dsfun 0.0.3

Helpful functions for Data Science

DSfun

This package contains useful loss function class for training algorithms. These are f1-loss related functions. The main features:

• It is differentiable so it works with tensorflow
• It is more eficient than standard implementations
• Great for task that require to optimize F1-score
• Works with missing data *(TO DO)
• Can be modified to perform arbitrary differential functions on confusion matrix (TO DO)

Limitations:

• As any machine learning framework, this loss function shouldn't be used without proper validation as it is not deeply understood
• If calculating on batches, it will give a biased estimation of global loss
• If there are no representatives of a class in a batch, it might not converge properly

Instalation

pip install dsfun

Usage

TO DO

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]]) f1_loss(y_true, y_pred, 'macro') > ? f1_score(y_true, y_pred, 'macro') > ?