CONditionals for Ordinal Regression and classification in PyTorch

Related tags

Deep Learningcondor_pytorch
Overview

CONDOR pytorch implementation for ordinal regression with deep neural networks.

Continuous Integration License Python 3


Documentation: https://GarrettJenkinson.github.io/condor_pytorch

About

CONDOR, short for CONDitionals for Ordinal Regression, is a method for ordinal regression with deep neural networks, which addresses the rank inconsistency issue of other ordinal regression frameworks.

It is compatible with any state-of-the-art deep neural network architecture, requiring only modification of the output layer, the labels, the loss function.

This repository implements the CONDOR functionality (neural network layer, loss function, and dataset utilities) for convenient use. Examples are provided via the "Tutorials" that can be found on the documentation website at https://GarrettJenkinson.github.io/condor_pytorch.

We also have CONDOR implemented for Tensorflow.

Installation or Docker

You can install the latest stable release of condor_pytorch directly from Python's package index via pip by executing the following code from your command line:

pip install condor-pytorch

We also provide Dockerfile's to help get up and started quickly with condor_pytorch. The cpu image can be built and ran as follows, with tutorial jupyter notebooks built in.

# Create a docker image, only done once
docker build -t cpu_pytorch -f cpu.Dockerfile ./

# run image to serve a jupyter notebook
docker run -it -p 8888:8888 --rm cpu_pytorch

# how to run bash inside container (with python that will have deps)
docker run -u $(id -u):$(id -g) -it -p 8888:8888 --rm cpu_pytorch bash

An NVIDIA based gpu optimized container can be built and run as follows (without interactive ipynb capabilities).

# only needs to be built once
docker build -t gpu_pytorch -f gpu.Dockerfile ./

# use the image after building it
docker run -it -p 8888:8888 --rm gpu_pytorch

Cite as

If you use CONDOR as part of your workflow in a scientific publication, please consider citing the CONDOR repository with the following DOI:

@article{condor2021,
title = "Universally rank consistent ordinal regression in neural networks",
journal = "arXiv",
volume = "2110.07470",
year = "2021",
url = "https://arxiv.org/abs/2110.07470",
author = "Garrett Jenkinson and Kia Khezeli and Gavin R. Oliver and John Kalantari and Eric W. Klee",
keywords = "Deep learning, Ordinal regression, neural networks, Machine learning, Biometrics"
}
You might also like...
To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types

To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types, from a Database Taken From Dr. Wolberg reports his Clinic Cases.

Astitva Veer Garg 1 Jul 31, 2022
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Machine Learning From Scratch About Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose

Erik Linder-Norén 21.8k Jan 9, 2023
Code for
Code for "3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop"

PyMAF This repository contains the code for the following paper: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop Hongwe

Hongwen Zhang 450 Dec 28, 2022
A very short and easy implementation of Quantile Regression DQN
A very short and easy implementation of Quantile Regression DQN

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"

NeuralSymbolicRegressionThatScales Pytorch implementation and pretrained models for the paper "Neural Symbolic Regression That Scales", presented at I

null 35 Nov 25, 2022
[TIP 2021] SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction
[TIP 2021] SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction

SADRNet Paper link: SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction Requirements python

Multimedia Computing Group, Nanjing University 99 Dec 30, 2022
Regression Metrics Calculation Made easy for tensorflow2 and scikit-learn

Regression Metrics Installation To install the package from the PyPi repository you can execute the following command: pip install regressionmetrics I

Ashish Patel 11 Dec 16, 2022
Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

advantage-weighted-regression Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning, by Peng et al. (

Omar D. Domingues 1 Dec 2, 2021
Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible

Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible, to be the most reliable with the least complexity possible

Nikolas B Virionis 2 Aug 1, 2022
Comments
  • src edits

    src edits

    Summary of edits:

    • added device as an argument of the functions to make them compatible when GPUs are used.
    • replaced torch.tile with repeat as it is unavailable in some versions of PyTorch.
    • worked with log probabilities and cumulative sum instead of product for numerical stability of ordinal_softmax
    opened by kolmogorov01 0
Releases(v1.1.0)
Owner
GitHub Repository
Code Release for ICCV 2021 (oral), "AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds"

AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds (ICCV 2021 oral) **Project Page | Arxiv ** Runsong Zhu¹, Yuan Liu², Zhen Dong¹, Te

40 Dec 30, 2022
Code for SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes (NeurIPS 2021)

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes (NeurIPS 2021) SyncTwin is a treatment effect estimation method tailored for observat

Zhaozhi Qian 3 Nov 03, 2022
This repository contains the PyTorch implementation of the paper STaCK: Sentence Ordering with Temporal Commonsense Knowledge appearing at EMNLP 2021.

STaCK: Sentence Ordering with Temporal Commonsense Knowledge This repository contains the pytorch implementation of the paper STaCK: Sentence Ordering

Deep Cognition and Language Research (DeCLaRe) Lab 23 Dec 16, 2022
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

Rethinking the Value of Labels for Improving Class-Imbalanced Learning This repository contains the implementation code for paper: Rethinking the Valu

Yuzhe Yang 656 Dec 28, 2022
[ICML 2021] A fast algorithm for fitting robust decision trees.

GROOT: Growing Robust Trees Growing Robust Trees (GROOT) is an algorithm that fits binary classification decision trees such that they are robust agai

Cyber Analytics Lab 17 Nov 21, 2022
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Generative Models Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Note: Gen

Agustinus Kristiadi 7k Jan 02, 2023
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2：ImageNet无限制对抗攻击 介绍 ： 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。

25 Dec 08, 2022
Diverse Object-Scene Compositions For Zero-Shot Action Recognition

Diverse Object-Scene Compositions For Zero-Shot Action Recognition This repository contains the source code for the use of object-scene compositions f

7 Sep 21, 2022
VISNOTATE: An Opensource tool for Gaze-based Annotation of WSI Data

VISNOTATE: An Opensource tool for Gaze-based Annotation of WSI Data Introduction Requirements Installation and Setup Supported Hardware and Software R

SigmaLab 1 Jun 14, 2022
Unofficial implementation of HiFi-GAN+ from the paper "Bandwidth Extension is All You Need" by Su, et al.

HiFi-GAN+ This project is an unoffical implementation of the HiFi-GAN+ model for audio bandwidth extension, from the paper Bandwidth Extension is All

Brent M. Spell 134 Dec 30, 2022
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

LSTM Neural Network for Time Series Prediction LSTM built using the Keras Python package to predict time series steps and sequences. Includes sine wav

Jakob Aungiers 4.1k Jan 02, 2023
A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

Neuraxle Pipelines Code Machine Learning Pipelines - The Right Way. Neuraxle is a Machine Learning (ML) library for building machine learning pipeline

Neuraxio 555 Dec 24, 2022
MAterial del programa Misión TIC 2022

Mision TIC 2022 Esta iniciativa, aparece como respuesta frente a los retos de la Cuarta Revolución Industrial, y tiene como objetivo la formación de 1

6 May 25, 2022
Python framework for Stochastic Differential Equations modeling

SDElearn: a Python package for SDE modeling This package implements functionalities for working with Stochastic Differential Equations models (SDEs fo

4 May 10, 2022
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet)

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet) (

Wei-Ting Chen 49 Dec 27, 2022
The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

Deep Residual Fourier Transformation for Single Image Deblurring Xintian Mao, Yiming Liu, Wei Shen, Qingli Li and Yan Wang News 2021.12.5 Release Deep

145 Jan 05, 2023
Fuwa-http - The http client implementation for the fuwa eco-system

Fuwa HTTP The HTTP client implementation for the fuwa eco-system Example import

Fuwa 2 Feb 16, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
Tutorial on scikit-learn and IPython for parallel machine learning

Parallel Machine Learning with scikit-learn and IPython Video recording of this tutorial given at PyCon in 2013. The tutorial material has been rearra

Olivier Grisel 1.6k Dec 26, 2022
FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

Anton Jeran Ratnarajah 89 Dec 22, 2022