EvalSuite

Unified, reproducible evaluation for machine learning and research.

EvalSuite brings classification and regression metrics (with clinical, statistical, segmentation and object-detection evaluation on the roadmap) into one consistent, validated, documented framework.

Status: in development (v0.1.0 in progress). The API may change before 0.1.0.

Installation

pip install evalsuite-python

The package is installed as evalsuite-python and imported as evalsuite:

import evalsuite as es

Why EvalSuite

• One consistent API. Every metric returns a result object that behaves like a number and exports to JSON, pandas, Markdown and LaTeX.
• Explicit conventions. Averaging, label order, the positive class and zero-division behaviour are stated and recorded in every result, never silently assumed.
• Validated. Each metric is tested against scikit-learn where definitions coincide, plus property-based tests and edge cases.
• Documented. Every metric carries its definition, formula, range, input requirements and references, available programmatically through metric_info().
• Efficient. evaluate() validates inputs once and computes the confusion matrix once for all metrics.
• Lightweight. Requires only NumPy, SciPy and pandas.

Quick start

import evalsuite as es

y_true = [0, 1, 1, 0, 1, 0]
y_pred = [0, 1, 0, 0, 1, 1]
y_prob = [0.1, 0.9, 0.4, 0.2, 0.8, 0.6]

result = es.evaluate(y_true, y_pred, y_prob=y_prob)
print(result.summary())

result["f1"]  # MetricResult(f1=0.666667)
f"{result['mcc']:.3f}"  # '0.333'
result.to_latex(caption="Test-set performance")
result.to_dataframe()

es.f1(y_true, y_pred)  # individual metrics
es.roc_auc(y_true, y_prob)
es.metric_info("classification.mcc").formula  # documentation
es.list_metrics("regression")

Metrics in this release

Classification (binary, multiclass, multilabel; micro/macro/weighted/samples/per-class averaging; sample weights): accuracy, balanced accuracy, precision, recall, specificity, NPV, F1, F-beta, Jaccard, MCC, Cohen's kappa (unweighted, linear, quadratic), Hamming loss, confusion matrix, ROC AUC (binary, one-vs-rest, one-vs-one), average precision, ROC and PR curves, log loss, Brier score, top-k accuracy.

Regression (single and multi-output; sample weights): MAE, MSE, RMSE, R², adjusted R², MAPE, sMAPE, MSLE, RMSLE, median absolute error, explained variance, max error, mean bias error, quantile (pinball) loss, Huber loss, relative absolute error, relative squared error.

Conventions

• average="auto" resolves to "binary" for binary targets and "macro" otherwise; the resolved value is stored in result.params["average"].
• Labels are sorted unless you pass labels=[...]; that order defines per-class outputs and the columns of 2-D y_prob.
• Undefined ratios (zero denominators) return 0 with an UndefinedMetricWarning; pass zero_division=np.nan to propagate NaN, or 0/1 to choose silently.
• Domain violations raise clear errors instead of being patched over (for example MAPE with zero targets).

Development

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest --cov=evalsuite
ruff check . && ruff format --check . && mypy

Links

• PyPI: https://pypi.org/project/evalsuite-python/
• Website and documentation: https://evalsuite-nine.vercel.app
• Website source: https://github.com/mkcs28/evalsuite

License

MIT. See LICENSE.