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GoJo - Make Machine/Deep Learning pipelines simple
==================================================

   Python library with a variety of pipeline implementations typically used to develop and evaluate Deep Learning and Machine Learning models.

.. important::

   Library still under development and subjected to changes (this is an alpha version). Check out the github_ page.

This Python library offers a comprehensive suite of pipeline implementations, specifically designed to streamline the entire process of developing and evaluating Deep Learning and Machine Learning models. These pipelines serve as an invaluable resource for researchers, data scientists, and engineers by providing a structured and efficient framework for tasks such as data preprocessing, feature engineering, model training, and performance evaluation. Whether you're working on computer vision, natural language processing, recommendation systems, or any other machine learning domain, this library equips you with the tools and utilities necessary to expedite your project development. With a rich set of modules and customizable components, it caters to a wide range of use cases and simplifies the complex journey of model creation and fine-tuning, ultimately contributing to more robust and accurate predictive models.


.. _github: https://github.com/FernandoGaGu/GoJo-ML



Installation
-------------

First clone the current repository:

.. code:: bash

   git clone git@github.com:FernandoGaGu/Gojo-ML.git


and after cloning the repository run the setup.py script

.. code:: bash

   cd Gojo-ML
   pip install -e .


Usage
-------------

Below is a basic example of use, for more examples see the notebooks located in the notebook folder. At the moment there are few examples and they are poorly documented, later, as the library stabilizes, more examples will be added.

.. code:: python

   # In this example, an SVM model (implemented in sklearn) is evaluated via cross-validation
   import pandas as pd
   from sklearn import datasets
   from sklearn.svm import SVC
   from gojo import core
   from gojo import interfaces
   from gojo import util

   # load test dataset (Wine)
   wine_dt = datasets.load_wine()

   # create the target variable. Classification problem 1 vs rest
   y = (wine_dt['target'] == 1).astype(int)
   X = wine_dt['data']

   # create a simple model using the sklearn interfaz
   model = interfaces.SklearnModelWrapper(
       SVC, kernel='poly', degree=3, coef0=50,
       cache_size=1000, class_weight=None)

   # evaluate the model using cross-validation
   cv_report = core.evalCrossVal(
       X=X, y=y,
       model=model,
       cv=util.getCrossValObj(cv=5, stratified=True, random_state=1997),
       n_jobs=-1)

   # compute classification metrics
   scores = cv_report.getScores(
       core.getDefaultMetrics('binary_classification', bin_threshold=0.5))

   # average scores across folds over the test dataset, and... THIS IS ALL!!
   test_scores = pd.DataFrame(scores['test'].mean(axis=0)).round(decimals=3)



Warranties
-------------

The code used, although it has been reviewed and tested with different test problems where it has been shown to lead to correct solutions, is still under development and it is possible to experience undetected bugs. If any errors appear, please report them to us via issues_.


.. _issues: https://github.com/FernandoGaGu/GoJo-ML/issues


Examples
========

Have a look at some of the examples:

.. toctree::
   :maxdepth: 1
   :caption: Hands-on

   examples/Example_1_Model_evaluation_by_cross_validation
   examples/Example_2_Neural_networks_integration
   examples/Example_3_Hyperparameter_optimization_by_nested_cross_validation
   examples/Example_4_Vanilla_Variational_Autoencoder
   examples/Example_5_Testing_different_CNN_architectures
   examples/Advanced_use



Modules
==================

.. toctree::
   :maxdepth: 3
   :caption: API Documentation

   gojo.core
   gojo.interfaces
   gojo.deepl
   gojo.plotting
   gojo.util
   gojo.experimental

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`