A waterfall-style chart showing how each feature contributes to pushing a model prediction from a base value (expected model output) to the final predicted value. Horizontal bar segments extend right for positive SHAP values and left for negative SHAP values, stacking cumulatively so the viewer can trace the path from baseline to prediction. This is a core ML explainability visualization for explaining individual predictions, complementing the SHAP summary plot which shows feature effects across many samples.
""" anyplot.ai
shap-waterfall: SHAP Waterfall Plot for Feature Attribution
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 85/100 | Created: 2026-05-07
"""
import os
import sys
import time
from pathlib import Path
# Prevent this file from shadowing the bokeh package
_this_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p) != _this_dir and p != ""]
from bokeh.io import output_file, save
from bokeh.models import Span
from bokeh.plotting import figure
from bokeh.resources import INLINE
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"
POS_COLOR = "#AE3030" # imprint red — positive SHAP (pushes prediction up)
NEG_COLOR = "#4467A3" # Okabe-Ito blue — negative SHAP (pushes prediction down)
LABEL_INK = "#F0EFE8" # near-white for text on colored bar segments
# Data — credit default risk model: explaining one loan applicant's prediction
features = [
"Credit Score (715)",
"Debt-to-Income Ratio",
"Monthly Income",
"Late Payments (2)",
"Employment Duration",
"Loan Amount",
"Savings Balance",
"Credit History Age",
"Credit Inquiries (3)",
"Credit Cards Count",
"Housing (Renter)",
"Education Level",
]
shap_values = [-0.18, 0.15, -0.12, 0.11, -0.08, 0.07, -0.05, -0.04, 0.04, 0.03, 0.02, -0.02]
base_value = 0.35
# Cumulative waterfall positions
n = len(features)
cumulative = [base_value]
for sv in shap_values:
cumulative.append(round(cumulative[-1] + sv, 6))
final_value = cumulative[-1]
bar_lefts = [min(cumulative[i], cumulative[i + 1]) for i in range(n)]
bar_rights = [max(cumulative[i], cumulative[i + 1]) for i in range(n)]
bar_colors = [POS_COLOR if sv > 0 else NEG_COLOR for sv in shap_values]
bar_labels = [f"{sv:+.2f}" for sv in shap_values]
# Y-axis: features[0] (largest |SHAP|) at top — Bokeh places y_range[0] at bottom
y_range = list(reversed(features))
# Plot
p = figure(
width=4800,
height=2700,
y_range=y_range,
title="Credit Default Risk · shap-waterfall · bokeh · anyplot.ai",
toolbar_location=None,
x_axis_label="Cumulative Default Probability",
)
# Waterfall bars
p.hbar(y=features, left=bar_lefts, right=bar_rights, height=0.6, color=bar_colors, alpha=0.88)
# Connector lines: vertical dashed segments at each cumulative transition
p.segment(
x0=[cumulative[i + 1] for i in range(n - 1)],
y0=[features[i] for i in range(n - 1)],
x1=[cumulative[i + 1] for i in range(n - 1)],
y1=[features[i + 1] for i in range(n - 1)],
line_color=INK_SOFT,
line_dash="dashed",
line_width=2,
line_alpha=0.6,
)
# SHAP value labels centered inside bars (p.text supports categorical y-axis)
p.text(
x=[(l + r) / 2 for l, r in zip(bar_lefts, bar_rights)],
y=features,
text=bar_labels,
text_align="center",
text_baseline="middle",
text_font_size="18pt",
text_color=LABEL_INK,
text_font_style="bold",
)
# Base value annotation above the topmost bar — right-aligned so it stays inside plot
p.text(
x=[base_value],
y=[features[0]],
text=[f"Base value: {base_value:.2f}"],
x_offset=-8,
y_offset=28,
text_align="right",
text_font_size="18pt",
text_color=INK_SOFT,
)
# Final prediction annotation below the bottommost bar
p.text(
x=[final_value],
y=[features[-1]],
text=[f"Prediction: {final_value:.2f}"],
x_offset=6,
y_offset=-28,
text_font_size="18pt",
text_color=INK,
text_font_style="bold",
)
# Reference lines for base value and final prediction
p.add_layout(
Span(location=base_value, dimension="height", line_color=INK_SOFT, line_dash="dashed", line_width=3, line_alpha=0.8)
)
p.add_layout(Span(location=final_value, dimension="height", line_color=INK, line_dash="solid", line_width=3))
# Theme-adaptive chrome
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.title.text_font_style = "bold"
p.xaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
p.xgrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_color = None
# Save HTML and take PNG screenshot with headless Chrome
output_file(f"plot-{THEME}.html")
save(p, resources=INLINE)
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(4)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of SHAP Waterfall Plot for Feature Attribution on anyplot.ai.