Metadata-Version: 2.1
Name: radon-defect-predictor
Version: 0.2.7
Summary: A Python library to train machine learning models for defect prediction of infrastructure code.
Home-page: https://github.com/radon-h2020/radon-defect-predictor
Author: Stefano Dalla Palma
Author-email: stefano.dallapalma0@gmail.com
Maintainer: Stefano Dalla Palma
License: UNKNOWN
Download-URL: https://github.com/radon-h2020/radon-defect-predictor/archive/0.2.7.tar.gz
Description: ![Build](https://github.com/radon-h2020/radon-defect-predictor/workflows/Build/badge.svg)
        ![Documentation](https://github.com/radon-h2020/radon-defect-predictor/workflows/Documentation/badge.svg)
        ![LGTM Grade](https://img.shields.io/lgtm/grade/python/github/radon-h2020/radon-defect-predictor)
        ![pypi-version](https://img.shields.io/pypi/v/radon-defect-predictor)
        ![pypi-status](https://img.shields.io/pypi/status/radon-defect-predictor)
        ![release-date](https://img.shields.io/github/release-date/radon-h2020/radon-defect-prediction-cli)
        ![python-version](https://img.shields.io/pypi/pyversions/radon-defect-predictor)
        
        # radon-defect-prediction
        The RADON command-line client for Infrastructure-as-Code Defect Prediction.
        
        
        ## How to Install
        
        From [PyPI](https://pypi.org/project/radon-defect-predictor/):
        
        `pip install radon-defect-predictor`
        
        From source code:
        
        ```text
        git clone https://github.com/radon-h2020/radon-defect-prediction.git
        cd radon-defect-predictor
        pip install -r requirements.txt
        pip install .
        ```
        
        ## Quick Start
        
        ```text
        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](https://radon-h2020.github.io/radon-repository-miner/cli/metrics/). 
        
        Run:
        
        `docker run -v /tmp/radon-dp-volume:/app radon-dp:latest radon-defect-predictor train metrics.csv ...`
        
        See the [docs](https://radon-h2020.github.io/radon-defect-prediction-cli/cli/train/) for more details about this command. 
        
        The built model can be accessed at `/tmp/radon-dp-volume/radondp_model.joblib`.
        
        
        
        ## Model
        
        Run:
        
        `docker run -v /tmp/radon-dp-volume:/app radon-dp:latest radon-defect-predictor download-model ...`
        
        See the [docs](https://radon-h2020.github.io/radon-defect-prediction-cli/cli/model/) for more details about this command. 
        
        The downloaded model can be accessed at `/tmp/radon-dp-volume/radondp_model.joblib`.
        
        
        
        ## Predict
        
        Move the model and the files to predict in the shared volume.
        For example, if you want to run the prediction on a .csar, then
        
        `cp patah/to/file.csar /tmp/radon-dp-volume`.
        
        Alternatively, you can create a volume from the folder containing the .csar (in that case, make sure to move the model within it).
        
        Run:
        
        `docker run -v /tmp/radon-dp-volume:/app radon-dp:latest radon-defect-predictor predict ...`
        
        See the [docs](https://radon-h2020.github.io/radon-defect-prediction-cli/cli/predict/) for more details about this command. 
        
        The predictions can be accessed at `/tmp/radon-dp-volume/radondp_predictions.json`.
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
