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modeling_xgboost.R
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##############################################
##############################################
##### Modeling eXtreme Gradient Boosting #####
##############################################
##############################################
source("00_loading_data.R")
{
#Only modeling common species
for(s in sp){
print(paste('Modeling', s))
sites_sp <- sites[, c("x", "y", "ua", s,
"PC1", "PC2", "PC3", "PC4", "PC5")]
names(sites_sp)[names(sites_sp) == s] <- "occ"
if(sum(sites_sp$occ) >= 30){
pa_data <- st_as_sf(sites_sp,
coords = c("x", "y"),
crs = crs(env_raster))
#Separating the data into blocks of 165 km size (no more spatial autocorrelation)
sb <- spatialBlock(speciesData = pa_data,
species = "occ",
rasterLayer = env_raster,
theRange = 165000, #Size of the blocks (m) from prior SAC analysis
k = 5, #Arbitrary
selection = "random",
iteration = 200, #Find evenly dispersed folds
biomod2Format = FALSE,
verbose = FALSE,
progress = FALSE
)
#Adding the partitioned blocks info to the initial data set
sites_sp$block <- sb$foldID
#Separate training-testing by pre-defined spatial blocks
split <- initial_split(sites_sp, prop = .7, strata = block) #70/30 in each block
train <- training(split)
test <- testing(split)
#Response must be a factor for {caret}
train$occ <- as.factor(train$occ)
levels(train$occ) <- c("C0", "C1")
#Creating a tuning grid
tune_grid <- expand.grid(nrounds = seq(from = 500, to = 5000, by = 250),
max_depth = seq(from = 1, to = 3, by = 1),
eta = c(0.001, 0.01, 0.1),
gamma = 0,
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1)
#Using caret for cross-validation
train_control <- trainControl(method = "cv",
number = 10,
classProbs = TRUE,
summaryFunction = twoClassSummary,
allowParallel = TRUE)
#Starting cluster
cluster <- makeCluster(8)
registerDoParallel(cluster)
#Running the XGBOOST model
#Model training here
xgb_fit <- caret::train(x = train[, 5:9],
y = train$occ,
method = "xgbTree",
metric = "ROC",
trControl = train_control,
tuneGrid = tune_grid,
verbose = TRUE)
#Ending cluster
stopCluster(cluster)
registerDoSEQ()
#Model testing here
test_pred <- predict(object = xgb_fit,
newdata = test[, 5:9],
type = "prob")$C1
#ROC-AUC and PRC-AUC evaluation
precrec_obj <- evalmod(scores = test_pred,
labels = test$occ)
#Prediction maps based on trained data
xgbpred <- raster::predict(model = xgb_fit,
object = env_raster,
type = "prob",
index = 2)
#Saving the output prediction raster
writeRaster(xgbpred,
paste("results/xgboost/rasters/", s, ".tif", sep = ""),
overwrite = TRUE)
#Evaluation table for each species
result = rbind(result,
data.frame(spp = s,
roc = round(precrec::auc(precrec_obj)$aucs[1], 4),
prc = round(precrec::auc(precrec_obj)$aucs[2], 4))
)
} else {
#Separate training-testing by pre-defined spatial blocks
split <- initial_split(sites_sp, prop = .7) #70/30 in each block
train <- training(split)
test <- testing(split)
#Response must be a factor
train$occ <- as.factor(train$occ)
levels(train$occ) <- c("C0", "C1")
#Creating a tuning grid
tune_grid <- expand.grid(nrounds = seq(from = 500, to = 5000, by = 250),
max_depth = seq(from = 1, to = 3, by = 1),
eta = c(0.001, 0.01, 0.1),
gamma = 0,
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1)
#Using caret for cross-validation
train_control <- trainControl(method = "cv",
number = 10,
classProbs = TRUE,
summaryFunction = twoClassSummary,
allowParallel = TRUE)
#Starting cluster
cluster <- makeCluster(8)
registerDoParallel(cluster)
#Running the XGBOOST model
#Model training here
xgb_fit <- caret::train(x = train[, 5:9],
y = train$occ,
method = "xgbTree",
metric = "ROC",
trControl = train_control,
tuneGrid = tune_grid,
verbose = TRUE)
#Ending cluster
stopCluster(cluster)
registerDoSEQ()
#Model testing here
test_pred <- predict(object = xgb_fit,
newdata = test[, 5:9],
type = "prob")$C1
#ROC-AUC and PRC-AUC evaluation
precrec_obj <- evalmod(scores = test_pred,
labels = test$occ)
#Prediction maps based on trained data
xgbpred <- raster::predict(model = xgb_fit,
object = env_raster,
type = "prob",
index = 2)
#Saving the output prediction raster
writeRaster(xgbpred,
paste("results/xgboost/rasters/", s, ".tif", sep = ""),
overwrite = TRUE)
#Evaluation table for each species
result = rbind(result,
data.frame(spp = s,
roc = round(precrec::auc(precrec_obj)$aucs[1], 4),
prc = round(precrec::auc(precrec_obj)$aucs[2], 4))
)
}
}
#Evaluation
write.csv(result, 'results/xgboost/evaluation.csv')
}