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This is a source code for AAAI 2019 paper Classification with Costly Features using Deep Reinforcement Learning wrote by Jaromír Janisch, Tomáš Pevný and Viliam Lisý: paper / slides / poster / code / blog

Updated version available: There is an enhanced version of the article under name Classification with Costly Features as a Sequential Decision-Making Problem (paper), which analyzes more settings (hard budget, lagrangian optimization of lambda and missing features). The code is available in the lagrange branch of this repository.

Cite as:

@inproceedings{janisch2019classification,
  title={Classification with Costly Features using Deep Reinforcement Learning},
  author={Janisch, Jaromír and Pevný, Tomáš and Lisý, Viliam},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2019}
}

Prerequisites:

• cuda capable hardware
• ubuntu 16.04
• cuda 8/9
• python 3.6 (numpy, pandas, pytorch 0.4)

Usage:

• use tools tools/conv_*.py to prepare datasets; read the headers of those files; data is expected to be in ../data
• pretrained HPC models are in trained_hpc, or you can use tools/hpc_svm.py to recreate them; they are needed in ../data
• run python3.6 main.py --dataset [dataset] --flambda [lambda] --use_hpc [0|1] --pretrain [0|1], choose dataset from config_datasets/
• the run will create multiple log files run*.dat
• you can use octave or matlab to analyze them with tools/debug.m
• you can also evaluate the agent on the test set with eval.py --dataset [dataset] --flambda [lambda]