nnetsauce

Randomized and quasi-randomized nnetworks for supervised learning and multivariate time series forecasting

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This is the R version of Python’s Techtonique/nnetsauce. See these posts for more details.

If you encounter errors on Windows, envisage using the Windows Subsystems for Linux.

Install 

# pip install uv # if necessary
uv venv venv
source venv/bin/activate
uv pip install pip nnetsauce

From GitHub

install.packages("remotes")
remotes::install_github("Techtonique/nnetsauce_r")

Examples

Keep in mind that there are many other models implemented. See these posts.

Read vignettes

Classification 

library(nnetsauce)

 set.seed(123)
 X <- as.matrix(iris[, 1:4])
 y <- as.integer(iris$Species) - 1L

 (index_train <- base::sample.int(n = nrow(X),
                                  size = floor(0.8*nrow(X)),
                                  replace = FALSE))
 X_train <- X[index_train, ]
 y_train <- y[index_train]
 X_test <- X[-index_train, ]
 y_test <- y[-index_train]

 obj <- nnetsauce::GLMClassifier(venv_path = "../venv")
 obj$fit(X_train, y_train)
 print(obj$score(X_test, y_test))

Regression 

X <- MASS::Boston[,-14] # dataset has an ethical problem
y <- MASS::Boston$medv

set.seed(13)
(index_train <- base::sample.int(n = nrow(X),
                                 size = floor(0.8*nrow(X)),
                                 replace = FALSE))
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]

obj <- LazyRegressor()
res <- obj$fit(X_train, X_test, y_train, y_test)
print(res[[1]])

Time series 

set.seed(123)
X <- matrix(rnorm(300), 100, 3)

(index_train <- base::sample.int(n = nrow(X),
                                 size = floor(0.8*nrow(X)),
                                 replace = FALSE))
X_train <- data.frame(X[index_train, ])
X_test <- data.frame(X[-index_train, ])

obj <- LazyMTS()

res <- obj$fit(X_train, X_test)
print(res[[1]])