Source: https://mlr3book.mlr-org.com/
Hello, Habr!In this post, we will consider the most thoughtful approach to machine learning in the R language today - the mlr3 package and the ecosystem around it. This approach is based on “normal” OOP using R6 classes and on representing all operations with data and models in the form of a computation graph. This allows you to create streamlined and flexible pipelines for machine learning tasks, but at first it may seem complicated and confusing. Below we will try to bring some clarity and motivate to use mlr3 in your projects.Content:
- A bit of history and comparison with competing solutions
- Technical details: R6 classes and data.table package
- The main components of ML pipline in mlr3
- Setting Hyperparameters
- Mlr3 ecosystem overview
- Pipes and graph of calculations
1.
caret — ,
caret R ( CRAN 2007 ). 2013 Applied Predictive Modeling, .
:
- ( - );
- (-), , , ;
- , caret- - ;
- — , xgboost
nrounds, max_depth, eta, gamma, colsample_bytree, min_child_weight subsample.
:
- — , . ;
- : , . recipes , ;
- - (nested resampling), caretEnsemble.
tidyverse strikes back
tidymodels, recipes ( «» , ), rsample ( ) tune ( ).
:
- «» , ;
- , embed textrecipes;
- , . ( tune);
- workflows «» .
:
- , tune . «» , ,
apply/map-; - . , 200 ;
- - .
mlr3 vs
mlr3 mlr , caret tidymodels. mlr , mlr3.
:
- R6-, data.table;
- . , ;
- API ,
learner — ; - .
:
2. : R6- data.table
mlr3 «» , R6-. R6- , . Advanced R, , .
R6- R6Class():
library(R6)
Accumulator <- R6Class("Accumulator", list(
sum = 0,
add = function(x = 1) {
self$sum <- self$sum + x
invisible(self)
})
)
— "Accumulator".
new(), (, , ) :
x <- Accumulator$new()
, , :
x$add(4)
x$sum
#> [1] 4
R6- :
y1 <- Accumulator$new()
y2 <- y1
y1$add(10)
c(y1 = y1$sum, y2 = y2$sum)
#> y1 y2
#> 10 10
clone() ( clone(deep = TRUE) ):
y1 <- Accumulator$new()
y2 <- y1$clone()
y1$add(10)
c(y1 = y1$sum, y2 = y2$sum)
#> y1 y2
#> 10 0
, R6 mlr3.
data.table ( , data.table data.table: R). - data.table, := . , , 2 , . , , .
3. ML- mlr3
: https://mlr3book.mlr-org.com/
mlr3 :
library(mlr3)
#
task <- TaskClassif$new(id = "iris",
backend = iris,
target = "Species")
task
# <TaskClassif:iris> (150 x 5)
# * Target: Species
# * Properties: multiclass
# * Features (4):
# - dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#
# learner_rpart <- mlr_learners$get("classif.rpart")
learner_rpart <- lrn("classif.rpart",
predict_type = "prob",
minsplit = 50)
learner_rpart
# <LearnerClassifRpart:classif.rpart>
# * Model: -
# * Parameters: xval=0, minsplit=50
# * Packages: rpart
# * Predict Type: prob
# * Feature types: logical, integer, numeric, factor, ordered
# * Properties: importance, missings, multiclass, selected_features, twoclass, weights
#
learner_rpart$param_set
# ParamSet:
# id class lower upper levels default value
# 1: minsplit ParamInt 1 Inf 20 50
# 2: minbucket ParamInt 1 Inf <NoDefault>
# 3: cp ParamDbl 0 1 0.01
# 4: maxcompete ParamInt 0 Inf 4
# 5: maxsurrogate ParamInt 0 Inf 5
# 6: maxdepth ParamInt 1 30 30
# 7: usesurrogate ParamInt 0 2 2
# 8: surrogatestyle ParamInt 0 1 0
# 9: xval ParamInt 0 Inf 10 0
#
learner_rpart$train(task, row_ids = 1:120)
learner_rpart$model
# n= 120
#
# node), split, n, loss, yval, (yprob)
# * denotes terminal node
#
# 1) root 120 70 setosa (0.41666667 0.41666667 0.16666667)
# 2) Petal.Length< 2.45 50 0 setosa (1.00000000 0.00000000 0.00000000) *
# 3) Petal.Length>=2.45 70 20 versicolor (0.00000000 0.71428571 0.28571429)
# 6) Petal.Length< 4.95 49 1 versicolor (0.00000000 0.97959184 0.02040816) *
# 7) Petal.Length>=4.95 21 2 virginica (0.00000000 0.09523810 0.90476190) *
: (task) (learner).
