A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Related tags

Machine Learningpythondata-sciencemachine-learninginterpretable-machine-learninglucidmode
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

Keivan Ipchi Hagh 1 Nov 22, 2021
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

Tangram 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 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

Amazon Web Services 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.

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.

FINRA 25 Dec 28, 2022
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.

Neuron 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

Microsoft 14.5k Jan 7, 2023
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.

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
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.

Krishna Priyatham Potluri 73 Dec 1, 2022
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.

null 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
lucidmode
GitHub Repository https://www.lucidmode.org
MLBox is a powerful Automated Machine Learning python library.

MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cle

Axel 1.4k Jan 06, 2023
Pandas DataFrames and Series as Interactive Tables in Jupyter

Pandas DataFrames and Series as Interactive Tables in Jupyter Star Turn pandas DataFrames and Series into interactive datatables in both your notebook

Marc Wouts 364 Jan 04, 2023
Case studies with Bayesian methods

Case studies with Bayesian methods

Baze Petrushev 8 Nov 26, 2022
This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev

MLProject_01 This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev Context Dataset English question data set file F

Hadi Nakhi 1 Dec 18, 2021
Implementation of linesearch Optimization Algorithms in Python

Nonlinear Optimization Algorithms During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Opti

Paul 3 Dec 06, 2022
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

Data Science on AWS - O'Reilly Book Get the book on Amazon.com Book Outline Quick Start Workshop (4-hours) In this quick start hands-on workshop, you

Data Science on AWS 2.8k Jan 03, 2023
Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and t

164 Jan 04, 2023
onelearn: Online learning in Python

onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o

15 Nov 06, 2022
Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale.

Model Search Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers sp

AriesTriputranto 1 Dec 13, 2021
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions

ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in

Computational Data Science Lab 182 Dec 31, 2022
Traingenerator šŸ§™ A web app to generate template code for machine learning āœØ

Traingenerator šŸ§™ A web app to generate template code for machine learning āœØ šŸŽ‰ Traingenerator is now live! šŸŽ‰

Johannes Rieke 1.2k Jan 07, 2023
Library for machine learning stacking generalization.

stacked_generalization Implemented machine learning *stacking technic[1]* as handy library in Python. Feature weighted linear stacking is also availab

114 Jul 19, 2022
A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

2.3k Jan 05, 2023
scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.

Sklearn-genetic-opt scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms. This is meant to be an alternativ

Rodrigo Arenas 180 Dec 20, 2022
Implementation of different ML Algorithms from scratch, written in Python 3.x

Implementation of different ML Algorithms from scratch, written in Python 3.x

Gautam J 393 Nov 29, 2022
Machine Learning University: Accelerated Natural Language Processing Class

Machine Learning University: Accelerated Natural Language Processing Class This repository contains slides, notebooks and datasets for the Machine Lea

AWS Samples 2k Jan 01, 2023
Machine Learning Techniques using python.

šŸ‘‹ Hi, Iā€™m Fahad from TEXAS TECH. šŸ‘€ Iā€™m interested in Optimization / Machine Learning/ Statistics šŸŒ± Iā€™m currently learning Machine Learning and Stat

FAHAD MOSTAFA 1 Jan 19, 2022
DaCeML - Machine learning powered by data-centric parallel programming.

Data-centric machine learning powered by DaCe

SPCL 48 Dec 12, 2022
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

Spark Python Notebooks This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, fro

Jose A Dianes 1.5k Jan 02, 2023
Pandas Machine Learning and Quant Finance Library Collection

Pandas Machine Learning and Quant Finance Library Collection

148 Dec 07, 2022