Return 0 instead of NA when heuristic fails

NEWS.md

@@ -7,10 +7,10 @@
 If no SHAP interaction values are available, by default, the color feature `v'` is selected by the heuristic `potential_interaction()`, which works as follows:
 
 1. If the feature `v` (the on the x-axis) is numeric, it is binned into `nbins` bins.
-2. Per bin, the SHAP values of `v` are regressed onto `v` and the R-squared is calculated.
-3. The R-squared are averaged over bins, weighted by the bin size.
+2. Per bin, the SHAP values of `v` are regressed onto `v'` and the R-squared is calculated. Rows with missing `v'` are discarded.
+3. The R-squared are averaged over bins, weighted by the number of non-missing `v'` values.
 
-This measures how much variability in the SHAP values is explained by `v'`, after accounting for `v`.
+This measures how much variability in the SHAP values of `v` is explained by `v'`, after accounting for `v`.
 
 We have introduced four parameters to control the heuristic. Their defaults are in line with the old behaviour.
 
@@ -35,7 +35,8 @@ We will continue to experiment with the defaults, which might change in the futu
 
 ## Other user-visible changes
 
-- `sv_dependence()`: If `color_var = "auto"` (default) and no color feature seems to be relevant (SHAP interaction is `NULL`, or heuristic returns no positive value), there won't be any color scale.
+- `sv_dependence()`: If `color_var = "auto"` (default) and no color feature seems to be relevant (SHAP interaction is `NULL`, or heuristic returns no positive value), there won't be any color scale. Furthermore, in some edge cases, a different
+color feature might be selected.
 - `mshapviz()` objects can now be rowbinded via `rbind()` or `+`. Implemented by [@jmaspons](https://github.com/jmaspons) in [#110](https://github.com/ModelOriented/shapviz/pull/110).
 - `mshapviz()` is more strict when combining multiple "shapviz" objects. These now need to have identical column names, see [#114](https://github.com/ModelOriented/shapviz/pull/114).
 

R/potential_interactions.R

@@ -1,18 +1,21 @@
 #' Interaction Strength
 #'
-#' Returns vector of interaction strengths between variable `v` and all other variables,
-#' see Details.
+#' Returns a vector of interaction strengths between variable `v` and all other
+#' variables, see Details.
 #'
 #' If SHAP interaction values are available, the interaction strength
 #' between feature `v` and another feature `v'` is measured by twice their
 #' mean absolute SHAP interaction values.
 #'
-#' Otherwise, we use a heuristic calculated as follows to calculate interaction strength
-#' between `v` and each other "color" feature `v':
+#' Otherwise, we use a heuristic calculated as follows:
 #' 1. If `v` is numeric, it is binned into `nbins` bins.
 #' 2. Per bin, the SHAP values of `v` are regressed onto `v`, and the R-squared
-#'   is calculated.
-#' 3. The R-squared are averaged over bins, weighted by the bin size.
+#'   is calculated. Rows with missing `v'` are discarded.
+#' 3. The R-squared are averaged over bins, weighted by the number of
+#'   non-missing `v'` values.
+#'
+#' This measures how much variability in the SHAP values of `v` is explained by `v'`,
+#' after accounting for `v`.
 #'
 #' Set `scale = TRUE` to multiply the R-squared by the within-bin variance
 #' of the SHAP values. This will put higher weight to bins with larger scatter.
@@ -22,6 +25,8 @@
 #'
 #' Finally, set `adjusted = TRUE` to use *adjusted* R-squared.
 #'
+#' The algorithm does not consider observations with missing `v'` values.
+#'
 #' @param obj An object of class "shapviz".
 #' @param v Variable name to calculate potential SHAP interactions for.
 #' @param nbins Into how many quantile bins should a numeric `v` be binned?
@@ -60,7 +65,7 @@ potential_interactions <- function(obj, v, nbins = NULL, color_num = TRUE,
     nbins <- ceiling(min(sqrt(nrow(X)), nrow(X) / 20))
   }
   out <- vapply(
-    X[v_other],  # data.frame is a list
+    X[v_other],
     FUN = heuristic,
     FUN.VALUE = 1.0,
     s = S[, v],
@@ -108,15 +113,16 @@ heuristic <- function(color, s, bins, color_num, scale, adjusted) {
 #'
 #' @inheritParams heuristic
 #' @returns
-#'   A (1x2) matrix with heuristic and number of observations.
+#'   A (1x2) matrix with the heuristic and the number of observations with non-missing
+#'   `v'`.
 heuristic_in_bin <- function(color, s, scale = FALSE, adjusted = FALSE) {
   ok <- !is.na(color)
   color <- color[ok]
   s <- s[ok]
   n <- length(s)
   var_s <- stats::var(s)
   if (n < 2L || var_s < .Machine$double.eps || length(unique(color)) < 2L) {
-    return(cbind(stat = NA, n = n))
+    return(cbind(stat = 0, n = n))
   }
   z <- stats::lm(s ~ color)
   var_r <- sum(z$residuals^2) / (if (adjusted) z$df.residual else n - 1)
@@ -125,7 +131,7 @@ heuristic_in_bin <- function(color, s, scale = FALSE, adjusted = FALSE) {
     stat <- stat * var_s
   }
   if (!is.finite(stat)) {
-    stat <- NA
+    stat <- 0
   }
   cbind(stat = stat, n = n)
 }