(TaskClassif , TaskRegr ..) new(). id, backend target; positive. : mlr_tasks$get("iris") tsk("iris").
mlr_learners get() train(), task , . : lrn("classif.rpart", predict_type = "prob", minsplit = 50). (predict_type = "prob") (minsplit = 50). : learner_rpart$predict_type <- "prob", learner_rpart$param_set$values$minsplit = 50.
predict_newdata():
#
preds <- learner_rpart$predict_newdata(newdata = iris[121:150, ])
preds
# <PredictionClassif> for 30 observations:
# row_id truth response prob.setosa prob.versicolor prob.virginica
# 1 virginica virginica 0 0.0952381 0.90476190
# 2 virginica versicolor 0 0.9795918 0.02040816
# 3 virginica virginica 0 0.0952381 0.90476190
# ---
# 28 virginica virginica 0 0.0952381 0.90476190
# 29 virginica virginica 0 0.0952381 0.90476190
# 30 virginica virginica 0 0.0952381 0.90476190
- 5 :
cv10 <- rsmp("cv", folds = 5)
resample_results <- resample(task, learner_rpart, cv10)
# INFO [09:37:05.993] Applying learner 'classif.rpart' on task 'iris' (iter 1/5)
# INFO [09:37:06.018] Applying learner 'classif.rpart' on task 'iris' (iter 2/5)
# INFO [09:37:06.042] Applying learner 'classif.rpart' on task 'iris' (iter 3/5)
# INFO [09:37:06.074] Applying learner 'classif.rpart' on task 'iris' (iter 4/5)
# INFO [09:37:06.098] Applying learner 'classif.rpart' on task 'iris' (iter 5/5)
resample_results
# <ResampleResult> of 5 iterations
# * Task: iris
# * Learner: classif.rpart
# * Warnings: 0 in 0 iterations
# * Errors: 0 in 0 iterations
# (-):
as.data.table(mlr_resamplings)
# key params iters
# 1: bootstrap repeats,ratio 30
# 2: custom 0
# 3: cv folds 10
# 4: holdout ratio 1
# 5: repeated_cv repeats,folds 100
# 6: subsampling repeats,ratio 30
. score() resample_results, — accuracy "classif.acc" classification error "classif.ce". , get(): mlr_measures$get("classif.ce"). msrs():
resample_results$score(msrs(c("classif.acc", "classif.ce")))[, 5:10]
#
# resampling resampling_id iteration prediction classif.acc classif.ce
# 1: <ResamplingCV> cv 1 <list> 0.8666667 0.13333333
# 2: <ResamplingCV> cv 2 <list> 0.9666667 0.03333333
# 3: <ResamplingCV> cv 3 <list> 0.9333333 0.06666667
# 4: <ResamplingCV> cv 4 <list> 0.9666667 0.03333333
# 5: <ResamplingCV> cv 5 <list> 0.9333333 0.06666667
4.
— . , .
. paradox:
library("paradox")
searchspace <- ParamSet$new(list(
ParamDbl$new("cp", lower = 0.001, upper = 0.1),
ParamInt$new("minsplit", lower = 1, upper = 10)
))
searchspace
# ParamSet:
# id class lower upper levels default value
# 1: cp ParamDbl 0.001 0.1 <NoDefault>
# 2: minsplit ParamInt 1.000 10.0 <NoDefault>
ParamSet, cp minsplit; rpart .
, searchspace . tune() Tuner. . resolution, , param_resolutions, . , , .
generate_design_grid() , :
generate_design_grid(searchspace,
param_resolutions = c("cp" = 2, "minsplit" = 3))
# <Design> with 6 rows:
# cp minsplit
# 1: 0.001 1
# 2: 0.001 5
# 3: 0.001 10
# 4: 0.100 1
# 5: 0.100 5
# 6: 0.100 10
: generate_design_random() generate_design_lhs() .
