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"
}

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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

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