covid19-backend

Code for running all the background services for Covid19 efforts.

Running locally

To run the server locally you will need to:

1. Set up your virtual environment

conda create -n covid19-backend python=3.7
conda activate covid19-backend
pip install -r requirements.txt
pip install -e .

2. Start the server

python app.py

Running via Docker

If the backend is running on the same server as the frontend, or you do not need SSL certification:

docker run -p 2020:2020 \
           -v $HOME/.cache:/root/.cache \
           --rm \
           rloganiv/covid19-backend:latest

If you need SSL certification:

docker run -p 2020:2020 \
           -v $HOME/.cache:/root/.cache \
           -v [DIRECTORY CONTAINING SSL CERTS]:/certs \
           --rm \
           rloganiv/covid19-backend:latest \
           -b 0.0.0.0:2020 \
           --certfile /certs/[CERTFILE NAME] \
           --keyfile /certs/[KEYFILE NAME]

Syncing to Google Sheets

For annotation purposes, we want to be able to transfer our data files to Google Sheets. To do that, follow the following steps:

1. Get the credentials.json file and copy to root folder, either from Google Drive folder or from here.

2. (Make sure you've run pip install as above recently)

3. Run the script, which by default copies the misconceptions.jsonl file to Google sheet in the shared folder.

python -m backend.utils.jsonl_to_gsheet

Setting up DB

python3 -m scripts.yaml2db --config configs/db/source.yaml --db backend.db
python3 -m scripts.yaml2db --config configs/db/label.yaml --db backend.db
python3 -m scripts.yaml2db --config configs/db/model.yaml --db backend.db
python3 -m scripts.jsonl2db --input misconceptions.jsonl --table Misinformation --custom initial_wiki --db backend.db
# Note: merged.csv is not available on the repository as it contains tweet texts
python3 -m scripts.csv2db --input merged.csv --tables Input Output --custom old_csv_format --db backend.db

Note,

• merged.csv consists of 7 columns in the following order with no headers: Random number 1, Misconception ID , Misconception, Tweet, Random Number 2, Annotated Label, Tweet ID

Train

Logistic Regression - Bag of Words

python3 -m scripts.ml.train_logreg --train PATH\TO\TRAIN_DATA.jsonl --dev PATH\TO\DEV_DATA.jsonl --output-dir PATH\TO\SAVE\MODEL -- c <INT> --feature-type bow

Logistic Regression - Avg. GloVE Emebeddings

python3 -m scripts.ml.train_logreg --train PATH\TO\TRAIN_DATA.jsonl --dev PATH\TO\DEV_DATA.jsonl --output-dir PATH\TO\SAVE\MODEL --c <INT> --feature-type boe

Note, use value of C which provides highest development set accuracy

BiLSTM

python3 -m scripts.ml.train_bilstm --train PATH\TO\TRAIN_DATA.jsonl --dev PATH\TO\DEV_DATA.jsonl --output-dir PATH\TO\SAVE\MODEL --epochs 20

SBERT

python3 -m scripts.ml.train_nli --model-name bert-base-cased --batch_size=10 --epochs=10 --lr=5e-5 --accumulation_steps 32 --train PATH\TO\TRAIN_DATA.jsonl --dev PATH\TO\DEV_DATA.jsonl --ckpt CKPT_NAME

SBERT - DA

python3 -m scripts.ml.train_nli --model-name digitalepidemiologylab/covid-twitter-bert --batch_size=10 --epochs=10 --lr=5e-5 --accumulation_steps 32 --train PATH\TO\TRAIN_DATA.jsonl --dev PATH\TO\DEV_DATA.jsonl --ckpt CKPT_NAME

Predict

python3 -m scripts.ml.predict --model_name MODEL_NAME --model_dir PATH\TO\MODEL --db_input backend.db --file PATH\TO\SAVE\PREDICTIONS.csv

Note,

• MODEL_NAME for each model can be found in model_names.txt
• Predictions can be written to .csv file and/or DB. Include --output_dir backend.db to write predictions to DB.

Evaluate

python3 -m scripts.ml.evaluate --db backend.db --model_name MODEL_NAME --eval_data Arjuna --file_name PATH\TO\PREDICTIONS.csv

Note,

• MODEL_NAME for each model can be found in model_names.txt
• Predictions can be read from .csv or DB. If --file_name not provided then will read predictions for given MODEL_NAME from DB