R/future_eapply.R, R/future_lapply.R, R/future_replicate.R, and 3 more
future_lapply.Rdfuture_lapply() implements base::lapply() using futures with perfect
replication of results, regardless of future backend used.
Analogously, this is true for all the other future_nnn() functions.
future_eapply(
env,
FUN,
...,
all.names = FALSE,
USE.NAMES = TRUE,
future.envir = parent.frame(),
future.label = "future_eapply-%d"
)
future_lapply(
X,
FUN,
...,
future.envir = parent.frame(),
future.stdout = TRUE,
future.conditions = "condition",
future.globals = TRUE,
future.packages = NULL,
future.seed = FALSE,
future.scheduling = 1,
future.chunk.size = NULL,
future.label = "future_lapply-%d"
)
future_replicate(
n,
expr,
simplify = "array",
future.seed = TRUE,
...,
future.envir = parent.frame(),
future.label = "future_replicate-%d"
)
future_sapply(
X,
FUN,
...,
simplify = TRUE,
USE.NAMES = TRUE,
future.envir = parent.frame(),
future.label = "future_sapply-%d"
)
future_tapply(
X,
INDEX,
FUN = NULL,
...,
default = NA,
simplify = TRUE,
future.envir = parent.frame(),
future.label = "future_tapply-%d"
)
future_vapply(
X,
FUN,
FUN.VALUE,
...,
USE.NAMES = TRUE,
future.envir = parent.frame(),
future.label = "future_vapply-%d"
)An R environment.
A function taking at least one argument.
If TRUE, the function will also be applied to variables
that start with a period (.), otherwise not.
See base::eapply() for details.
See base::sapply().
An environment passed as argument envir to
future::future() as-is.
If a character string, then each future is assigned
a label sprintf(future.label, chunk_idx). If TRUE, then the
same as future.label = "future_lapply-%d". If FALSE, no labels
are assigned.
An R object for which a split method
exists. Typically vector-like, allowing subsetting with
[, or a data frame.
If TRUE (default), then the standard output of the
underlying futures is captured, and re-outputted as soon as possible.
If FALSE, any output is silenced (by sinking it to the null device
as it is outputted).
If NA (not recommended), output is not intercepted.
A character string of conditions classes to be
captured and relayed. The default is the same as the condition
argument of future::Future().
To not intercept conditions, use conditions = character(0L).
Errors are always relayed.
A logical, a character vector, or a named list for controlling how globals are handled. For details, see below section.
(optional) a character vector specifying packages to be attached in the R environment evaluating the future.
A logical or an integer (of length one or seven),
or a list of length(X) with pre-generated random seeds.
For details, see below section.
Average number of futures ("chunks") per worker.
If 0.0, then a single future is used to process all elements
of X.
If 1.0 or TRUE, then one future per worker is used.
If 2.0, then each worker will process two futures
(if there are enough elements in X).
If Inf or FALSE, then one future per element of
X is used.
Only used if future.chunk.size is NULL.
The average number of elements per future ("chunk").
If Inf, then all elements are processed in a single future.
If NULL, then argument future.scheduling is used.
The number of replicates.
An R expression to evaluate repeatedly.
See base::sapply() and base::tapply(), respectively.
A list of one or more factors, each of same length as X.
The elements are coerced to factors by
as.factor(). Can also be a formula, which
is useful if X is a data frame; see the f argument in
split() for interpretation.
See base::tapply().
A template for the required return value from
each FUN(X[ii], ...).
Types may be promoted to a higher type within the ordering
logical < integer < double < complex, but not demoted.
See base::vapply() for details.
(optional) Additional arguments passed to FUN().
For future_*apply() functions and replicate(), any future.* arguments
part of \ldots are passed on to future_lapply() used internally.
A named (unless USE.NAMES = FALSE) list.
See base::eapply() for details.
For future_lapply(), a list with same length and names as X.
See base::lapply() for details.
future_replicate() is a wrapper around future_sapply() and return
simplified object according to the simplify argument.
See base::replicate() for details.
Since future_replicate() usually involves random number generation (RNG),
it uses future.seed = TRUE by default in order produce sound random
numbers regardless of future backend and number of background workers used.
For future_sapply(), a vector with same length and names as X.
See base::sapply() for details.
future_tapply() returns an array with mode "list", unless
simplify = TRUE (default) and FUN returns a scalar, in which
case the mode of the array is the same as the returned scalars.
See base::tapply() for details.
For future_vapply(), a vector with same length and names as X.
See base::vapply() for details.
Argument future.globals may be used to control how globals
should be handled similarly how the globals argument is used with
future().
Since all function calls use the same set of globals, this function can do
any gathering of globals upfront (once), which is more efficient than if
it would be done for each future independently.
If TRUE (default), then globals are automatically identified and gathered.
If a character vector of names is specified, then those globals are gathered.
If a named list, then those globals are used as is.
In all cases, FUN and any \ldots arguments are automatically
passed as globals to each future created as they are always needed.
Unless future.seed is FALSE or NULL, this function guarantees to
generate the exact same sequence of random numbers given the same initial
seed / RNG state - this regardless of type of futures, scheduling
("chunking") strategy, and number of workers.
RNG reproducibility is achieved by pregenerating the random seeds for all
iterations (over X) by using L'Ecuyer-CMRG RNG streams. In each
iteration, these seeds are set before calling FUN(X[[ii]], ...).
