Nothing
inst/doc/mlexperiments_knn_multiclass.R
In mlexperiments: Machine Learning Experiments
## ----setup--------------------------------------------------------------------
# nolint start
library(mlexperiments)
## -----------------------------------------------------------------------------
library(mlbench)
data("DNA")
dataset <- DNA |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[1:180]
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(
l = 2,
test = parse(text = "fold_test$x"),
use.all = FALSE
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(type = "response")
performance_metric <- metric("bacc")
performance_metric_args <- NULL
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
k = seq(4, 68, 6)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(k = c(2L, 80L))
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerKnn$new(),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner$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)
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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 k l use.all
#> 1: 1 0.1669134 16 2 FALSE
#> 2: 2 0.1256584 64 2 FALSE
#> 3: 3 0.1870928 10 2 FALSE
#> 4: 4 0.1364111 34 2 FALSE
#> 5: 5 0.1243125 58 2 FALSE
#> 6: 6 0.1462841 28 2 FALSE
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerKnn$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$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id k gpUtility acqOptimum inBounds Elapsed Score metric_optim_mean errorMessage l use.all
#> 1: 0 1 16 NA FALSE TRUE 1.061 -0.1651140 0.1651140 NA 2 FALSE
#> 2: 0 2 64 NA FALSE TRUE 1.131 -0.1261065 0.1261065 NA 2 FALSE
#> 3: 0 3 10 NA FALSE TRUE 1.060 -0.1835086 0.1835086 NA 2 FALSE
#> 4: 0 4 34 NA FALSE TRUE 1.074 -0.1377516 0.1377516 NA 2 FALSE
#> 5: 0 5 58 NA FALSE TRUE 1.101 -0.1247624 0.1247624 NA 2 FALSE
#> 6: 0 6 28 NA FALSE TRUE 1.046 -0.1462823 0.1462823 NA 2 FALSE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = LearnerKnn$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
validator$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 progress [======================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> CV fold: Fold3
#> CV progress [==========================================================================================================] 3/3 (100%)
head(validator_results)
#> fold performance k l use.all
#> 1: Fold1 0.8931022 58 2 FALSE
#> 2: Fold2 0.8445084 58 2 FALSE
#> 3: Fold3 0.9010913 58 2 FALSE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerKnn$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
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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%)
#> CV fold: Fold2
#> CV progress [======================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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%)
#> CV fold: Fold3
#> CV progress [==========================================================================================================] 3/3 (100%)
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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(validator_results)
#> fold performance k l use.all
#> 1: Fold1 0.8863818 64 2 FALSE
#> 2: Fold2 0.8396360 64 2 FALSE
#> 3: Fold3 0.9000926 64 2 FALSE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerKnn$new(),
strategy = "bayesian",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$parameter_grid <- parameter_grid
validator$learner_args <- learner_args
validator$split_type <- "stratified"
validator$parameter_bounds <- parameter_bounds
validator$optim_args <- optim_args
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
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 k l use.all
#> 1: Fold1 0.8702444 28 2 FALSE
#> 2: Fold2 0.8396360 64 2 FALSE
#> 3: Fold3 0.9010913 58 2 FALSE
## ----include=FALSE------------------------------------------------------------
# nolint end
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mlexperiments documentation built on April 12, 2025, 1:40 a.m.
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## ----setup--------------------------------------------------------------------
# nolint start
library(mlexperiments)
## -----------------------------------------------------------------------------
library(mlbench)
data("DNA")
dataset <- DNA |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[1:180]
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(
l = 2,
test = parse(text = "fold_test$x"),
use.all = FALSE
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(type = "response")
performance_metric <- metric("bacc")
performance_metric_args <- NULL
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
k = seq(4, 68, 6)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(k = c(2L, 80L))
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerKnn$new(),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner$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)
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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 k l use.all
#> 1: 1 0.1669134 16 2 FALSE
#> 2: 2 0.1256584 64 2 FALSE
#> 3: 3 0.1870928 10 2 FALSE
#> 4: 4 0.1364111 34 2 FALSE
#> 5: 5 0.1243125 58 2 FALSE
#> 6: 6 0.1462841 28 2 FALSE
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerKnn$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$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id k gpUtility acqOptimum inBounds Elapsed Score metric_optim_mean errorMessage l use.all
#> 1: 0 1 16 NA FALSE TRUE 1.061 -0.1651140 0.1651140 NA 2 FALSE
#> 2: 0 2 64 NA FALSE TRUE 1.131 -0.1261065 0.1261065 NA 2 FALSE
#> 3: 0 3 10 NA FALSE TRUE 1.060 -0.1835086 0.1835086 NA 2 FALSE
#> 4: 0 4 34 NA FALSE TRUE 1.074 -0.1377516 0.1377516 NA 2 FALSE
#> 5: 0 5 58 NA FALSE TRUE 1.101 -0.1247624 0.1247624 NA 2 FALSE
#> 6: 0 6 28 NA FALSE TRUE 1.046 -0.1462823 0.1462823 NA 2 FALSE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = LearnerKnn$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
validator$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 progress [======================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> CV fold: Fold3
#> CV progress [==========================================================================================================] 3/3 (100%)
head(validator_results)
#> fold performance k l use.all
#> 1: Fold1 0.8931022 58 2 FALSE
#> 2: Fold2 0.8445084 58 2 FALSE
#> 3: Fold3 0.9010913 58 2 FALSE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerKnn$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
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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%)
#> CV fold: Fold2
#> CV progress [======================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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%)
#> CV fold: Fold3
#> CV progress [==========================================================================================================] 3/3 (100%)
#>
#> Parameter settings [===================>------------------------------------------------------------------------------] 2/10 ( 20%)
#> 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(validator_results)
#> fold performance k l use.all
#> 1: Fold1 0.8863818 64 2 FALSE
#> 2: Fold2 0.8396360 64 2 FALSE
#> 3: Fold3 0.9000926 64 2 FALSE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerKnn$new(),
strategy = "bayesian",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$parameter_grid <- parameter_grid
validator$learner_args <- learner_args
validator$split_type <- "stratified"
validator$parameter_bounds <- parameter_bounds
validator$optim_args <- optim_args
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
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 k l use.all
#> 1: Fold1 0.8702444 28 2 FALSE
#> 2: Fold2 0.8396360 64 2 FALSE
#> 3: Fold3 0.9010913 58 2 FALSE
## ----include=FALSE------------------------------------------------------------
# nolint end
Try the mlexperiments 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.
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