OPerating PROfile PLOT
Opproplot is an operating profile plot for binary classifiers: a single figure that shows score distributions by class plus TPR/FPR/Accuracy as you move the decision threshold. It makes threshold selection, ROC/PR intuition, and calibration discussion concrete in one view.
Docs: https://rollingstorms.github.io/opproplot
What is an Operating Profile Plot?
An Operating Profile Plot (Opproplot) is a unified visualization for binary classifiers that shows how a model behaves across every possible decision threshold. It combines:
- a stacked histogram of predicted scores for positives vs. negatives
- threshold-dependent performance metrics such as Recall (TPR) and False Positive Rate (FPR)
- computed at the midpoint of each score bin, treating each as a candidate threshold
This creates a complete operating profile of the model in a single view — letting you see where the model is confident, where the classes overlap, and how performance changes as you move the threshold.
It is a compact, multidimensional readout of model behavior: score distribution by class plus operating curves (TPR/FPR/accuracy) on the same axis. Comparing profiles across models or datasets shows whether a model separates classes cleanly, where overlap drives errors, and how threshold choices shift business metrics.
Rather than switching between ROC curves, PR curves, histograms, and calibration plots, Opproplot places the score distribution and the operating characteristics on the same axis, making it easy to:
- identify thresholds with optimal trade-offs
- diagnose where errors occur in score space
- communicate model quality in an interpretable, visual way
What it is: Opproplot visualizes the family of decision rules h_t(x) = 1{f(x) >= t} and their induced operating characteristics (TPR, FPR, Accuracy), alongside the empirical score distributions p(s | Y=1) and p(s | Y=0).
Why it matters: You see where positives and negatives sit in score space, how recall and false positives trade off at every cutoff, and where accuracy peaks—no context-switching between ROC curves, confusion matrices, and histograms.
When to use it: model validation, stakeholder reviews, threshold tuning for production alerting, class-imbalance checks, and calibration audits.
From the repo root:
pip install -e .import numpy as np
from opproplot import operating_profile_plot
rng = np.random.default_rng(0)
y_true = rng.integers(0, 2, size=5000)
scores = rng.random(size=5000)
fig, ax_hist, ax_metric = operating_profile_plot(y_true, scores, bins=30)The resulting operating profile lets you see where positives and negatives concentrate in score space, how recall and false positive rate trade off as you move the threshold, and where accuracy peaks. It is a single, interpretable view of all possible thresholds for a scoring model.
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(
data.data, data.target, test_size=0.25, random_state=0, stratify=data.target
)
clf = LogisticRegression(max_iter=1000)
clf.fit(X_train, y_train)
y_score = clf.predict_proba(X_test)[:, 1]
operating_profile_plot(y_test, y_score, bins=30)
- Package code lives in
src/opproplot. - Tests live in
tests/. - Documentation for GitHub Pages lives in
docs/(see below). - Regenerate doc images with
python scripts/generate_docs_images.py(requires numpy, matplotlib, scikit-learn).
Enable GitHub Pages with the docs/ folder as the root. The scaffold includes:
docs/index.md: landing page with value proposition and a hero plot.docs/getting_started.md: install, notebook walkthrough, common models.docs/theory.md: decision rules, distributions, and metric integrals.docs/examples.md: real datasets and comparisons.docs/api.md: core functions and parameters.docs/roadmap.md: features and status.
Fill in each page as you iterate; the structure is ready to publish.
pytest