Data preparator for LightGBM datasets with rules (numeric)

Description

Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.

Usage

lgb.prepare_rules(data, rules = NULL)

Arguments

Value

A list with the cleaned dataset (data) and the rules (rules). The data must be converted to a matrix format (as.matrix) for input in lgb.Dataset.

Examples

library(lightgbm)
data(iris)

str(iris)

new_iris <- lgb.prepare_rules(data = iris) # Autoconverter
str(new_iris$data)

data(iris) # Erase iris dataset
iris$Species[1L] <- "NEW FACTOR" # Introduce junk factor (NA)

# Use conversion using known rules
# Unknown factors become 0, excellent for sparse datasets
newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$rules)

# Unknown factor is now zero, perfect for sparse datasets
newer_iris$data[1L, ] # Species became 0 as it is an unknown factor

newer_iris$data[1L, 5L] <- 1.0 # Put back real initial value

# Is the newly created dataset equal? YES!
all.equal(new_iris$data, newer_iris$data)

# Can we test our own rules?
data(iris) # Erase iris dataset

# We remapped values differently
personal_rules <- list(Species = c("setosa" = 3L,
                                   "versicolor" = 2L,
                                   "virginica" = 1L))
newest_iris <- lgb.prepare_rules(data = iris, rules = personal_rules)
str(newest_iris$data) # SUCCESS!