R/R_preprocessing.R

In theo-s/Rforestry_R: Random Forests

# ------------------------------------------------------------------------------
# globally import the ability to create classes methods etc

#' @import methods
NULL
# This is just here, because methods will be used in many places for creating
# classes, methods etc etc.


#-------------------------------------------------------------------------------
# Methods for Preprocessing Data

#' @title preprocess_training
#' @description Perform preprocessing for the training data, including
#' converting data to dataframe, and encoding categorical data into numerical
#' representation.
#' @param x A data frame of all training predictors.
#' @param y A vector of all training responses.
#' @import plyr
#' @return A list of two datasets along with necessary information that
#' encoding the preprocessing.
preprocess_training <- function(x, y) {
  x <- as.data.frame(x)

  # Check if the input dimension of x matches y
  if (nrow(x) != length(y)) {
    stop("The dimension of input dataset x doesn't match the output vector y.")
  }

  # Track the order of all features
  featureNames <- colnames(x)
  if (is.null(featureNames)) {
    warning("No names are given for each column.")
  }

  # Track all categorical features (both factors and characters)
  featureFactorCols <- which(sapply(x, is.factor) == TRUE)
  featureCharacterCols <- which(sapply(x, is.character) == TRUE)
  categoricalFeatureCols <-
    c(featureFactorCols, featureCharacterCols)
  if (length(categoricalFeatureCols) == 0) {
    categoricalFeatureCols <- list()
  } else {
    categoricalFeatureCols <- list(categoricalFeatureCols)
  }

  # For each categorical feature, encode x into numeric representation and
  # save the encoding mapping
  categoricalFeatureMapping <- list()
  dummyIndex <- 1
  for (categoricalFeatureCol in unlist(categoricalFeatureCols)) {
    uniqueFeatureValues <- unique(x[, categoricalFeatureCol])
    numericFeatureValues <- 1:length(uniqueFeatureValues)
    x[, categoricalFeatureCol] <-
      plyr::mapvalues(x = x[, categoricalFeatureCol],
                      from = uniqueFeatureValues,
                      to = numericFeatureValues)
    categoricalFeatureMapping[[dummyIndex]] <- list(
      "categoricalFeatureCol" = categoricalFeatureCol,
      "uniqueFeatureValues" = uniqueFeatureValues,
      "numericFeatureValues" = numericFeatureValues
    )
    dummyIndex <- dummyIndex + 1
  }

  # Return transformed data and encoding information
  return(
    list(
      "x" = x,
      "categoricalFeatureCols" = categoricalFeatureCols,
      "categoricalFeatureMapping" = categoricalFeatureMapping
    )
  )
}

#' @title preprocess_testing
#' @description Perform preprocessing for the testing data, including
#' converting data to dataframe, and testing if the columns are consistent
#' with the training data and encoding categorical data into numerical
#' representation in the same way as training data.
#' @param x A data frame of all training predictors.
#' @param categoricalFeatureCols A list of index for all categorical data. Used
#' for trees to detect categorical columns.
#' @param categoricalFeatureMapping A list of encoding details for each
#' categorical column, including all unique factor values and their
#' corresponding numeric representation.
#' @import plyr
#' @return A preprocessed training dataaset x
preprocess_testing <- function(x,
                               categoricalFeatureCols,
                               categoricalFeatureMapping) {
  x <- as.data.frame(x)

  # Track the order of all features
  testingFeatureNames <- colnames(x)
  if (is.null(testingFeatureNames)) {
    warning("No names are given for each column.")
  }

  # Track all categorical features (both factors and characters)
  featureFactorCols <- which(sapply(x, is.factor) == TRUE)
  featureCharacterCols <- which(sapply(x, is.character) == TRUE)
  testingCategoricalFeatureCols <-
    c(featureFactorCols, featureCharacterCols)
  if (length(testingCategoricalFeatureCols) == 0) {
    testingCategoricalFeatureCols <- list()
  } else {
    testingCategoricalFeatureCols <- list(testingCategoricalFeatureCols)
  }

  if (length(setdiff(categoricalFeatureCols,
                     testingCategoricalFeatureCols)) != 0) {
    stop("Categorical columns are different between testing and training data.")
  }

  # For each categorical feature, encode x into numeric representation
  for (categoricalFeatureMapping_ in categoricalFeatureMapping) {
    categoricalFeatureCol <-
      categoricalFeatureMapping_$categoricalFeatureCol
    # Get all unique feature values
    testingUniqueFeatureValues <- unique(x[, categoricalFeatureCol])
    uniqueFeatureValues <-
      categoricalFeatureMapping_$uniqueFeatureValues
    numericFeatureValues <-
      categoricalFeatureMapping_$numericFeatureValues

    # If testing dataset contains more, adding new factors to the mapping list
    diffUniqueFeatureValues <- setdiff(testingUniqueFeatureValues,
                                       uniqueFeatureValues)
    if (length(diffUniqueFeatureValues) != 0) {
      uniqueFeatureValues <-
        c(uniqueFeatureValues, diffUniqueFeatureValues)
      numericFeatureValues <- 1:length(uniqueFeatureValues)
    }

    x[, categoricalFeatureCol] <-
      plyr::mapvalues(x = x[, categoricalFeatureCol],
                      from = uniqueFeatureValues,
                      to = numericFeatureValues)
  }

  # Return transformed data and encoding information
  return(x)
}