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+# 8-VIC modeling 
+
+
+# 10/31/24
+
+# Dave White
+start.time <- Sys.time()
+
+# load and install packages
+required.packages <- c( "caret", "sf", "terra", "randomForest", "doParallel", "aqp", "parallel", "snow", "foreign", "foreach")
+new.packages <- required.packages[!(required.packages %in% installed.packages()[,"Package"])]
+if(length(new.packages)) install.packages(new.packages)
+lapply(required.packages, require, character.only=T)
+rm(required.packages, new.packages)
+
+
+
+# bring in covariate data and create a stack
+
+# set working directory to dem covs
+setwd("~/data/8-vic/cov")
+
+# read in raster file names as a list
+rList=list.files(getwd(), pattern="tif$", full.names = FALSE)
+
+# create a raster stack of dem covs
+rStack <- rast(rList)
+names(rStack) <- gsub(".tif", "",rList)
+names(rStack) <- gsub("-", "", names(rStack))
+names(rStack) <- gsub("_", "", names(rStack))
+names(rStack)
+
+
+# Bring in training data points 
+# read in shapefile 
+# bring in pedon data
+
+#set working directory to training data.
+setwd("~/data/8-vic/data")
+
+pts <- read_sf("pedons.shp")
+
+# extract raster covariate values at each training point for the all.pts dataset
+pts.sv <- terra::extract(rStack, pts, xy=F, bind=TRUE, na.rm=TRUE) 
+
+# convert SpatVector to sf 
+all.pts <- sf::st_as_sf(pts.sv) 
+
+names(all.pts)
+
+# remove unwanted cols
+all.pts <- all.pts[-c(1:14)]
+colnames(all.pts)[1] <- "class"
+names(all.pts)
+all.pts$class <- as.factor(all.pts$class)
+names(all.pts)
+#
+levels(all.pts$class)
+
+
+# convert sf to a dataframe
+pts.all <- as.data.frame(all.pts)
+names(pts.all)
+
+comp <- pts.all[,!(colnames(pts.all)%in% c("geometry"))]
+names(comp)
+
+
+# remove any observations with NAs
+comp <- comp[complete.cases(comp),]
+
+# convert Class col to a factor
+#comp$Class <- as.factor(comp$Class)
+is.factor(comp$class)
+levels(comp$class)
+summary((comp$class))
+
+# There are a few classes with < 2 observations remove those classes
+
+nlevels(comp$class)
+
+comp = droplevels(comp[comp$class %in% names(table(comp$class)) [table(comp$class) >2],])
+
+nlevels(comp$class)
+
+levels(comp$class)
+summary(comp$class)
+
+
+length(levels(comp$class))
+gc()
+
+#---------------------------------------------------------------------------
+# Recursive Feature Selection (RFE)
+
+
+subsets <- seq(50, length(names(rStack)), 5)
+
+#set number and repeats for cross validation
+number = 10
+repeats = 1
+
+# set seeds to get reproducible results when running the process in parallel
+set.seed(12)
+seeds <- vector(mode = "list", length=(number*repeats+1))
+for(i in 1:(number*repeats)) seeds[[i]] <- sample.int(1000, length(subsets)+1)
+seeds[[(number*repeats+1)]] <- sample.int(1000, 1)
+
+
+cl <- makeCluster(121) # base R only recognizes 128 cores and about 5 need to be left for OS
+registerDoParallel(cl)
+
+# set up the rfe control
+ctrl.RFE <- rfeControl(functions = rfFuncs,
+                       method = "repeatedcv",
+                       number = number,
+                       repeats = repeats,
+                       seeds = seeds,
+                       verbose = F)
+
+## highlight and run everything from c1 to stopCluster(c1) to run RFE
+rfe <- rfe(x = comp[,-c(1)],
+           y = comp$class,
+           sizes = subsets,
+           rfeControl = ctrl.RFE,
+           allowParallel = TRUE
+)
+stopCluster(cl)             
+gc()
+
+
+# Look at the results
+rfe
+plot(rfe)
+
+
+
+rfe$fit$confusion
+rfe$results
+
+# see list of predictors
+predictors(rfe)
+
+# the rfe function retuns the covariates with the highest accuracy for the rf model
+# view the highest accuracy noted by the *
+rfe
+
+# take the accuracy and subtract the accuracySD. look in the results of rf.RFE and find the accuracy that is > or = to this value. this is the number of covariates to use below
+#predictors(rfeLand) # top number of covariates
+
+# look at the top number of covariates that are equal to greater than the accuracy minus the accuracySD
+#predictors(rf.RFE)[c(1:9,24:26)]
+
+# assign this to a variable
+a <- predictors(rfe)#[c(1:50)]
+
+
+# subset the covariate stack and the data frame by the selected covariates the variable a is your selected covariates
+
+# subsed the raser stack to the selected covariates
+rsm <- subset(rStack, a)
+
+
+# subset the data frame points with the number of covariates selected
+names(comp)
+comp.sub <- (comp[,c("class", a)])
+
+
+names(comp.sub)
+is.factor(comp.sub$class)
+
+subsets <- seq(1, length(names(rsm)), 5)
+
+#set number and repeats for cross validation
+number = 10
+repeats = 5
+
+# set seeds to get reproducible results when running the process in parallel
+set.seed(12)
+seeds <- vector(mode = "list", length=(number*repeats+1))
+for(i in 1:(number*repeats)) seeds[[i]] <- sample.int(1000, length(subsets)+1)
+seeds[[(number*repeats+1)]] <- sample.int(1000, 1)
+
+
+# set up the train control
+fitControl <- trainControl(method = "repeatedcv", 
+                           number = number,
+                           repeats = repeats,
+                           p = 0.8, #30% used for test set, 70% used for training set
+                           selectionFunction = 'best', 
+                           classProbs = T,
+                           savePredictions = T, 
+                           returnResamp = 'final',
+                           search = "random",
+                           seeds = seeds)
+
+cl <- makeCluster(121)
+registerDoParallel(cl)
+# Random Forest - Parallel process
+rfm = train(x = comp.sub[,-c(1)],
+            y = comp.sub$class,
+            "rf", 
+            trControl = fitControl, 
+            ntree = 500, #number of trees default is 500, which seems to work best anyway. 
