Nothing
## bagging
run_bagging <- function(
dat_train,
dat_test,
dat_total,
params,
indcv,
iter,
budget
) {
# split/cross-validation
cv <- params$cv
## train
fit_train <- train_bagging(dat_train)
## test
fit_test <- test_bagging(
fit_train, dat_test, dat_total, params$n_df, params$n_tb,
indcv, iter, budget, cv
)
return(list(test = fit_test, train = fit_train))
}
train_bagging <- function(dat_train) {
## format data
training_data_elements_bagging = create_ml_args_bagging(dat_train)
## train formula
formula_bagging = training_data_elements_bagging[["formula"]]
## tunning parameter
tune_parameter = ncol(training_data_elements_bagging[["data"]]) -1
## fit
fit <- randomForest::randomForest(formula_bagging,
data = training_data_elements_bagging[["data"]],
mtry=tune_parameter, ntree = 500,
norm.votes=TRUE)
return(fit)
}
#'@importFrom stats predict runif
test_bagging <- function(
fit_train, dat_test, dat_total, n_df, n_tb, indcv, iter, budget, cv
) {
## format data
testing_data_elements_bagging = create_ml_args_bagging(dat_test)
total_data_elements_bagging = create_ml_args_bagging(dat_total)
## outcome
outcome = testing_data_elements_bagging[["data"]][["Y"]]
if(cv == TRUE){
if(length(unique(outcome)) > 2){
## predict
Y0t_total = predict(
fit_train,
newdata = total_data_elements_bagging[["data0t"]])
Y1t_total = predict(
fit_train,
newdata = total_data_elements_bagging[["data1t"]])
}else{
## predict
Y0t_total = predict(
fit_train,
newdata = total_data_elements_bagging[["data0t"]],
type = "prob")[, 2]
Y1t_total = predict(
fit_train,
newdata = total_data_elements_bagging[["data1t"]],
type = "prob")[, 2]
}
tau_total = Y1t_total - Y0t_total + runif(n_df,-1e-6,1e-6)
## compute quantities of interest
tau_test <- tau_total[indcv == iter]
That <- as.numeric(tau_total > 0)
That_p <- as.numeric(tau_total >= sort(tau_test, decreasing = TRUE)[floor(budget*length(tau_test))+1])
## output
cf_output <- list(
tau = c(tau_test, rep(NA, length(tau_total) - length(tau_test))),
tau_cv = tau_total,
That_cv = That,
That_pcv = That_p
)
}
if(cv == FALSE){
if(length(unique(outcome)) > 2){
## predict
Y0t_test = predict(
fit_train,
newdata = testing_data_elements_bagging[["data0t"]])
Y1t_test = predict(
fit_train,
newdata = testing_data_elements_bagging[["data1t"]])
}else{
## predict
Y0t_test = predict(
fit_train,
newdata = testing_data_elements_bagging[["data0t"]],
type = "prob")[, 2]
Y1t_test = predict(
fit_train,
newdata = testing_data_elements_bagging[["data1t"]],
type = "prob")[, 2]
}
tau_test = Y1t_test - Y0t_test
## compute quantities of interest
That = as.numeric(tau_test > 0)
That_p = numeric(length(That))
That_p[sort(tau_test,decreasing =TRUE,index.return=TRUE)$ix[1:(floor(budget*length(tau_test))+1)]] = 1
## output
cf_output <- list(
tau = tau_test,
tau_cv = tau_test,
That_cv = That,
That_pcv = That_p
)
}
return(cf_output)
}
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