Miscellaneous helper functions for mlr3
Home Page: https://mlr3misc.mlr-org.com
License: GNU Lesser General Public License v3.0
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According to book chapter 2.3, we can use mlr3::insert_named() to set specific arguments without resetting existing arguments. However, when I tried to use it for a learner of class "regr.glmnet" the function didn't appear to work at all:
library(mlr3)
library(mlr3learners)
library(mlr3misc)
learner <- mlr_learners$get("regr.glmnet")
learner$param_set
<ParamSet>
id class lower upper levels default parents value
1: alignment ParamFct NA NA lambda,fraction lambda
2: alpha ParamDbl 0 1 1
3: big ParamDbl -Inf Inf 9.9e+35
4: devmax ParamDbl 0 1 0.999
5: dfmax ParamInt 0 Inf <NoDefault[3]>
6: eps ParamDbl 0 1 1e-06
7: epsnr ParamDbl 0 1 1e-08
8: exact ParamLgl NA NA TRUE,FALSE FALSE
9: exclude ParamInt 1 Inf <NoDefault[3]>
10: exmx ParamDbl -Inf Inf 250
11: family ParamFct NA NA gaussian,poisson gaussian gaussian
12: fdev ParamDbl 0 1 1e-05
13: gamma ParamDbl -Inf Inf 1 relax
14: grouped ParamLgl NA NA TRUE,FALSE TRUE
15: intercept ParamLgl NA NA TRUE,FALSE TRUE
16: keep ParamLgl NA NA TRUE,FALSE FALSE
17: lambda ParamUty NA NA <NoDefault[3]>
18: lambda.min.ratio ParamDbl 0 1 <NoDefault[3]>
19: lower.limits ParamUty NA NA <NoDefault[3]>
20: maxit ParamInt 1 Inf 100000
21: mnlam ParamInt 1 Inf 5
22: mxit ParamInt 1 Inf 100
23: mxitnr ParamInt 1 Inf 25
24: newoffset ParamUty NA NA <NoDefault[3]>
25: nlambda ParamInt 1 Inf 100
26: offset ParamUty NA NA
27: parallel ParamLgl NA NA TRUE,FALSE FALSE
28: penalty.factor ParamUty NA NA <NoDefault[3]>
29: pmax ParamInt 0 Inf <NoDefault[3]>
30: pmin ParamDbl 0 1 1e-09
31: prec ParamDbl -Inf Inf 1e-10
32: predict.gamma ParamDbl -Inf Inf 1
33: relax ParamLgl NA NA TRUE,FALSE FALSE
34: s ParamDbl 0 Inf 0.01
35: standardize ParamLgl NA NA TRUE,FALSE TRUE
36: thresh ParamDbl 0 Inf 1e-07
37: trace.it ParamInt 0 1 0
38: type.gaussian ParamFct NA NA covariance,naive <NoDefault[3]> family
39: type.logistic ParamFct NA NA Newton,modified.Newton <NoDefault[3]>
40: type.measure ParamFct NA NA deviance,class,auc,mse,mae deviance
41: type.multinomial ParamFct NA NA ungrouped,grouped <NoDefault[3]>
42: upper.limits ParamUty NA NA <NoDefault[3]>
id class lower upper levels default parents value
## insert new params
insert_named(learner$param_set$values,
list(family="poisson",
lambda=0))
learner$param_set
<ParamSet>
id class lower upper levels default parents value
1: alignment ParamFct NA NA lambda,fraction lambda
2: alpha ParamDbl 0 1 1
3: big ParamDbl -Inf Inf 9.9e+35
4: devmax ParamDbl 0 1 0.999
5: dfmax ParamInt 0 Inf <NoDefault[3]>
6: eps ParamDbl 0 1 1e-06
7: epsnr ParamDbl 0 1 1e-08
8: exact ParamLgl NA NA TRUE,FALSE FALSE
9: exclude ParamInt 1 Inf <NoDefault[3]>
10: exmx ParamDbl -Inf Inf 250
11: family ParamFct NA NA gaussian,poisson gaussian gaussian
12: fdev ParamDbl 0 1 1e-05
13: gamma ParamDbl -Inf Inf 1 relax
14: grouped ParamLgl NA NA TRUE,FALSE TRUE
15: intercept ParamLgl NA NA TRUE,FALSE TRUE
16: keep ParamLgl NA NA TRUE,FALSE FALSE
17: lambda ParamUty NA NA <NoDefault[3]>
