Added crps + profit logging and updated plots for non autoregressive models

src/training_scripts/autoregressive_quantiles.py

@@ -3,7 +3,7 @@ from src.utils.clearml import ClearMLHelper
 #### ClearML ####
 clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
 task = clearml_helper.get_task(
-    task_name="Autoregressive Quantile Regression: Non Linear"
+    task_name="Non Autoregressive Quantile Regression: Non Linear"
 )
 task.execute_remotely(queue_name="default", exit_process=True)
 
@@ -11,7 +11,10 @@ from src.policies.PolicyEvaluator import PolicyEvaluator
 from src.policies.simple_baseline import BaselinePolicy, Battery
 from src.models.lstm_model import GRUModel
 from src.data import DataProcessor, DataConfig
-from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer
+from src.trainers.quantile_trainer import (
+    AutoRegressiveQuantileTrainer,
+    NonAutoRegressiveQuantileRegression,
+)
 from src.trainers.trainer import Trainer
 from src.utils.clearml import ClearMLHelper
 from src.models import *
@@ -46,7 +49,7 @@ data_processor.set_full_day_skip(False)
 
 
 #### Hyperparameters ####
-data_processor.set_output_size(1)
+data_processor.set_output_size(96)
 inputDim = data_processor.get_input_size()
 epochs = 300
 
@@ -77,7 +80,7 @@ time_embedding = TimeEmbedding(
 # lstm_model = GRUModel(time_embedding.output_dim(inputDim), len(quantiles), hidden_size=model_parameters["hidden_size"], num_layers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
 non_linear_model = NonLinearRegression(
     time_embedding.output_dim(inputDim),
-    len(quantiles),
+    len(quantiles) * 96,
     hiddenSize=model_parameters["hidden_size"],
     numLayers=model_parameters["num_layers"],
     dropout=model_parameters["dropout"],
@@ -85,6 +88,7 @@ non_linear_model = NonLinearRegression(
 # linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
 
 model = nn.Sequential(time_embedding, non_linear_model)
+model.output_size = 96
 optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
 
 ### Policy Evaluator ###
@@ -93,7 +97,18 @@ baseline_policy = BaselinePolicy(battery, data_path="")
 policy_evaluator = PolicyEvaluator(baseline_policy, task)
 
 #### Trainer ####
-trainer = AutoRegressiveQuantileTrainer(
+# trainer = AutoRegressiveQuantileTrainer(
+#     model,
+#     inputDim,
+#     optimizer,
+#     data_processor,
+#     quantiles,
+#     "cuda",
+#     policy_evaluator=policy_evaluator,
+#     debug=False,
+# )
+
+trainer = NonAutoRegressiveQuantileRegression(
     model,
     inputDim,
     optimizer,

src/training_scripts/diffusion_training.py

@@ -1,7 +1,9 @@
 from src.utils.clearml import ClearMLHelper
 
 clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
-task = clearml_helper.get_task(task_name="Diffusion Training")
+task = clearml_helper.get_task(
+    task_name="Diffusion Training: hidden_sizes=[64, 64], lr=0.0001, time_dim=8"
+)
 task.execute_remotely(queue_name="default", exit_process=True)
 
 from src.models import *
@@ -37,9 +39,9 @@ inputDim = data_processor.get_input_size()
 print("Input dim: ", inputDim)
 
 model_parameters = {
-    "epochs": 5000,
+    "epochs": 8000,
     "learning_rate": 0.0001,
-    "hidden_sizes": [128, 128],
+    "hidden_sizes": [64, 64],
     "time_dim": 8,
 }