A new version of nnetsauce, v0.10.0, is available on Pypi (for Python)
and GitHub (for R). To those who’ve never heard about nnetsauce: it’s a package for supervised learning (as of February 2022, you can solve regression, classification, and time series forecasting problems with nnetsauce) based on various combinations of components \(g(XW+b)\), with:
- \(X\), a matrix of explanatory variables or multivariate (univariate works too, but hasn’t been tested enough yet) time series
- \(W\), a matrix which contains quasirandom numbers, that help in achieving a kind of automated feature engineering (\(XW\))
- \(b\), a bias term
- \(g\), an activation function, used for model nonlinearity (otherwise, without it, the model would be linear)
For example, here is how nnetsauce can be used to create a nonlinear model from a linear model:
In nnetsauce v0.10.0, the most important change is a – potentially breaking – change in the API: classes’ attributes (mostly, computed in method fit) which do not belong to the interface have been renamed with a suffix “_”. As in scikit-learn.
Multiple Python examples can be found on GitHub, along with notebooks. Here is an example of use of the package in R (on Ubuntu):
library(devtools)
devtools::install_github("Techtonique/nnetsauce/R-package")
library(nnetsauce)
set.seed(123)
(n <- nrow(iris))
(index_train <- sample.int(n, size = floor(0.8*n), replace = FALSE))
X_train <- as.matrix(iris[index_train, 1:4])
y_train <- as.integer(iris[index_train, 5]) - 1L
X_test <- as.matrix(iris[-index_train, 1:4])
y_test <- as.integer(iris[-index_train, 5]) - 1L
obj <- nnetsauce::Ridge2MultitaskClassifier()
print(obj$get_params())
obj$fit(X_train, y_train)
print(obj$score(X_test, y_test)) # accuracy
R session info:
> sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] nnetsauce_0.10.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8 magrittr_2.0.2 rappdirs_0.3.3 munsell_0.5.0 colorspace_2.0-2
[6] here_1.0.1 lattice_0.20-45 R6_2.5.1 rlang_1.0.1 fansi_1.0.2
[11] tools_4.1.2 grid_4.1.2 gtable_0.3.0 png_0.1-7 utf8_1.2.2
[16] cli_3.1.1 ellipsis_0.3.2 rprojroot_2.0.2 tibble_3.1.6 lifecycle_1.0.1
[21] crayon_1.4.2 Matrix_1.3-4 ggplot2_3.3.5 vctrs_0.3.8 glue_1.6.1
[26] compiler_4.1.2 pillar_1.7.0 scales_1.1.1 reticulate_1.24 jsonlite_1.7.3
[31] pkgconfig_2.0.3