intro_R6.RmdThe tisthemachinelearner package provides a simple R
interface to scikit-learn models through Python’s
tisthemachinelearner package. This vignette demonstrates
how to use the package with R’s built-in mtcars
dataset.
First, let’s load the required packages:
library(tisthemachinelearner)
#> Loading required package: reticulate
#> Loading required package: Matrix
library(reticulate)We’ll use the classic mtcars dataset to predict miles
per gallon (mpg) based on other car characteristics:
# Load data
data(mtcars)
head(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# Split features and target
X <- as.matrix(mtcars[, -1]) # all columns except mpg
y <- mtcars[, 1] # mpg column
# Create train/test split
set.seed(42)
train_idx <- sample(nrow(mtcars), size = floor(0.8 * nrow(mtcars)))
X_train <- X[train_idx, ]
X_test <- X[-train_idx, ]
y_train <- y[train_idx]
y_test <- y[-train_idx]
# R6 interface
model <- Regressor$new(model_name = "LinearRegression")
start <- proc.time()[3]
model$fit(X_train, y_train)
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")
#> Time taken: 0.003 seconds
start <- proc.time()[3]
preds <- model$predict(X_test)
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")
#> Time taken: 0.002 seconds
print(preds)
#> [1] 19.97085 20.25755 29.09144 29.61140 19.25911 26.09742 17.92195
# S3 interface
start <- proc.time()[3]
model <- regressor(X_train, y_train, model_name = "LinearRegression")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")
#> Time taken: 0.107 seconds
start <- proc.time()[3]
preds <- predict(model, X_test)
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")
#> Time taken: 0.003 seconds
print(preds)
#> [1] 19.97085 20.25755 29.09144 29.61140 19.25911 26.09742 17.92195This example demonstrates how to:
The tisthemachinelearner package makes it easy to use
scikit-learn models with R data, combining the familiarity of R data
structures with the power of Python’s machine learning ecosystem.
sessionInfo()
#> R version 4.3.3 (2024-02-29)
#> Platform: x86_64-apple-darwin20 (64-bit)
#> Running under: macOS Sonoma 14.2
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: Europe/Paris
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] tisthemachinelearner_0.3.0 Matrix_1.6-5
#> [3] reticulate_1.42.0
#>
#> loaded via a namespace (and not attached):
#> [1] cli_3.6.4 knitr_1.49 rlang_1.1.6 xfun_0.51
#> [5] png_0.1-8 textshaping_1.0.0 jsonlite_2.0.0 htmltools_0.5.8.1
#> [9] ragg_1.3.3 sass_0.4.9 rmarkdown_2.29 grid_4.3.3
#> [13] evaluate_1.0.3 jquerylib_0.1.4 fastmap_1.2.0 yaml_2.3.10
#> [17] lifecycle_1.0.4 compiler_4.3.3 fs_1.6.5 htmlwidgets_1.6.4
#> [21] Rcpp_1.0.14 systemfonts_1.1.0 lattice_0.22-5 digest_0.6.37
#> [25] R6_2.6.1 bslib_0.9.0 tools_4.3.3 pkgdown_2.1.1
#> [29] cachem_1.1.0 desc_1.4.3