18: lambda.min.ratio ParamDbl 0 1 <NoDefault[3]>
19: lower.limits ParamUty NA NA <NoDefault[3]>
20: maxit ParamInt 1 Inf 100000
21: mnlam ParamInt 1 Inf 5
22: mxit ParamInt 1 Inf 100
23: mxitnr ParamInt 1 Inf 25
24: newoffset ParamUty NA NA <NoDefault[3]>
25: nlambda ParamInt 1 Inf 100
26: offset ParamUty NA NA
27: parallel ParamLgl NA NA TRUE,FALSE FALSE
28: penalty.factor ParamUty NA NA <NoDefault[3]>
29: pmax ParamInt 0 Inf <NoDefault[3]>
30: pmin ParamDbl 0 1 1e-09
31: prec ParamDbl -Inf Inf 1e-10
32: predict.gamma ParamDbl -Inf Inf 1
33: relax ParamLgl NA NA TRUE,FALSE FALSE
34: s ParamDbl 0 Inf 0.01
35: standardize ParamLgl NA NA TRUE,FALSE TRUE
36: thresh ParamDbl 0 Inf 1e-07
37: trace.it ParamInt 0 1 0
38: type.gaussian ParamFct NA NA covariance,naive <NoDefault[3]> family
39: type.logistic ParamFct NA NA Newton,modified.Newton <NoDefault[3]>
40: type.measure ParamFct NA NA deviance,class,auc,mse,mae deviance
41: type.multinomial ParamFct NA NA ungrouped,grouped <NoDefault[3]>
42: upper.limits ParamUty NA NA <NoDefault[3]>
id class lower upper levels default parents value
However it does appear to work when I overwrite it directly:
learner$param_set$values <- list(family="poisson",
lambda=0)
learner$param_set
<ParamSet>
id class lower upper levels default parents value
1: alignment ParamFct NA NA lambda,fraction lambda
2: alpha ParamDbl 0 1 1
3: big ParamDbl -Inf Inf 9.9e+35
4: devmax ParamDbl 0 1 0.999
5: dfmax ParamInt 0 Inf <NoDefault[3]>
6: eps ParamDbl 0 1 1e-06
7: epsnr ParamDbl 0 1 1e-08
8: exact ParamLgl NA NA TRUE,FALSE FALSE
9: exclude ParamInt 1 Inf <NoDefault[3]>
10: exmx ParamDbl -Inf Inf 250
11: family ParamFct NA NA gaussian,poisson gaussian poisson
12: fdev ParamDbl 0 1 1e-05
13: gamma ParamDbl -Inf Inf 1 relax
14: grouped ParamLgl NA NA TRUE,FALSE TRUE
15: intercept ParamLgl NA NA TRUE,FALSE TRUE
16: keep ParamLgl NA NA TRUE,FALSE FALSE
17: lambda ParamUty NA NA <NoDefault[3]> 0
18: lambda.min.ratio ParamDbl 0 1 <NoDefault[3]>
19: lower.limits ParamUty NA NA <NoDefault[3]>
20: maxit ParamInt 1 Inf 100000
21: mnlam ParamInt 1 Inf 5
22: mxit ParamInt 1 Inf 100
23: mxitnr ParamInt 1 Inf 25
24: newoffset ParamUty NA NA <NoDefault[3]>
25: nlambda ParamInt 1 Inf 100
26: offset ParamUty NA NA
27: parallel ParamLgl NA NA TRUE,FALSE FALSE
28: penalty.factor ParamUty NA NA <NoDefault[3]>
29: pmax ParamInt 0 Inf <NoDefault[3]>
30: pmin ParamDbl 0 1 1e-09
31: prec ParamDbl -Inf Inf 1e-10
32: predict.gamma ParamDbl -Inf Inf 1
33: relax ParamLgl NA NA TRUE,FALSE FALSE
34: s ParamDbl 0 Inf 0.01
35: standardize ParamLgl NA NA TRUE,FALSE TRUE
36: thresh ParamDbl 0 Inf 1e-07
37: trace.it ParamInt 0 1 0
38: type.gaussian ParamFct NA NA covariance,naive <NoDefault[3]> family
39: type.logistic ParamFct NA NA Newton,modified.Newton <NoDefault[3]>
40: type.measure ParamFct NA NA deviance,class,auc,mse,mae deviance
41: type.multinomial ParamFct NA NA ungrouped,grouped <NoDefault[3]>
42: upper.limits ParamUty NA NA <NoDefault[3]>
id class lower upper levels default parents value
I'm running R 4.0.2, mlr3 0.9.0, mlr3learners 0.4.3, mlr3misc 0.6.0.
