Introduction to tisthemachinelearner, S3 interface

Introduction

The 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.

Setup

First, let’s load the required packages:

library(tisthemachinelearner)
library(reticulate)

Data Preparation

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]

Linear Regression

Let’s start with a simple linear regression model:

# Fit linear regression model
reg_linear <- regressor(X_train, y_train, "LinearRegression")

# Make predictions
predictions <- predict(reg_linear, X_test)

# Calculate RMSE
rmse <- sqrt(mean((predictions - y_test)^2))
cat("Linear Regression RMSE:", rmse, "\n")
#> Linear Regression RMSE: 4.876167

# Compare actual vs predicted values
results <- data.frame(
  Actual = y_test,
  Predicted = predictions,
  Difference = y_test - predictions
)
print(results)
#>   Actual Predicted Difference
#> 1   18.1  19.97085  -1.870846
#> 2   24.4  20.25755   4.142451
#> 3   30.4  29.09144   1.308558
#> 4   33.9  29.61140   4.288603
#> 5   15.2  19.25911  -4.059112
#> 6   15.8  26.09742 -10.297416
#> 7   19.7  17.92195   1.778054

Ridge Regression with Cross-Validation

Now let’s try Ridge regression with cross-validation for hyperparameter tuning:

# Fit ridge regression model
reg_ridge <- regressor(X_train, y_train, "RidgeCV", 
                      alphas = c(0.01, 0.1, 1, 10))

# Make predictions
predictions_ridge <- predict(reg_ridge, X_test)

# Calculate RMSE
rmse_ridge <- sqrt(mean((predictions_ridge - y_test)^2))
cat("Ridge Regression RMSE:", rmse_ridge, "\n")
#> Ridge Regression RMSE: 3.822053

Visualization

Let’s visualize how well our predictions match the actual values:

# Create scatter plot of actual vs predicted values
par(mfrow = c(1, 2))

# Linear Regression plot
plot(y_test, predictions,
     xlab = "Actual MPG",
     ylab = "Predicted MPG",
     main = "Linear Regression",
     pch = 16)
abline(a = 0, b = 1, col = "red", lty = 2)

# Ridge Regression plot
plot(y_test, predictions_ridge,
     xlab = "Actual MPG",
     ylab = "Predicted MPG",
     main = "Ridge Regression",
     pch = 16)
abline(a = 0, b = 1, col = "red", lty = 2)

Model Comparison

Compare the performance of both models:

comparison <- data.frame(
  Model = c("Linear Regression", "Ridge Regression"),
  RMSE = c(rmse, rmse_ridge)
)
print(comparison)
#>               Model     RMSE
#> 1 Linear Regression 4.876167
#> 2  Ridge Regression 3.822053

Conclusion

This example demonstrates how to:

Prepare R data for use with the regressor
Fit different types of regression models
Make predictions on new data
Calculate and compare model performance
Visualize results

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.

Session Info

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] reticulate_1.41.0          tisthemachinelearner_0.3.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] cli_3.6.4         knitr_1.49        rlang_1.1.5       xfun_0.50        
#>  [5] png_0.1-8         textshaping_1.0.0 jsonlite_1.9.0    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          Rcpp_1.0.14      
#> [21] htmlwidgets_1.6.4 systemfonts_1.1.0 lattice_0.22-5    digest_0.6.37    
#> [25] R6_2.6.1          bslib_0.9.0       Matrix_1.6-5      tools_4.3.3      
#> [29] pkgdown_2.1.1     cachem_1.1.0      desc_1.4.3

T. Moudiki

2025-02-26