lecrapaud

🐸 LeCrapaud

An all-in-one machine learning framework

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-feature for new features
- fix/your-bugfix for bug fixes
- docs/your-change for documentation
Write or update tests when changing behavior
Run the test suite before submitting:
```
make test
```
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.

Pierre Gallet 2025

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lecrapaud-2.4.7.tar.gz (243.7 kB view details)

Uploaded Jul 8, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

lecrapaud-2.4.7-py3-none-any.whl (291.0 kB view details)

Uploaded Jul 8, 2026 Python 3

File details

Details for the file lecrapaud-2.4.7.tar.gz.

File metadata

Download URL: lecrapaud-2.4.7.tar.gz
Upload date: Jul 8, 2026
Size: 243.7 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: uv/0.11.15 {"installer":{"name":"uv","version":"0.11.15","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for lecrapaud-2.4.7.tar.gz
Algorithm	Hash digest
SHA256	`f6e1c8e79c80ca030cf61f12c21a8bf6c2f6767b5ec69f85d757aea72270b7b9`
MD5	`da80c581ed5dd211b638ddb15525d5c4`
BLAKE2b-256	`34ba69862cc1e7821275f154bb7c15165e01a5e3d0c32d6531a24bf1d9e83598`

See more details on using hashes here.

File details

Details for the file lecrapaud-2.4.7-py3-none-any.whl.

File metadata

Download URL: lecrapaud-2.4.7-py3-none-any.whl
Upload date: Jul 8, 2026
Size: 291.0 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: uv/0.11.15 {"installer":{"name":"uv","version":"0.11.15","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for lecrapaud-2.4.7-py3-none-any.whl
Algorithm	Hash digest
SHA256	`ca03c9f6e5ee86e58c91b3eb0c5699647d3a7aa746d9521a2f4dce28d5ce5817`
MD5	`6a0fd82eb13b24df58a08210a4a8ad64`
BLAKE2b-256	`38bc6cea3f1e4de98c23177cc6bb89f2105b63f8f94f67d18fcdee62007e0609`