## ----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)]
## -----------------------------------------------------------------------------
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)
options("mlexperiments.optim.xgb.nrounds" = 100L)
options("mlexperiments.optim.xgb.early_stopping_rounds" = 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$test, get("time")],
type = "right"
)
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = split_vector_train,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# required learner arguments, not optimized
learner_args <- list(
objective = "survival:cox",
eval_metric = "cox-nloglik"
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- NULL
performance_metric <- c_index
performance_metric_args <- NULL
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
subsample = seq(0.6, 1, .2),
colsample_bytree = seq(0.6, 1, .2),
min_child_weight = seq(1, 5, 4),
learning_rate = seq(0.1, 0.2, 0.1),
max_depth = seq(1, 5, 4)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(
subsample = c(0.2, 1),
colsample_bytree = c(0.2, 1),
min_child_weight = c(1L, 10L),
learning_rate = c(0.1, 0.2),
max_depth = c(1L, 10L)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerSurvXgboostCox$new(
metric_optimization_higher_better = FALSE
),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner$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$execute(k = 3)
#>
#> Parameter settings [==============================================================================================================>----------------------------] 8/10 ( 80%)
#> Parameter settings [============================================================================================================================>--------------] 9/10 ( 90%)
#> Parameter settings [==========================================================================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective eval_metric
#> 1: 1 4.866125 26 0.6 0.8 5 0.2 1 survival:cox cox-nloglik
#> 2: 2 4.896370 14 1.0 0.8 5 0.1 5 survival:cox cox-nloglik
#> 3: 3 4.860956 72 0.8 0.8 5 0.1 1 survival:cox cox-nloglik
#> 4: 4 4.867604 6 0.6 0.8 5 0.2 5 survival:cox cox-nloglik
#> 5: 5 4.893917 14 1.0 0.8 1 0.1 5 survival:cox cox-nloglik
#> 6: 6 4.883471 13 0.8 0.8 5 0.1 5 survival:cox cox-nloglik
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = LearnerSurvXgboostCox$new(
metric_optimization_higher_better = FALSE
),
strategy = "bayesian",
ncores = ncores,
seed = seed
)
tuner$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$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id subsample colsample_bytree min_child_weight learning_rate max_depth gpUtility acqOptimum inBounds Elapsed Score metric_optim_mean nrounds errorMessage
#> 1: 0 1 0.6 0.8 5 0.2 1 NA FALSE TRUE 1.792 -4.867594 4.867594 22 NA
#> 2: 0 2 1.0 0.8 5 0.1 5 NA FALSE TRUE 1.826 -4.901912 4.901912 12 NA
#> 3: 0 3 0.8 0.8 5 0.1 1 NA FALSE TRUE 1.833 -4.874152 4.874152 48 NA
#> 4: 0 4 0.6 0.8 5 0.2 5 NA FALSE TRUE 1.836 -4.870687 4.870687 5 NA
#> 5: 0 5 1.0 0.8 1 0.1 5 NA FALSE TRUE 0.813 -4.883240 4.883240 14 NA
#> 6: 0 6 0.8 0.8 5 0.1 5 NA FALSE TRUE 0.861 -4.895220 4.895220 13 NA
#> objective eval_metric
#> 1: survival:cox cox-nloglik
#> 2: survival:cox cox-nloglik
#> 3: survival:cox cox-nloglik
#> 4: survival:cox cox-nloglik
#> 5: survival:cox cox-nloglik
#> 6: survival:cox cox-nloglik
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = LearnerSurvXgboostCox$new(
metric_optimization_higher_better = FALSE
),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
validator$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 subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> 1: Fold1 0.6433838 0.3015909 0.5804647 1 0.2 1 27 survival:cox cox-nloglik
#> 2: Fold2 0.6979611 0.3015909 0.5804647 1 0.2 1 27 survival:cox cox-nloglik
#> 3: Fold3 0.6536441 0.3015909 0.5804647 1 0.2 1 27 survival:cox cox-nloglik
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerSurvXgboostCox$new(
metric_optimization_higher_better = FALSE
),
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 [==============================================================================================================>----------------------------] 8/10 ( 80%)
#> Parameter settings [============================================================================================================================>--------------] 9/10 ( 90%)
#> Parameter settings [==========================================================================================================================================] 10/10 (100%)
#> CV fold: Fold2
#> CV progress [=================================================================================================>-------------------------------------------------] 2/3 ( 67%)
#>
#> 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 [=======================================================>-----------------------------------------------------------------------------------] 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 nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective eval_metric
#> 1: Fold1 0.6355074 47 0.6 1.0 1 0.2 1 survival:cox cox-nloglik
#> 2: Fold2 0.6699094 13 0.8 0.8 5 0.1 5 survival:cox cox-nloglik
#> 3: Fold3 0.6832026 20 0.6 0.8 5 0.2 1 survival:cox cox-nloglik
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = LearnerSurvXgboostCox$new(
metric_optimization_higher_better = FALSE
),
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 subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> 1: Fold1 0.6420432 0.6394793 0.9881643 4 0.1268116 1 53 survival:cox cox-nloglik
#> 2: Fold2 0.6563499 1.0000000 1.0000000 5 0.1000000 5 11 survival:cox cox-nloglik
#> 3: Fold3 0.6573680 0.7495501 0.4383327 7 0.1000000 5 20 survival:cox cox-nloglik
## -----------------------------------------------------------------------------
preds_xgboost <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
perf_xgboost <- mlexperiments::performance(
object = validator,
prediction_results = preds_xgboost,
y_ground_truth = test_y
)
perf_xgboost
#> model performance
#> 1: Fold1 0.6384856
#> 2: Fold2 0.6118066
#> 3: Fold3 0.6356952
## ----include=FALSE------------------------------------------------------------
# nolint end