Add greedyOptimizer class

NAMESPACE

@@ -14,7 +14,9 @@ S3method(plot,caretList)
 S3method(plot,caretStack)
 S3method(predict,caretList)
 S3method(predict,caretStack)
+S3method(predict,greedyMSE)
 S3method(print,caretStack)
+S3method(print,greedyMSE)
 S3method(print,summary.caretList)
 S3method(print,summary.caretStack)
 S3method(summary,caretList)
@@ -26,6 +28,7 @@ export(caretList)
 export(caretModelSpec)
 export(caretStack)
 export(extractMetric)
+export(greedyMSE)
 export(is.caretList)
 export(is.caretStack)
 export(permutationImportance)

R/greedyOpt.R

@@ -0,0 +1,86 @@
+#' @title Greedy optimization for MSE
+#' @description Greedy optimization for minimizing the mean squared error.
+#' Works for classification and regression.
+#' @param X A numeric matrix of features.
+#' @param Y A numeric matrix of target values.
+#' @param max_iter An integer scalar of the maximum number of iterations.
+#' @return A list with components:
+#' \item{model_weights}{A numeric matrix of model_weights.}
+#' \item{RMSE}{A numeric scalar of the root mean squared error.}
+#' \item{max_iter}{An integer scalar of the maximum number of iterations.}
+#' @export
+greedyMSE <- function(X, Y, max_iter = 100L) {
+  stopifnot(
+    is.matrix(X), is.matrix(Y),
+    is.numeric(X), is.numeric(Y),
+    is.finite(X), is.finite(Y),
+    nrow(X) == nrow(Y), ncol(X) >= 1L, ncol(Y) >= 1L,
+    is.integer(max_iter), max_iter > 0L
+  )
+
+  model_weights <- matrix(0L, nrow = ncol(X), ncol = ncol(Y))
+  model_update <- diag(ncol(X))
+
+  for (iter in seq_len(max_iter)) {
+    for (y_col in seq_len(ncol(Y))) {
+      target <- Y[, y_col]
+      w <- model_weights[, y_col]
+
+      # Calculate MSE for incrementing each weight
+      w_new <- w + model_update
+      w_new <- w_new / colSums(w_new)
+      predictions <- X %*% w_new
+      MSE <- colMeans((predictions - target)^2.0)
+
+      # Update the best weight
+      best_id <- which.min(MSE)
+      model_weights[best_id, y_col] <- model_weights[best_id, y_col] + 1L
+    }
+  }
+
+  # Output
+  model_weights <- model_weights / colSums(model_weights)
+  rownames(model_weights) <- colnames(X)
+  colnames(model_weights) <- colnames(Y)
+  RMSE <- sqrt(mean((X %*% model_weights - Y)^2.0))
+  out <- list(
+    model_weights = model_weights,
+    RMSE = RMSE,
+    max_iter = max_iter
+  )
+  class(out) <- "greedyMSE"
+  out
+}
+
+#' @title Print method for greedyMSE
+#' @description Print method for greedyMSE objects.
+#' @param x A greedyMSE object.
+#' @param ... Additional arguments. Ignored.
+#' @export
+print.greedyMSE <- function(x, ...) {
+  cat("Greedy MSE\n")
+  cat("RMSE: ", x$RMSE, "\n")
+  cat("Weights:\n")
+  print(x$model_weights)
+}
+
+#' @title Predict method for greedyMSE
+#' @description Predict method for greedyMSE objects.
+#' @param object A greedyMSE object.
+#' @param newdata A numeric matrix of new data.
+#' @param ... Additional arguments. Ignored.
+#' @return A numeric matrix of predictions.
+#' @export
+predict.greedyMSE <- function(object, newdata, ...) {
+  stopifnot(
+    is.matrix(newdata),
+    is.numeric(newdata),
+    is.finite(newdata),
+    ncol(newdata) == nrow(object$model_weights)
+  )
+  out <- newdata %*% object$model_weights
+  if (ncol(out) > 1L) {
+    out <- out / rowSums(out)
+  }
+  out
+}