README.md

@@ -66,7 +66,7 @@ dia_2000 <- diamonds[sample(nrow(diamonds), 2000), x]
 shp <- shapviz(fit, X_pred = data.matrix(dia_2000), X = dia_2000)
 
 sv_importance(shp, show_numbers = TRUE)
-sv_dependence(shp, v = x)}
+sv_dependence(shp, v = x)
 ```
 
 ![](man/figures/README-imp.svg)

tests/testthat/test-potential_interactions.R

@@ -66,84 +66,84 @@ test_that("heuristic_in_bin() returns R-squared", {
   )
 })
 
-test_that("Failing heuristic_in_bin() returns NA", {
-  expect_equal(heuristic_in_bin(c(NA, NA), 1:2), cbind(stat = NA, n = 0))
+test_that("Failing heuristic_in_bin() returns 0", {
+  expect_equal(heuristic_in_bin(c(NA, NA), 1:2), cbind(stat = 0, n = 0))
 })
 
-test_that("heuristic_in_bin() returns NA for constant response", {
+test_that("heuristic_in_bin() returns 0 for constant response", {
   expect_equal(
     heuristic_in_bin(color = 1:3, s = c(1, 1, 1)),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
   expect_equal(
     heuristic_in_bin(color = 1:3, s = c(1, 1, 1), scale = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
   expect_equal(
     heuristic_in_bin(color = 1:3, s = c(1, 1, 1), adjust = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
   expect_equal(
     heuristic_in_bin(color = 1:3, s = c(1, 1, 1), adjust = TRUE, scale = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
 })
 
-test_that("heuristic_in_bin() returns NA for constant color", {
+test_that("heuristic_in_bin() returns 0 for constant color", {
   expect_equal(
     heuristic_in_bin(s = 1:3, color = c(1, 1, 1)),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
   expect_equal(
     heuristic_in_bin(s = 1:3, color = c(1, 1, 1), scale = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
   expect_equal(
     heuristic_in_bin(s = 1:3, color = c(1, 1, 1), adjust = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
   expect_equal(
     heuristic_in_bin(s = 1:3, color = c(1, 1, 1), adjust = TRUE, scale = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
 })
 
 test_that("heuristic_in_bin() returns 0 if response and color are constant", {
   z <- c(1, 1)
   expect_equal(
     heuristic_in_bin(color = z, s = z),
-    cbind(stat = NA, n = 2L)
+    cbind(stat = 0, n = 2L)
   )
   expect_equal(
     heuristic_in_bin(color = z, s = z, scale = TRUE),
-    cbind(stat = NA, n = 2L)
+    cbind(stat = 0, n = 2L)
   )
   expect_equal(
     heuristic_in_bin(color = z, s = z, adjust = TRUE),
-    cbind(stat = NA, n = 2L)
+    cbind(stat = 0, n = 2L)
   )
   expect_equal(
     heuristic_in_bin(color = z, s = z, adjust = TRUE, scale = TRUE),
-    cbind(stat = NA, n = 2L)
+    cbind(stat = 0, n = 2L)
   )
 })
 
-test_that("heuristic_in_bin() returns NA for single obs", {
+test_that("heuristic_in_bin() returns 0 for single obs", {
   expect_equal(
     heuristic_in_bin(color = 2, s = 2),
-    cbind(stat = NA, n = 1L)
+    cbind(stat = 0, n = 1L)
   )
   expect_equal(
     heuristic_in_bin(color = 2, s = 2, scale = TRUE),
-    cbind(stat = NA, n = 1L)
+    cbind(stat = 0, n = 1L)
   )
   expect_equal(
     heuristic_in_bin(color = 2, s = 2, adjust = TRUE),
-    cbind(stat = NA, n = 1L)
+    cbind(stat = 0, n = 1L)
   )
   expect_equal(
     heuristic_in_bin(color = 2, s = 2, adjust = TRUE, scale = TRUE),
-    cbind(stat = NA, n = 1L)
+    cbind(stat = 0, n = 1L)
   )
 })
 
@@ -159,11 +159,11 @@ test_that("heuristic_in_bin() returns NA for single obs", {
   )
   expect_equal(
     heuristic_in_bin(color = cc, s = 1:3, adjust = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
   expect_equal(
     heuristic_in_bin(color = cc, s = 2*(1:3), adjust = TRUE, scale = TRUE),
-    cbind(stat = NA, n = 3L)
+    cbind(stat = 0, n = 3L)
   )
 })