---
title: "Introduction to tisthemachinelearner, S3 interface with calibration"
author: "T. Moudiki"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to tisthemachinelearner, S3 interface with calibration}
  %\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 ridge regression model
start <- proc.time()[3]
reg_ridge <- tisthemachinelearner::regressor(X_train, y_train, "Ridge", 
                      #alphas = c(0.01, 0.1, 1, 10),
                      calibration = TRUE, venv_path = "../venv")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")

# Make predictions
start <- proc.time()[3]
predictions_ridge_splitconformal <- predict(reg_ridge, X_test, method = "splitconformal")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")

start <- proc.time()[3]
predictions_ridge_surrogate <- predict(reg_ridge, X_test, method = "surrogate")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")

start <- proc.time()[3]
predictions_ridge_bootstrap <- predict(reg_ridge, X_test, method = "bootstrap")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")

# Calculate coverage
coverage_ridge_splitconformal <- mean(y_test >= predictions_ridge_splitconformal[, "lwr"] & y_test <= predictions_ridge_splitconformal[, "upr"])
coverage_ridge_surrogate <- mean(y_test >= predictions_ridge_surrogate[, "lwr"] & y_test <= predictions_ridge_surrogate[, "upr"])
coverage_ridge_bootstrap <- mean(y_test >= predictions_ridge_bootstrap[, "lwr"] & y_test <= predictions_ridge_bootstrap[, "upr"])

cat("Ridge Regression Split Conformal Coverage:", coverage_ridge_splitconformal, "\n")
cat("Ridge Regression Surrogate Coverage:", coverage_ridge_surrogate, "\n")
cat("Ridge Regression Bootstrap Coverage:", coverage_ridge_bootstrap, "\n")
```

## Session Info

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