labicompare 0.2.0

A low-dependency library for statistical comparison and visualization for machine learning models.

LabiCompare

LabiCompare is a Python library focused on the evaluation and statistical comparison of Machine Learning models. It simplifies the process of running hypothesis tests (such as Friedman and Wilcoxon-Holm) and generates visualizations, including Critical Difference (CD) Diagrams.

Instalation

Via PyPI (future - WIP):

pip install labicompare

Or install it locally from the source code:

git clone https://github.com/jose-gilberto/labicompare.git
cd labicompare
pip install -e .

Quick Start

Here is how easy it is to compare the performance of multiple models across different datasets or folds:

import pandas as pd
from labicompare.core.data import EvaluationData

from labicompare.stats import evaluate_models 
from labicompare.plots.ranking import plot_cd_diagram

# 1. Prepare your data (Rows = Datasets/Folds, Columns = Models)
data_dict = {
    'Model_A': [0.85, 0.88, 0.82, 0.89],
    'Model_B': [0.86, 0.89, 0.84, 0.90],
    'Model_C': [0.70, 0.75, 0.72, 0.71],
    'Proposed_Model': [0.91, 0.93, 0.89, 0.95]
}
df = pd.DataFrame(data_dict)

# 2. Wrap the data (Accuracy: higher_is_better=True)
eval_data = EvaluationData(df, higher_is_better=True)

# 3. Run the statistical tests (e.g., Friedman + Wilcoxon-Holm)
summary = wilcoxon_holm(eval_data, alpha=0.05)

print(summary)
# Output: ComparisonSummary(Friedman P-Value=0.0012, H0=REJECTED, Models=4)

# 4. Generate the Critical Difference Diagram (CD Diagram)
fig = plot_cd_diagram(
    data=eval_data,
    summary=summary,
    title="Model Comparison (Accuracy)"
)
fig.savefig("cd_diagram.png", dpi=300, bbox_inches='tight')

Core Components

1. EvaluationData

The base class that ingests your pandas.DataFrame.

Key Parameter: higher_is_better (Boolean). Use True for metrics like Accuracy and F1-Score, and False for error metrics like RMSE or MAE. The library automatically handles ranking inversions under the hood.

2. ComparisonSummary

The object returned by the statistical testing functions. It stores:

• Global results (Friedman's P-value).
• Pairwise results (pairwise_results), including which model won and whether the difference is statistically significant.
• Built-in Export: You can use summary.to_dataframe() to export the results into a tabular format, making it easy to convert to LaTeX or Markdown for your papers.

3. Critical Difference Diagrams (plot_cd_diagram)

The visual tool for comparing models. Our implementation features an enhanced UX tailored for academic publishing:

• Bilateral Layout: Models are split evenly on both sides to prevent text overlap.
• Maximal Cliques: Thick bars group models that have no statistically significant difference (ties), automatically preventing redundant sub-lines.
• Inline Rankings: The exact average rank is displayed cleanly beneath each model's name.

Highlighting Your Model

If you are proposing a new model and want it to stand out in the diagram, use the highlight parameters:

fig = plot_cd_diagram(
    data=eval_data,
    summary=summary,
    highlight_models=['Proposed_Model'],
    highlight_color='#d97706' # Optional custom color (Default: Amber/Orange)
)

Contributing

We welcome contributions from the community! Whether you want to fix a bug, add a new statistical test, or improve the documentation, your help is highly appreciated.

Development Setup

1. Fork the repository and clone it locally:

   git clone https://github.com/jose-gilberto/labicompare.git
   cd labicompare
   

2. Create a virtual environment and install the development dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate
   pip install -e ".[dev]"   # Ensure you have a [dev] extra in your pyproject.toml or setup.py
   

Testing

All new features and bug fixes should be accompanied by unit tests. We use pytest as our testing framework.

To run the test suite:

pytest tests/

Pull Request Process

Create a new branch for your feature or bugfix (git checkout -b feature/my-awesome-feature).

Make your changes and commit them with descriptive messages.

Ensure all tests pass and the code is properly formatted.

Push your branch to your fork (git push origin feature/my-awesome-feature).

Open a Pull Request against the main branch of this repository. Include a clear description of the changes and any related issue numbers.

Reporting Issues

If you find a bug or have a feature request, please open an issue on GitHub. Provide as much detail as possible, including steps to reproduce bugs or a clear rationale for new features.

🎓 Citation

If you use labicompare in your research or project, please consider citing it.

@misc{labicompare2026,
  author       = {José Gilberto Barbosa de Medeiros Júnior},
  title        = {labicompare: statistical comparison and visualization for Machine Learning models},
  year         = {2026},
  publisher    = {GitHub},
  journal      = {GitHub repository},
  howpublished = {\url{[https://github.com/jose-gilberto/labicompare](https://github.com/jose-gilberto/labicompare)}},
}