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Added plots thresholds densities

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Victor Mylle
2024-02-14 18:12:11 +00:00
parent d10f8a5ff6
commit 7bd0476085
2 changed files with 83 additions and 8 deletions
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89
src/policies/policy_executer.py
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@@ -124,6 +124,9 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
baseline_profits_cycles = {i: [0, 0] for i in penalties}
_charge_thresholds = {}
_discharge_thresholds = {}
initial, nrvs, target = predict_NRV(model, date, data_processor, test_loader)
initial = np.repeat(initial, nrvs.shape[0])
@@ -139,6 +142,10 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
for penalty in penalties:
found_charge_thresholds, found_discharge_thresholds = baseline_policy.get_optimal_thresholds(reconstructed_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
_charge_thresholds[penalty] = found_charge_thresholds
_discharge_thresholds[penalty] = found_discharge_thresholds
next_day_charge_threshold = found_charge_thresholds.mean(axis=0)
next_day_discharge_threshold = found_discharge_thresholds.mean(axis=0)
yesterday_charge_thresholds, yesterday_discharge_thresholds = baseline_policy.get_optimal_thresholds(yesterday_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
@@ -153,23 +160,29 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
baseline_profits_cycles[penalty][0] += yesterday_profit.item()
baseline_profits_cycles[penalty][1] += yesterday_charge_cycles.item()
return predicted_nrv_profits_cycles, baseline_profits_cycles
return predicted_nrv_profits_cycles, baseline_profits_cycles, _charge_thresholds, _discharge_thresholds
def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: callable):
penalties = [0, 10, 50, 150, 300, 500, 600, 800, 1000, 1500, 2000, 2500]
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
baseline_profits_cycles = {i: [0, 0] for i in penalties}
charge_thresholds = {}
discharge_thresholds = {}
# get all dates in test set
dates = baseline_policy.test_data["DateTime"].dt.date.unique()
# dates back to datetime
dates = pd.to_datetime(dates)
for date in tqdm(dates):
for date in tqdm(dates[:10]):
try:
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles, new_charge_thresholds, new_discharge_thresholds = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
charge_thresholds[date] = new_charge_thresholds
discharge_thresholds[date] = new_discharge_thresholds
for penalty in penalties:
predicted_nrv_profits_cycles[penalty][0] += new_predicted_nrv_profits_cycles[penalty][0]
predicted_nrv_profits_cycles[penalty][1] += new_predicted_nrv_profits_cycles[penalty][1]
@@ -179,15 +192,15 @@ def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: call
except Exception as e:
# print(f"Error for date {date}")
continue
raise e
return predicted_nrv_profits_cycles, baseline_profits_cycles
return predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds
def main():
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="Policy Test")
task.execute_remotely(queue_name="default", exit_process=True)
# task.execute_remotely(queue_name="default", exit_process=True)
configuration, model, data_processor, test_loader = load_model(args.task_id)
@@ -205,7 +218,69 @@ def main():
ipc = ImbalancePriceCalculator(data_path="")
predicted_nrv_profits_cycles, baseline_profits_cycles = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
# the charge_thresholds is a dictionary with date as key. The values of the dictionary is another dictionary with keys as penalties and values as the charge thresholds
# create density plot that shows a density plot of the charge thresholds for each penalty (use seaborn displot) (One plot with a different color for each penalty)
import seaborn as sns
import matplotlib.pyplot as plt
charge_thresholds_for_penalty = {}
for d in charge_thresholds.values():
for penalty, thresholds in d.items():
if penalty not in charge_thresholds_for_penalty:
charge_thresholds_for_penalty[penalty] = []
charge_thresholds_for_penalty[penalty].extend(thresholds)
discharge_thresholds_for_penalty = {}
for d in discharge_thresholds.values():
for penalty, thresholds in d.items():
if penalty not in discharge_thresholds_for_penalty:
discharge_thresholds_for_penalty[penalty] = []
discharge_thresholds_for_penalty[penalty].extend(thresholds)
### Plot charge thresholds distribution ###
data_to_plot = []
for penalty, values in charge_thresholds_for_penalty.items():
for value in values:
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
df = pd.DataFrame(data_to_plot)
print(df.head())
palette = sns.color_palette("bright", len(charge_thresholds.keys()))
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
plt.title('Density of Charge Thresholds by Penalty')
plt.xlabel('Charge Threshold')
plt.ylabel('Density')
plt.legend(title='Penalty')
task.get_logger().report_matplotlib_figure(
"Policy Results",
"Charge Thresholds",
iteration=0,
figure=fig
)
plt.close()
### Plot discharge thresholds distribution ###
data_to_plot = []
for penalty, values in discharge_thresholds_for_penalty.items():
for value in values:
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
df = pd.DataFrame(data_to_plot)
palette = sns.color_palette("bright", len(discharge_thresholds.keys()))
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
plt.title('Density of Charge Thresholds by Penalty')
plt.xlabel('Charge Threshold')
plt.ylabel('Density')
plt.legend(title='Penalty')
task.get_logger().report_matplotlib_figure(
"Policy Results",
"Discharge Thresholds",
iteration=0,
figure=fig
)
plt.close()
# create dataframe with columns "name", "penalty", "profit", "cycles"
df = pd.DataFrame(columns=["name", "penalty", "profit", "cycles"])

2
src/utils/clearml.py
View File

@@ -10,6 +10,6 @@ class ClearMLHelper:
Task.ignore_requirements("torchvision")
Task.ignore_requirements("tensorboard")
task = Task.init(project_name=self.project_name, task_name=task_name, continue_last_task=False)
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime --env GIT_SSL_NO_VERIFY=true --env CLEARML_AGENT_GIT_USER=VictorMylle --env CLEARML_AGENT_GIT_PASS=Voetballer1" )
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime")
task.set_packages("requirements.txt")
return task