We implemented a random forest classifier that uses age, sex, and the root mean square of the ECG lead signals as features. This simple example illustrates how to format your Python entry for the Challenge, and it should finish running on any of the Challenge training datasets in a minute or two on a personal computer. However, it is not designed to score well (or, more accurately, it is designed not to score well), so you should not use it as a baseline for your model's performance.
This code uses four main scripts, as described below, to train and test your model for the 2021 Challenge.
You can run this classifier code by installing the requirements
pip install requirements.txt
and running
python train_model.py training_data model
python test_model.py model test_data test_outputs
where training_data is a folder of training data files, model is a folder for saving your models, test_data is a folder of test data files (you can use the training data locally for debugging and cross-validation), and test_outputs is a folder for saving your models' outputs. The PhysioNet/CinC Challenge 2021 webpage provides training databases with data files and a description of the contents and structure of these files.
After training your model and obtaining test outputs with above commands, you can evaluate the scores of your models using the PhysioNet/CinC Challenge 2021 evaluation code by running
python evaluate_model.py labels outputs scores.csv class_scores.csv
where labels is a folder containing files with one or more labels for each ECG recording, such as the training database on the PhysioNet webpage; outputs is a folder containing files with outputs produced by your models for those recordings; scores.csv (optional) is a collection of scores for your models; and class_scores.csv (optional) is a collection of per-class scores for your models.
We will run the train_model.py and test_model.py scripts to run your training code and testing code, so please check these scripts and the functions that they call.
Our example code uses four main scripts to train and test your model for the 2021 Challenge:
Please edit the following script to add your training and testing code:
team_code.pyis a script with functions for training your model and running your trained models.
Please do not edit the following scripts. We will use the unedited versions of these scripts.
train_model.pyis a script for calling your training code on the training data.test_model.pyis a script for calling your trained models on the test data.helper_code.pyis a script with helper variables and functions that we used for our code. You are welcome to use them in your code.
These four scripts must remain in the root path of your repository, but you can put other scripts and other files elsewhere in your repository.
To train and save your models, please edit the training_code function in the team_code.py script. Please do not edit the input arguments or output arguments of the training_code function.
To load and run your trained model, please edit the load_twelve_lead_model, load_six_lead_model, load_three_lead_model, and load_two_lead_model functions as well as the run_twelve_lead_model, run_six_lead_model, run_three_lead_model and run_two_lead_model functions in the team_code.py script, which takes an ECG recording as an input and returns the class labels and probabilities for the ECG recording as outputs. Please do not edit the input or output arguments of the functions for loading or running your models.
This README has instructions for running the example code and writing and running your own code.
We also included a script, extract_leads_wfdb.py, for extracting reduced-lead sets from the training data. You can use this script to produce reduced-lead data that you can use with your code. You can run this script using the following commands:
python extract_leads_wfdb.py -i twelve_lead_directory -o two_lead_directory -l II V5
python extract_leads_wfdb.py -i twelve_lead_directory -o three_lead_directory -l I II V2
python extract_leads_wfdb.py -i twelve_lead_directory -o six_lead_directory -l I II III aVL aVR aVF
Here, the -i argument gives the input folder, the -o argument gives the output folder, and the -l argument gives the leads.
Docker and similar platforms allow you to containerize and package your code with specific dependencies that you can run 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 subset of the training data.
If you have trouble running your code, then please try the follow steps to run the example code, which is known to work.
-
Create a folder
examplein your home directory with several subfolders.user@computer:~$ cd ~/ user@computer:~$ mkdir example user@computer:~$ cd example user@computer:~/example$ mkdir training_data test_data model test_outputs -
Download the training data from the Challenge website. Put some of the training data in
training_dataandtest_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). -
Download or clone this repository in your terminal.
user@computer:~/example$ git clone https://github.com/physionetchallenges/python-classifier-2021.git -
Build a Docker image and run the example code in your terminal.
user@computer:~/example$ ls model python-classifier-2021 test_data test_outputs training_data user@computer:~/example$ ls training_data/ A0001.hea A0001.mat A0002.hea A0002.mat A0003.hea ... user@computer:~/example$ cd python-classifier-2021/ user@computer:~/example/python-classifier-2021$ docker build -t image . Sending build context to Docker daemon 30.21kB [...] Successfully tagged image:latest user@computer:~/example/python-classifier-2021$ 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 root@[...]:/physionet# ls Dockerfile model test_data train_model.py extract_leads_wfdb.py README.md test_model.py helper_code.py requirements.txt test_outputs LICENSE team_code.py training_data root@[...]:/physionet# python train_model.py training_data model root@[...]:/physionet# python test_model.py model test_data test_outputs root@[...]:/physionet# exit Exit user@computer:~/example/python-classifier-2021$ cd .. user@computer:~/example$ ls test_outputs/ A0006.csv A0007.csv A0008.csv A0009.csv A0010.csv ...
Please see the PhysioNet/CinC Challenge 2021 webpage for more details. Please post questions and concerns on the Challenge discussion forum.