Python example classifier code for the George B. Moody PhysioNet Challenge 2022

What’s in this repository?

This repository contains a simple example to illustrate how to format a Python entry for the George B. Moody PhysioNet Challenge 2022.. You can try it by running the following commands on the Challenge training sets. These commands should take a few minutes or less to run from start to finish on a recent personal computer.

For this example, we implemented a random forest classifier with several features. You can use a different classifier, features, and libraries for your entry. This simpple example is designed not not to perform well, so you should not use it as a baseline for your model’s performance.

This code uses four main scripts, described below, to train and run a model for the 2022 Challenge.

How do I run these scripts?

You can install the dependencies for these scripts by creating a Docker image (see below) and running

pip install requirements.txt

You can train and run your model by running

python train_model.py training_data model
python run_model.py model test_data test_outputs

where training_data is a folder with the training data files, model is a folder for saving your model, test_data is a folder with the test data files (you can use the training data for debugging and cross-validation), and test_outputs is a folder for saving your model outputs. The 2022 Challenge website provides a training database with a description of the contents and structure of the data files.

You can evaluate your model by pulling or downloading the evaluation code and running

python evaluate_model.py labels outputs scores.csv class_scores.csv

where labels is a folder with labels for the data, such as the training database on the PhysioNet webpage; outputs is a folder containing files with your model’s outputs ofr th edata; scores.csv (optional) is a collection of scores for your model; and class_scores.csv (optional) is a collection of per-class scores for your model.

Which scripts I can edit?

We will run the train_model.py and run_model.py scripts to train and run your model, so please check these scripts and the functions that they call.

Please edit the following script to add your training and testing code:

  • team_code.py is a script with functions for training and running your model.

Please do not edit the following scripts. We will use the unedited versions of these scripts when running your code:

  • train_model.py is a script for training your model.
  • run_model.py is a script for running your trained model.
  • evaluate_model.py is a script with evaluating your model.
  • helper_code.py is a script with helper functions that we used for our code. You are welcome to use them in your code.

These scripts must remain in the root path of your repository, but you can put other scripts and other files elsewhere in your repository.

How do I train, save, load, and run my model?

To train and save your models, please edit the train_challenge_model function in the team_code.py script. Please do not edit the input or output arguments of the train_challenge_model function.

To load and run your trained model, please edit the load_challenge_model and run_challenge_model functions in the team_code.py script. Please do not edit the input or output arguments of the functions of the load_challenge_model and run_challenge_model functions.

How do I run these scripts in Docker?

Docker and similar platforms allow you to containerize and package your code with specific dependencies so that you can run your code reliably in other computing environments and operating systems.

To guarantee that we can run your code, please install Docker, build a Docker image from your code, and run it on the training data. To quickly check your code for bugs, you may want to run it on a small subset of the training data.

If you have trouble running your code, then please try the follow steps to run the example code.

  1. Create a folder example in your home directory with several subfolders.

     [email protected]:~$ cd ~/
     [email protected]:~$ mkdir example
     [email protected]:~$ cd example
     [email protected]:~/example$ mkdir training_data test_data model test_outputs
    
  2. Download the training data from the Challenge website. Put some of the training data in training_data and test_data. You can use some of the training data to check your code (and should perform cross-validation on the training data to evaluate your algorithm).

  3. Download or clone this repository in your terminal.

     [email protected]:~/example$ git clone https://github.com/physionetchallenges/python-classifier-2022.git
    
  4. Build a Docker image and run the example code in your terminal.

     [email protected]:~/example$ ls
     model  python-classifier-2022  test_data  test_outputs  training_data
    
     [email protected]:~/example$ cd python-classifier-2022/
    
     [email protected]:~/example/python-classifier-2022$ docker build -t image .
    
     Sending build context to Docker daemon  [...]kB
     [...]
     Successfully tagged image:latest
    
     [email protected]:~/example/python-classifier-2022$ docker run -it -v ~/example/model:/physionet/model -v ~/example/test_data:/physionet/test_data -v ~/example/test_outputs:/physionet/test_outputs -v ~/example/training_data:/physionet/training_data image bash
    
     [email protected][...]:/physionet# ls
         Dockerfile             README.md         test_outputs
         evaluate_model.py      requirements.txt  training_data
         helper_code.py         team_code.py      train_model.py
         LICENSE                run_model.py
    
     [email protected][...]:/physionet# python train_model.py training_data model
    
     [email protected][...]:/physionet# python run_model.py model test_data test_outputs
    
     [email protected][...]:/physionet# python evaluate_model.py model test_data test_outputs
     [...]
    
     [email protected][...]:/physionet# exit
     Exit
    

How do I learn more?

Please see the Challenge website for more details. Please post questions and concerns on the Challenge discussion forum.

Useful links

GitHub

View Github