Added diffusion validation set

src/policies/PolicyEvaluator.py

@@ -15,7 +15,17 @@ class PolicyEvaluator:
         self.baseline_policy = baseline_policy
 
         self.ipc = ImbalancePriceCalculator(data_path="")
+
         self.dates = baseline_policy.test_data["DateTime"].dt.date.unique()
+
+        # also add dates from last 2 months of 2023
+        self.dates = np.append(
+            self.dates,
+            pd.date_range(
+                start="2022-11-01", end="2022-12-31", freq="D"
+            ).to_pydatetime(),
+        )
+
         self.dates = pd.to_datetime(self.dates)
 
         ### Load Imbalance Prices ###
@@ -116,6 +126,10 @@ class PolicyEvaluator:
             # Calculate the gradient (difference) between the simulated and target charge cycles
             gradient = simulated_charge_cycles - target_charge_cycles
 
+            if abs(gradient) < tolerance:
+                print(f"Optimal penalty found after {iteration+1} iterations")
+                break
+
             # Optionally, adjust learning rate based on the change of gradient direction to avoid oscillation
             if previous_gradient is not None and gradient * previous_gradient < 0:
                 learning_rate *= learning_rate_decay
@@ -129,9 +143,7 @@ class PolicyEvaluator:
             previous_gradient = gradient
 
             # Check if the charge cycles are close enough to the target
-            if abs(gradient) < tolerance:
-                print(f"Optimal penalty found after {iteration+1} iterations")
-                break
+            
 
         else:
             print(
@@ -218,7 +230,7 @@ class PolicyEvaluator:
                 raise KeyboardInterrupt
 
             except Exception as e:
-                print(e)
+                # print(e)
                 pass
 
         self.profits = pd.DataFrame(
@@ -243,6 +255,8 @@ class PolicyEvaluator:
 
         loggings = []
 
+        total_dates = 0
+
         for date in tqdm(self.dates):
             try:
                 (
@@ -272,15 +286,18 @@ class PolicyEvaluator:
                 }
 
                 loggings.append(new_info)
+                total_dates += 1
 
             except KeyboardInterrupt:
                 print("Interrupted")
                 raise KeyboardInterrupt
 
             except Exception as e:
-                print(e)
+                # print(e)
                 pass
 
+        print(f"Total Evaluated Dates: {total_dates}")
+
         if log_metrics:
             log_df = pd.DataFrame(loggings)