This project aims to make experiment for SDP much more easier. All the components are combined and examined the performance of every combinations.
- Python 3.5
- pip3
- (Optional)
virtualenv env -p python3&source env/bin/activate pip3 install -r requirements.txt- May have some errors while installing graphic related package on different system
cd srcpython3 run.py
- data
- Empty directory, synchronized data will be saved at here
- origin_data
- Original source of the dataset
- report
- Empty directory, generated report will be here
- src
- Source code
- setting.py
- Contains all the framework variables ( e.g. dataset path, selected dataset, selected methods, selected feature selection methods )
- run.py
- Entry module that trigger everyother modules to start the process
- dataset.py
- Handling dataset preprocessing
- feature.py
- Contain functions that take feature and label as input and return the data with selected metrics depending on different implementations
- models.py
- Core module of the framework that contain unsupervised methods for SDP. They are expected to take data as input, and output an onedimensional array which classify every entities. 1 as defective and 0 as nondefective