Adding intermediate table with non linear model results

src/training_scripts/autoregressive_quantiles.py

@@ -2,7 +2,9 @@ from src.utils.clearml import ClearMLHelper
 
 #### ClearML ####
 clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
-task = clearml_helper.get_task(task_name="AQR: Non-Linear (2 - 256 - 0.2)")
+task = clearml_helper.get_task(
+    task_name="AQR: Non-Linear (8 - 512 - 0.2) + Load + PV + Wind + Net Position + QE (dim 5)"
+)
 task.execute_remotely(queue_name="default", exit_process=True)
 
 from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -28,19 +30,19 @@ 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.QUARTER = False
+data_config.QUARTER = True
 data_config.DAY_OF_WEEK = False
 
-data_config.NOMINAL_NET_POSITION = False
+data_config.NOMINAL_NET_POSITION = True
 
 
 data_config = task.connect(data_config, name="data_features")
@@ -68,8 +70,8 @@ else:
 
 model_parameters = {
     "learning_rate": 0.0001,
-    "hidden_size": 256,
-    "num_layers": 2,
+    "hidden_size": 512,
+    "num_layers": 8,
     "dropout": 0.2,
     "time_feature_embedding": 5,
 }
@@ -125,7 +127,7 @@ trainer = AutoRegressiveQuantileTrainer(
 trainer.add_metrics_to_track(
     [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
 )
-trainer.early_stopping(patience=5)
+trainer.early_stopping(patience=10)
 trainer.plot_every(15)
 trainer.train(task=task, epochs=epochs, remotely=True)