VictorMylle/Thesis
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Policy evaluation during training

Browse Source
This commit is contained in:
Victor Mylle
2024-02-25 22:13:00 +01:00
parent 90751866a4
commit f1b54df2c9
5 changed files with 450 additions and 158 deletions
Download Patch File Download Diff File Expand all files Collapse all files

168
src/policies/PolicyEvaluator.py
View File

@@ -18,9 +18,11 @@ class PolicyEvaluator:
self.dates = pd.to_datetime(self.dates)
### Load Imbalance Prices ###
imbalance_prices = pd.read_csv('data/imbalance_prices.csv', sep=';')
imbalance_prices["DateTime"] = pd.to_datetime(imbalance_prices['DateTime'], utc=True)
self.imbalance_prices = imbalance_prices.sort_values(by=['DateTime'])
imbalance_prices = pd.read_csv("data/imbalance_prices.csv", sep=";")
imbalance_prices["DateTime"] = pd.to_datetime(
imbalance_prices["DateTime"], utc=True
)
self.imbalance_prices = imbalance_prices.sort_values(by=["DateTime"])
self.penalties = [0, 100, 300, 500, 800, 1000, 1500]
self.profits = []
@@ -28,30 +30,46 @@ class PolicyEvaluator:
self.task = task
def get_imbanlance_prices_for_date(self, date):
imbalance_prices_day = self.imbalance_prices[self.imbalance_prices["DateTime"].dt.date == date]
return imbalance_prices_day['Positive imbalance price'].values
imbalance_prices_day = self.imbalance_prices[
self.imbalance_prices["DateTime"].dt.date == date
]
return imbalance_prices_day["Positive imbalance price"].values
def evaluate_for_date(self, date, idx_samples, test_loader):
charge_thresholds = np.arange(-100, 250, 25)
discharge_thresholds = np.arange(-100, 250, 25)
idx = test_loader.dataset.get_idx_for_date(date.date())
print("Evaluated for idx: ", idx)
(initial, samples) = idx_samples[idx]
initial = initial.cpu().numpy()[0][-1]
if len(initial.shape) == 2:
initial = initial.cpu().numpy()[0][-1]
else:
initial = initial.cpu().numpy()[-1]
samples = samples.cpu().numpy()
initial = np.repeat(initial, samples.shape[0])
combined = np.concatenate((initial.reshape(-1, 1), samples), axis=1)
reconstructed_imbalance_prices = self.ipc.get_imbalance_prices_2023_for_date_vectorized(date, combined)
reconstructed_imbalance_prices = torch.tensor(reconstructed_imbalance_prices, device="cuda")
reconstructed_imbalance_prices = (
self.ipc.get_imbalance_prices_2023_for_date_vectorized(date, combined)
)
reconstructed_imbalance_prices = torch.tensor(
reconstructed_imbalance_prices, device="cuda"
)
real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
for penalty in self.penalties:
found_charge_thresholds, found_discharge_thresholds = self.baseline_policy.get_optimal_thresholds(
reconstructed_imbalance_prices, charge_thresholds, discharge_thresholds, penalty
found_charge_thresholds, found_discharge_thresholds = (
self.baseline_policy.get_optimal_thresholds(
reconstructed_imbalance_prices,
charge_thresholds,
discharge_thresholds,
penalty,
)
)
predicted_charge_threshold = found_charge_thresholds.mean(axis=0)
@@ -59,13 +77,25 @@ class PolicyEvaluator:
### Determine Profits and Charge Cycles ###
simulated_profit, simulated_charge_cycles = self.baseline_policy.simulate(
torch.tensor([[real_imbalance_prices]]), torch.tensor([predicted_charge_threshold]), torch.tensor([predicted_discharge_threshold])
torch.tensor([[real_imbalance_prices]]),
torch.tensor([predicted_charge_threshold]),
torch.tensor([predicted_discharge_threshold]),
)
self.profits.append([date, penalty, simulated_profit[0][0].item(), simulated_charge_cycles[0][0].item(), predicted_charge_threshold.item(), predicted_discharge_threshold.item()])
self.profits.append(
[
date,
penalty,
simulated_profit[0][0].item(),
simulated_charge_cycles[0][0].item(),
predicted_charge_threshold.item(),
predicted_discharge_threshold.item(),
]
)
def evaluate_test_set(self, idx_samples, test_loader):
self.profits = []
try:
print(self.dates)
for date in tqdm(self.dates):
self.evaluate_for_date(date, idx_samples, test_loader)
except KeyboardInterrupt:
@@ -76,14 +106,31 @@ class PolicyEvaluator:
print(e)
pass
self.profits = pd.DataFrame(self.profits, columns=["Date", "Penalty", "Profit", "Charge Cycles", "Charge Threshold", "Discharge Threshold"])
self.profits = pd.DataFrame(
self.profits,
columns=[
"Date",
"Penalty",
"Profit",
"Charge Cycles",
"Charge Threshold",
"Discharge Threshold",
],
)
print("Profits calculated")
print(self.profits.head())
def plot_profits_table(self):
