One of the most important features of this package is the complete microbial taxonomic database, supplied by the Catalogue of Life. We created a function as.mo() that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.
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.
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.
-microbenchmark <- microbenchmark::microbenchmark
+library(microbenchmark)
library(AMR)
library(dplyr)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, the taxonomic kingdom).
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(
+S.aureus <- microbenchmark(
as.mo("sau"), # WHONET code
as.mo("stau"),
as.mo("STAU"),
@@ -223,40 +223,47 @@
as.mo("Sthafilokkockus aaureuz"), # incorrect spelling
as.mo("MRSA"), # Methicillin Resistant S. aureus
as.mo("VISA"), # Vancomycin Intermediate S. aureus
- as.mo("VRSA"), # Vancomycin Resistant S. aureus
- times = 10)
+ times = 25)
print(S.aureus, unit = "ms", signif = 2)
# Unit: milliseconds
-# expr min lq mean median uq max neval
-# as.mo("sau") 11.0 12.0 27.0 13.0 49.0 51 10
-# as.mo("stau") 53.0 57.0 76.0 74.0 93.0 100 10
-# as.mo("STAU") 53.0 54.0 69.0 56.0 58.0 190 10
-# as.mo("staaur") 11.0 12.0 21.0 13.0 42.0 44 10
-# as.mo("STAAUR") 11.0 12.0 16.0 13.0 14.0 48 10
-# as.mo("S. aureus") 27.0 27.0 38.0 32.0 35.0 75 10
-# as.mo("S aureus") 27.0 29.0 38.0 30.0 36.0 73 10
-# as.mo("Staphylococcus aureus") 3.1 3.2 6.9 3.5 3.7 38 10
-# as.mo("Staphylococcus aureus (MRSA)") 250.0 260.0 270.0 260.0 280.0 290 10
-# as.mo("Sthafilokkockus aaureuz") 160.0 200.0 200.0 200.0 210.0 230 10
-# as.mo("MRSA") 10.0 11.0 12.0 11.0 13.0 14 10
-# as.mo("VISA") 19.0 20.0 26.0 22.0 24.0 61 10
-# as.mo("VRSA") 19.0 20.0 24.0 21.0 22.0 56 10
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 100 milliseconds, this is only 10 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations can take up to 500-1000 times as much time.
-To improve performance, two important calculations take almost no time at all: repetitive results and already precalculated results.
+# expr min lq mean median uq max neval +# as.mo("sau") 9.3 10 11.0 10 11.0 13.0 25 +# as.mo("stau") 52.0 55 73.0 58 92.0 100.0 25 +# as.mo("STAU") 50.0 54 73.0 58 96.0 110.0 25 +# as.mo("staaur") 9.7 10 14.0 11 12.0 57.0 25 +# as.mo("STAAUR") 8.9 10 14.0 10 11.0 52.0 25 +# as.mo("S. aureus") 26.0 28 41.0 29 67.0 76.0 25 +# as.mo("S aureus") 27.0 28 41.0 30 65.0 76.0 25 +# as.mo("Staphylococcus aureus") 2.6 3 3.2 3 3.3 4.6 25 +# as.mo("Staphylococcus aureus (MRSA)") 240.0 260 270.0 260 270.0 380.0 25 +# as.mo("Sthafilokkockus aaureuz") 160.0 190 200.0 200 200.0 300.0 25 +# as.mo("MRSA") 9.3 10 15.0 10 12.0 49.0 25 +# as.mo("VISA") 18.0 19 31.0 21 54.0 67.0 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 200 times slower to determine.
+To improve performance, we implemented two important algorithms to save unnecessary calculations: repetitive results and already precalculated results.
Repetitive results are unique values that are present more than once. Unique values will only be calculated once by as.mo(). We will use mo_name() for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses as.mo() internally.
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.
-# take all MO codes from the example_isolates data set
-x <- example_isolates$mo %>%
+# 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>
+# [1] B_STPHY_AURS B_STRPT_GRPC B_STPHY_CONS B_STPHY_EPDR B_STRPT_PNMN
+# [6] B_PROTS_VLGR
-# as the example_isolates has 2,000 rows, we should have 2 million items
+# as the example_isolates data set has 2,000 rows, we should have 2 million items
length(x)
# [1] 2000000
@@ -265,32 +272,32 @@
# [1] 90
# now let's see:
-run_it <- microbenchmark(mo_name(x),
+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
-# mo_name(x) 137 146 178 172 193 282 10So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.172 seconds. You only lose time on your unique input values.
+# mo_name(x) 157 187 222 206 224 372 10So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.206 seconds. That is 2.471 milliseconds per unique item on average. You only lose time on your unique input values.
What about precalculated results? If the input is an already precalculated result of a helper function like mo_name(), it almost doesn’t take any time at all (see ‘C’ below):
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"),
+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
# expr min lq mean median uq max neval
-# A 7.12 7.56 7.89 7.7 8.39 8.69 10
-# B 23.90 24.50 35.10 24.8 27.40 77.50 10
-# C 1.73 1.84 1.95 1.9 2.09 2.36 10So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0019 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 0.0018 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"),
+run_it <- microbenchmark(A = mo_species("aureus"),
B = mo_genus("Staphylococcus"),
C = mo_name("Staphylococcus aureus"),
D = mo_family("Staphylococcaceae"),
@@ -302,15 +309,15 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 1.54 1.57 1.73 1.69 1.88 2.00 10
-# B 1.51 1.52 1.75 1.70 1.86 2.41 10
-# C 1.55 1.60 1.70 1.70 1.81 1.86 10
-# D 1.55 1.59 1.71 1.66 1.83 1.94 10
-# E 1.50 1.55 1.68 1.61 1.78 2.10 10
-# F 1.51 1.62 1.75 1.79 1.82 2.02 10
-# G 1.52 1.53 1.68 1.60 1.80 2.02 10
-# H 1.53 1.54 1.63 1.60 1.67 1.84 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 ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.
Of 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.
Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
+# expr min lq mean median uq max neval +# en 17.12 17.40 22.65 17.54 18.39 78.39 100 +# de 19.93 20.26 22.60 20.48 20.97 81.27 100 +# nl 24.87 25.24 30.93 25.50 26.90 87.17 100 +# es 20.00 20.26 24.39 20.58 21.13 82.01 100 +# it 19.92 20.26 26.54 20.66 21.38 79.15 100 +# fr 19.62 19.90 24.74 20.10 21.04 77.20 100 +# pt 19.74 20.02 26.76 20.41 22.68 82.61 100 +Currently supported non-English languages are German, Dutch, Spanish, Italian, French and Portuguese.
diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png index c643f012..f8429aaf 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index ec8d95b2..28653326 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -81,7 +81,7 @@ diff --git a/docs/authors.html b/docs/authors.html index 29ad2cfc..245ceccf 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -81,7 +81,7 @@ diff --git a/docs/index.html b/docs/index.html index d631d734..3689a9d8 100644 --- a/docs/index.html +++ b/docs/index.html @@ -43,7 +43,7 @@ diff --git a/docs/news/index.html b/docs/news/index.html index 5388f298..5e1588e0 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -81,7 +81,7 @@ @@ -236,13 +236,13 @@ Source:NEWS.md
-