【R vs Python】机器学习

R语言
python
机器学习
R语言（tidymodels 和 mlr3）和python(sklearn)机器学习对比
作者

不止BI

发布于

2024年2月23日

数据集

企鹅数据集（penguins）是一个包含南极洲三种企鹅物种的生物测量数据的公开数据集。该数据集由美国南极洲 Palmer 站的科学家在 1971 年至 2022 年间收集。

  • species: 企鹅物种，取值为 Adelie、Chinstrap 或 Gentoo

  • island: 企鹅栖息的岛屿，取值为 Biscoe、Dream 或 Torgersen

  • sex: 企鹅性别，取值为 male 或 female

  • bill_length_mm: 企鹅喙长（毫米）

  • bill_depth_mm: 企鹅喙深（毫米）

  • flipper_length_mm: 企鹅鳍长（毫米）

  • body_mass_g: 企鹅体重（克）

  • year: 数据收集年份

  • age: 企鹅年龄（年）

代码 
library(dplyr)

tb_penguins <-
  read.csv(
    "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv"
  ) %>%
  as_tibble()
head(tb_penguins)
# A tibble: 6 × 7
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <chr>   <chr>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
5 Adelie  Torgersen           36.7          19.3               193        3450
6 Adelie  Torgersen           39.3          20.6               190        3650
# ℹ 1 more variable: sex <chr>
代码 
library(data.table)

dt_penguins <- as.data.table(
  read.csv(
    "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv"
  )
)
head(dt_penguins)
   species    island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
    <char>    <char>          <num>         <num>             <int>       <int>
1:  Adelie Torgersen           39.1          18.7               181        3750
2:  Adelie Torgersen           39.5          17.4               186        3800
3:  Adelie Torgersen           40.3          18.0               195        3250
4:  Adelie Torgersen             NA            NA                NA          NA
5:  Adelie Torgersen           36.7          19.3               193        3450
6:  Adelie Torgersen           39.3          20.6               190        3650
      sex
   <char>
1:   MALE
2: FEMALE
3: FEMALE
4:       
5: FEMALE
6:   MALE
代码 
import pandas as pd

df_penguins = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv")
df_penguins.head(5)
  species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex
0  Adelie  Torgersen            39.1  ...              181.0       3750.0    MALE
1  Adelie  Torgersen            39.5  ...              186.0       3800.0  FEMALE
2  Adelie  Torgersen            40.3  ...              195.0       3250.0  FEMALE
3  Adelie  Torgersen             NaN  ...                NaN          NaN     NaN
4  Adelie  Torgersen            36.7  ...              193.0       3450.0  FEMALE

[5 rows x 7 columns]

数据可视化

代码 
library(GGally)
library(tidyverse)

ggpairs(tb_penguins %>% dplyr::select(ends_with("mm"), species), mapping = ggplot2::aes(color = species))

代码 
library(GGally)
ggpairs(tb_penguins, mapping = ggplot2::aes(color = species))

代码 
import seaborn as sns
plot1 = sns.pairplot(data = df_penguins,hue = 'species')

基本流程

观察数据

代码 
glimpse(tb_penguins)
Rows: 344
Columns: 7
$ species           <chr> "Adelie", "Adelie", "Adelie", "Adelie", "Adelie", "A…
$ island            <chr> "Torgersen", "Torgersen", "Torgersen", "Torgersen", …
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <chr> "MALE", "FEMALE", "FEMALE", "", "FEMALE", "MALE", "F…
代码 
skimr::skim(data = tb_penguins)
Data summary
Name tb_penguins
Number of rows 344
Number of columns 7
_______________________
Column type frequency:
character 3
numeric 4
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
species 0 1 6 9 0 3 0
island 0 1 5 9 0 3 0
sex 0 1 0 6 11 3 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
bill_length_mm 2 0.99 43.92 5.46 32.1 39.23 44.45 48.5 59.6 ▃▇▇▆▁
bill_depth_mm 2 0.99 17.15 1.97 13.1 15.60 17.30 18.7 21.5 ▅▅▇▇▂
flipper_length_mm 2 0.99 200.92 14.06 172.0 190.00 197.00 213.0 231.0 ▂▇▃▅▂
body_mass_g 2 0.99 4201.75 801.95 2700.0 3550.00 4050.00 4750.0 6300.0 ▃▇▆▃▂

数据拆分

代码 
library(tidymodels)
set.seed(123)

tb_penguins <- tb_penguins %>%
  mutate(RowId = row_number(), species = factor(species))

# 分层抽样
data_split <- initial_split(tb_penguins, prop = 3 / 4, strata = species)

train_data <- training(data_split)
test_data <- testing(data_split)


# 交叉验证

folds <- vfold_cv(train_data, v = 10, repeats = 10, strata = species)
folds
#  10-fold cross-validation repeated 10 times using stratification 
# A tibble: 100 × 3
   splits           id       id2   
   <list>           <chr>    <chr> 
 1 <split [230/28]> Repeat01 Fold01
 2 <split [231/27]> Repeat01 Fold02
 3 <split [231/27]> Repeat01 Fold03
 4 <split [232/26]> Repeat01 Fold04
 5 <split [233/25]> Repeat01 Fold05
 6 <split [233/25]> Repeat01 Fold06
 7 <split [233/25]> Repeat01 Fold07
 8 <split [233/25]> Repeat01 Fold08
 9 <split [233/25]> Repeat01 Fold09
10 <split [233/25]> Repeat01 Fold10
# ℹ 90 more rows

创建配方

代码 
penguins_rec <-
  recipe(species ~ ., data = train_data) %>%
  update_role(RowId, new_role = "ID") %>%
  step_dummy(all_nominal_predictors()) %>%
  step_naomit(all_predictors()) %>%
  step_zv(all_predictors())

创建模型

代码 
# 逻辑回归
# lgr_mod <- logistic_reg()

# 随机森林模型，指定参数、计算引擎(即采用的算法包)、计算模式(分类或回归)
rf_mod <-
  rand_forest(trees = 1000) %>%
  set_engine("ranger") %>%
  set_mode("classification")


# 超参数调参
tune_spec <-
  decision_tree(
    cost_complexity = tune(),
    tree_depth = tune()
  ) %>%
  set_engine("rpart") %>%
  set_mode("classification")
tree_grid <- grid_regular(cost_complexity(),
  tree_depth(),
  levels = 5
)

创建工作流

代码 
# 包含数据预处理及模型的工作流
penguins_wflow <- workflow() %>%
  add_recipe(penguins_rec) %>%
  add_model(rf_mod)
penguins_wflow
══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()

── Preprocessor ────────────────────────────────────────────────────────────────
3 Recipe Steps

• step_dummy()
• step_naomit()
• step_zv()

── Model ───────────────────────────────────────────────────────────────────────
Random Forest Model Specification (classification)

Main Arguments:
  trees = 1000

Computational engine: ranger
代码 
# 包含数据预处理及模型超参数调参的工作流
penguins_wflow_tune <- workflow() %>%
  add_recipe(penguins_rec) %>%
  add_model(tune_spec)
penguins_wflow_tune
══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: decision_tree()

── Preprocessor ────────────────────────────────────────────────────────────────
3 Recipe Steps

• step_dummy()
• step_naomit()
• step_zv()

── Model ───────────────────────────────────────────────────────────────────────
Decision Tree Model Specification (classification)

Main Arguments:
  cost_complexity = tune()
  tree_depth = tune()

Computational engine: rpart

拟合数据

代码 
# 直接拟合训练集
penguins_fit <- penguins_wflow %>% fit(train_data)
penguins_fit
══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()

