Nothing
inst/doc/mllrnrs_ranger_multiclass.R
In mllrnrs: R6-Based ML Learners for 'mlexperiments'
## ----setup--------------------------------------------------------------------
# nolint start
library(mlexperiments)
library(mllrnrs)
## -----------------------------------------------------------------------------
library(mlbench)
data("DNA")
dataset <- DNA |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[160:180]
target_col <- "Class"
## -----------------------------------------------------------------------------
seed <- 123
if (isTRUE(as.logical(Sys.getenv("_R_CHECK_LIMIT_CORES_")))) {
# on cran
ncores <- 2L
} else {
ncores <- ifelse(
test = parallel::detectCores() > 4,
yes = 4L,
no = ifelse(
test = parallel::detectCores() < 2L,
yes = 1L,
no = parallel::detectCores()
)
)
}
options("mlexperiments.bayesian.max_init" = 10L)
## -----------------------------------------------------------------------------
data_split <- splitTools::partition(
y = dataset[, get(target_col)],
p = c(train = 0.7, test = 0.3),
type = "stratified",
seed = seed
)
train_x <- model.matrix(
~ -1 + .,
dataset[data_split$train, .SD, .SDcols = feature_cols]
)
train_y <- dataset[data_split$train, get(target_col)]
test_x <- model.matrix(
~ -1 + .,
dataset[data_split$test, .SD, .SDcols = feature_cols]
)
test_y <- dataset[data_split$test, get(target_col)]
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# required learner arguments, not optimized
learner_args <- list(probability = TRUE, classification = TRUE)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(reshape = TRUE)
performance_metric <- metric("bacc")
performance_metric_args <- NULL
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
num.trees = seq(500, 1000, 500),
mtry = seq(2, 6, 2),
min.node.size = seq(1, 9, 4),
max.depth = seq(1, 9, 4),
sample.fraction = seq(0.5, 0.8, 0.3)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(
num.trees = c(100L, 1000L),
mtry = c(2L, 9L),
min.node.size = c(1L, 20L),
max.depth = c(1L, 40L),
sample.fraction = c(0.3, 1.)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner$parameter_grid <- parameter_grid
tuner$learner_args <- learner_args
tuner$split_type <- "stratified"
tuner$set_data(
x = train_x,
y = train_y
)
tuner_results_grid <- tuner$execute(k = 3)
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
head(tuner_results_grid)
#> setting_id metric_optim_mean num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: 1 0.4786887 500 2 9 5 0.5 TRUE TRUE
#> 2: 2 0.4791386 500 2 5 5 0.8 TRUE TRUE
#> 3: 3 0.4419159 500 4 9 9 0.5 TRUE TRUE
#> 4: 4 0.4809325 1000 2 9 1 0.5 TRUE TRUE
#> 5: 5 0.4809325 500 2 9 1 0.8 TRUE TRUE
#> 6: 6 0.4329589 1000 6 1 9 0.5 TRUE TRUE
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "bayesian",
ncores = ncores,
seed = seed
)
tuner$parameter_grid <- parameter_grid
tuner$parameter_bounds <- parameter_bounds
tuner$learner_args <- learner_args
tuner$optim_args <- optim_args
tuner$split_type <- "stratified"
tuner$set_data(
x = train_x,
y = train_y
)
tuner_results_bayesian <- tuner$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id num.trees mtry min.node.size max.depth sample.fraction gpUtility acqOptimum inBounds Elapsed Score
#> 1: 0 1 500 2 9 5 0.5 NA FALSE TRUE 1.597 -0.4791386
#> 2: 0 2 500 2 5 5 0.8 NA FALSE TRUE 1.641 -0.4786887
#> 3: 0 3 500 4 9 9 0.5 NA FALSE TRUE 2.161 -0.4392295
#> 4: 0 4 1000 2 9 1 0.5 NA FALSE TRUE 1.635 -0.4809325
#> 5: 0 5 500 2 9 1 0.8 NA FALSE TRUE 0.416 -0.4809325
#> 6: 0 6 1000 6 1 9 0.5 NA FALSE TRUE 3.373 -0.4378800
#> metric_optim_mean errorMessage probability classification
#> 1: 0.4791386 NA TRUE TRUE
#> 2: 0.4786887 NA TRUE TRUE
#> 3: 0.4392295 NA TRUE TRUE
#> 4: 0.4809325 NA TRUE TRUE
#> 5: 0.4809325 NA TRUE TRUE
#> 6: 0.4378800 NA TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerRanger$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
