model_opt.r 9.51 KB
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#' Perform Habitat Sampling and Probability Mapping
#'
#' This function finds the best model (mmax) for a set of sampled points.
#'
#' @param k Iteration value for the models.
#' @param raster satellite time series stack (rasterBrickObject) or just any type of image (*rasterObject)
#' @param sample_type distribution of spatial locations c("random","regular")
#' @param buffer distance (in m) for new sample collection around initial samples (depends on pixel size)
#' @param model which machine learning classifier to use c("rf", "svm") for random forest or support vector machine implementation
#' @param area extent where the the classification is happening
#' @param seed set seed for reproducible results
#' @param n number of iterations for model accuracy
#' @param sample_size number of spatial locations
#' @param n_channel number of channels
#' @param seed2 spatial points sample
#' @param mtry number of predictor used at random forest splitting nodes (mtry << n predictors)
#' @param pbtn1 matrix for points
#' @param pbtn2 matrix for points
#' @param ras_vx velox raster
#' @param max_samples_per_class maximum number of samples per class
#' 
#' @return a list with 4 elements:
#' 1) k To identify the used model \cr
#' 2) model The model - mmax \cr
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#' 3) points List of points used part of the sample. \cr
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#' 4) oobe The accuracy achieved by the model \cr
#' @keywords internal
model_opt_r <- function(k,
                        raster,
                        sample_type,
                        buffer,
                        model,
                        area,
                        seed,
                        n,
                        sample_size,
                        n_channel,
                        seed2,
                        mtry,
                        pbtn1,
                        pbtn2,
                        ras_vx,
                        max_samples_per_class) {
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  points <- NULL
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  models <- NULL
  oobe <- matrix(NA, nrow = n, ncol = 1)
  for (j in 1:n) {
    ###Vorbereitung Klassifizierung
    if (j == 1) {
      classes <- as.factor(c(1, 1))
      if (sample_type == "random") {
        set.seed(seed2[k])
        pbt <-
          raster::sampleRandom(raster, size = sample_size, sp = T)
      }
      if (sample_type == "regular") {
        pbt <- raster::sampleRegular(raster, size = sample_size, sp = T)
      }
      pbt <- spatialEco::point.in.poly(pbt, area)[, 1:n_channel]
      
      #f <- which(is.na(pbt@data[1]))
      #if (length(f) != 0) {
      #  pbt <- pbt[-f,]
      #}
      pbt@data <- pbt@data[complete.cases(pbt@data[1]), ]
      
      set.seed(seed2[k])
      classes <-
        as.factor(sample(c(1:2), size = nrow(pbt), replace = T))
      if (length(levels(classes)) < 2) {
        break
      }
      
      data <- as.data.frame(cbind(classes, pbt@data))
    }
    ########################################################################
    if (model == "rf") {
      model1 <-
        randomForest::randomForest(as.factor(classes) ~ .,
                                   data = data,
                                   mtry = mtry)
      if (is.na(mean(model1$err.rate[, 1])) == TRUE) {
        break
      }
      oobe[j, 1] <- mean(model1$err.rate[, 1])
    }
    ###
    if (model == "svm") {
      model1 <- e1071::svm(as.factor(classes) ~ ., data = data)
      co <-
        length(which(
          as.numeric(as.character(model1$fitted)) - as.numeric(as.character(classes)) == 0
        ))
      if (co == 0) {
        break
      }
      oobe[j, 1] <- 1 - (co / length(classes))
    }
    
    model_pre <- model1
    pbtn1_pre <- pbtn1
    pbtn2_pre <- pbtn2
    #if ( j > 1) {if (oobe[j,k] < 0.02 || abs(oobe[(j-1),k]-oobe[j,k]) <= 0.011 )
    if (j > 1) {
      if (oobe[j, 1] < 0.02) {
        models <- model1
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        points <- rbind(pbtn1, pbtn2)
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        break
      }
      
      if (oobe[(j - 1), 1] <= oobe[j, 1]) {
        models <- model_pre
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        points <- rbind(pbtn1_pre, pbtn2_pre)
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        break
      }
      
      if (j == n & oobe[j, 1] >= 0.02) {
        models <- NULL
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        points <- NULL
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        break
      }
    }
    model_pre <- model1
    pbtn1_pre <- pbtn1
    pbtn2_pre <- pbtn2
    oobe <- oobe
    ########################################################################
    if (model == "rf") {
      correct <-
        which(as.numeric(as.character(classes)) - as.numeric(as.character(model1$predicted)) == 0)
    }
    if (model == "svm") {
      correct <-
        which(as.numeric(as.character(model1$fitted)) - as.numeric(as.character(classes)) == 0)
    }
    ########################################################################
    which_classes_correct <- which(classes[correct] == 1)
    if (length(which_classes_correct) == 0) {
      if (j == 1) {
        break
      } else{
        pbtn1 <- pbtn1
      }
    } else {
      d1 <- correct[which_classes_correct]
      