── Preprocessor ────────────────────────────────────────────────────────────────
3 Recipe Steps

• step_dummy()
• step_naomit()
• step_zv()

── Model ───────────────────────────────────────────────────────────────────────
Ranger result

Call:
 ranger::ranger(x = maybe_data_frame(x), y = y, num.trees = ~1000,      num.threads = 1, verbose = FALSE, seed = sample.int(10^5,          1), probability = TRUE) 

Type:                             Probability estimation 
Number of trees:                  1000 
Sample size:                      256 
Number of independent variables:  8 
Mtry:                             2 
Target node size:                 10 
Variable importance mode:         none 
Splitrule:                        gini 
OOB prediction error (Brier s.):  0.01622186
代码 
# 交叉验证拟和
penguins_fit_fold <- penguins_wflow %>% fit_resamples(resamples = folds)
collect_metrics(penguins_fit_fold)
# A tibble: 3 × 6
  .metric     .estimator   mean     n  std_err .config             
  <chr>       <chr>       <dbl> <int>    <dbl> <chr>               
1 accuracy    multiclass 0.986    100 0.00209  Preprocessor1_Model1
2 brier_class multiclass 0.0176   100 0.00109  Preprocessor1_Model1
3 roc_auc     hand_till  1.00     100 0.000166 Preprocessor1_Model1
代码 
# 调参拟和
penguins_fit_fold_tune <- penguins_wflow_tune %>%
  tune_grid(resamples = folds, grid = tree_grid)
collect_metrics(penguins_fit_fold_tune)
# A tibble: 75 × 8
   cost_complexity tree_depth .metric     .estimator  mean     n std_err .config
             <dbl>      <int> <chr>       <chr>      <dbl> <int>   <dbl> <chr>  
 1    0.0000000001          1 accuracy    multiclass 0.783   100 0.00363 Prepro…
 2    0.0000000001          1 brier_class multiclass 0.158   100 0.00270 Prepro…
 3    0.0000000001          1 roc_auc     hand_till  0.813   100 0.00241 Prepro…
 4    0.0000000178          1 accuracy    multiclass 0.783   100 0.00363 Prepro…
 5    0.0000000178          1 brier_class multiclass 0.158   100 0.00270 Prepro…
 6    0.0000000178          1 roc_auc     hand_till  0.813   100 0.00241 Prepro…
 7    0.00000316            1 accuracy    multiclass 0.783   100 0.00363 Prepro…
 8    0.00000316            1 brier_class multiclass 0.158   100 0.00270 Prepro…
 9    0.00000316            1 roc_auc     hand_till  0.813   100 0.00241 Prepro…
10    0.000562              1 accuracy    multiclass 0.783   100 0.00363 Prepro…
# ℹ 65 more rows

抽取结果

代码 
# 抽取模型(随机森林不支持)
# penguins_fit %>%
#   extract_fit_parsnip() %>%
#   tidy()

预测新数据

代码 
# 只显示目标变量
penguins_pred <- predict(penguins_fit, test_data)
# 目标变量 与 预测数据结合
penguins_aug <- augment(penguins_fit, test_data)
penguins_aug
# A tibble: 86 × 12
   .pred_class .pred_Adelie .pred_Chinstrap .pred_Gentoo species island   
   <fct>              <dbl>           <dbl>        <dbl> <fct>   <chr>    
 1 Adelie             0.999        0.000443     0.000150 Adelie  Torgersen
 2 Adelie             0.986        0.0111       0.00259  Adelie  Torgersen
 3 Adelie             0.990        0.00662      0.00295  Adelie  Torgersen
 4 Adelie             0.992        0.00651      0.00135  Adelie  Torgersen
 5 Adelie             0.989        0.00158      0.00961  Adelie  Torgersen
 6 Adelie             0.949        0.0353       0.0159   Adelie  Torgersen
 7 Adelie             0.996        0.00297      0.00119  Adelie  Torgersen
 8 Adelie             0.983        0.0106       0.00642  Adelie  Biscoe   
 9 Adelie             0.922        0.0760       0.00164  Adelie  Dream    
10 Adelie             0.899        0.0999       0.00161  Adelie  Dream    
# ℹ 76 more rows
# ℹ 6 more variables: bill_length_mm <dbl>, bill_depth_mm <dbl>,
#   flipper_length_mm <int>, body_mass_g <int>, sex <chr>, RowId <int>

模型评价

代码 
penguins_aug %>%
  roc_curve(truth = species, .pred_Adelie:.pred_Gentoo) %>%
  autoplot()

代码 
penguins_aug %>% roc_auc(truth = species, .pred_Adelie:.pred_Gentoo)
# A tibble: 1 × 3
  .metric .estimator .estimate
  <chr>   <chr>          <dbl>
1 roc_auc hand_till      0.999
代码 
penguins_aug %>% accuracy(truth = species, estimate = .pred_class)
# A tibble: 1 × 3
  .metric  .estimator .estimate
  <chr>    <chr>          <dbl>
1 accuracy multiclass     0.988

观察数据

代码 
library(mlr3verse)
summary(tb_penguins)
      species       island          bill_length_mm  bill_depth_mm  
 Adelie   :152   Length:344         Min.   :32.10   Min.   :13.10  
 Chinstrap: 68   Class :character   1st Qu.:39.23   1st Qu.:15.60  
 Gentoo   :124   Mode  :character   Median :44.45   Median :17.30  
                                    Mean   :43.92   Mean   :17.15  
                                    3rd Qu.:48.50   3rd Qu.:18.70  
                                    Max.   :59.60   Max.   :21.50  
                                    NA's   :2       NA's   :2      
 flipper_length_mm  body_mass_g       sex                RowId       
 Min.   :172.0     Min.   :2700   Length:344         Min.   :  1.00  
 1st Qu.:190.0     1st Qu.:3550   Class :character   1st Qu.: 86.75  
 Median :197.0     Median :4050   Mode  :character   Median :172.50  
 Mean   :200.9     Mean   :4202                      Mean   :172.50  
 3rd Qu.:213.0     3rd Qu.:4750                      3rd Qu.:258.25  
 Max.   :231.0     Max.   :6300                      Max.   :344.00  
 NA's   :2         NA's   :2

新建学习任务

代码 
tsk_penguins <- as_task_classif(tb_penguins, target = "species", id = "penguins")
tsk_penguins
<TaskClassif:penguins> (344 x 8)
* Target: species
* Properties: multiclass
* Features (7):
  - int (3): RowId, body_mass_g, flipper_length_mm
  - dbl (2): bill_depth_mm, bill_length_mm
  - chr (2): island, sex

mlr3中的tsk是一个R6类,可以通过$调取类方法和属性.需要注意的是，tsk类中的方法会直接作用于对象，如果需要保留原始对象，需要使用$clone将tsk复制后进行操作

