One of the most important features of this package is the complete
microbial taxonomic database, supplied by the Catalogue of Life (CoL) and
the List of Prokaryotic names with
Standing in Nomenclature (LPSN). We created a function
as.mo() that transforms any user input value to a valid
microbial ID by using intelligent rules combined with the microbial
taxonomy.
Using the microbenchmark package, we can review the
calculation performance of this function. Its function
microbenchmark() runs different input expressions
independently of each other and measures their time-to-result.
In the next test, we try to ‘coerce’ different input values into the microbial code of Staphylococcus aureus. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.
The actual result is the same every time: it returns its
microorganism code B_STPHY_AURS (B stands for
Bacteria, its taxonomic kingdom).
But the calculation time differs a lot:
S.aureus <- microbenchmark(
as.mo("sau"), # WHONET code
as.mo("stau"),
as.mo("STAU"),
as.mo("staaur"),
as.mo("STAAUR"),
as.mo("S. aureus"),
as.mo("S aureus"),
as.mo("Staphylococcus aureus"), # official taxonomic name
as.mo("Staphylococcus aureus (MRSA)"), # additional text
as.mo("Sthafilokkockus aaureuz"), # incorrect spelling
as.mo("MRSA"), # Methicillin Resistant S. aureus
as.mo("VISA"), # Vancomycin Intermediate S. aureus
times = 25)
print(S.aureus, unit = "ms", signif = 2)
# Unit: milliseconds
<<<<<<< HEAD
# expr min lq mean median uq max neval
# as.mo("sau") 12.0 13 18.0 14.0 15.0 48 25
# as.mo("stau") 54.0 59 80.0 91.0 96.0 99 25
# as.mo("STAU") 53.0 61 77.0 66.0 94.0 100 25
# as.mo("staaur") 12.0 13 19.0 14.0 16.0 62 25
# as.mo("STAAUR") 12.0 13 16.0 14.0 15.0 48 25
# as.mo("S. aureus") 28.0 30 38.0 33.0 35.0 69 25
# as.mo("S aureus") 27.0 31 46.0 34.0 65.0 73 25
# as.mo("Staphylococcus aureus") 3.7 4 6.7 4.3 4.5 36 25
# as.mo("Staphylococcus aureus (MRSA)") 260.0 270 290.0 280.0 290.0 360 25
# as.mo("Sthafilokkockus aaureuz") 190.0 210 220.0 210.0 220.0 330 25
# as.mo("MRSA") 12.0 13 20.0 14.0 16.0 68 25
# as.mo("VISA") 22.0 23 32.0 25.0 27.0 63 25
In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 67 times slower to determine.
To improve performance, we implemented two important algorithms to save unnecessary calculations: repetitive results and already precalculated results.
======= # expr min lq mean median uq max neval # as.mo("sau") 20.0 20.0 25.0 20.0 21 57 25 # as.mo("stau") 94.0 94.0 110.0 95.0 130 130 25 # as.mo("STAU") 94.0 95.0 110.0 130.0 130 130 25 # as.mo("staaur") 20.0 20.0 28.0 20.0 21 58 25 # as.mo("STAAUR") 20.0 20.0 25.0 20.0 21 57 25 # as.mo("S. aureus") 56.0 56.0 72.0 57.0 92 97 25 # as.mo("S aureus") 55.0 56.0 66.0 56.0 75 93 25 # as.mo("Staphylococcus aureus") 5.7 5.8 7.3 5.9 6 41 25 # as.mo("Staphylococcus aureus (MRSA)") 370.0 370.0 390.0 380.0 410 410 25 # as.mo("Sthafilokkockus aaureuz") 260.0 290.0 300.0 290.0 300 330 25 # as.mo("MRSA") 20.0 20.0 23.0 20.0 20 56 25 # as.mo("VISA") 34.0 35.0 48.0 35.0 35 220 25
In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 65 times slower to determine.
To improve performance, we implemented two important algorithms to save unnecessary calculations: repetitive results and already precalculated results.
