Trying out more linear baselines

Reports/Thesis/sections/nrv_prediction.tex

@@ -159,11 +159,23 @@ where:
 \begin{itemize}
     \item \( Q_{\tau}(y | \mathbf{x}) \) is the \( \tau \)-th quantile of the conditional distribution of the NRV
     \item \( \mathbf{X} \) is the input features (e.g. the 96 previous NRV values)
-    \item \(\beta_{0,\tau}, \beta_{1,\tau}, \beta_{2,\tau}, \ldots, \beta_{n,\tau} \) are the coefficients
+    \item \(\beta_{0,\tau}, \beta_{1,\tau}, \beta_{2,\tau}, \ldots, \beta_{n,\tau} \) are the coefficients including the bias
 \end{itemize}
 
-The linear model outputs the values for the chosen quantiles. The total amount of parameters depends on the input features and the number of chosen quantiles. Assuming the input features are the 96 previous NRV values and 13 quantiles are chosen, the total amount of parameters is 96 * 13 + 13 = 1261. 
+The linear model outputs the values for the chosen quantiles. The total amount of parameters depends on the input features and the number of chosen quantiles. Assuming the input features are the 96 previous NRV values and 13 quantiles are chosen, the total amount of parameters is $96 * 13 + 13 = 1261$.
 
-TODO: add results for this model
-
-More input features can be used as well. 
+\begin{table}[ht]
+\centering
+\begin{tabular}{@{}lccccc@{}} % Corrected to six columns
+\toprule
+    & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{MAE} & CRPS \\ 
+    \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-6}
+    & Train & Test & Train & Test & Test \\
+\midrule
+Model 1 & 0.012 & 0.015 & 0.085 & 0.090 & 0.070 \\
+Model 2 & 0.010 & 0.013 & 0.080 & 0.085 & 0.065 \\
+Model 3 & 0.008 & 0.011 & 0.075 & 0.080 & 0.060 \\
+\bottomrule
+\end{tabular}
+\caption{Performance Metrics of the Model}
+\end{table}

Reports/Thesis/verslag.tex

@@ -22,6 +22,8 @@
 \usepackage{multirow}
 \usepackage{float}
 \usepackage{bbm}
+\usepackage{booktabs}
+
 
 \newcolumntype{C}{>{\centering\arraybackslash}X}
 

src/training_scripts/autoregressive_quantiles.py

@@ -27,8 +27,8 @@ from src.models.time_embedding_layer import TimeEmbedding
 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
@@ -125,32 +125,32 @@ trainer.plot_every(2)
 trainer.train(task=task, epochs=epochs, remotely=True)
 
 ### Policy Evaluation ###
-idx_samples = trainer.test_set_samples
-_, test_loader = trainer.data_processor.get_dataloaders(
-    predict_sequence_length=trainer.model.output_size, full_day_skip=False
-)
+# idx_samples = trainer.test_set_samples
+# _, test_loader = trainer.data_processor.get_dataloaders(
+#     predict_sequence_length=trainer.model.output_size, full_day_skip=False
+# )
 
 # policy_evaluator.evaluate_test_set(idx_samples, test_loader)
 # policy_evaluator.plot_profits_table()
 # policy_evaluator.plot_thresholds_per_day()
 
-optimal_penalty, profit, charge_cycles = (
-    policy_evaluator.optimize_penalty_for_target_charge_cycles(
-        idx_samples=idx_samples,
-        test_loader=test_loader,
-        initial_penalty=1000,
-        target_charge_cycles=283,
-        initial_learning_rate=3,
-        max_iterations=150,
-        tolerance=1,
-    )
-)
+# optimal_penalty, profit, charge_cycles = (
+#     policy_evaluator.optimize_penalty_for_target_charge_cycles(
+#         idx_samples=idx_samples,
+#         test_loader=test_loader,
+#         initial_penalty=1000,
+#         target_charge_cycles=283,
+#         initial_learning_rate=3,
+#         max_iterations=150,
+#         tolerance=1,
+#     )
+# )
 
-print(
-    f"Optimal Penalty: {optimal_penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}"
-)
-task.get_logger().report_single_value(name="Optimal Penalty", value=optimal_penalty)
-task.get_logger().report_single_value(name="Optimal Profit", value=profit)
-task.get_logger().report_single_value(name="Optimal Charge Cycles", value=charge_cycles)
+# print(
+#     f"Optimal Penalty: {optimal_penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}"
+# )
+# task.get_logger().report_single_value(name="Optimal Penalty", value=optimal_penalty)
+# task.get_logger().report_single_value(name="Optimal Profit", value=profit)
+# task.get_logger().report_single_value(name="Optimal Charge Cycles", value=charge_cycles)
 
 task.close()