| Installation | Examples | Citation
This repo implements the experiments for UCI and genomics dataset for our paper Semi-Parametric Inducing Point Networks and Neural Process. For experiments with SPIN Neural Processes please see SPIN Neural Processes repo
This repo uses NPT codebase and framework https://github.com/OATML/non-parametric-transformers and modifies the NPT architecture to inducing point architecture, which we call SPIN
Set up and activate the Python environment by executing
conda env create -f environment.yml
conda activate npt
Scripts in the paper are stored in ./scripts/SPIN_scripts.sh.
SLURM system can be used to run jobs. An example script for submitting SLURM job is given in ./scripts/combined_sbatch.sub.
In the scripts folder, customize the script init_env.sh for your environment and path. This path is then referenced in ./scripts/combined_sbatch.sub .For imputation experiments, the data link for vcf file, marker panel, test and validation samples is specified in the ./scripts/init_env.sh script.
For running genomics experiments, the vcf file ("chr20.1kg.phase3.v5a.vcf.gz") from http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes_phase3_v5a/b37.vcf/ and
Human omni marker panel ("HumanOmni2-5-8-v1-1-C-b37.strand") from https://www.well.ox.ac.uk/~wrayner/strand/ are used.
We have processed these raw files and they can be downloaded from google drive links below
1000 genomes train/val/test csv files
cleaned chromosome 20 snps in chunks of 10K snps per file
Specify their location in ./scripts/init_env.sh for the following environment variables
USER_PATH: local directory path where the repo exists
LOG_PATH: path for logs
DATA_IN: path where genomics input data - specifically - human omni marker panel and train/val/test splits for 1000 genomes dataset is stored
CLEANED_CHR20_SNPS: path where cleaned and processed snps for genomic data are stored
DATA_OUT: path where model checkpoint will be stored
ENV_NAME: name of the conda environment
python run.py --data_set imputation \
--exp_batch_size 256 \
--SPIN True --startAttBlock ABA --num_inds_I 10 --num_inds_F 10 --project Genotype_Imputation
SPIN is the configuration of SPIN that uses XABD, XABA and ABLA (on H_A) by default.
The number of inducing points for datapoints (h in the paper) is configured by num_inds_I and number of inducing points for attributs (f in the paper) is configured by num_inds_F
If you find SPIN useful for your research, please consider citing our paper:
@inproceedings{
rastogi2023semiparametric,
title={Semi-Parametric Inducing Point Networks and Neural Processes},
author={Richa Rastogi and Yair Schiff and Alon Hacohen and Zhaozhi Li and Ian Lee and Yuntian Deng and Mert R. Sabuncu and Volodymyr Kuleshov},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=FE99-fDrWd5}
}
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