Avoid generating NaNs in UnwoundPathSum (#3943)
* Avoid generating NaNs in UnwoundPathSum. Kudos to Jakub Zakrzewski for tracking down the bug. * Add a test
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@ -81,6 +81,39 @@ test_that("predict feature interactions works", {
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expect_lt(max(abs(intr - gt_intr)), 0.1)
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})
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test_that("SHAP contribution values are not NAN", {
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d <- data.frame(
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x1 = c(-2.3, 1.4, 5.9, 2, 2.5, 0.3, -3.6, -0.2, 0.5, -2.8, -4.6, 3.3, -1.2,
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-1.1, -2.3, 0.4, -1.5, -0.2, -1, 3.7),
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x2 = c(291.179171, 269.198331, 289.942097, 283.191669, 269.673332,
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294.158346, 287.255835, 291.530838, 285.899586, 269.290833,
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268.649586, 291.530841, 280.074593, 269.484168, 293.94042,
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294.327506, 296.20709, 295.441669, 283.16792, 270.227085),
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y = c(9, 15, 5.7, 9.2, 22.4, 5, 9, 3.2, 7.2, 13.1, 7.8, 16.9, 6.5, 22.1,
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5.3, 10.4, 11.1, 13.9, 11, 20.5),
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fold = c(2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2))
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ivs <- c("x1", "x2")
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fit <- xgboost(
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verbose = 0,
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params = list(
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objective = "reg:linear",
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eval_metric = "rmse"),
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data = as.matrix(subset(d, fold == 2)[, ivs]),
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label = subset(d, fold == 2)$y,
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nthread = 1,
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nrounds = 3)
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shaps <- as.data.frame(predict(fit,
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newdata = as.matrix(subset(d, fold == 1)[, ivs]),
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predcontrib = T))
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result <- cbind(shaps, sum = rowSums(shaps), pred = predict(fit,
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newdata = as.matrix(subset(d, fold == 1)[, ivs])))
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expect_true(identical(TRUE, all.equal(result$sum, result$pred, tol = 1e-6)))
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})
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test_that("multiclass feature interactions work", {
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dm <- xgb.DMatrix(as.matrix(iris[,-5]), label=as.numeric(iris$Species)-1)
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@ -293,9 +293,12 @@ bst_float UnwoundPathSum(const PathElement *unique_path, unsigned unique_depth,
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total += tmp;
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next_one_portion = unique_path[i].pweight - tmp * zero_fraction * ((unique_depth - i)
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/ static_cast<bst_float>(unique_depth + 1));
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} else {
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} else if (zero_fraction != 0) {
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total += (unique_path[i].pweight / zero_fraction) / ((unique_depth - i)
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/ static_cast<bst_float>(unique_depth + 1));
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} else {
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CHECK_EQ(unique_path[i].pweight, 0)
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<< "Unique path " << i << " must have zero weight";
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}
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}
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return total;
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