🤲 💳 🎠 Pembelajaran mesin dalam bahasa R menggunakan paket mlr3 🤵🏻 🧖🏾 🏨

Halo, Habr!

Dalam posting ini, kami akan mempertimbangkan pendekatan yang paling bijaksana untuk pembelajaran mesin dalam bahasa R hari ini - paket mlr3 dan ekosistem di sekitarnya. Pendekatan ini didasarkan pada OOP "normal" menggunakan kelas R6 dan mewakili semua operasi dengan data dan model dalam bentuk grafik perhitungan. Ini memungkinkan Anda membuat saluran pipa yang ramping dan fleksibel untuk tugas-tugas pembelajaran mesin, tetapi pada awalnya mungkin terlihat rumit dan membingungkan. Di bawah ini kami akan mencoba memberikan kejelasan dan memotivasi untuk menggunakan mlr3 dalam proyek Anda.

Kandungan:

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 .

Pembelajaran mesin dalam bahasa R menggunakan paket mlr3