Hi guys, here are some instructions on getting this running in a console environment. You should probably be doing this in a Google Cloud VM as described in week 4 discussion slides, rather than on your own computer. You can find details to setup the VM and driver specific to GCN at https://docs.google.com/document/d/1gf1dW5k5gNkuaaF5UAjBzXH8papBvuiqUk4bTTeKbcY/edit?usp=sharing if you want to replicate our environment

First time setup

Cloning the Repo

git clone https://github.com/rizvi-ha/team2_gcn.git

cd team2_gcn

Setting up Python environment

python3 -m venv venv

source venv/bin/activate

pip install -r requirements.txt

Downloading the data

wget https://www.dropbox.com/scl/fi/o8du146aafl3vrb87tm45/IND-WhoIsWho.zip?rlkey=cg6tbubqo532hb1ljaz70tlxe&dl=1

wait a min or two... then run

unzip IND-WhoIsWho.zip?rlkey=cg6tbubqo532hb1ljaz70tlxe

rm -rf __MACOSX

mkdir dataset

mv IND-WhoIsWho dataset

rm IND-WhoIsWho.zip?rlkey=cg6tbubqo532hb1ljaz70tlxe

rm wget-log

Everytime you reopen terminal

Pull any updates from GitHub

if there's merge conflicts or anything like that let Hassan know, but this usually should go smoothly.

git pull origin

Jump into the python environment

source venv/bin/activate

Running the model

Training and Validation to Leaderboard

python encoding.py --path ./dataset/IND-WhoIsWho/pid_to_info_all.json --save_path ./dataset/roberta_embeddings.pkl

python build_graph.py --author_dir ./dataset/IND-WhoIsWho/train_author.json  --save_dir ./dataset/train.pkl --embeddings_dir ./dataset/roberta_embeddings.pkl --pub_dir ./dataset/IND-WhoIsWho/pid_to_info_all.json

python build_graph.py --author_dir ./dataset/IND-WhoIsWho/ind_valid_author.json --save_dir ./dataset/valid.pkl --embeddings_dir ./dataset/roberta_embeddings.pkl --pub_dir ./dataset/IND-WhoIsWho/pid_to_info_all.json

python train.py  --train_dir ./dataset/train.pkl  --test_dir ./dataset/valid.pkl --saved_dir gcn --log_name gcn-log [--usecoo] [--usecov] [--threshold 0.5]

gcn/res.json is the submission json, and gcn-log is the relevant log file. [...] are optional params. The first 3 commands do standard data prepocessing, but take a long time. If you would just like to directly get the data .pkl files, please contact CS145 Team 2 to get them.

Our optimal final combination uses the final training command:

python train.py  --train_dir ./dataset/train.pkl  --test_dir ./dataset/valid.pkl --saved_dir gcn --log_name gcn-log --usecoo --threshold 0.1

Final submission after training and validating on the Leaderboard

wget https://open-data-set.oss-cn-beijing.aliyuncs.com/oag-benchmark/kddcup-2024/IND-WhoIsWho/IND-test-public.zip

<UNZIP AND MOVE TO dataset/IND-test-public> then run:

python build_graph.py --author_dir ./dataset/IND-test-public/ind_test_author_filter_public.json --save_dir ./dataset/test.pkl --embeddings_dir ./dataset/roberta_embeddings.pkl --pub_dir ./dataset/IND-WhoIsWho/pid_to_info_all.json

to build test.pkl and then:

python train.py  --train_dir ./dataset/train.pkl  --test_dir ./dataset/test.pkl --saved_dir gcn --log_name gcn-log [--usecoo] [--usecov] [--threshold 0.5]

to get a final gcn/res.json to submit to https://www.biendata.xyz/competition/ind_kdd_2024/final-submission/