How to force an error if non-finite values (NA, NaN, or Inf) are encountered

Question

There's a conditional debugging flag I miss from Matlab: dbstop if infnan described here. If set, this condition will stop code execution when an Inf or NaN is encountered (IIRC, Matlab doesn't have NAs).

How might I achieve this in R in a more efficient manner than testing all objects after every assignment operation?

At the moment, the only ways I see to do this are via hacks like the following:

1. Manually insert a test after all places where these values might be encountered (e.g. a division, where division by 0 may occur). The testing would be to use is.finite(), described in this Q & A, on every element.
2. Use body() to modify the code to call a separate function, after each operation or possibly just each assignment, which tests all of the objects (and possibly all objects in all environments).
3. Modify R's source code (?!?)
4. Attempt to use tracemem to identify those variables that have changed, and check only these for bad values.
5. (New - see note 2) Use some kind of call handlers / callbacks to invoke a test function.

The 1st option is what I am doing at present. This is tedious, because I can't guarantee I've checked everything. The 2nd option will test everything, even if an object hasn't been updated. That is a massive waste of time. The 3rd option would involve modifying assignments of NA, NaN, and infinite values (+/- Inf), so that an error is produced. That seems like it's better left to R Core. The 4th option is like the 2nd - I'd need a call to a separate function listing all of the memory locations, just to ID those that have changed, and then check the values; I'm not even sure this will work for all objects, as a program may do an in-place modification, which seems like it would not invoke the duplicate function.

Is there a better approach that I'm missing? Maybe some clever tool by Mark Bravington, Luke Tierney, or something relatively basic - something akin to an options() parameter or a flag when compiling R?

Example code Here is some very simple example code to test with, incorporating the addTaskCallback function proposed by Josh O'Brien. The code isn't interrupted, but an error does occur in the first scenario, while no error occurs in the second case (i.e. badDiv(0,0,FALSE) doesn't abort). I'm still investigating callbacks, as this looks promising.

badDiv  <- function(x, y, flag){
    z = x / y
    if(flag == TRUE){
        return(z)
    } else {
        return(FALSE)
    }
}

addTaskCallback(stopOnNaNs)
badDiv(0, 0, TRUE)

addTaskCallback(stopOnNaNs)
badDiv(0, 0, FALSE)

---

Note 1. I'd be satisfied with a solution for standard R operations, though a lot of my calculations involve objects used via data.table or bigmemory (i.e. disk-based memory mapped matrices). These appear to have somewhat different memory behaviors than standard matrix and data.frame operations.

Note 2. The callbacks idea seems a bit more promising, as this doesn't require me to write functions that mutate R code, e.g. via the body() idea.

Note 3. I don't know whether or not there is some simple way to test the presence of non-finite values, e.g. meta information about objects that indexes where NAs, Infs, etc. are stored in the object, or if these are stored in place. So far, I've tried Simon Urbanek's inspect package, and have not found a way to divine the presence of non-numeric values.

Follow-up: Simon Urbanek has pointed out in a comment that such information is not available as meta information for objects.

Note 4. I'm still testing the ideas presented. Also, as suggested by Simon, testing for the presence of non-finite values should be fastest in C/C++; that should surpass even compiled R code, but I'm open to anything. For large datasets, e.g. on the order of 10-50GB, this should be a substantial savings over copying the data. One may get further improvements via use of multiple cores, but that's a bit more advanced.

Answer 1

The idea sketched below (and its implementation) is very imperfect. I'm hesitant to even suggest it, but: (a) I think it's kind of interesting, even in all of its ugliness; and (b) I can think of situations where it would be useful. Given that it sounds like you are right now manually inserting a check after each computation, I'm hopeful that your situation is one of those.

Mine is a two-step hack. First, I define a function nanDetector() which is designed to detect NaNs in several of the object types that might be returned by your calculations. Then, it using addTaskCallback() to call the function nanDetector() on .Last.value after each top-level task/calculation is completed. When it finds an NaN in one of those returned values, it throws an error, which you can use to avoid any further computations.

