A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Last update: Aug 12, 2022

Overview

Currently a Beta-Version

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

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

Installation

With package manager (coming soon)

Install by using pip package manager:

pip install lucidmode

Cloning repository

Clone entire github project

[email protected]: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)

Important Links

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/

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 [email protected]

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

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Releases(v0.4-beta1.0)

v0.4-beta1.0(Apr 29, 2021)
Metrics

Calculation of several metrics for classification sensitivity (TPR), specificity (TNR), accuracy (acc), likelihood ratio (positive), likelihood ratio (negative), confusion matrix (binary and multiclass) confusion tensor (binary for every class in multi-class)

Sequential Class

Move the cost_f and cost_r parameters to be specified from the formation method, leave the class instantiation with just the model architecture

Move the init_weights method to be specified from the formation method

Execution

Create formation method in the Sequential Class, with the following parameters init, cost, metrics, optimizer

Store selected metrics in Train and Validation History

Visualizations

Select metrics for verbose output

Source code(tar.gz)
Source code(zip)
v0.3-beta1.0(Apr 27, 2021)
Regularization:

On weights and biases, location: gradients

L1, L2 and ElasticNet

On weights and biases, location: cost function

L1, L2 and ElasticNet

Numerical Stability:

in functions.py, in cost, added a 1e-25 value to A, to avoid a divide by zero and invalid multiply cases in computations of np.log(A)

Data Handling:

train and validation cost

Visualization:

print: verbose of cost evolution

Documentation:

Improve README

Source code(tar.gz)
Source code(zip)
v0.2-beta1.0(Apr 27, 2021)
Files:

complete data set: MNIST

complete data set: 'fashion-MNIST'

Tests passed:

fashion MNIST

previous release tests

Topology

single hidden layer (tested)

1 - 2 hidden layers (tested)

different activation functions among hidden layer

Activation functions:

For hidden -> Sigmoid, Tanh, ReLU (tested and not working)

For output -> Softmax

Cost Functions:

'binary-logloss' (Binary-class Cross-Entropy)

'multi-logloss' (Multi-class Cross-Entropy)

Metrics:

Confusion matrix (Multi-class)

Accuracy (Multi-class)

Source code(tar.gz)
Source code(zip)
v0.1-beta1.0(Apr 26, 2021)
First release!

Tests passed:

Random XOR data classification

Sequential model:

hidden_l: Number of neurons per hidden layer (list of int, with a length of l_hidden)

hidden_a: Activation of hidden layers (list of str, with length l_hidden)

output_n: Number of neurons in the output layer (1)

output_a: Activation of output layer (str)

Layer transformations:

linear

Activation functions:

For hidden -> Sigmoid, Tanh

For output -> Sigmoid (Binary)

Weights Initialization:

Xavier normal, Xavier uniform, common uniform, according to [1]

Training Schemes:

Gradient Descent

Cost Functions:

Sum of Squared Error (SSE) or Residual Sum of Squares (RSS)

Metrics:

Accuracy (Binary)

Source code(tar.gz)
Source code(zip)
LucidNet_v0.1-beta1.0.zip(111.97 MB)

Owner

lucidmode

A lucid framework for interpretable machine learning models

GitHub Repository https://www.lucidmode.org

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