validator$learner_args <- tuner$results$best.setting[-1]
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: Fold1 0.4206685 500 4 9 9 0.8 TRUE TRUE
#> 2: Fold2 0.4011889 500 4 9 9 0.8 TRUE TRUE
#> 3: Fold3 0.4252033 500 4 9 9 0.8 TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "grid",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$parameter_grid <- parameter_grid
validator$learner_args <- learner_args
validator$split_type <- "stratified"
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: Fold1 0.4505456 1000 6 1 9 0.5 TRUE TRUE
#> 2: Fold2 0.4162822 1000 6 1 9 0.5 TRUE TRUE
#> 3: Fold3 0.4508978 1000 6 1 9 0.5 TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "bayesian",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = 312
)
validator$parameter_grid <- parameter_grid
validator$learner_args <- learner_args
validator$split_type <- "stratified"
validator$parameter_bounds <- parameter_bounds
validator$optim_args <- optim_args
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- TRUE
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> Registering parallel backend using 4 cores.
#>
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Registering parallel backend using 4 cores.
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> Registering parallel backend using 4 cores.
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: Fold1 0.4470914 1000 6 1 9 0.5000000 TRUE TRUE
#> 2: Fold2 0.4419416 636 6 2 12 0.9378338 TRUE TRUE
#> 3: Fold3 0.4737314 388 6 5 14 0.7457303 TRUE TRUE
## -----------------------------------------------------------------------------
preds_ranger <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
perf_ranger <- mlexperiments::performance(
object = validator,
prediction_results = preds_ranger,
y_ground_truth = test_y
)
perf_ranger
#> model performance
#> 1: Fold1 0.4466305
#> 2: Fold2 0.4601201
#> 3: Fold3 0.4742046
## -----------------------------------------------------------------------------
# define the target weights
y_weights <- ifelse(train_y == "n", 0.8, ifelse(train_y == "ei", 1.2, 1))
head(y_weights)
#> [1] 1.2 1.2 0.0 0.8 0.8 0.0
## -----------------------------------------------------------------------------
tuner_w_weights <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner_w_weights$parameter_grid <- parameter_grid
tuner_w_weights$learner_args <- c(
learner_args,
list(case_weights = y_weights)
)
tuner_w_weights$split_type <- "stratified"
tuner_w_weights$set_data(
x = train_x,
y = train_y
)
tuner_results_grid <- tuner_w_weights$execute(k = 3)
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean num.trees mtry min.node.size max.depth sample.fraction probability classification
#> <int> <num> <num> <num> <num> <num> <num> <lgcl> <lgcl>
#> 1: 1 0.4665865 500 2 9 5 0.5 TRUE TRUE
#> 2: 2 0.4656941 500 2 5 5 0.8 TRUE TRUE
#> 3: 3 0.4486635 500 4 9 9 0.5 TRUE TRUE
#> 4: 4 0.4809325 1000 2 9 1 0.5 TRUE TRUE
#> 5: 5 0.4809325 500 2 9 1 0.8 TRUE TRUE
#> 6: 6 0.4544915 1000 6 1 9 0.5 TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerRanger$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
# append the optimized setting from above with the newly created weights
validator$learner_args <- c(
tuner$results$best.setting[-1],
list("case_weights" = y_weights)
)
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> <char> <num> <num> <num> <num> <num> <num> <lgcl> <lgcl>
#> 1: Fold1 0.4565664 500 4 9 9 0.8 TRUE TRUE
#> 2: Fold2 0.4266609 500 4 9 9 0.8 TRUE TRUE
#> 3: Fold3 0.4576471 500 4 9 9 0.8 TRUE TRUE
## ----include=FALSE------------------------------------------------------------
# nolint end
Try the mllrnrs package in your browser
Any scripts or data that you put into this service are public.
