Added baseline with perfect predictions

src/policies/baselines/perfect_baseline.py

@@ -0,0 +1,54 @@
+from src.utils.clearml import ClearMLHelper
+
+#### ClearML ####
+clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
+task = clearml_helper.get_task(task_name="Perfect Baseline")
+task.execute_remotely(queue_name="default", exit_process=True)
+
+from src.policies.simple_baseline import BaselinePolicy, Battery
+from src.data import DataProcessor, DataConfig
+from policies.baselines.PerfectBaseline import PerfectBaseline
+
+### Data Processor ###
+data_config = DataConfig()
+data_config.NRV_HISTORY = True
+data_config.LOAD_HISTORY = True
+data_config.LOAD_FORECAST = True
+
+data_config.WIND_FORECAST = True
+data_config.WIND_HISTORY = True
+
+data_config.QUARTER = False
+data_config.DAY_OF_WEEK = False
+
+data_config.NOMINAL_NET_POSITION = True
+
+data_processor = DataProcessor(data_config, path="", lstm=False)
+data_processor.set_batch_size(64)
+data_processor.set_full_day_skip(True)
+
+### Policy Evaluator ###
+battery = Battery(2, 1)
+baseline_policy = BaselinePolicy(battery, data_path="")
+policy_evaluator = PerfectBaseline(baseline_policy, task)
+
+penalty, profit, charge_cycles = (
+    policy_evaluator.optimize_penalty_for_target_charge_cycles(
+        data_processor=data_processor,
+        initial_penalty=0,
+        target_charge_cycles=283,
+        learning_rate=2,
+        max_iterations=100,
+        tolerance=1,
+    )
+)
+# policy_evaluator.plot_profits_table()
+print()
+print("Test Set Results")
+print(f"Penalty: {penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}")
+
+task.get_logger().report_single_value(name="Optimal Penalty", value=penalty)
+task.get_logger().report_single_value(name="Optimal Profit", value=profit)
+task.get_logger().report_single_value(name="Optimal Charge Cycles", value=charge_cycles)
+
+task.close()