Public repository for "Shapley-Detect: A Novel Approach for Robust Drift Detection in Multivariate Dynamic Environments"

Setup

Use poetry python package manager to install the correct dependencies :

 python3 -m venv ./venv
 source venv/bin/activate
 python3 -m pip install -r requirements.txt

Then you will need to edit a .env file with by specifying the following path variables (replace YOUR_PATH by your current working directory):

 RESULTS_ROOT_PATH="YOUR_PATH/Shap-Adwin/results/"
 FIGURES_PATH="YOUR_PATH/Shap-Adwin/figures/"
 DATA_PATH="YOUR_PATHShap-Adwin/data/"

Important files

./src/notebooks/bench.ipynb : generates the results used in the paper;
./src/notebooks/display_results.ipynb : generates the figures and results vizualisations used in the paper ;

To add a new dataset place your dataset in .csv file in the /data/ and then do exactly the same as with sine1 or sine2 or stagger case.

Use a table of content extension to navigate the notebooks. They are quite lengthy and most of the time you only need to access a section s most sections isolated with proper markdowns cells are idependant you can go there and exectute the partial group of cells.
To run the notebook make sure you are using the virtual env previously created with the correct dependencies installed.