Remove temporary code snippets

Author: robin-janssen

codes/benchmark/bench_fcts.py

@@ -96,8 +96,6 @@ def run_benchmark(surr_name: str, surrogate_class, conf: dict) -> dict[str, Any]
         tolerance=conf["dataset"]["tolerance"],
         per_species=conf["dataset"].get("normalise_per_species", False),
     )
-    # TEMP
-    print(conf["dataset"]["name"], train_data.shape, val_data.shape, test_data.shape)
 
     model_config = get_model_config(surr_name, conf)
     n_timesteps = train_data.shape[1]
@@ -1282,10 +1280,6 @@ def compare_errors(metrics: dict[str, dict], config: dict) -> None:
     if log_errors:
         plot_errors_over_time(mean_log, median_log, timesteps, config, mode="deltadex")
         plot_error_distribution_comparative(log_errors, config, mode="deltadex")
-        # TEMP
-        dataset = config["dataset"]["name"]
-        os.makedirs(f"scripts/pp/{dataset}", exist_ok=True)
-        np.savez(f"scripts/pp/{dataset}/all_log_errors.npz", log_errors)
 
 
 def compare_iterative(metrics: dict[str, dict], config: dict) -> None:
@@ -1315,11 +1309,6 @@ def compare_iterative(metrics: dict[str, dict], config: dict) -> None:
                 iterative_errors[surrogate], axis=(0, 2)
             )
 
-    # TEMP
-    dataset = config["dataset"]["name"]
-    os.makedirs(f"scripts/pp/{dataset}", exist_ok=True)
-    np.savez(f"scripts/pp/{dataset}/all_iterative_errors.npz", iterative_errors)
-
     plot_errors_over_time(
         mean_iterative_errors,
         median_iterative_errors,
@@ -1602,12 +1591,6 @@ def compare_UQ(all_metrics: dict, config: dict) -> None:
         show_title=True,
     )
 
-    # TEMP
-    dataset = config["dataset"]["name"]
-    os.makedirs(f"scripts/pp/{dataset}", exist_ok=True)
-    np.savez(f"scripts/pp/{dataset}/all_uq_errors.npz", ensemble_errors)
-    np.savez(f"scripts/pp/{dataset}/all_uq_std.npz", ensemble_std)
-
 
 def tabular_comparison(all_metrics: dict, config: dict) -> None:
     """

codes/benchmark/bench_plots.py

@@ -2036,8 +2036,6 @@ def inference_time_bar_plot(
     # Calculate the upper y-limit to provide space for text
     max_bar = max(means[i] + stds[i] for i in range(len(means)))
     # min_bar = min(means[i] - stds[i] for i in range(len(means)))
-    # Temp!
-    # ax.set_ylim(min_bar * 0.3, max_bar * 2)  # Set limits with some padding
     ax.set_ylim(0, max_bar * 1.2)  # Set limits with some padding
 
     # Add inference time as text to the bars using the format_time function
@@ -2054,8 +2052,6 @@ def inference_time_bar_plot(
 
     ax.set_xlabel("Surrogate Model")
     ax.set_ylabel("Mean Inference Time per Run")
-    # Temp!
-    # ax.set_yscale("log")
     if show_title:
         ax.set_title("Surrogate Mean Inference Time Comparison")
 
@@ -2490,10 +2486,6 @@ def plot_catastrophic_detection_curves(
     names = list(errors_log.keys())
     colors = plt.cm.viridis(np.linspace(0, 0.95, len(names)))
     summary: dict[str, dict[float, dict[str, float]]] = {}
-    # TEMP
-    recall_99 = np.zeros((len(names), len(flag_fractions)))
-    recall_90 = np.zeros((len(names), len(flag_fractions)))
-    dataset_name = conf["dataset"]["name"]
 
     # --- Recall vs fraction flagged (per catastrophic percentile) ---
     for ax, perc in zip(axes[:-1], percentiles):
@@ -2522,11 +2514,6 @@ def plot_catastrophic_detection_curves(
                     recall = (flagged & is_cat).sum() / n_cat if n_cat > 0 else 0.0
                     xs.append(100.0 * flagged.mean())
                     ys.append(100.0 * recall)
-                # TEMP
-                if perc == 99.0:
-                    recall_99[i, flag_fractions.index(f)] = recall
-                if perc == 90.0:
-                    recall_90[i, flag_fractions.index(f)] = recall
 
             ax.plot(
                 xs,
@@ -2553,9 +2540,6 @@ def plot_catastrophic_detection_curves(
                 f"Detection @ {perc}th percentile (Top {100 - perc:.0f}% Δdex)"
             )
 
-    np.savez(f"scripts/pp/{dataset_name}/catastrophic_recall_99.npz", recall_99)
-    np.savez(f"scripts/pp/{dataset_name}/catastrophic_recall_90.npz", recall_90)
-
     # MAE improvement plot
     ax_mae = axes[-1]
     for i, name in enumerate(names):
@@ -3006,8 +2990,6 @@ def rel_errors_and_uq(
 
     ax1.set_xlabel("Time")
     ax1.set_xlim(timesteps[0], timesteps[-1])
-    # Temp!
-    # ax1.set_ylim(3e-4, 1)
     ax1.set_ylabel("Relative Error")
     ax1.set_yscale("log")
     ax1.set_title("Comparison of Relative Errors Over Time")
@@ -3038,8 +3020,6 @@ def rel_errors_and_uq(
 
     ax2.set_xlabel("Time")
     ax2.set_xlim(timesteps[0], timesteps[-1])
-    # Temp!
-    # ax2.set_ylim(0, 0.04)
     ax2.set_ylabel("Uncertainty/Absolute Error")
     if show_title:
         ax2.set_title("Comparison of Predictive Uncertainty Over Time")