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Added diffusion validation set

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This commit is contained in:
Victor Mylle
2024-05-17 16:11:17 +00:00
parent 11ae0e1949
commit 8a219d0d19
24 changed files with 64 additions and 36 deletions
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8
src/data/preprocessing.py
View File

@@ -50,8 +50,8 @@ class DataProcessor:
datetime(year=2022, month=11, day=30, tzinfo=pytz.UTC),
)
self.val_range = (
datetime(year=2022, month=10, day=1, tzinfo=pytz.UTC),
datetime(year=2022, month=11, day=30, tzinfo=pytz.UTC),
datetime(year=2022, month=11, day=1, tzinfo=pytz.UTC),
datetime(year=2022, month=12, day=30, tzinfo=pytz.UTC),
)
self.test_range = (datetime(year=2023, month=1, day=1, tzinfo=pytz.UTC), np.inf)
@@ -297,10 +297,10 @@ class DataProcessor:
):
val_df = self.all_features.copy()
if self.test_range[0] != -np.inf:
if self.val_range[0] != -np.inf:
val_df = val_df[(val_df["datetime"] >= self.val_range[0])]
if self.test_range[1] != np.inf:
if self.val_range[1] != np.inf:
val_df = val_df[(val_df["datetime"] <= self.val_range[1])]
if transform:

27
src/policies/PolicyEvaluator.py
View File

@@ -15,7 +15,17 @@ class PolicyEvaluator:
self.baseline_policy = baseline_policy
self.ipc = ImbalancePriceCalculator(data_path="")
self.dates = baseline_policy.test_data["DateTime"].dt.date.unique()
# also add dates from last 2 months of 2023
self.dates = np.append(
self.dates,
pd.date_range(
start="2022-11-01", end="2022-12-31", freq="D"
).to_pydatetime(),
)
self.dates = pd.to_datetime(self.dates)
### Load Imbalance Prices ###
@@ -116,6 +126,10 @@ class PolicyEvaluator:
# Calculate the gradient (difference) between the simulated and target charge cycles
gradient = simulated_charge_cycles - target_charge_cycles
if abs(gradient) < tolerance:
print(f"Optimal penalty found after {iteration+1} iterations")
break
# Optionally, adjust learning rate based on the change of gradient direction to avoid oscillation
if previous_gradient is not None and gradient * previous_gradient < 0:
learning_rate *= learning_rate_decay
@@ -129,9 +143,7 @@ class PolicyEvaluator:
previous_gradient = gradient
# Check if the charge cycles are close enough to the target
if abs(gradient) < tolerance:
print(f"Optimal penalty found after {iteration+1} iterations")
break
else:
print(
@@ -218,7 +230,7 @@ class PolicyEvaluator:
raise KeyboardInterrupt
except Exception as e:
print(e)
# print(e)
pass
self.profits = pd.DataFrame(
@@ -243,6 +255,8 @@ class PolicyEvaluator:
loggings = []
total_dates = 0
for date in tqdm(self.dates):
try:
(
@@ -272,15 +286,18 @@ class PolicyEvaluator:
}
loggings.append(new_info)
total_dates += 1
except KeyboardInterrupt:
print("Interrupted")
raise KeyboardInterrupt
except Exception as e:
print(e)
# print(e)
pass
print(f"Total Evaluated Dates: {total_dates}")
if log_metrics:
log_df = pd.DataFrame(loggings)

