Any]:
         """
         Calculate the ablation importance for each hyperparameter.

deepcave/evaluators/mo_lpi.py

@@ -76,12 +76,12 @@ def __init__(self, run: AbstractRun):
         self.importances: Optional[pd.DataFrame] = None
 
     def calculate(
-            self,
-            objectives: Optional[Union[Objective, List[Objective]]] = None,
-            budget: Optional[Union[int, float]] = None,
-            continous_neighbors: int = 500,
-            n_trees: int = 10,
-            seed: int = 0,
+        self,
+        objectives: Optional[Union[Objective, List[Objective]]] = None,
+        budget: Optional[Union[int, float]] = None,
+        continous_neighbors: int = 500,
+        n_trees: int = 10,
+        seed: int = 0,
     ) -> None:
         """
         Prepare the data and train a RandomForest model.
@@ -129,7 +129,7 @@ def calculate(
         for obj in objectives:
             normed = obj.name + "_normed"
             df[normed] = (df[obj.name] - df[obj.name].min()) / (
-                    df[obj.name].max() - df[obj.name].min()
+                df[obj.name].max() - df[obj.name].min()
             )
             if obj.optimize == "upper":
                 df[normed] = 1 - df[normed]

deepcave/plugins/hyperparameter/ablation_paths.py

@@ -212,8 +212,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]:
             "show_confidence": {"options": get_select_options(binary=True), "value": "false"},
         }
 
-    def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[
-        str, Any]:  # type: ignore # noqa: E501
+    def load_dependency_inputs(self, run, _, inputs) -> Dict[str, Any]:  # type: ignore # noqa: E501
         """
         Works like 'load_inputs' but called after inputs have changed.
 
@@ -477,7 +476,7 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]:  # type: ignore
             barmode="group",
             title={
                 "text": "Ablation Path when Iteratively Setting the Hyperparameters to Their "
-                        "Incumbent Value",
+                "Incumbent Value",
                 "font": {"size": config.FIGURE_FONT_SIZE + 2},
             },
             yaxis_title=objective.name,

deepcave/plugins/hyperparameter/importances.py

@@ -224,8 +224,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]:
             "budget_ids": {"options": get_checklist_options(), "value": []},
         }
 
-    def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[
-        str, Any]:  # type: ignore # noqa: E501
+    def load_dependency_inputs(self, run, _, inputs) -> Dict[str, Any]:  # type: ignore # noqa: E501
         """
         Works like 'load_inputs' but called after inputs have changed.