## ----setup--------------------------------------------------------------------
# nolint start
library(mlexperiments)
library(mlsurvlrnrs)
## -----------------------------------------------------------------------------
dataset <- survival::colon |>
data.table::as.data.table() |>
na.omit()
dataset <- dataset[get("etype") == 2, ]
surv_cols <- c("status", "time", "rx")
feature_cols <- colnames(dataset)[3:(ncol(dataset) - 1)]
cat_vars <- c("sex", "obstruct", "perfor", "adhere", "differ", "extent",
"surg", "node4", "rx")
## -----------------------------------------------------------------------------
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)
## -----------------------------------------------------------------------------
split_vector <- splitTools::multi_strata(
df = dataset[, .SD, .SDcols = surv_cols],
strategy = "kmeans",
k = 4
)
data_split <- splitTools::partition(
y = split_vector,
p = c(train = 0.7, test = 0.3),
type = "stratified",
seed = seed
)
train_x <- model.matrix(
~ -1 + .,
dataset[
data_split$train, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])
]
)
train_y <- survival::Surv(
event = (dataset[data_split$train, get("status")] |>
as.character() |>
as.integer()),
time = dataset[data_split$train, get("time")],
type = "right"
)
split_vector_train <- splitTools::multi_strata(
df = dataset[data_split$train, .SD, .SDcols = surv_cols],
strategy = "kmeans",
k = 4
)
test_x <- model.matrix(
~ -1 + .,
dataset[data_split$test, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])]
)
test_y <- survival::Surv(
event = (dataset[data_split$test, get("status")] |>
as.character() |>
as.integer()),
time = dataset[data_split$test, get("time")],
type = "right"
)
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = split_vector_train,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# required learner arguments, not optimized
learner_args <- NULL
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- NULL
performance_metric <- c_index
performance_metric_args <- NULL
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
alpha = seq(0, 1, 0.05)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(
alpha = c(0., 1.)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerSurvGlmnetCox$new(),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner$parameter_grid <- parameter_grid
tuner$learner_args <- learner_args
tuner$split_type <- "stratified"
tuner$split_vector <- split_vector_train
tuner$set_data(
x = train_x,
y = train_y
)
tuner_results_grid <- tuner$execute(k = 3)
#>
#> Parameter settings [===========================>---------------------------------------------------------------------------------------------------------------] 2/10 ( 20%)
#> Parameter settings [=========================================>-------------------------------------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=======================================================>-----------------------------------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [=====================================================================>---------------------------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [==================================================================================>--------------------------------------------------------] 6/10 ( 60%)
#> Parameter settings [================================================================================================>------------------------------------------] 7/10 ( 70%)
#> Parameter settings [==============================================================================================================>----------------------------] 8/10 ( 80%)
#> Parameter settings [============================================================================================================================>--------------] 9/10 ( 90%)
#> Parameter settings [==========================================================================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean lambda alpha
#> 1: 1 0.6420939 0.1571721 0.70
#> 2: 2 0.6473427 0.1222450 0.90
#> 3: 3 0.6420939 0.1692623 0.65
#> 4: 4 0.6503151 0.9134093 0.10
#> 5: 5 0.6448394 0.2227701 0.45
#> 6: 6 0.6516264 2.0049311 0.05
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerSurvGlmnetCox$new(),
strategy = "bayesian",
ncores = ncores,
seed = seed
)
tuner$parameter_grid <- parameter_grid
tuner$parameter_bounds <- parameter_bounds
tuner$learner_args <- learner_args
tuner$optim_args <- optim_args
tuner$split_type <- "stratified"
tuner$split_vector <- split_vector_train
tuner$set_data(
x = train_x,
y = train_y
)
tuner_results_bayesian <- tuner$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id alpha gpUtility acqOptimum inBounds Elapsed Score metric_optim_mean lambda errorMessage
#> 1: 0 1 0.70 NA FALSE TRUE 1.186 0.6420939 0.6420939 0.1571721 NA
#> 2: 0 2 0.90 NA FALSE TRUE 1.157 0.6473427 0.6473427 0.1222450 NA
#> 3: 0 3 0.65 NA FALSE TRUE 1.163 0.6420939 0.6420939 0.1692623 NA
#> 4: 0 4 0.10 NA FALSE TRUE 1.177 0.6503151 0.6503151 0.9134093 NA
#> 5: 0 5 0.45 NA FALSE TRUE 0.240 0.6448394 0.6448394 0.2227701 NA
#> 6: 0 6 0.05 NA FALSE TRUE 0.286 0.6516264 0.6516264 2.0049311 NA
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = LearnerSurvGlmnetCox$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
validator$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
head(validator_results)
#> fold performance alpha lambda
#> 1: Fold1 0.5959883 0.03836977 2.612644
#> 2: Fold2 0.6688831 0.03836977 2.612644
#> 3: Fold3 0.6899724 0.03836977 2.612644
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerSurvGlmnetCox$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$split_type <- "stratified"
validator$split_vector <- split_vector_train
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 lambda alpha
#> 1: Fold1 0.5881450 1.2565031 0.05
#> 2: Fold2 0.6187568 0.4969800 0.10
#> 3: Fold3 0.6882968 0.1076019 0.05
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerSurvGlmnetCox$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$split_type <- "stratified"
validator$split_vector <- split_vector_train
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 alpha lambda
#> 1: Fold1 0.5921167 0.001528976 28.32153046
#> 2: Fold2 0.6033518 0.450000000 0.05758351
#> 3: Fold3 0.6908924 0.150000000 0.40290596
## -----------------------------------------------------------------------------
train_x_coxph <- data.matrix(
dataset[
data_split$train, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])
]
)
test_x_coxph <- data.matrix(
dataset[
data_split$test, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])
]
)
## -----------------------------------------------------------------------------
validator_coxph <- mlexperiments::MLCrossValidation$new(
learner = LearnerSurvCoxPHCox$new(),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
validator_coxph$performance_metric <- performance_metric
validator_coxph$performance_metric_args <- performance_metric_args
validator_coxph$return_models <- TRUE
validator_coxph$set_data(
x = train_x_coxph,
y = train_y,
cat_vars = cat_vars
)
validator_coxph_results <- validator_coxph$execute()
#>
#> CV fold: Fold1
#> Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
#>
#> CV fold: Fold2
#> Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
#>
#> CV fold: Fold3
#> Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
head(validator_coxph_results)
#> fold performance
#> 1: Fold1 0.5895801
#> 2: Fold2 0.5992298
#> 3: Fold3 0.6732488
## -----------------------------------------------------------------------------
mlexperiments::validate_fold_equality(
experiments = list(validator, validator_coxph)
)
## -----------------------------------------------------------------------------
preds_glmnet <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
preds_coxph <- mlexperiments::predictions(
object = validator_coxph,
newdata = test_x_coxph
)
## -----------------------------------------------------------------------------
perf_glmnet <- mlexperiments::performance(
object = validator,
prediction_results = preds_glmnet,
y_ground_truth = test_y
)
perf_glmnet
#> model performance
#> 1: Fold1 0.6660022
#> 2: Fold2 0.6846061
#> 3: Fold3 0.6636560
## -----------------------------------------------------------------------------
perf_coxph <- mlexperiments::performance(
object = validator_coxph,
prediction_results = preds_coxph,
y_ground_truth = test_y
)
perf_coxph
#> model performance
#> 1: Fold1 0.6758025
#> 2: Fold2 0.6782526
#> 3: Fold3 0.6437025
## ----include=FALSE------------------------------------------------------------
# nolint end