Metadata-Version: 1.2
Name: torchtable
Version: 0.1.0
Summary: Tools for processing tabular datasets for PyTorch
Home-page: https://github.com/keitakurita/torchtable
Author: Keita Kurita
Author-email: keita.kurita@gmail.com
License: MIT
Description: .. image:: https://circleci.com/gh/keitakurita/torchtable.svg?style=svg
            :target: https://circleci.com/gh/keitakurita/torchtable
        
        .. image:: https://readthedocs.org/projects/torchtable/badge/?version=master
            :target: https://torchtable.readthedocs.io/en/master/?badge=master
            :alt: Documentation Status
        
        torchtable
        ++++++++++
        
        Torchtable is a library for handling tabular datasets in PyTorch. It is heavily inspired by torchtext and uses a similar API but without some of the limitations (e.g. only one field per column).
        Torchtable aims to be **simple to use** and **easily extensible**. 
        It provides sensible defaults while allowing the user to define their own custom pipelines, putting all of this behind an intuitive interface.
        
        Installation
        ============
        Install via pip.
        
        `$ pip install torchtable`
        
        Documentation
        =============
        Documentation is a work in progress, but the current docs can be read `here <https://torchtable.readthedocs.io/en/master/>`_.
        In addition, you can read the notebooks in the examples directory or dev_nb directory to learn more.
        
        Usage
        =====
        
        Torchtable uses a declarative API similar to torchtext.
        Here is an example of how you might handle an imaginary dataset where you are supposed to predict the price of some product.
        
        .. code-block:: python
        
          >>> train = TabularDataset.from_csv('data/train.csv',
          ...    fields={'seller_id': CategoricalField(min_freq=3),
          ...            'timestamp': [DayofWeekField(), HourField()],
          ...            'price': NumericalField(fill_missing="median", is_target=True)
          ...    })
          ...
        
        See the examples directory for more examples.
        
        TODO
        ====
        - Add more models
        - Implement default field selection
        - Implement text field/operations
        - Implement swap noise
        - Implement input/output validation
        
Keywords: PyTorch,deep learning,machine learning
Platform: UNKNOWN
Requires-Python: >=3.6
