fastMatMR
fastMatMR
provides R bindings for reading and writing to Matrix
Market files using the
high-performance fast_matrix_market C++
library (version
1.7.4).
Matrix Market files
are crucial to much of the data-science ecosystem. The fastMatMR
package focuses on high-performance read and write operations for Matrix
Market files, serving as a key tool for data extraction in computational
and data science pipelines.
The target audience and scientific applications primarily include data scientists or researchers developing numerical methods who may wish to either test standard NIST (National Institute of Standards and Technology) which include:
comparative studies of algorithms for numerical linear algebra, featuring nearly 500 sparse matrices from a variety of applications, as well as matrix generation tools and services.
Additionally, being able to use the matrix market file format, means it
is easier to interface R
analysis with those in Python
(e.g. SciPy
uses the same underlying C++
library). These files can also be used
with the Tensor Algebra
Compiler (TACO).
Extended Support: fastMatMR
supports standard R vectors,
matrices, as well as Matrix
sparse objects.
Performance: The package is a thin wrapper around one of the
fastest C++ libraries for reading and writing .mtx
files.
Correctness: Unlike Matrix
, roundtripping with NA
and NaN
values works by coercing to NaN
instead of to arbitrarily high
numbers.
We have vignettes for both read and write operations to demonstrate the performance claims.
Matrix
package allows reading and writing sparse matrices in the
.mtx
(matrix market) format..mtx
files, it can only handles sparse matrices for
writing and reading.NA
and
NaN
values produces arbitrarily high numbers instead of preserving
NaN
/ handling NA
For the latest CRAN
version:
install.packages("fastMatMR")
For the latest development version of fastMatMR
:
install.packages("fastMatMR",
repos = "https://ropensci.r-universe.dev")
For the latest commit, one can use:
# install.packages("devtools")
devtools::install_github("ropensci/fastMatMR")
library(fastMatMR)
spmat <- Matrix::Matrix(c(1, 0, 3, 2), nrow = 2, sparse = TRUE)
write_fmm(spmat, "sparse.mtx")
fmm_to_sparse_Matrix("sparse.mtx")
The resulting .mtx
file is language agnostic, and can even be read
back in python
as an example:
pip install fast_matrix_market
python -c 'import fast_matrix_market as fmm; print(fmm.read_array_or_coo("sparse.mtx"))'
((array([1., 3., 2.]), (array([0, 0, 1], dtype=int32), array([0, 1, 1], dtype=int32))), (2, 2))
python -c 'import fast_matrix_market as fmm; print(fmm.read_array("sparse.mtx"))'
array([[1., 3.],
[0., 2.]])
Similarly, fastMatMR
supports writing and reading from other R
objects (e.g. standard R vectors and matrices), as seen in the getting
started
vignette.
Contributions are very welcome. Please see the Contribution Guide and our Code of Conduct.
This project is licensed under the MIT License.
The logo was generated via a non-commercial use prompt on hotpot.ai, both blue, and green, as a riff on the NIST Matrix Market logo. The text was added in a presentation software (WPS Presentation). Hexagonal cropping was accomplished in a hexb compatible design using hexsticker.
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