Prepare for release:
git pullurlchecker::url_check()devtools::check(remote = TRUE, manual = TRUE)devtools::check_win_devel()revdepcheck::revdep_check(num_workers = 4)cran-comments.mdgit pushSubmit to CRAN:
usethis::use_version('minor')devtools::submit_cran()Wait for CRAN...
usethis::use_github_release()usethis::use_dev_version(push = TRUE)i want to peel off exactly one level of a list of list
so this should be
x = list(list(iris[1:2,]), list(iris[1:2,]))
list(iris[1:2,], iris[1:2,])
library("mlr3")
rsmp("holdout", x = 1)
#> Error: object of type 'closure' is not subsettable
#> Environment:
#> 1: rsmp("holdout", x = 1)
#> 2: dictionary_sugar_get(mlr_resamplings, .key, ...)
#> 3: stopf("Cannot set argument '%s' for '%s' (not a constructor argument, not a
#> 4: stop(simpleError(str_wrap(sprintf(msg, ...), width = wrap), call = NULL))
#> 5: simpleError(str_wrap(sprintf(msg, ...), width = wrap), call = NULL)
#> 6: structure(list(message = as.character(message), call = call), class = class
#> 7: str_wrap(sprintf(msg, ...), width = wrap)
#> 8: sprintf(msg, ...)
#> 9: did_you_mean(nn, c(constructor_args, param_ids, fields(obj$value)))
#> 10: unique(candidates)
#> 11: fields(obj$value)
#> 12: setdiff(names(x$public_methods), c("initialize", "clone", "print", "format"
#> 13: as.vector(x)problem seems to be that rsmp()-entries are functions and not R6-constructors, which is explicitly allowed for dictionaries.
this is replicate(simplify = FALSE)
R's default sucks
I want to save an anonymous function in an object and serialize that object, but that object should not grow huge. It is then necessary that the function's environment hierarchy doesn't contain too much other stuff.
x = 1
large_obj = [huge object that takes lots of memory]
want_to_serialize = function(arg) return(arg + x)
saveRDS(want_to_serialize, "savefile.rds")want_to_serialize's environment now contains large_obj and the savefile grows large. Instead I would want a function detachEnv(fun, keep, basis):
want_to_serialize_lean = detachEnv(want_to_serialize, keep = "x")
saveRDS(want_to_serialize_lean, "savefile.rds")code for this:
# removes the environment from fun and potentially wraps it in a new environment
detachEnv <- function(fun, keep = character(0), basis = topenv(parent.frame())) {
assertEnvironment(basis)
assertCharacter(keep, any.missing = FALSE)
assertFunction(fun)
if (length(keep)) {
keepvals <- mget(keep, parent.frame(), inherits = TRUE)
basis <- new.env(parent = basis, size = length(keepvals))
mapply(assign, names(keepvals), keepvals, MoreArgs = list(envir = basis))
}
environment(fun) <- basis
fun
}name up for debate
Let insert_named(NULL, x) just be x (assuming x is named)?
I tried to install it on my rstudio, but it just won't work. I did:
Every time when I run the library(ml3rverse) or library(mlr3), I get this error:
Error: package or namespace load failed for ‘mlr3verse’ in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]):
there is no package called ‘mlr3misc’
I have the latest Rstudio, is there something else that's wrong?
why? i just wanted to clone one.
can we please at least document the reason?
As mlr3 populates dictionaries from packages like mlr3 or mlr3pipelines when loading extensions, those objects should be removed again when unloading the extension.
