calc_errors() returns inconsistent results errors

[chintan-sd]

calc_errors() sometimes returns NA for $errors, while sometimes returns valid errors
I have observed this particularly happening depending on the batch size

Ex 1: $errors has errors

z1 <- list(list(x=ts(c(20000,40000,10000,150000,650000), frequency = 4), h = 2))
z1 <- temp_holdout(z1)
z1 <- calc_forecasts(z1, forec_methods(), n.cores=4)
z1 <- calc_errors(z1)

Ex 2: using same series as in earlier example, with an additional series errors are now valid

z2 <- list(list(x=ts(c(20000,40000,10000,150000,650000), frequency = 4), h = 2),list(x=ts(c(20000,40000,10000,150000,650000,130000,125000,325000,160000,150000), frequency = 4), h = 2))
z2 <- temp_holdout(z2)
z2 <- calc_forecasts(z2, forec_methods(), n.cores=4)
z2 <- calc_errors(z2)

this seems to be happening, coz total_snaive_errors is an average across all values in the dataset. However, if there is an issue in any one of the series then all $errors values are NA, causing the subsequent code to fail.

Ex 3: to series from Ex 2, we add one more series. Resulting errors for all three series will be NA

z2 <- list(
          list(x=ts(c(20000,40000,10000,150000,650000), frequency = 4), h = 2),
          list(x=ts(c(20000,40000,10000,150000,650000,130000,125000,325000,160000,150000), frequency = 4), h = 2),
          list(x=ts(c(50,50,50,50,50,50,50,50,50,50), frequency = 4), h = 2)
      )
z2 <- temp_holdout(z2)
z2 <- calc_forecasts(z2, forec_methods(), n.cores=4)
z2 <- calc_errors(z2)

[HamidM6]

in https://github.com/robjhyndman/M4metalearning/blob/master/R/process_dataset.R line number 95 and 96

total_snaive_errors <- total_snaive_errors + c(mean(lentry$snaive_mase), mean(lentry$snaive_smape))

I have added na.rm = TRUE to mean function to hack around the issue but not sure how that affects the results.