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Trying out more linear baselines

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Victor Mylle
2024-04-17 12:55:46 +02:00
parent 0edcc91e65
commit 6b02c9aab8
5 changed files with 42 additions and 28 deletions
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46
src/training_scripts/autoregressive_quantiles.py
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@@ -27,8 +27,8 @@ from src.models.time_embedding_layer import TimeEmbedding
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.WIND_FORECAST = False
data_config.WIND_HISTORY = False
@@ -125,32 +125,32 @@ trainer.plot_every(2)
trainer.train(task=task, epochs=epochs, remotely=True)
### Policy Evaluation ###
idx_samples = trainer.test_set_samples
_, test_loader = trainer.data_processor.get_dataloaders(
predict_sequence_length=trainer.model.output_size, full_day_skip=False
)
# idx_samples = trainer.test_set_samples
# _, test_loader = trainer.data_processor.get_dataloaders(
# predict_sequence_length=trainer.model.output_size, full_day_skip=False
# )
# policy_evaluator.evaluate_test_set(idx_samples, test_loader)
# policy_evaluator.plot_profits_table()
# policy_evaluator.plot_thresholds_per_day()
optimal_penalty, profit, charge_cycles = (
policy_evaluator.optimize_penalty_for_target_charge_cycles(
idx_samples=idx_samples,
test_loader=test_loader,
initial_penalty=1000,
target_charge_cycles=283,
initial_learning_rate=3,
max_iterations=150,
tolerance=1,
)
)
# optimal_penalty, profit, charge_cycles = (
# policy_evaluator.optimize_penalty_for_target_charge_cycles(
# idx_samples=idx_samples,
# test_loader=test_loader,
# initial_penalty=1000,
# target_charge_cycles=283,
# initial_learning_rate=3,
# max_iterations=150,
# tolerance=1,
# )
# )
print(
f"Optimal Penalty: {optimal_penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}"
)
task.get_logger().report_single_value(name="Optimal Penalty", value=optimal_penalty)
task.get_logger().report_single_value(name="Optimal Profit", value=profit)
task.get_logger().report_single_value(name="Optimal Charge Cycles", value=charge_cycles)
# print(
# f"Optimal Penalty: {optimal_penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}"
# )
# task.get_logger().report_single_value(name="Optimal Penalty", value=optimal_penalty)
# task.get_logger().report_single_value(name="Optimal Profit", value=profit)
# task.get_logger().report_single_value(name="Optimal Charge Cycles", value=charge_cycles)
task.close()