fixup: Format Python code with Black

base.py

@@ -618,7 +618,11 @@ class BaseModel(abc.ABC):
             )
         elif search_algorithm == "grid":
             search = GridSearchCV(
-                self.model, param_grid=params, refit=True, cv=cv, scoring=scoring_func,
+                self.model,
+                param_grid=params,
+                refit=True,
+                cv=cv,
+                scoring=scoring_func,
             )
         elif search_algorithm == "random":
             search = RandomizedSearchCV(

ddm_predictor.py

@@ -163,7 +163,9 @@ def env_setup():
 
 
 def test_random_policy(
-    num_episodes: int = 500, num_iterations: int = 250, sim: Simulator = None,
+    num_episodes: int = 500,
+    num_iterations: int = 250,
+    sim: Simulator = None,
 ):
     """Test a policy using random actions over a fixed number of episodes
 
@@ -310,7 +312,9 @@ def main(cfg: DictConfig):
             while True:
                 # Advance by the new state depending on the event type
                 sim_state = SimulatorState(
-                    sequence_id=sequence_id, state=sim.get_state(), halted=sim.halted(),
+                    sequence_id=sequence_id,
+                    state=sim.get_state(),
+                    halted=sim.halted(),
                 )
                 try:
                     event = client.session.advance(

ddm_test_validate.py

@@ -20,14 +20,14 @@ from all_models import available_models
 
 ## Add a local simulator in a `sim` folder to validate data-driven model
 ## Example: Quanser from a Microsoft Bonsai
-'''
+"""
 ├───ddm_test_validate.py
 ├───main.py
 ├───sim
 │   ├───quanser
 │   │   ├───sim
 │   │   |    ├───qube_simulator.py
-'''
+"""
 # TODO: from main import TemplateSimulatorSession, env_setup
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -52,7 +52,9 @@ class Simulator(BaseModel):
         self.config_keys = configs
         self.state_keys = states
         self.action_keys = actions
-        self.sim_orig = sim_orig()  # include simulator function if comparing to simulator
+        self.sim_orig = (
+            sim_orig()
+        )  # include simulator function if comparing to simulator
         self.diff_state = diff_state
         if log_file == "enable":
             current_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
@@ -205,10 +207,10 @@ def test_sim_model(
     for episode in range(num_episodes):
         iteration = 0
         terminal = False
-        '''
+        """
         TODO: Add episode_start(config) so sim works properly and not initializing
         with unrealistic initial conditions.
-        '''
+        """
         sim.episode_start()
         ddm_state = sim.get_state()
         sim_state = sim.get_sim_state()
@@ -267,17 +269,16 @@ def main(cfg: DictConfig):
 
     input_cols = input_cols + augmented_cols
 
-
     ddModel = available_models[model_name]
     model = ddModel()
 
-    #model.build_model(**cfg["model"]["build_params"])
+    # model.build_model(**cfg["model"]["build_params"])
     if model_name.lower() == "pytorch":
         model.load_model(
             input_dim=len(input_cols),
             output_dim=len(output_cols),
             filename=save_path,
-            scale_data=scale_data
+            scale_data=scale_data,
         )
     else:
         model.load_model(filename=save_path, scale_data=scale_data)

ddm_trainer.py

@@ -73,12 +73,20 @@ def main(cfg: DictConfig) -> None:
     )
     train_id_end = floor(X.shape[0] * (1 - test_perc))
     X_train, y_train = (
-        X[:train_id_end,],
-        y[:train_id_end,],
+        X[
+            :train_id_end,
+        ],
+        y[
+            :train_id_end,
+        ],
     )
     X_test, y_test = (
-        X[train_id_end:,],
-        y[train_id_end:,],
+        X[
+            train_id_end:,
+        ],
+        y[
+            train_id_end:,
+        ],
     )
 
     # save training and test sets

torch_models.py

@@ -120,7 +120,11 @@ class PyTorchModel(BaseModel):
         self.model.fit(X, y, **fit_params)
 
     def load_model(
-        self, input_dim: str, output_dim: str, filename: str, scale_data: bool = False,
+        self,
+        input_dim: str,
+        output_dim: str,
+        filename: str,
+        scale_data: bool = False,
     ):
 
         self.scale_data = scale_data