wehub-resource-sync
95 lines
2.7 KiB
R
95 lines
2.7 KiB
R
DataProcessor <- R6::R6Class(
|
|
classname = "lgb.DataProcessor",
|
|
public = list(
|
|
factor_levels = NULL,
|
|
|
|
process_label = function(label, objective, params) {
|
|
|
|
if (is.character(label)) {
|
|
label <- factor(label)
|
|
}
|
|
|
|
if (is.factor(label)) {
|
|
|
|
self$factor_levels <- levels(label)
|
|
if (length(self$factor_levels) <= 1L) {
|
|
stop("Labels to predict is a factor with <2 possible values.")
|
|
}
|
|
|
|
label <- as.numeric(label) - 1.0
|
|
out <- list(label = label)
|
|
if (length(self$factor_levels) == 2L) {
|
|
if (objective == "auto") {
|
|
objective <- "binary"
|
|
}
|
|
if (!(objective %in% .BINARY_OBJECTIVES())) {
|
|
stop("Two-level factors as labels only allowed for objective='binary' or objective='auto'.")
|
|
}
|
|
} else {
|
|
if (objective == "auto") {
|
|
objective <- "multiclass"
|
|
}
|
|
if (!(objective %in% .MULTICLASS_OBJECTIVES())) {
|
|
stop(
|
|
sprintf(
|
|
"Factors with >2 levels as labels only allowed for multi-class objectives. Got: %s (allowed: %s)"
|
|
, objective
|
|
, toString(.MULTICLASS_OBJECTIVES())
|
|
)
|
|
)
|
|
}
|
|
data_num_class <- length(self$factor_levels)
|
|
params <- .check_wrapper_param(
|
|
main_param_name = "num_class"
|
|
, params = params
|
|
, alternative_kwarg_value = data_num_class
|
|
)
|
|
if (params[["num_class"]] != data_num_class) {
|
|
warning(
|
|
sprintf(
|
|
"Found num_class=%d in params, but 'label' is a factor with %d levels. 'num_class' will be ignored."
|
|
, params[["num_class"]]
|
|
, data_num_class
|
|
)
|
|
)
|
|
params$num_class <- data_num_class
|
|
}
|
|
}
|
|
out$objective <- objective
|
|
out$params <- params
|
|
return(out)
|
|
|
|
} else {
|
|
|
|
label <- as.numeric(label)
|
|
if (objective == "auto") {
|
|
objective <- "regression"
|
|
}
|
|
out <- list(
|
|
label = label
|
|
, objective = objective
|
|
, params = params
|
|
)
|
|
return(out)
|
|
|
|
}
|
|
},
|
|
|
|
process_predictions = function(pred, type) {
|
|
if (NROW(self$factor_levels)) {
|
|
if (type == "class") {
|
|
pred <- as.integer(pred) + 1L
|
|
attributes(pred)$levels <- self$factor_levels
|
|
attributes(pred)$class <- "factor"
|
|
} else if (type %in% c("response", "raw")) {
|
|
if (is.matrix(pred) && ncol(pred) == length(self$factor_levels)) {
|
|
colnames(pred) <- self$factor_levels
|
|
}
|
|
}
|
|
}
|
|
|
|
return(pred)
|
|
}
|
|
)
|
|
)
|