Condor Ordinal regression in Tensorflow Keras

Tensorflow Keras implementation of CONDOR Ordinal Regression (aka ordinal classification) by Garrett Jenkinson et al (2021).

CONDOR is compatible with any state-of-the-art deep neural network architecture, requiring only modification of the output layer, the labels, and the loss function. Read our full documentation to learn more.

We also have implemented CONDOR for pytorch.

This package includes:

• Ordinal tensorflow loss function: CondorOrdinalCrossEntropy
• Ordinal tensorflow error metric: OrdinalMeanAbsoluteError
• Ordinal tensorflow error metric: OrdinalEarthMoversDistance
• Ordinal tensorflow sparse loss function: CondorSparseOrdinalCrossEntropy
• Ordinal tensorflow sparse error metric: SparseOrdinalMeanAbsoluteError
• Ordinal tensorflow sparse error metric: SparseOrdinalEarthMoversDistance
• Ordinal tensorflow activation function: ordinal_softmax
• Ordinal sklearn label encoder: CondorOrdinalEncoder

Installation

Install the stable version via pip:

pip install condor-tensorflow

Alternatively install the most recent code on GitHub via pip:

pip install git+https://github.com/GarrettJenkinson/condor_tensorflow/

condor_tensorflow should now be available for use as a Python library. The dependencies can be pip installed also using the included requirements.txt:

pip install -r requirements.txt

Docker container

As an alternative to the above, we provide a convenient Dockerfile that will build a container with condor_tensorflow along with all of its dependencies (Python 3.6+, Tensorflow 2.2+, sklearn, numpy). This can be used as follows:

# Clone this git repository
git clone https://github.com/GarrettJenkinson/condor_tensorflow/

# Change directory to the cloned repository root
cd condor_tensorflow

# Create a docker image
docker build -t cpu_tensorflow -f cpu.Dockerfile ./

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

# how to run bash inside container (with Python that will have required dependencies available)
docker run -u $(id -u):$(id -g) -it -p 8888:8888 --rm cpu_tensorflow bash

Assuming a GPU enabled machine with NVIDIA drivers installed replace cpu above with gpu.

Example

This is a quick example to show basic model implementation syntax.
Example assumes existence of input data (variable 'X') and ordinal labels (variable 'labels').

import tensorflow as tf
import condor_tensorflow as condor
NUM_CLASSES = 5
# Ordinal 'labels' variable has 5 labels, 0 through 4.
enc_labs = condor.CondorOrdinalEncoder(nclasses=NUM_CLASSES).fit_transform(labels)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(32, activation = "relu"))
model.add(tf.keras.layers.Dense(NUM_CLASSES-1)) # Note the "-1"
model.compile(loss = condor.CondorOrdinalCrossEntropy(),
              metrics = [condor.OrdinalMeanAbsoluteError()])
model.fit(x = X, y = enc_labs)

See this colab notebook for extended examples of ordinal regression with MNIST and Amazon reviews (universal sentence encoder).

Please post any issues to the issue queue.

Acknowledgments: Many thanks to the CORAL ordinal authors and the CORAL pytorch authors whose repos provided a roadmap for this codebase.

References

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