GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

Last update: Dec 05, 2022

Overview

GalaXC

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

@InProceedings{Saini21,
	author       = {Saini, D. and Jain, A.K. and Dave, K. and Jiao, J. and Singh, A. and Zhang, R. and Varma, M.},
	title        = {GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification},
	booktitle    = {Proceedings of The Web Conference},
	month = "April",
	year = "2021",
	}

Setup GalaXC

git clone https://github.com/Extreme-classification/GalaXC.git
conda env create -f GalaXC/environment.yml
conda activate galaxc
pip install hnswlib
git clone https://github.com/kunaldahiya/pyxclib.git
cd pyxclib
python setup.py install
cd ../GalaXC

Dataset Structure

Your dataset should have the following structure:

DatasetName (e.g. LF-AmazonTitles-131K)
│   trn_X.txt   (text for trn documents, one text in each line)
|   tst_X.tst   (text for tst documents, one text in each line)
|   Y.txt       (text for labels, one text in each line)
│   trn_X_Y.txt (trn labels in spmat format)
|   tst_X_Y.txt (tst labels in spmat format)
|   filter_labels_test.txt (filter labels where label and test documents are same)
│
└───XXCondensedData (embeddings for tst, trn documents and labels, for benchmark datasets, XX=DX[Astec])
    │   trn_point_embs.npy (2D numpy matrix for trn document embeddings)
    │   tst_point_embs.npy (2D numpy matrix for tst document embeddings)
    |   label_embs.npy     (2D numpy matrix for label embeddings)

We have provided the DX(embeddings from Module 1 of Astec) embeddings for public benchmark datasets for ease of use. Got better(higher recall) embeddings from somewhere? Just plug the new ones and GalaXC will have better preformance, no need to make any code change! These files for LF-AmazonTitles-131K, LF-WikiSeeAlsoTitles-320K and LF-AmazonTitles-1.3M can be found here. Except the files in DXCondensedData, all other files are copy of the datasets from The Extreme Classification Repository.

Sample Runs

To reproduce the numbers on public benchmark datasets reported in the paper, the sample runs are

LF-AmazonTitles-131K

python -u -W ignore train_main.py --dataset /your/path/to/data/LF-AmazonTitles-131K --save-model 0  --devices cuda:0  --num-epochs 30  --num-HN-epochs 0  --batch-size 256  --lr 0.001  --attention-lr 0.001 --adjust-lr 5,10,15,20,25,28  --dlr-factor 0.5  --mpt 0  --restrict-edges-num -1  --restrict-edges-head-threshold 20  --num-random-samples 30000  --random-shuffle-nbrs 0  --fanouts 4,3,2  --num-HN-shortlist 500   --embedding-type DX  --run-type NR  --num-validation 25000  --validation-freq -1  --num-shortlist 500 --predict-ova 0  --A 0.6  --B 2.6

LF-WikiSeeAlsoTitles-320K

python -u -W ignore train_main.py --dataset /your/path/to/data/LF-WikiSeeAlsoTitles-320K --save-model 0  --devices cuda:0  --num-epochs 30  --num-HN-epochs 0  --batch-size 256  --lr 0.001  --attention-lr 0.05 --adjust-lr 5,10,15,20,25,28  --dlr-factor 0.5  --mpt 0  --restrict-edges-num -1  --restrict-edges-head-threshold 20  --num-random-samples 32000  --random-shuffle-nbrs 0  --fanouts 4,3,2  --num-HN-shortlist 500  --repo 1  --embedding-type DX --run-type NR  --num-validation 25000  --validation-freq -1  --num-shortlist 500  --predict-ova 0  --A 0.55  --B 1.5

LF-AmazonTitles-1.3M

python -u -W ignore train_main.py --dataset /your/path/to/data/LF-AmazonTitles-1.3M --save-model 0  --devices cuda:0  --num-epochs 24  --num-HN-epochs 15  --batch-size 512  --lr 0.001  --attention-lr 0.05 --adjust-lr 4,8,12,16,18,20,22  --dlr-factor 0.5  --mpt 0  --restrict-edges-num 5  --restrict-edges-head-threshold 20  --num-random-samples 100000  --random-shuffle-nbrs 1  --fanouts 3,3,3  --num-HN-shortlist 500   --embedding-type DX  --run-type NR  --num-validation 25000  --validation-freq -1  --num-shortlist 500 --predict-ova 0  --A 0.6  --B 2.6

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

Related tags

Overview

GalaXC

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

Setup GalaXC

Dataset Structure

Sample Runs

YOU MAY ALSO LIKE

Owner

Extreme Classification

FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

JAX bindings to the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) library

Region-aware Contrastive Learning for Semantic Segmentation, ICCV 2021

Task Transformer Network for Joint MRI Reconstruction and Super-Resolution (MICCAI 2021)

This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

A framework that allows people to write their own Rocket League bots.

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

Bayesian Inference Tools in Python

ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.

Implementation of momentum^2 teacher

NHL 94 AI contests

The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.

Rotary Transformer

This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset

Python TFLite scripts for detecting objects of any class in an image without knowing their label.

Dungeons and Dragons randomized content generator

Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

Lazy, a tool for running things in idle time