diff --git a/analysis/base_loss/centroid_convergence_plotter.py b/analysis/base_loss/centroid_convergence_plotter.py index 1904c46..55102c1 100644 --- a/analysis/base_loss/centroid_convergence_plotter.py +++ b/analysis/base_loss/centroid_convergence_plotter.py @@ -43,7 +43,7 @@ def replicate_base_loss(theta_array, desired_theta, base_key): # --------------------------------------------------------------------- # Helper: safe regression function (handles log transforms, filters invalid data) # --------------------------------------------------------------------- -def safe_compute_best_fit(x, y, log_x=False, log_y=True): +def safe_compute_best_fit(x, y, log_x=False, log_y=False): """ Computes a best-fit line using linear regression, optionally on log(x) and/or log(y). Returns: @@ -215,7 +215,7 @@ def process_condition(condition_name): ax_top.set_ylabel(f"Final Loss @ epoch {final_epoch}") ax_top.set_title(f"{condition_name}: Final Loss vs. Exponent") # best-fit line - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["final_loss"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["final_loss"], log_x=False, log_y=False) if xs is not None: ax_top.plot(xs, y_fit, "k--", label=f"slope={slope:.3f}, int={intercept:.3f}, R²={R2:.3f}") ax_top.legend(fontsize=8) @@ -236,7 +236,7 @@ def process_condition(condition_name): ax_bot.set_ylabel("Epochs to Convergence") ax_bot.set_title(f"{condition_name}: Convergence vs. Exponent") # best-fit line - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["epochs_to_convergence"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["epochs_to_convergence"], log_x=False, log_y=False) if xs is not None: ax_bot.plot(xs, y_fit, "k--", label=f"slope={slope:.3f}, int={intercept:.3f}, R²={R2:.3f}") ax_bot.legend(fontsize=8) diff --git a/analysis/base_loss/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png index 432b03e..4ed1407 100644 Binary files a/analysis/base_loss/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png index 3a8a4af..d262258 100644 Binary files a/analysis/base_loss/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png index 1ddad65..2073d3a 100644 Binary files a/analysis/base_loss/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png index 49abd67..6381e44 100644 Binary files a/analysis/base_loss/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png index 0911cae..1cedd3a 100644 Binary files a/analysis/base_loss/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss_learning_rate_sweep/centroid_convergence_plotter.py b/analysis/base_loss_learning_rate_sweep/centroid_convergence_plotter.py index 919a22b..7f6f019 100644 --- a/analysis/base_loss_learning_rate_sweep/centroid_convergence_plotter.py +++ b/analysis/base_loss_learning_rate_sweep/centroid_convergence_plotter.py @@ -43,7 +43,7 @@ def replicate_base_loss(theta_array, desired_theta, base_key): # --------------------------------------------------------------------- # Helper: safe regression function (handles log transforms, filters invalid data) # --------------------------------------------------------------------- -def safe_compute_best_fit(x, y, log_x=False, log_y=True): +def safe_compute_best_fit(x, y, log_x=False, log_y=False): """ Computes a best-fit line using linear regression, optionally on log(x) and/or log(y). Returns: @@ -215,7 +215,7 @@ def process_condition(condition_name): ax_top.set_ylabel(f"Final Loss @ epoch {final_epoch}") ax_top.set_title(f"{condition_name}: Final Loss vs. Exponent") # best-fit line - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["final_loss"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["final_loss"], log_x=False, log_y=False) if xs is not None: ax_top.plot(xs, y_fit, "k--", label=f"slope={slope:.3f}, int={intercept:.3f}, R²={R2:.3f}") ax_top.legend(fontsize=8) @@ -236,7 +236,7 @@ def process_condition(condition_name): ax_bot.set_ylabel("Epochs to Convergence") ax_bot.set_title(f"{condition_name}: Convergence vs. Exponent") # best-fit line - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["epochs_to_convergence"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["Exponent"], df["epochs_to_convergence"], log_x=False, log_y=False) if