catasta 0.0.4

Catasta is a Python library designed to simplify and accelerate the process of machine learning model experimentation. It encapsulates the complexities of model training and evaluation, offering researchers and developers a straightforward pipeline for rapid model assessment with minimal setup required.

Catasta: Streamlined Model Experimentation

Catasta is a Python library designed to simplify and accelerate the process of machine learning model experimentation. It encapsulates the complexities of model training and evaluation, offering researchers and developers a straightforward pipeline for rapid model assessment with minimal setup required.

Note: Catasta only supports regression at the moment. Other techniques such as classification or prediction are being developed.

Important: Catasta is subject of change until a major version is launched.

Key features

Catasta is a very simple package, containing only five modules each one with an specific purpose.

Models

The models module houses a variety of machine learning models.

Datasets

The datasets module provides an easy way to import the data contained in directories, being also able to modify the data shape in an easy way.

Transformations

The transformations module lets you apply transformations to the data when its loaded to a dataset.

Scaffolds

The scaffolds module is where models and datasets are integrated for training. Scaffolds handle training and evaluation.

Archways

The archways module takes a trained model and handles the inference task.

Installation

Install via pip

Catasta is available as a PyPi package:

pip install catasta

Install from source

Clone the repository

git clone https://github.com/vistormu/catasta

and install the dependencies

pip install -r requirements.txt

Documentation

Work in progress