[R-package] Add lgb.convert from smart matrix building (#561)

* Add lgb.prepare

* Update roxygen to 6.0.1 and add lgb.prepare

* Add prepared rules.

* Add recommendation when needing rules for transformation.

R-package/DESCRIPTION

@@ -35,4 +35,4 @@ Imports:
     data.table (>= 1.9.6),
     magrittr (>= 1.5),
     jsonlite
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1

R-package/NAMESPACE

@@ -23,6 +23,10 @@ export(lgb.load)
 export(lgb.model.dt.tree)
 export(lgb.plot.importance)
 export(lgb.plot.interpretation)
+export(lgb.prepare)
+export(lgb.prepare2)
+export(lgb.prepare_rules)
+export(lgb.prepare_rules2)
 export(lgb.save)
 export(lgb.train)
 export(lgb.unloader)

R-package/R/lgb.prepare.R

+#' Data preparator for LightGBM datasets (integer)
+#'
+#' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). Please use \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
+#' 
+#' @param data A data.frame or data.table to prepare.
+#' 
+#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
+#' 
+#' @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 ...
+#'   
+#'   str(lgb.prepare(data = iris)) # Convert all factors/chars to integer
+#'   # '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 ...
+#'   
+#'   # When lightgbm package is installed, and you do not want to load it
+#'   # You can still use the function!
+#'   lgb.unloader()
+#'   str(lightgbm::lgb.prepare(data = 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     : int  1 1 1 1 1 1 1 1 1 1 ...
+#'   
+#' }
+#' 
+#' @export
+lgb.prepare <- function(data) {
+  
+  # data.table not behaving like data.frame
+  if ("data.table" %in% class(data)) {
+    
+    # Get data classes
+    list_classes <- sapply(data, class)
+    
+    # Convert characters to factors only (we can change them to numeric after)
+    is_char <- which(list_classes == "character")
+    if (length(is_char) > 0) {
+      data[, (is_char) := lapply(.SD, function(x) {as.integer(as.factor(x))}), .SDcols = is_char]
+    }
+    
+    # Convert factors to numeric (integer is more efficient actually)
+    is_fact <- c(which(list_classes == "factor"), is_char)
+    if (length(is_fact) > 0) {
+      data[, (is_fact) := lapply(.SD, function(x) {as.integer(x)}), .SDcols = is_fact]
+    }
+    
+  } else {
+    
+    # Default routine (data.frame)
+    if ("data.frame" %in% class(data)) {
+      
+      # Get data classes
+      list_classes <- sapply(data, class)
+      
+      # Convert characters to factors to numeric (integer is more efficient actually)
+      is_char <- which(list_classes == "character")
+      if (length(is_char) > 0) {
+        data[is_char] <- lapply(data[is_char], function(x) {as.integer(as.factor(x))})
+      }
+      
+      # Convert factors to numeric (integer is more efficient actually)
+      is_fact <- which(list_classes == "factor")
+      if (length(is_fact) > 0) {
+        data[is_fact] <- lapply(data[is_fact], function(x) {as.integer(x)})
+      }
+      
+    } else {
+      
+      # What do you think you are doing here? Throw error.
+      stop("lgb.prepare: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
+      
+    }
+    
+  }
+  
+  return(data)
+  
+}

