Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection - Code and Data

<p>This repository contains the code and data for reproducibility of the paper 'Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection'.&nbsp;</p> <p>The following files are included:</p> <ul> <li>best_algo.csv : labels for the classification and median performance of algorithms;</li> <li>ML_models.ipyb : jupyter notebook with the definition of the neural networks for both classifiers;</li> <li>pickle.zip : pickled models for the hardness classification and the algorithm selector;</li> <li>trajectories.zip : raw data files containing parts of the trajectories of each algorithm;</li> <li>Results.zip : results obtained using the approach on the stream of instances.</li> </ul>