lgb.prepare_rules2.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.prepare_rules2.R
\name{lgb.prepare_rules2}
\alias{lgb.prepare_rules2}
\title{Data preparator for LightGBM datasets with rules (integer)}
\usage{
lgb.prepare_rules2(data, rules = NULL)
}
\arguments{
\item{data}{A data.frame or data.table to prepare.}

\item{rules}{A set of rules from the data preparator, if already used.}
}
\value{
A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
}
\description{
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.
}
\examples{
\dontrun{
  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 ...
  
}

}