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ssr

An R package with Semi-Supervised Regression Methods

https://github.com/enriquegit/ssr

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    DOI references
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    1 of 2 committers (50.0%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (17.9%) to scientific vocabulary

Keywords

data-science machine-learning r regression semi-supervised-learning
Last synced: 6 months ago · JSON representation

Repository

An R package with Semi-Supervised Regression Methods

Basic Info
  • Host: GitHub
  • Owner: enriquegit
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 185 KB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
data-science machine-learning r regression semi-supervised-learning
Created over 6 years ago · Last pushed over 6 years ago
Metadata Files
Readme

README.md

ssr

CRAN\_Status\_Badge Travis build status <!-- badges: end -->

An R package for semi-supervised regression.

The ssr package implements Co-training by Committee and self-learning semi-supervised learning (SSL) algorithms for regression. In semi-supervised learning, algorithms learn model's parameters not only from labeled data but also from unlabeled data. In many applications, it is difficult, expensive, time-consuming, etc. to label data. Thus, semi-supervised methods learn by combining the limited labeled data points and the unlabeled data points.

The ssr package provides the following functionalities:

  • Train Co-training by Committee models.
  • Train self-learning models.
  • Track and plot performance during training.
  • Generate plots to quickly visualize the results.
  • User can specify the base regressors to be used by the Co-training committee and self-learning from the caret package, other packages or custom functions.

Installation

You can install the ssr package from CRAN:

{r} install.packages("ssr")

or you can install the development version from GitHub.

```{r}

install.packages("devtools")

devtools::install_github("enriquegit/ssr") ```

Example

The following example shows how to train a Co-training Committee of two regressors: a linear model and a KNN.

```{r} library(ssr)

dataset <- friedman1 # Load friedman1 dataset.

set.seed(1234)

Prepare de data

split1 <- splittraintest(dataset, pctTrain = 70) split2 <- splittraintest(split1$trainset, pctTrain = 5) L <- split2$trainset U <- split2$testset[, -11] # Remove the labels. testset <- split1$testset

Define list of regressors.

regressors <- list(linearRegression=lm, knn=caret::knnreg)

Fit the model.

model <- ssr("Ytrue ~ .", L, U, regressors = regressors, testdata = testset)

Plot RMSE.

plot(model)

Get the predictions on the testset.

predictions <- predict(model, testset)

Calculate RMSE on the test set.

sqrt(mean((predictions - testset$Ytrue)^2))

```

For detailed explanations and more examples refer to the package vignettes.

Citation

To cite package ssr in publications use:

{r} Enrique Garcia-Ceja (2019). ssr: Semi-Supervised Regression Methods. R package https://CRAN.R-project.org/package=ssr

BibTex entry for LaTeX:

{r} @Manual{enriqueSSR, title = {ssr: Semi-Supervised Regression Methods}, author = {Enrique Garcia-Ceja}, year = {2019}, note = {R package}, url = {https://CRAN.R-project.org/package=ssr}, }

Owner

  • Name: Enrique
  • Login: enriquegit
  • Kind: user
  • Location: Oslo, Norway

GitHub Events

Total
Last Year

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 19
  • Total Committers: 2
  • Avg Commits per committer: 9.5
  • Development Distribution Score (DDS): 0.105
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Enrique Garcia-Ceja e****a@g****m 17
enrique e****x@i****g 2
Committer Domains (Top 20 + Academic)
ieee.org: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 211 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: ssr

Semi-Supervised Regression Methods

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 211 Last month
Rankings
Forks count: 28.8%
Dependent packages count: 29.8%
Stargazers count: 31.7%
Dependent repos count: 35.5%
Average: 41.8%
Downloads: 83.5%
Maintainers (1)
e.g.mx@ieee.org
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.6.0 depends
  • caret * imports
  • e1071 * imports
  • knitr * suggests
  • rmarkdown * suggests
  • tgp * suggests