R/lgb.prepare_rules.R
lgb.prepare_rules.RdAttempts 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.
lgb.prepare_rules(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.
#> '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 1 1 1 1 1 1 ...# '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.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$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)#> Warning: invalid factor level, NA generated# Warning message: # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L, : # invalid factor level, NA generated # Use conversion using known rules # Unknown factors become 0, excellent for sparse datasets newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$rules) # Unknown factor is now zero, perfect for sparse datasets newer_iris$data[1, ] # Species became 0 as it is an unknown factor#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.2 0# Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 0 newer_iris$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# [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 0 3 3 3 3 3 3 3 3 3 ...# '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 ...