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Formula vs matricial interfaces

library(unifiedml)
## Loading required package: doParallel
## Loading required package: foreach
## Loading required package: iterators
## Loading required package: parallel
## Loading required package: R6
# Example 1: lm with matrix interface (including factors)
demo_lm_matrix <- function() {
  cat("\n=== Example 1: lm with matrix interface ===\n")
  
  lm_matrix <- unifiedml::formula_to_matrix(lm)  # Uses generic predict()
  
  # Create data with factor and numeric columns
  X <- data.frame(
    wt = mtcars$wt,
    hp = mtcars$hp,
    cyl = factor(mtcars$cyl)
  )
  y <- mtcars$mpg
  weights <- rep(1, nrow(X))
  
  model <- lm_matrix$fit(X, y, weights = weights)
  preds <- lm_matrix$predict(model, X[1:5, ])
  
  cat("Predictions:\n")
  print(preds)
  cat("\nCoefficients:\n")
  print(coef(model))
}

# Example 2: glmnet with formula interface (with factors)
demo_glmnet_formula <- function() {
  cat("\n=== Example 2: glmnet with formula interface ===\n")
  
  if (!requireNamespace("glmnet", quietly = TRUE)) {
    cat("glmnet package not installed, skipping example\n")
    return(invisible(NULL))
  }
  
  glmnet_formula <- unifiedml::matrix_to_formula(
    fit_func = glmnet::glmnet,
    predict_func = function(model, newX, ...) {
      # Wrapper to provide default s parameter
      glmnet::predict.glmnet(model, newx = newX, s = 0.01, ...)
    }
  )
  
  # Formula with factor - model.matrix will auto-create dummies
  model <- glmnet_formula$fit(mpg ~ wt + hp + factor(cyl), data = mtcars)
  preds <- glmnet_formula$predict(model, newdata = mtcars[1:5, ])
  
  cat("Predictions:\n")
  print(preds)
}

# Example 3: Special characters in column names
demo_special_names <- function() {
  cat("\n=== Example 3: Column names with special characters ===\n")
  
  lm_matrix <- unifiedml::formula_to_matrix(lm)
  
  # Create problematic column names
  X <- data.frame(
    `x-1` = mtcars$wt,      # Dash
    `a:b` = mtcars$hp,       # Colon
    `log(x)` = log(mtcars$disp),  # Parentheses
    check.names = FALSE
  )
  y <- mtcars$mpg
  
  model <- lm_matrix$fit(X, y)
  preds <- lm_matrix$predict(model, X[1:3, ])
  
  cat("Predictions:\n")
  print(preds)
  cat("\nModel handled special column names correctly!\n")
}

# Run examples (uncomment to test)
demo_lm_matrix()
## 
## === Example 1: lm with matrix interface ===
## Predictions:
##        1        2        3        4        5 
## 21.60851 20.79725 26.31500 19.71558 17.67011 
## 
## Coefficients:
## (Intercept)          wt          hp        cyl6        cyl8 
## 35.84599532 -3.18140405 -0.02311981 -3.35902490 -3.18588444
demo_glmnet_formula()
## 
## === Example 2: glmnet with formula interface ===
## Predictions:
##                     s=0.01
## Mazda RX4         21.65238
## Mazda RX4 Wag     20.83774
## Datsun 710        26.26479
## Hornet 4 Drive    19.75155
## Hornet Sportabout 17.70497
demo_special_names()
## 
## === Example 3: Column names with special characters ===
## Predictions:
##        1        2        3 
## 22.92749 22.38846 25.67092 
## 
## Model handled special column names correctly!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.