xs is not None: ax_bot.plot(xs, y_fit, "k--", label=f"slope={slope:.3f}, int={intercept:.3f}, R²={R2:.3f}") ax_bot.legend(fontsize=8) diff --git a/analysis/base_loss_learning_rate_sweep/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss_learning_rate_sweep/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png index d20cdd9..abd10fb 100644 Binary files a/analysis/base_loss_learning_rate_sweep/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss_learning_rate_sweep/extreme_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss_learning_rate_sweep/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss_learning_rate_sweep/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png index 6cf6404..2406223 100644 Binary files a/analysis/base_loss_learning_rate_sweep/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss_learning_rate_sweep/large_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss_learning_rate_sweep/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss_learning_rate_sweep/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png index fa3fc11..6b1fca9 100644 Binary files a/analysis/base_loss_learning_rate_sweep/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss_learning_rate_sweep/overshoot_angle_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss_learning_rate_sweep/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss_learning_rate_sweep/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png index ddc96ad..9a0edd1 100644 Binary files a/analysis/base_loss_learning_rate_sweep/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss_learning_rate_sweep/overshoot_vertical_test/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/base_loss_learning_rate_sweep/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png b/analysis/base_loss_learning_rate_sweep/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png index 8efaefb..5f1b302 100644 Binary files a/analysis/base_loss_learning_rate_sweep/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png and b/analysis/base_loss_learning_rate_sweep/small_perturbation/plots/degree_convergence/exponent_vs_loss_and_convergence.png differ diff --git a/analysis/time_weighting/centroid_convergence_plotter.py b/analysis/time_weighting/centroid_convergence_plotter.py index 9c048cc..eb51723 100644 --- a/analysis/time_weighting/centroid_convergence_plotter.py +++ b/analysis/time_weighting/centroid_convergence_plotter.py @@ -127,7 +127,7 @@ def find_convergence_epoch(data_dict, desired_theta, threshold): # --------------------------------------------------------------------- # Helper: Compute best-fit line (with R^2) for scatter plot data. # --------------------------------------------------------------------- -def safe_compute_best_fit(x, y, log_x=False, log_y=True): +def safe_compute_best_fit(x, y, log_x=False, log_y=False): """ Computes the best-fit line using linear regression. Filters out NaN and non-positive values if log transforms are used. @@ -256,7 +256,7 @@ for cond_name in all_subdirs: axes[0].set_ylabel(f"Loss at Epoch {final_epoch}") axes[0].set_yscale("log") axes[0].set_title(f"{cond_name}: Loss vs. $t_{{median}}$") - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["t_median"], df["final_loss"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["t_median"], df["final_loss"], log_x=False, log_y=False) if xs is not None: axes[0].plot(xs, y_fit, "k--", label=f"Fit: slope={slope:.3f}, int={intercept:.3f}\n$R^2$={R2:.3f}") axes[0].legend(fontsize=8) @@ -273,7 +273,7 @@ for cond_name in all_subdirs: axes[1].set_ylabel("Epochs to Convergence") axes[1].set_yscale("log") axes[1].set_title(f"{cond_name}: Convergence vs. $t_{{median}}$") - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["t_median"], df["epochs_to_convergence"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["t_median"], df["epochs_to_convergence"], log_x=False, log_y=False) if xs is not None: axes[1].plot(xs, y_fit, "k--", label=f"Fit: slope={slope:.3f}, int={intercept:.3f}\n$R^2$={R2:.3f}") axes[1].legend(fontsize=8) diff --git a/analysis/time_weighting/extreme_perturbation/plots/centroid_convergence/t_median_composite.png b/analysis/time_weighting/extreme_perturbation/plots/centroid_convergence/t_median_composite.png index e82311b..82f5737 100644 Binary files a/analysis/time_weighting/extreme_perturbation/plots/centroid_convergence/t_median_composite.png and b/analysis/time_weighting/extreme_perturbation/plots/centroid_convergence/t_median_composite.png differ diff --git a/analysis/time_weighting/large_perturbation/plots/centroid_convergence/t_median_composite.png b/analysis/time_weighting/large_perturbation/plots/centroid_convergence/t_median_composite.png index fcdfeb9..e3da872 100644 Binary files a/analysis/time_weighting/large_perturbation/plots/centroid_convergence/t_median_composite.png and b/analysis/time_weighting/large_perturbation/plots/centroid_convergence/t_median_composite.png differ diff --git a/analysis/time_weighting/overshoot_angle_test/plots/centroid_convergence/t_median_composite.png b/analysis/time_weighting/overshoot_angle_test/plots/centroid_convergence/t_median_composite.png index 8aa56d3..1ed3e24 100644 Binary files a/analysis/time_weighting/overshoot_angle_test/plots/centroid_convergence/t_median_composite.png and b/analysis/time_weighting/overshoot_angle_test/plots/centroid_convergence/t_median_composite.png differ diff --git a/analysis/time_weighting/overshoot_vertical_test/plots/centroid_convergence/t_median_composite.png b/analysis/time_weighting/overshoot_vertical_test/plots/centroid_convergence/t_median_composite.png index ba94c8b..c6f1162 100644 Binary files a/analysis/time_weighting/overshoot_vertical_test/plots/centroid_convergence/t_median_composite.png and b/analysis/time_weighting/overshoot_vertical_test/plots/centroid_convergence/t_median_composite.png differ diff --git a/analysis/time_weighting/small_perturbation/plots/centroid_convergence/t_median_composite.png b/analysis/time_weighting/small_perturbation/plots/centroid_convergence/t_median_composite.png index 4626aaa..5df4bc0 100644 Binary files a/analysis/time_weighting/small_perturbation/plots/centroid_convergence/t_median_composite.png and b/analysis/time_weighting/small_perturbation/plots/centroid_convergence/t_median_composite.png differ diff --git a/analysis/time_weighting_learning_rate_sweep/centroid_convergence_plotter.py b/analysis/time_weighting_learning_rate_sweep/centroid_convergence_plotter.py index f009df6..072ffd7 100644 --- a/analysis/time_weighting_learning_rate_sweep/centroid_convergence_plotter.py +++ b/analysis/time_weighting_learning_rate_sweep/centroid_convergence_plotter.py @@ -127,7 +127,7 @@ def find_convergence_epoch(data_dict, desired_theta, threshold): # --------------------------------------------------------------------- # Helper: Compute best-fit line (with R^2) for scatter plot data. # --------------------------------------------------------------------- -def safe_compute_best_fit(x, y, log_x=False, log_y=True): +def safe_compute_best_fit(x, y, log_x=False, log_y=False): """ Computes the best-fit line using linear regression. Filters out NaN and non-positive values if log transforms are used. @@ -256,7 +256,7 @@ for cond_name in all_subdirs: axes[0].set_ylabel(f"Loss at Epoch {final_epoch}") axes[0].set_yscale("log") axes[0].set_title(f"{cond_name}: Loss vs. $t_{{median}}$") - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["t_median"], df["final_loss"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["t_median"], df["final_loss"], log_x=False, log_y=False) if xs is not None: axes[0].plot(xs, y_fit, "k--", label=f"Fit: slope={slope:.3f}, int={intercept:.3f}\n$R^2$={R2:.3f}") axes[0].legend(fontsize=8) @@ -273,7 +273,7 @@ for cond_name in all_subdirs: axes[1].set_ylabel("Epochs to Convergence") axes[1].set_yscale("log") axes[1].set_title(f"{cond_name}: Convergence vs. $t_{{median}}$") - xs, y_fit, slope, intercept, R2 = safe_compute_best_fit(df["t_median"], df["epochs_to_convergence"], log_x=False, log_y=True) + xs, y_fit, slope, intercept, R2 = 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