R/som.nn.validate.R

In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

#    SOMnn topology-based classifier
#    Copyright (C) 2017  Andreas Dominik
#                        THM University of Applied Sciences
#                        Gießen, Germany
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
#    (at your option) any later version.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
#
#    You should have received a copy of the GNU General Public License
#    along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
#' Predict class labels for a validation dataset
#'
#' A model of type \code{SOMnn} is tested with a validation dataset. The dataset must
#' include a column with correct class labels.
#' The model is used to predict class labels. Confusion table, 
#' specificity, sensitivity and accuracy for each class are calculated.
#' 
#' Parameters stored in the model are applied for k-NN-like prediction. If necessary 
#' the parameters can be changed by \code{\link{som.nn.set}} before testing.
#' 
#' The funcion is only a wrapper and actually calls \code{som.nn.continue} with the test data and 
#' without training (i.e. \code{len = 0}).
#'
#' @param model    model of type \code{SOMnn}.
#' @param x        data.fame with validation data. Samples are requested as rows.
#'                 \code{x} must include the same columns as the data.frame with which the model
#'                 have been trained originally.
#'                 A column with correct class labels is needed. The column with class
#'                 lables is selected by the slot \code{class.idx} of the model.
#'
#' @return         S4 object of type \code{\link{SOMnn}} with the unchanged model and the
#'                 test statistics for the test data.
#'
#' @example man/examples/example.train.R
#' 
#' @export 
som.nn.validate <- function( model, x){

  name = paste("Validation of", model@name)
  
  # make data for prediction and calculations:
  class.idx <- model@class.idx
  unk <- x[-class.idx]
  class.labels <- x[[class.idx]]

  #
  # validate:
  #
  cat("Calculate predictions for training data ...")
  prediction <- predict(model, unk)
  
  cat("Calculate accuracy for training data ...")
  confusion <- som.nn.confusion(prediction, class.labels)
  measures <- som.nn.accuracy(prediction, class.labels)
  accuracy <- som.nn.all.accuracy(prediction, x[[class.idx]])
  
  # create model object:
  new.model <- methods::new("SOMnn", name = name,
                            codes = model@codes,
                            qerror = model@qerror,
                            classes = model@classes, class.idx= class.idx,
                            class.counts = model@class.counts, class.freqs = model@class.freqs,
                            xdim = model@xdim, ydim = model@ydim, len.total = model@len.total, toroidal = model@toroidal,
                            norm = model@norm, norm.center = model@norm.center, norm.scale = model@norm.scale,
                            dist.fun = model@dist.fun, max.dist = model@max.dist, strict = model@strict,
                            confusion = confusion, measures = measures, accuracy = accuracy)
  
  return( new.model)
}