The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

glmnet: Survival Analysis

library(mlexperiments)
library(mlsurvlrnrs)

See https://github.com/kapsner/mlsurvlrnrs/blob/main/R/learner_surv_glmnet_cox.R for implementation details.

Preprocessing

Import and Prepare Data

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")

General Configurations

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)

Generate Training- and Test Data

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"
)

Generate Training Data Folds

fold_list <- splitTools::create_folds(
  y = split_vector_train,
  k = 3,
  type = "stratified",
  seed = seed
)

Experiments

Prepare Experiments

# 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"
)

Hyperparameter Tuning

Bayesian Optimization

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

k-Fold Cross Validation

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

Nested Cross Validation

Inner Bayesian Optimization

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

Comparison with Cox Proportional Hazards Regression

See https://github.com/kapsner/mlsurvlrnrs/blob/main/R/learner_surv_coxph_cox.R for implementation details.

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

Test Fold Equality

mlexperiments::validate_fold_equality(
  experiments = list(validator, validator_coxph)
)

Predict Outcome in Holdout Test Dataset

preds_glmnet <- mlexperiments::predictions(
  object = validator,
  newdata = test_x
)
preds_coxph <- mlexperiments::predictions(
  object = validator_coxph,
  newdata = test_x_coxph
)

Evaluate Performance on Holdout Test Dataset

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.