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Improved policy executer

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Victor Mylle
2024-01-16 23:22:05 +00:00
parent d1074281c4
commit b87ad1bf42
7 changed files with 1328 additions and 101 deletions
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9
src/training_scripts/autoregressive_quantiles.py
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@@ -13,7 +13,7 @@ from src.models.time_embedding_layer import TimeEmbedding
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: GRU + Quarter + DoW + Load + Wind + Net")
task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: Non Linear + Quarter + DoW + Load + Wind + Net")
#### Data Processor ####
@@ -35,7 +35,7 @@ data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="", lstm=True)
data_processor = DataProcessor(data_config, path="", lstm=False)
data_processor.set_batch_size(512)
data_processor.set_full_day_skip(False)
@@ -67,9 +67,10 @@ 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), hidden_size=model_parameters["hidden_size"], num_layers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
# lstm_model = GRUModel(time_embedding.output_dim(inputDim), len(quantiles), hidden_size=model_parameters["hidden_size"], num_layers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
non_linear_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=model_parameters["hidden_size"], numLayers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
model = nn.Sequential(time_embedding, lstm_model)
model = nn.Sequential(time_embedding, non_linear_model)
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
#### Trainer ####