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tests for multiplex graphs #7
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@TomKellyGenetics
TomKellyGenetics committed Nov 23, 2020
1 parent 24944d9 commit 4b036df
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Showing 3 changed files with 318 additions and 8 deletions.
41 changes: 39 additions & 2 deletions R/leiden.R
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Original file line number Diff line number Diff line change
Expand Up @@ -127,12 +127,32 @@ leiden.matrix <- function(object,
laplacian = FALSE,
legacy = FALSE
) {
if(length(partition_type) > 1) partition_type <- partition_type[[1]][1]
partition_type <- match.arg(partition_type)

#import python modules with reticulate
numpy <- import("numpy", delay_load = TRUE)
leidenalg <- import("leidenalg", delay_load = TRUE)
ig <- import("igraph", delay_load = TRUE)

py_graph <- make_py_object(object, weights = weights)
#convert matrix input (corrects for sparse matrix input)
if(is.matrix(object) || is(object, "dgCMatrix")){
object <- object
} else{
object <- as.matrix(object)
}

#compute weights if non-binary adjacency matrix given
is_pure_adj <- all(as.logical(object) == object)
if (is.null(weights) && !is_pure_adj) {
if(!is.matrix(object)) object <- as.matrix(object)
#assign weights to edges (without dependancy on igraph)
t_mat <- t(object)
weights <- t_mat[t_mat!=0]
#remove zeroes from rows of matrix and return vector of length edges
}

py_graph <- make_py_graph(object, weights = weights)

#compute partitions
partition <- find_partition(py_graph, partition_type = partition_type,
Expand Down Expand Up @@ -176,6 +196,9 @@ leiden.Matrix <- function(object,
laplacian = FALSE,
legacy = FALSE
) {
if(length(partition_type) > 1) partition_type <- partition_type[[1]][1]
partition_type <- match.arg(partition_type)

#cast to sparse matrix
adj_mat <- as(object, "dgCMatrix")
#run as igraph object (passes to reticulate)
Expand Down Expand Up @@ -243,8 +266,14 @@ leiden.list <- function(object,
legacy = legacy
)
} else{
py_list <- r_to_py(lapply(object, make_py_graph))
#import python modules with reticulate
numpy <- reticulate::import("numpy", delay_load = TRUE)
leidenalg <- import("leidenalg", delay_load = TRUE)
ig <- import("igraph", delay_load = TRUE)

py_list <- r_to_py(lapply(object, function(r_graph){
make_py_graph(r_graph, weights = weights)
}))

if(partition_type == 'ModularityVertexPartition.Bipartite') partition_type <- "ModularityVertexPartition"
if(partition_type == 'CPMVertexPartition.Bipartite') partition_type <- "CPMVertexPartition"
Expand Down Expand Up @@ -294,6 +323,10 @@ leiden.igraph <- function(object,
if(length(partition_type) > 1) partition_type <- partition_type[[1]][1]
partition_type <- match.arg(partition_type)

#import python modules with reticulate
numpy <- reticulate::import("numpy", delay_load = TRUE)
leidenalg <- import("leidenalg", delay_load = TRUE)
ig <- import("igraph", delay_load = TRUE)

