Added baseline with perfect predictions

src/policies/baselines/PerfectBaseline.py

@@ -0,0 +1,48 @@
+from clearml import Task
+from policies.simple_baseline import BaselinePolicy
+from src.policies.baselines.YesterdayBaselinePolicyExecutor import (
+    YesterdayBaselinePolicyEvaluator,
+)
+import torch
+import numpy as np
+
+
+class PerfectBaseline(YesterdayBaselinePolicyEvaluator):
+    def __init__(self, baseline_policy: BaselinePolicy, task: Task = None):
+        super().__init__(baseline_policy, task)
+
+    def evaluate_for_date(
+        self,
+        date,
+        charge_thresholds=np.arange(-100, 250, 25),
+        discharge_thresholds=np.arange(-100, 250, 25),
+        penalty: int = 0,
+        current_state_of_charge=0.0,
+    ):
+
+        real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
+
+        best_charge_thresholds, best_discharge_thresholds = (
+            self.baseline_policy.get_optimal_thresholds(
+                real_imbalance_prices,
+                charge_thresholds,
+                discharge_thresholds,
+                penalty,
+                battery_state_of_charge=current_state_of_charge,
+            )
+        )
+
+        best_profit, best_charge_cycles, new_state_of_charge = (
+            self.baseline_policy.simulate(
+                torch.tensor([[real_imbalance_prices]]),
+                torch.tensor([best_charge_thresholds.mean(axis=0)]),
+                torch.tensor([best_discharge_thresholds.mean(axis=0)]),
+                battery_state_of_charge=torch.tensor([current_state_of_charge]),
+            )
+        )
+
+        return (
+            best_profit[0][0].item(),
+            best_charge_cycles[0][0].item(),
+            new_state_of_charge.squeeze(0).item(),
+        )