45
src/trainers/diffusion_trainer.py
View File

@@ -98,7 +98,7 @@ class DiffusionTrainer:
self.best_score = None
self.policy_evaluator = policy_evaluator
self.prev_optimal_penalty = 0
self.prev_optimal_penalty = 600
def noise_time_series(self, x: torch.tensor, t: int):
"""Add noise to time series
@@ -198,6 +198,8 @@ class DiffusionTrainer:
early_stopping = 0
best_crps = None
best_profit = None
best_charge_cycles = None
if task:
self.init_clearml_task(task)
@@ -206,6 +208,12 @@ class DiffusionTrainer:
predict_sequence_length=self.ts_length, validation=True
)
# val loader total samples
val_loader_total_samples = len(val_loader.dataset)
print("Train Loader Total Samples: ", len(train_loader.dataset))
print(f"Val Loader Total Samples: {val_loader_total_samples}")
print(f"Test Loader Total Samples: {len(test_loader.dataset)}")
train_sample_indices = self.random_samples(train=True, num_samples=5)
test_sample_indices = self.random_samples(train=False, num_samples=5)
@@ -229,16 +237,16 @@ class DiffusionTrainer:
running_loss /= len(train_loader.dataset)
if epoch % 75 == 0 and epoch != 0:
crps, _ = self.test(val_loader, epoch, task)
if epoch % 30 == 0 and epoch != 0:
crps, profit, charge_cycles, _ = self.test(val_loader, epoch, task)
if best_crps is None or crps < best_crps:
best_crps = crps
if best_profit is None or profit > best_profit:
best_profit = profit
early_stopping = 0
else:
early_stopping += 1
if early_stopping > 5:
if early_stopping > 20:
break
if task:
@@ -249,7 +257,7 @@ class DiffusionTrainer:
value=loss.item(),
)
if epoch % 150 == 0 and epoch != 0:
if epoch % 300 == 0 and epoch != 0:
self.debug_plots(
task, True, train_loader, train_sample_indices, epoch
)
@@ -580,15 +588,12 @@ class DiffusionTrainer:
name="test_MSELoss", value=mean_inversed_mse
)
if self.best_score is None or mean_crps < self.best_score:
self.save_checkpoint(mean_crps, task, epoch)
if task:
task.get_logger().report_scalar(
title="CRPS", series="val", value=mean_crps, iteration=epoch
)
if self.policy_evaluator:
if self.policy_evaluator and epoch != -1:
_, val_loader, _ = self.data_processor.get_dataloaders(
predict_sequence_length=self.ts_length,
full_day_skip=True,
@@ -600,10 +605,10 @@ class DiffusionTrainer:
idx_samples=generated_samples,
test_loader=val_loader,
initial_penalty=self.prev_optimal_penalty,
target_charge_cycles=283,
initial_learning_rate=1,
target_charge_cycles=58*400/356,
initial_learning_rate=20,
max_iterations=50,
tolerance=1,
tolerance=0.3,
iteration=epoch,
)
)
@@ -612,22 +617,28 @@ class DiffusionTrainer:
task.get_logger().report_scalar(
title="Optimal Penalty",
series="test",
series="val",
value=optimal_penalty,
iteration=epoch,
)
task.get_logger().report_scalar(
title="Optimal Profit", series="test", value=profit, iteration=epoch
title="Optimal Profit", series="val", value=profit, iteration=epoch
)
task.get_logger().report_scalar(
title="Optimal Charge Cycles",
series="test",
series="val",
value=charge_cycles,
iteration=epoch,
)
if self.best_score is None or profit > self.best_score:
self.save_checkpoint(profit, task, epoch)
return mean_crps, profit, charge_cycles, generated_samples
return mean_crps, generated_samples
def save_checkpoint(self, val_loss, task, iteration: int):

20
src/training_scripts/diffusion_training.py
View File

@@ -2,9 +2,9 @@ from src.utils.clearml import ClearMLHelper
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(
task_name="Diffusion Training: hidden_sizes=[256, 256] (100 steps), lr=0.0001, time_dim=8",
task_name="Diffusion Training: hidden_sizes=[512, 512] (100 steps), lr=0.0001, time_dim=8",
)
task.execute_remotely(queue_name="default", exit_process=True)
# task.execute_remotely(queue_name="default", exit_process=True)
from src.models import *
from src.losses import *
@@ -19,16 +19,16 @@ from src.policies.PolicyEvaluator import PolicyEvaluator
data_config = DataConfig()
data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = False
data_config.LOAD_FORECAST = False
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.PV_FORECAST = False
data_config.PV_HISTORY = False
data_config.PV_FORECAST = True
data_config.PV_HISTORY = True
data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = False
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.NOMINAL_NET_POSITION = False
data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features")
@@ -42,7 +42,7 @@ print("Input dim: ", inputDim)
model_parameters = {
"epochs": 15000,
"learning_rate": 0.0001,
"hidden_sizes": [256, 256],
"hidden_sizes": [512, 512],
"time_dim": 8,
}