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.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

1 Nov 22, 2021

Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.

Tangram Website | Discord Tangram makes it easy for programmers to train, deploy, and monitor machine learning models. Run tangram train to train a mo

1.4k Jan 5, 2023

SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

SageMaker Python SDK SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the S

1.8k Jan 1, 2023

Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

25 Dec 28, 2022

easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.

5 Jun 18, 2022

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

14.5k Jan 7, 2023

Automated modeling and machine learning framework FEDOT

This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML). It can build custom modeling pipelines for different real-world processes in an automated way using an evolutionary approach. FEDOT supports classification (binary and multiclass), regression, clustering, and time series prediction tasks.

National Center for Cognitive Research of ITMO University

148 Jul 5, 2021

machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service

This is a machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service. We initially made this project as a requirement for an internship at Indian Servers. We are now making it open to contribution.

73 Dec 1, 2022

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 2, 2023

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

Machine Learning for RC Cars

Suiron Machine Learning for RC Cars Prediction visualization (green = actual, blue = prediction) Click the video below to see it in action! Dependenci

706 Jan 02, 2023

Traingenerator 🧙 A web app to generate template code for machine learning ✨

Traingenerator 🧙 A web app to generate template code for machine learning ✨ 🎉 Traingenerator is now live! 🎉

1.2k Jan 07, 2023

ML Optimizers from scratch using JAX

Toy implementations of some popular ML optimizers using Python/JAX

38 Jul 29, 2022

ML-powered Loan-Marketer Customer Filtering Engine

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very length

13 Jul 02, 2022

Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application

Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application (with docker-compose).

2 Dec 03, 2021

TensorFlow implementation of an arbitrary order Factorization Machine

This is a TensorFlow implementation of an arbitrary order (=2) Factorization Machine based on paper Factorization Machines with libFM. It supports: d

785 Dec 21, 2022

A python library for Bayesian time series modeling

PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W

438 Dec 17, 2022

PySpark ML Bank Churn Prediction

PySpark-Bank-Churn Surname: corresponds to the record (row) number and has no effect on the output. CreditScore: contains random values and has no eff

2 Nov 11, 2021

A model to predict steering torque fully end-to-end

torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f

4 Jun 03, 2022

2021 Machine Learning Security Evasion Competition

2021 Machine Learning Security Evasion Competition This repository contains code samples for the 2021 Machine Learning Security Evasion Competition. P

8 May 01, 2022

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

27 Aug 19, 2022

A Python toolkit for rule-based/unsupervised anomaly detection in time series

Anomaly Detection Toolkit (ADTK) Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection. As

888 Dec 30, 2022

Neighbourhood Retrieval (Nearest Neighbours) with Distance Correlation.

Neighbourhood Retrieval with Distance Correlation Assign Pseudo class labels to datapoints in the latent space. NNDC is a slim wrapper around FAISS. N

1 Jan 16, 2022

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. Documenta

2.5k Jan 07, 2023