An idea brought forward by Lukas is to automate this, which I think is a good idea:
mlr-org/mlr3proba#301 (comment)
A simple solution would be to tag the dictionary entries with the package that added them.
Then, during the .onUnload function, we can remove all objects that were added by the package that is being unloaded.
mlr3misc uses backports::errorCondition, so to the degree it imports backports, it should require the backports version that introduced that. Otherwise it's very hard for a user who happens to have an old backports version installed to find out what the problem is.
I could use this in the pipelines.
Is it broken, or does it make sense to copy this?
i would like to do this
foo = function(mu) {
setDT(as.data.frame(rnorm(2, mean = mu)))
}
d = map_dtc(1:3, foo) # doenst work
print(d)
# this works
d = lapply(1:3, foo)
d = do.call(cbind, d)
print(d)
I think we should really offer this set_params public method for Learners, Graphs etc. so we can use it in the book.
Last time @mllg mentioned that we could just offer a s3 method that does something akin to
set_params.R6 = function(x, ...) {
if (!is.null(x$param_set)) {
x$param_set$values = insert_named(x$param_set$values, list(...))
}
invisible(x)
}admittedly that is less work and we could simply add it in mlr3misc without touching other packages. But I think the downsight is that this is isomewhat inconsistent. The user then has to guess when something is implemented as a method vs a s function (?) I think offering both would be fine as well but I definitely think that the following should work.
learner$set_params(x = 1, y = 2)
This is also related to the suggestion that @mb706 made at some point, that it might make sense to introduce another class into the hierarchy that represents an object that has a param set and some other standardized properties? But even if this would make sense it would definitely take some time so I would suggest we make an mlr3misc release with this set_params utility that is already implemented and then add the method set_params to
I am not sure whether I forgot something.
I can do this if you are ok @mllg @be-marc
a = data.table(y = list(0), opt_x = list(list(z_1 = 100, z_2 = 200)))
unnest(a, "opt_x")
# > x y z_1 z_2
# > 1: 1 0 0 0
a = data.table(z = list(0), opt_x = list(list(z_1 = 100, z_2 = 200)))
unnest(a, "opt_x")
# > x z z_1 z_2
# > 1: 1 0 100 200
The bug occurs when the unnested columns are added to the data.table with rcbind since y is used as the variable name for the unnested columns.
rcbind
function (x, y)
{
assert_data_table(x)
assert_data_table(y)
if (ncol(x) == 0L) {
return(y)
}
if (ncol(y) == 0L) {
return(x)
}
if (nrow(x) != nrow(y)) {
stopf("Tables have different number of rows (x: %i, y: %i)",
nrow(x), nrow(y))
}
dup = intersect(names(x), names(y))
if (length(dup)) {
stopf("Duplicated names: %s", str_collapse(dup))
}
x[, `:=`(names(y), y)][]
}
right now it returns an error
It is e.g. impossible for a learner with a parameter / field that partially matches dict to be set during a lrn() call as the value of this parameter will be used as the dict argument.
UI something like
register_mlr3 = function() { ... }
register_mlr3pipelines = function() { ... }
.onLoad = function(libname, pkgname) {
mlr3misc::react_to_namespace("mlr3", register_mlr3)
mlr3misc::react_to_namespace("mlr3pipelines", register_mlr3pipelines)
}should look something like
react_to_namespace = function(namespace, callback) {
if (already_loaded(namespace)) callback()
setHook(packageEvent(namespace, "onLoad"), .......)
setHook(packageEvent(namespace_name_of(parent.frame()), "onUnload"), function(...) { < do the onUnload stuff > })
}Object sizes can grow very large when packages are installed with the --with-keep.source option. We should give a warning (that can be disabled) when users have mlr3 with sourcerefs installed. It should be possible to disable this warning using an option.
