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Interpretable Machine Learning Models

Project description


https://raw.githubusercontent.com/lucidmode/lucidmode/main/images/lucidmode_logo.png


Documentation Status Version License Version Visits

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.

Documentation

Installation

  • 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

Models

Artificial Neural Network

Feedforward Multilayer perceptron with backpropagation.

  • fit: Fit model to data
  • predict: Prediction according to model

Initialization, Activations, Cost functions, regularization, optimization

  • 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

  • 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)

Author/Principal Maintainer

Francisco Munnoz (IFFranciscoME) Is an associate professor of financial engineering and financial machine learning ITESO (Western Institute of Technology and Higher Education)

License

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

Project details


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