代码 
class(tsk_penguins)
[1] "TaskClassif"    "TaskSupervised" "Task"           "R6"
代码 
# 查看列信息
tsk_penguins$col_info
Key: <id>
                  id      type                  levels  label fix_factor_levels
              <char>    <char>                  <list> <char>            <lgcl>
1:          ..row_id   integer                  [NULL]   <NA>             FALSE
2:             RowId   integer                  [NULL]   <NA>             FALSE
3:     bill_depth_mm   numeric                  [NULL]   <NA>             FALSE
4:    bill_length_mm   numeric                  [NULL]   <NA>             FALSE
5:       body_mass_g   integer                  [NULL]   <NA>             FALSE
6: flipper_length_mm   integer                  [NULL]   <NA>             FALSE
7:            island character                  [NULL]   <NA>             FALSE
8:               sex character                  [NULL]   <NA>             FALSE
9:           species    factor Adelie,Chinstrap,Gentoo   <NA>             FALSE
代码 
# 查看分类
tsk_penguins$class_names
[1] "Adelie"    "Chinstrap" "Gentoo"
代码 
# 特征名称
tsk_penguins$feature_names
[1] "RowId"             "bill_depth_mm"     "bill_length_mm"   
[4] "body_mass_g"       "flipper_length_mm" "island"           
[7] "sex"
代码 
# 筛选数据（在原始对象）
# tsk_penguins$filter(1:4)
# 筛选数据（在复制对象，原始对象保持不变）
tsk_penguins_clone <- tsk_penguins$clone()
tsk_penguins_clone$filter(1:3)
# 原始对象数据不变
tsk_penguins$data()
     species RowId bill_depth_mm bill_length_mm body_mass_g flipper_length_mm
      <fctr> <int>         <num>          <num>       <int>             <int>
  1:  Adelie     1          18.7           39.1        3750               181
  2:  Adelie     2          17.4           39.5        3800               186
  3:  Adelie     3          18.0           40.3        3250               195
  4:  Adelie     4            NA             NA          NA                NA
  5:  Adelie     5          19.3           36.7        3450               193
 ---                                                                         
340:  Gentoo   340            NA             NA          NA                NA
341:  Gentoo   341          14.3           46.8        4850               215
342:  Gentoo   342          15.7           50.4        5750               222
343:  Gentoo   343          14.8           45.2        5200               212
344:  Gentoo   344          16.1           49.9        5400               213
        island    sex
        <char> <char>
  1: Torgersen   MALE
  2: Torgersen FEMALE
  3: Torgersen FEMALE
  4: Torgersen       
  5: Torgersen FEMALE
 ---                 
340:    Biscoe       
341:    Biscoe FEMALE
342:    Biscoe   MALE
343:    Biscoe FEMALE
344:    Biscoe   MALE
代码 
# 复制对象数据已被筛选
tsk_penguins_clone$data()
   species RowId bill_depth_mm bill_length_mm body_mass_g flipper_length_mm
    <fctr> <int>         <num>          <num>       <int>             <int>
1:  Adelie     1          18.7           39.1        3750               181
2:  Adelie     2          17.4           39.5        3800               186
3:  Adelie     3          18.0           40.3        3250               195
      island    sex
      <char> <char>
1: Torgersen   MALE
2: Torgersen FEMALE
3: Torgersen FEMALE

数据拆分

代码 
# 拆分训练集与测试集
splits <- partition(tsk_penguins, ratio = 0.75)
splits
$train
  [1]   2   3   4   5   6   8   9  11  14  15  16  17  19  20  21  22  23  24
 [19]  25  26  27  30  31  33  34  35  36  37  38  40  41  42  44  45  46  47
 [37]  49  50  51  52  53  55  56  57  58  60  62  63  64  67  69  70  71  72
 [55]  73  74  76  77  78  80  81  82  84  85  86  87  88  89  90  92  93  94
 [73]  96  97 100 101 104 105 106 107 108 110 112 115 116 117 118 119 121 122
 [91] 123 126 127 128 129 130 131 132 133 134 135 139 140 141 142 143 144 145
[109] 146 147 148 149 150 152 154 155 157 158 160 162 163 164 165 168 169 170
[127] 171 172 173 174 176 177 178 180 181 182 183 184 185 186 188 189 191 192
[145] 193 194 195 196 197 200 201 202 203 204 205 206 207 209 212 213 215 216
[163] 218 220 222 226 230 231 232 235 236 241 242 243 245 246 247 248 249 250
[181] 251 252 253 255 256 257 258 259 260 261 262 263 264 265 266 268 269 270
[199] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 287 288 289
[217] 290 291 292 293 294 295 296 297 299 300 301 302 303 304 305 306 307 308
[235] 309 312 313 314 315 316 318 321 322 323 325 326 327 328 332 333 334 335
[253] 337 338 339 340 342 344

$test
 [1]   1   7  10  12  13  18  28  29  32  39  43  48  54  59  61  65  66  68  75
[20]  79  83  91  95  98  99 102 103 109 111 113 114 120 124 125 136 137 138 151
[39] 153 156 159 161 166 167 175 179 187 190 198 199 208 210 211 214 217 219 221
[58] 223 224 225 227 228 229 233 234 237 238 239 240 244 254 267 286 298 310 311
[77] 317 319 320 324 329 330 331 336 341 343

$validation
integer(0)
代码 
# 交叉验证
resampling <- rsmp("repeated_cv", folds = 10L, repeats = 10)

创建模型

代码 
# 查看支持的学习器
lrn()
<DictionaryLearner> with 51 stored values
Keys: classif.cv_glmnet, classif.debug, classif.featureless,
  classif.glmnet, classif.kknn, classif.lda, classif.log_reg,
  classif.multinom, classif.naive_bayes, classif.nnet, classif.qda,
  classif.ranger, classif.rpart, classif.svm, classif.xgboost,
  clust.agnes, clust.ap, clust.bico, clust.birch, clust.cmeans,
  clust.cobweb, clust.dbscan, clust.dbscan_fpc, clust.diana, clust.em,
  clust.fanny, clust.featureless, clust.ff, clust.hclust,
  clust.hdbscan, clust.kkmeans, clust.kmeans, clust.MBatchKMeans,
  clust.mclust, clust.meanshift, clust.optics, clust.pam,
  clust.SimpleKMeans, clust.xmeans, regr.cv_glmnet, regr.debug,
  regr.featureless, regr.glmnet, regr.kknn, regr.km, regr.lm,
  regr.nnet, regr.ranger, regr.rpart, regr.svm, regr.xgboost
代码 
# 定义学习器

lrn_ranger <- lrn("classif.ranger")
# 查看学习器参数
lrn_ranger$param_set
<ParamSet(28)>
Key: <id>
                              id    class lower upper nlevels        default
                          <char>   <char> <num> <num>   <num>         <list>
 1:       always.split.variables ParamUty    NA    NA     Inf <NoDefault[0]>
 2:                class.weights ParamUty    NA    NA     Inf         [NULL]
 3:                      holdout ParamLgl    NA    NA       2          FALSE
 4:                   importance ParamFct    NA    NA       4 <NoDefault[0]>
 5:                   keep.inbag ParamLgl    NA    NA       2          FALSE
 6:                    max.depth ParamInt     1   Inf     Inf         [NULL]
 7:                   min.bucket ParamUty    NA    NA     Inf              1
 8:                min.node.size ParamUty    NA    NA     Inf         [NULL]
 9:                         mtry ParamInt     1   Inf     Inf <NoDefault[0]>
10:                   mtry.ratio ParamDbl     0     1     Inf <NoDefault[0]>
11:                    na.action ParamFct    NA    NA       3       na.learn
12:                   node.stats ParamLgl    NA    NA       2          FALSE
13:            num.random.splits ParamInt     1   Inf     Inf              1
14:                  num.threads ParamInt     1   Inf     Inf              1
15:                    num.trees ParamInt     1   Inf     Inf            500
16:                    oob.error ParamLgl    NA    NA       2           TRUE
17:        regularization.factor ParamUty    NA    NA     Inf              1
18:      regularization.usedepth ParamLgl    NA    NA       2          FALSE
19:                      replace ParamLgl    NA    NA       2           TRUE
20:    respect.unordered.factors ParamFct    NA    NA       3 <NoDefault[0]>
21:              sample.fraction ParamDbl     0     1     Inf <NoDefault[0]>
22:                  save.memory ParamLgl    NA    NA       2          FALSE
23: scale.permutation.importance ParamLgl    NA    NA       2          FALSE
24:                         seed ParamInt  -Inf   Inf     Inf         [NULL]
25:         split.select.weights ParamUty    NA    NA     Inf         [NULL]
26:                    splitrule ParamFct    NA    NA       3           gini
27:                      verbose ParamLgl    NA    NA       2           TRUE
28:                 write.forest ParamLgl    NA    NA       2           TRUE
                              id    class lower upper nlevels        default
       parents  value
        <list> <list>
 1:     [NULL] [NULL]
 2:     [NULL] [NULL]
 3:     [NULL] [NULL]
 4:     [NULL] [NULL]
 5:     [NULL] [NULL]
 6:     [NULL] [NULL]
 7:     [NULL] [NULL]
 8:     [NULL] [NULL]
 9:     [NULL] [NULL]
10:     [NULL] [NULL]
11:     [NULL] [NULL]
12:     [NULL] [NULL]
13:  splitrule [NULL]
14:     [NULL]      1
15:     [NULL] [NULL]
16:     [NULL] [NULL]
17:     [NULL] [NULL]
18:     [NULL] [NULL]
19:     [NULL] [NULL]
20:     [NULL] [NULL]
21:     [NULL] [NULL]
22:     [NULL] [NULL]
23: importance [NULL]
24:     [NULL] [NULL]
25:     [NULL] [NULL]
26:     [NULL] [NULL]
27:     [NULL] [NULL]
28:     [NULL] [NULL]
       parents  value
代码 
# 设置超参数调参
# lrn_ranger$param_set$set_values(
#   max.depth = to_tune(1, 10),
#   min.node.size = to_tune(1, 50),
#   min.bucket = to_tune(1, 10)
# )

