fix part devtools::check and remove NLPCA (commented in code)

a2175a63 · De Boissieu Florian · 53d17265 · a2175a63 · a2175a63 · a2175a63
Commit a2175a63 authored Jul 05, 2019 by De Boissieu Florian
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -8,7 +8,7 @@ Description: this packages allows processing image data based on the method desc
    It expects an image file as input, with a specific data format.
    ENVI HDR image with BIL interleave required
 Encoding: UTF-8
-License: GPL3
+License: GPL-3
 LazyData: true
 Imports: 
    dissUtils,
@@ -23,6 +23,7 @@ Imports:
    rgdal,
    R.utils,
    snow,
+    stringr,
    tools,
    vegan,
    zip

--- a/LICENSE.txt
+++ b/LICENSE.txt
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -22,5 +22,7 @@ importFrom(future.apply,future_lapply)
 importFrom(labdsv,pco)
 importFrom(matlab,padarray)
 importFrom(matrixStats,rowSds)
+importFrom(raster,writeRaster)
 importFrom(rgdal,readOGR)
 importFrom(snow,splitRows)
+importFrom(stringr,str_count)
--- a/R/Lib_ImageProcess.R
+++ b/R/Lib_ImageProcess.R
@@ -997,9 +997,10 @@ Where.To.Write.Kernel <- function(HDR.SS, HDR.SSD, nbPieces, SE.Size) {
 # @param headerFpath Path of the hdr file
 #
 # @return
+#' @importFrom stringr str_count
 write.ENVI.header <- function(header, headerFpath) {
  h <- lapply(header, function(x) {
-    if (length(x) > 1 || (is.character(x) && stringr::str_count(x, "\\w+") > 1)) {
+    if (length(x) > 1 || (is.character(x) && str_count(x, "\\w+") > 1)) {
      x <- paste0("{", paste(x, collapse = ","), "}")
    }
    # convert last numerics

--- a/R/Lib_MapBetaDiversity.R
+++ b/R/Lib_MapBetaDiversity.R
@@ -129,7 +129,6 @@ Get.Sunlit.Pixels <- function(ImPathSunlit, MinSun) {
 #' @importFrom future.apply future_lapply
 Compute.NMDS <- function(MatBCdist) {
  nbiterNMDS <- 4
-  library(doParallel)
  if (Sys.info()["sysname"] == "Windows") {
    nbCoresNMDS <- 2
  } else if (Sys.info()["sysname"] == "Linux") {

--- a/R/Lib_PerformPCA.R
+++ b/R/Lib_PerformPCA.R
@@ -14,7 +14,7 @@
 #' @param ImNames Path and name of the images to be processed
 #' @param Output.Dir output directory
 #' @param Continuum.Removal boolean: should continuum removal be applied?
-#' @param TypePCA Type of PCA (PCA, SPCA, NLPCA...)
+#' @param TypePCA Type of PCA: "PCA" or "SPCA"
 #' @param FilterPCA boolean. If TRUE 2nd filtering based on PCA
 #' @param Excluded.WL boolean. Water Vapor Absorption domains (in nanometers). Can also be used to exclude spectific domains
 #' @param NbIter numeric. Number of iteration to estimate diversity from the raster.
@@ -61,12 +61,12 @@ Perform.PCA.Image <- function(ImPath, ImPathShade, Output.Dir, Continuum.Removal
  print("perform PCA#1 on the subset image")
  if (TypePCA == "PCA" | TypePCA == "SPCA") {
    PCA.model <- pca(DataSubset, TypePCA)
-  } else if (TypePCA == "NLPCA") {
-    print("performing NL-PCA with autoencoder")
-    print("Make sure you properly installed and defined python environment if using this functionality")
-    tic()
-    PCA.model <- nlpca(DataSubset)
-    toc()
+  # } else if (TypePCA == "NLPCA") {
+  #   print("performing NL-PCA with autoencoder")
+  #   print("Make sure you properly installed and defined python environment if using this functionality")
+  #   tic()
+  #   PCA.model <- nlpca(DataSubset)
+  #   toc()
  }

