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: bokeh 3.9.0 | Python 3.13.13
Quality: 85/100 | Updated: 2026-05-17
"""
import os
import sys
import time
from pathlib import Path
import numpy as np
# Workaround for bokeh.py shadowing: clear current dir from sys.path and inject site-packages at beginning
_site_packages = next((p for p in sys.path if "site-packages" in p), None)
sys.path = [p for p in sys.path if not (p == "" or p == "." or "site-packages" in p)]
if _site_packages:
sys.path.insert(0, _site_packages)
def main():
"""Main implementation function to avoid module name shadowing."""
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, Whisker
from bokeh.palettes import Cividis256
from bokeh.plotting import figure
from bokeh.transform import linear_cmap
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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 = [
"Square Footage",
"Number of Bedrooms",
"Neighborhood Score",
"Year Built",
"Lot Size",
"Distance to City Center",
"Number of Bathrooms",
"Garage Capacity",
"School Rating",
"Crime Index",
"Property Tax Rate",
"Previous Sale Price",
"Days on Market",
"Walk Score",
"Public Transit Access",
]
# Generate importance values (higher = more important)
importance_mean = np.array(
[0.182, 0.145, 0.128, 0.095, 0.078, 0.065, 0.052, 0.041, 0.035, 0.028, 0.018, 0.012, 0.005, -0.003, -0.008]
)
# Standard deviations (variability across shuffles)
importance_std = np.array(
[0.025, 0.022, 0.020, 0.018, 0.015, 0.014, 0.012, 0.011, 0.010, 0.009, 0.008, 0.007, 0.006, 0.005, 0.004]
)
# Sort by importance (highest first)
sort_idx = np.argsort(importance_mean)[::-1]
features_sorted = [features[i] for i in sort_idx]
importance_mean_sorted = importance_mean[sort_idx]
importance_std_sorted = importance_std[sort_idx]
# Reverse for plotting (highest at top)
features_plot = features_sorted[::-1]
importance_mean_plot = importance_mean_sorted[::-1]
importance_std_plot = importance_std_sorted[::-1]
# Create data source
source = ColumnDataSource(
data={
"features": features_plot,
"importance": importance_mean_plot,
"std": importance_std_plot,
"upper": importance_mean_plot + importance_std_plot,
"lower": importance_mean_plot - importance_std_plot,
}
)
# Create figure with categorical y-axis
p = figure(
width=4800,
height=2700,
y_range=features_plot,
x_axis_label="Mean Decrease in Model Score",
title="bar-permutation-importance · bokeh · anyplot.ai",
)
# Color mapper for bars (darker = more important)
mapper = linear_cmap(
field_name="importance", palette=Cividis256, low=min(importance_mean_plot), high=max(importance_mean_plot)
)
# Draw horizontal bars
p.hbar(
y="features",
right="importance",
left=0,
height=0.7,
source=source,
fill_color=mapper,
line_color=INK_SOFT,
line_width=1,
)
# Add error bars (whiskers)
whisker = Whisker(
source=source,
base="features",
upper="upper",
lower="lower",
dimension="width",
line_color=INK_SOFT,
line_width=2,
upper_head=None,
lower_head=None,
)
p.add_layout(whisker)
# Add vertical reference line at x=0
p.line(x=[0, 0], y=[-1, len(features_plot)], line_color=INK_SOFT, line_width=2, line_dash="dashed")
# Add hover tool
hover = HoverTool(
tooltips=[("Feature", "@features"), ("Importance", "@importance{0.000}"), ("Std Dev", "@std{0.000}")]
)
p.add_tools(hover)
# Styling
p.title.text_font_size = "28pt"
p.title.text_font_style = "bold"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_color = None
# Axis styling
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.axis_line_width = 1
p.yaxis.axis_line_width = 1
p.outline_line_color = INK_SOFT
p.outline_line_width = 1
# Background
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
# Save HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome using Selenium
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
if __name__ == "__main__":
main()
Part of Permutation Feature Importance Plot on anyplot.ai.