The warning should point to the FAQ section of the mlr-org website: mlr-org/mlr3website#132
Hi,
I just wanted to install your wonderful mlr3verse package (from CRAN). But it stopped at the installation of mlr3misc with the following error I can not understand:
Error: package or namespace load failed for ‘mlr3misc’ in dyn.load(file, DLLpath = DLLpath, ...):
unable to load shared object '/applvg/os/usr/lib64/R/library/mlr3misc/libs/mlr3misc.so':
/applvg/os/usr/lib64/R/library/mlr3misc/libs/mlr3misc.so: undefined symbol: REAL_RO
Error: loading failed
Execution halted
ERROR: loading failed
Further, I get the following warnings:
gcc -m64 -std=gnu99 -I/applvg/os/apps/gcc/gmp/include -I/applvg/os/apps/gcc/mpfr/include -I/usr/include/R -DNDEBUG -I/usr/local/include -fpic -O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector-strong --param=ssp-buffer-size=4 -grecord-gcc-switches -m64 -mtune=generic -c count_missing.c -o count_missing.o
count_missing.c: In function ‘count_missing_logical’:
count_missing.c:11:5: warning: implicit declaration of function ‘LOGICAL_RO’ [-Wimplicit-function-declaration]
const int * xp = LOGICAL_RO(x);
^
count_missing.c:11:22: warning: initialization makes pointer from integer without a cast
const int * xp = LOGICAL_RO(x);
^
count_missing.c: In function ‘count_missing_integer’:
count_missing.c:26:5: warning: implicit declaration of function ‘INTEGER_RO’ [-Wimplicit-function-declaration]
const int * xp = INTEGER_RO(x);
^
count_missing.c:26:22: warning: initialization makes pointer from integer without a cast
const int * xp = INTEGER_RO(x);
^
count_missing.c: In function ‘count_missing_double’:
count_missing.c:41:5: warning: implicit declaration of function ‘REAL_RO’ [-Wimplicit-function-declaration]
const double * xp = REAL_RO(x);
^
count_missing.c:41:25: warning: initialization makes pointer from integer without a cast
const double * xp = REAL_RO(x);
^
count_missing.c: In function ‘count_missing_complex’:
count_missing.c:52:5: warning: implicit declaration of function ‘COMPLEX_RO’ [-Wimplicit-function-declaration]
const Rcomplex * xp = COMPLEX_RO(x);
^
count_missing.c:52:27: warning: initialization makes pointer from integer without a cast
const Rcomplex * xp = COMPLEX_RO(x);
^
I have a Redhat 6 Unix System with R 3.4.3 and my default gcc is a 4.8.5. I also tried with the newer version 4.9.4.
Are there any dependencies I can not see? Do I need a newer gcc?
The version of the package I got from CRAN is 0.9.0
Best
Ulf
Just .mapply(list, design, list())?
So we can remove: mlr-org/paradox#115
For many objects there is lrn("xxx")$help(), but the dictionary can also contain entries that do not map 1-to-1 with R6-classes, such as ppl(), which constructs different Graphs. However, we have the nomenclature of <dictname>_<key> for help files. I therefore suggest that Dictionary$help(<key>) tries to show help for the <dictname>_<key> help entry (and, failing that, tries to construct <key> and call its help() function.
We could define operators such as %+=%, %-=% etc. What I seem to need a lot is
x = c(x, <something>)so maybe we could have an %c=% operator for that.
See here for my implementation in an old project.
The transpose function name-clashes with the data.table::transpose function. We usually import both these packages, so we end up having to write mlr3misc::transpose, while we usually don't prefix mlr3misc functions. Renaming this function to transpose_list would probably be more convenient. (We would be breaking with purrr function names).
also see this here
mlr-org/paradox#245
aggregating lists down to a single element is an important operatuon.
base R has "Reduce". I will at least have a look at what purrr does and see if it is beneficial
https://purrr.tidyverse.org/reference/modify.html
This is useful for some graph modifications in mlr3pipelines
it is not for any other type. maybe make this optional
Unfortunately the futures package does not offer this (and R seems to suck at synchronized parallelization in general) so we would have to hack this in a way using polling.
I can write this, but I am wondering if this should be in misc or belongs to its own package?
