CONditionals for Ordinal Regression and classification in PyTorch

Last update: Jul 25, 2022

Related tags

Deep Learning condor_pytorch

Overview

CONDOR pytorch implementation for ordinal regression with deep neural networks.

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:

Jenkinson, Khezeli, Oliver, Kalantari, Klee. Universally rank consistent ordinal regression in neural networks, arXiv:2110.07470, 2021.

@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"
}

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.

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

21.8k Jan 9, 2023

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

450 Dec 28, 2022

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

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

35 Nov 25, 2022

[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

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. (

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

2 Aug 1, 2022

Comments

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)

v1.1.0(Feb 19, 2022)

This release includes the condor_negloglikeloss function that allows for maximum likelihood estimation of the network parameters.
Source code(tar.gz)
Source code(zip)
v1.0.1(Nov 1, 2021)

Source code(tar.gz)
Source code(zip)

CONditionals for Ordinal Regression and classification in PyTorch

Related tags

Overview

About

Installation or Docker

Cite as

You might also like...

To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types

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.

Code for "3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop"

A very short and easy implementation of Quantile Regression DQN

Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"

[TIP 2021] SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction

Regression Metrics Calculation Made easy for tensorflow2 and scikit-learn

Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible

Comments

src edits

Releases(v1.1.0)

v1.1.0(Feb 19, 2022)

v1.0.1(Nov 1, 2021)

Owner

Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.

Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

Repository for RNNs using TensorFlow and Keras - LSTM and GRU Implementation from Scratch - Simple Classification and Regression Problem using RNNs

Flexible Option Learning - NeurIPS 2021

A trusty face recognition research platform developed by Tencent Youtu Lab

Simulations for Turring patterns on an apically expanding domain. T

Repositório criado para abrigar os notebooks com a listas de exercícios propostos pelo professor Gustavo Guanabara do canal Curso em Vídeo do YouTube durante o Curso de Python 3

Simple object detection app with streamlit

Noise Conditional Score Networks (NeurIPS 2019, Oral)

Hybrid Neural Fusion for Full-frame Video Stabilization

Bling's Object detection tool

Trainable Bilateral Filter Layer (PyTorch)

To build a regression model to predict the concrete compressive strength based on the different features in the training data.

The materials used in the SaxonJS tutorial presented at Declarative Amsterdam, 2021

FACIAL: Synthesizing Dynamic Talking Face With Implicit Attribute Learning. ICCV, 2021.

LaneAF: Robust Multi-Lane Detection with Affinity Fields

PyTorch - Python + Nim

TensorFlow implementation of "Variational Inference with Normalizing Flows"

Colab notebook and additional materials for Python-driven analysis of redlining data in Philadelphia

某学校选课系统GIF验证码数据集 + Baseline模型 + 上下游相关工具