# mlr3tuningspace中收集了常用的搜索空间，对于决策树、随机森林、xgboost这种著名模型，只需要用lts()方法，不需要手动设置值
# lrn_ranger = lts(lrn_ranger)

图学习器

代码 
# 查看支持的图数据处理
po()
<DictionaryPipeOp> with 75 stored values
Keys: adas, blsmote, boxcox, branch, chunk, classbalancing, classifavg,
  classweights, colapply, collapsefactors, colroles, copy,
  datefeatures, decode, encode, encodeimpact, encodelmer,
  encodeplquantiles, encodepltree, featureunion, filter, fixfactors,
  histbin, ica, imputeconstant, imputehist, imputelearner, imputemean,
  imputemedian, imputemode, imputeoor, imputesample, kernelpca,
  learner, learner_cv, learner_pi_cvplus, learner_quantiles, missind,
  modelmatrix, multiplicityexply, multiplicityimply, mutate, nearmiss,
  nmf, nop, ovrsplit, ovrunite, pca, proxy, quantilebin,
  randomprojection, randomresponse, regravg, removeconstants,
  renamecolumns, replicate, rowapply, scale, scalemaxabs, scalerange,
  select, smote, smotenc, spatialsign, subsample, targetinvert,
  targetmutate, targettrafoscalerange, textvectorizer, threshold,
  tomek, tunethreshold, unbranch, vtreat, yeojohnson
代码 
preproc <- po("imputemedian") %>>%
  # po("collapsefactors", no_collapse_above_prevalence = 0.03) %>>%
  po("fixfactors") %>>%
  po("encode", method = "one-hot") %>>%
  po("scale")
graph <- preproc %>>% lrn_ranger # 接上学习器
graph$plot(html = TRUE, horizontal = TRUE) # 可视化
代码 
glrn <- as_learner(graph) # 转化为图学习器

拟合数据

代码 
## 拟合训练数据
glrn$train(task = tsk_penguins, row_ids = splits$train)

glrn$model
$imputemedian
$imputemedian$affected_cols
[1] "RowId"             "bill_depth_mm"     "bill_length_mm"   
[4] "body_mass_g"       "flipper_length_mm"

$imputemedian$intasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   integer
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   integer
5: flipper_length_mm   integer
6:            island character
7:               sex character

$imputemedian$model
$imputemedian$model$RowId
[1] 174

$imputemedian$model$bill_depth_mm
[1] 17.3

$imputemedian$model$bill_length_mm
[1] 44.45

$imputemedian$model$body_mass_g
[1] 4050

$imputemedian$model$flipper_length_mm
[1] 197


$imputemedian$outtasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   integer
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   integer
5: flipper_length_mm   integer
6:            island character
7:               sex character

$imputemedian$imputed_train
[1] "bill_depth_mm"     "bill_length_mm"    "body_mass_g"      
[4] "flipper_length_mm"


$fixfactors
$fixfactors$levels
named list()

$fixfactors$affected_cols
[1] "RowId"             "island"            "sex"              
[4] "bill_depth_mm"     "bill_length_mm"    "body_mass_g"      
[7] "flipper_length_mm"

$fixfactors$intasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   integer
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   integer
5: flipper_length_mm   integer
6:            island character
7:               sex character

$fixfactors$outtasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   integer
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   integer
5: flipper_length_mm   integer
6:            island character
7:               sex character

$fixfactors$outtaskshell
Empty data.table (0 rows and 8 cols): species,RowId,island,sex,bill_depth_mm,bill_length_mm...


$encode
$encode$dt_columns
character(0)

$encode$affected_cols
[1] "RowId"             "island"            "sex"              
[4] "bill_depth_mm"     "bill_length_mm"    "body_mass_g"      
[7] "flipper_length_mm"

$encode$intasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   integer
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   integer
5: flipper_length_mm   integer
6:            island character
7:               sex character

$encode$outtasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   integer
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   integer
5: flipper_length_mm   integer
6:            island character
7:               sex character

$encode$outtaskshell
Empty data.table (0 rows and 8 cols): species,RowId,island,sex,bill_depth_mm,bill_length_mm...


$scale
$scale$center
            RowId     bill_depth_mm    bill_length_mm       body_mass_g 
        173.86434          17.18915          43.91473        4215.89147 
flipper_length_mm 
        201.27519 

$scale$scale
            RowId     bill_depth_mm    bill_length_mm       body_mass_g 
       100.048903          1.998861          5.362109        768.028693 
flipper_length_mm 
        13.724848 

$scale$dt_columns
[1] "RowId"             "bill_depth_mm"     "bill_length_mm"   
[4] "body_mass_g"       "flipper_length_mm"

$scale$affected_cols
[1] "RowId"             "island"            "sex"              
[4] "bill_depth_mm"     "bill_length_mm"    "body_mass_g"      
[7] "flipper_length_mm"

$scale$intasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   integer
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   integer
5: flipper_length_mm   integer
6:            island character
7:               sex character

$scale$outtasklayout
Key: <id>
                  id      type
              <char>    <char>
1:             RowId   numeric
2:     bill_depth_mm   numeric
3:    bill_length_mm   numeric
4:       body_mass_g   numeric
5: flipper_length_mm   numeric
6:            island character
7:               sex character

$scale$outtaskshell
Empty data.table (0 rows and 8 cols): species,island,sex,RowId,bill_depth_mm,bill_length_mm...


