RECON

This repo contains the codes for the VLDB 2023 paper Coresets over multiple tables for feature-rich and data-efficient machine learning.

Quick Start

Folder Structure

.
├── preprocess              # Data preprocessing codes
├── RECON                   # RECON codes for coreset construction
├── MLModel                 # ML models training codes to test the performance of RECON
├── linear-universal.py     # Evaluation of regression models
├── logistic-universal.py   # Evaluation of classification models
└── README.md               

Requirements

Before running the codes, please make sure your C++ version is above C++14. Library cnpy is also needed to save results in the format of npz.

The dataset path is configured by variable DATAPATH (line 9 in global.h), which should also be configured properly before running the codes. The datasets can be downloaded from dataset link.

• Python 3.7+

• C++14

• cnpy: a library to read/write .npy and .npz files in C/C++ link

Usage

RECON on IMDB / IMDB-Large:

First build ./RECON by:

• cd RECON

• cmake .

• make

and then perform RECON on different datasets by passing different arguments.

parameter setting:

[dataName] [proportion] [0:IMDB 1:IMDB-Large] [0:Classification 1:Regression]

• IMDB, p=0.0128 for classification: ./RECON IMDB 0.0128 0 0
• IMDB, p=0.0032 for regression: ./RECON IMDB 0.0032 0 1
• IMDB-Large, p=0.0016 for classification: ./RECON IMDB 0.0016 1 0
• IMDB-Large, p=0.0016 for regression ./RECON IMDB 0.0016 1 1

RECON on stack / Brazil / taxi:

parameter setting:

[dataName] [proportion]

• stack, p=0.0032: ./RECON stack 0.0032
• Brazil, p=0.0016: ./RECON Brazil 0.0016
• taxi, p=0.0032: ./RECON taxi 0.0032

Note: '-L/usr/local/lib/ -lcnpy -lz' may also need to be added to the program arguments, which depends on the method to install cnpy.

Note: Before running RECON, make sure the variable DATAPATH (line 9 in global.h) is configured as the path of dataset. Besides, make sure the vaiable CSPATH (line 10 in gloabl.h) is configured as the location to save RECON's output, i.e., coresets.

Training Logistic Regression

Run logsitic-universal.py to train logistic regression models.

• IMDB: python logistic-universal.py --data IMDBC5 --method sgd -s 0.0128

• IMDB-Large: python logistic-universal.py --data IMDBLargeC5 --method sgd -s 0.0016

• Brazil: python logistic-universal.py --data Brazilnew --method sgd -s 0.0016

Training Linear Regression

Run linear-universal.py to train linear regression models.

• IMDB: python linear-universal.py --data IMDBCLinear --method sgd -s 0.0032

• IMDB-Large: python linear-universal.py --data IMDBLargeCLinear --method sgd -s 0.0016

• stack: python linear-universal.py --data stackn --method sgd -s 0.0032

• taxi: python linear-universal.py --data taxi --method sgd -s 0.0032

Note: Before training models, make sure variable DATAPATH (line 1 in Global.py) is configured as the path of datasets. And CSPATH(line 2 in Global.py) is configured as the path to RECON's output (path of coreset).

Other Baselines

• Sample-Join: The argument --greedy [0:Uniform Sampling 1:Coreset (default)] specifies the subset for training. Sample-Join can be achieved by setting --greedy 0. For example, to train a logistic regression model on a uniform sampling of IMDB, you may use:

python logistic-universal.py --data IMDBC5 --method sgd -s 0.0128 --greedy 0

• Full: Full can be achieved by setting -s to 1 on top of Sample-Join. For example, to train a logistic regression model using full data of IMDB, you may use:

python logistic-universal.py --data IMDBC5 --method sgd -s 1 --greedy 0

• Coreset-Join and Join-Coreset: You can find their official implementations from link.

Data Preprocessing

In general, our preprocessing of each dataset in the preprocess directory can be summarized as data cleaning, normalization, and partition by label. We provide the preprocessed data in dataset link. The raw datasets can be found in their original sources.

License

The project is available under the MIT license.

Citation

If our work is helpful to you, please cite our paper:

@article{wang2022coresets,
  title={Coresets over multiple tables for feature-rich and data-efficient machine learning},
  author={Wang, Jiayi and Chai, Chengliang and Tang, Nan and Liu, Jiabin and Li, Guoliang},
  journal={Proceedings of the VLDB Endowment},
  volume={16},
  number={1},
  pages={64--76},
  year={2022},
  publisher={VLDB Endowment}
}