Note, for large length(X) this may introduce a large overhead.
If future.seed = TRUE, then .Random.seed
is used if it holds a L'Ecuyer-CMRG RNG seed, otherwise one is created
randomly.
If future.seed = FALSE, it is expected that none of the
FUN(X[[ii]], ...) function calls use random number generation.
If they do, then an informative warning or error is produces depending
on settings. See future::future for more details.
Using future.seed = NULL, is like future.seed = FALSE but without
the check whether random numbers were generated or not.
As input, future.seed may also take a fixed initial seed (integer),
either as a full L'Ecuyer-CMRG RNG seed (vector of 1+6 integers), or
as a seed generating such a full L'Ecuyer-CMRG seed. This seed will
be used to generated length(X) L'Ecuyer-CMRG RNG streams.
In addition to the above, it is possible to specify a pre-generated
sequence of RNG seeds as a list such that
length(future.seed) == length(X) and where each element is an
integer seed vector that can be assigned to
.Random.seed. One approach to generate a
set of valid RNG seeds based on fixed initial seed (here 42L) is:
seeds <- future_lapply(seq_along(X), FUN = function(x) .Random.seed,
future.chunk.size = Inf, future.seed = 42L)Note that as.list(seq_along(X)) is not a valid set of such
.Random.seed values.
In all cases but future.seed = FALSE and NULL, the RNG state of the
calling R processes after this function returns is guaranteed to be
"forwarded one step" from the RNG state that was before the call and
in the same way regardless of future.seed, future.scheduling
and future strategy used. This is done in order to guarantee that an R
script calling future_lapply() multiple times should be numerically
reproducible given the same initial seed.
Attribute ordering of future.chunk.size or future.scheduling can
be used to control the ordering the elements are iterated over, which
only affects the processing order and not the order values are returned.
This attribute can take the following values:
index vector - an numeric vector of length length(X)
function - an function taking one argument which is called as
ordering(length(X)) and which must return an
index vector of length length(X), e.g.
function(n) rev(seq_len(n)) for reverse ordering.
"random" - this will randomize the ordering via random index
vector sample.int(length(X)).
For example, future.scheduling = structure(TRUE, ordering = "random").
Note, when elements are processed out of order, then captured standard
output and conditions are also relayed in that order, that is out of order.
## ---------------------------------------------------------
## lapply(), sapply(), tapply()
## ---------------------------------------------------------
x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE, FALSE, FALSE, TRUE))
y0 <- lapply(x, FUN = quantile, probs = 1:3/4)
y1 <- future_lapply(x, FUN = quantile, probs = 1:3/4)
print(y1)
#> $a
#> 25% 50% 75%
#> 3.25 5.50 7.75
#>
#> $beta
#> 25% 50% 75%
#> 0.2516074 1.0000000 5.0536690
#>
#> $logic
#> 25% 50% 75%
#> 0.0 0.5 1.0
#>
stopifnot(all.equal(y1, y0))
y0 <- sapply(x, FUN = quantile)
y1 <- future_sapply(x, FUN = quantile)
print(y1)
#> a beta logic
#> 0% 1.00 0.04978707 0.0
#> 25% 3.25 0.25160736 0.0
#> 50% 5.50 1.00000000 0.5
#> 75% 7.75 5.05366896 1.0
#> 100% 10.00 20.08553692 1.0
stopifnot(all.equal(y1, y0))
y0 <- vapply(x, FUN = quantile, FUN.VALUE = double(5L))
y1 <- future_vapply(x, FUN = quantile, FUN.VALUE = double(5L))
print(y1)
#> a beta logic
#> 0% 1.00 0.04978707 0.0
#> 25% 3.25 0.25160736 0.0
#> 50% 5.50 1.00000000 0.5
#> 75% 7.75 5.05366896 1.0
#> 100% 10.00 20.08553692 1.0
stopifnot(all.equal(y1, y0))
## ---------------------------------------------------------
## Parallel Random Number Generation
## ---------------------------------------------------------
# \donttest{
## Regardless of the future plan, the number of workers, and
## where they are, the random numbers produced are identical
plan(multisession)
set.seed(0xBEEF)
y1 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE)
str(y1)
#> List of 5
#> $ : num -1.32
#> $ : num [1:2] -1.5 0.293
#> $ : num [1:3] 2.787 0.929 -0.461
#> $ : num [1:4] -0.193 -0.184 0.504 1.255
#> $ : num [1:5] -1.704 1.142 2.352 -1.474 -0.559
plan(sequential)
set.seed(0xBEEF)
y2 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE)
str(y2)
#> List of 5
#> $ : num -1.32
#> $ : num [1:2] -1.5 0.293
#> $ : num [1:3] 2.787 0.929 -0.461
#> $ : num [1:4] -0.193 -0.184 0.504 1.255
#> $ : num [1:5] -1.704 1.142 2.352 -1.474 -0.559
stopifnot(all.equal(y1, y2))
# }
## ---------------------------------------------------------
## Process chunks of data.frame rows in parallel
## ---------------------------------------------------------
iris <- datasets::iris
chunks <- split(iris, seq(1, nrow(iris), length.out = 3L))
y0 <- lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length))
y0 <- do.call(sum, y0)
y1 <- future_lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length))
y1 <- do.call(sum, y1)
print(y1)
#> [1] 876.5
stopifnot(all.equal(y1, y0))