Distribution Guided Active Feature Acquisition

This is the official code repo for Distribution Guided Active Feature A cquisition.

Code Structure

• datasets: define the datasets and dataloader
• envs: environment for RL based acquisition
  • img: image data acquisition with a fixed budget
  • img_term: image data acquisition with a given cost for each feature
  • vec: vector data acquisition with a fixed budget
  • vec_term: vector data acquisition with a given cost for each feature
• models: surrogate model and runner
  • acflow: ACFlow for 2D images
  • actan: ACFlow for 1D vectors
  • acflow_classifier: class conditioned ACFlow for 2D images
  • actan_classifier: class conditioned ACFlow for 1D vectors
  • runner: train and evaluate the model
  • model_wrapper: model wrapper for RL agent
  • group: action space grouping using a trained model
• detectors: OOD detector
  • PO3D: partially observed OOD detector
  • po3d_wrapper: detector wrapper for RL agent
• agents: AFA agents
  • img_cls_rand: random policy for image classification
  • img_cls_ppo: PPO policy for image classification
  • img_cls_hppo: hierarchical PPO policy with action space grouping
  • img_cls_hgcppo: sub-goal conditioned policy (sub-goal is a pair of classes)
  • img_cls_hgkppo: sub-goal conditioned policy (sub-goal is a set of clusters)
  • img_rec_rand: random policy for image reconstruction
  • img_rec_ppo: PPO policy for image reconstruction
  • img_rec_hppo: hierarchical PPO policy with action space grouping
  • img_rec_hgkppo: sub-goal conditioned policy (sub-goal is a set of clusters)
  • vec_cls_ppo; PPO policy for vector data classification
  • vec_cls_hppo: hierarchical PPO policy with action space grouping
  • vec_cls_gkppo: sub-goal conditioned policy (sub-goal is a set of clusters)
• scripts: running scripts
  • run_model: train the surrogate model
  • run_group: action space grouping
  • run_agent: training the AFA agent
• utils: utility functions

Requirements

scikit-learn
tensorflow-gpu==1.14.0
tensorflow-probability==0.7.0

Usage

Please see exp folder for examples of the config files.

Train the Surrogate Model

python scripts/run_model.py --cfg_file=path/to/config --mode=train

Action Space Grouping

python scripts/run_group.py --cfg_file=path/to/config

Train OOD Detector

see `detectors/PO3D/README.md`

Train GMM Model

use `sklearn`, see a code snippet in `exp/mnist/gmm.run.py`

Train the AFA Agent

python scripts/run_agent --cfg_file=path/to/config --mode=train