P2HNNS: Point-to-Hyperplane Nearest Neighbor Search

Welcome to the P2HNNS GitHub!

P2HNNS is a toolbox for the problem of Point-to-Hyperplane Nearest Neighbor Search (P2HNNS). Given a set of data points and a hyperplane query, the problem of P2HNNS aims to find the nearest data point to the hyperplane query. It has plenty of applications in large-scale active learning with SVMs, maximum margin clustering, large-margin dimensionality reduction, etc.

This toolbox provides the implementations and experiments of our work Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere in SIGMOD 2021. We also implement three state-of-the-art hyperplane hashing schemes (i.e., Embedding Hyperplane Hashing (EH), Bilinear Hyperplane Hashing (BH), and Multilinear Hyperplane Hashing (MH)) and two heuristic linear scan methods Random-Scan and Sorted-Scan.

Datasets and Queries

We choose five real-life datasets Yelp, Music-100, GloVe, Tiny-1M, and Msong for perfromance validation. For each dataset, we generate 100 hyperplane queries for evaluation. The statistics of datasets and queries are summarized as follows.

Datasets	#Data Objects	Dimensionality	#Queries	Data Size	Type
Yelp	77,079	50	100	14.7 MB	Rating
Music-100	1,000,000	100	100	381.5 MB	Rating
GloVe	1,183,514	100	100	451.5 MB	Text
Tiny-1M	1,000,000	384	100	1.43 GB	Image
Msong	992,272	420	100	1.55 GB	Audio

We also show their heat-maps of |cos 𝜃| and ∥o∥ and histograms in the following figure, where the histograms of |cos 𝜃| and ∥o∥ are depicted on the right and the top of the heat-map, respectively.

Compilation

This toolbox requires g++-8 with c++17 support. Before the compilation, please check whether the g++-8 is installed. If not, we provide a way to install g++-8 in Ubuntu 18.04 (or higher versions) as follows.

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install g++-8
sudo apt-get install gcc-8 (optional)

Users can use the following commands to compile the C++ source codes:

git clone git@github.com/HuangQiang/P2HNNS.git
cd P2HNNS/methods/
make -j

Usages

We provide bash scripts to reproduce all the experiments reported in our SIGMOD 2021 paper. Suppose you have cloned the project and you are in the folder P2HNNS/.

Step 1: Get the Datasets and Generate Hyperplane Queries

Please download the datasets and copy them to the directory data/original/. For example, when you get Msong.bin, please move it to the path data/original/Msong.bin.

Once you have finished copying the datasets to data/original/, you can get the datasets and generate the hyperplane queries from data/bin/ and data/bin_normalized/ with the following commands:

cd data/original/
bash run.sh

Currently, we also share the datasets and hyperplane queries for bin/ and bin_normalized/ via datasets. Users can download the datasets and copy them to data/.

Step 2: Reproduce Experiments

After preparing the datasets, users can reproduce all the experiments by simply typing the following commands:

cd methods
bash run_all.sh

In order to make a fair comparison for different methods and analyse the trade-off among the query accuracy, efficiency and indexing overhead, we run each method for each dataset using the grid search of their parameters. Thus, it might be time consuming to finish all the experiments. In our case, we spend at least one month to get the whole results. One can run the datasets one by one to save time and/or computing resource.

Step 3: Draw Figures

Finally, we provide python scripts (i.e., plot.py and plot_heatmap.py in scripts/) to reproduce all the figures that were appeared in our SIGMOD 2021 paper. These scripts require python 3.7 (or higher versions) with numpy, scipy, and matplotlib installed. If not, you might need to use anaconda to create a new virtual environment and use pip to install those packages.

With the experimental results from step 2, users can reproduce all the figures with the following commands.

cd scripts/
python3 plot.py
python3 plot_heatmap.py

cd scripts/
python plot.py
python plot_heatmap.py

Here we show the results on the five real-life datasets without and with normalization on data points as follows.

More results and analyses can be found in our SIGMOD 2021 paper.

Reference

Thank you so much for being so patient to read the user manual. We will appreciate using the following BibTeX to cite this work when you use P2HNNS in your paper.

@inproceedings{huang2021point,
  title={Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere},
  author={Huang, Qiang and Lei, Yifan and Tung, Anthony KH},
  booktitle={Proceedings of the 2021 International Conference on Management of Data (SIGMOD)},
  pages={777--789},
  year={2021}
}

It is welcome to contact me (huangq@comp.nus.edu.sg) if you meet any issue. Thank you.