  # if PCA based filtering:
@@ -111,11 +111,11 @@ Perform.PCA.Image <- function(ImPath, ImPathShade, Output.Dir, Continuum.Removal
    print("perform PCA#2 on the subset image")
    if (TypePCA == "PCA" | TypePCA == "SPCA") {
      PCA.model <- pca(DataSubset, TypePCA)
-    } else if (TypePCA == "NLPCA") {
-      print("performing NL-PCA with autoencoder")
-      tic()
-      PCA.model <- nlpca(DataSubset)
-      toc()
+    # } else if (TypePCA == "NLPCA") {
+    #   print("performing NL-PCA with autoencoder")
+    #   tic()
+    #   PCA.model <- nlpca(DataSubset)
+    #   toc()
    }
  }
  # Number of PCs computed and written in the PCA file: 30 if hyperspectral
@@ -242,24 +242,24 @@ Filter.PCA <- function(ImPath, HDR, ImPathShade, Shade.Update, Spectral, CR, PCA
    # Apply PCA
    if (TypePCA == "PCA" | TypePCA == "SPCA") {
      Image.Chunk <- t(t(PCA.model$eiV[, PCsel]) %*% t(Center.Reduce(Image.Chunk, PCA.model$mu, PCA.model$scale)))
-    } else if (TypePCA == "NLPCA") {
-      # apply a transformation to the dataset
-      Image.Chunk <- apply(Image.Chunk, 2, minmax, mode = "apply", MinX = PCA.model$MinVal, MaxX = PCA.model$MaxVal)
-      Image.Chunk2 <- matrix(NA, nrow = length(Image.Chunk[[1]]$data), ncol = length(Image.Chunk))
-      for (ii in 1:length(Image.Chunk)) {
-        Image.Chunk2[, ii] <- matrix(Image.Chunk[[ii]]$data, ncol = 1)
-      }
-      Image.Chunk <- Image.Chunk2
-      rm(Image.Chunk2)
-      intermediate_layer_model <- keras_model(inputs = PCA.model$Model$input, outputs = get_layer(PCA.model$Model, "bottleneck")$output)
-
-      # # use multithread to apply autoencoder...
-      # plan(multiprocess, workers = nbCPU) ## Parallelize using four cores
-      # Image.Chunk     = splitRows(Image.Chunk, nbCPU)
-      # Image.Chunk     = future_lapply(Image.Chunk,FUN = predict.from.NLPCA,model  = intermediate_layer_model,future.scheduling = 1.0)
-      # plan(sequential)
-      # Image.Chunk     = rbind(Image.Chunk)
-      Image.Chunk <- predict(intermediate_layer_model, Image.Chunk)
+    # } else if (TypePCA == "NLPCA") {
+    #   # apply a transformation to the dataset
+    #   Image.Chunk <- apply(Image.Chunk, 2, minmax, mode = "apply", MinX = PCA.model$MinVal, MaxX = PCA.model$MaxVal)
+    #   Image.Chunk2 <- matrix(NA, nrow = length(Image.Chunk[[1]]$data), ncol = length(Image.Chunk))
+    #   for (ii in 1:length(Image.Chunk)) {
+    #     Image.Chunk2[, ii] <- matrix(Image.Chunk[[ii]]$data, ncol = 1)
+    #   }
+    #   Image.Chunk <- Image.Chunk2
+    #   rm(Image.Chunk2)
+    #   intermediate_layer_model <- keras_model(inputs = PCA.model$Model$input, outputs = get_layer(PCA.model$Model, "bottleneck")$output)
+    #
+    #   # # use multithread to apply autoencoder...
+    #   # plan(multiprocess, workers = nbCPU) ## Parallelize using four cores
+    #   # Image.Chunk     = splitRows(Image.Chunk, nbCPU)
+    #   # Image.Chunk     = future_lapply(Image.Chunk,FUN = predict.from.NLPCA,model  = intermediate_layer_model,future.scheduling = 1.0)
+    #   # plan(sequential)
+    #   # Image.Chunk     = rbind(Image.Chunk)
+    #   Image.Chunk <- predict(intermediate_layer_model, Image.Chunk)
    }

