lecrapaud 0.20.1

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

Install the package

pip install lecrapaud

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 import LeCrapaud

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

# 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)

Database Configuration (Required)

LeCrapaud requires access to a MySQL database to store experiments and results. You must either:

• Pass a valid MySQL URI to the LeCrapaud constructor:

  app = LeCrapaud(uri="mysql+pymysql://user:password@host:port/dbname")
  

• OR set the following environment variables before using the package:
  • DB_USER, DB_PASSWORD, DB_HOST, DB_PORT, DB_NAME
  • Or set DB_URI directly with your full connection string.

If neither is provided, database operations will not work.

Using OpenAI Embeddings (Optional)

If you want to use the columns_pca embedding feature (for advanced feature engineering), you must set the OPENAI_API_KEY environment variable with your OpenAI API key:

export OPENAI_API_KEY=sk-...

If this variable is not set, features relying on OpenAI embeddings will not be available.

Experiment Context Arguments

Below are the main arguments you can pass to create_experiment (or the Experiment class):

Argument	Type	Description	Example/Default
columns_binary	list	Columns to treat as binary	['flag']
columns_boolean	list	Columns to treat as boolean	['is_active']
columns_date	list	Columns to treat as dates	['date']
columns_drop	list	Columns to drop during feature engineering	['col1', 'col2']
columns_frequency	list	Columns to frequency encode	['category']
columns_onehot	list	Columns to one-hot encode	['sector']
columns_ordinal	list	Columns to ordinal encode	['grade']
columns_pca	list	Columns to use for PCA/embeddings (requires OPENAI_API_KEY if using OpenAI embeddings)	['text_col']
columns_te_groupby	list	Columns for target encoding groupby	['sector']
columns_te_target	list	Columns for target encoding target	['target']
data	DataFrame	Your main dataset (required for new experiment)	your_dataframe
date_column	str	Name of the date column	'date'
experiment_name	str	Name for the training session	'my_session'
group_column	str	Name of the group column	'stock_id'
max_timesteps	int	Max timesteps for time series models	30
models_idx	list	Indices of models to use for model selection	[0, 1, 2]
number_of_trials	int	Number of trials for hyperparameter optimization	20
perform_crossval	bool	Whether to perform cross-validation	True/False
perform_hyperopt	bool	Whether to perform hyperparameter optimization	True/False
plot	bool	Whether to plot results	True/False
preserve_model	bool	Whether to preserve the best model	True/False
target_clf	list	List of classification target column indices/names	[1, 2, 3]
target_mclf	list	Multi-class classification targets (not yet implemented)	[11]
target_numbers	list	List of regression target column indices/names	[1, 2, 3]
test_size	int/float	Test set size (count or fraction)	0.2
time_series	bool	Whether the data is time series	True/False
val_size	int/float	Validation set size (count or fraction)	0.2

Note:

• Not all arguments are required; defaults may exist for some.
• For columns_pca with OpenAI embeddings, you must set the OPENAI_API_KEY environment variable.

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)

⚠️ Using Alembic in Your Project (Important for Integrators)

If you use Alembic for migrations in your own project and you share the same database with LeCrapaud, you must ensure that Alembic does not attempt to drop or modify LeCrapaud tables (those prefixed with {LECRAPAUD_TABLE_PREFIX}_).

By default, Alembic's autogenerate feature will propose to drop any table that exists in the database but is not present in your project's models. To prevent this, add the following filter to your env.py:

def include_object(object, name, type_, reflected, compare_to):
    if type_ == "table" and name.startswith(f"{LECRAPAUD_TABLE_PREFIX}_"):
        return False  # Ignore LeCrapaud tables
    return True

context.configure(
    # ... other options ...
    include_object=include_object,
)

This will ensure that Alembic ignores all tables created by LeCrapaud when generating migrations for your own project.

---

🤝 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

4. Create environment

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

5. Install dependencies

$ make install

6. Deactivate virtualenv (if needed)

$ deactivate

---

Pierre Gallet © 2025