| lgb.prepare_rules2 {lightgbm} | R Documentation |
Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). In addition, keeps rules created so you can convert other datasets using this converter. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
lgb.prepare_rules2(data, rules = NULL)
data |
A data.frame or data.table to prepare. |
rules |
A set of rules from the data preparator, if already used. |
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.
library(lightgbm)
data(iris)
str(iris)
# 'data.frame': 150 obs. of 5 variables:
# $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
# $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
# $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
# $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
# $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
str(new_iris$data)
# 'data.frame': 150 obs. of 5 variables:
# $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
# $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
# $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
# $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
# $ Species : int 1 1 1 1 1 1 1 1 1 1 ...
data(iris) # Erase iris dataset
iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
# Warning message:
# In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L, :
# invalid factor level, NA generated
# Use conversion using known rules
# Unknown factors become 0, excellent for sparse datasets
newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
# Unknown factor is now zero, perfect for sparse datasets
newer_iris$data[1, ] # Species became 0 as it is an unknown factor
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1 5.1 3.5 1.4 0.2 0
newer_iris$data[1, 5] <- 1 # Put back real initial value
# Is the newly created dataset equal? YES!
all.equal(new_iris$data, newer_iris$data)
# [1] TRUE
# 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_rules2(data = iris, rules = personal_rules)
str(newest_iris$data) # SUCCESS!
# 'data.frame': 150 obs. of 5 variables:
# $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
# $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
# $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
# $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
# $ Species : int 3 3 3 3 3 3 3 3 3 3 ...