R-package/R/lgb.prepare2.R

+#' Data preparator for LightGBM datasets (numeric)
+#'
+#' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric without integers. This is useful if you have a specific need for numeric dataset instead of integer dataset. There are programs which do not support integer-only input. Consider this is a fallback solution if you cannot use integers. Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets.
+#' 
+#' @param data A data.frame or data.table to prepare.
+#' 
+#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
+#' 
+#' @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 ...
+#'   
+#'   str(lgb.prepare2(data = iris)) # Convert all factors/chars to numeric
+#'   # '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 ...
+#'   
+#'   # When lightgbm package is installed, and you do not want to load it
+#'   # You can still use the function!
+#'   lgb.unloader()
+#'   str(lightgbm::lgb.prepare2(data = 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     : num  1 1 1 1 1 1 1 1 1 1 ...
+#' }
+#' 
+#' @export
+lgb.prepare2 <- function(data) {
+  
+  # data.table not behaving like data.frame
+  if ("data.table" %in% class(data)) {
+    
+    # Get data classes
+    list_classes <- sapply(data, class)
+    
+    # Convert characters to factors only (we can change them to numeric after)
+    is_char <- which(list_classes == "character")
+    if (length(is_char) > 0) {
+      data[, (is_char) := lapply(.SD, function(x) {as.numeric(as.factor(x))}), .SDcols = is_char]
+    }
+    
+    # Convert factors to numeric (integer is more efficient actually)
+    is_fact <- c(which(list_classes == "factor"), is_char)
+    if (length(is_fact) > 0) {
+      data[, (is_fact) := lapply(.SD, function(x) {as.numeric(x)}), .SDcols = is_fact]
+    }
+    
+  } else {
+    
+    # Default routine (data.frame)
+    if ("data.frame" %in% class(data)) {
+      
+      # Get data classes
+      list_classes <- sapply(data, class)
+      
+      # Convert characters to factors to numeric (integer is more efficient actually)
+      is_char <- which(list_classes == "character")
+      if (length(is_char) > 0) {
+        data[is_char] <- lapply(data[is_char], function(x) {as.numeric(as.factor(x))})
+      }
+      
+      # Convert factors to numeric (integer is more efficient actually)
+      is_fact <- which(list_classes == "factor")
+      if (length(is_fact) > 0) {
+        data[is_fact] <- lapply(data[is_fact], function(x) {as.numeric(x)})
+      }
+      
+    } else {
+      
+      # What do you think you are doing here? Throw error.
+      stop("lgb.prepare2: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
+      
+    }
+    
+  }
+  
+  return(data)
+  
+}

R-package/R/lgb.prepare_rules.R

+#' Data preparator for LightGBM datasets with rules (integer)
+#'
+#' 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.
+#' 
+#' @param data A data.frame or data.table to prepare.
+#' @param rules A set of rules from the data preparator, if already used.
+#' 
+#' @return 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.
+#' 
+#' @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_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     : int  1 1 1 1 1 1 1 1 1 1 ...
+#'   
+#'   data(iris) # Erase iris dataset
+#'   iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
+#'   # Warning message:
+#'   In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
+#'     invalid factor level, NA generated
+#'   
+#'   # Use conversion using known rules
+#'   # Unknown factors become 0, excellent for sparse datasets
+#'   newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$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" = 3L,
+#'                                      "versicolor" = 2L,
+#'                                      "virginica" = 1L))
+#'   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     : int  3 3 3 3 3 3 3 3 3 3 ...
+#'   
+#' }
+#' 
+#' @export
+lgb.prepare_rules <- function(data, rules = NULL) {
+  
+  # data.table not behaving like data.frame
+  if ("data.table" %in% class(data)) {
+    
+    # Must use existing rules
+    if (!is.null(rules)) {
+      
+      # Loop through rules
+      for (i in names(rules)) {
+        
+        set(data, j = i, value = unname(rules[[i]][data[[i]]]))
+        data[[i]][is.na(data[[i]])] <- 0L # Overwrite NAs by 0s as integer
+        
+      }
+      
+    } else {
+      
+      # Get data classes
+      list_classes <- sapply(data, class)
+      
+      # Map characters/factors
+      is_fix <- which(list_classes %in% c("character", "factor"))
+      ruleset <- list()
+      
+      # Need to create rules?
+      if (length(is_fix) > 0) {
+        
+        # Go through all characters/factors
+        for (i in is_fix) {
+          
+          # Store column elsewhere
+          mini_data <- data[[i]]
+          
+          # Get unique values
+          if (class(mini_data) == "factor") {
+            mini_unique <- levels(mini_data) # Factor
+            mini_numeric <- 1:length(mini_unique) # Respect ordinal if needed
+          } else {
+            mini_unique <- as.factor(unique(mini_data)) # Character
+            mini_numeric <- as.integer(mini_unique) # No respect of ordinality
+          }
+          
+          # Create ruleset
+          indexed <- colnames(data)[i] # Index value
+          ruleset[[indexed]] <- mini_numeric # Numeric content
+          names(ruleset[[indexed]]) <- mini_unique # Character equivalent
+          
+          # Apply to real data column
+          set(data, j = i, value = unname(ruleset[[indexed]][mini_data]))
+          
+        }
+        
+      }
+      
+    }
+    
+  } else {
+    
+    # Must use existing rules
+    if (!is.null(rules)) {
+      
+      # Loop through rules
+      for (i in names(rules)) {
+        
+        data[[i]] <- unname(rules[[i]][data[[i]]])
+        data[[i]][is.na(data[[i]])] <- 0L # Overwrite NAs by 0s as integer
+        
+      }
+      
+    } else {
+      
+      # Default routine (data.frame)
+      if ("data.frame" %in% class(data)) {
+        
+        # Get data classes
+        list_classes <- sapply(data, class)
+        
+        # Map characters/factors
+        is_fix <- which(list_classes %in% c("character", "factor"))
+        ruleset <- list()
+        
+        # Need to create rules?
+        if (length(is_fix) > 0) {
+          
+          # Go through all characters/factors
+          for (i in is_fix) {
+            
+            # Store column elsewhere
+            mini_data <- data[[i]]
+            
+            # Get unique values
+            if (class(mini_data) == "factor") {
+              mini_unique <- levels(mini_data) # Factor
+              mini_numeric <- 1:length(mini_unique) # Respect ordinal if needed
+            } else {
+              mini_unique <- as.factor(unique(mini_data)) # Character
+              mini_numeric <- as.integer(mini_unique) # No respect of ordinality
+            }
+            
+            # Create ruleset
+            indexed <- colnames(data)[i] # Index value
+            ruleset[[indexed]] <- mini_numeric # Numeric content
+            names(ruleset[[indexed]]) <- mini_unique # Character equivalent
+            
+            # Apply to real data column
+            data[[i]] <- unname(ruleset[[indexed]][mini_data])
+            
+          }
+          
+        }
+        
+      } else {
+        
+        # What do you think you are doing here? Throw error.
+        stop("lgb.prepare: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
+        
+      }
+      
+    }
+    
+  }
+  
+  return(list(data = data, rules = ruleset))
+  
+}

