https://github.com/alrobles/plus
Positive and unlabeled Learning from Unbalanced cases and Sparse structures
Science Score: 49.0%
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Low similarity (10.7%) to scientific vocabulary
Last synced: 6 months ago
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Repository
Positive and unlabeled Learning from Unbalanced cases and Sparse structures
Basic Info
- Host: GitHub
- Owner: alrobles
- License: gpl-3.0
- Language: R
- Default Branch: main
- Homepage: https://alrobles.github.io/plus/
- Size: 25.3 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Created over 1 year ago
· Last pushed 7 months ago
Metadata Files
Readme
License
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# plus
Positive and unlabeled Learning from Unbalanced cases and Sparse structures (PLUS)
## Installation
You can install the development version of plus from [GitHub](https://github.com/alrobles/PLUS) with:
``` r
# install.packages("devtools")
devtools::install_github("alrobles/plus")
```
The goal of plus is implement the Positive and unlabeled Learning from Unbalanced cases and Sparse structures (PLUS) algorithm, originally published in:
[PLoS](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009956)
## Example
The use of plus is very simple:
```{r example, warning=FALSE}
library(plus)
data(binexample)
x = binexample$x
y = binexample$y
plus(x, y)
```
You can predict new data:
```{r predict, warning=FALSE}
data(binexample)
x = binexample$x
y = binexample$y
train <- sample(seq(length(y)), 85, replace = FALSE)
x_train <- x[train, ]
x_test <- x[-train, ]
y_train <- y[train]
y_test <- y[-train]
fit <- plus(x_train, y_train)
predict(fit, newx = x_test)
```
Additionally you can get evaluation performance metrics for the plus models
```{r roc, echo = FALSE, warning=FALSE}
#only Area under ROC curve
data(binexample)
x = binexample$x
y = binexample$y
train <- sample(seq(length(y)), 75, replace = FALSE)
x_train <- x[train, ]
x_test <- x[-train, ]
y_train <- y[train]
y_test <- y[-train]
fit <- plus(x_train, y_train)
#Get the AUC
get_auc(fit, x_test, y_test)
#get different performance metrics
assess(fit, newx = x_test, newy = y_test)
```
The plus algorithm can be applied in different datasets
Owner
- Name: Angel Luis Robles Fernández
- Login: alrobles
- Kind: user
- Location: Xalapa Mexico
- Company: Vida Analytics
- Website: https://vidaanalytics.com/
- Repositories: 60
- Profile: https://github.com/alrobles
PhD student at Arizona State University
GitHub Events
Total
- Release event: 1
- Push event: 1
- Create event: 1
Last Year
- Release event: 1
- Push event: 1
- Create event: 1
Dependencies
DESCRIPTION
cran
- R >= 2.10 depends
- Matrix * imports
- glmnet * imports
- stats * imports
- survival * imports
.github/workflows/pkgdown.yaml
actions
- JamesIves/github-pages-deploy-action v4.5.0 composite
- actions/checkout v4 composite
- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite