R: Data preparator for LightGBM datasets with rules (numeric)

lgb.prepare_rules {lightgbm} | R Documentation

## 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

`data` | A data.frame or data.table to prepare.
`rules` | A set of rules from the data preparator, if already used.

### 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!

[Package lightgbm version 2.3.1 Index]