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SHAP values for feature contributions (#2438)

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* SHAP values for feature contributions

* Fix commenting error

* New polynomial time SHAP value estimation algorithm

* Update API to support SHAP values

* Fix merge conflicts with updates in master

* Correct submodule hashes

* Fix variable sized stack allocation

* Make lint happy

* Add docs

* Fix typo

* Adjust tolerances

* Remove unneeded def

* Fixed cpp test setup

* Updated R API and cleaned up

* Fixed test typo
This commit is contained in:
Scott Lundberg
2017-10-12 12:35:51 -07:00
committed by GitHub
parent ff9180cd73
commit 78c4188cec
16 changed files with 369 additions and 143 deletions
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37
R-package/tests/testthat/test_helpers.R
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@@ -52,9 +52,9 @@ test_that("xgb.dump works", {
test_that("xgb.dump works for gblinear", {
expect_length(xgb.dump(bst.GLM), 14)
# also make sure that it works properly for a sparse model where some coefficients
# also make sure that it works properly for a sparse model where some coefficients
# are 0 from setting large L1 regularization:
bst.GLM.sp <- xgboost(data = sparse_matrix, label = label, eta = 1, nthread = 2, nrounds = 1,
bst.GLM.sp <- xgboost(data = sparse_matrix, label = label, eta = 1, nthread = 2, nrounds = 1,
alpha=2, objective = "binary:logistic", booster = "gblinear")
d.sp <- xgb.dump(bst.GLM.sp)
expect_length(d.sp, 14)
@@ -80,28 +80,35 @@ test_that("predict feature contributions works", {
expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-6)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
# gbtree binary classifier (approximate method)
expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE, approxcontrib = TRUE), regexp = NA)
expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
# gblinear binary classifier
expect_error(pred_contr <- predict(bst.GLM, sparse_matrix, predcontrib = TRUE), regexp = NA)
expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
pred <- predict(bst.GLM, sparse_matrix, outputmargin = TRUE)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 2e-6)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
# manual calculation of linear terms
coefs <- xgb.dump(bst.GLM)[-c(1,2,4)] %>% as.numeric
coefs <- c(coefs[-1], coefs[1]) # intercept must be the last
pred_contr_manual <- sweep(cbind(sparse_matrix, 1), 2, coefs, FUN="*")
expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual), 2e-6)
expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual), 1e-5)
# gbtree multiclass
# gbtree multiclass
pred <- predict(mbst.Tree, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
pred_contr <- predict(mbst.Tree, as.matrix(iris[, -5]), predcontrib = TRUE)
expect_is(pred_contr, "list")
expect_length(pred_contr, 3)
for (g in seq_along(pred_contr)) {
expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 2e-6)
expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 1e-5)
}
# gblinear multiclass (set base_score = 0, which is base margin in multiclass)
@@ -192,10 +199,10 @@ test_that("xgb.model.dt.tree works with and without feature names", {
expect_equal(names.dt.trees, names(dt.tree))
expect_equal(dim(dt.tree), c(188, 10))
expect_output(str(dt.tree), 'Feature.*\\"Age\\"')
dt.tree.0 <- xgb.model.dt.tree(model = bst.Tree)
expect_equal(dt.tree, dt.tree.0)
# when model contains no feature names:
bst.Tree.x <- bst.Tree
bst.Tree.x$feature_names <- NULL
@@ -219,20 +226,20 @@ test_that("xgb.importance works with and without feature names", {
expect_equal(dim(importance.Tree), c(7, 4))
expect_equal(colnames(importance.Tree), c("Feature", "Gain", "Cover", "Frequency"))
expect_output(str(importance.Tree), 'Feature.*\\"Age\\"')
importance.Tree.0 <- xgb.importance(model = bst.Tree)
expect_equal(importance.Tree, importance.Tree.0)
# when model contains no feature names:
bst.Tree.x <- bst.Tree
bst.Tree.x$feature_names <- NULL
importance.Tree.x <- xgb.importance(model = bst.Tree)
expect_equal(importance.Tree[, -1, with=FALSE], importance.Tree.x[, -1, with=FALSE])
imp2plot <- xgb.plot.importance(importance_matrix = importance.Tree)
expect_equal(colnames(imp2plot), c("Feature", "Gain", "Cover", "Frequency", "Importance"))
xgb.ggplot.importance(importance_matrix = importance.Tree)
# for multiclass
imp.Tree <- xgb.importance(model = mbst.Tree)
expect_equal(dim(imp.Tree), c(4, 4))
@@ -247,7 +254,7 @@ test_that("xgb.importance works with GLM model", {
imp2plot <- xgb.plot.importance(importance.GLM)
expect_equal(colnames(imp2plot), c("Feature", "Weight", "Importance"))
xgb.ggplot.importance(importance.GLM)
# for multiclass
imp.GLM <- xgb.importance(model = mbst.GLM)
expect_equal(dim(imp.GLM), c(12, 3))