Updated some stuff

Reports/Thesis/verslag.log

@@ -1,4 +1,4 @@
-This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) (preloaded format=pdflatex 2023.9.17)  20 MAR 2024 22:13
+This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) (preloaded format=pdflatex 2023.9.17)  20 MAR 2024 22:16
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  restricted \write18 enabled.
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Result-Reports/Policies.md

@@ -198,8 +198,24 @@ Tabellen die we gaan bespreken -> updaten met nieuwe data dan
 !!!!! Fix the test set (maybe save pickle)
 
 Plot profits per year (maybe with charge cycles) for the different models and baselines.
-Spread plotten van difference between charge and discharge thresholds
 
 RESULTATEN FIXEN
 
 Nog eens 3e meeting opbrengen voor 2e deel maart.
+
+Baseline -> thresholds bepalen training data, penalty aanpassen na evaluatie op test
+
+Schaal van plotjes aan zelfde
+
+NRV generaties plotten
+
+Profit during training
+
+
+Beste diffusion and best GRU model -> plotjes van profits during training
+
+
+# !!!! Baseline + Non autoregressive !!!!!!!!!!!!!!!
+
+
+# profit evaluation done day by day. Start with fresh battery. Maybe electritiy bought but not sold -> negative profits? What to do with this? 

Result-Reports/Profit.md

@@ -0,0 +1,3 @@
+| Experiment | Model Type | Input Parameters | Profit | Charge Cycles |
+|------------|------------|------------------|--------|----------------|
+| [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/432396923e354d579494048656228c32/info-output/metrics/scalar) | Diffusion Model | 

src/training_scripts/autoregressive_quantiles.py

@@ -114,17 +114,6 @@ trainer = AutoRegressiveQuantileTrainer(
     debug=False,
 )
 
-# trainer = NonAutoRegressiveQuantileRegression(
-#     model,
-#     inputDim,
-#     optimizer,
-#     data_processor,
-#     quantiles,
-#     "cuda",
-#     policy_evaluator=policy_evaluator,
-#     debug=False,
-# )
-
 trainer.add_metrics_to_track(
     [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
 )

src/training_scripts/diffusion_training.py

@@ -18,6 +18,7 @@ from src.policies.PolicyEvaluator import PolicyEvaluator
 #### Data Processor ####
 data_config = DataConfig()
 data_config.NRV_HISTORY = True
+
 data_config.LOAD_HISTORY = False
 data_config.LOAD_FORECAST = False