    # get PCA of the group of line and rearrange the data to write it correctly in the output file
@@ -395,24 +395,24 @@ Create.PCA.Image <- function(ImPath, ImPathShade, PCA.Path, PCA.model, Spectral,
    # Apply PCA
    if (TypePCA == "PCA" | TypePCA == "SPCA") {
      Image.Chunk <- t(t(PCA.model$eiV[, 1:Nb.PCs]) %*% t(Center.Reduce(Image.Chunk, PCA.model$mu, PCA.model$scale)))
-    } else if (TypePCA == "NLPCA") {
-      # apply a transformation to the dataset
-      Image.Chunk <- apply(Image.Chunk, 2, minmax, mode = "apply", MinX = PCA.model$MinVal, MaxX = PCA.model$MaxVal)
-      Image.Chunk2 <- matrix(NA, nrow = length(Image.Chunk[[1]]$data), ncol = length(Image.Chunk))
-      for (ii in 1:length(Image.Chunk)) {
-        Image.Chunk2[, ii] <- matrix(Image.Chunk[[ii]]$data, ncol = 1)
-      }
-      Image.Chunk <- Image.Chunk2
-      rm(Image.Chunk2)
-      intermediate_layer_model <- keras_model(inputs = PCA.model$Model$input, outputs = get_layer(PCA.model$Model, "bottleneck")$output)
-
-      # # use multithread to apply autoencoder...
-      # plan(multiprocess, workers = nbCPU) ## Parallelize using four cores
-      # Image.Chunk     = splitRows(Image.Chunk, nbCPU)
-      # Image.Chunk     = future_lapply(Image.Chunk,FUN = predict.from.NLPCA,model  = intermediate_layer_model,future.scheduling = 1.0)
-      # plan(sequential)
-      # Image.Chunk     = rbind(Image.Chunk)
-      Image.Chunk <- predict(intermediate_layer_model, Image.Chunk)
+    # } else if (TypePCA == "NLPCA") {
+    #   # apply a transformation to the dataset
+    #   Image.Chunk <- apply(Image.Chunk, 2, minmax, mode = "apply", MinX = PCA.model$MinVal, MaxX = PCA.model$MaxVal)
+    #   Image.Chunk2 <- matrix(NA, nrow = length(Image.Chunk[[1]]$data), ncol = length(Image.Chunk))
+    #   for (ii in 1:length(Image.Chunk)) {
+    #     Image.Chunk2[, ii] <- matrix(Image.Chunk[[ii]]$data, ncol = 1)
+    #   }
+    #   Image.Chunk <- Image.Chunk2
+    #   rm(Image.Chunk2)
+    #   intermediate_layer_model <- keras_model(inputs = PCA.model$Model$input, outputs = get_layer(PCA.model$Model, "bottleneck")$output)
+    #
+    #   # # use multithread to apply autoencoder...
+    #   # plan(multiprocess, workers = nbCPU) ## Parallelize using four cores
+    #   # Image.Chunk     = splitRows(Image.Chunk, nbCPU)
+    #   # Image.Chunk     = future_lapply(Image.Chunk,FUN = predict.from.NLPCA,model  = intermediate_layer_model,future.scheduling = 1.0)
+    #   # plan(sequential)
+    #   # Image.Chunk     = rbind(Image.Chunk)
+    #   Image.Chunk <- predict(intermediate_layer_model, Image.Chunk)
    }

    # get PCA of the group of line and rearrange the data to write it correctly in the output file
@@ -444,97 +444,97 @@ Create.PCA.Image <- function(ImPath, ImPathShade, PCA.Path, PCA.model, Spectral,
 }


