readme.txt

File Description:

utils.py: load dataset for given task; define some evaluation functions.

trainer.py: implement the policy; perform model training.

adaptive_trainer.py: similar to trainer.py, but trains the policy on gradually increasing data set.

synthetic.ipynb: generate synthetic data; choose supervised classifier and its corresponding hyper-parameters; get results reported in Table 1 of the paper.

checkerboard.ipynb: choose supervised classifier and its corresponding hyper-parameters; plot classification boundaries on original dataset and the sampled dataset with our proposed method as reported in Figure 1 of the paper.

page.ipynb: choose supervised classifier and its corresponding hyper-parameters on page dataset; apply typical data sampling methods to this dataset and the chosen classifier.

spam.ipynb: similar to page.ipynb but performs on the spam message dataset.

vehicle.ipynb: similar to page.ipynb but performs on the vehicle dataset.

vehicle.ipynb: similar to page.ipynb but performs on the creditcard dataset.