use cov matrix with kernel width for sampling

R/surv_lime.R

@@ -43,7 +43,8 @@ surv_lime <- function(explainer, new_observation,
         N,
         categorical_variables,
         sampling_method,
-        sample_around_instance
+        sample_around_instance,
+        kernel_width
     )
 
 
@@ -122,7 +123,8 @@ generate_neighbourhood <- function(data_org,
                                    n_samples = 100,
                                    categorical_variables = NULL,
                                    sampling_method = "gaussian",
-                                   sample_around_instance = TRUE) {
+                                   sample_around_instance = TRUE,
+                                   kernel_width = NULL) {
 
     # change categorical_variables to column names
     if (is.numeric(categorical_variables)) categorical_variables <- colnames(data_org)[categorical_variables]
@@ -131,6 +133,13 @@ generate_neighbourhood <- function(data_org,
     categorical_variables <- unique(c(additional_categorical_variables, factor_variables))
     data_row <- data_row[colnames(data_org)]
 
+    if  (is.character(kernel_width) && kernel_width == "silverman"){
+        p <- ncol(data_org)
+        b <- (4/(n_samples*(p+2)))^(1/(p+4))
+    } else {
+        b <- 1
+    }
+
     feature_frequencies <- list(length(categorical_variables))
     scaled_data <- scale(data_org[, !colnames(data_org) %in% categorical_variables])
 
@@ -154,9 +163,9 @@ generate_neighbourhood <- function(data_org,
 
     if (sample_around_instance) {
         to_add <- data_row[, !colnames(data_row) %in% categorical_variables]
-        data <- data %*% diag(sc) + to_add[col(data)]
+        data <- data %*% (b * diag(sc)) + to_add[col(data)]
     } else {
-        data <- data %*% diag(sc) + me[col(data)]
+        data <- data %*% (b * diag(sc)) + me[col(data)]
     }
 
     data <- as.data.frame(data)