$classif.ranger
$model
Ranger result

Call:
 ranger::ranger(dependent.variable.name = task$target_names, data = task$data(),      probability = self$predict_type == "prob", case.weights = task$weights$weight,      num.threads = 1L) 

Type:                             Classification 
Number of trees:                  500 
Sample size:                      258 
Number of independent variables:  7 
Mtry:                             2 
Target node size:                 1 
Variable importance mode:         none 
Splitrule:                        gini 
OOB prediction error:             0.00 % 

$param_vals
$param_vals$num.threads
[1] 1


$log
Empty data.table (0 rows and 3 cols): stage,class,msg

$train_time
[1] 0.03

$task_hash
[1] "62c6849cbed0a047"

$feature_names
[1] "island"            "sex"               "RowId"            
[4] "bill_depth_mm"     "bill_length_mm"    "body_mass_g"      
[7] "flipper_length_mm"

$validate
NULL

$mlr3_version
[1] '0.23.0'

$data_prototype
Empty data.table (0 rows and 8 cols): species,island,sex,RowId,bill_depth_mm,bill_length_mm...

$task_prototype
Empty data.table (0 rows and 8 cols): species,island,sex,RowId,bill_depth_mm,bill_length_mm...

$train_task
<TaskClassif:penguins> (258 x 8)
* Target: species
* Properties: multiclass
* Features (7):
  - dbl (5): RowId, bill_depth_mm, bill_length_mm, body_mass_g,
    flipper_length_mm
  - chr (2): island, sex

attr(,"class")
[1] "learner_state" "list"         

attr(,"class")
[1] "graph_learner_model" "list"
代码 
## 交叉验证拟合

# rr = resample(tsk_penguins, glrn, resampling)


## 超参数调参

at <- auto_tuner(
  tuner = tnr("random_search"),
  learner = preproc %>>% lrn("classif.ranger", max.depth = to_tune(1, 10)) %>% as_learner(),
  resampling = resampling,
  measure = msr("classif.ce"),
  term_evals = 4
)

