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)"
+    task_name="NAQR: Non Linear (4 - 512)"
 )
 task.execute_remotely(queue_name="default", exit_process=True)
 
@@ -45,7 +45,7 @@ data_config.NOMINAL_NET_POSITION = False
 
 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(64)
 data_processor.set_full_day_skip(True)
 
@@ -68,8 +68,8 @@ else:
 
 model_parameters = {
     "learning_rate": 0.0001,
-    "hidden_size": 256,
-    "num_layers": 2,
+    "hidden_size": 512,
+    "num_layers": 4,
     "dropout": 0.2,
 }
 
@@ -77,24 +77,15 @@ model_parameters = task.connect(model_parameters, name="model_parameters")
 
 # 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"],
-# )
-
-lstm_model = GRUModel(
+non_linear_model = NonLinearRegression(
     inputDim,
     len(quantiles) * 96,
-    hidden_size=model_parameters["hidden_size"],
-    num_layers=model_parameters["num_layers"],
+    hiddenSize=model_parameters["hidden_size"],
+    numLayers=model_parameters["num_layers"],
     dropout=model_parameters["dropout"],
 )
 
-
-model = lstm_model
+model = non_linear_model
 model.output_size = 96
 optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])