| import seaborn as sns |
| from matplotlib import pyplot as plt |
| import numpy as np |
| import os |
| import pandas as pd |
| import torch |
| import seaborn as sns |
| import torch.nn.functional as F |
|
|
|
|
| def plot_distribution(args, id_scores, ood_scores, out_dataset, score=None): |
| sns.set(style="white", palette="muted") |
| palette = ['#A8BAE3', '#55AB83'] |
|
|
| data = { |
| "ID": [-1 * id_score for id_score in id_scores], |
| "OOD": [-1 * ood_score for ood_score in ood_scores] |
| } |
| sns.displot(data, label="id", kind="kde", palette=palette, fill=True, alpha=0.8) |
| if score is not None: |
| plt.savefig(os.path.join(args.output_dir,f"{out_dataset}_{args.T}_{score}.png"), bbox_inches='tight') |
| else: |
| plt.savefig(os.path.join(args.output_dir,f"{out_dataset}_{args.T}.png"), bbox_inches='tight') |
|
|
|
|
| def show_values_on_bars(axs): |
| def _show_on_single_plot(ax): |
| for p in ax.patches: |
| _x = p.get_x() + p.get_width() / 2 |
| _y = p.get_y() + p.get_height() |
| value = '{:.2f}'.format(p.get_height()) |
| ax.text(_x, _y, value, ha="center", fontsize=9) |
| if isinstance(axs, np.ndarray): |
| for idx, ax in np.ndenumerate(axs): |
| _show_on_single_plot(ax) |
| else: |
| _show_on_single_plot(axs) |
|
|
