A horizontal bar chart displaying permutation feature importance from machine learning models, showing the decrease in model score when each feature is randomly shuffled. Unlike model-specific feature importances, permutation importance is model-agnostic and measures how much the model's performance degrades when a feature's relationship with the target is broken. Error bars indicate variability across multiple shuffles, providing a confidence measure for each importance score.
""" anyplot.ai
bar-permutation-importance: Permutation Feature Importance Plot
Library: pygal 3.1.0 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-17
"""
import os
import sys
import numpy as np
# Temporarily remove current directory from path to avoid name collision with pygal module
_cwd = sys.path[0] if sys.path[0] else "."
if _cwd in sys.path:
sys.path.remove(_cwd)
import pygal
from pygal.style import Style
# Restore path
sys.path.insert(0, _cwd)
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Data - Simulated permutation importance results
np.random.seed(42)
features = [
"Year Built",
"Bathrooms",
"Garage Size",
"Lot Area",
"Bedrooms",
"Basement Area",
"Total Rooms",
"Living Area",
"Neighborhood",
"Overall Quality",
]
importance_mean = np.array([0.002, 0.008, 0.015, 0.024, 0.032, 0.048, 0.067, 0.095, 0.128, 0.185])
importance_std = np.array([0.003, 0.005, 0.008, 0.011, 0.014, 0.018, 0.022, 0.028, 0.035, 0.042])
# Generate viridis color gradient for importance values
# Inline color generation without helper function
min_imp = importance_mean.min()
max_imp = importance_mean.max()
imp_range = max_imp - min_imp if max_imp != min_imp else 1.0
viridis_stops = [(0.0, 68, 1, 84), (0.25, 58, 82, 139), (0.5, 32, 144, 140), (0.75, 94, 201, 97), (1.0, 253, 231, 36)]
bar_colors = []
for imp in importance_mean:
t = (imp - min_imp) / imp_range
for j in range(len(viridis_stops) - 1):
t0, r0, g0, b0 = viridis_stops[j]
t1, r1, g1, b1 = viridis_stops[j + 1]
if t0 <= t <= t1:
seg_t = (t - t0) / (t1 - t0)
r = int(r0 + (r1 - r0) * seg_t)
g = int(g0 + (g1 - g0) * seg_t)
b = int(b0 + (b1 - b0) * seg_t)
bar_colors.append(f"#{r:02x}{g:02x}{b:02x}")
break
else:
bar_colors.append("#fde724")
# Custom style with theme-adaptive colors and large fonts
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_SOFT,
colors=tuple(bar_colors),
title_font_size=28,
label_font_size=22,
major_label_font_size=18,
legend_font_size=16,
value_font_size=14,
stroke_width=3,
)
# Create horizontal bar chart
chart = pygal.HorizontalBar(
width=4800,
height=2700,
style=custom_style,
title="bar-permutation-importance · pygal · anyplot.ai",
x_title="Mean Decrease in R² Score",
show_legend=False,
print_values=True,
print_values_position="top",
show_y_guides=False,
show_x_guides=True,
range=(0, importance_mean.max() + importance_std.max() + 0.02),
margin_bottom=120,
margin_left=360,
margin_right=80,
margin_top=80,
)
# Set feature labels
chart.x_labels = features
# Add mean importance bars with viridis gradient colors
chart.add(
"Importance",
[
{"value": mean, "color": color, "xlink": {"href": "#"}, "label": f"Mean: {mean:.3f} ± {std:.3f}"}
for mean, std, color in zip(importance_mean, importance_std, bar_colors, strict=True)
],
formatter=lambda x: f"{x:.3f}" if x else "",
)
# Save outputs
chart.render_to_file(f"plot-{THEME}.html")
chart.render_to_png(f"plot-{THEME}.png")
Part of Permutation Feature Importance Plot on anyplot.ai.