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(v0.7.1.9073) as.mo() self-learning algorithm

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dr. M.S. (Matthijs) Berends
2019-09-15 22:57:30 +02:00
parent cd178ee569
commit 398c5bdc4f
31 changed files with 1030 additions and 2360 deletions
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52
vignettes/benchmarks.Rmd
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@ -20,7 +20,7 @@ knitr::opts_chunk$set(
comment = "#",
fig.width = 7.5,
fig.height = 4.5,
dpi = 150
dpi = 75
)
```
@ -110,26 +110,40 @@ That takes `r round(mean(T.islandicus$time, na.rm = TRUE) / mean(S.aureus$time,
In the figure below, we compare *Escherichia coli* (which is very common) with *Prevotella brevis* (which is moderately common) and with *Thermus islandicus* (which is uncommon):
```{r, echo = FALSE}
ggplot.bm(
microbenchmark(as.mo("Escherichia coli"),
as.mo("E. coli"),
times = 10), title = "Very common")
# ggplot.bm(
# microbenchmark(as.mo("Escherichia coli"),
# as.mo("E. coli"),
# times = 10), title = "Very common")
#
# ggplot.bm(
# microbenchmark(as.mo("Prevotella brevis"),
# as.mo("P. brevis"),
# times = 10), title = "Moderately common")
#
# ggplot.bm(
# microbenchmark(as.mo("Thermus islandicus"),
# as.mo("T. islandicus"),
# times = 10), title = "Uncommon")
ggplot.bm(
microbenchmark(as.mo("Prevotella brevis"),
as.mo("P. brevis"),
times = 10), title = "Moderately common")
ggplot.bm(
microbenchmark(as.mo("Thermus islandicus"),
as.mo("T. islandicus"),
times = 10), title = "Uncommon")
par(mar = c(5, 16, 4, 2))
boxplot(microbenchmark(
'as.mo("Thermus islandicus")' = as.mo("Thermus islandicus"),
'as.mo("Prevotella brevis")' = as.mo("Prevotella brevis"),
'as.mo("Escherichia coli")' = as.mo("Escherichia coli"),
'as.mo("T. islandicus")' = as.mo("T. islandicus"),
'as.mo("P. brevis")' = as.mo("P. brevis"),
'as.mo("E. coli")' = as.mo("E. coli"),
times = 10),
horizontal = TRUE, las = 1, unit = "s", log = FALSE,
xlab = "", ylab = "Time in seconds", ylim = c(0, 0.5),
main = "Benchmarks per prevalence")
```
```{r, echo = FALSE, eval = FALSE}
# In reality, the `as.mo()` functions **learns from its own output to speed up determinations for next times**. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use `as.mo()` yourself:
In reality, the `as.mo()` functions **learns from its own output to speed up determinations for next times**. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use `as.mo()` yourself:
clean_mo_history()
```{r, echo = FALSE}
clear_mo_history()
par(mar = c(5, 16, 4, 2))
boxplot(microbenchmark(
'as.mo("Thermus islandicus")' = as.mo("Thermus islandicus", force_mo_history = TRUE),
@ -142,10 +156,10 @@ boxplot(microbenchmark(
horizontal = TRUE, las = 1, unit = "s", log = FALSE,
xlab = "", ylab = "Time in seconds", ylim = c(0, 0.5),
main = "Benchmarks per prevalence")
# The highest outliers are the first times. All next determinations were done in only thousands of seconds. For now, learning only works per session. If R is closed or terminated, the algorithms reset. This will probably be resolved in a next version.
```
The highest outliers are the first times. All next determinations were done in only thousands of seconds.
Uncommon microorganisms take a lot more time than common microorganisms. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: **repetitive results** and **already precalculated results**.
### Repetitive results