dsfun 0.0.4

Helpful functions for Data Science

DSfun

This package is under development and limited for now. Eventually, it will contain a special categorical loss function class for training machine learning algorithms. The goal features include:

• Wrapping in a simple interface for many useful loss functions based on confusion matrix
• Differentiability (compatibility with tensorflow)
• Scalable to multilabel problems
• Time efficiency optimizations
• Working with missing labels

Limitations:

• Loss function that aren't widely studied, should be used with caution and proper validation
• If calculating on batches, it might give a biased estimation of global loss
• As the class is broad, some of the functions might not converge at all

Instalation

pip install dsfun

Usage example

import tensorflow as tf
from dsfun import f1_loss, f1_score

y_true = tf.constant([[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 1.0]])
y_pred = tf.constant([[0.5, 0.5], [0.5, 0.5], [1.0, 0.0], [0.0, 1.0]])

print(f1_loss(y_true, y_pred, 'macro'))
print(f1_score(y_true, y_pred, 'macro'))