Package: fastadi 0.1.1

Alex Hayes

fastadi: Self-Tuning Data Adaptive Matrix Imputation

Implements the AdaptiveImpute matrix completion algorithm of 'Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion', <https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2018.1518238>. AdaptiveImpute is useful for embedding sparsely observed matrices, often out performs competing matrix completion algorithms, and self-tunes its hyperparameter, making usage easy.

Authors:Alex Hayes [aut, cre, cph], Juhee Cho [aut], Donggyu Kim [aut], Karl Rohe [aut]

fastadi_0.1.1.tar.gz
fastadi_0.1.1.tar.gz(r-4.5-noble)fastadi_0.1.1.tar.gz(r-4.4-noble)
fastadi_0.1.1.tgz(r-4.4-emscripten)fastadi_0.1.1.tgz(r-4.3-emscripten)
fastadi.pdf |fastadi.html✨
fastadi/json (API)
NEWS

# Install 'fastadi' in R:
install.packages('fastadi', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/rohelab/fastadi/issues10 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

1.70 score 229 downloads 3 exports 11 dependencies

Last updated 3 years agofrom:ef09f2312e. Checks:1 OK, 2 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-linux-x86_64NOTEMar 10 2025
R-4.4-linux-x86_64NOTEMar 10 2025

Exports:adaptive_imputeadaptive_initializecitation_impute

Dependencies:ellipsisgluelatticeloggerLRMF3MatrixRcppRcppArmadilloRcppEigenrlangRSpectra

Citation

To cite package ‘fastadi’ in publications use:

Hayes A, Cho J, Kim D, Rohe K (2022). fastadi: Self-Tuning Data Adaptive Matrix Imputation. R package version 0.1.1, https://CRAN.R-project.org/package=fastadi.

Corresponding BibTeX entry: 

  @Manual{,
    title = {fastadi: Self-Tuning Data Adaptive Matrix Imputation},
    author = {Alex Hayes and Juhee Cho and Donggyu Kim and Karl Rohe},
    year = {2022},
    note = {R package version 0.1.1},
    url = {https://CRAN.R-project.org/package=fastadi},
  }

Readme and manuals

fastadi

fastadi implements the AdaptiveImpute matrix completion algorithm. fastadi is a self-tuning alternative to algorithms such as SoftImpute (implemented in the softImpute package), truncated SVD, maximum margin matrix factorization, and weighted regularized matrix factorization (implemented in the rsparse package). In simulations fastadi often outperforms softImpute by a small margin.

You may find fastadi useful if you are developing embeddings for sparsely observed data, if you are working in natural language processing, or building a recommendation system.

Installation

You can install the released version from CRAN with:

install.packages("fastadi")

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("RoheLab/fastadi")

Example usage

Here we embed users and items in the MovieLens 100K dataset.

library(fastadi)
#> Loading required package: LRMF3
#> Loading required package: Matrix

mf <- adaptive_impute(ml100k, rank = 3L, max_iter = 5L)
#> Warning: 
#> Reached maximum allowed iterations. Returning early.

mf
#> 
#> Adaptively Imputed Low Rank Matrix Factorization
#> ------------------------------------------------
#> 
#> Rank: 3
#> 
#> Rows: 943
#> Cols: 1682
#> 
#> d[rank]: 467.486
#> alpha:   144.663
#> 
#> Components
#> 
#> u: 943 x 3 [matrix] 
#> d: 3      [numeric] 
#> v: 1682 x 3 [matrix]

Note that the vignettes are currently scratch work for reference by the developers and are not yet ready for general consumption.

References

  1. Alex Hayes and Karl Rohe. “Finding Topics in Citation Data”. 2022+

  2. Cho, Juhee, Donggyu Kim, and Karl Rohe. “Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-Observed Low Rank Matrix with Noise.” Statistica Sinica, 2018. https://doi.org/10.5705/ss.202016.0205.

  3. ———. “Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion: Some Statistical and Algorithmic Theory for Adaptive-Impute.” Journal of Computational and Graphical Statistics 28, no. 2 (April 3, 2019): 323–33. https://doi.org/10.1080/10618600.2018.1518238.

  4. Mazumder, Rahul, Trevor Hastie, and Robert Tibshirani. “Spectral Regularization Algorithms for Learning Large Incomplete Matrices.” Journal of Machine Learning Research, 2010. https://web.stanford.edu/~hastie/Papers/mazumder10a.pdf.

You can find the original implementation accompanying these papers here.

Help Manual

Help pageTopics
Create an Adaptive Imputation objectadaptive_imputation
AdaptiveImputeadaptive_impute adaptive_impute.LRMF adaptive_impute.sparseMatrix
AdaptiveInitializeadaptive_initialize adaptive_initialize.sparseMatrix
CitationImputecitation_impute citation_impute.LRMF citation_impute.sparseMatrix