R/permutationImportance.R

@@ -105,7 +105,6 @@ permutationImportance <- function(
     is.numeric(preds_orig),
     is.finite(preds_orig)
   )
-
   # Error of shuffled variables
   mae_vars <- shuffled_mae(model, newdata, preds_orig, pred_type, shuffle_idx)
 

inst/WORDLIST

@@ -27,6 +27,7 @@ ggplot
 ggplot2
 github
 glm
+greedyMSE
 importances
 kable
 knitr

tests/testthat/test-greedyMSE.R

@@ -0,0 +1,171 @@
+# Helper function to create a simple dataset
+create_dataset <- function(n_samples, n_features = 5L, n_targets = 1L, coef = NULL, noise = 0L) {
+  X <- matrix(stats::runif(n_samples * n_features), nrow = n_samples)
+
+  if (is.null(coef)) {
+    coef <- matrix(stats::runif(n_features * n_targets), nrow = n_features)
+  } else if (is.vector(coef)) {
+    coef <- matrix(coef, ncol = 1L)
+  }
+
+  # Normalize coefficients
+  coef <- apply(coef, 2L, function(col) col / sum(abs(col)))
+
+  Y <- X %*% coef + matrix(stats::rnorm(n_samples * n_targets, 0.0, noise), nrow = n_samples)
+
+  if (n_targets > 1L) {
+    # Ensure all values are positive
+    Y <- Y - min(Y) + 1e-6
+    # Normalize rows to sum to 1
+    Y <- Y / rowSums(Y)
+  }
+
+  list(X = X, Y = Y, coef = coef)
+}
+
+# Create datasets for reuse
+set.seed(42L)
+N <- 100L
+regression_data <- create_dataset(N, noise = 0.1)
+multi_regression_data <- create_dataset(N, n_targets = 3L)
+
+Y_binary <- matrix(as.integer(regression_data$Y > mean(regression_data$Y)), nrow = N)
+Y_multi_binary <- matrix(as.integer(multi_regression_data$Y > mean(multi_regression_data$Y)), nrow = N)
+
+# Test for regression (one col)
+testthat::test_that("greedyMSE works for regression", {
+  model <- greedyMSE(regression_data$X, regression_data$Y)
+  testthat::expect_lt(model$RMSE, stats::sd(regression_data$Y)) # Model should be better than baseline
+  # High correlation with true values
+  testthat::expect_gt(stats::cor(predict(model, regression_data$X), regression_data$Y), 0.8)
+
+  testthat::expect_output(print(model), "Greedy MSE")
+  testthat::expect_output(print(model), "RMSE")
+  testthat::expect_output(print(model), "Weights")
+})
+
+# Test for binary classification (one col)
+testthat::test_that("greedyMSE works for binary classification", {
+  model <- greedyMSE(regression_data$X, Y_binary)
+  predictions <- predict(model, regression_data$X)
+  accuracy <- mean((predictions > 0.5) == Y_binary)
+  testthat::expect_gt(accuracy, 0.7) # Accuracy should be better than random guessing
+
+  testthat::expect_output(print(model), "Greedy MSE")
+  testthat::expect_output(print(model), "RMSE")
+  testthat::expect_output(print(model), "Weights")
+})
+
+# Test for multiple regression (many cols)
+testthat::test_that("greedyMSE works for multiple regression", {
+  model <- greedyMSE(multi_regression_data$X, multi_regression_data$Y)
+
+  testthat::expect_lt(model$RMSE, 0.3)
+  predictions <- predict(model, multi_regression_data$X)
+  correlations <- diag(stats::cor(predictions, multi_regression_data$Y))
+  testthat::expect_gt(min(correlations), 0.7) # High correlation for all targets
+})
+
+# Test for multiclass classification (many cols)
+testthat::test_that("greedyMSE works for multiclass classification", {
+  model <- greedyMSE(multi_regression_data$X, Y_multi_binary)
+  predictions <- predict(model, multi_regression_data$X)
+  accuracy <- mean(apply(predictions, 1L, which.max) == apply(multi_regression_data$Y, 1L, which.max))
