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Fixed the non autoregressive final metric calculations

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Victor Mylle
2024-04-18 16:53:17 +00:00
parent dc102926fa
commit 98a7244995
3 changed files with 28 additions and 22 deletions
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29
src/training_scripts/non_autoregressive_quantiles.py
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@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NAQR: Linear")
task = clearml_helper.get_task(task_name="NAQR: Non Linear")
task = clearml_helper.get_task(task_name="NAQR: Linear + Load + PV + Wind + Net Position")
task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -27,22 +27,22 @@ 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
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.PV_FORECAST = False
data_config.PV_HISTORY = False
data_config.PV_FORECAST = True
data_config.PV_HISTORY = True
data_config.NOMINAL_NET_POSITION = False
data_config.NOMINAL_NET_POSITION = True
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_batch_size(64)
data_processor.set_full_day_skip(True)
@@ -72,9 +72,6 @@ model_parameters = {
model_parameters = task.connect(model_parameters, name="model_parameters")
time_embedding = TimeEmbedding(
data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"]
)
# lstm_model = GRUModel(
# time_embedding.output_dim(inputDim),
# len(quantiles),
@@ -91,9 +88,9 @@ time_embedding = TimeEmbedding(
# dropout=model_parameters["dropout"],
# )
linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
linear_model = LinearRegression(inputDim, len(quantiles) * 96)
model = nn.Sequential(time_embedding, linear_model)
model = linear_model
model.output_size = 96
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
@@ -117,8 +114,8 @@ trainer = NonAutoRegressiveQuantileRegression(
trainer.add_metrics_to_track(
[PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
)
trainer.early_stopping(patience=10)
trainer.plot_every(5)
trainer.early_stopping(patience=5)
trainer.plot_every(20)
trainer.train(task=task, epochs=epochs, remotely=True)
### Policy Evaluation ###