Disallow factor predictions

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: hstats
 Title: Interaction Statistics
-Version: 1.1.2
+Version: 1.2.0
 Authors@R: 
     person("Michael", "Mayer", , "[email protected]", role = c("aut", "cre"))
 Description: Fast, model-agnostic implementation of different H-statistics

NEWS.md

@@ -1,3 +1,14 @@
+# hstats 1.2.0
+
+## Major changes
+
+- Factor-valued predictions are no longer possible.
+- Consequently, also removed "classification_error" loss.
+
+## Minor changes
+
+- Code simplifications.
+
 # hstats 1.1.2
 
 ## ICE plots

R/average_loss.R

@@ -22,25 +22,19 @@
 #'   or as discrete vector with corresponding levels. 
 #'   The latter case is turned into a dummy matrix by a fast version of
 #'   `model.matrix(~ as.factor(y) + 0)`.
-#' - "classification_error": Misclassification error. Both the 
-#'   observed values `y` and the predictions can be character/factor. This
-#'   loss function can be used in non-probabilistic classification settings.
-#'   BUT: Probabilistic classification (with "mlogloss") is clearly preferred in most
-#'   situations.
 #' - A function with signature `f(actual, predicted)`, returning a numeric 
 #'   vector or matrix of the same length as the input.
 #'
 #' @inheritParams hstats
 #' @param y Vector/matrix of the response, or the corresponding column names in `X`.
 #' @param loss One of "squared_error", "logloss", "mlogloss", "poisson",
-#'   "gamma", "absolute_error", "classification_error". Alternatively, a loss function 
+#'   "gamma", or "absolute_error". Alternatively, a loss function 
 #'   can be provided that turns observed and predicted values into a numeric vector or 
 #'   matrix of unit losses of the same length as `X`.
 #'   For "mlogloss", the response `y` can either be a dummy matrix or a discrete vector. 
 #'   The latter case is handled via a fast version of `model.matrix(~ as.factor(y) + 0)`.
-#'   For "classification_error", both predictions and responses can be non-numeric.
-#'   For "squared_error", both predictions and responses can be factors with identical
-#'   levels. In this case, squared error is evaulated for each one-hot-encoded column.
+#'   For "squared_error", the response can be a factor with levels in column order of
+#'   the predictions. In this case, squared error is evaluated for each one-hot-encoded column.
 #' @param agg_cols Should multivariate losses be summed up? Default is `FALSE`.
 #'   In combination with the squared error loss, `agg_cols = TRUE` gives
 #'   the Brier score for (probabilistic) classification.
@@ -116,7 +110,8 @@ average_loss.default <- function(object, X, y,
 #' @describeIn average_loss Method for "ranger" models.
 #' @export
 average_loss.ranger <- function(object, X, y, 
-                                pred_fun = function(m, X, ...) stats::predict(m, X, ...)$predictions,
+                                pred_fun = function(m, X, ...)
+                                  stats::predict(m, X, ...)$predictions,
                                 loss = "squared_error",
                                 agg_cols = FALSE,
                                 BY = NULL, by_size = 4L, 

R/hstats.R

@@ -188,7 +188,7 @@ hstats.default <- function(object, X, v = NULL,
   }
 
   # Predictions ("F" in Friedman and Popescu) always calculated (cheap)
-  f <- wcenter(prepare_pred(pred_fun(object, X, ...), ohe = TRUE), w = w)
+  f <- wcenter(prepare_pred(pred_fun(object, X, ...)), w = w)
   mean_f2 <- wcolMeans(f^2, w = w)  # A vector
 
   # Initialize first progress bar

R/ice.R

@@ -117,7 +117,6 @@ ice.default <- function(object, v, X, pred_fun = stats::predict,
     grid = grid, 
     pred_fun = pred_fun, 
     pred_only = FALSE,
-    ohe = TRUE,
     ...
   )
   pred <- ice_out[["pred"]]

R/losses.R

@@ -1,18 +1,43 @@
+#' Input Checks for Losses
+#' 
+#' Internal function with general input checks.
+#' 
+#' @noRd
+#' @keywords internal
+#'
+#' @param actual Actual values.
+#' @param predicted Predictions.
+#' @returns `TRUE`
+check_loss <- function(actual, predicted) {
+  stopifnot(
+    is.vector(actual) || is.matrix(actual),
+    is.vector(predicted) || is.matrix(predicted),
+    is.numeric(actual) || is.logical(actual),
+    is.numeric(predicted) || is.logical(predicted),
+    NROW(actual) == NROW(predicted),
+    NCOL(actual) == 1L || NCOL(actual) == NCOL(predicted)
+  )
+  return(TRUE)
+}
+
 #' Squared Error Loss
 #' 
 #' Internal function. Calculates squared error.
 #' 
 #' @noRd
 #' @keywords internal
 #'
-#' @param actual A numeric vector/matrix, or factor.
-#' @param predicted A numeric vector/matrix, or factor.
+#' @param actual A numeric vector or matrix, or a factor with levels in the same order 
+#'   as the column names of `predicted`.
+#' @param predicted A numeric vector or matrix.
 #' @returns Vector or matrix of numeric losses.
 loss_squared_error <- function(actual, predicted) {
-  actual <- drop(prepare_pred(actual, ohe = TRUE))
-  predicted <- prepare_pred(predicted, ohe = TRUE)
-
-  (actual - predicted)^2
+  if (is.factor(actual)) {
+    actual <- fdummy(actual)
+  }
+  check_loss(actual, predicted)
+
+  return((drop(actual) - predicted)^2)
 }
 