, . Terminator-, , ( ), . mlr3tuning:
library("mlr3tuning")
evals20 <- term("evals", n_evals = 20)
evals20
# <TerminatorEvals>
# * Parameters: n_evals=20
#
as.data.table(mlr_terminators)
# key
# 1: clock_time
# 2: combo
# 3: evals
# 4: model_time
# 5: none
# 6: perf_reached
# 7: stagnation
TuningInstance:
tuning_instance <- TuningInstance$new(
task = TaskClassif$new(id = "iris",
backend = iris,
target = "Species"),
learner = lrn("classif.rpart",
predict_type = "prob"),
resampling = rsmp("cv", folds = 5),
measures = msr("classif.ce"),
param_set = ParamSet$new(
list(ParamDbl$new("cp", lower = 0.001, upper = 0.1),
ParamInt$new("minsplit", lower = 1, upper = 10)
)
),
terminator = term("evals", n_evals = 20)
)
tuning_instance
# <TuningInstance>
# * State: Not tuned
# * Task: <TaskClassif:iris>
# * Learner: <LearnerClassifRpart:classif.rpart>
# * Measures: classif.ce
# * Resampling: <ResamplingCV>
# * Terminator: <TerminatorEvals>
# * bm_args: list()
# * n_evals: 0
# ParamSet:
# id class lower upper levels default value
# 1: cp ParamDbl 0.001 0.1 <NoDefault>
# 2: minsplit ParamInt 1.000 10.0 <NoDefault>
— Tuner, :
tuner <- tnr("grid_search",
resolution = 5,
batch_size = 2)
#
# as.data.table(mlr_tuners)
# key
# 1: design_points
# 2: gensa
# 3: grid_search
# 4: random_search
resolution = 5, 25 . 20 , terminator = term("evals", n_evals = 20). batch_size — , . mlr3 — , .
tnr("design_points"): , ( — , mlr3 ).
, :
result <- tuner$tune(tuning_instance)
result
# NULL
, result . , tuner$tune() tuning_instance:
tuning_instance$result
# $tune_x
# $tune_x$cp
# [1] 0.001
#
# $tune_x$minsplit
# [1] 5
#
#
# $params
# $params$xval
# [1] 0
#
# $params$cp
# [1] 0.001
#
# $params$minsplit
# [1] 5
#
#
# $perf
# classif.ce
# 0.04
result <- tuning_instance$archive(unnest = "params")
result[order(classif.ce), c("cp", "minsplit", "classif.ce")]
# cp minsplit classif.ce
# 1: 0.00100 5 0.04000000
# 2: 0.00100 3 0.04000000
# 3: 0.00100 8 0.04000000
# 4: 0.00100 1 0.04000000
# 5: 0.00100 10 0.04666667
# 6: 0.02575 10 0.06000000
# 7: 0.07525 5 0.06000000
# 8: 0.02575 8 0.06000000
# 9: 0.02575 3 0.06000000
# 10: 0.05050 1 0.06000000
# 11: 0.07525 3 0.06000000
# 12: 0.07525 1 0.06000000
# 13: 0.05050 3 0.06000000
# 14: 0.02575 5 0.06000000
# 15: 0.05050 5 0.06000000
# 16: 0.05050 8 0.06000000
# 17: 0.10000 3 0.06000000
# 18: 0.10000 8 0.06000000
# 19: 0.05050 10 0.06000000
# 20: 0.10000 1 0.06000000
library(ggplot2)
ggplot(result,
aes(x = cp, y = classif.ce, color = as.factor(minsplit))) +
geom_line() +
geom_point(size = 3)
, tune():
Tuner ( batch_size);Learner Task . ResampleResult ( BenchmarkResult);Terminator , . , 1, , ;- ;
- , ( , ,
msr("classif.ce", aggregator = "median").