at$train(tsk_penguins)
INFO  [16:11:21.424] [bbotk] Starting to optimize 1 parameter(s) with '<OptimizerBatchRandomSearch>' and '<TerminatorEvals> [n_evals=4, k=0]'
INFO  [16:11:21.462] [bbotk] Evaluating 1 configuration(s)
INFO  [16:11:21.484] [mlr3] Running benchmark with 100 resampling iterations
INFO  [16:11:21.532] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 1/100)
INFO  [16:11:21.787] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 2/100)
INFO  [16:11:22.015] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 3/100)
INFO  [16:11:22.229] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 4/100)
INFO  [16:11:22.881] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 5/100)
INFO  [16:11:23.132] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 6/100)
INFO  [16:11:23.359] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 7/100)
INFO  [16:11:23.602] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 8/100)
INFO  [16:11:23.834] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 9/100)
INFO  [16:11:24.048] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 10/100)
INFO  [16:11:24.282] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 11/100)
INFO  [16:11:24.521] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 12/100)
INFO  [16:11:24.745] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 13/100)
INFO  [16:11:24.972] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 14/100)
INFO  [16:11:25.217] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 15/100)
INFO  [16:11:25.469] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 16/100)
INFO  [16:11:25.737] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 17/100)
INFO  [16:11:26.001] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 18/100)
INFO  [16:11:26.262] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 19/100)
INFO  [16:11:26.488] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 20/100)
INFO  [16:11:26.760] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 21/100)
INFO  [16:11:27.017] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 22/100)
INFO  [16:11:27.269] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 23/100)
INFO  [16:11:27.497] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 24/100)
INFO  [16:11:27.724] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 25/100)
INFO  [16:11:27.984] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 26/100)
INFO  [16:11:28.218] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 27/100)
INFO  [16:11:28.470] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 28/100)
INFO  [16:11:28.721] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 29/100)
INFO  [16:11:28.973] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 30/100)
INFO  [16:11:29.213] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 31/100)
INFO  [16:11:29.436] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 32/100)
INFO  [16:11:29.669] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 33/100)
INFO  [16:11:29.902] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 34/100)
INFO  [16:11:30.181] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 35/100)
INFO  [16:11:30.443] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 36/100)
INFO  [16:11:30.683] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 37/100)
INFO  [16:11:30.933] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 38/100)
INFO  [16:11:31.147] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 39/100)
INFO  [16:11:31.383] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 40/100)
INFO  [16:11:31.623] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 41/100)
INFO  [16:11:31.859] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 42/100)
INFO  [16:11:32.110] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 43/100)
INFO  [16:11:32.350] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 44/100)
INFO  [16:11:32.580] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 45/100)
INFO  [16:11:32.820] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 46/100)
INFO  [16:11:33.058] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 47/100)
INFO  [16:11:33.288] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 48/100)
INFO  [16:11:33.535] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 49/100)
INFO  [16:11:33.776] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 50/100)
INFO  [16:11:34.037] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 51/100)
INFO  [16:11:34.267] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 52/100)
INFO  [16:11:34.542] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 53/100)
INFO  [16:11:34.816] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 54/100)
INFO  [16:11:35.144] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 55/100)
INFO  [16:11:35.391] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 56/100)
INFO  [16:11:35.659] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 57/100)
INFO  [16:11:35.890] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 58/100)
INFO  [16:11:36.159] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 59/100)
INFO  [16:11:36.412] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 60/100)
INFO  [16:11:36.657] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 61/100)
INFO  [16:11:36.929] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 62/100)
INFO  [16:11:37.174] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 63/100)
INFO  [16:11:37.422] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 64/100)
INFO  [16:11:37.650] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 65/100)
INFO  [16:11:37.942] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 66/100)
INFO  [16:11:38.178] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 67/100)
INFO  [16:11:38.412] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 68/100)
INFO  [16:11:38.682] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 69/100)
INFO  [16:11:38.923] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 70/100)
INFO  [16:11:39.182] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 71/100)
INFO  [16:11:39.425] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 72/100)
INFO  [16:11:39.653] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 73/100)
INFO  [16:11:39.939] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 74/100)
INFO  [16:11:40.216] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 75/100)
INFO  [16:11:40.439] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 76/100)
INFO  [16:11:40.734] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 77/100)
INFO  [16:11:40.995] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 78/100)
INFO  [16:11:41.222] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 79/100)
INFO  [16:11:41.501] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 80/100)
INFO  [16:11:41.758] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 81/100)
INFO  [16:11:41.998] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 82/100)
INFO  [16:11:42.260] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 83/100)
INFO  [16:11:42.514] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 84/100)
INFO  [16:11:42.740] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 85/100)
INFO  [16:11:42.987] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 86/100)
INFO  [16:11:43.292] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 87/100)
INFO  [16:11:43.503] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 88/100)
INFO  [16:11:43.773] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 89/100)
INFO  [16:11:44.005] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 90/100)
INFO  [16:11:44.298] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 91/100)
INFO  [16:11:44.622] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 92/100)
INFO  [16:11:44.910] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 93/100)
INFO  [16:11:45.200] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 94/100)
INFO  [16:11:45.476] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 95/100)
INFO  [16:11:45.730] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 96/100)
INFO  [16:11:45.995] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 97/100)
INFO  [16:11:46.273] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 98/100)
INFO  [16:11:46.578] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 99/100)
INFO  [16:11:46.808] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 100/100)
INFO  [16:11:47.091] [mlr3] Finished benchmark
INFO  [16:11:47.184] [bbotk] Result of batch 1:
INFO  [16:11:47.186] [bbotk]  classif.ranger.max.depth   classif.ce warnings errors runtime_learners
INFO  [16:11:47.186] [bbotk]                        10 0.0008739496        0      0             24.1
INFO  [16:11:47.186] [bbotk]                                 uhash
INFO  [16:11:47.186] [bbotk]  eec08ecf-a954-4dbc-9648-efce7b1c553d
INFO  [16:11:47.192] [bbotk] Evaluating 1 configuration(s)
INFO  [16:11:47.197] [mlr3] Running benchmark with 100 resampling iterations
INFO  [16:11:47.204] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 1/100)
INFO  [16:11:47.495] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 2/100)
INFO  [16:11:47.751] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 3/100)
INFO  [16:11:48.042] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 4/100)
INFO  [16:11:48.308] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 5/100)
INFO  [16:11:48.572] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 6/100)
INFO  [16:11:48.829] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 7/100)
INFO  [16:11:49.099] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 8/100)
INFO  [16:11:49.392] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 9/100)
INFO  [16:11:49.716] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 10/100)
INFO  [16:11:49.980] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 11/100)
INFO  [16:11:50.279] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 12/100)
INFO  [16:11:50.544] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 13/100)
INFO  [16:11:50.789] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 14/100)
INFO  [16:11:51.033] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 15/100)
INFO  [16:11:51.311] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 16/100)
INFO  [16:11:51.566] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 17/100)
INFO  [16:11:51.810] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 18/100)
INFO  [16:11:52.075] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 19/100)
INFO  [16:11:52.430] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 20/100)
INFO  [16:11:52.678] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 21/100)
INFO  [16:11:52.946] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 22/100)
INFO  [16:11:53.212] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 23/100)
INFO  [16:11:53.496] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 24/100)
INFO  [16:11:53.794] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 25/100)
INFO  [16:11:54.078] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 26/100)
INFO  [16:11:54.378] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 27/100)
INFO  [16:11:54.665] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 28/100)
INFO  [16:11:54.922] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 29/100)
INFO  [16:11:55.191] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 30/100)
INFO  [16:11:55.439] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 31/100)
INFO  [16:11:55.712] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 32/100)
INFO  [16:11:55.972] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 33/100)
INFO  [16:11:56.220] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 34/100)
INFO  [16:11:56.499] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 35/100)
INFO  [16:11:56.781] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 36/100)
INFO  [16:11:57.079] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 37/100)
INFO  [16:11:57.343] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 38/100)
INFO  [16:11:57.620] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 39/100)
INFO  [16:11:57.941] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 40/100)
INFO  [16:11:58.224] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 41/100)
INFO  [16:11:58.503] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 42/100)
INFO  [16:11:58.790] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 43/100)
INFO  [16:11:59.057] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 44/100)
INFO  [16:11:59.317] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 45/100)
INFO  [16:11:59.594] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 46/100)
INFO  [16:11:59.860] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 47/100)
INFO  [16:12:00.103] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 48/100)
INFO  [16:12:00.395] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 49/100)
INFO  [16:12:00.644] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 50/100)
INFO  [16:12:00.873] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 51/100)
INFO  [16:12:01.161] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 52/100)
INFO  [16:12:01.475] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 53/100)
INFO  [16:12:01.761] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 54/100)
INFO  [16:12:02.083] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 55/100)
INFO  [16:12:02.317] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 56/100)
INFO  [16:12:02.634] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 57/100)
INFO  [16:12:02.917] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 58/100)
INFO  [16:12:03.180] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 59/100)
INFO  [16:12:03.487] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 60/100)
INFO  [16:12:03.769] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 61/100)
INFO  [16:12:04.104] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 62/100)
INFO  [16:12:04.425] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 63/100)
INFO  [16:12:04.697] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 64/100)
INFO  [16:12:05.010] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 65/100)
INFO  [16:12:05.273] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 66/100)
INFO  [16:12:05.582] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 67/100)
INFO  [16:12:05.913] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 68/100)
INFO  [16:12:06.186] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 69/100)
INFO  [16:12:06.486] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 70/100)
INFO  [16:12:06.783] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 71/100)
INFO  [16:12:07.071] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 72/100)
INFO  [16:12:07.372] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 73/100)
INFO  [16:12:07.639] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 74/100)
INFO  [16:12:07.945] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 75/100)
INFO  [16:12:08.244] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 76/100)
INFO  [16:12:08.535] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 77/100)
INFO  [16:12:08.859] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 78/100)
INFO  [16:12:09.170] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 79/100)
INFO  [16:12:09.450] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 80/100)
INFO  [16:12:09.755] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 81/100)
INFO  [16:12:10.049] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 82/100)
INFO  [16:12:10.326] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 83/100)
INFO  [16:12:10.624] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 84/100)
INFO  [16:12:10.913] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 85/100)
INFO  [16:12:11.616] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 86/100)
INFO  [16:12:11.890] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 87/100)
INFO  [16:12:12.142] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 88/100)
INFO  [16:12:12.386] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 89/100)
INFO  [16:12:12.628] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 90/100)
INFO  [16:12:12.948] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 91/100)
INFO  [16:12:13.208] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 92/100)
INFO  [16:12:13.452] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 93/100)
INFO  [16:12:13.691] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 94/100)
INFO  [16:12:13.934] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 95/100)
INFO  [16:12:14.156] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 96/100)
INFO  [16:12:14.388] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 97/100)
INFO  [16:12:14.640] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 98/100)
INFO  [16:12:14.891] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 99/100)
INFO  [16:12:15.116] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 100/100)
INFO  [16:12:15.369] [mlr3] Finished benchmark
INFO  [16:12:15.456] [bbotk] Result of batch 2:
INFO  [16:12:15.458] [bbotk]  classif.ranger.max.depth  classif.ce warnings errors runtime_learners
INFO  [16:12:15.458] [bbotk]                         5 0.001445378        0      0            26.43
INFO  [16:12:15.458] [bbotk]                                 uhash
INFO  [16:12:15.458] [bbotk]  06641505-374e-4cb8-981b-cf4fc6a1e270
INFO  [16:12:15.463] [bbotk] Evaluating 1 configuration(s)
INFO  [16:12:15.468] [mlr3] Running benchmark with 100 resampling iterations
