lecrapaud 0.2.0

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

🐸

Welcome to LeCrapaud

An all-in-one machine learning framework

🚀 Introduction

LeCrapaud is a high-level Python library for end-to-end machine learning workflows on tabular data, with a focus on financial and stock datasets. It provides a simple API to handle feature engineering, model selection, training, and prediction, all in a reproducible and modular way.

✨ Key Features

• 🧩 Modular pipeline: Feature engineering, preprocessing, selection, and modeling as independent steps
• 🤖 Automated model selection and hyperparameter optimization
• 📊 Easy integration with pandas DataFrames
• 🔬 Supports both regression and classification tasks
• 🛠️ Simple API for both full pipeline and step-by-step usage
• 📦 Ready for production and research workflows

⚡ Quick Start

1. Create environment

$ pip install virtualenv
$ python -m venv .venv
$ source .venv/bin/activate

2. Install dependencies

$ make install

3. Deactivate virtualenv (if needed)

$ deactivate

🛠️ How it works

This package provides a high-level API to manage experiments for feature engineering, model selection, and prediction on tabular data (e.g. stock data).

Typical workflow

from lecrapaud.api import LeCrapaud

# 1. Create the main app
app = LeCrapaud()

# 2. Define your experiment context (see your notebook or api.py for all options)
context = {
    "data": your_dataframe,
    "columns_drop": [...],
    "columns_date": [...],
    # ... other config options
}

# 3. Create an experiment
experiment = app.create_experiment(**context)

# 4. Run the full training pipeline
experiment.train(your_dataframe)

# 5. Make predictions on new data
predictions = experiment.predict(new_data)

Modular usage

You can also use each step independently:

data_eng = experiment.feature_engineering(data)
train, val, test = experiment.preprocess_feature(data_eng)
features = experiment.feature_selection(train)
std_data, reshaped_data = experiment.preprocess_model(train, val, test)
experiment.model_selection(std_data, reshaped_data)

🤝 Contributing

Reminders for Github usage

1. Creating Github repository

$ brew install gh
$ gh auth login
$ gh repo create

2. Initializing git and first commit to distant repository

$ git init
$ git add .
$ git commit -m 'first commit'
$ git remote add origin <YOUR_REPO_URL>
$ git push -u origin master

3. Use conventional commits
   https://www.conventionalcommits.org/en/v1.0.0/#summary

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Pierre Gallet © 2024