tuning_instance$bmr, BenchmarkResult, score() as.data.table(tuning_instance$bmr). , , ResampleResult tuning_instance$archive():
tuning_instance$archive()[1, resample_result][[1]]$score()[, 4:9]
# learner_id resampling resampling_id iteration prediction classif.ce
# 1: classif.rpart <ResamplingCV> cv 1 <list> 0.06666667
# 2: classif.rpart <ResamplingCV> cv 2 <list> 0.16666667
# 3: classif.rpart <ResamplingCV> cv 3 <list> 0.03333333
# 4: classif.rpart <ResamplingCV> cv 4 <list> 0.03333333
# 5: classif.rpart <ResamplingCV> cv 5 <list> 0.00000000
, :
res <- tuning_instance$archive(unnest = "params")
res[, ce_resemples := lapply(resample_result, function(x) x$score()[, classif.ce])]
ce_resemples <- res[, .(ce_resemples = unlist(ce_resemples)), by = nr]
res[ce_resemples, on = "nr"]
5. mlr3
: mlr3, mlr3tuning paradox. , - mlr3verse:
# install.packages("mlr3verse")
library(mlr3verse)
## Loading required package: mlr3
## Loading required package: mlr3db
## Loading required package: mlr3filters
## Loading required package: mlr3learners
## Loading required package: mlr3pipelines
## Loading required package: mlr3tuning
## Loading required package: mlr3viz
## Loading required package: paradox
- mlr3db dbplyr data.table.
- mlr3filters , ( ).
- mlr3learners (
regr.glmnet, regr.kknn, regr.km, regr.lm, regr.ranger, regr.svm, regr.xgboost) (classif.glmnet, classif.kknn, classif.lda, classif.log_reg, classif.multinom, classif.naive_bayes, classif.qda, classif.ranger, classif.svm, classif.xgboost). . - mlr3pipelines (pipelines), . , , CRAN, :
remotes::install_github("https://github.com/mlr-org/mlr3pipelines"). - mlr3tuning .
- mlr3viz , .
- mlr3measures — ~40 . mlr3verse , .
, .
6.
(pipelines) , , .
— , , — . PipeOpLearner(), — PipeOpFilter(), — PipeOp(). ( po()) :
pca <- po("pca")
filter <- po("filter",
filter = mlr3filters::flt("variance"),
filter.frac = 0.5)
learner_po <- po("learner",
learner = lrn("classif.rpart"))
%>>%:
graph <- pca %>>% filter %>>% learner_po
graph$plot()
. , :
gr <- Graph$new()$
add_pipeop(mlr_pipeops$get("copy", outnum = 2))$
add_pipeop(mlr_pipeops$get("scale"))$
add_pipeop(mlr_pipeops$get("pca"))$
add_pipeop(mlr_pipeops$get("featureunion", innum = 2))
gr$
add_edge("copy", "scale", src_channel = 1)$
add_edge("copy", "pca", src_channel = "output2")$
add_edge("scale", "featureunion", dst_channel = 1)$
add_edge("pca", "featureunion", dst_channel = 2)
gr$plot(html = FALSE)
, (po("learner", learner = lrn("classif.rpart"))). , :
glrn <- GraphLearner$new(graph)
glrn
# <GraphLearner:pca.variance.classif.rpart>
# * Model: -
# * Parameters: variance.filter.frac=0.5, variance.na.rm=TRUE, classif.rpart.xval=0
# * Packages: -
# * Predict Type: response
# * Feature types: logical, integer, numeric, character, factor, ordered, POSIXct
# * Properties: importance, missings, multiclass, oob_error, selected_features, twoclass,
# weights
GraphLearner Learner. , Learner-, :
resample(tsk("iris"), glrn, rsmp("cv"))
# INFO [17:17:00.358] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 1/10)
# INFO [17:17:00.615] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 2/10)
# INFO [17:17:00.881] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 3/10)
# INFO [17:17:01.087] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 4/10)
# INFO [17:17:01.303] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 5/10)
# INFO [17:17:01.518] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 6/10)
# INFO [17:17:01.716] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 7/10)
# INFO [17:17:01.927] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 8/10)
# INFO [17:17:02.129] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 9/10)
# INFO [17:17:02.337] Applying learner 'pca.variance.classif.rpart' on task 'iris' (iter 10/10)
# <ResampleResult> of 10 iterations
# * Task: iris
# * Learner: pca.variance.classif.rpart
# * Warnings: 0 in 0 iterations
# * Errors: 0 in 0 iterations
, issue How to deal with different preprocessing steps as hyperparameters:
gr <- pipeline_branch(list(pca = po("pca"), nothing = po("nop")))
gr$plot()
caret tidymodels !
, mlr3. mlr3 book .