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