Among its shortcomings:

• If you do something like setting stop(error = recover), it's hard to tell where the error was triggered, since the error is always thrown from inside of stopOnNaNs().

• When it throws an error, stopOnNaNs() is terminated before it can return TRUE. As a consequence, it is removed from the task list, and you'll need to reset with addTaskCallback(stopOnNaNs) it you want to use it again. (See the 'Arguments' section of ?addTaskCallback for more details).

Without further ado, here it is:

---

# Sketch of a function that tests for NaNs in several types of objects
nanDetector <- function(X) {
   # To examine data frames
   if(is.data.frame(X)) { 
       return(any(unlist(sapply(X, is.nan))))
   }
   # To examine vectors, matrices, or arrays
   if(is.numeric(X)) {
       return(any(is.nan(X)))
   }
   # To examine lists, including nested lists
   if(is.list(X)) {
       return(any(rapply(X, is.nan)))
   }
   return(FALSE)
}

# Set up the taskCallback
stopOnNaNs <- function(...) {
    if(nanDetector(.Last.value)) {stop("NaNs detected!\n")}
    return(TRUE)
}
addTaskCallback(stopOnNaNs)


# Try it out
j <- 1:00
y <- rnorm(99)
l <- list(a=1:4, b=list(j=1:4, k=NaN))
# Error in function (...)  : NaNs detected!

# Subsequent time consuming code that could be avoided if the
# error thrown above is used to stop its evaluation.

Answer 2

I fear there is no such shortcut. In theory on unix there is SIGFPE that you could trap on, but in practice

1. there is no standard way to enable FP operations to trap it (even C99 doesn't include a provision for that) - it is highly system-specifc (e.g. feenableexcept on Linux, fp_enable_all on AIX etc.) or requires the use of assembler for your target CPU
2. FP operations are nowadays often done in vector units like SSE so you can't be even sure that FPU is involved and
3. R intercepts some operations on things like NaNs, NAs and handles them separately so they won't make it to the FP code

That said, you could hack yourself an R that will catch some exceptions for your platform and CPU if you tried hard enough (disable SSE etc.). It is not something we would consider building into R, but for a special purpose it may be doable.

However, it would still not catch NaN/NA operations unless you change R internal code. In addition, you would have to check every single package you are using since they may be using FP operations in their C code and may also handle NA/NaN separately.

If you are only worried about things like division by zero or over/underflows, the above will work and is probably the closest to something like a solution.

Just checking your results may not be very reliable, because you don't know whether a result is based on some intermediate NaN calculation that changed an aggregated value which may not need to be NaN as well. If you are willing to discard such case, then you could simply walk recursively through your result objects or the workspace. That should not be extremely inefficient, because you only need to worry about REALSXP and not anything else (unless you don't like NAs either - then you'd have more work).

---

This is an example code that could be used to traverse R object recursively:

static int do_isFinite(SEXP x) {
    /* recurse into generic vectors (lists) */
    if (TYPEOF(x) == VECSXP) {
        int n = LENGTH(x);
        for (int i = 0; i < n; i++)
            if (!do_isFinite(VECTOR_ELT(x, i))) return 0;
    }
    /* recurse into pairlists */ 
    if (TYPEOF(x) == LISTSXP) {
         while (x != R_NilValue) {
             if (!do_isFinite(CAR(x))) return 0;
             x = CDR(x);
         }
         return 1;
    }
    /* I wouldn't bother with attributes except for S4
       where attributes are slots */
    if (IS_S4_OBJECT(x) && !do_isFinite(ATTRIB(x))) return 0;
    /* check reals */
    if (TYPEOF(x) == REALSXP) {
        int n = LENGTH(x);
        double *d = REAL(x);
        for (int i = 0; i < n; i++) if (!R_finite(d[i])) return 0;
    }
    return 1; 
}

SEXP isFinite(SEXP x) { return ScalarLogical(do_isFinite(x)); }

# in R: .Call("isFinite", x)