 #' Absolute Error Loss
@@ -26,13 +51,9 @@ loss_squared_error <- function(actual, predicted) {
 #' @param predicted A numeric vector/matrix.
 #' @returns Vector or matrix of numeric losses.
 loss_absolute_error <- function(actual, predicted) {
-  actual <- drop(prepare_pred(actual))
-  predicted <- prepare_pred(predicted)
-  if (is.factor(actual) || is.factor(predicted)) {
-    stop("Absolute loss does not make sense for factors.")
-  }
-
-  abs(actual - predicted)
+  check_loss(actual, predicted)
+
+  return(abs(drop(actual) - predicted))
 }
 
 #' Poisson Deviance Loss
@@ -46,20 +67,18 @@ loss_absolute_error <- function(actual, predicted) {
 #' @param predicted A numeric vector/matrix with non-negative values.
 #' @returns Vector or matrix of numeric losses.
 loss_poisson <- function(actual, predicted) {
-  actual <- drop(prepare_pred(actual))
-  predicted <- prepare_pred(predicted)
-  if (is.factor(actual) || is.factor(predicted)) {
-    stop("Poisson loss does not make sense for factors.")
-  }
+  check_loss(actual, predicted)
   stopifnot(
     all(predicted >= 0),
     all(actual >= 0)
   )
 
+  actual <- drop(actual)
+
   out <- predicted
   p <- actual > 0
   out[p] <- (actual * log(actual / predicted) - (actual  - predicted))[p]
-  2 * out
+  return(2 * out)
 }
 
 #' Gamma Deviance Loss
@@ -73,34 +92,15 @@ loss_poisson <- function(actual, predicted) {
 #' @param predicted A numeric vector/matrix with positive values.
 #' @returns Vector or matrix of numeric losses.
 loss_gamma <- function(actual, predicted) {
-  actual <- drop(prepare_pred(actual))
-  predicted <- prepare_pred(predicted)
-  if (is.factor(actual) || is.factor(predicted)) {
-    stop("Gamma loss does not make sense for factors.")
-  }
+  check_loss(actual, predicted)
   stopifnot(
     all(predicted > 0), 
     all(actual > 0)
   )
 
-  -2 * (log(actual / predicted) - (actual - predicted) / predicted)
-}
-
-#' Classification Error Loss
-#' 
-#' Internal function. Calculates per-row misclassification errors.
-#' 
-#' @noRd
-#' @keywords internal
-#'
-#' @param actual A vector/factor/matrix with discrete values.
-#' @param predicted A vector/factor/matrix with discrete values.
-#' @returns Vector or matrix of numeric losses.
-loss_classification_error <- function(actual, predicted) {
-  actual <- drop(prepare_pred(actual))
-  predicted <- prepare_pred(predicted)
+  actual <- drop(actual)
 
-  (actual != predicted) * 1.0
+  return(-2 * (log(actual / predicted) - (actual - predicted) / predicted))
 }
 
 #' Log Loss
@@ -115,19 +115,17 @@ loss_classification_error <- function(actual, predicted) {
 #' @param predicted A numeric vector/matrix with values between 0 and 1.
 #' @returns Vector or matrix of numeric losses.
 loss_logloss <- function(actual, predicted) {
-  actual <- drop(prepare_pred(actual))
-  predicted <- prepare_pred(predicted)
-  if (is.factor(actual) || is.factor(predicted)) {
-    stop("Log loss does not make sense for factors.")
-  }
+  check_loss(actual, predicted)
   stopifnot(
     all(predicted >= 0), 
     all(predicted <= 1),
     all(actual >= 0),
     all(actual <= 1)
   )
 
-  -xlogy(actual, predicted) - xlogy(1 - actual, 1 - predicted)
+  actual <- drop(actual)
+
+  return(-xlogy(actual, predicted) - xlogy(1 - actual, 1 - predicted))
 }
 