R-package/R/lgb.prepare_rules2.R

+#' Data preparator for LightGBM datasets with rules (numeric)
+#'
+#' 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. This is useful if you have a specific need for numeric dataset instead of integer dataset. There are programs which do not support integer-only input. Consider this is a fallback solution if you cannot use integers.
+#' 
+#' @param data A data.frame or data.table to prepare.
+#' @param rules A set of rules from the data preparator, if already used.
+#' 
+#' @return 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.
+#' 
+#' @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     : num  1 1 1 1 1 1 1 1 1 1 ...
+#'   
+#'   data(iris) # Erase iris dataset
+#'   iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
+#'   # Warning message:
+#'   In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
+#'     invalid factor level, NA generated
+#'   
+#'   # Use conversion using known rules
+#'   # Unknown factors become 0, excellent for sparse datasets
+#'   newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$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" = 3,
+#'                                      "versicolor" = 2,
+#'                                      "virginica" = 1))
+#'   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     : num  3 3 3 3 3 3 3 3 3 3 ...
+#'   
+#' }
+#' 
+#' @export
+lgb.prepare_rules2 <- function(data, rules = NULL) {
+  
+  # data.table not behaving like data.frame
+  if ("data.table" %in% class(data)) {
+    
+    # Must use existing rules
+    if (!is.null(rules)) {
+      
+      # Loop through rules
+      for (i in names(rules)) {
+        
+        set(data, j = i, value = unname(rules[[i]][data[[i]]]))
+        data[[i]][is.na(data[[i]])] <- 0 # Overwrite NAs by 0s
+        
+      }
+      
+    } else {
+      
+      # Get data classes
+      list_classes <- sapply(data, class)
+      
+      # Map characters/factors
+      is_fix <- which(list_classes %in% c("character", "factor"))
+      ruleset <- list()
+      
+      # Need to create rules?
+      if (length(is_fix) > 0) {
+        
+        # Go through all characters/factors
+        for (i in is_fix) {
+          
+          # Store column elsewhere
+          mini_data <- data[[i]]
+          
+          # Get unique values
+          if (class(mini_data) == "factor") {
+            mini_unique <- levels(mini_data) # Factor
+            mini_numeric <- numeric(length(mini_unique))
+            mini_numeric[1:length(mini_unique)] <- 1:length(mini_unique) # Respect ordinal if needed
+          } else {
+            mini_unique <- as.factor(unique(mini_data)) # Character
+            mini_numeric <- as.numeric(mini_unique) # No respect of ordinality
+          }
+          
+          # Create ruleset
+          indexed <- colnames(data)[i] # Index value
+          ruleset[[indexed]] <- mini_numeric # Numeric content
+          names(ruleset[[indexed]]) <- mini_unique # Character equivalent
+          
+          # Apply to real data column
+          set(data, j = i, value = unname(ruleset[[indexed]][mini_data]))
+          
+        }
+        
+      }
+      
+    }
+    
+  } else {
+    
+    # Must use existing rules
+    if (!is.null(rules)) {
+      
+      # Loop through rules
+      for (i in names(rules)) {
+        
+        data[[i]] <- unname(rules[[i]][data[[i]]])
+        data[[i]][is.na(data[[i]])] <- 0 # Overwrite NAs by 0s
+        
+      }
+      
+    } else {
+      
+      # Default routine (data.frame)
+      if ("data.frame" %in% class(data)) {
+        
+        # Get data classes
+        list_classes <- sapply(data, class)
+        
+        # Map characters/factors
+        is_fix <- which(list_classes %in% c("character", "factor"))
+        ruleset <- list()
+        
+        # Need to create rules?
+        if (length(is_fix) > 0) {
+          
+          # Go through all characters/factors
+          for (i in is_fix) {
+            
+            # Store column elsewhere
+            mini_data <- data[[i]]
+            
+            # Get unique values
+            if (class(mini_data) == "factor") {
+              mini_unique <- levels(mini_data) # Factor
+              mini_numeric <- numeric(length(mini_unique))
+              mini_numeric[1:length(mini_unique)] <- 1:length(mini_unique) # Respect ordinal if needed
+            } else {
+              mini_unique <- as.factor(unique(mini_data)) # Character
+              mini_numeric <- as.numeric(mini_unique) # No respect of ordinality
+            }
+            
+            # Create ruleset
+            indexed <- colnames(data)[i] # Index value
+            ruleset[[indexed]] <- mini_numeric # Numeric content
+            names(ruleset[[indexed]]) <- mini_unique # Character equivalent
+            
+            # Apply to real data column
+            data[[i]] <- unname(ruleset[[indexed]][mini_data])
+            
+          }
+          
+        }
+        
+      } else {
+        
+        # What do you think you are doing here? Throw error.
+        stop("lgb.prepare: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
+        
+      }
+      
+    }
+    
+  }
+  
+  return(list(data = data, rules = ruleset))
+  
+}

