Grid and random search with mlr

Hyperparameter Tuning in R

Dr. Shirin Elsinghorst

Senior Data Scientist

Hyperparameter tuning with mlr

In mlr you have to define

the search space for every hyperparameter
the tuning method (e.g. grid or random search)
the resampling method

Defining the search space

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))

Defining the tuning method

Grid search

ctrl_grid <- makeTuneControlGrid()
ctrl_grid

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

Tune threshold: FALSE
Further arguments: resolution=10

Can only deal with discrete parameter sets!

Random search

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

Define resampling strategy

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)

Tuning hyperparameters

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

Let's practice!

Hyperparameter Tuning in R