rtemis Machine Learning and Visualization

A platform for advanced Machine Learning research and applications.
The goal of rtemis is to make data science efficient and accessible with no compromise on flexibility.

Documentation

• Documentation and vignettes

Requirements

R version 4.1 or higher

Installation

Install rtemis usikg pak, remotes, or devtools:

pak::pkg_install("egenn/rtemis")

or

remotes::install_github("egenn/rtemis")

or

devtools::install_github("egenn/rtemis")

See here for more setup and installation instructions.

Note: Make sure to keep your installation updated by running remotes::install_github("egenn/rtemis") regularly: it will only proceed if there are updates available.

30-second intro to rtemis

Install dependencies if they are not already installed:

packages <- c("future", "ranger")
.add <- !packages %in% installed.packages()
install.packages(packages[.add])

Get cross-validated random forest performance on the iris dataset:

library(rtemis)
mod <- train(iris)

What's new

We are working towards the 1.0 release, which will feature updates to the API as well as the backend, and preparing for CRAN submission. This will be accompanied by expansion of the documentation.

The Python and Julia ports, rtemispy and Rtemis.jl, which are not yet publicly available, are in parallel development. With the upcoming 1.0 release of rtemis, the aim is to offer a unified API across all three languages.

0.95.5

• Transition to train() complete; elevate()andelevate1()` removed
• Updated s_LightGBM and s_LightRuleFit for better handling of categorical variables
• Updated tests
• General cleanup in preparation for 1.0 release & CRAN submission

0.95.0 in rcpp branch

Adding support for Rcpp.

0.91

Switched the main parallelizable functions (resLearn for outer resamples; gridSearchlearn for inner resamples) to use future for parallelization and progressr for progress reporting.

0.90

Multiple additions and updates.
Major change: Renamed modeling and visualization functions to substitute dots with underscores:

• Supervised learning: s. => s_
• Decomposition: d. => d_
• Clustering: u. => c_
• Cross-decomposition: x. => x_
• Static graphics: mplot3. => mplot3_; mplot. => mplot_; gplot3. => gplot3_
• Interactive graphics: dplot3. => dplot3_

0.82

• Themes: New darkgray theme now always on whether you like it or not - jk: it's the new default but you can always set your own default using e.g. options(rt.theme = "lightgrid"). Also, new lightgray theme.
• New option to set default plotting font: e.g. options(rt.font = "Inter")
• Many improvements / additions to dplot3* functions.
• Plenty more I haven't had a chance to document here

0.80.0

An accumulation of updates and added functionality, algorithms, graphics.
Majority of mplot3 and dplot3 functions now work with the new theme system provided by theme_* functions like theme_lightgrid and theme_darkgrid.

0.79

07-02-2019: "Super Papaya" Release out

0.78

04-02-2019: rtemis moved to public repo

Features

• Visualization

  • Static: mplot3 family (base graphics)
  • Dynamic: dplot3 family (plotly)

• Unsupervised Learning

  • Clustering: c*_
  • Decomposition: d*_

• Supervised Learning

  • Classification, Regression, Survival Analysis: s*_

• Cross-Decomposition

  • Sparse Canonical Correlation / Sparse Decomposition: x*_

• Meta-Models

  • Model Stacking: metaMod()
  • Modality Stacking: metaFeat()
  • Group-weighted Stacking: metaGroup()

  (metaFeat and metaGroup have been removed for updating)

rtemislive

rtemislive is rtemis' web interface / GUI. It makes advanced visualization and modeling instantly accessible by all. It is currently available for beta testing at UCSF, and will be made publicly available once funding is secured for a hosting server.

VS Code theme

Get the rtemis-dark VS Code theme.

Recommended font is Fira Code with its pretty ligatures.

©2022 E.D. Gennatas MBBS AICSM PhD