INFO  [16:12:15.473] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 1/100)
INFO  [16:12:15.706] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 2/100)
INFO  [16:12:15.969] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 3/100)
INFO  [16:12:16.204] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 4/100)
INFO  [16:12:16.432] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 5/100)
INFO  [16:12:16.667] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 6/100)
INFO  [16:12:16.907] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 7/100)
INFO  [16:12:17.143] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 8/100)
INFO  [16:12:17.371] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 9/100)
INFO  [16:12:17.609] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 10/100)
INFO  [16:12:17.873] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 11/100)
INFO  [16:12:18.138] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 12/100)
INFO  [16:12:18.353] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 13/100)
INFO  [16:12:18.598] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 14/100)
INFO  [16:12:18.813] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 15/100)
INFO  [16:12:19.061] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 16/100)
INFO  [16:12:19.267] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 17/100)
INFO  [16:12:19.467] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 18/100)
INFO  [16:12:19.707] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 19/100)
INFO  [16:12:19.927] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 20/100)
INFO  [16:12:20.136] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 21/100)
INFO  [16:12:20.356] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 22/100)
INFO  [16:12:20.576] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 23/100)
INFO  [16:12:20.823] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 24/100)
INFO  [16:12:21.070] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 25/100)
INFO  [16:12:21.278] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 26/100)
INFO  [16:12:21.513] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 27/100)
INFO  [16:12:21.728] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 28/100)
INFO  [16:12:21.970] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 29/100)
INFO  [16:12:22.192] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 30/100)
INFO  [16:12:22.439] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 31/100)
INFO  [16:12:22.669] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 32/100)
INFO  [16:12:22.939] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 33/100)
INFO  [16:12:23.262] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 34/100)
INFO  [16:12:23.483] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 35/100)
INFO  [16:12:23.742] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 36/100)
INFO  [16:12:23.974] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 37/100)
INFO  [16:12:24.204] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 38/100)
INFO  [16:12:24.436] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 39/100)
INFO  [16:12:24.656] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 40/100)
INFO  [16:12:24.874] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 41/100)
INFO  [16:12:25.116] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 42/100)
INFO  [16:12:25.379] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 43/100)
INFO  [16:12:25.614] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 44/100)
INFO  [16:12:25.839] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 45/100)
INFO  [16:12:26.091] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 46/100)
INFO  [16:12:26.350] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 47/100)
INFO  [16:12:26.626] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 48/100)
INFO  [16:12:26.861] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 49/100)
INFO  [16:12:27.116] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 50/100)
INFO  [16:12:27.356] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 51/100)
INFO  [16:12:27.576] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 52/100)
INFO  [16:12:27.826] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 53/100)
INFO  [16:12:28.075] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 54/100)
INFO  [16:12:28.298] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 55/100)
INFO  [16:12:28.496] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 56/100)
INFO  [16:12:28.728] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 57/100)
INFO  [16:12:28.968] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 58/100)
INFO  [16:12:29.199] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 59/100)
INFO  [16:12:29.402] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 60/100)
INFO  [16:12:29.622] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 61/100)
INFO  [16:12:29.876] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 62/100)
INFO  [16:12:30.143] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 63/100)
INFO  [16:12:30.346] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 64/100)
INFO  [16:12:30.558] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 65/100)
INFO  [16:12:30.824] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 66/100)
INFO  [16:12:31.065] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 67/100)
INFO  [16:12:31.351] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 68/100)
INFO  [16:12:31.579] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 69/100)
INFO  [16:12:31.822] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 70/100)
INFO  [16:12:32.053] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 71/100)
INFO  [16:12:32.334] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 72/100)
INFO  [16:12:32.539] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 73/100)
INFO  [16:12:32.788] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 74/100)
INFO  [16:12:33.018] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 75/100)
INFO  [16:12:33.250] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 76/100)
INFO  [16:12:33.482] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 77/100)
INFO  [16:12:33.723] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 78/100)
INFO  [16:12:33.929] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 79/100)
INFO  [16:12:34.136] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 80/100)
INFO  [16:12:34.455] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 81/100)
INFO  [16:12:34.677] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 82/100)
INFO  [16:12:34.937] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 83/100)
INFO  [16:12:35.196] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 84/100)
INFO  [16:12:35.469] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 85/100)
INFO  [16:12:35.716] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 86/100)
INFO  [16:12:35.948] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 87/100)
INFO  [16:12:36.226] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 88/100)
INFO  [16:12:36.449] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 89/100)
INFO  [16:12:36.742] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 90/100)
INFO  [16:12:36.982] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 91/100)
INFO  [16:12:37.251] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 92/100)
INFO  [16:12:37.472] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 93/100)
INFO  [16:12:37.692] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 94/100)
INFO  [16:12:37.932] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 95/100)
INFO  [16:12:38.145] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 96/100)
INFO  [16:12:38.408] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 97/100)
INFO  [16:12:38.624] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 98/100)
INFO  [16:12:38.868] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 99/100)
INFO  [16:12:39.095] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 100/100)
INFO  [16:12:39.370] [mlr3] Finished benchmark
INFO  [16:12:39.479] [bbotk] Result of batch 3:
INFO  [16:12:39.481] [bbotk]  classif.ranger.max.depth  classif.ce warnings errors runtime_learners
INFO  [16:12:39.481] [bbotk]                         2 0.009285714        0      0            22.31
INFO  [16:12:39.481] [bbotk]                                 uhash
INFO  [16:12:39.481] [bbotk]  956f96fa-bd00-446f-87b0-5b29626ff65b
INFO  [16:12:39.489] [bbotk] Evaluating 1 configuration(s)
INFO  [16:12:39.496] [mlr3] Running benchmark with 100 resampling iterations
INFO  [16:12:39.505] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 1/100)
INFO  [16:12:39.745] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 2/100)
INFO  [16:12:40.014] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 3/100)
INFO  [16:12:40.258] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 4/100)
INFO  [16:12:40.509] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 5/100)
INFO  [16:12:40.764] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 6/100)
INFO  [16:12:40.979] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 7/100)
INFO  [16:12:41.265] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 8/100)
INFO  [16:12:41.502] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 9/100)
INFO  [16:12:41.780] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 10/100)
INFO  [16:12:42.018] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 11/100)
INFO  [16:12:42.313] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 12/100)
INFO  [16:12:42.543] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 13/100)
INFO  [16:12:42.807] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 14/100)
INFO  [16:12:43.029] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 15/100)
INFO  [16:12:43.230] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 16/100)
INFO  [16:12:43.481] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 17/100)
INFO  [16:12:43.713] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 18/100)
INFO  [16:12:43.963] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 19/100)
INFO  [16:12:44.181] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 20/100)
INFO  [16:12:44.436] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 21/100)
INFO  [16:12:44.666] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 22/100)
INFO  [16:12:44.934] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 23/100)
INFO  [16:12:45.160] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 24/100)
INFO  [16:12:45.401] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 25/100)
INFO  [16:12:45.655] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 26/100)
INFO  [16:12:46.002] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 27/100)
INFO  [16:12:46.221] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 28/100)
INFO  [16:12:46.504] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 29/100)
INFO  [16:12:46.728] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 30/100)
INFO  [16:12:46.933] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 31/100)
INFO  [16:12:47.175] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 32/100)
INFO  [16:12:47.409] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 33/100)
INFO  [16:12:47.690] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 34/100)
INFO  [16:12:47.916] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 35/100)
INFO  [16:12:48.146] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 36/100)
INFO  [16:12:48.391] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 37/100)
INFO  [16:12:48.607] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 38/100)
INFO  [16:12:48.846] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 39/100)
INFO  [16:12:49.045] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 40/100)
INFO  [16:12:49.288] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 41/100)
INFO  [16:12:49.549] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 42/100)
INFO  [16:12:49.805] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 43/100)
INFO  [16:12:50.028] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 44/100)
INFO  [16:12:50.255] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 45/100)
INFO  [16:12:50.507] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 46/100)
INFO  [16:12:50.754] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 47/100)
INFO  [16:12:51.055] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 48/100)
INFO  [16:12:51.311] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 49/100)
INFO  [16:12:51.600] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 50/100)
INFO  [16:12:51.815] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 51/100)
INFO  [16:12:52.081] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 52/100)
INFO  [16:12:52.323] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 53/100)
INFO  [16:12:52.587] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 54/100)
INFO  [16:12:52.807] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 55/100)
INFO  [16:12:53.104] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 56/100)
INFO  [16:12:53.376] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 57/100)
INFO  [16:12:53.623] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 58/100)
INFO  [16:12:53.885] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 59/100)
INFO  [16:12:54.139] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 60/100)
INFO  [16:12:54.385] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 61/100)
INFO  [16:12:54.609] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 62/100)
INFO  [16:12:54.861] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 63/100)
INFO  [16:12:55.092] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 64/100)
INFO  [16:12:55.396] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 65/100)
INFO  [16:12:55.650] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 66/100)
INFO  [16:12:55.893] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 67/100)
INFO  [16:12:56.100] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 68/100)
INFO  [16:12:56.332] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 69/100)
INFO  [16:12:56.561] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 70/100)
INFO  [16:12:56.826] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 71/100)
INFO  [16:12:57.032] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 72/100)
INFO  [16:12:57.310] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 73/100)
INFO  [16:12:57.543] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 74/100)
INFO  [16:12:57.812] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 75/100)
INFO  [16:12:58.082] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 76/100)
INFO  [16:12:58.317] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 77/100)
INFO  [16:12:58.579] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 78/100)
INFO  [16:12:58.847] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 79/100)
INFO  [16:12:59.093] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 80/100)
INFO  [16:12:59.317] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 81/100)
INFO  [16:12:59.550] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 82/100)
INFO  [16:12:59.824] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 83/100)
INFO  [16:13:00.099] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 84/100)
INFO  [16:13:00.403] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 85/100)
INFO  [16:13:00.672] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 86/100)
INFO  [16:13:00.960] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 87/100)
INFO  [16:13:01.213] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 88/100)
INFO  [16:13:01.522] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 89/100)
INFO  [16:13:01.759] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 90/100)
INFO  [16:13:02.028] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 91/100)
INFO  [16:13:02.289] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 92/100)
INFO  [16:13:02.531] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 93/100)
INFO  [16:13:02.797] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 94/100)
INFO  [16:13:03.023] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 95/100)
INFO  [16:13:03.280] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 96/100)
INFO  [16:13:03.519] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 97/100)
INFO  [16:13:03.801] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 98/100)
INFO  [16:13:04.031] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 99/100)
INFO  [16:13:04.282] [mlr3] Applying learner 'imputemedian.fixfactors.encode.scale.classif.ranger' on task 'penguins' (iter 100/100)
INFO  [16:13:04.581] [mlr3] Finished benchmark
INFO  [16:13:04.675] [bbotk] Result of batch 4:
INFO  [16:13:04.676] [bbotk]  classif.ranger.max.depth classif.ce warnings errors runtime_learners
INFO  [16:13:04.676] [bbotk]                         1  0.1986387        0      0            23.43
INFO  [16:13:04.676] [bbotk]                                 uhash
INFO  [16:13:04.676] [bbotk]  2de630e8-670b-4cf6-9005-9e1ba40f3716
INFO  [16:13:04.696] [bbotk] Finished optimizing after 4 evaluation(s)
INFO  [16:13:04.696] [bbotk] Result:
INFO  [16:13:04.698] [bbotk]  classif.ranger.max.depth learner_param_vals  x_domain   classif.ce
INFO  [16:13:04.698] [bbotk]                     <int>             <list>    <list>        <num>
INFO  [16:13:04.698] [bbotk]                        10          <list[5]> <list[1]> 0.0008739496

