Datasets are each given a custom class that handles preprocessing in npt/datasets/.
These classes handle assignment of target columns (e.g. targets for prediction) and feature columns and generate a random mask to hide feature values.
Datasets all store raw values in .data_table attribute and have a make_missing(p: float=proportion_to_mask) function to return an np.ndarray mask of the data values.
Dataset classes also store two lists, .num_target_cols, .cat_target_cols, each containing integer indices denoting the location of numeric and categorical target columns in .data_table.
There are analagous lists .cat_features, .num_features. Each of these is hard coded in the dataset class.
Overview | Abstract | Installation | Examples | Citation
Hi, good to see you here! 👋
Thanks for checking out the code for Non-Parametric Transformers (NPTs).
This codebase will allow you to reproduce experiments from the paper as well as use NPTs for your own research.
We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. Our approach uses self-attention to reason about relationships between datapoints explicitly, which can be seen as realizing non-parametric models using parametric attention mechanisms. However, unlike conventional non-parametric models, we let the model learn end-to-end from the data how to make use of other datapoints for prediction. Empirically, our models solve cross-datapoint lookup and complex reasoning tasks unsolvable by traditional deep learning models. We show highly competitive results on tabular data, early results on CIFAR-10, and give insight into how the model makes use of the interactions between points.
Set up and activate the Python environment by executing
conda env create -f environment.yml
conda activate npt
For now, we recommend installing CUDA <= 10.2:
See issue with CUDA >= 11.0 here.
If you are running this on a system without a GPU, use the above with environment_no_gpu.yml instead.
We now give some basic examples of running NPT.
NPT downloads all supported datasets automatically, so you don't need to worry about that.
We use wandb to log experimental results.
Wandb allows us to conveniently track run progress online.
If you do not want wandb enabled, you can run wandb off in the shell where you execute NPT.
For example, run this to explore NPT with default configuration on Breast Cancer
python run.py --data_set breast-cancer
Another example: A run on the poker-hand dataset may look like this
python run.py --data_set poker-hand \
--exp_batch_size 4096 \
--exp_print_every_nth_forward 100
You can find all possible config arguments and descriptions in NPT/configs.py or using python run.py --help.
In scripts/ we provide a list with the runs and correct hyperparameter configurations presented in the paper.
We hope you enjoy using the code and please feel free to reach out with any questions 😊
If you find this code helpful for your work, please cite our paper Paper as
@article{kossen2021self,
title={Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning},
author={Kossen, Jannik and Band, Neil and Gomez, Aidan N. and Lyle, Clare and Rainforth, Tom and Gal, Yarin},
journal={arXiv:2106.02584},
year={2021}
}