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)
    # '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_rules(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     : num  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_rules(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" = 3,
                                       "versicolor" = 2,
                                       "virginica" = 1))
    newest_iris <- lgb.prepare_rules(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     : num  3 3 3 3 3 3 3 3 3 3 ...

[Package lightgbm version 2.2.3 Index]