预测新数据

代码 
penguins_pred_mlr3 <- glrn$predict(task = tsk_penguins, row_ids = splits$test)

penguins_pred_mlr3_at <- at$predict(task = tsk_penguins, row_ids = splits$test)

模型评价

代码 
# 混淆矩阵
penguins_pred_mlr3$confusion
           truth
response    Adelie Chinstrap Gentoo
  Adelie        38         0      0
  Chinstrap      0        18      0
  Gentoo         0         0     30
代码 
penguins_pred_mlr3_at$confusion
           truth
response    Adelie Chinstrap Gentoo
  Adelie        38         0      0
  Chinstrap      0        18      0
  Gentoo         0         0     30
代码 
# 查看评估指标
msrs()
<DictionaryMeasure> with 75 stored values
Keys: aic, bic, ci, ci.con_z, ci.cor_t, ci.holdout, ci.ncv, ci.wald_cv,
  classif.acc, classif.auc, classif.bacc, classif.bbrier, classif.ce,
  classif.costs, classif.dor, classif.fbeta, classif.fdr, classif.fn,
  classif.fnr, classif.fomr, classif.fp, classif.fpr, classif.logloss,
  classif.mauc_au1p, classif.mauc_au1u, classif.mauc_aunp,
  classif.mauc_aunu, classif.mauc_mu, classif.mbrier, classif.mcc,
  classif.npv, classif.ppv, classif.prauc, classif.precision,
  classif.recall, classif.sensitivity, classif.specificity, classif.tn,
  classif.tnr, classif.tp, classif.tpr, clust.ch, clust.dunn,
  clust.silhouette, clust.wss, debug_classif, internal_valid_score,
  oob_error, regr.bias, regr.ktau, regr.mae, regr.mape, regr.maxae,
  regr.medae, regr.medse, regr.mse, regr.msle, regr.pbias,
  regr.pinball, regr.rae, regr.rmse, regr.rmsle, regr.rrse, regr.rse,
  regr.rsq, regr.sae, regr.smape, regr.srho, regr.sse,
  selected_features, sim.jaccard, sim.phi, time_both, time_predict,
  time_train
代码 
measure <- msrs(c("classif.acc", "classif.ce"))

penguins_pred_mlr3$score(measure)
classif.acc  classif.ce 
          1           0
代码 
# 交叉验证评估
# rr$score(measure)[, .(task_id, learner_id, iteration, classif.acc)]

观察数据

代码 
import pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.feature_selection import VarianceThreshold
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_curve, roc_auc_score, accuracy_score


print(df_penguins.head())
  species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex
0  Adelie  Torgersen            39.1  ...              181.0       3750.0    MALE
1  Adelie  Torgersen            39.5  ...              186.0       3800.0  FEMALE
2  Adelie  Torgersen            40.3  ...              195.0       3250.0  FEMALE
3  Adelie  Torgersen             NaN  ...                NaN          NaN     NaN
4  Adelie  Torgersen            36.7  ...              193.0       3450.0  FEMALE

[5 rows x 7 columns]
代码 
print(df_penguins.describe())
       bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g
count      342.000000     342.000000         342.000000   342.000000
mean        43.921930      17.151170         200.915205  4201.754386
std          5.459584       1.974793          14.061714   801.954536
min         32.100000      13.100000         172.000000  2700.000000
25%         39.225000      15.600000         190.000000  3550.000000
50%         44.450000      17.300000         197.000000  4050.000000
75%         48.500000      18.700000         213.000000  4750.000000
max         59.600000      21.500000         231.000000  6300.000000

数据拆分

代码 

df_penguins['species'] = pd.Categorical(df_penguins['species'])
X = df_penguins.drop(['species'], axis=1)
y = df_penguins['species']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=123, stratify=y)

# 交叉验证
skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=123)

创建配方

代码 
numeric_features = X.select_dtypes(include=['int', 'float']).columns
categorical_features = X.select_dtypes(include=['object', 'category']).columns

preprocessor = ColumnTransformer([
    ('onehot', OneHotEncoder(), categorical_features),
    ('imputer', SimpleImputer(), numeric_features),
    ('variance_threshold', VarianceThreshold(), numeric_features)
])

创建模型

代码 
rf = RandomForestClassifier(n_estimators=1000)
param_grid = {
    'max_depth': [None, 5, 10, 15, 20],
    'min_samples_split': [2, 5, 10]
}
grid_search = GridSearchCV(rf, param_grid, cv=skf)

创建工作流

代码 
pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('model', rf)
])

拟合数据

代码 
pipeline.fit(X_train, y_train)
Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('onehot', OneHotEncoder(),
                                                  Index(['island', 'sex'], dtype='object')),
                                                 ('imputer', SimpleImputer(),
                                                  Index(['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'], dtype='object')),
                                                 ('variance_threshold',
                                                  VarianceThreshold(),
                                                  Index(['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'], dtype='object'))])),
                ('model', RandomForestClassifier(n_estimators=1000))])
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预测新数据

代码 
y_pred = pipeline.predict(X_test)

模型评价

代码 
y_pred_proba = pipeline.predict_proba(X_test)
# fpr, tpr, _ = roc_curve(y_test, y_pred_proba[:, 1])
# roc_auc = roc_auc_score(y_test, y_pred_proba[:, 1])
accuracy = accuracy_score(y_test, y_pred)

print(accuracy)
1.0
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