utsavrai/NHS-AKI-Detection-Model-Training

PythonMIT

NHS-AKI-Detection-Model-Training

Instructions

Prerequisites:

Before you begin, ensure you have installed the required packages as specified in requirements.txt.

Steps:

To install package dependencies, execute:
```
pip install -r requirements.txt
```

Running `train.py`:

This script is responsible for training the machine learning model.

Steps:

Make sure you are in the directory containing both the train.py file and training.csv.
Run the script with the command:
```
python3 train.py
```
- This will process the training data from training.csv, train the model, and save it as rf_model.joblib.
- If your training data file has a different name, adjust the script accordingly.

Running `test_utils.py` (Unit Tests):

These tests aim to verify the functionality of utilities within the project.

Steps:

Ensure you are in the directory that contains the test_utils.py file.
Run the unit tests with:
```
python3 -m unittest test_utils.py
```
- This will execute all test cases in test_utils.py, confirming that the functions in utils.py operate correctly.

Running `model.py`:

model.py can be executed using Docker or directly on your local machine.

Docker Method:

Be in the directory containing the Dockerfile.
Build the Docker image using:
```
docker build -t aki .
```
Run the Docker container, ensuring the current directory is mounted to /data inside the container:
```
docker run -v ${PWD}:/data aki
```

Local Method:

Go to the directory containing model.py.
Execute the script with:
```
python3 model.py
```