DelayedArray-class: DelayedArray objects

Description Usage Arguments In-memory versus on-disk realization Accessors Subsetting See Also Examples

Description

Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism.

Usage

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DelayedArray(seed)  # constructor function
seed(x)             # seed getter
nseed(x)            # seed counter
path(object, ...)   # path getter
type(x)

Arguments

seed

An array-like object.

x, object

A DelayedArray object. For type(), x can also be any array-like object, that is, any object for which dim(x) is not NULL.

...

Additional arguments passed to methods.

In-memory versus on-disk realization

To realize a DelayedArray object (i.e. to trigger execution of the delayed operations carried by the object and return the result as an ordinary array), call as.array on it. However this realizes the full object at once in memory which could require too much memory if the object is big. A big DelayedArray object is preferrably realized on disk e.g. by calling writeHDF5Array on it (this function is defined in the HDF5Array package) or coercing it to an HDF5Array object with as(x, "HDF5Array"). Other on-disk backends can be supported. This uses a block processing strategy so that the full object is not realized at once in memory. Instead the object is processed block by block i.e. the blocks are realized in memory and written to disk one at a time. See ?writeHDF5Array in the HDF5Array package for more information about this.

Accessors

DelayedArray objects support the same set of getters as ordinary arrays i.e. dim(), length(), and dimnames(). In addition, they support seed(), nseed(), path(), and type().

type() is the DelayedArray equivalent of typeof() (or storage.mode()) for ordinary arrays and vectors. Note that, for convenience and consistency, type() also supports ordinary arrays and vectors. It also supports any array-like object, that is, any object x for which dim(x) is not NULL.

dimnames(), seed(), and path() also work as setters.

Subsetting

A DelayedArray object can be subsetted with [ like an ordinary array, but with the following differences:

Subsetting with [[ is supported but only the linear form of it at the moment i.e. the x[[i]] form where i is a single numeric value >= 1 and <= length(x). It is equivalent to x[i][[1]].

Subassignment to a DelayedArray object with [<- is also supported like with an ordinary array, but with the following restrictions:

These 3 forms of subassignment are implemented as delayed operations so are very light.

Single value replacement (x[[...]] <- value) is not supported yet.

See Also

Examples

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## ---------------------------------------------------------------------
## A. WRAP AN ORDINARY ARRAY IN A DelayedArray OBJECT
## ---------------------------------------------------------------------
a <- array(runif(1500000), dim=c(10000, 30, 5))
A <- DelayedArray(a)
A
## The seed of a DelayedArray object is **always** treated as a
## "read-only" object so will never be modified by the operations
## we perform on A:
stopifnot(identical(a, seed(A)))
type(A)

## Multi-dimensional single bracket subsetting:
m <- a[11:20 , 5, -3]  # an ordinary matrix
M <- A[11:20 , 5, -3]  # a DelayedMatrix object
stopifnot(identical(m, as.array(M)))

## Linear single bracket subsetting:
A[11:20]
A[A <= 1e-5]
stopifnot(identical(a[a <= 1e-5], A[A <= 1e-5]))

## Subassignment:
A[A < 0.2] <- NA
a[a < 0.2] <- NA
stopifnot(identical(a, as.array(A)))

A[2:5, 1:2, ] <- array(1:40, c(4, 2, 5))
a[2:5, 1:2, ] <- array(1:40, c(4, 2, 5))
stopifnot(identical(a, as.array(A)))

## Other operations:
crazy <- function(x) (5 * x[ , , 1] ^ 3 + 1L) * log(x[, , 2])
b <- crazy(a)
head(b)

B <- crazy(A)  # very fast! (all operations are delayed)
B

cs <- colSums(b)
CS <- colSums(B)
stopifnot(identical(cs, CS))

## ---------------------------------------------------------------------
## B. WRAP A DataFrame OBJECT IN A DelayedArray OBJECT
## ---------------------------------------------------------------------
## Generate random coverage and score along an imaginary chromosome:
cov <- Rle(sample(20, 5000, replace=TRUE), sample(6, 5000, replace=TRUE))
score <- Rle(sample(100, nrun(cov), replace=TRUE), runLength(cov))

DF <- DataFrame(cov, score)
A2 <- DelayedArray(DF)
A2
seed(A2)  # 'DF'

## Coercion of a DelayedMatrix object to DataFrame produces a DataFrame
## object with Rle columns:
as(A2, "DataFrame")
stopifnot(identical(DF, as(A2, "DataFrame")))

t(A2)  # transposition is delayed so is very fast and memory-efficient
colSums(A2)

