LeCrapaud is a high-level Python library for end-to-end machine learning on tabular and time series data. It handles feature engineering, model selection, training, and prediction in one command.
Key Features
- 🔄 End-to-end ML pipeline — feature engineering, preprocessing, feature selection, hyperparameter optimization, and training in a single
fit()call - 🤖 11+ models — from Linear Regression to XGBoost, LightGBM, CatBoost, and deep learning architectures (LSTM, GRU, TCN, Transformer)
- 🎯 Automated feature selection — ensemble of 10+ methods (Chi2, ANOVA, Mutual Information, SHAP, RFE, etc.)
- ⚡ Hyperparameter optimization — HyperOpt (TPE) and Ray Tune with cross-validation support
- 🔍 Explainability — built-in SHAP, LIME, feature importance, and tree visualization
- 🗄️ Experiment tracking — every experiment is stored in the database (PostgreSQL or MySQL) with full reproducibility
- 🧩 Modular — use the full pipeline or individual components (FeatureEngineer, FeaturePreprocessor, FeatureSelector) in sklearn-compatible pipelines
Why LeCrapaud?
Most ML tools solve one piece of the puzzle. LeCrapaud handles the entire workflow in a single fit() call.
| LeCrapaud | MLflow | scikit-learn | Auto-sklearn / TPOT | |
|---|---|---|---|---|
| Feature engineering | ✅ Automated (Fourier dates, target encoding, imputation) | ❌ Manual | ❌ Manual | ❌ Generic only |
| Feature selection | ✅ Ensemble of 10+ methods with voting | ❌ Manual | ❌ One method at a time | ⚠️ Implicit |
| Hyperparameter optimization | ✅ HyperOpt + Ray Tune | ❌ Manual | ⚠️ GridSearchCV | ✅ Built-in |
| Multi-target support | ✅ Native (regression + classification) | ❌ | ❌ | ❌ |
| Deep learning models | ✅ LSTM, GRU, TCN, Transformer | ❌ | ⚠️ MLP only | ❌ |
| Time series support | ✅ Fourier features, temporal CV, RNNs | ❌ | ⚠️ Basic | ❌ |
| Explainability | ✅ SHAP + LIME + feature importance | ❌ | ⚠️ Feature importance only | ❌ |
| Experiment tracking | ✅ Full artifacts in PostgreSQL/MySQL | ✅ Tracking server | ❌ | ❌ |
| Reproducibility | ✅ Reload any experiment with get(id=...) |
✅ | ❌ | ⚠️ |
| sklearn compatibility | ✅ fit/transform pattern | ❌ | ✅ Native | ✅ |
In short:
- MLflow tracks experiments but doesn't train models or engineer features — you still write all the ML code yourself
- scikit-learn provides building blocks but requires manual pipeline composition, no experiment tracking, and limited model support
- AutoML tools (auto-sklearn, TPOT) automate model selection but act as black boxes with no feature engineering transparency, no explainability, and no time series support
- LeCrapaud combines automated feature engineering, ensemble feature selection, hyperparameter optimization, multi-target training, explainability, and experiment tracking — all in one
fit()call, while remaining transparent and customizable
Prerequisites
- Python 3.12 (strictly required)
- PostgreSQL or MySQL database for experiment storage
- macOS only — libomp for LightGBM/XGBoost:
brew install libomp
Installation
📦 From PyPI (recommended)
Install the latest stable release:
pip install lecrapaud
Or pin a specific version:
pip install lecrapaud==2.4.6
🔧 From source
Install the latest development version directly from GitHub:
pip install git+https://github.com/PierreGallet/lecrapaud.git
Or clone the repository and install locally:
git clone https://github.com/PierreGallet/lecrapaud.git
cd lecrapaud
pip install .
Quick Start
from lecrapaud import LeCrapaud
LeCrapaud.set_uri("mysql+pymysql://user:password@host:port/dbname")
lc = LeCrapaud(
experiment_name="my_experiment",
target_numbers=[1],
target_clf=[1],
models_idx=["lgb", "xgb"],
)
lc.fit(data)
predictions = lc.predict(new_data)
# eval scores (when new_data has TARGET columns): lc.regression_scores / lc.classification_scores
Documentation
Full documentation available at lecrapaud.pierregallet.com
Contributing
Contributions are welcome! Here's how to get started.
Development Setup
git clone https://github.com/PierreGallet/lecrapaud.git
cd lecrapaud
python3.12 -m venv .venv
source .venv/bin/activate
make install
Workflow
- Open an issue first to discuss the change you'd like to make
- Fork the repo and create a branch from
main:feat/your-featurefor new featuresfix/your-bugfixfor bug fixesdocs/your-changefor documentation
- Write or update tests when changing behavior
- Run the test suite before submitting:
make test
- Open a Pull Request against
mainwith a clear description
Commit Convention
We use Conventional Commits. Every commit message and PR title must follow this format:
type: short description
| Type | Usage |
|---|---|
feat: |
New feature |
fix: |
Bug fix |
docs: |
Documentation only |
refactor: |
Code change that neither fixes a bug nor adds a feature |
test: |
Adding or updating tests |
perf: |
Performance improvement |
ci: |
CI/CD changes |
chore: |
Maintenance tasks |
Examples:
feat: add catboost model support
fix: handle missing target column in predict
docs: update getting started guide
Guidelines
- Keep PRs focused and small — one concern per PR
- Update documentation when APIs change
- Follow the existing code style
- All tests must pass before merging
License
LeCrapaud is licensed under the Apache License 2.0. You are free to use, modify, and distribute this software in compliance with the license terms.
Pierre Gallet 2025
Bases locales (Docker)
Prérequis : Docker (Desktop ou OrbStack). Rien d'autre — ni serveur ni
client Homebrew. Les cibles Make exécutent les clients (psql, createdb,
mysql…) dans les conteneurs, qui les embarquent déjà.
docker-compose.dev.yml déclare deux conteneurs MySQL : un pour le dev
(volume nommé, les données survivent à make db-down) et un pour les tests
(sans volume, jetable). Serveur en utf8mb4 / utf8mb4_unicode_ci.
| Cible | Effet |
|---|---|
make db-up |
démarre les conteneurs et attend les healthchecks |
make db-down |
arrête (les données de dev sont conservées) |
make test-db-setup dépend de db-up : rien à démarrer à la main avant les
tests.
Ports
Ports décalés par rapport aux valeurs par défaut (5432 / 3306 / 6379), pour ne pas entrer en conflit avec un serveur déjà installé sur la machine.
| Service | Dev | Test |
|---|---|---|
| mysql | 3309 | 3310 |
Les conteneurs n'écoutent que sur 127.0.0.1, jamais 0.0.0.0.
Viser une base distante
Toutes les variables restent surchargeables : renseigner les endpoints dans
.env suffit à pointer une base distante, sans toucher au Makefile.
1 maintainer
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