coral-ordinal 0.1.4

Tensorflow Keras implementation of CORAL ordinal regression output layer, loss, activation, and metrics

Ordinal regression in Tensorflow Keras

Tensorflow Keras implementation of ordinal regression (aka ordinal classification) using consistent rank logits (CORAL) by Cao, Mirjalili, & Raschka (2019).

This package includes:

• Ordinal output layer: CoralOrdinal()
• Ordinal loss function: OrdinalCrossEntropy()
• Ordinal error metric: MeanAbsoluteErrorLabels()
• Ordinal activation function: ordinal_softmax()

This is a work in progress, so please post any issues to the issue queue.

Acknowledgments: Many thanks to Sebastian Raschka for the help in porting from the PyTorch source repository.

Key pending items:

• Function docstrings
• Docs
• Tests

Installation

Install the stable version via pip:

pip install coral-ordinal

Install the most recent code on GitHub via pip:

pip install git+https://github.com/ck37/coral-ordinal/

Dependencies

This package relies on Python 3.6+, Tensorflow 2.2+, and numpy.

Example

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(32, activation = "relu"))
model.add(coral.CoralOrdinal(num_classes = 5)) # Ordinal variable has 5 labels, 0 through 4.
model.compile(loss = coral.OrdinalCrossEntropy(), metrics = [coral.MeanAbsoluteErrorLabels])

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

Note that the minimum value of the ordinal variable needs to be 0. If your labeled data ranges from 1 to 5, you will need to subtract 1 so that it is scaled to be 0 to 4.

References

Cao, W., Mirjalili, V., & Raschka, S. (2019). Consistent rank logits for ordinal regression with convolutional neural networks. arXiv preprint arXiv:1901.07884, 6.