mllrnrs documentation built on Sept. 11, 2024, 8:30 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## ----setup--------------------------------------------------------------------
# nolint start
library(mlexperiments)
library(mllrnrs)
## -----------------------------------------------------------------------------
library(mlbench)
data("DNA")
dataset <- DNA |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[160:180]
target_col <- "Class"
## -----------------------------------------------------------------------------
seed <- 123
if (isTRUE(as.logical(Sys.getenv("_R_CHECK_LIMIT_CORES_")))) {
# on cran
ncores <- 2L
} else {
ncores <- ifelse(
test = parallel::detectCores() > 4,
yes = 4L,
no = ifelse(
test = parallel::detectCores() < 2L,
yes = 1L,
no = parallel::detectCores()
)
)
}
options("mlexperiments.bayesian.max_init" = 10L)
## -----------------------------------------------------------------------------
data_split <- splitTools::partition(
y = dataset[, get(target_col)],
p = c(train = 0.7, test = 0.3),
type = "stratified",
seed = seed
)
train_x <- model.matrix(
~ -1 + .,
dataset[data_split$train, .SD, .SDcols = feature_cols]
)
train_y <- dataset[data_split$train, get(target_col)]
test_x <- model.matrix(
~ -1 + .,
dataset[data_split$test, .SD, .SDcols = feature_cols]
)
test_y <- dataset[data_split$test, get(target_col)]
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# required learner arguments, not optimized
learner_args <- list(probability = TRUE, classification = TRUE)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(reshape = TRUE)
performance_metric <- metric("bacc")
performance_metric_args <- NULL
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
num.trees = seq(500, 1000, 500),
mtry = seq(2, 6, 2),
min.node.size = seq(1, 9, 4),
max.depth = seq(1, 9, 4),
sample.fraction = seq(0.5, 0.8, 0.3)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(
num.trees = c(100L, 1000L),
mtry = c(2L, 9L),
min.node.size = c(1L, 20L),
max.depth = c(1L, 40L),
sample.fraction = c(0.3, 1.)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner$parameter_grid <- parameter_grid
tuner$learner_args <- learner_args
tuner$split_type <- "stratified"
tuner$set_data(
x = train_x,
y = train_y
)
tuner_results_grid <- tuner$execute(k = 3)
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
head(tuner_results_grid)
#> setting_id metric_optim_mean num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: 1 0.4786887 500 2 9 5 0.5 TRUE TRUE
#> 2: 2 0.4791386 500 2 5 5 0.8 TRUE TRUE
#> 3: 3 0.4419159 500 4 9 9 0.5 TRUE TRUE
#> 4: 4 0.4809325 1000 2 9 1 0.5 TRUE TRUE
#> 5: 5 0.4809325 500 2 9 1 0.8 TRUE TRUE
#> 6: 6 0.4329589 1000 6 1 9 0.5 TRUE TRUE
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "bayesian",
ncores = ncores,
seed = seed
)
tuner$parameter_grid <- parameter_grid
tuner$parameter_bounds <- parameter_bounds
tuner$learner_args <- learner_args
tuner$optim_args <- optim_args
tuner$split_type <- "stratified"
tuner$set_data(
x = train_x,
y = train_y
)
tuner_results_bayesian <- tuner$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id num.trees mtry min.node.size max.depth sample.fraction gpUtility acqOptimum inBounds Elapsed Score
#> 1: 0 1 500 2 9 5 0.5 NA FALSE TRUE 1.597 -0.4791386
#> 2: 0 2 500 2 5 5 0.8 NA FALSE TRUE 1.641 -0.4786887
#> 3: 0 3 500 4 9 9 0.5 NA FALSE TRUE 2.161 -0.4392295
#> 4: 0 4 1000 2 9 1 0.5 NA FALSE TRUE 1.635 -0.4809325
#> 5: 0 5 500 2 9 1 0.8 NA FALSE TRUE 0.416 -0.4809325
#> 6: 0 6 1000 6 1 9 0.5 NA FALSE TRUE 3.373 -0.4378800
#> metric_optim_mean errorMessage probability classification
#> 1: 0.4791386 NA TRUE TRUE
#> 2: 0.4786887 NA TRUE TRUE
#> 3: 0.4392295 NA TRUE TRUE
#> 4: 0.4809325 NA TRUE TRUE
#> 5: 0.4809325 NA TRUE TRUE
#> 6: 0.4378800 NA TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerRanger$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
validator$learner_args <- tuner$results$best.setting[-1]
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: Fold1 0.4206685 500 4 9 9 0.8 TRUE TRUE
#> 2: Fold2 0.4011889 500 4 9 9 0.8 TRUE TRUE
#> 3: Fold3 0.4252033 500 4 9 9 0.8 TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "grid",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$parameter_grid <- parameter_grid
validator$learner_args <- learner_args
validator$split_type <- "stratified"
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Classification: using 'classification error rate' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Classification: using 'classification error rate' as optimization metric.