-# Function to perform NLPCA
+# # Function to perform NLPCA
+# #
+# # @param DataSubset matrix to apply NLPCA on
+# #
+# # @return list of PCA parameters (PCs from X, mean, eigenvectors and values)
+# nlpca <- function(DataSubset) {
 #
-# @param DataSubset matrix to apply NLPCA on
+#   # put between 0 and 1
+#   x_train <- apply(DataSubset, 2, minmax, mode = "define")
+#   Subset <- list()
+#   nb.Vars <- ncol(DataSubset)
+#   Subset$DataSubset <- matrix(NA, nrow = nrow(DataSubset), ncol = nb.Vars)
+#   for (i in 1:nb.Vars) {
+#     Subset$DataSubset[, i] <- matrix(x_train[[i]]$data, ncol = 1)
+#     Subset$MinVal[i] <- x_train[[i]]$MinX
+#     Subset$MaxVal[i] <- x_train[[i]]$MaxX
+#   }
+#   # define number of pixels to be used for NL-PCA
+#   nbSubsamples.NLPCA <- 100000
+#   # autoencoder in keras
+#   # set training data
+#   x_train <- as.matrix(Subset$DataSubset)
 #
-# @return list of PCA parameters (PCs from X, mean, eigenvectors and values)
-nlpca <- function(DataSubset) {
-
-  # put between 0 and 1
-  x_train <- apply(DataSubset, 2, minmax, mode = "define")
-  Subset <- list()
-  nb.Vars <- ncol(DataSubset)
-  Subset$DataSubset <- matrix(NA, nrow = nrow(DataSubset), ncol = nb.Vars)
-  for (i in 1:nb.Vars) {
-    Subset$DataSubset[, i] <- matrix(x_train[[i]]$data, ncol = 1)
-    Subset$MinVal[i] <- x_train[[i]]$MinX
-    Subset$MaxVal[i] <- x_train[[i]]$MaxX
-  }
-  # define number of pixels to be used for NL-PCA
-  nbSubsamples.NLPCA <- 100000
-  # autoencoder in keras
-  # set training data
-  x_train <- as.matrix(Subset$DataSubset)
-
-  # set model
-  model <- keras_model_sequential()
-  if (nb.Vars < 12) {
-    model %>%
-      layer_dense(units = 6, activation = "tanh", input_shape = ncol(x_train)) %>%
-      layer_dense(units = 3, activation = "tanh", name = "bottleneck") %>%
-      layer_dense(units = 6, activation = "tanh") %>%
-      layer_dense(units = ncol(x_train))
-  } else if (nb.Vars > 100) {
-    model %>%
-      layer_dense(units = 100, activation = "tanh", input_shape = ncol(x_train)) %>%
-      layer_dense(units = 80, activation = "tanh") %>%
-      layer_dense(units = 60, activation = "tanh") %>%
-      layer_dense(units = 40, activation = "tanh") %>%
-      layer_dense(units = 20, activation = "tanh", name = "bottleneck") %>%
-      layer_dense(units = 40, activation = "tanh") %>%
-      layer_dense(units = 60, activation = "tanh") %>%
-      layer_dense(units = 80, activation = "tanh") %>%
-      layer_dense(units = 100, activation = "tanh") %>%
-      layer_dense(units = ncol(x_train))
-  } else if (nb.Vars <= 100) {
-    model %>%
-      layer_dense(units = 80, activation = "tanh", input_shape = ncol(x_train)) %>%
-      layer_dense(units = 60, activation = "tanh") %>%
-      layer_dense(units = 40, activation = "tanh") %>%
-      layer_dense(units = 20, activation = "tanh", name = "bottleneck") %>%
-      layer_dense(units = 40, activation = "tanh") %>%
-      layer_dense(units = 60, activation = "tanh") %>%
-      layer_dense(units = 80, activation = "tanh") %>%
-      layer_dense(units = ncol(x_train))
-  }
-  # view model layers
-  summary(model)
-  # compile model
-  model %>% keras::compile(
-    loss = "mean_squared_error",
-    optimizer = "adam"
-  )
-
-  # fit model
-  Sampling <- sample(nrow(x_train))
-  model %>% keras::fit(
-    x = x_train[Sampling[1:nbSubsamples.NLPCA], ],
-    y = x_train[Sampling[1:nbSubsamples.NLPCA], ],
-    epochs = 100,
-    verbose = 0
-  )
-  # # evaluate the performance of the model
-  # mse.ae2 <- keras::evaluate(model, x_train, x_train)
-  # mse.ae2
-  # extract the bottleneck layer