#pass weights to igraph if not found
if(!is_weighted(object) && !is.null(weights)){
Expand Down Expand Up @@ -444,6 +477,7 @@ numpy <- NULL
install.packages("devtools", quiet = TRUE)
devtools::install_github("rstudio/reticulate", ref = "86ebb56", quiet = TRUE)
if(!reticulate::py_module_available("numpy")) suppressWarnings(suppressMessages(reticulate::conda_install(envname = "r-reticulate", packages = "numpy")))
if(!reticulate::py_module_available("pandas")) suppressWarnings(suppressMessages(reticulate::conda_install(envname = "r-reticulate", packages = "pandas")))
if(!reticulate::py_module_available("igraph")) suppressWarnings(suppressMessages(reticulate::conda_install(envname = "r-reticulate", packages = "python-igraph")))
if(!reticulate::py_module_available("mkl")) suppressWarnings(suppressMessages(reticulate::conda_install(envname = "r-reticulate", packages = "mkl", channel = "intel")))
if(!reticulate::py_module_available("umap")) suppressWarnings(suppressMessages(reticulate::conda_install(envname = "r-reticulate", packages = "umap-learn", channel = "conda-forge")))
Expand All @@ -453,6 +487,7 @@ numpy <- NULL
utils::install.packages("reticulate", quiet = TRUE)
} else {
if(!reticulate::py_module_available("numpy")) suppressWarnings(suppressMessages(reticulate::conda_install("r-reticulate", "numpy")))
if(!reticulate::py_module_available("pandas")) suppressWarnings(suppressMessages(reticulate::conda_install(envname = "r-reticulate", packages = "pandas")))
if(!reticulate::py_module_available("igraph")) suppressWarnings(suppressMessages(reticulate::conda_install("r-reticulate", "python-igraph")))
if(!reticulate::py_module_available("umap")) suppressWarnings(suppressMessages(reticulate::conda_install("r-reticulate", "umap-learn", forge = TRUE)))
if(!reticulate::py_module_available("leidenalg")) suppressWarnings(suppressMessages(reticulate::conda_install("r-reticulate", "leidenalg", forge = TRUE)))
Expand All @@ -471,6 +506,7 @@ numpy <- NULL
# system("conda init")
# system("conda activate r-reticulate")
if(!reticulate::py_module_available("numpy")) suppressWarnings(suppressMessages(reticulate::py_install("numpy")))
if(!reticulate::py_module_available("pandas")) suppressWarnings(suppressMessages(reticulate::conda_install(envname = "r-reticulate", packages = "pandas")))
if(!reticulate::py_module_available("igraph")) suppressWarnings(suppressMessages(reticulate::py_install("python-igraph", method = method, conda = conda)))
if(!reticulate::py_module_available("umap")) suppressWarnings(suppressMessages(reticulate::py_install("umap-learn")))
if(!reticulate::py_module_available("leidenalg")) suppressWarnings(suppressMessages(reticulate::py_install("leidenalg", method = method, conda = conda, forge = TRUE)))
Expand Down Expand Up @@ -507,6 +543,7 @@ numpy <- NULL
if (modules) {
## assignment in parent environment!
numpy <- reticulate::import("numpy", delay_load = TRUE)
pd <- reticulate::import("pandas", delay_load = TRUE)
leidenalg <- reticulate::import("leidenalg", delay_load = TRUE)
ig <- reticulate::import("igraph", delay_load = TRUE)
}
Expand Down
38 changes: 32 additions & 6 deletions R/py_objects.R
View file
Original file line number Diff line number Diff line change
Expand Up @@ -16,23 +16,23 @@ make_py_object.matrix <- function(object, weights = NULL){

#convert matrix input (corrects for sparse matrix input)
if(is.matrix(object) || is(object, "dgCMatrix")){
adj_mat <- object
object <- object
} else{
adj_mat <- as.matrix(object)
object <- as.matrix(object)
}

#compute weights if non-binary adjacency matrix given
is_pure_adj <- all(as.logical(adj_mat) == adj_mat)
is_pure_adj <- all(as.logical(object) == object)
if (is.null(weights) && !is_pure_adj) {
if(!is.matrix(object)) adj_mat <- as.matrix(adj_mat)
if(!is.matrix(object)) object <- as.matrix(object)
#assign weights to edges (without dependancy on igraph)
t_mat <- t(adj_mat)
t_mat <- t(object)
weights <- t_mat[t_mat!=0]
#remove zeroes from rows of matrix and return vector of length edges
}

##convert to python numpy.ndarray, then a list
adj_mat_py <- r_to_py(adj_mat, convert = T)
adj_mat_py <- r_to_py(object, convert = TRUE)
if(is(object, "dgCMatrix")){
adj_mat_py <- adj_mat_py$toarray()
}
Expand Down Expand Up @@ -74,11 +74,34 @@ make_py_object.igraph <- function(object, weights = NULL){
py_graph
}

make_py_object.data.frame <- function(object, weights = NULL){
pd <- reticulate::import("pandas", delay_load = TRUE)
adj_df_py <- pd$DataFrame(data = r_to_py(object, convert = TRUE))

adj_df_py
}

##' convert to python igraph object
##' @param object an igraph object or matrix
##' @param weights Parameters to pass to the Python leidenalg function (defaults initial_membership=None, weights=None). Weights are derived from weighted igraph objects and non-zero integer values of adjacency matrices.
##' @noRd
##' @description internal function to compute partitions by calling Python with reticulate
##' @keywords internal
make_py_graph <- function(object, weights = NULL) {
UseMethod("make_py_graph", object)
}

make_py_graph.matrix <- function(object, weights = NULL){
#compute weights if non-binary adjacency matrix given
is_pure_adj <- all(as.logical(object) == object)
if (is.null(weights) && !is_pure_adj) {
if(!is.matrix(object)) object <- as.matrix(object)
#assign weights to edges (without dependancy on igraph)
t_mat <- t(object)
weights <- t_mat[t_mat!=0]
#remove zeroes from rows of matrix and return vector of length edges
}

#import python modules with reticulate
numpy <- reticulate::import("numpy", delay_load = TRUE)
leidenalg <- import("leidenalg", delay_load = TRUE)
Expand All @@ -97,3 +120,6 @@ make_py_graph.matrix <- function(object, weights = NULL){

make_py_graph.igraph <- make_py_object.igraph

make_py_graph.data.frame <- function(object, weights = NULL){
py_graph <- make_py_graph(as.matrix(object), weights = weights)
}
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