R-package/man/agaricus.test.Rd

@@ -29,4 +29,3 @@ Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
 School of Information and Computer Science.
 }
 \keyword{datasets}
-

R-package/man/agaricus.train.Rd

@@ -29,4 +29,3 @@ Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
 School of Information and Computer Science.
 }
 \keyword{datasets}
-

R-package/man/dim.Rd

@@ -34,4 +34,3 @@ stopifnot(all(dim(dtrain) == dim(train$data)))
 }
 
 }
-

R-package/man/dimnames.lgb.Dataset.Rd

@@ -37,4 +37,3 @@ print(dtrain, verbose = TRUE)
 }
 
 }
-

R-package/man/getinfo.Rd

@@ -48,4 +48,3 @@ stopifnot(all(labels2 == 1 - labels))
 }
 
 }
-

R-package/man/lgb.Dataset.Rd

@@ -43,4 +43,3 @@ lgb.Dataset.construct(dtrain)
 }
 
 }
-

R-package/man/lgb.Dataset.construct.Rd

@@ -22,4 +22,3 @@ lgb.Dataset.construct(dtrain)
 }
 
 }
-

R-package/man/lgb.Dataset.create.valid.Rd

@@ -33,4 +33,3 @@ dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 }
 
 }
-

R-package/man/lgb.Dataset.save.Rd

@@ -28,4 +28,3 @@ lgb.Dataset.save(dtrain, "data.bin")
 }
 
 }
-

R-package/man/lgb.Dataset.set.categorical.Rd

@@ -29,4 +29,3 @@ lgb.Dataset.set.categorical(dtrain, 1:2)
 }
 
 }
-

R-package/man/lgb.Dataset.set.reference.Rd

@@ -30,4 +30,3 @@ lgb.Dataset.set.reference(dtest, dtrain)
 }
 
 }
-

R-package/man/lgb.dump.Rd

@@ -39,4 +39,3 @@ json_model <- lgb.dump(model)
 }
 
 }
-

R-package/man/lgb.get.eval.result.Rd

@@ -24,4 +24,3 @@ vector of evaluation result
 \description{
 Get record evaluation result from booster
 }
-

R-package/man/lgb.importance.Rd

@@ -41,4 +41,3 @@ tree_imp2 <- lgb.importance(model, percentage = FALSE)
 }
 
 }
-