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: Fold1 0.4505456 1000 6 1 9 0.5 TRUE TRUE
#> 2: Fold2 0.4162822 1000 6 1 9 0.5 TRUE TRUE
#> 3: Fold3 0.4508978 1000 6 1 9 0.5 TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "bayesian",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = 312
)
validator$parameter_grid <- parameter_grid
validator$learner_args <- learner_args
validator$split_type <- "stratified"
validator$parameter_bounds <- parameter_bounds
validator$optim_args <- optim_args
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- TRUE
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> Registering parallel backend using 4 cores.
#>
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Registering parallel backend using 4 cores.
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> Registering parallel backend using 4 cores.
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> 1: Fold1 0.4470914 1000 6 1 9 0.5000000 TRUE TRUE
#> 2: Fold2 0.4419416 636 6 2 12 0.9378338 TRUE TRUE
#> 3: Fold3 0.4737314 388 6 5 14 0.7457303 TRUE TRUE
## -----------------------------------------------------------------------------
preds_ranger <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
perf_ranger <- mlexperiments::performance(
object = validator,
prediction_results = preds_ranger,
y_ground_truth = test_y
)
perf_ranger
#> model performance
#> 1: Fold1 0.4466305
#> 2: Fold2 0.4601201
#> 3: Fold3 0.4742046
## -----------------------------------------------------------------------------
# define the target weights
y_weights <- ifelse(train_y == "n", 0.8, ifelse(train_y == "ei", 1.2, 1))
head(y_weights)
#> [1] 1.2 1.2 0.0 0.8 0.8 0.0
## -----------------------------------------------------------------------------
tuner_w_weights <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner_w_weights$parameter_grid <- parameter_grid
tuner_w_weights$learner_args <- c(
learner_args,
list(case_weights = y_weights)
)
tuner_w_weights$split_type <- "stratified"
tuner_w_weights$set_data(
x = train_x,
y = train_y
)
tuner_results_grid <- tuner_w_weights$execute(k = 3)
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean num.trees mtry min.node.size max.depth sample.fraction probability classification
#> <int> <num> <num> <num> <num> <num> <num> <lgcl> <lgcl>
#> 1: 1 0.4665865 500 2 9 5 0.5 TRUE TRUE
#> 2: 2 0.4656941 500 2 5 5 0.8 TRUE TRUE
#> 3: 3 0.4486635 500 4 9 9 0.5 TRUE TRUE
#> 4: 4 0.4809325 1000 2 9 1 0.5 TRUE TRUE
#> 5: 5 0.4809325 500 2 9 1 0.8 TRUE TRUE
#> 6: 6 0.4544915 1000 6 1 9 0.5 TRUE TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerRanger$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
# append the optimized setting from above with the newly created weights
validator$learner_args <- c(
tuner$results$best.setting[-1],
list("case_weights" = y_weights)
)
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
#> <char> <num> <num> <num> <num> <num> <num> <lgcl> <lgcl>
#> 1: Fold1 0.4565664 500 4 9 9 0.8 TRUE TRUE
#> 2: Fold2 0.4266609 500 4 9 9 0.8 TRUE TRUE
#> 3: Fold3 0.4576471 500 4 9 9 0.8 TRUE TRUE
## ----include=FALSE------------------------------------------------------------
# nolint end
Try the mllrnrs package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.