lucidmode 0.4.3.12
Interpretable Machine Learning Models
Requires Python: >=3.6.0

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        :alt: License
        
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**Currently a Beta-Version**

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**lucidmode** is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning models. It has built in machine learning methods optimized for visual interpretation of some of the most relevant calculations. 

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Documentation
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- Oficial Website: https://www.lucidmode.org
- Documentation: https://lucidmode.readthedocs.io
- Python Package Index (PyPI) repository: https://pypi.org/project/lucidmode/
- Github repository: https://github.com/lucidmode/lucidmode

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Installation
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- With package manager *(coming soon)*

Install by using pip package manager:
        
        pip install lucidmode

- Cloning repository
  
Clone entire github project

        git@github.com:lucidmode/lucidmode.git

and then install dependencies

        pip install -r requirements.txt
        
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Models
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Artificial Neural Network
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Feedforward Multilayer perceptron with backpropagation.

- **fit**: Fit model to data
- **predict**: Prediction according to model

Initialization, Activations, Cost functions, regularization, optimization
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- **Weights Initialization**: With 4 types of criterias (zeros, xavier, common, he)
- **Activation Functions**: sigmoid, tanh, ReLU
- **Cost Functions**: Sum of Squared Error, Binary Cross-Entropy, Multi-Class Cross-Entropy
- **Regularization**: L1, L2, ElasticNet for weights in cost function and in gradient updating
- **Optimization**: Weights optimization with Gradient Descent (GD, SGD, Batch) with learning rate
- **Execution**: Callback (metric threshold), History (Cost and metrics)
- **Hyperparameter Optimization**: Random Grid Search with Memory

Complementary
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- **Metrics**: Accuracy, Confusion Matrix (Binary and Multiclass), Confusion Tensor (Multiclass OvR)
- **Visualizations**: Cost evolution
- **Public Datasets**: MNIST, Fashion MNIST
- **Special Datasets**: OHLCV + Symbolic Features of Cryptocurrencies (ETH, BTC)

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Important Links
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- Release notes: https://github.com/lucidmode/lucidmode/releases
- Issues: https://github.com/lucidmode/lucidmode/issues
- Example Notebooks: https://github.com/lucidmode/lucidmode/tree/main/notebooks
- Documentation: https://lucidmode.readthedocs.io
- Python Package Index (PyPI) repository: https://pypi.org/project/lucidmode/

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Author
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J.Francisco Munnoz - `IFFranciscoME`_ - Is an Associate Professor in the Mathematics and Physics Department, at `ITESO`_ University.

.. _ITESO: https://iteso.mx/
.. _IFFranciscoME: https://iffranciscome.com/


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Current Contributors
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.. image:: https://contrib.rocks/image?repo=IFFranciscoME/T-Fold-SV
        :target: https://github.com/IFFranciscoME/T-Fold-SV/graphs/contributors
        :alt: Contributors


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License
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**GNU General Public License v3.0** 

*Permissions of this strong copyleft license are conditioned on making available 
complete source code of licensed works and modifications, which include larger 
works using a licensed work, under the same license. Copyright and license notices 
must be preserved. Contributors provide an express grant of patent rights.*

*Contact: For more information in reggards of this repo, please contact francisco.me@iteso.mx*