The Source Code for SOSIA (ICDE 2024)

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Introduction

This is a source code for the algorithm described in the paper [Efficient Approximate Maximum Inner Product Search over Sparse Vectors (Submitted to ICDE 2024)]. We call it as sos project.

Compilation

The sos project is written by C++ (under C++17 standard) and is simple and easy to use. It can be complied by g++ in Linux or MSVC in Windows. To completely support C++17 standard, the g++ version is suggested to be version 9 and MSVC version is suggested to be at least MSVC 19.15 (Visual Studio 2017 15.8).

Installation

Windows

We can use Visual Studio 2019 (Other version of Visual Studio should also work but remains untested) to build the project with importing all the files in the directory ./src/.

Linux

make

The excutable file is then in the main directory, called as sos

Command Usage

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./sos mode datasetName queriesName

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(the first parameter specifies the procedure be executed and change)

FOR EXAMPLE, YOU CAN RUN THE FOLLOWING CODE IN COMMAND LINE AFTER BUILDING ALL THE TOOLS:

cd .
make
./sos 0 base_small queries

Parameter explanation

• mode : 0-4, an integer, the running mode. mode 0 runs the sos in the default parameters; mode 1 runs the sos by varying the base l; mode 2 runs the sos by varying the number of minHash functions m; mode 3 runs the sos by varying the k of top-k results; mode 4 runs the sos for obtaining the recall-time curves.
• datasetName : the dataset file name
• queries : the query file name

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Dataset

In our project, the format of the input file (such as base_small.csr) is a binary file but not a text file, because binary file has many advantages. The binary file is organized as the following format:

{nrow(int64)} {ncol(int64)}{nnz(int64)} {indptr[0:(nrow)](int64)} {indices[0:nnz-1](int32)} {data[0:nnz-1](float32)}

Parameter explanation

• nrow : a positive integer, the cardinality of the dataset
• ncol : a positive integer, the dimensionality of the dataset
• nnz : a positive integer, the number of non-zero values in the datasets
• indptr : an array with size nrow+1. indptr[i] stores the number of non-zero values of the first i-1 points in the dataset. indptr[0]=0
• indices : an array with size nnz. indices stores all the non-zero indices in the dataset by the order of points
• data : an array with size nnz. indices stores all the non-zero values in the dataset by the order of points

FOR EXAMPLE, for the dataset $D={x_0,x_1}$ where $x_0=(0.1,0.2,0)$ and $x_1=(0.3,0,0.5)$, nrow=2, ncol=3 and nnz=4. indptr=[0,2,4], indices=[0,1,0,2] and data=[0.1,0.2,0.3,0.5]. So, the input file should be as following:

2(int64)3(int64)4(int64)0(int64)...0.5(float32)

The input file is read via C++ as following. You can understand the data format via this C++ function.

//Load Sparse Data
void Preprocess::load_data(const std::string& path, SparseData* sd)
{
	std::ifstream in(path.c_str(), std::ios::binary);
	if (!in) {
		std::cout << "Fail to open the file:\n" << path << "!\n";
		exit(-10086);
	}


	size_t header[3] = {};
	in.read((char*)header, sizeof(header));

	size_t nrow = header[0];
	size_t ncol = header[1];
	size_t nnz = header[2];

	sd->dim = ncol;
	sd->n = nrow;
	sd->nnz = nnz;

	sd->indptr = new size_t[nrow + 1];
	in.read((char*)sd->indptr, sizeof(size_t) * (nrow + 1));

	sd->indices = new int[nnz];
	in.read((char*)sd->indices, sizeof(int) * nnz);

	sd->val = new float[nnz];
	in.read((char*)sd->val, sizeof(float) * nnz);

	std::cout << "Load the sparse data from the file: " << path << "\n";
	sd->showInfo();
	in.close();
}

A sample dataset base_small and queries.dev.csr have been put in the directory ./datasets. Also, you can download them from here and here.

For your application, you should also transform your dataset and queries into this binary format, then rename them as [datasetName].csr and [queries].dev.csr. Then, put them in the directory ./datasetss.

Download the datasets

There are 3 datasets that can be downloaded from the internet, which are base_small, base_1M and base_full. base_1M and base_full are used in the experiments in our paper (SPLADE1M and SPLADE-Full). To download them, we can follow the guidance below (Take base_1M as example):

Windows

We can download base_1M and the query sets directly from here and here via browser and then unzip them.

Linux

mkdir datasets
cd datasets
rm *
wget https://storage.googleapis.com/ann-challenge-sparse-vectors/csr/queries.dev.csr.gz
wget https://storage.googleapis.com/ann-challenge-sparse-vectors/csr/base_1M.csr.gz
gunzip *.gz
cd ..

Reproduction in Linux

To quickly reproduce the results in our paper, a one-click shell run.sh is prepared. We can run it as following:

cd .
./run.sh datasetID

datasetID can be 0, 1 or 2, which represents the datasets base_small, base_1M and base_full, respectively.

Results

The experimental result is saved in the directory ./results/ as the file Running_result.txt.

Reference

Efficient Approximate Maximum Inner Product Search over Sparse Vectors (Submitted to ICDE 2024)