Updated thesis

src/training_scripts/non_autoregressive_quantiles.py

@@ -5,7 +5,7 @@ clearml_helper = ClearMLHelper(
     project_name="Thesis/NrvForecast"
 )
 task = clearml_helper.get_task(
-    task_name="NAQR: Non Linear (2 - 256) + All"
+    task_name="NAQR: Linear"
 )
 task.execute_remotely(queue_name="default", exit_process=True)
 
@@ -31,16 +31,16 @@ 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.PV_FORECAST = True
-data_config.PV_HISTORY = 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")
@@ -75,17 +75,17 @@ model_parameters = {
 
 model_parameters = task.connect(model_parameters, name="model_parameters")
 
-# linear_model = LinearRegression(inputDim, len(quantiles) * 96)
+linear_model = LinearRegression(inputDim, len(quantiles) * 96)
 
-non_linear_model = NonLinearRegression(
-    inputDim,
-    len(quantiles) * 96,
-    hiddenSize=model_parameters["hidden_size"],
-    numLayers=model_parameters["num_layers"],
-    dropout=model_parameters["dropout"],
-)
+# non_linear_model = NonLinearRegression(
+#     inputDim,
+#     len(quantiles) * 96,
+#     hiddenSize=model_parameters["hidden_size"],
+#     numLayers=model_parameters["num_layers"],
+#     dropout=model_parameters["dropout"],
+# )
 
-model = non_linear_model
+model = linear_model
 model.output_size = 96
 optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])