Pencarian grid dan acak dengan mlr

Penyetelan Hyperparameter di R

Dr. Shirin Elsinghorst

Senior Data Scientist

Tuning hyperparameter dengan mlr

Di mlr Anda harus mendefinisikan

ruang pencarian untuk setiap hyperparameter
metode tuning (mis. grid atau pencarian acak)
metode resampling

Menentukan ruang pencarian

makeParamSet(
  makeNumericParam(),
  makeIntegerParam(),
  makeDiscreteParam(),
  makeLogicalParam(),
  makeDiscreteVectorParam()
)

                                       Type  len             Def
autoencoder                         logical    -           FALSE
use_all_factor_level                logical    -            TRUE 
activation                         discrete    -       Rectifier
hidden                        integervector <NA>         200,200 
epochs                              numeric    -              10
train_samples_per_iteration         numeric    -              -2 
seed                                integer    -               -

getParamSet("classif.h2o.deeplearning")

adaptive_rate                        logical    -            TRUE
rho                                  numeric    -            0.99
epsilon                              numeric    -           1e-08
rate                                 numeric    -           0.005

                                       Type  len             Def
autoencoder                         logical    -           FALSE
use_all_factor_level                logical    -            TRUE
activation                         discrete    -       Rectifier
hidden                        integervector <NA>         200,200
epochs                              numeric    -              10
train_samples_per_iteration         numeric    -              -2
seed                                integer    -               -
adaptive_rate                       logical    -            TRUE

rho                                 numeric    -            0.99
epsilon                             numeric    -           1e-08
rate                                numeric    -           0.005

param_set <- makeParamSet(
  makeDiscreteParam("hidden", values = list(one = 10, two = c(10, 5, 10))),
  makeDiscreteParam("activation", values = c("Rectifier", "Tanh")),
  makeNumericParam("l1", lower = 0.0001, upper = 1),
  makeNumericParam("l2", lower = 0.0001, upper = 1))

Menentukan metode tuning

Pencarian grid

ctrl_grid <- makeTuneControlGrid()
ctrl_grid

Tune control: TuneControlGrid
Same resampling instance: TRUE
Imputation value: <worst>
Start: <NULL>

Tune threshold: FALSE
Further arguments: resolution=10

Hanya dapat menangani set parameter diskret!

Pencarian acak

ctrl_random <- makeTuneControlRandom()
ctrl_random

Tune control: TuneControlRandom
Same resampling instance: TRUE
Imputation value: <worst>
Start: <NULL>
Budget: 100
Tune threshold: FALSE
Further arguments: maxit=100

Tetapkan strategi resampling

cross_val <- makeResampleDesc("RepCV", 
                              predict = "both",
                              folds = 5 * 3)

param_set <- makeParamSet(...)
ctrl_grid <- makeTuneControlGrid()

task <- makeClassifTask(data = knowledge_train_data, 
                        target = "UNS")
lrn <- makeLearner("classif.h2o.deeplearning", 
                   predict.type = "prob", 
                   fix.factors.prediction = TRUE)

lrn_tune <- tuneParams(lrn,
                       task,
                       resampling = cross_val,
                       control = ctrl_grid,
                       par.set = param_set)

Menyetel hyperparameter

lrn_tune <- tuneParams(lrn, task, resampling = cross_val, control = ctrl_grid, par.set = param_set)

[Tune-y] 27: mmce.test.mean=0.6200000; time: 0.0 min
[Tune-x] 28: hidden=two; activation=Rectifier; l1=0.578; l2=1
[Tune-y] 28: mmce.test.mean=0.6800000; time: 0.0 min
[Tune-x] 29: hidden=one; activation=Rectifier; l1=0.156; l2=0.68
[Tune-y] 29: mmce.test.mean=0.4400000; time: 0.0 min
[Tune-x] 30: hidden=one; activation=Rectifier; l1=0.717; l2=0.427
[Tune-y] 30: mmce.test.mean=0.6600000; time: 0.0 min

[Tune] Result: hidden=two; activation=Tanh; l1=0.113; 
l2=0.0973 : mmce.test.mean=0.2000000

# tictoc
26.13 sec elapsed

Ayo berlatih!

Penyetelan Hyperparameter di R