Computes Proximity in Large Sparse Matrices

Computes proximity between rows or columns of large matrices efficiently in C++. Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

proxyC computes proximity between rows or columns of large matrices efficiently in C++. It is optimized for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

This code was originally written for quanteda to compute similarity/distance between documents or features in large corpora, but separated as a stand-alone package to make it available for broader data scientific purposes.

install.packages("proxyC")

require(Matrix)
## Loading required package: Matrix
require(microbenchmark)
## Loading required package: microbenchmark
require(RcppParallel)
## Loading required package: RcppParallel
require(ggplot2)
## Loading required package: ggplot2
 
# Set number of threads
setThreadOptions(8)
 
# Make a matrix with 99% zeros
sm1k <- rsparsematrix(1000, 1000, 0.01) # 1,000 columns
sm10k <- rsparsematrix(1000, 10000, 0.01) # 10,000 columns
 
# Convert to dense format
dm1k <- as.matrix(sm1k) 
dm10k <- as.matrix(sm10k)

Cosine similarity between columns

With sparse matrices, proxyC is roughly 10 to 100 times faster than proxy.

bm1 <- microbenchmark(
    "proxyC 1k" = proxyC::simil(sm1k, margin = 2, method = "cosine"),
    "proxy 1k" = proxy::simil(dm1k, method = "cosine"),
    "proxyC 10k" = proxyC::simil(sm10k, margin = 2, method = "cosine"),
    "proxy 10k" = proxy::simil(dm10k, method = "cosine"),
    times = 10
)
autoplot(bm1)
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Top-10 cosine similarity

If rank is used, proxyC becomes even faster as many similarity scores are discarded (rounded to zero).

bm2 <- microbenchmark(
    "proxyC rank" = proxyC::simil(sm1k, margin = 2, method = "cosine", rank = 10),
    "proxyC all" = proxyC::simil(sm1k, margin = 2, method = "cosine"),
    times = 10
)
autoplot(bm2)
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Correlation greater than 0.9

min_simil also makes proxyC faster.

bm3 <- microbenchmark(
    "proxyC min_simil" = proxyC::simil(sm1k, margin = 2, method = "correlation", min_simil = 0.9),
    "proxyC all" = proxyC::simil(sm1k, margin = 2, method = "correlation"),
    times = 10
)
autoplot(bm3)
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.