A Python library to train machine learning models for defect prediction of infrastructure code.
Project description
radon-defect-prediction
The RADON command-line client for Infrastructure-as-Code Defect Prediction.
How to Install
From source code:
git clone https://github.com/radon-h2020/radon-defect-prediction.git
cd radon-defect-predictor
pip install .
Quick Start
usage: radon-defect-predictor [-h] [-v] {train,predict,model} ...
A Python library to train machine learning models for defect prediction of infrastructure code
positional arguments:
{train,predict,model}
train train a brand new model from scratch
model get a pre-trained model to test unseen instances
predict predict unseen instances
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
How to build Docker container
docker build --tag radon-dp:latest .
How to run Docker container
First, create a host volume to share data and results between the host machine and the Docker container:
mkdir /tmp/radon-dp-volume/
Train
Create a training dataset metrics.csv
and copy/move it to /tmp/radon-dp-volume/
.
See how to generate the training data for defect prediction here.
Run:
docker run -v /tmp/radon-dp-volume:/app radon-dp:latest radon-defect-predictor train metrics.csv ...
See the docs for more details about this command.
The built model can be accessed at /tmp/radon-dp-volume/radon_modoel.joblib
.
See the Docs for details and examples of usage.
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