The model training and visualization pipeline lives in: modeling/basic_model_framework.ipynb
which will train and visualize the outputs of various models.
The high level wrapper for training and predicting values is the fit_and_predict function in modeling/fit_and_predict.py
this allows you to train a few models by passing in different arguments. For more details please see the function documentation.
To simply get the predictions for the best model, use the add_preds
function:
from fit_and_predict import add_preds
df = add_preds(df, NUM_DAYS_LIST=[1, 3, 5]) # adds keys like "Predicted Deaths 1-day", "Predicted Deaths 3-day"
# NUM_DAYS_LIST is list of number of days in the future to predict
- to reproduce all the results in the paper, first run
predict_all_deaths.py
and then runreproduce_paper_results.py