Minor edits

interpretable_sdm.r

@@ -3,13 +3,9 @@ library(mlr)
 library(lime)
 library(dplyr)
 
-
 set.seed(42)
-
-# obtain occurence and environmental data
 occ_data_raw <- get_benchmarking_data("Loxodonta africana", limit = 1000)
 
-# rename environmental data features to be more understandable
 names(occ_data_raw$raster_data$climate_variables) <- c(
   "mean_temp",
   "mean_diurnal",
@@ -55,47 +51,44 @@ names(occ_data_raw$df_data) <- c(
   "label"
 )
 
-# minor data processing
 occ_data <- occ_data_raw$df_data
 occ_data$label <- as.factor(occ_data$label)
 
-coordinates.df <- rbind(occ_data_raw$raster_data$coords_presence,
+coordinates_df <- rbind(occ_data_raw$raster_data$coords_presence,
                         occ_data_raw$raster_data$background)
-occ_data <- cbind(occ_data, coordinates.df)
+occ_data <- cbind(occ_data, coordinates_df)
 occ_data <- na.omit(occ_data)
 
-# split data into training and testing
 train_test_split <- rsample::initial_split(occ_data, prop = 0.7)
-data.train <- rsample::training(train_test_split)
+data_train <- rsample::training(train_test_split)
 data.test  <- rsample::testing(train_test_split)
 
-train.coords <- dplyr::select(data.train, c("x", "y"))
-data.train$x <- NULL
-data.train$y <- NULL
+data_train$x <- NULL
+data_train$y <- NULL
 
-test.coords <- dplyr::select(data.test, c("x", "y"))
-data.test$x <- NULL
-data.test$y <- NULL
+data_test_subset <- data.test %>% filter(label == 1)
+sample_data <- sample_n(data_test_subset, 3)
+sample_data_coords <- dplyr::select(sample_data, c("x", "y"))
+sample_data$x <- NULL
+sample_data$y <- NULL
 
-# fit Random Forest
-task <- makeClassifTask(id = "model", data = data.train, target = "label")
+task <- makeClassifTask(id = "model", data = data_train, target = "label")
 lrn <- makeLearner("classif.randomForest", predict.type = "prob")
 mod <- train(lrn, task)
 
-explainer <- lime(data.train, mod)
-explanation <- lime::explain(sample_n(data.test, 3), explainer, n_labels = 1, n_features = 12)
+explainer <- lime(data_train, mod)
+explanation <- lime::explain(sample_data, explainer, n_labels = 1, n_features = 5)
 plot_features(explanation)
 
-# create a custom function to make model object work with raster predictions
 customPredictFun <- function(model, data) {
   v <- predict(model, data, type = "prob")
   v <- as.data.frame(v)
   colnames(v) <- c("absence", "presence")
   return(v$presence)
 }
 
-# predict and create Habitat Suitability Map
 pr <- dismo::predict(occ_data_raw$raster_data$climate_variables, mlr::getLearnerModel(mod, TRUE), fun = customPredictFun)
 
-rf_map <- raster::spplot(pr, main = "Predicted Habitat Suitability Map (Random Forests)", ylab = "Suitability")
+rf_map <- raster::spplot(pr, main = "Predicted Habitat Suitability Map (Random Forest)", ylab = "Suitability")
+rf_map