+  testthat::expect_gt(accuracy, 0.6) # Accuracy should be better than random guessing
+})
+
+# Edge cases
+testthat::test_that("greedyMSE handles edge cases", {
+  # 1. Single feature
+  data <- create_dataset(100L, 1L, 1L)
+  testthat::expect_equal(greedyMSE(data$X, data$Y)$RMSE, 0.0, tolerance = 1e-6)
+
+  # 2. Perfect multicollinearity
+  X <- matrix(1L, nrow = 100L, ncol = 2L)
+  Y <- X[, 1L] + stats::rnorm(100L, 0.0, 0.1)
+  testthat::expect_equal(greedyMSE(data$X, data$Y)$RMSE, 0.0, tolerance = 1e-6)
+
+  # 3. All zero features
+  X <- matrix(0L, nrow = 100L, ncol = 5L)
+  Y <- matrix(stats::rnorm(100L), ncol = 1L)
+  model <- greedyMSE(X, Y)
+  testthat::expect_equal(model$RMSE, stats::sd(Y), tolerance = 1e-2)
+
+  # 4. Constant target
+  X <- matrix(stats::runif(500L), nrow = 100L)
+  Y <- matrix(rep(1L, 100L), ncol = 1L)
+  model <- greedyMSE(X, Y)
+  testthat::expect_equal(model$RMSE, 0.5, tolerance = 0.1)
+
+  # 5. Very large values
+  X <- matrix(stats::runif(500L, 1.0e6, 1.0e7), nrow = 100L)
+  Y <- matrix(rowSums(X) + stats::rnorm(100L, 0L, 1.0e5), ncol = 1L)
+  model <- greedyMSE(X, Y)
+  pred <- predict(model, X)
+  testthat::expect_gt(cor(pred, Y), 0.4)
+})
+
+# Regression ensembling test
+testthat::test_that("greedyMSE can be used for regression ensembling with GLM, rpart, and RF", {
+  X <- regression_data$X
+  Y <- regression_data$Y
+
+  # GLM
+  glm_model <- stats::glm(Y ~ X, family = stats::gaussian())
+  glm_pred <- stats::predict(glm_model, newdata = data.frame(X))
+
+  # rpart
+  rpart_model <- rpart::rpart(Y ~ X)
+  rpart_pred <- stats::predict(rpart_model, newdata = data.frame(X))
+
+  # greedyMSE
+  greedy_model <- greedyMSE(X, Y)
+  greedy_pred <- predict(greedy_model, X)
+
+  # Ensemble
+  ensemble_X <- cbind(glm_pred, rpart_pred, greedy_pred)
+  ensemble_model <- greedyMSE(ensemble_X, Y)
+
+  # Check if ensemble performs better than the best individual model
+  individual_rmse <- c(
+    sqrt(mean((Y - glm_pred)^2L)),
+    sqrt(mean((Y - rpart_pred)^2L)),
+    greedy_model$RMSE
+  )
+  testthat::expect_lte(ensemble_model$RMSE, min(individual_rmse))
+})
+
+# Classification ensembling test
+testthat::test_that("greedyMSE can be used for classification ensembling with GLM, rpart, and RF", {
+  X <- regression_data$X
+  Y_binary <- as.integer(regression_data$Y > stats::median(regression_data$Y))
+
+  # GLM (logistic regression)
+  glm_model <- stats::glm(Y_binary ~ X, family = stats::binomial())
+  glm_pred <- stats::predict(glm_model, newdata = data.frame(X), type = "response")
+
+  # rpart
+  rpart_model <- rpart::rpart(Y_binary ~ X, method = "class")
+  rpart_pred <- stats::predict(rpart_model, newdata = data.frame(X), type = "prob")[, 2L]
+
+  # Random Forest
+  rf_model <- randomForest::randomForest(X, as.factor(Y_binary))
+  rf_pred <- stats::predict(rf_model, newdata = X, type = "prob")[, 2L]
+
+  # greedyMSE
+  greedy_model <- greedyMSE(X, matrix(Y_binary, ncol = 1L))
+  greedy_pred <- predict(greedy_model, X)
+
+  # Ensemble
+  ensemble_X <- cbind(glm_pred, rpart_pred, rf_pred, greedy_pred)
+  ensemble_model <- greedyMSE(ensemble_X, matrix(Y_binary, ncol = 1L))
+
+  # Check if ensemble performs better than the best individual model
+  individual_rmse <- c(
+    sqrt(mean((Y_binary - glm_pred)^2L)),
+    sqrt(mean((Y_binary - rpart_pred)^2L)),
+    sqrt(mean((Y_binary - rf_pred)^2L)),
+    greedy_model$RMSE
+  )
+  testthat::expect_lte(ensemble_model$RMSE, min(individual_rmse))
+})