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context("scipy sparse matrix")
library(methods)
library(Matrix)
# https://github.com/r-lib/testthat/issues/1556
if (!inherits("t", "standardGeneric"))
setGeneric("t")
check_matrix_conversion <- function(r_matrix, python_matrix) {
# check that the conversion to python works
expect_true(all(py_to_r(python_matrix$toarray()) == as.matrix(r_matrix)))
# check that the conversion to r works
expect_true(all(py_to_r(python_matrix) == r_matrix))
# check that S3 methods work
expect_equal(dim(python_matrix), dim(r_matrix))
expect_equal(length(python_matrix), length(r_matrix))
}
test_that("Conversion to scipy sparse matrix S3 methods behave with null pointers", {
skip_on_cran()
skip_if_no_scipy()
N <- 1000
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
result <- r_to_py(x)
temp_file <- file.path(tempdir(), "sparse_matrix.rds")
saveRDS(result, temp_file)
result <- readRDS(temp_file)
# check that S3 methods behave with null pointers
expect_true(is(result, "scipy.sparse.csc.csc_matrix") || is(result, "scipy.sparse._csc.csc_matrix"))
expect_true(is.null(dim(result)))
expect_true(length(result) == 0L)
file.remove(temp_file)
})
test_that("Conversion between Matrix::dgCMatrix and Scipy sparse CSC matrix works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1000
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
result <- r_to_py(x)
# check that we are testing the right classes
expect_true(is(result, "scipy.sparse.csc.csc_matrix") || is(result, "scipy.sparse._csc.csc_matrix"))
expect_true(is(py_to_r(result), "dgCMatrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion between a small Matrix::dgCMatrix and Scipy sparse CSC matrix works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
result <- r_to_py(x)
# check that we are testing the right classes
expect_true(is(result, "scipy.sparse.csc.csc_matrix") || is(result, "scipy.sparse._csc.csc_matrix"))
expect_true(is(py_to_r(result), "dgCMatrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion between Matrix::dgRMatrix and Scipy sparse CSR matrix works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1000
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
x <- as(x, "RsparseMatrix")
result <- r_to_py(x)
# check that we are testing the right classes
expect_true(is(result, "scipy.sparse.csr.csr_matrix") || is(result, "scipy.sparse._csr.csr_matrix"))
expect_true(is(py_to_r(result), "dgRMatrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion between a small Matrix::dgRMatrix and Scipy sparse CSR matrix works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
x <- as(x, "RsparseMatrix")
result <- r_to_py(x)
# check that we are testing the right classes
expect_true(is(result, "scipy.sparse.csr.csr_matrix") || is(result, "scipy.sparse._csr.csr_matrix"))
expect_true(is(py_to_r(result), "dgRMatrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion between Matrix::dgTMatrix and Scipy sparse COO matrix works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1000
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
x <- as(x, "TsparseMatrix")
result <- r_to_py(x)
# check that we are testing the right classes
expect_true(is(result, "scipy.sparse.coo.coo_matrix") || is(result, "scipy.sparse._coo.coo_matrix"))
expect_true(is(py_to_r(result), "dgTMatrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion between a small Matrix::dgTMatrix and Scipy sparse COO matrix works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
x <- as(x, "TsparseMatrix")
result <- r_to_py(x)
# check that we are testing the right classes
expect_true(is(result, "scipy.sparse.coo.coo_matrix") || is(result, "scipy.sparse._coo.coo_matrix"))
expect_true(is(py_to_r(result), "dgTMatrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion between Scipy sparse matrices without specific conversion functions works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1000
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
result <- r_to_py(x)$tolil()
# check that we are testing the right classes
expect_true(is(result, "scipy.sparse.lil.lil_matrix") || is(result, "scipy.sparse._lil.lil_matrix"))
expect_true(is(py_to_r(result), "dgCMatrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion between R sparse matrices without specific conversion functions works", {
skip_on_cran()
skip_if_no_scipy()
N <- 1000
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
# symmetrize
x <- x + t(x)
x <- as(x, "symmetricMatrix")
result <- r_to_py(x)
# check that we are testing the right classes
expect_true(is(x, "dsCMatrix"))
expect_true(is(result, "scipy.sparse.csc.csc_matrix") || is(result, "scipy.sparse._csc.csc_matrix"))
check_matrix_conversion(x, result)
})
test_that("Conversion with unsorted values works in csc", {
skip_on_cran()
skip_if_no_scipy()
sp <- import("scipy.sparse", convert = FALSE)
# Test data
indices <- c(1L, 0L, 2L, 1L, 0L)
indptr <- c(0L, 3L, 5L)
data <- c(2, 1, 3, 5, 4)
# create csr matrix and try to convert
mat_py <- sp$csc_matrix(
tuple(
np_array(data),
np_array(indices),
np_array(indptr),
convert = FALSE
),
shape = c(3L, 2L)
)
mat_py_to_r <- py_to_r(mat_py)
mat_r <- Matrix::sparseMatrix(
i = indices + 1,
p = indptr,
x = data,
dims = c(3L, 2L)
)
expect_equal(as.matrix(mat_py_to_r), as.matrix(mat_r))
})
test_that("Conversion with unsorted values works in csr", {
skip_on_cran()
skip_if_no_scipy()
sp <- import("scipy.sparse", convert = FALSE)
# Test data
indices <- c(1L, 0L, 2L, 1L, 0L)
indptr <- c(0L, 3L, 5L)
data <- c(2, 1, 3, 5, 4)
# create csr matrix and try to convert
mat_py <- sp$csr_matrix(
tuple(
np_array(data),
np_array(indices),
np_array(indptr),
convert = FALSE
),
shape = c(2L, 3L)
)
mat_py_to_r <- py_to_r(mat_py)
mat_r <- Matrix::sparseMatrix(
j = indices + 1,
p = indptr,
x = data,
dims = c(2L, 3L)
)
expect_equal(as.matrix(mat_py_to_r), as.matrix(mat_r))
})
test_that("Conversion with unsorted values works in coo", {
skip_on_cran()
skip_if_no_scipy()
sp <- import("scipy.sparse", convert = FALSE)
# Test data
row <- c(1L, 0L, 2L, 1L, 0L)
col <- c(1L, 0L, 0L, 0L, 1L)
data <- c(5, 1, 3, 2, 4)
# create csr matrix and try to convert
mat_py <- sp$coo_matrix(
tuple(
np_array(data),
tuple(
np_array(row),
np_array(col)
),
convert = FALSE
),
shape = c(3L, 2L)
)
mat_py_to_r <- py_to_r(mat_py)
mat_r <- as.matrix(Matrix::sparseMatrix(
i = row + 1,
j = col + 1,
x = data,
dims = c(3L, 2L)
))
dimnames(mat_r) <- NULL
expect_equal(as.matrix(mat_py_to_r), mat_r)
})
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