>>>>>>> 8c9feea087f568fd4abbdb325140d1d628e6856fRepetitive results
Repetitive results are values that are present more than once in a
vector. Unique values will only be calculated once by
as.mo(). So running
as.mo(c("E. coli", "E. coli")) will check the value
"E. coli" only once.
To prove this, we will use mo_name() for testing - a
helper function that returns the full microbial name (genus, species and
possibly subspecies) which uses as.mo() internally.
# start with the example_isolates data set
x <- example_isolates %>%
# take all MO codes from the 'mo' column
pull(mo) %>%
# and copy them a thousand times
rep(1000) %>%
# then scramble them
sample()
# what do these values look like? They are of class <mo>:
head(x)
# Class <mo>
<<<<<<< HEAD
# [1] B_ESCHR_COLI B_STRPT_MITS B_STRPT_ANGN B_STPHY_CONS B_ESCHR_COLI
# [6] B_ESCHR_COLI
=======
# [1] B_PROTS_MRBL B_STPHY_CONS B_ESCHR_COLI B_PSDMN_AERG B_ENTRC_FCLS
# [6] B_STPHY_AURS
>>>>>>> 8c9feea087f568fd4abbdb325140d1d628e6856f
# as the example_isolates data set has 2,000 rows, we should have 2 million items
length(x)
# [1] 2000000
# and how many unique values do we have?
n_distinct(x)
# [1] 90
# now let's see:
run_it <- microbenchmark(mo_name(x),
times = 10)
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
<<<<<<< HEAD
# mo_name(x) 207 225 288 233 370 414 10So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.233 seconds. That is 117 nanoseconds on average. You only lose time on your unique input values.
======= # mo_name(x) 265 269 357 298 471 516 10So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.298 seconds. That is 149 nanoseconds on average. You only lose time on your unique input values.
>>>>>>> 8c9feea087f568fd4abbdb325140d1d628e6856fPrecalculated results
What about precalculated results? If the input is an already
precalculated result of a helper function such as
mo_name(), it almost doesn’t take any time at all. In other
words, if you run mo_name() on a valid taxonomic name, it
will return the results immediately (see ‘C’ below):
run_it <- microbenchmark(A = mo_name("STAAUR"),
B = mo_name("S. aureus"),
C = mo_name("Staphylococcus aureus"),
times = 10)
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
<<<<<<< HEAD
# expr min lq mean median uq max neval
# A 10.3 10.90 11.20 11.10 11.20 12.40 10
# B 31.3 32.70 38.20 33.90 35.20 79.80 10
# C 2.5 2.64 2.79 2.78 2.85 3.13 10So going from mo_name("Staphylococcus aureus") to
"Staphylococcus aureus" takes 0.0028 seconds - it doesn’t
even start calculating if the result would be the same as the
expected resulting value. That goes for all helper functions:
So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0031 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:
run_it <- microbenchmark(A = mo_species("aureus"),
B = mo_genus("Staphylococcus"),
C = mo_name("Staphylococcus aureus"),
D = mo_family("Staphylococcaceae"),
E = mo_order("Bacillales"),
F = mo_class("Bacilli"),
G = mo_phylum("Firmicutes"),
H = mo_kingdom("Bacteria"),
times = 10)
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
<<<<<<< HEAD
# A 2.30 2.55 2.68 2.66 2.85 2.94 10
# B 2.27 2.38 2.59 2.56 2.83 2.88 10
# C 2.22 2.25 2.51 2.47 2.74 2.87 10
# D 2.21 2.40 2.68 2.73 2.94 3.08 10
# E 2.22 2.28 2.46 2.45 2.56 2.81 10
# F 2.19 2.34 2.52 2.48 2.71 3.04 10
# G 2.23 2.40 2.52 2.46 2.62 2.88 10
# H 2.13 2.25 2.42 2.47 2.50 2.77 10Of course, when running mo_phylum("Firmicutes") the
function has zero knowledge about the actual microorganism, namely
S. aureus. But since the result would be
"Firmicutes" anyway, there is no point in calculating the
result. And because this package contains all phyla of all known
bacteria, it can just return the initial value immediately.