betacal

library1.1.0pypypi✓ verified 22d ago

BetaCal provides a Python implementation of Beta Calibration, a method for calibrating predicted probabilities from binary classifiers. It offers a well-founded and easily implemented improvement over traditional logistic calibration, particularly effective when classifiers suffer from scores that tend too much to the extremes or when an already well-calibrated model might be uncalibrated by logistic methods. The current version is 1.1.0, last released in April 2021. While functional and widely cited, the package's release cadence is slow, suggesting a maintenance rather than active development phase.

ai-ml

pip install betacal

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · BETACAL

betacal

ai-mlpythonv1.1.0

Install

9.8s avg

Import

3698ms

Disk

280MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.1.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 9.8s · import 3.698s · 270MB

280MB installed

● package 280MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

BetaCalibration

✓ from betacal import BetaCalibration

Demonstrates how to initialize, fit, and use BetaCalibration to transform raw classifier scores into calibrated probabilities. It uses synthetic data to illustrate the basic workflow.

import numpy as np from betacal import BetaCalibration # Generate some dummy data np.random.seed(42) scores = np.random.rand(100) # Raw classifier scores (probabilities) labels = np.random.randint(0, 2, 100) # True binary labels # Initialize BetaCalibration with default 'abm' parameters # 'abm' (alpha, beta, mu) is a three-parameter model bc = BetaCalibration(parameters='abm') # Fit the calibrator to the scores and true labels bc.fit(scores, labels) # Predict calibrated probabilities calibrated_scores = bc.predict(scores) print(f"Original scores (first 5): {scores[:5].round(3)}") print(f"Calibrated scores (first 5): {calibrated_scores[:5].round(3)}")

Debug

Known issues

gotchaThe `betacal` library has not seen a new release since April 2021. While the existing version is stable and functional, users should be aware that active development and new features are not frequently added.

fix

Review the GitHub repository (https://github.com/betacal/python) for any unreleased updates or forks if requiring more recent maintenance or features.

affects: 1.1.0 and earlier

gotchaChoosing the correct `parameters` for `BetaCalibration` ('abm', 'ab', 'am') is crucial. 'abm' is a three-parameter model, 'ab' is a two-parameter model, and 'am' is a one-parameter model. An incorrect choice without understanding the underlying Beta distribution models can lead to suboptimal calibration, especially if the data distribution doesn't align with the chosen model complexity.

fix

Consult the `betacal` documentation and tutorials to understand the implications of each parameter choice. Experiment with different `parameters` settings and evaluate calibration performance (e.g., using reliability diagrams) on validation data.

affects: All

gotchaBeta Calibration is designed to address specific shortcomings of Logistic Calibration, such as uncalibrating already well-calibrated models or performing poorly when scores are at the extremes. It's an alternative to logistic and isotonic calibration, not a direct drop-in replacement without considering these specific benefits.

fix

Understand the theoretical background and use cases for Beta Calibration. If transitioning from other calibration methods, evaluate `betacal`'s performance thoroughly on your specific dataset to confirm its benefits for your use case.

affects: All

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'betacal'

The `betacal` package is not installed in the current Python environment, or the environment where it's installed is not active.

fix

Install the package using pip: `pip install betacal`

ValueError: 'invalid_param' is not a valid parameter choice. Choose from 'abm', 'ab', 'am'.

The `parameters` argument provided to the `BetaCalibration` constructor is not one of the allowed string values ('abm', 'ab', 'am').

fix

Use one of the valid parameter choices for `BetaCalibration`: `cal = BetaCalibration(parameters='abm')` (or 'ab', 'am').

ValueError: Expected 2D array, got 1D array instead: array=[...]. Reshape your data using X.reshape(-1, 1) if it contains a single feature or X.reshape(1, -1) if it contains a single sample.

The input probability scores (X) provided to the `fit` or `predict` method of `BetaCalibration` are a 1D array, but the scikit-learn-like API expects a 2D array (e.g., `(n_samples, 1)` for a single feature).

fix

Reshape your 1D input array `X` to a 2D array before passing it to `fit` or `predict`: `X_reshaped = X.reshape(-1, 1)`

Upgrade

Version history

1.1.0latest on PyPI · released Apr 1, 2021

Audit

Dependencies

numpyrequiredFundamental package for numerical computation.

scikit-learnrequiredMachine learning utilities and compatibility.

Agent activity

26 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/betacal/python ↗

packagehttps://pypi.org/project/betacal/ ↗

homepagehttps://betacal.github.io/ ↗