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Non-autoregressive Linear baseline update + wrote further at thesis

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Victor Mylle
2024-04-18 00:30:25 +02:00
parent 8fb2a7fc7b
commit dc102926fa
13 changed files with 1557 additions and 101 deletions
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80
src/training_scripts/non_autoregressive_quantiles.py
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@@ -1,7 +1,7 @@
from src.utils.clearml import ClearMLHelper
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
clearml_helper = ClearMLHelper(project_name="Thesis/NAQR: Linear")
task = clearml_helper.get_task(task_name="NAQR: Non Linear")
task.execute_remotely(queue_name="default", exit_process=True)
@@ -27,23 +27,23 @@ from src.models.time_embedding_layer import TimeEmbedding
data_config = DataConfig()
data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.LOAD_HISTORY = False
data_config.LOAD_FORECAST = False
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = False
data_config.QUARTER = True
data_config.DAY_OF_WEEK = True
data_config.PV_FORECAST = False
data_config.PV_HISTORY = False
data_config.NOMINAL_NET_POSITION = True
data_config.NOMINAL_NET_POSITION = False
data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="", lstm=False)
data_processor.set_batch_size(512)
data_processor.set_full_day_skip(False)
data_processor.set_full_day_skip(True)
#### Hyperparameters ####
@@ -83,17 +83,17 @@ time_embedding = TimeEmbedding(
# dropout=model_parameters["dropout"],
# )
non_linear_model = NonLinearRegression(
time_embedding.output_dim(inputDim),
len(quantiles) * 96,
hiddenSize=model_parameters["hidden_size"],
numLayers=model_parameters["num_layers"],
dropout=model_parameters["dropout"],
)
# non_linear_model = NonLinearRegression(
# time_embedding.output_dim(inputDim),
# len(quantiles) * 96,
# hiddenSize=model_parameters["hidden_size"],
# numLayers=model_parameters["num_layers"],
# dropout=model_parameters["dropout"],
# )
# linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
model = nn.Sequential(time_embedding, non_linear_model)
model = nn.Sequential(time_embedding, linear_model)
model.output_size = 96
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
@@ -110,7 +110,7 @@ trainer = NonAutoRegressiveQuantileRegression(
data_processor,
quantiles,
"cuda",
policy_evaluator=policy_evaluator,
policy_evaluator=None,
debug=False,
)
@@ -122,32 +122,32 @@ trainer.plot_every(5)
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,
learning_rate=15,
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,
# learning_rate=15,
# 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()