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| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from pandas.api.types import CategoricalDtype | |
| def plot_metrics(metrics, condition_to_latex, title, color_palette): | |
| # Add a new column to your dataframe with the latex representation | |
| metrics['condition_latex'] = metrics['condition'].map(condition_to_latex) | |
| # Order condition_latex as per the condition_to_latex dictionary | |
| cat_type = CategoricalDtype(categories=condition_to_latex.values(), ordered=True) | |
| metrics['condition_latex'] = metrics['condition_latex'].astype(cat_type) | |
| # Compute mean and std for each condition for each metric | |
| grouped = metrics.groupby('condition_latex')[['mel', 'frechet']].agg(['mean', 'std']) | |
| fig, axs = plt.subplots(2, 1, figsize=(7, 5.25)) | |
| # Set the main title for the figure | |
| fig.suptitle(title, fontsize=16) | |
| # Get color for each bar in the plot | |
| bar_colors = [color_palette[condition] for condition in grouped.index] | |
| # Plot mel | |
| sns.boxplot(x='condition_latex', y='mel', data=metrics, ax=axs[0], palette=color_palette, showfliers=False) | |
| axs[0].set_ylabel('Mel Spectrogram Loss \u2190') | |
| axs[0].set_xlabel('') # Remove x-axis label | |
| axs[0].set_xticklabels(grouped.index, rotation=0, ha='center') | |
| # Plot frechet | |
| axs[1].bar(grouped.index, grouped['frechet']['mean'], yerr=grouped['frechet']['std'], color=bar_colors) | |
| axs[1].set_ylabel('FAD \u2190') | |
| axs[1].set_xlabel('') # Remove x-axis label | |
| axs[1].set_xticklabels(grouped.index, rotation=0, ha='center') | |
| # Adjust the space between plots | |
| plt.subplots_adjust(hspace=0.1) | |
| # Remove any unnecessary space around the plot | |
| plt.tight_layout(rect=[0, 0, 1, 0.96]) | |
| # Reduce the space between suptitle and the plot | |
| plt.subplots_adjust(top=0.92) |