| """ |
| Module for visualizations. |
| """ |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
|
|
| def plot_error_distribution(all_scores, all_labels, save_path="docs/error_dist.png"): |
| """ |
| Function that plots the error distribution between normal and anomaly scores using label masking. |
| """ |
| |
| |
| normal_scores = all_scores[all_labels == 0] |
| anomaly_scores = all_scores[all_labels == 1] |
|
|
| |
| plt.figure(figsize=(10, 5)) |
| plt.hist(normal_scores, bins=50, alpha=0.6, label="Normal", density=True) |
| plt.hist(anomaly_scores, bins=50, alpha=0.6, label="Anomaly", density=True) |
| plt.xlabel("Reconstruction error") |
| plt.ylabel("Density") |
| plt.title("Per-frame reconstruction error: normal vs anomaly") |
| plt.legend() |
| plt.savefig(save_path, dpi=120, bbox_inches="tight") |
| print(f"saved: {save_path}") |
