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Updated literature study and validation set for autoregressive models

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Victor Mylle
2024-05-19 00:08:43 +02:00
parent 1d1436612c
commit 26807eae22
33 changed files with 1282 additions and 742 deletions
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22
src/trainers/quantile_trainer.py
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@@ -176,22 +176,22 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
crps_from_samples_metric.append(crps[0].mean().item())
if epoch is not None:
if epoch is not None and task is not None:
task.get_logger().report_scalar(
title="CRPS_from_samples",
series="test",
series="val",
value=np.mean(crps_from_samples_metric),
iteration=epoch,
)
# using the policy evaluator, evaluate the policy with the generated samples
if self.policy_evaluator is not None:
if self.policy_evaluator is not None and epoch != -1:
optimal_penalty, profit, charge_cycles = (
self.policy_evaluator.optimize_penalty_for_target_charge_cycles(
idx_samples=generated_samples,
test_loader=dataloader,
initial_penalty=900,
target_charge_cycles=283,
target_charge_cycles=58 * 400 / 356,
initial_learning_rate=5,
max_iterations=100,
tolerance=1,
@@ -205,22 +205,30 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
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,
)
return (
np.mean(crps_from_samples_metric),
profit,
charge_cycles,
optimal_penalty,
generated_samples,
)
return np.mean(crps_from_samples_metric), generated_samples
def log_final_metrics(self, task, dataloader, train: bool = True):