A Precision-Recall curve plots precision (positive predictive value) against recall (sensitivity) at various classification thresholds. This visualization is essential for evaluating binary classifiers on imbalanced datasets where accuracy alone is misleading. The area under the curve (Average Precision) summarizes classifier performance, with higher values indicating better performance.
""" anyplot.ai
precision-recall: Precision-Recall Curve
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-10
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, Legend
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
from sklearn.metrics import average_precision_score, precision_recall_curve
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
# 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"
BRAND = "#009E73" # Okabe-Ito position 1
ACCENT = "#C475FD" # Okabe-Ito position 2
# Data - Generate imbalanced classification dataset
np.random.seed(42)
X, y_true = make_classification(
n_samples=2000, n_features=20, n_informative=10, n_redundant=5, n_classes=2, weights=[0.7, 0.3], random_state=42
)
# Split into train and test for realistic evaluation
X_train, X_test, y_train, y_test = train_test_split(X, y_true, test_size=0.5, random_state=42, stratify=y_true)
# Train two classifiers for comparison
lr_model = LogisticRegression(random_state=42, max_iter=1000)
nb_model = GaussianNB()
lr_model.fit(X_train, y_train)
nb_model.fit(X_train, y_train)
# Get prediction probabilities on test set
lr_scores = lr_model.predict_proba(X_test)[:, 1]
nb_scores = nb_model.predict_proba(X_test)[:, 1]
# Calculate precision-recall curves
lr_precision, lr_recall, _ = precision_recall_curve(y_test, lr_scores)
nb_precision, nb_recall, _ = precision_recall_curve(y_test, nb_scores)
# Calculate Average Precision scores
lr_ap = average_precision_score(y_test, lr_scores)
nb_ap = average_precision_score(y_test, nb_scores)
# Baseline (random classifier) - positive class ratio
baseline = np.mean(y_test)
# Plot
p = figure(
width=4800,
height=2700,
title="precision-recall · bokeh · anyplot.ai",
x_axis_label="Recall",
y_axis_label="Precision",
x_range=(-0.02, 1.05),
y_range=(0, 1.08),
)
# Create data sources for stepped lines
lr_source = ColumnDataSource(data={"recall": lr_recall, "precision": lr_precision})
nb_source = ColumnDataSource(data={"recall": nb_recall, "precision": nb_precision})
# Plot Precision-Recall curves with step style
lr_line = p.step(x="recall", y="precision", source=lr_source, line_width=5, color=BRAND, alpha=0.9, mode="after")
nb_line = p.step(x="recall", y="precision", source=nb_source, line_width=5, color=ACCENT, alpha=0.9, mode="after")
# Baseline reference line (random classifier)
baseline_source = ColumnDataSource(data={"x": [0, 1], "y": [baseline, baseline]})
baseline_line = p.line(
x="x", y="y", source=baseline_source, line_width=4, line_dash="dashed", color=INK_SOFT, alpha=0.6
)
# Create legend with AP scores
legend = Legend(
items=[
(f"Logistic Regression (AP = {lr_ap:.3f})", [lr_line]),
(f"Naive Bayes (AP = {nb_ap:.3f})", [nb_line]),
(f"Random Classifier (baseline = {baseline:.2f})", [baseline_line]),
],
location="top_right",
label_text_font_size="18pt",
glyph_width=50,
glyph_height=30,
spacing=20,
padding=25,
background_fill_alpha=0.95,
background_fill_color=ELEVATED_BG,
border_line_color=INK_SOFT,
border_line_width=2,
)
p.add_layout(legend)
# Style the plot
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_alpha = 0.10
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
# Axis styling
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.axis_line_width = 3
p.yaxis.axis_line_width = 3
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
p.xaxis.major_tick_line_width = 3
p.yaxis.major_tick_line_width = 3
# Background
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
p.outline_line_width = 2
# Min border for padding
p.min_border_left = 100
p.min_border_right = 100
p.min_border_top = 80
p.min_border_bottom = 100
# Save
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome
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 Precision-Recall Curve on anyplot.ai.