Precision-Recall Curve — Pygal

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.

Precision-Recall Curve rendered with Pygal

Python source (Pygal)

""" anyplot.ai
precision-recall: Precision-Recall Curve
Library: pygal 3.1.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-10
"""

import os

import numpy as np
import pygal
from pygal.style import Style
from sklearn.datasets import make_classification
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import average_precision_score, precision_recall_curve
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233")

# Data - Generate balanced binary classification dataset
np.random.seed(42)
X, y = make_classification(
    n_samples=2000,
    n_features=20,
    n_informative=10,
    n_redundant=5,
    n_classes=2,
    weights=[0.5, 0.5],  # Balanced: 50% class 0, 50% class 1
    flip_y=0.05,
    random_state=42,
)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)

# Train two classifiers for comparison
# Support Vector Machine
svm_model = SVC(kernel="rbf", probability=True, random_state=42, gamma="scale")
svm_model.fit(X_train, y_train)
svm_scores = svm_model.predict_proba(X_test)[:, 1]
svm_precision, svm_recall, _ = precision_recall_curve(y_test, svm_scores)
svm_ap = average_precision_score(y_test, svm_scores)

# Gradient Boosting
gb_model = GradientBoostingClassifier(n_estimators=50, max_depth=5, random_state=42)
gb_model.fit(X_train, y_train)
gb_scores = gb_model.predict_proba(X_test)[:, 1]
gb_precision, gb_recall, _ = precision_recall_curve(y_test, gb_scores)
gb_ap = average_precision_score(y_test, gb_scores)

# Baseline (random classifier) - horizontal line at positive class ratio
baseline = np.mean(y_test)

# Custom style for large canvas (4800x2700)
custom_style = Style(
    background=PAGE_BG,
    plot_background=PAGE_BG,
    foreground=INK,
    foreground_strong=INK,
    foreground_subtle=INK_MUTED,
    colors=IMPRINT,
    title_font_size=28,
    label_font_size=22,
    major_label_font_size=18,
    legend_font_size=16,
    value_font_size=14,
    stroke_width=3,
)

# Create XY chart for precision-recall curve
chart = pygal.XY(
    width=4800,
    height=2700,
    title="precision-recall · pygal · anyplot.ai",
    x_title="Recall",
    y_title="Precision",
    style=custom_style,
    show_dots=False,
    stroke_style={"width": 4},
    show_x_guides=True,
    show_y_guides=True,
    legend_at_bottom=True,
    truncate_legend=-1,
    range=(0, 1),
    xrange=(0, 1),
    show_minor_x_labels=False,
    show_minor_y_labels=False,
)

# Downsample curves for cleaner visualization
n_points = 100
svm_indices = np.linspace(0, len(svm_recall) - 1, n_points, dtype=int)
svm_recall_ds = svm_recall[svm_indices]
svm_precision_ds = svm_precision[svm_indices]

gb_indices = np.linspace(0, len(gb_recall) - 1, n_points, dtype=int)
gb_recall_ds = gb_recall[gb_indices]
gb_precision_ds = gb_precision[gb_indices]

# Create stepped data points for threshold-based visualization
svm_stepped_points = []
for i in range(len(svm_recall_ds)):
    if i > 0:
        svm_stepped_points.append((svm_recall_ds[i], svm_precision_ds[i - 1]))
    svm_stepped_points.append((svm_recall_ds[i], svm_precision_ds[i]))

gb_stepped_points = []
for i in range(len(gb_recall_ds)):
    if i > 0:
        gb_stepped_points.append((gb_recall_ds[i], gb_precision_ds[i - 1]))
    gb_stepped_points.append((gb_recall_ds[i], gb_precision_ds[i]))

# Add curves
chart.add(f"SVM (AP={svm_ap:.3f})", svm_stepped_points)
chart.add(f"Gradient Boosting (AP={gb_ap:.3f})", gb_stepped_points)

# Add baseline
baseline_points = [(0, baseline), (1, baseline)]
chart.add(f"Random Baseline ({baseline:.2f})", baseline_points, stroke_dasharray="10,5")

# Save as PNG and HTML
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
    f.write(chart.render())

Part of Precision-Recall Curve on anyplot.ai.

Precision-Recall Curve — Pygal

Python source (Pygal)

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