Installation

Install via pip.

$ pip install torchtable

Documentation

Documentation is a work in progress, but the current docs can be read here. 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.

>>> 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