BayesCard

Environment setup

The following command using conda should setup the environment in linux CentOS.

conda env create -f environment.yml

If not, you need to manually download the following packages Required dependence: numpy, scipy, pandas, Pgmpy, pomegranate, networkx, tqdm, joblib, pytorch, psycopg2, scikit-learn, spflow, tables

Additional dependence: numba, bz2, Pyro (These packages are not required to reproduce the result in the paper.)

Dataset download:

The optimal trained models for each dataset are already stored. If you are only interested in verifying the paper's result, you can skip the dataset download and model training, and directly execute the evaluate the learnt model.

DMV dataset: The DMV dataset is publically available at catalog.data.gov. The data is continuously updated but there shouldn't be any shift in distribution. If you would like to reproduce the exact result as in paper, please contact the author for the snapshot that he downloaded.
Census dataset: The census dataset is publically available on UCI website: https://archive.ics.uci.edu/ml/datasets/US+Census+Data+%281990%29.
IMDB dataset: The imdb dataset can be downloaded here: http://homepages.cwi.nl/~boncz/job/imdb.tgz

Reproducing DMV result:

In order to reproduce the single table result for DMV, First run the following command to train the model

python run_experiment.py --dataset dmv
       --generate_models
       --csv_path .../DMV/DMV.csv
       --model_path Benchmark/DMV
       --learning_algo chow-liu
       --max_parents 1
       --sample_size 200000

model_path specifies the location to save the model

csv_path points the dataset you just downloaded

algo: one can choose between chow-liu, greedy, exact, junction. Expect for chow-liu, other methods contain a large amount of randomness, so not garuantee to reproduce the exactly same result as paper.

Then, evaluate the learnt model

python run_experiment.py --dataset dmv
       --evaluate_cardinalities
       --model_path Benchmark/DMV/chow-liu_1.pkl
       --query_file_location Benchmark/DMV/query.sql
       --infer_algo exact-jit

infer_algo: one can choose between exact, exact-jit, exact-jit-torch, progressive_sampling, BP and sampling. I'm current working on BP's optimization, so there might be some unexpected bugs.

Reproducing Census result:

Similar to DMV, first train the model

python run_experiment.py --dataset census
       --generate_models
       --csv_path ../Census/Census.csv
       --model_path Benchmark/Census
       --learning_algo chow-liu
       --max_parents 1
       --sample_size 200000

Then, evaluate the learnt model

python run_experiment.py --dataset census
       --evaluate_cardinalities
       --model_path Benchmark/Census/chow-liu_1.pkl
       --query_file_location Benchmark/Census/query.sql
       --infer_algo exact-jit

Reproducing stability and scalability experiment on synthetic datasets:

Please refer to jupyter notebook: Testing/stability_experiment.ipybn, which contains the step-by-step guide to reproduce the result. Other notebooks in that directory are for debug purposes only.

Reproducing IMDB results:

First prepare the data, i.e. adding some fanout columns

python run_experiment.py --dataset imdb 
       --generate_hdf 
       --csv_path ../imdb-benchmark
       --hdf_path ../imdb-benchmark/gen_hdf

Then train a Bayescard ensemble of BNs

python run_experiment.py --dataset imdb 
       --generate_models
       --hdf_path ../imdb-benchmark/gen_hdf
       --model_path Benchmark/IMDB
       --learning_algo chow-liu
       --max_parents 1
       --sample_size 200000

Evaluate the learnt Bayescard

python run_experiment.py --dataset imdb 
       --evaluate_cardinalities
       --model_path Benchmark/IMDB
       --query_file_location Benchmark/IMDB/job-light.sql
       --learning_algo chow-liu
       --max_parents 1
       --infer_algo exact-jit

If you find the code helpful, please cite our paper:

@article{wu2020bayescard,
 title={BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation},
 author={Wu, Ziniu and Shaikhha, Amir and Zhu, Rong and Zeng, Kai and Han, Yuxing and Zhou, Jingren},
 journal={arXiv preprint arXiv:2012.14743},
 year={2020}
 }