---
title: "Introduction to tisthemachinelearner, S3 interface booster"
author: "T. Moudiki"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to tisthemachinelearner, S3 interface booster}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## 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:

```{r}
library(reticulate)
library(tisthemachinelearner)
```

## Data Preparation

We'll use the classic `mtcars` dataset to predict miles per gallon (mpg) based on other car characteristics:

```{r, eval=FALSE}
# Load data

# Split features and target
X <- as.matrix(MASS::Boston[, -14])  # all columns except mpg
y <- MASS::Boston[, 14]              # mpg column

# Create train/test split
set.seed(42)
train_idx <- sample(nrow(X), size = floor(0.8 * nrow(X)))
X_train <- X[train_idx, ]
X_test <- X[-train_idx, ]
y_train <- y[train_idx]
y_test <- y[-train_idx]
```

## Ridge Regression with Cross-Validation

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

```{r, eval=FALSE}
# Fit booster model
time <- proc.time()[3]
reg_booster <- tisthemachinelearner::booster(X_train, y_train, "ExtraTreeRegressor",
                                            n_estimators = 100L,
                                            learning_rate = 0.1,
                                            show_progress = TRUE,
                                            verbose = TRUE, 
                                            venv_path = "../venv")
time <- proc.time()[3] - time
cat("Time taken:", time, "seconds\n")

# Make predictions
time <- proc.time()[3]
predictions <- predict(reg_booster, X_test)
time <- proc.time()[3] - time
cat("Time taken:", time, "seconds\n")

# RMSE 
rmse <- sqrt(mean((y_test - predictions)^2))
cat("RMSE:", rmse, "\n")
```

## Session Info

```{r}
sessionInfo()
```