Added yesterday policy evaluator

src/policies/baselines/YesterdayBaselinePolicyExecutor.py

@@ -0,0 +1,76 @@
+from datetime import timedelta
+from clearml import Task
+from src.policies.simple_baseline import BaselinePolicy
+from src.policies.PolicyEvaluator import PolicyEvaluator
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import torch
+
+
+class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
+    def __init__(self, baseline_policy: BaselinePolicy, task: Task = None):
+        super(YesterdayBaselinePolicyEvaluator, self).__init__(baseline_policy, task)
+
+    def evaluate_for_date(
+        self,
+        date,
+        charge_thresholds=np.arange(-100, 250, 25),
+        discharge_thresholds=np.arange(-100, 250, 25),
+    ):
+
+        real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
+        yesterday_imbalance_prices = self.get_imbanlance_prices_for_date(
+            date.date() - timedelta(days=1)
+        )
+        yesterday_imbalance_prices = torch.tensor(
+            np.array([yesterday_imbalance_prices]), device="cpu"
+        )
+
+        for penalty in self.penalties:
+            yesterday_charge_thresholds, yesterday_discharge_thresholds = (
+                self.baseline_policy.get_optimal_thresholds(
+                    yesterday_imbalance_prices,
+                    charge_thresholds,
+                    discharge_thresholds,
+                    penalty,
+                )
+            )
+
+            yesterday_profit, yesterday_charge_cycles = self.baseline_policy.simulate(
+                torch.tensor([[real_imbalance_prices]]),
+                torch.tensor([yesterday_charge_thresholds.mean(axis=0)]),
+                torch.tensor([yesterday_discharge_thresholds.mean(axis=0)]),
+            )
+
+            self.profits.append(
+                [
+                    date,
+                    penalty,
+                    yesterday_profit[0][0].item(),
+                    yesterday_charge_cycles[0][0].item(),
+                    yesterday_charge_thresholds.mean(axis=0).item(),
+                    yesterday_discharge_thresholds.mean(axis=0).item(),
+                ]
+            )
+
+    def evaluate_test_set(self):
+        self.profits = []
+        try:
+            for date in tqdm(self.dates):
+                self.evaluate_for_date(date)
+        except Exception as e:
+            print(e)
+            pass
+
+        self.profits = pd.DataFrame(
+            self.profits,
+            columns=[
+                "Date",
+                "Penalty",
+                "Profit",
+                "Charge Cycles",
+                "Charge Threshold",
+                "Discharge Threshold",
+            ],
+        )

src/policies/baselines/yesterday_baseline.py

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+from src.utils.clearml import ClearMLHelper
+
+#### ClearML ####
+clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
+task = clearml_helper.get_task(task_name="Global Thresholds Baseline")
+task.execute_remotely(queue_name="default", exit_process=True)
+
+from src.policies.baselines.BaselinePolicyEvaluator import BaselinePolicyEvaluator
+from src.policies.simple_baseline import BaselinePolicy, Battery
+from src.policies.baselines.YesterdayBaselinePolicyExecutor import (
+    YesterdayBaselinePolicyEvaluator,
+)
+
+### Policy Evaluator ###
+battery = Battery(2, 1)
+baseline_policy = BaselinePolicy(battery, data_path="")
+policy_evaluator = YesterdayBaselinePolicyEvaluator(baseline_policy, task)
+
+policy_evaluator.evaluate_test_set()
+policy_evaluator.plot_profits_table()
+
+task.close()