@berndbischl @mllg
library(mlr3misc)
f = function() NULL
fc = crate(f)
f()
#> NULL
f()
#> NULL
fc()
#> NULL
fc()
#> NULL
f
#> function() NULL
#> <bytecode: 0x55fcfee0baa0>
fc
#> function() NULL
#> <environment: 0x55fcfec76550>Created on 2023-10-30 with reprex v2.0.2
we currently use code like this instead
any(map_lgl(xs, identical, x)))
xs = list(a = data.table(foo = 1), b = data.table(bar = 2))
d = map_dtc(xs, identity)
print(d)
leads to
a.foo b.bar
1: 1 2
this is pretty much always unwanted i guess. i would suggest to either always
unname the xs list, or have a flag for that, which is by default true
i have this code now here
https://github.com/mlr-org/paradox/blob/master/R/generate_design_grid.R
# the un / renaming sucks a bit here, caused by dotdotdot-interface of CJ. would like to have a better way, but dont know
# FIXME: mini helper in mlr3misc for this?
ns = names(res); res = unname(res)
res = do.call(CJ, c(res, sorted = FALSE))
set_names(res, ns)
it sucks. problem is that res can contain "sorted".
should we add a helper CJ(list, sorted) ?
I would rather avoid this, but the above sucks a lot and keeping CJ as it is, is buggy and unsafe
To prevent the following
library(mlr3learners)
library(mlr3)
foo = mlr_learners$mget("classif.rpart", "classif.svm")
length(foo)
#> [1] 1Created on 2019-07-29 by the reprex package (v0.3.0)
In this case passing the learners as a vector is required.
Since the possible ellipsis in mget() usually needs to be named, we could somehow account for cases like above and maybe even get rid of the c() wrapping?
it says chunks takes a vec, it takes a length arg
Unfortunately we can't do tab completion, but this is the closest thing
When the function in map_dtc returns a data.table with many rows, map_dtc appears to be slower than it needs to be by a factor of about 100.
system.time(mlr3misc::map_dtc(1:3, function(x) runif(1e6, max = x)))
#> user system elapsed
#> 0.043 0.000 0.044
system.time(mlr3misc::map_dtc(1:3, function(x) data.table(x = runif(1e6, max = x))))
#> user system elapsed
#> 5.124 0.006 5.147 profvis tells me this this is because name_dots is called in data.table.
I wrote something similar to carrier::crate here. The difference is that .fn does not need to be a verbatim function definition, with the cost that all values being used need to be declared in .... Do we want this in mlr3misc? I think it would be generally helpful in places where functions are given to objects, e.g. in ParamUty constructor argument custom_check.
library(mlr3misc)
l = list(a = 1)
crate(function() print(a), a = l$a)
#> Error in vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...): values must be length 1,
#> but FUN(X[[2]]) result is length 3Created on 2023-08-27 with reprex v2.0.2
The object size below depends on how many packages are loaded before
library(mlr3)
library(mlr3verse)
library(data.table)
task = tsk("iris")
learner = lrn("classif.rpart")
learner$fallback = lrn("classif.featureless")
learner$train(task)
pth = tempfile(fileext = ".rds")
saveRDS(learner$state, pth)
x = readRDS(pth)
pryr::object_size(x)
#> 19.78 MBCreated on 2023-09-13 with reprex v2.0.2
it is the underlying issue reported here
mlr-org/mlr3tuning#201
i guess the case / problem is very clear
a) we document this at least as a restriction that unnesting non-scalar columns is not possible
b) probably better: unnest the rest into individual list-columns
I think we should sanitize our inputs, even if .mapply doesn't.
> mlr3misc::pmap(1:4, function(x) x)
*** caught segfault ***
address 0x200000001, cause 'memory not mapped'
Traceback:
1: .mapply(.f, .x, list(...))
2: mlr3misc::pmap(1:4, function(x) x)Prepare for release:
git pullurlchecker::url_check()devtools::check(remote = TRUE, manual = TRUE)devtools::check_win_devel()revdepcheck::revdep_check(num_workers = 4)cran-comments.mdgit pushSubmit to CRAN:
usethis::use_version('minor')devtools::submit_cran()Wait for CRAN...
usethis::use_github_release()usethis::use_dev_version(push = TRUE)i need to do special NA-handling in paradox, when a generated dt contains in some elements NAs.
when transponse generates the list of row-configurations, the user should have the option to filter out NAs quickly (before config-evaluation)
for this, I could set a class for returned dt-objects in paradox and then specialize transpose in paradox
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