Updated thesis

src/training_scripts/non_autoregressive_quantiles.py

@@ -2,10 +2,10 @@ from src.utils.clearml import ClearMLHelper
 
 #### ClearML ####
 clearml_helper = ClearMLHelper(
-    project_name="Thesis/NAQR: Non Linear (4 - 256) + Load + PV + Wind + NP"
+    project_name="Thesis/NrvForecast"
 )
 task = clearml_helper.get_task(
-    task_name="NAQR: Non Linear (4 - 256) + Load + PV + Wind + NP"
+    task_name="NAQR: Non Linear (2 - 512)"
 )
 task.execute_remotely(queue_name="default", exit_process=True)
 
@@ -30,17 +30,17 @@ from src.models.time_embedding_layer import TimeEmbedding
 #### Data Processor ####
 data_config = DataConfig()
 
-data_config.NRV_HISTORY = True
-data_config.LOAD_HISTORY = True
-data_config.LOAD_FORECAST = True
+data_config.NRV_HISTORY = False
+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")
@@ -53,7 +53,7 @@ data_processor.set_full_day_skip(True)
 #### Hyperparameters ####
 data_processor.set_output_size(96)
 inputDim = data_processor.get_input_size()
-epochs = 300
+epochs = 5
 
 # add parameters to clearml
 quantiles = task.get_parameter("general/quantiles", cast=True)
@@ -69,7 +69,7 @@ else:
 model_parameters = {
     "learning_rate": 0.0001,
     "hidden_size": 512,
-    "num_layers": 8,
+    "num_layers": 2,
     "dropout": 0.2,
 }
 
@@ -111,15 +111,15 @@ trainer = NonAutoRegressiveQuantileRegression(
     data_processor,
     quantiles,
     "cuda",
-    policy_evaluator=None,
+    policy_evaluator=policy_evaluator,
     debug=False,
 )
 
 trainer.add_metrics_to_track(
     [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
 )
-trainer.early_stopping(patience=5)
-trainer.plot_every(20)
+trainer.early_stopping(patience=8)
+trainer.plot_every(4)
 trainer.train(task=task, epochs=epochs, remotely=True)
 
 ### Policy Evaluation ###
@@ -138,7 +138,7 @@ optimal_penalty, profit, charge_cycles = (
         test_loader=test_loader,
         initial_penalty=1000,
         target_charge_cycles=283,
-        learning_rate=15,
+        initial_learning_rate=15,
         max_iterations=150,
         tolerance=1,
     )