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AMR/vignettes/benchmarks.Rmd

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---
title: "Benchmarks"
author: "Matthijs S. Berends"
date: '`r format(Sys.Date(), "%d %B %Y")`'
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{Benchmarks}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
fig.width = 7.5,
fig.height = 4.5,
dpi = 75
)
```
One of the most important features of this package is the complete microbial taxonomic database, supplied by the [Catalogue of Life](http://catalogueoflife.org). We created a function `as.mo()` that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.
Using the `microbenchmark` package, we can review the calculation performance of this function. Its function `microbenchmark()` runs different input expressions independently of each other and measures their time-to-result.
```{r, message = FALSE, echo = FALSE}
library(ggplot2)
ggplot.bm <- function(df, title = NULL) {
s <- summary(df)[order(summary(df)$median), ]
suppressWarnings(
print(
s %>%
ggplot(aes(x = expr, y = median)) +
geom_linerange(aes(ymin = 0, ymax = median), colour = "#555555") +
geom_text(aes(label = round(s$median, 0), hjust = -0.5), size = 3) +
geom_point(size = 3, colour = "#555555") +
coord_flip() +
scale_y_log10(breaks = c(1, 2, 5,
10, 20, 50,
100, 200, 500,
1000, 2000, 5000),
limits = c(1, max(s$median) * 2)) +
labs(x = "Expression", y = "Median time in milliseconds (log scale)", title = title)
)
)
}
```
```{r, message = FALSE}
microbenchmark <- microbenchmark::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 ``r as.character(as.mo("Staphylococcus aureus"))`` (*B* stands for *Bacteria*, the taxonomic kingdom).
But the calculation time differs a lot:
```{r, warning=FALSE}
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
as.mo("VRSA"), # Vancomycin Resistant S. aureus
as.mo(22242419), # Catalogue of Life ID
times = 10)
print(S.aureus, unit = "ms", signif = 2)
```
```{r, echo = FALSE}
ggplot.bm(S.aureus)
```
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.
To achieve this speed, the `as.mo` function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of *Methanosarcina semesiae* (`B_MTHNSR_SEMS`), a bug probably never found before in humans:
```{r, warning=FALSE}
M.semesiae <- microbenchmark(as.mo("metsem"),
as.mo("METSEM"),
as.mo("M. semesiae"),
as.mo("M. semesiae"),
as.mo("Methanosarcina semesiae"),
times = 10)
print(M.semesiae, unit = "ms", signif = 4)
```
Looking up arbitrary codes of less prevalent microorganisms costs the most time. Full names (like *Methanosarcina semesiae*) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.
In the figure below, we compare *Escherichia coli* (which is very common) with *Prevotella brevis* (which is moderately common) and with *Methanosarcina semesiae* (which is uncommon):
```{r, echo = FALSE, fig.width=12}
par(mar = c(5, 16, 4, 2))
boxplot(microbenchmark(
as.mo("Meth. semesiae"),
as.mo("Prev. brevis"),
as.mo("Esc. coli"),
times = 100),
horizontal = TRUE, las = 1, unit = "s", log = TRUE,
xlab = "", ylab = "Time in seconds (log)",
main = "Benchmarks per prevalence")
```
Uncommon microorganisms take some more time than common microorganisms. To further improve performance, two important calculations take almost no time at all: **repetitive results** and **already precalculated results**.
### Repetitive 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.
```{r, message = FALSE}
# take all MO codes from the example_isolates data set
x <- example_isolates$mo %>%
# keep only the unique ones
unique() %>%
# pick 50 of them at random
sample(50) %>%
# paste that 10,000 times
rep(10000) %>%
# scramble it
sample()
# got indeed 50 times 10,000 = half a million?
length(x)
# and how many unique values do we have?
n_distinct(x)
# now let's see:
run_it <- microbenchmark(mo_name(x),
times = 10)
print(run_it, unit = "ms", signif = 3)
```
So transforming 500,000 values (!!) of `r n_distinct(x)` unique values only takes `r round(median(run_it$time, na.rm = TRUE) / 1e9, 2)` seconds. You only lose time on your unique input values.
### Precalculated results
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):
```{r}
run_it <- microbenchmark(A = mo_name("B_STPHY_AURS"),
B = mo_name("S. aureus"),
C = mo_name("Staphylococcus aureus"),
times = 10)
print(run_it, unit = "ms", signif = 3)
```
So going from `mo_name("Staphylococcus aureus")` to `"Staphylococcus aureus"` takes `r format(round(run_it %>% filter(expr == "C") %>% pull(time) %>% median() / 1e9, 4), scientific = FALSE)` 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:
```{r}
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)
```
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 'knows' all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.
### Results in other languages
When the system language is non-English and supported by this `AMR` package, some functions will have a translated result. This almost does't take extra time:
```{r}
mo_name("CoNS", language = "en") # or just mo_name("CoNS") on an English system
mo_name("CoNS", language = "es") # or just mo_name("CoNS") on a Spanish system
mo_name("CoNS", language = "nl") # or just mo_name("CoNS") on a Dutch system
run_it <- microbenchmark(en = mo_name("CoNS", language = "en"),
de = mo_name("CoNS", language = "de"),
nl = mo_name("CoNS", language = "nl"),
es = mo_name("CoNS", language = "es"),
it = mo_name("CoNS", language = "it"),
fr = mo_name("CoNS", language = "fr"),
pt = mo_name("CoNS", language = "pt"),
times = 100)
print(run_it, unit = "ms", signif = 4)
```
Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
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