المصدر: https://mlr3book.mlr-org.com/
مرحبا يا هابر!في هذا المنشور ، سننظر في النهج الأكثر عمقًا لتعلم الآلة باللغة R اليوم - حزمة mlr3 والنظام البيئي من حولها. يعتمد هذا النهج على OOP "العادي" باستخدام فئات R6 وعلى تمثيل جميع العمليات بالبيانات والنماذج في شكل رسم بياني حسابي. يتيح لك ذلك إنشاء خطوط أنابيب انسيابية ومرنة لمهام تعلُم الآلة ، ولكن في البداية قد يبدو الأمر معقدًا ومربكًا. أدناه سنحاول تحقيق بعض الوضوح والتحفيز على استخدام mlr3 في مشاريعك.المحتوى:
- القليل من التاريخ والمقارنة مع الحلول المنافسة
- التفاصيل الفنية: فئات R6 وحزمة data.table
- المكونات الرئيسية لخط أنابيب ML في mlr3
- تعيين معلمات هايبر
- نظرة عامة على النظام البيئي Mlr3
- أنابيب ورسم بياني للحسابات
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 .