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5f6b40b 9583919 5f6b40b 9583919 5f6b40b 9583919 5f6b40b 9583919 5f6b40b 9583919 5f6b40b 9583919 5f6b40b 9583919 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | 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)
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