+            tuneLength=10, 
+            metric = 'Kappa', 
+            na.action=na.pass,
+            keep.forest=TRUE, # added this line and the next for partial dependence plots
+            importance=TRUE)
+stopCluster(cl)
+gc()
+
+
+# Inspect rfFit
+rfm$finalModel
+
+
+#look at overall accuracy, kappa, and SD associated with each mtry value
+print(rfm)
+rfm$results
+
+
+# Convert caret wrapper into randomforest model object for raster prediction
+rfm <- rfm$finalModel
+
+
+
+
+
+
+
+# make predictions
+
+# set wd to store tiles for prediction - tiles are written to hard drive, make sure there is enough room
+setwd("~/data/8-vic/data/tiles/")
+
+
+#numTiles <- 11 # numTiles^2 should <= the number of cores - in this case I have 123 cores available and 11x11 would give 121 tiles
+numTiles <- (round(sqrt(121), digits = 0))
+
+x <- rast(ncol=numTiles, nrow=numTiles, extent=ext(rsm))
+
+tl <- makeTiles(rsm, x, overwrite=T, extend=T)
+tl <- list.files("~/data/8-vic/data/tiles/", pattern=".tif$", full.names=FALSE)
+
+gc()
+# read in raster file names as a list
+#rList=list.files(getwd(), pattern="tif$", full.names = FALSE)
+
+
+cl <- parallel::makeCluster(round(length(tl)/2))
+registerDoParallel(cl)
+
+pred <- foreach(i = 1:length(tl), .packages = c("terra", "randomForest")) %dopar% {
+                  pred <- wrap(terra::predict(rast(tl[i]),rfm, na.rm=T))#wopt=list(steps=40)
+                  return(pred)
+                }
+pred <- do.call(terra::merge,lapply(pred,terra::rast))
+plot(pred)
+setwd("~/data/8-vic/results")
+# write rasters
+writeRaster(pred, overwrite = TRUE, filename = "class.tif", gdal=c("TFW=YES"),datatype='INT1U')
+# write raster attribute table
+#library(foreign)
+write.dbf(levels(pred)[[1]], file='class.tif.vat.dbf') # make sure the first part of the file name is exactly the same as the predicted raster
+stopCluster(cl)
+gc()
+
+
+
+
+#set directory to prob tiles
+setwd("~/data/8-vic/data/tiles/")
+
+# read in raster file names as a list
+tl=list.files(getwd(), pattern="tif$", full.names = TRUE)
+tlnames=list.files(getwd(), pattern="tif$", full.names = FALSE)
+tlnames <- gsub(".tif", "", tlnames)
+tlnames <- gsub("tile", "prob", tlnames)
+
+cl <- parallel::makeCluster(round(length(tl)/2))
+registerDoParallel(cl)
+
+foreach(i = 1:length(tl), .packages = c("terra", "randomForest")) %dopar% {
+  wrap(terra::predict(rast(tl[i]), rfm, na.rm=T, type="prob", 
+                      filename = paste0("~/data/8-vic/results/prob/",tlnames[i],".tif"), 
+                      gdal=c("TFW=YES"),
+                      overwrite=T))
+                    }
+
+stopCluster(cl)
+gc()
+
+
+
+
+
+
+#set directory to prob tiles
+setwd("~/data/8-vic/results/prob")
+
+# read in raster file names as a list
+rList=list.files(getwd(), pattern="tif$", full.names = FALSE)
+
+
+
+cl <- parallel::makeCluster(round(length(tl)/2))
+registerDoParallel(cl)
+shan <- foreach(i = 1:length(rList),
+                .packages = c("terra", "aqp")) %dopar% {
+                  shan <- wrap(aqp::shannonEntropy(terra::rast(rList[i])))
+                  return(shan)
+                }
+shan <- do.call(terra::merge,lapply(shan, terra::rast))
+plot(shan)
+
+gc()
+
+#normalized shan entropy
+b <- length(levels(pred)[[1]])
+shanNorm <- foreach(i = 1:length(rList),
+                     .packages = c("terra", "aqp")) %dopar% {
+                       shanNorm1 <- wrap(aqp::shannonEntropy(terra::rast(rList[i]), b=b))
+                       return(shanNorm1)
+                     }
+shanNorm <- do.call(terra::merge,lapply(shanNorm, terra::rast))
+plot(shanNorm)
+
+setwd("~/data/8-vic/results")
+writeRaster(shan, overwrite = TRUE, filename = "shan.tif", gdal=c("TFW=YES"))
+writeRaster(shanNorm, overwrite = TRUE, filename = "shanNorm.tif", gdal=c("TFW=YES"))
+
+stopCluster(cl)
+
+# get confusion matrix from model
+cm <- confusionMatrix(rfm$predicted, rfm$y)
+
+
+end.time <- Sys.time()
+
+time.taken <- end.time - start.time
+
+time.taken
+saveRDS(time.taken, "timeTaken.rds")
+
+
+## Transfer the script to the storage bucket:
+## Copy the line below and run in the terminal
+## change the name of file and path as necessary
+##gsutil -m cp ~/data/8-vic/scripts/modeling.R gs://sbs-aws-migration-s2026-8-vic/8-vic/scripts/