## ---------------------------------------------------------------------
## C. AN HDF5Array OBJECT IS A (PARTICULAR KIND OF) DelayedArray OBJECT
## ---------------------------------------------------------------------
library(HDF5Array)
A3 <- as(a, "HDF5Array")   # write 'a' to an HDF5 file
A3
is(A3, "DelayedArray")     # TRUE
seed(A3)                   # an HDF5ArraySeed object

B3 <- crazy(A3)            # very fast! (all operations are delayed)
B3                         # not an HDF5Array object anymore because
                           # now it carries delayed operations
CS3 <- colSums(B3)
stopifnot(identical(cs, CS3))

## ---------------------------------------------------------------------
## D. PERFORM THE DELAYED OPERATIONS
## ---------------------------------------------------------------------
as(B3, "HDF5Array")        # "realize" 'B3' on disk

## If this is just an intermediate result, you can either keep going
## with B3 or replace it with its "realized" version:
B3 <- as(B3, "HDF5Array")  # no more delayed operations on new 'B3'
seed(B3)
path(B3)

## For convenience, realize() can be used instead of explicit coercion.
## The current "realization backend" controls where realization
## happens e.g. in memory if set to NULL or in an HDF5 file if set
## to "HDF5Array":
D <- cbind(B3, exp(B3))
D
setRealizationBackend("HDF5Array")
D <- realize(D)
D
## See '?realize' for more information about "realization backends".

## ---------------------------------------------------------------------
## E. MODIFY THE PATH OF A DelayedArray OBJECT
## ---------------------------------------------------------------------
## This can be useful if the file containing the array data is on a
## shared partition but the exact path to the partition depends on the
## machine from which the data is being accessed.
## For example:

## Not run: 
library(HDF5Array)
A <- HDF5Array("/path/to/lab_data/my_precious_data.h5")
path(A)

## Operate on A...
## Now A carries delayed operations.
## Make sure path(A) still works:
path(A)

## Save A:
save(A, file="A.rda")

## A.rda should be small (it doesn't contain the array data).
## Send it to a co-worker that has access to my_precious_data.h5.

## Co-worker loads it:
load("A.rda")
path(A)

## A is broken because path(A) is incorrect for co-worker:
A  # error!

## Co-worker fixes the path (in this case this is better done using the
## dirname() setter rather than the path() setter):
dirname(A) <- "E:/other/path/to/lab_data"

## A "works" again:
A

## End(Not run)

## ---------------------------------------------------------------------
## F. WRAP A SPARSE MATRIX IN A DelayedArray OBJECT
## ---------------------------------------------------------------------
## Not run: 
library(Matrix)
M <- 75000L
N <- 1800L
p <- sparseMatrix(sample(M, 9000000, replace=TRUE),
                  sample(N, 9000000, replace=TRUE),
                  x=runif(9000000), dims=c(M, N))
P <- DelayedArray(p)
P
p2 <- as(P, "sparseMatrix")
stopifnot(identical(p, p2))

## The following is based on the following post by Murat Tasan on the
## R-help mailing list:
##   https://stat.ethz.ch/pipermail/r-help/2017-May/446702.html

## As pointed out by Murat, the straight-forward row normalization
## directly on sparse matrix 'p' would consume too much memory:
row_normalized_p <- p / rowSums(p^2)  # consumes too much memory
## because the rowSums() result is being recycled (appropriately) into a
## *dense* matrix with dimensions equal to dim(p).

## Murat came up with the following solution that is very fast and
## memory-efficient:
row_normalized_p1 <- Diagonal(x=1/sqrt(Matrix::rowSums(p^2))) 

## With a DelayedArray object, the straight-forward approach uses a
## block processing strategy behind the scene so it doesn't consume
## too much memory.

## First, let's see the block processing in action:
DelayedArray:::set_verbose_block_processing(TRUE)
## and check the automatic block size:
getAutoBlockSize()

row_normalized_P <- P / sqrt(DelayedArray::rowSums(P^2))

## Increasing the block size increases the speed but also memory usage:
setAutoBlockSize(2e8)
row_normalized_P2 <- P / sqrt(DelayedArray::rowSums(P^2))
stopifnot(all.equal(row_normalized_P, row_normalized_P2))

## Back to sparse representation:
DelayedArray:::set_verbose_block_processing(FALSE)
row_normalized_p2 <- as(row_normalized_P, "sparseMatrix")
stopifnot(all.equal(row_normalized_p1, row_normalized_p2))

setAutoBlockSize()

## End(Not run)

DelayedArray documentation built on Nov. 1, 2018, 2:27 a.m.