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: plotnine 0.15.4 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-10
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
annotate,
element_line,
element_rect,
element_text,
geom_hline,
geom_step,
ggplot,
labs,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
# 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 for baseline
# Data - Simulated binary classification results
np.random.seed(42)
n_samples = 500
# Create realistic classification scenario (imbalanced - ~20% positive class)
y_true = np.random.choice([0, 1], size=n_samples, p=[0.8, 0.2])
# Generate scores: positive class gets higher scores on average
y_scores = np.where(
y_true == 1,
np.clip(np.random.beta(5, 2, size=n_samples), 0, 1),
np.clip(np.random.beta(2, 5, size=n_samples), 0, 1),
)
# Calculate precision-recall curve
sorted_indices = np.argsort(y_scores)[::-1]
y_true_sorted = y_true[sorted_indices]
tp_cumsum = np.cumsum(y_true_sorted)
total_positives = y_true.sum()
n_predictions = np.arange(1, len(y_true_sorted) + 1)
precision = tp_cumsum / n_predictions
recall = tp_cumsum / total_positives
# Add start point (recall=0, precision=1)
precision = np.concatenate([[1], precision])
recall = np.concatenate([[0], recall])
# Calculate average precision (area under PR curve)
recall_diff = np.diff(recall)
ap_score = np.sum(recall_diff * precision[1:])
# Baseline (positive class ratio)
baseline = y_true.mean()
# Create DataFrame for plotting
df = pd.DataFrame({"Recall": recall, "Precision": precision})
# Plot
plot = (
ggplot(df, aes(x="Recall", y="Precision"))
+ geom_step(color=BRAND, size=2, direction="vh")
+ geom_hline(yintercept=baseline, linetype="dashed", color=ACCENT, size=1.5)
+ annotate(
"text",
x=0.95,
y=baseline + 0.05,
label=f"Random Classifier (baseline = {baseline:.2f})",
ha="right",
size=14,
color=ACCENT,
)
+ annotate(
"rect", xmin=0.55, xmax=0.95, ymin=0.75, ymax=0.95, fill=ELEVATED_BG, alpha=0.95, color=INK_SOFT, size=0.3
)
+ annotate(
"text", x=0.75, y=0.85, label=f"Average Precision (AP) = {ap_score:.3f}", size=16, color=INK, fontweight="bold"
)
+ labs(
x="Recall (Sensitivity)",
y="Precision (Positive Predictive Value)",
title="precision-recall · plotnine · anyplot.ai",
)
+ scale_x_continuous(limits=(0, 1), breaks=[0, 0.2, 0.4, 0.6, 0.8, 1.0])
+ scale_y_continuous(limits=(0, 1), breaks=[0, 0.2, 0.4, 0.6, 0.8, 1.0])
+ theme_minimal()
+ theme(
figure_size=(16, 9),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
panel_grid_minor=element_line(color=INK, size=0.2, alpha=0.05),
panel_border=element_rect(color=INK_SOFT, fill=None, size=0.3),
axis_title=element_text(size=20, color=INK),
axis_text=element_text(size=16, color=INK_SOFT),
axis_line=element_line(color=INK_SOFT, size=0.3),
text=element_text(size=14),
plot_title=element_text(size=24, color=INK, ha="center"),
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)
Part of Precision-Recall Curve on anyplot.ai.