# Check if task or penalties are not set
if self.task is None or not hasattr(self, 'penalties') or not hasattr(self, 'profits'):
if (
self.task is None
or not hasattr(self, "penalties")
or not hasattr(self, "profits")
):
print("Task, penalties, or profits not defined.")
return
if self.profits.empty:
print("Profits DataFrame is empty.")
return
@@ -92,23 +139,32 @@ class PolicyEvaluator:
aggregated = self.profits.groupby("Penalty").agg(
Total_Profit=("Profit", "sum"),
Total_Charge_Cycles=("Charge Cycles", "sum"),
Num_Days=("Date", "nunique")
Num_Days=("Date", "nunique"),
)
aggregated["Profit_Per_Year"] = (
aggregated["Total_Profit"] / aggregated["Num_Days"] * 365
)
aggregated["Charge_Cycles_Per_Year"] = (
aggregated["Total_Charge_Cycles"] / aggregated["Num_Days"] * 365
)
aggregated["Profit_Per_Year"] = aggregated["Total_Profit"] / aggregated["Num_Days"] * 365
aggregated["Charge_Cycles_Per_Year"] = aggregated["Total_Charge_Cycles"] / aggregated["Num_Days"] * 365
# Reset index to make 'Penalty' a column again and drop unnecessary columns
final_df = aggregated.reset_index().drop(columns=["Total_Profit", "Total_Charge_Cycles", "Num_Days"])
final_df = aggregated.reset_index().drop(
columns=["Total_Profit", "Total_Charge_Cycles", "Num_Days"]
)
# Rename columns to match expected output
final_df.columns = ["Penalty", "Total Profit", "Total Charge Cycles"]
# Profits till 400
profits_till_400 = self.get_profits_till_400()
# aggregate the final_df and profits_till_400 with columns: Penalty, total profit, total charge cycles, profit till 400, total charge cycles
final_df = final_df.merge(profits_till_400, on="Penalty")
# Log the final results table
self.task.get_logger().report_table(
"Policy Results",
"Policy Results",
iteration=0,
table_plot=final_df
"Policy Results", "Policy Results", iteration=0, table_plot=final_df
)
def plot_thresholds_per_day(self):
@@ -116,10 +172,10 @@ class PolicyEvaluator:
return
fig = px.line(
self.profits[self.profits["Penalty"] == 0],
x="Date",
y=["Charge Threshold", "Discharge Threshold"],
title="Charge and Discharge Thresholds per Day"
self.profits[self.profits["Penalty"] == 0],
x="Date",
y=["Charge Threshold", "Discharge Threshold"],
title="Charge and Discharge Thresholds per Day",
)
fig.update_layout(
@@ -129,24 +185,62 @@ class PolicyEvaluator:
)
self.task.get_logger().report_plotly(
"Thresholds per Day",
"Thresholds per Day",
iteration=0,
figure=fig
"Thresholds per Day", "Thresholds per Day", iteration=0, figure=fig
)
def get_profits_as_scalars(self):
aggregated = self.profits.groupby("Penalty").agg(
Total_Profit=("Profit", "sum"),
Total_Charge_Cycles=("Charge Cycles", "sum"),
Num_Days=("Date", "nunique")
Num_Days=("Date", "nunique"),
)
aggregated["Profit_Per_Year"] = (
aggregated["Total_Profit"] / aggregated["Num_Days"] * 365
)
aggregated["Charge_Cycles_Per_Year"] = (
aggregated["Total_Charge_Cycles"] / aggregated["Num_Days"] * 365
)
aggregated["Profit_Per_Year"] = aggregated["Total_Profit"] / aggregated["Num_Days"] * 365
aggregated["Charge_Cycles_Per_Year"] = aggregated["Total_Charge_Cycles"] / aggregated["Num_Days"] * 365
# Reset index to make 'Penalty' a column again and drop unnecessary columns
final_df = aggregated.reset_index().drop(columns=["Total_Profit", "Total_Charge_Cycles", "Num_Days"])
final_df = aggregated.reset_index().drop(
columns=["Total_Profit", "Total_Charge_Cycles", "Num_Days"]
)
# Rename columns to match expected output
final_df.columns = ["Penalty", "Total Profit", "Total Charge Cycles"]
return final_df
return final_df
def get_profits_till_400(self):
# calculates profits until 400 charge cycles per year are reached
number_of_days = len(self.profits["Date"].unique())
usable_charge_cycles = (400 / 365) * number_of_days
# now sum the profit until the usable charge cycles are reached
penalty_profits = {}
penalty_charge_cycles = {}
for index, row in self.profits.iterrows():
penalty = row["Penalty"]
profit = row["Profit"]
charge_cycles = row["Charge Cycles"]
if penalty not in penalty_profits:
penalty_profits[penalty] = 0
penalty_charge_cycles[penalty] = 0
if penalty_charge_cycles[penalty] < usable_charge_cycles:
penalty_profits[penalty] += profit
penalty_charge_cycles[penalty] += charge_cycles
df = pd.DataFrame(
list(
zip(
penalty_profits.keys(),
penalty_profits.values(),
penalty_charge_cycles.values(),
)
),
columns=["Penalty", "Profit_till_400", "Cycles_till_400"],
)
return df