Forming classifier ensembles with deterministic feature subspaces
Python code for forming classifier ensembles with deterministic feature subspace approach. Contains deterministic subspace classifier compatible with scikit-learn interface and tools necessary to easily repeat conducted experiments.
More details about the method, results of experiments and related papers can be found at mkoziarski.com/deterministic-feature-subspace-method.
Tested on Python 2.7.9. Remaining packages used are enclosed in requirements.txt.
To download necessary datasets and create databases in which results will be stored go to main project directory and execute
Results reported in last paper were obtained on precalculated folds, enclosed in this repository. If you want to evaluate different set of folds, you can run
Experiment was designed to be run from several processes at once. Because of that queue of pending trials has to be filled first.
By default, all trials described in the last paper will be run. If you want to change that, you can modify schedule_experiment.py accordingly. After that, execute
Main script will try to pull pending trials and evaluate them as long as they are present.
Exporting results to CSV file
After the experiment is done, you can convert results to CSV format by running
python -c 'import database; database.export()'