Metadata-Version: 2.1
Name: dsfun
Version: 0.0.3
Summary: Helpful functions for Data Science
Home-page: https://github.com/DawidDabkowski/dsfun
Author: Dawid Dabkowski
Author-email: dav.dabkowski@gmail.com
License: UNKNOWN
Description: [![Build Status](https://travis-ci.com/DawidDabkowski/dsfun.svg?branch=master)](https://travis-ci.com/DawidDabkowski/dsfun) [![codecov](https://codecov.io/gh/DawidDabkowski/dsfun/branch/master/graph/badge.svg)](https://codecov.io/gh/DawidDabkowski/dsfun)
        
        
        # 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')`
        `> ?`
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: developing
