Skip to content

Latest commit

 

History

History
84 lines (75 loc) · 3.64 KB

README.md

File metadata and controls

84 lines (75 loc) · 3.64 KB
Raw

Toward Optimal Probabilistic Active Learning Using a Bayesian Approach

Project Structure

  • data: contains .csv-files of data sets being not available at OpenML
  • data_set_ids.csv: contains the list of data sets with their IDs at OpenML
  • images: path where the visualizations of the results and utility plots will be saved
  • src: Python package consisting of several sub-packages
    • base: implementation of DataSet and QueryStrategy class
    • classifier: implementation of Parzen Window Classifier (PWC)
    • evaluation: scripts for experimental setup and SLURM
    • notebooks: jupyter notebooks for the investigation of the different query strategies
    • query_strategies: implementation of all query strategies
    • utils: helper functions

How to execute an experiment?

Due to the large number of experiments, we executed the experiments on a computer cluster consisting of four nodes with about 100 CPU units. Using these nodes, we were able to execute 100 experiments simultaneously. An exemplary script how showing how we executed the experiments on our computer cluster with SLURM is given by projectpath/src/evaluation/evaluate_competitive_strategies.sh.

Without such a computer cluster, it will probably take several days to reproduce all results of the article. Nevertheless, one can execute the experiments on a local personal computer by following the upcoming steps.

  1. Setup Python environment:
projectpath$ sudo apt-get install python3-pip
projectpath$ pip3 install virtualenv
projectpath$ virtualenv xpal
projectpath$ source xpal/bin/activate
projectpath$ pip3 install -r requirements.txt
  1. Get information about the available hyperparameters (argparse) for the experiments.
projectpath$ cd src/evaluation/
projectpath/src/evaluation$ python3 experimental_setup_csv.py -h
  1. Example experiment: To test xPAL on the dataset iris with:
    • a budget of 200 samples,
    • a test ratio of 40%,
    • the RBF kernel,
    • the mean criterion as bandwidth,
    • and using the seed 1,

we have to execute the following commands.

projectpath/src/evaluation$ python3 experimental_setup_csv.py \
  --query_strategy xpal-0.001 \
  --data_set iris \
  --results_path ../../results \
  --test_ratio 0.4 \
  --bandwidth mean \
  --budget 200 \
  --seed 1

The results are saved in the directory projectpath/results/ as a .csv-file. The names of the possible data sets are given in the file xpal/dat_set_ids.csv. The available kernels are: rbf, categorical, and cosine. To create the ranking plot, there must be at least one run for each strategy on a data set. For example, you could execute the above command with the different strategies by setting the query_strategy parameter to each of the following values: xpal-0.001, pal-1, lc, alce, qbc, zero-one-loss, random, qbc.

How to plot the experimental results?

Start jupyter-notebook and run the jupyter-notebook file xpal/src/notebooks/evaluation_csv.ipynb. Remark: The ranking plots can only be created when we have for each dataset and each strategy the same number of executed experiments.

projectpath$ source xpal/bin/activate
projectpath$ jupyter-notebook

How to reproduce the utility plots?

Start jupyter-notebook and run the jupyter-notebook file xpal/src/notebooks/visualization.ipynb.

projectpath$ source xpal/bin/activate
projectpath$ jupyter-notebook

How to reproduce the runtime plots?

Start jupyter-notebook and run the jupyter-notebook file projectpath/src/projectpath/complexity.ipynb.

projectpath$ source xpal/bin/activate
projectpath$ jupyter-notebook