Quelle: https://mlr3book.mlr-org.com/
Hallo Habr!In diesem Beitrag werden wir den durchdachtesten Ansatz fĂŒr maschinelles Lernen in der heutigen R-Sprache betrachten - das mlr3- Paket und das Ăkosystem um es herum. Dieser Ansatz basiert auf ânormalemâ OOP unter Verwendung von R6-Klassen und auf der Darstellung aller Operationen mit Daten und Modellen in Form eines Berechnungsdiagramms. Auf diese Weise können Sie optimierte und flexible Pipelines fĂŒr maschinelle Lernaufgaben erstellen. Dies kann jedoch zunĂ€chst kompliziert und verwirrend erscheinen. Im Folgenden werden wir versuchen, Klarheit zu schaffen und zu motivieren, mlr3 in Ihren Projekten zu verwenden.Inhalt:
- Ein bisschen Geschichte und Vergleich mit konkurrierenden Lösungen
- Technische Details: R6-Klassen und data.table-Paket
- Die Hauptkomponenten der ML-Pipline in mlr3
- Hyperparameter einstellen
- Ăberblick ĂŒber das Mlr3-Ăkosystem
- Rohre und Diagramm der Berechnungen
1.
caret â ,
caret R ( CRAN 2007 ). 2013 Applied Predictive Modeling, .
:
- ( - );
- (-), , , ;
- , caret- - ;
- â , xgboost
nrounds, max_depth, eta, gamma, colsample_bytree, min_child_weight subsample.
:
- â , . ;
- : , . 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 â ; - .
:
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)
})
)
â "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$predict_newdata(newdata = iris[121:150, ])
preds
# <PredictionClassif> for 30 observations:
# row_id truth response prob.setosa prob.versicolor prob.virginica
# 1 virginica virginica 0 0.0952381 0.90476190
# 2 virginica versicolor 0 0.9795918 0.02040816
# 3 virginica virginica 0 0.0952381 0.90476190
# ---
# 28 virginica virginica 0 0.0952381 0.90476190
# 29 virginica virginica 0 0.0952381 0.90476190
# 30 virginica virginica 0 0.0952381 0.90476190
- 5 :
cv10 <- rsmp("cv", folds = 5)
resample_results <- resample(task, learner_rpart, cv10)
# INFO [09:37:05.993] Applying learner 'classif.rpart' on task 'iris' (iter 1/5)
# INFO [09:37:06.018] Applying learner 'classif.rpart' on task 'iris' (iter 2/5)
# INFO [09:37:06.042] Applying learner 'classif.rpart' on task 'iris' (iter 3/5)
# INFO [09:37:06.074] Applying learner 'classif.rpart' on task 'iris' (iter 4/5)
# INFO [09:37:06.098] Applying learner 'classif.rpart' on task 'iris' (iter 5/5)
resample_results
# <ResampleResult> of 5 iterations
# * Task: iris
# * Learner: classif.rpart
# * Warnings: 0 in 0 iterations
# * Errors: 0 in 0 iterations
# (-):
as.data.table(mlr_resamplings)
# key params iters
# 1: bootstrap repeats,ratio 30
# 2: custom 0
# 3: cv folds 10
# 4: holdout ratio 1
# 5: repeated_cv repeats,folds 100
# 6: subsampling repeats,ratio 30
. score() resample_results, â accuracy "classif.acc" classification error "classif.ce". , get(): mlr_measures$get("classif.ce"). msrs():
resample_results$score(msrs(c("classif.acc", "classif.ce")))[, 5:10]
#
# resampling resampling_id iteration prediction classif.acc classif.ce
# 1: <ResamplingCV> cv 1 <list> 0.8666667 0.13333333
# 2: <ResamplingCV> cv 2 <list> 0.9666667 0.03333333
# 3: <ResamplingCV> cv 3 <list> 0.9333333 0.06666667
# 4: <ResamplingCV> cv 4 <list> 0.9666667 0.03333333
# 5: <ResamplingCV> cv 5 <list> 0.9333333 0.06666667
4.
â . , .
. paradox:
library("paradox")
searchspace <- ParamSet$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$new("minsplit", lower = 1, upper = 10)
)
),
terminator = term("evals", n_evals = 20)
)
tuning_instance
# <TuningInstance>
# * State: Not tuned
# * Task: <TaskClassif:iris>
# * Learner: <LearnerClassifRpart:classif.rpart>
# * Measures: classif.ce
# * Resampling: <ResamplingCV>
# * Terminator: <TerminatorEvals>
# * bm_args: list()
# * n_evals: 0
# ParamSet:
# id class lower upper levels default value
# 1: cp ParamDbl 0.001 0.1 <NoDefault>
# 2: minsplit ParamInt 1.000 10.0 <NoDefault>
â Tuner, :
tuner <- tnr("grid_search",
resolution = 5,
batch_size = 2)
#
# as.data.table(mlr_tuners)
# key
# 1: design_points
# 2: gensa
# 3: grid_search
# 4: random_search
resolution = 5, 25 . 20 , terminator = term("evals", n_evals = 20). batch_size â , . mlr3 â , .
tnr("design_points"): , ( â , mlr3 ).
, :
result <- tuner$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$score()[, classif.ce])]
ce_resemples <- res[, .(ce_resemples = unlist(ce_resemples)), by = nr]
res[ce_resemples, on = "nr"]
5. mlr3
: mlr3, mlr3tuning paradox. , - mlr3verse:
# install.packages("mlr3verse")
library(mlr3verse)
## Loading required package: mlr3
## Loading required package: mlr3db
## Loading required package: mlr3filters
## Loading required package: mlr3learners
## Loading required package: mlr3pipelines
## Loading required package: mlr3tuning
## Loading required package: mlr3viz
## Loading required package: paradox
- mlr3db dbplyr data.table.
- mlr3filters , ( ).
- mlr3learners (
regr.glmnet, regr.kknn, regr.km, regr.lm, regr.ranger, regr.svm, regr.xgboost) (classif.glmnet, classif.kknn, classif.lda, classif.log_reg, classif.multinom, classif.naive_bayes, classif.qda, classif.ranger, classif.svm, classif.xgboost). . - mlr3pipelines (pipelines), . , , CRAN, :
remotes::install_github("https://github.com/mlr-org/mlr3pipelines"). - mlr3tuning .
- mlr3viz , .
- mlr3measures â ~40 . mlr3verse , .
, .
6.
(pipelines) , , .
â , , â . PipeOpLearner(), â PipeOpFilter(), â PipeOp(). ( po()) :
pca <- po("pca")
filter <- po("filter",
filter = mlr3filters::flt("variance"),
filter.frac = 0.5)
learner_po <- po("learner",
learner = lrn("classif.rpart"))
%>>%:
graph <- pca %>>% filter %>>% learner_po
graph$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 .