-  intermediate_layer_model <- keras_model(inputs = model$input, outputs = get_layer(model, "bottleneck")$output)
-  intermediate_output <- predict(intermediate_layer_model, x_train)
-  my_list <- list("Model" = model, "dataPCA" = intermediate_output, "MinVal" = Subset$MinVal, "MaxVal" = Subset$MaxVal)
-  return(my_list)
-}
-
-# applies NLPCA to data
+#   # set model
+#   model <- keras_model_sequential()
+#   if (nb.Vars < 12) {
+#     model %>%
+#       layer_dense(units = 6, activation = "tanh", input_shape = ncol(x_train)) %>%
+#       layer_dense(units = 3, activation = "tanh", name = "bottleneck") %>%
+#       layer_dense(units = 6, activation = "tanh") %>%
+#       layer_dense(units = ncol(x_train))
+#   } else if (nb.Vars > 100) {
+#     model %>%
+#       layer_dense(units = 100, activation = "tanh", input_shape = ncol(x_train)) %>%
+#       layer_dense(units = 80, activation = "tanh") %>%
+#       layer_dense(units = 60, activation = "tanh") %>%
+#       layer_dense(units = 40, activation = "tanh") %>%
+#       layer_dense(units = 20, activation = "tanh", name = "bottleneck") %>%
+#       layer_dense(units = 40, activation = "tanh") %>%
+#       layer_dense(units = 60, activation = "tanh") %>%
+#       layer_dense(units = 80, activation = "tanh") %>%
+#       layer_dense(units = 100, activation = "tanh") %>%
+#       layer_dense(units = ncol(x_train))
+#   } else if (nb.Vars <= 100) {
+#     model %>%
+#       layer_dense(units = 80, activation = "tanh", input_shape = ncol(x_train)) %>%
+#       layer_dense(units = 60, activation = "tanh") %>%
+#       layer_dense(units = 40, activation = "tanh") %>%
+#       layer_dense(units = 20, activation = "tanh", name = "bottleneck") %>%
+#       layer_dense(units = 40, activation = "tanh") %>%
+#       layer_dense(units = 60, activation = "tanh") %>%
+#       layer_dense(units = 80, activation = "tanh") %>%
+#       layer_dense(units = ncol(x_train))
+#   }
+#   # view model layers
+#   summary(model)
+#   # compile model
+#   model %>% keras::compile(
+#     loss = "mean_squared_error",
+#     optimizer = "adam"
+#   )
 #
-# @param Data matrix to apply NLPCA on
-# @param model.AutoEncod model defined previously
-#
-# @return Image.Chunk after NLPCA
-#' @importFrom future.apply future_lapply
-predict.from.NLPCA <- function(Data, model.AutoEncod) {
-  Image.Chunk <- future_lapply(Data, FUN = predict.from.NLPCA, model = model.AutoEncod, future.scheduling = 1.0)
-  return(Image.Chunk)
-}
+#   # fit model
+#   Sampling <- sample(nrow(x_train))
+#   model %>% keras::fit(
+#     x = x_train[Sampling[1:nbSubsamples.NLPCA], ],
+#     y = x_train[Sampling[1:nbSubsamples.NLPCA], ],
+#     epochs = 100,
+#     verbose = 0
+#   )
+#   # # evaluate the performance of the model
+#   # mse.ae2 <- keras::evaluate(model, x_train, x_train)
+#   # mse.ae2
+#   # extract the bottleneck layer
+#   intermediate_layer_model <- keras_model(inputs = model$input, outputs = get_layer(model, "bottleneck")$output)
+#   intermediate_output <- predict(intermediate_layer_model, x_train)
+#   my_list <- list("Model" = model, "dataPCA" = intermediate_output, "MinVal" = Subset$MinVal, "MaxVal" = Subset$MaxVal)
+#   return(my_list)
+# }
+
+# # applies NLPCA to data
+# #
+# # @param Data matrix to apply NLPCA on
+# # @param model.AutoEncod model defined previously
+# #
+# # @return Image.Chunk after NLPCA
+# # @importFrom future.apply future_lapply
+# predict.from.NLPCA <- function(Data, model.AutoEncod) {
+#   Image.Chunk <- future_lapply(Data, FUN = predict.from.NLPCA, model = model.AutoEncod, future.scheduling = 1.0)
+#   return(Image.Chunk)
+# }

