Enhanced Input Argument Validation

Oct 13, 2025 by sorusgentrification.com 55 views

Introduction

The add_manual_targets() function in the prioritizr package is used to add manual targets to a problem. However, when trying to compile a multi-zone problem with improperly specified targets, it can result in a segfault. This issue arises when specifying a nested list that has >2 levels of nestedness. In this article, we will discuss the problem, provide a reprex, and propose a solution to enhance the input argument validation checks to catch this invalid input.

Understanding the Issue

When trying to compile a multi-zone problem with improperly specified targets, the add_manual_targets() function can result in a segfault. This happens when specifying a nested list that has >2 levels of nestedness. For example, in the reprex provided below, the target_data is a nested list with two levels of nestedness.

target_data <-
  tibble(
    feature = feature_names(ft_data)[[1]],
    zone = list(list(zone_names(ft_data))),
    type = "absolute",
    sense = ">=", 
    target = 5
  )

Reprex

Here's a reprex based on the dev version of prioritizr that demonstrates the issue:

# initialization
## load packages
devtools::load_all()
library(terra)
library(tibble)
library(dplyr)

# preliminary processing
### import data
pu_data <- get_sim_zones_pu_raster()
ft_data <- get_sim_zones_features()

## prepare target data for prioritizr
target_data <-
  tibble(
    feature = feature_names(ft_data)[[1]],
    zone = list(list(zone_names(ft_data))),
    type = "absolute",
    sense = ">=", 
    target = 5
  )

# create problem
p1 <- 
  problem(pu_data, ft_data) %>%
  add_min_set_objective() %>%
  add_manual_targets(target_data) %>%
  add_binary_decisions()

# compile problem
o1 <- compile(p1)

#  *** caught segfault ***
# address 0x63d970df7350, cause 'memory not mapped'
# 
# Traceback:
#  1: .Call(`_prioritizr_rcpp_add_rij_data`, x, rij_list, targets_list,     compressed_formulation)
#  2: rcpp_add_rij_data(op$ptr, x$get_data("rij_matrix"), as.list(targets),     compressed_formulation)
#  3: compile.ConservationProblem(p1)
#  4: compile(p1)

Proposed Solution

To resolve this issue, the input argument validation checks should be enhanced to catch this invalid input. This can be achieved by adding a check to ensure that the zone column in the target_data is not a nested list with >2 levels of nestedness.

Enhanced Input Argument Validation

Here's an example of how the input argument validation can be enhanced:

add_manual_targets <- function(x, targets) {
  # check if zone is a nested list with >2 levels of nestedness
  if (is.list(targets$zone) && length(targets$zone) > 2) {
    stop("Invalid input: zone should not be a nested list with >2 levels of nestedness")
  }
  
  # add manual targets
  x$targets <- targets
  return(x)
}

Conclusion

In conclusion, the add_manual_targets() function in the prioritizr package can result in a segfault when trying to compile a multi-zone problem with improperly specified targets. This issue arises when specifying a nested list that has >2 levels of nestedness. To resolve this issue, the input argument validation checks should be enhanced to catch this invalid input. By adding a check to ensure that the zone column in the target_data is not a nested list with >2 levels of nestedness, we can prevent this issue from occurring.

Future Work

In the future, we can further enhance the input argument validation checks to catch other types of invalid input. This can include checking for invalid values in the type, sense, and target columns. Additionally, we can add more robust error handling to provide more informative error messages to users.

Recommendations

Based on our analysis, we recommend the following:

Enhance the input argument validation checks to catch invalid input.
Add a check to ensure that the zone column in the target_data is not a nested list with >2 levels of nestedness.
Provide more informative error messages to users.
Further enhance the input argument validation checks to catch other types of invalid input.

Q: What is the issue with the add_manual_targets() function in the prioritizr package?

A: The add_manual_targets() function in the prioritizr package can result in a segfault when trying to compile a multi-zone problem with improperly specified targets. This issue arises when specifying a nested list that has >2 levels of nestedness.

Q: What is a nested list with >2 levels of nestedness?

A: A nested list with >2 levels of nestedness is a list that contains another list, which in turn contains another list, and so on. For example:

target_data <-
  tibble(
    feature = feature_names(ft_data)[[1]],
    zone = list(list(zone_names(ft_data))),
    type = "absolute",
    sense = ">=", 
    target = 5
  )

In this example, the zone column is a nested list with 2 levels of nestedness.

Q: How can I prevent this issue from occurring?

A: To prevent this issue from occurring, you can enhance the input argument validation checks to catch this invalid input. This can be achieved by adding a check to ensure that the zone column in the target_data is not a nested list with >2 levels of nestedness.

Q: How can I enhance the input argument validation checks?

A: You can enhance the input argument validation checks by adding a check to ensure that the zone column in the target_data is not a nested list with >2 levels of nestedness. Here's an example of how you can do this:

add_manual_targets <- function(x, targets) {
  # check if zone is a nested list with >2 levels of nestedness
  if (is.list(targets$zone) && length(targets$zone) > 2) {
    stop("Invalid input: zone should not be a nested list with >2 levels of nestedness")
  }
  
  # add manual targets
  x$targets <- targets
  return(x)
}

Q: What are some other types of invalid input that I should check for?

A: Some other types of invalid input that you should check for include:

Invalid values in the type column
Invalid values in the sense column
Invalid values in the target column

Q: How can I add more robust error handling to provide more informative error messages to users?

A: You can add more robust error handling by using the stop() function to provide more informative error messages to users. For example:

add_manual_targets <- function(x, targets) {
  # check if zone is a nested list with >2 levels of nestedness
  if (is.list(targets$zone) && length(targets$zone) > 2) {
    stop("Invalid input: zone should not be a nested list with >2 levels of nestedness. Please check the documentation for more information.")
  }
  
  # add manual targets
  x$targets <- targets
  return(x)
}

Q: What are some best practices for writing robust and user-friendly code?

A: Some best practices for writing robust and user-friendly code include:

Enhancing input argument validation checks to catch invalid input
Providing more informative error messages to users
Using robust error handling to handle unexpected errors
Documenting code thoroughly to provide users with clear instructions on how to use the code

By following these best practices, you can write robust and user-friendly code that is easy to use and maintain.