lecrapaud 0.31.4

Framework for machine and deep learning, with regression, classification and time series analysis

🐸 LeCrapaud

An all-in-one machine learning framework

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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

pip install lecrapaud

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, scores_reg, scores_clf = lc.predict(new_data)

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

1. Open an issue first to discuss the change you'd like to make
2. Fork the repo and create a branch from main:
   • feat/your-feature for new features
   • fix/your-bugfix for bug fixes
   • docs/your-change for documentation
3. Write or update tests when changing behavior
4. Run the test suite before submitting:

   make test
   

5. Open a Pull Request against main with 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.

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Pierre Gallet 2025