A logistic regression visualization showing the characteristic S-shaped (sigmoid) probability curve for binary classification. The plot displays data points colored by their binary class, the fitted logistic curve representing predicted probabilities, confidence intervals around the curve, and an optional decision threshold line. This visualization is essential for understanding how a logistic model maps continuous input features to class probabilities.
""" anyplot.ai
logistic-regression: Logistic Regression Curve Plot
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-18
"""
import os
# Remove the current directory from sys.path to avoid circular imports with bokeh.py
import sys
import time
from pathlib import Path
sys.path = [p for p in sys.path if p not in ("", ".", os.getcwd(), os.path.dirname(__file__))]
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, Label, Span
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito colors
COLOR_0 = "#009E73" # Class 0 - bluish green (brand color)
COLOR_1 = "#C475FD" # Class 1 - vermillion
# Data - Generate binary classification data
np.random.seed(42)
X, y = make_classification(
n_samples=200, n_features=1, n_informative=1, n_redundant=0, n_clusters_per_class=1, flip_y=0.1, random_state=42
)
x = X.flatten()
# Scale x to meaningful range (e.g., test score 20-80)
x = (x - x.min()) / (x.max() - x.min()) * 60 + 20
# Fit logistic regression
model = LogisticRegression()
model.fit(x.reshape(-1, 1), y)
# Generate smooth curve for logistic regression
x_curve = np.linspace(x.min() - 2, x.max() + 2, 300)
prob_curve = model.predict_proba(x_curve.reshape(-1, 1))[:, 1]
# Compute confidence intervals (using asymptotic approximation)
p_val = prob_curve
se = np.sqrt(p_val * (1 - p_val) / len(x))
z = 1.96
ci_lower = np.clip(prob_curve - z * se * 3, 0, 1)
ci_upper = np.clip(prob_curve + z * se * 3, 0, 1)
# Jitter y values for visibility
jitter = np.random.uniform(-0.03, 0.03, len(y))
y_jittered = y + jitter
# Separate data by class
class_0_mask = y == 0
class_1_mask = y == 1
# Create data sources
source_class0 = ColumnDataSource(data={"x": x[class_0_mask], "y": y_jittered[class_0_mask]})
source_class1 = ColumnDataSource(data={"x": x[class_1_mask], "y": y_jittered[class_1_mask]})
source_curve = ColumnDataSource(data={"x": x_curve, "prob": prob_curve, "ci_lower": ci_lower, "ci_upper": ci_upper})
# Create figure
p = figure(
width=4800,
height=2700,
title="logistic-regression · python · bokeh · anyplot.ai",
x_axis_label="Test Score",
y_axis_label="Probability of Passing",
x_range=(x.min() - 5, x.max() + 5),
y_range=(-0.08, 1.08),
)
# Confidence interval band
p.varea(
x="x", y1="ci_lower", y2="ci_upper", source=source_curve, fill_color=COLOR_0, fill_alpha=0.15, legend_label="95% CI"
)
# Logistic curve
p.line(x="x", y="prob", source=source_curve, line_color=COLOR_0, line_width=5, legend_label="Logistic Curve")
# Data points - Class 0
p.scatter(
x="x",
y="y",
source=source_class0,
size=20,
fill_color=COLOR_0,
fill_alpha=0.6,
line_color=PAGE_BG,
line_width=2,
legend_label="Class 0 (Failed)",
)
# Data points - Class 1
p.scatter(
x="x",
y="y",
source=source_class1,
size=20,
fill_color=COLOR_1,
fill_alpha=0.6,
line_color=PAGE_BG,
line_width=2,
legend_label="Class 1 (Passed)",
)
# Decision threshold line at p=0.5
threshold = Span(location=0.5, dimension="width", line_color=INK_SOFT, line_width=4, line_dash="dashed")
p.add_layout(threshold)
# Add threshold label
threshold_label = Label(
x=x.max() - 5,
y=0.53,
text="Decision Threshold (p=0.5)",
text_font_size="20pt",
text_color=INK_SOFT,
text_align="right",
)
p.add_layout(threshold_label)
# Model accuracy annotation
accuracy = model.score(x.reshape(-1, 1), y)
accuracy_label = Label(
x=x.min() + 2, y=0.92, text=f"Model Accuracy: {accuracy:.1%}", text_font_size="22pt", text_color=INK
)
p.add_layout(accuracy_label)
# Styling
p.title.text_font_size = "28pt"
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
# Axis styling
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_alpha = 0.10
# Legend styling - repositioned to top-left to avoid data overlap
p.legend.location = "top_left"
p.legend.label_text_font_size = "18pt"
p.legend.label_text_color = INK_SOFT
p.legend.glyph_height = 35
p.legend.glyph_width = 35
p.legend.spacing = 12
p.legend.background_fill_color = ELEVATED_BG
p.legend.background_fill_alpha = 0.9
p.legend.border_line_color = INK_SOFT
p.legend.padding = 15
# Background
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Save HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with 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()
Part of Logistic Regression Curve Plot on anyplot.ai.