A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Overview

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



Documentation Status Version License Version Visits

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

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

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]

You might also like...
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

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

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

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.

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.

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

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

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

Automated modeling and machine learning framework FEDOT
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.

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
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.

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

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

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
lucidmode
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.

7.4k Jan 04, 2023
High performance implementation of Extreme Learning Machines (fast randomized neural networks).

High Performance toolbox for Extreme Learning Machines. Extreme learning machines (ELM) are a particular kind of Artificial Neural Networks, which sol

Anton Akusok 174 Dec 07, 2022
This project has Classification and Clustering done Via kNN and K-Means respectfully

This project has Classification and Clustering done Via kNN and K-Means respectfully. It later tests its efficiency via F1/accuracy/recall/precision for kNN and Davies-Bouldin Index for Clustering. T

Mohammad Ali Mustafa 0 Jan 20, 2022
Learning --> Numpy January 2022 - winter'22

Numerical-Python Numpy NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along

Shahzaneer Ahmed 0 Mar 12, 2022
Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Highly interpretable, sklearn-compatible classifier based on decision rules This is a scikit-learn compatible wrapper for the Bayesian Rule List class

Tamas Madl 482 Nov 19, 2022
BudouX is the successor to Budou, the machine learning powered line break organizer tool.

BudouX Standalone. Small. Language-neutral. BudouX is the successor to Budou, the machine learning powered line break organizer tool. It is standalone

Google 868 Jan 05, 2023
Basic Docker Compose for Machine Learning Purposes

Docker-compose for Machine Learning How to use: cd docker-ml-jupyterlab

Chris Chen 1 Oct 29, 2021
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! 🎉

Johannes Rieke 1.2k Jan 07, 2023
Given the names and grades for each student in a class N of students, store them in a nested list and print the name(s) of any student(s) having the second lowest grade.

Hackerank-Nested-List Given the names and grades for each student in a class N of students, store them in a nested list and print the name(s) of any s

Sangeeth Mathew John 2 Dec 14, 2021
UpliftML: A Python Package for Scalable Uplift Modeling

UpliftML is a Python package for scalable unconstrained and constrained uplift modeling from experimental data. To accommodate working with big data, the package uses PySpark and H2O models as base l

Booking.com 254 Dec 31, 2022
CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

ZhihuiYangCS 8 Jun 07, 2022
List of Data Science Cheatsheets to rule the world

Data Science Cheatsheets List of Data Science Cheatsheets to rule the world. Table of Contents Business Science Business Science Problem Framework Dat

Favio André Vázquez 11.7k Dec 30, 2022
A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

âš¡ funk-svd funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize co

Geoffrey Bolmier 171 Dec 19, 2022
ML Kaggle Titanic Problem using LogisticRegrission

-ML-Kaggle-Titanic-Problem-using-LogisticRegrission here you will find the solution for the titanic problem on kaggle with comments and step by step c

Mahmoud Nasser Abdulhamed 3 Oct 23, 2022
MLflow App Using React, Hooks, RabbitMQ, FastAPI Server, Celery, Microservices

Katana ML Skipper This is a simple and flexible ML workflow engine. It helps to orchestrate events across a set of microservices and create executable

Tom Xu 8 Nov 17, 2022
Simulation of early COVID-19 using SIR model and variants (SEIR ...).

COVID-19-simulation Simulation of early COVID-19 using SIR model and variants (SEIR ...). Made by the Laboratory of Sustainable Life Assessment (GYRO)

José Paulo Pereira das Dores Savioli 1 Nov 17, 2021
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

Toolkit for Building Robust ML models that generalize to unseen domains (RobustDG) Divyat Mahajan, Shruti Tople, Amit Sharma Privacy & Causal Learning

Microsoft 149 Jan 06, 2023
Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Amplo 10 May 15, 2022
WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can b

Shigang Li 6 Jun 18, 2022
Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining

**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.** S

Sebastian Raschka 4k Dec 30, 2022