 #' Multi-Column Log Loss
@@ -139,28 +137,33 @@ loss_logloss <- function(actual, predicted) {
 #' @keywords internal
 #'
 #' @param actual A numeric matrix with values between 0 and 1, or a 
-#'   discrete vector that will be one-hot-encoded by a fast version of
-#'   `model.matrix(~ as.factor(actual) + 0)`.
+#'   factor, or a discrete numeric vector that will be one-hot-encoded by a 
+#'   fast version of `model.matrix(~ as.factor(actual) + 0)`.
 #'   The column order of `predicted` must be in line with this!
 #' @param predicted A numeric matrix with values between 0 and 1.
-#' @returns `TRUE` (or an error message).
+#' @returns A numeric vector of losses.
 loss_mlogloss <- function(actual, predicted) {
-  actual <- prepare_pred(actual)
-  predicted <- prepare_pred(predicted)
   if (NCOL(actual) == 1L) {  # not only for factors
     actual <- fdummy(actual)
   }
+
   stopifnot(
+    is.matrix(actual),
     is.matrix(predicted),
+
+    is.numeric(actual) || is.logical(actual),
+    is.numeric(predicted) || is.logical(predicted),
+
+    dim(actual) == dim(predicted),
     ncol(predicted) >= 2L,
-    ncol(actual) == ncol(predicted),
+
     all(predicted >= 0), 
     all(predicted <= 1),
     all(actual >= 0),
     all(actual <= 1)
   )
 
-  unname(-rowSums(xlogy(actual, predicted)))
+  return(unname(-rowSums(xlogy(actual, predicted))))
 }
 
 #' Calculates x*log(y)
@@ -176,6 +179,7 @@ loss_mlogloss <- function(actual, predicted) {
 xlogy <- function(x, y) {
   out <- x * log(y)
   out[x == 0] <- 0
+
   return(out)
 }
 
@@ -198,7 +202,6 @@ get_loss_fun <- function(loss) {
     poisson = loss_poisson,
     gamma = loss_gamma,
     absolute_error = loss_absolute_error,
-    classification_error = loss_classification_error,
     stop("Unknown loss function.")
   )
 }

R/pd_raw.R

@@ -54,11 +54,10 @@ pd_raw <- function(object, v, X, grid, pred_fun = stats::predict,
 #'   predictions. Otherwise, a list with two elements: `pred` (predictions)
 #'   and `grid_pred` (the corresponding grid values in the same mode as the input, 
 #'   but replicated over `X`).
-#' @param ohe Should factor output be one-hot encoded? Default is `FALSE`.
 #' @returns 
 #'   Either a vector/matrix of predictions or a list with predictions and grid.
 ice_raw <- function(object, v, X, grid, pred_fun = stats::predict, 
-                    pred_only = TRUE, ohe = FALSE, ...) {
+                    pred_only = TRUE, ...) {
   D1 <- length(v) == 1L
   n <- nrow(X)
   n_grid <- NROW(grid)
@@ -79,7 +78,7 @@ ice_raw <- function(object, v, X, grid, pred_fun = stats::predict,
   }
 
   # Calculate matrix/vector of predictions
-  pred <- prepare_pred(pred_fun(object, X_pred, ...), ohe = ohe)
+  pred <- prepare_pred(pred_fun(object, X_pred, ...))
 
   if (pred_only) {
     return(pred)

R/perm_importance.R

@@ -2,7 +2,7 @@
 #' 
 #' Calculates permutation importance for a set of features or a set of feature groups. 
 #' By default, importance is calculated for all columns in `X` (except column names
-#' used as response `y` or case weight `w`).
+#' used as response `y` or as case weight `w`).
 #' 
 #' The permutation importance of a feature is defined as the increase in the average
 #' loss when shuffling the corresponding feature values before calculating predictions.
@@ -31,7 +31,7 @@
 #' # MODEL 1: Linear regression
 #' fit <- lm(Sepal.Length ~ ., data = iris)
 #' s <- perm_importance(fit, X = iris, y = "Sepal.Length")
-
+#'
 #' s
 #' s$M
 #' s$SE  # Standard errors are available thanks to repeated shuffling
@@ -105,7 +105,7 @@ perm_importance.default <- function(object, X, y, v = NULL,
     X <- X[ix, , drop = FALSE]
     if (is.vector(y) || is.factor(y)) {
       y <- y[ix]
-    } else {
+    } else {  # matrix case
       y <- y[ix, , drop = FALSE]
     }
     if (!is.null(w)) {
@@ -128,8 +128,8 @@ perm_importance.default <- function(object, X, y, v = NULL,
     X <- rep_rows(X, ind)
     if (is.vector(y) || is.factor(y)) {
       y <- y[ind]
-    } else {
-      y <- rep_rows(y, ind)
+    } else {  # matrix case
+      y <- y[ind, , drop = FALSE]
     }
   }
 
@@ -206,7 +206,8 @@ perm_importance.default <- function(object, X, y, v = NULL,
 #' @describeIn perm_importance Method for "ranger" models.
 #' @export
 perm_importance.ranger <- function(object, X, y, v = NULL,
-                                   pred_fun = function(m, X, ...) stats::predict(m, X, ...)$predictions,
+                                   pred_fun = function(m, X, ...)
+                                     stats::predict(m, X, ...)$predictions,
                                    loss = "squared_error", m_rep = 4L, 
                                    agg_cols = FALSE, 
                                    normalize = FALSE, n_max = 10000L,