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LSHKNN

CPython module for fast calculation of k nearest neighbor (KNN) graphs in high-dimensional vector spaces using Pearson correlation distance and local sensitive hashing (LSH).

The current application is analysis of single cell RNA-Seq data and is the result of a collaboration between Fabio Zanini (now @UNSW) and Paolo Carnevali @ Chan Zuckerberg Initiative, who is the owner of the algorithm code, which is also under MIT license:

https://github.com/chanzuckerberg/ExpressionMatrix2

Requirements

• A CPU with SSE 4.2 instructions (chances are you have it)
• A C++ 11/14 compiler, e.g. gcc 4.8 or later
• Python 3.4+ or 2.7
• eigen 3
• numpy
• setuptools
• pkg-config
• pkgconfig
• pybind11

Installation

(you may need superuser priviledges)

pip install lshknn

For the development version:

git clone https://github.com/iosonofabio/lshknn.git
cd lshknn
python setup.py install

Usage

import numpy as np
import lshknn

# Make mock data
# 2 features (rows), 4 samples (columns)
data = np.array(
        [[1, 0, 1, 0],
         [0, 1, 0, 1]],
        dtype=np.float64)

# Instantiate class
c = lshknn.Lshknn(
        data=data,
        k=1,
        threshold=0.2,
        m=10,
        slice_length=4)

# Call subroutine
knn, similarity, n_neighbors = c()

# Check result
assert (knn == [[2], [3], [0], [1]]).all()