 # Function to perform PCA on a matrix
 #
@@ -620,7 +620,7 @@ Define.Pixels.Per.Iter <- function(ImNames, NbIter = NbIter) {
 #' @param Output.Dir output directory
 #' @param Input.Image.File path for image to be processed
 #' @param PCA.Files path of PCA files
-#' @param TypePCA Type of PCA (PCA, SPCA, NLPCA...)
+#' @param TypePCA Type of PCA: "PCA" or "SPCA"
 #'
 #' @return nothing
 #' @export

--- a/R/Lib_Validation_biodivMapR.R
+++ b/R/Lib_Validation_biodivMapR.R
@@ -313,10 +313,10 @@ Get.Diversity.From.Plots = function(Raster, Plots,NbClusters = 50,Name.Plot = FA
 # @param quiet
 # @return NULL
 #' @importFrom rgdal readOGR
+#' @importFrom raster writeRaster
 gdal_polygonizeR = function(x, outshape=NULL, gdalformat = 'ESRI Shapefile',
                            pypath=NULL, readpoly=TRUE, quiet=TRUE) {

-  if (readpoly==TRUE) require(rgdal)
  if (is.null(pypath)) {
    pypath <- Sys.which('gdal_polygonize.py')
  }
@@ -335,7 +335,6 @@ gdal_polygonizeR = function(x, outshape=NULL, gdalformat = 'ESRI Shapefile',
    #                             sep='.')[f.exists])), call.=FALSE)
  } else outshape <- tempfile()
  if (is(x, 'Raster')) {
-    require(raster)
    writeRaster(x, {f <- tempfile(fileext='.tif')})
    rastpath <- normalizePath(f)
  } else if (is.character(x)) {

--- a/TODOLIST.txt
+++ b/TODOLIST.txt
--- a/man/Perform.PCA.Image.Rd
+++ b/man/Perform.PCA.Image.Rd
@@ -13,7 +13,7 @@ Perform.PCA.Image(ImPath, ImPathShade, Output.Dir,

 \item{Continuum.Removal}{boolean: should continuum removal be applied?}

-\item{TypePCA}{Type of PCA (PCA, SPCA, NLPCA...)}
+\item{TypePCA}{Type of PCA: "PCA" or "SPCA"}

 \item{FilterPCA}{boolean. If TRUE 2nd filtering based on PCA}


--- a/man/Select.Components.Rd
+++ b/man/Select.Components.Rd
@@ -14,7 +14,7 @@ Select.Components(Input.Image.File, Output.Dir, PCA.Files,

 \item{PCA.Files}{path of PCA files}

-\item{TypePCA}{Type of PCA (PCA, SPCA, NLPCA...)}
+\item{TypePCA}{Type of PCA: "PCA" or "SPCA"}
 }
 \value{
 nothing