      ###generate new samples from only correctly classified samples [label 1]
      p1 <- pbt@coords[d1, ]
      pbtn1 <-
        as.data.frame(cbind(classes[d1], matrix(p1, ncol = 2)))
      sp::coordinates(pbtn1) <- c("V2", "V3")
      sp::proj4string(pbtn1) <- sp::proj4string(pbt)
      
      poly <- rgeos::gBuffer(spgeom = pbtn1,
                             width = buffer,
                             byid = TRUE)
      test <- ras_vx$extract(sp = poly)
      
      for (i in 1:length(test)) {
        s1 <- dim(test[[i]])[1]
        #if (s1 <= 5) {
        #  test[[i]] <-
        #    test[[i]]
        #} else {
        if (s1 > 5) {
          set.seed(seed)
          test[[i]] <-
            test[[i]][sample(c(1:s1), 5, replace = F), ]
        }
      }
      
      for (i in 1:length(test)) {
        if (i == 1) {
          co <- raster::xyFromCell(raster, test[[i]][, 1])
        } else {
          co <- rbind(co, raster::xyFromCell(raster, test[[i]][, 1]))
        }
      }
      pbtn1 <- as.data.frame(cbind(rep(1, nrow(co)), co))
      sp::coordinates(pbtn1) <- c("x", "y")
      
      test1 <- as.matrix(do.call(rbind, test)[, -1])
      if (ncol(test1) == 1) {
        test1 <- t(test1)
      }
      colnames(test1) <- names(raster)
      if (length(which(is.na(test1))) > 0) {
        pbtn1 <- pbtn1[complete.cases(test1), ]
        test1 <- test1[complete.cases(test1), ]
      }
    }
    if (class(test1)[1] == "numeric") {
      test1 <- t(matrix(test1))
    }
    if (nrow(test1) == 0) {
      break
    }
    ##############################
    ##############################
    which_classes_correct_2 <- which(classes[correct] == 2)
    if (length(which_classes_correct_2) == 0) {
      if (j == 1) {
        break
      } else{
        pbtn2 <- pbtn2
      }
    } else {
      d2 <- correct[which_classes_correct_2]
      
      ###generate new samples from only correctly classified samples [label 2]
      p2 <- pbt@coords[d2, ]
      pbtn2 <-
        as.data.frame(cbind(classes[d2], matrix(p2, ncol = 2)))
      sp::coordinates(pbtn2) <- c("V2", "V3")
      sp::proj4string(pbtn2) <- sp::proj4string(pbt)
      
      poly <- rgeos::gBuffer(spgeom = pbtn2,
                             width = buffer,
                             byid = TRUE)
      test <- ras_vx$extract(sp = poly)
      
      for (i in 1:length(test)) {
        s1 <- dim(test[[i]])[1]
        #if (s1 <= 5) {
        #  test[[i]] <-
        #    test[[i]]
        #} else {
        if (s1 > 5) {
          set.seed(seed)
          test[[i]] <-
            test[[i]][sample(c(1:s1), 5, replace = F), ]
        }
      }
      
      for (i in 1:length(test)) {
        if (i == 1) {
          co <- raster::xyFromCell(raster, test[[i]][, 1])
        } else {
          co <- rbind(co, raster::xyFromCell(raster, test[[i]][, 1]))
        }
      }
      pbtn2 <- as.data.frame(cbind(rep(2, nrow(co)), co))
      sp::coordinates(pbtn2) <- c("x", "y")
      
      test2 <- as.matrix(do.call(rbind, test)[, -1])
      if (ncol(test2) == 1) {
        test2 <- t(test2)
      }
      colnames(test2) <- names(raster)
      if (length(which(is.na(test2))) > 0) {
        pbtn2 <- pbtn2[complete.cases(test2), ]
        test2 <- test2[complete.cases(test2), ]
      }
    }
    if (class(test2)[1] == "numeric") {
      test2 <- t(matrix(test2))
    }
    if (nrow(test2) == 0) {
      break
    }
    ######################################
    ###Gleichverteilung samples in Klassen
    di <- c(nrow(pbtn1), nrow(pbtn2))
    if (abs(nrow(pbtn1) - nrow(pbtn2)) > min(di) * 0.3) {
      if (which.min(di) == 2) {
        set.seed(seed)
        d3 <- sample(1:nrow(pbtn1), nrow(pbtn2), replace = F)
        pbtn1 <- pbtn1[d3, ]
        test1 <- test1[d3, ]
      } else {
        set.seed(seed)
        d4 <- sample(1:nrow(pbtn2), nrow(pbtn1), replace = F)
        pbtn2 <- pbtn2[d4, ]
        test2 <- test2[d4, ]
      }
    }
    #####################################
    ###max Klassenbelegungswert
    if (nrow(pbtn1) > max_samples_per_class) {
      set.seed(seed)
      dr <-
        sample(1:nrow(pbtn1), max_samples_per_class, replace = F)
      pbtn1 <- pbtn1[dr, ]
      test1 <- test1[dr, ]
    }
    if (nrow(pbtn2) > max_samples_per_class) {
      set.seed(seed)
      dr <-
        sample(1:nrow(pbtn2), max_samples_per_class, replace = F)
      pbtn2 <- pbtn2[dr, ]
      test2 <- test2[dr, ]
    }
    ########################################################################
    data <-
      as.data.frame(cbind(append(pbtn1@data$V1, pbtn2@data$V1),
                          rbind(test1, test2))) ##data
    names(data)[1] <- "classes"
    classes <- data$classes
    pbt <- rbind(pbtn1, pbtn2)
  }
  return(list(
    "k" = k,
    "models" = models,
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    "points" = points,
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    "oobe" = oobe
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  ))
}