Fixed Budget Context Aware Active Learning

Setting

• OS: Ubuntu 18.04
• CPU: Core i7
• GPU: GTX 1080 Ti
• using Nvidia-docker

pip install -r requirements.txt

Datasets are listed in references in the paper．

how to use

If you only want to check the result

In "compare_input_domain.ipynb", you can see the difference in the result when the learning domain (artificial data) is changed. In "compare_methods.ipynb", you can see the difference of the result by the method. Also, in comparison with oracle, "result / ral_compare_oracle ..." is the result of the proposed method, "result / oracle_random ..." is the result of random, and "oracle_trip ..." is the result of oracle. So you can check there.

If you want to do an experiment

In order to carry out a follow-up experiment, an overview of each file is given.

• artificial_data_maker.py

Generate artificial data. The data for learning and the data for testing are generated here. First of all.

• learning.py

This is the code for model learning.

• artificial_data_test, real_world_data_test, oracle_test

These are codes for performing experiments with artificial data, experiments with real data, and experiments comparing with Oracle, respectively. The result is output to "result/".

• compare

The code of the comparison method is arranged.

"integrate.py" can output the results of all comparison methods.