Non linear results section done

src/training_scripts/autoregressive_quantiles.py

@@ -2,8 +2,8 @@ from src.utils.clearml import ClearMLHelper
 
 #### ClearML ####
 clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
-task = clearml_helper.get_task(task_name="AQR: Linear + QE (dim 2)")
-# task.execute_remotely(queue_name="default", exit_process=True)
+task = clearml_helper.get_task(task_name="AQR: Non-Linear (16 - 256) + QE (dim 2)")
+task.execute_remotely(queue_name="default", exit_process=True)
 
 from src.policies.PolicyEvaluator import PolicyEvaluator
 from src.policies.simple_baseline import BaselinePolicy, Battery
@@ -67,8 +67,8 @@ else:
 
 model_parameters = {
     "learning_rate": 0.0001,
-    "hidden_size": 512,
-    "num_layers": 8,
+    "hidden_size": 256,
+    "num_layers": 16,
     "dropout": 0.2,
     "time_feature_embedding": 2,
 }
@@ -89,17 +89,17 @@ time_embedding = TimeEmbedding(
 #     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"],
-# )
+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"],
+)
 
-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, linear_model)
+model = nn.Sequential(time_embedding, non_linear_model)
 
 model.output_size = 1
 optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])