Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jchiquet/aricode
R package for computation of (adjusted) rand-index and other such scores
https://github.com/jchiquet/aricode
bucket-sort clustering clustering-comparison-measures
Last synced: 29 days ago
JSON representation
R package for computation of (adjusted) rand-index and other such scores
- Host: GitHub
- URL: https://github.com/jchiquet/aricode
- Owner: jchiquet
- Created: 2016-09-06T11:46:09.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-02-27T18:31:44.000Z (10 months ago)
- Last Synced: 2024-10-28T17:24:31.977Z (about 2 months ago)
- Topics: bucket-sort, clustering, clustering-comparison-measures
- Language: R
- Homepage: https://jchiquet.github.io/aricode
- Size: 878 KB
- Stars: 25
- Watchers: 5
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- Authors: AUTHORS
Awesome Lists containing this project
README
---
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.path = "man/figures/"
)
```
# aricode
[](https://github.com/jchiquet/aricode/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=aricode)
[](https://codecov.io/gh/jchiquet/aricode)
[](https://www.tidyverse.org/lifecycle/#stable)
[](https://github.com/jchiquet/aricode/commits/master)
A package for efficient computations of standard clustering comparison measures
## Installation
Stable version on the [CRAN](https://cran.rstudio.com/web/packages/aricode/).
```{r install_cran, eval = FALSE}
install.packages("aricode")
```
The development version is available via:
```{r install_github, eval = FALSE}
devtools::install_github("jchiquet/aricode")
```
## Description
Computation of measures for clustering comparison (ARI, AMI, NID and even the $\chi^2$ distance) are usually based on the contingency table. Traditional implementations (e.g., function `adjustedRandIndex` of package **mclust**) are in $\Omega(n + u v)$ where
- $n$ is the size of the vectors the classifications of which are to be compared,
- $u$ and $v$ are the respective number of classes in each vectors.
In **aricode** we propose an implementation, based on radix sort, that is in $\Theta(n)$ in time and space.
Importantly, the complexity does not depends on $u$ and $v$.
Our implementation of the ARI for instance is one or two order of magnitude faster than some standard implementation in `R`.
## Available measures and functions
The functions included in aricode are:
- `ARI`: computes the adjusted rand index
- `Chi2`: computes the Chi-square statistics
- `MARI/MARIraw`: computes the modified adjusted rand index (Sundqvist et al, in preparation)
- `NVI`: computes the the normalized variation information
- `NID`: computes the normalized information distance
- `NMI`: computes the normalized mutual information
- `AMI`: computes the adjusted mutual information
- `expected_MI`: computes the expected mutual information
- `entropy`: computes the conditional and joint entropies
- `clustComp`: computes all clustering comparison measures at once
## Timings
Here are some timings to compare the cost of computing the adjusted Rand Index with **aricode** or with the commonly used function `adjustedRandIndex` of the *mclust* package: the cost of the latter can be prohibitive for large vectors:
```{r timings_function, echo=FALSE, message=FALSE, warning=FALSE}
library(aricode)
library(mclust)
library(ggplot2)
time.aricode <- function(times, c1, c2){
replicate(times, system.time(ARI(c1, c2))[3])
}
time.mclust <- function(times, c1, c2){
replicate(times, system.time(mclust::adjustedRandIndex(c1, c2))[3])
}
time.method <- function(times, c1, c2, n){
rbind(
data.frame(time = time.aricode(times, c1, c2), expr = "aricode", n = n),
data.frame(time = time.mclust(times, c1, c2), expr = "mclust", n = n)
)
}
# with similar classif, number of classes grows with n
sim.timings <- function(n, times = 10) {
c1 <- sample(1:(n/200), n, replace=TRUE);c2 <- c1;
i_change <- sample(1:n, n/50, replace=FALSE)
c2[i_change] <- c2[rev(i_change)]
out <- time.method(times, c1, c2, n)
data.frame(time=out$time, method=out$expr, n = n)
}
```
```{r timings_run, echo=FALSE, message=FALSE, warning=FALSE, cache=TRUE}
# with similar classif, number of classes grows with n
ns <- sort(c(200 * 2^(3:14), 150 * 2^(3:15)))
timings <- do.call("rbind", lapply(ns, sim.timings))
```
```{r timings_plot, echo=FALSE, message=FALSE, warning=FALSE}
p.timings <- ggplot(timings, aes(x=n, y=time, colour=method)) +
geom_smooth(data = dplyr::filter(timings, n > 1e4), method = "lm") + geom_point(size=0.25, alpha=0.9) + labs(y="time (sec.)") +
scale_x_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
) +
scale_y_log10(breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))) +
annotation_logticks()
p.timings + ggtitle("number of classes grows with n") + theme_bw()
```