Nothing
#' @importFrom stats terms
process_input_specs <- function(x, model) {
if (!is.null(model$formula)) {
predictor_cols <- labels(terms(model$formula, data = x))
x <- x[, which(names(x) %in% predictor_cols)]
}
x
}
process_input_and_label_specs <- function(x, y, formula) {
if (!is.null(formula)) {
if (!inherits(x, "data.frame")) {
stop(
"'x' must be a data.frame when predictor column(s) and response ",
"column are specified using the formula syntax."
)
}
response_col <- all.vars(formula)[[1]]
predictor_cols <- labels(terms(formula, data = x))
y <- x[, response_col]
x <- x[, which(names(x) %in% predictor_cols)]
} else if (!is.numeric(y)) {
stop(
"'y' must be a numeric vector if predictor(s) and responses are not",
" specified using the formula syntax."
)
}
list(x, y)
}
new_model <- function(cls, mode, xptr, formula = NULL, resp_var = NULL, ...) {
structure(
c(
list(
mode = mode,
xptr = xptr,
formula = formula,
resp_var_cls = class(resp_var),
resp_var_attrs = attributes(resp_var)
),
rlang::dots_list(...)
),
class = cls
)
}
postprocess_classification_results <- function(predictions, model) {
if (!is.null(model$resp_var_cls)) {
class(predictions) <- model$resp_var_cls
}
if (!is.null(model$resp_var_attrs)) {
attributes(predictions) <- model$resp_var_attrs
}
predictions
}
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