adding feature contributions to R and gblinear (#2295)
* [gblinear] add features contribution prediction; fix DumpModel bug * [gbtree] minor changes to PredContrib * [R] add feature contribution prediction to R * [R] bump up version; update NEWS * [gblinear] fix the base_margin issue; fixes #1969 * [R] list of matrices as output of multiclass feature contributions * [gblinear] make order of DumpModel coefficients consistent: group index changes the fastest
This commit is contained in:
Vadim Khotilovich
committed by
Yuan (Terry) Tang
Yuan (Terry) Tang
parent
e5e721722e
commit
b52db87d5c
@@ -14,18 +14,19 @@ df[,ID := NULL]
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sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
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label <- df[, ifelse(Improved == "Marked", 1, 0)]
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nrounds <- 12
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bst.Tree <- xgboost(data = sparse_matrix, label = label, max_depth = 9,
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eta = 1, nthread = 2, nrounds = 10, verbose = 0,
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eta = 1, nthread = 2, nrounds = nrounds, verbose = 0,
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objective = "binary:logistic", booster = "gbtree")
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bst.GLM <- xgboost(data = sparse_matrix, label = label,
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eta = 1, nthread = 2, nrounds = 10, verbose = 0,
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eta = 1, nthread = 1, nrounds = nrounds, verbose = 0,
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objective = "binary:logistic", booster = "gblinear")
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feature.names <- colnames(sparse_matrix)
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test_that("xgb.dump works", {
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expect_length(xgb.dump(bst.Tree), 172)
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expect_length(xgb.dump(bst.Tree), 200)
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expect_true(xgb.dump(bst.Tree, 'xgb.model.dump', with_stats = T))
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expect_true(file.exists('xgb.model.dump'))
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expect_gt(file.size('xgb.model.dump'), 8000)
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@@ -33,7 +34,7 @@ test_that("xgb.dump works", {
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# JSON format
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dmp <- xgb.dump(bst.Tree, dump_format = "json")
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expect_length(dmp, 1)
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expect_length(grep('nodeid', strsplit(dmp, '\n')[[1]]), 162)
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expect_length(grep('nodeid', strsplit(dmp, '\n')[[1]]), 188)
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})
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test_that("xgb.dump works for gblinear", {
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@@ -52,13 +53,74 @@ test_that("xgb.dump works for gblinear", {
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expect_length(grep('\\d', strsplit(dmp, '\n')[[1]]), 11)
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})
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test_that("predict leafs works", {
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# no error for gbtree
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expect_error(pred_leaf <- predict(bst.Tree, sparse_matrix, predleaf = TRUE), regexp = NA)
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expect_equal(dim(pred_leaf), c(nrow(sparse_matrix), nrounds))
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# error for gblinear
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expect_error(predict(bst.GLM, sparse_matrix, predleaf = TRUE))
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})
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test_that("predict feature contributions works", {
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# gbtree binary classifier
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expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE), regexp = NA)
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expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
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expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
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pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-6)
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# gblinear binary classifier
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expect_error(pred_contr <- predict(bst.GLM, sparse_matrix, predcontrib = TRUE), regexp = NA)
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expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
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expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
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pred <- predict(bst.GLM, sparse_matrix, outputmargin = TRUE)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 2e-6)
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# manual calculation of linear terms
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coefs <- xgb.dump(bst.GLM)[-c(1,2,4)] %>% as.numeric
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coefs <- c(coefs[-1], coefs[1]) # intercept must be the last
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pred_contr_manual <- sweep(cbind(sparse_matrix, 1), 2, coefs, FUN="*")
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expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual), 2e-6)
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# gbtree multiclass
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lb <- as.numeric(iris$Species) - 1
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb, verbose = 0,
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max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
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objective = "multi:softprob", num_class = 3)
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pred <- predict(bst, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
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pred_contr <- predict(bst, as.matrix(iris[, -5]), predcontrib = TRUE)
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expect_is(pred_contr, "list")
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expect_length(pred_contr, 3)
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for (g in seq_along(pred_contr)) {
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expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
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expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 2e-6)
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}
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# gblinear multiclass (set base_score = 0, which is base margin in multiclass)
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb, verbose = 0,
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booster = "gblinear", eta = 0.1, nthread = 1, nrounds = 10,
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objective = "multi:softprob", num_class = 3, base_score = 0)
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pred <- predict(bst, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
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pred_contr <- predict(bst, as.matrix(iris[, -5]), predcontrib = TRUE)
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expect_length(pred_contr, 3)
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coefs_all <- xgb.dump(bst)[-c(1,2,6)] %>% as.numeric
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for (g in seq_along(pred_contr)) {
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expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
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expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 2e-6)
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# manual calculation of linear terms
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coefs <- coefs_all[seq(g, length(coefs_all), by = 3)]
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coefs <- c(coefs[-1], coefs[1]) # intercept needs to be the last
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pred_contr_manual <- sweep(as.matrix(cbind(iris[,-5], 1)), 2, coefs, FUN="*")
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expect_equal(as.numeric(pred_contr[[g]]), as.numeric(pred_contr_manual), 2e-6)
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}
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})
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test_that("xgb-attribute functionality", {
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val <- "my attribute value"
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list.val <- list(my_attr=val, a=123, b='ok')
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list.ch <- list.val[order(names(list.val))]
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list.ch <- lapply(list.ch, as.character)
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# note: iter is 0-index in xgb attributes
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list.default <- list(niter = "9")
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list.default <- list(niter = as.character(nrounds - 1))
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list.ch <- c(list.ch, list.default)
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# proper input:
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expect_error(xgb.attr(bst.Tree, NULL))
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@@ -85,7 +147,9 @@ test_that("xgb-attribute functionality", {
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expect_null(xgb.attributes(bst))
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})
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if (grepl('Windows', Sys.info()[['sysname']]) || grepl('Linux', Sys.info()[['sysname']]) || grepl('Darwin', Sys.info()[['sysname']])) {
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if (grepl('Windows', Sys.info()[['sysname']]) ||
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grepl('Linux', Sys.info()[['sysname']]) ||
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grepl('Darwin', Sys.info()[['sysname']])) {
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test_that("xgb-attribute numeric precision", {
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# check that lossless conversion works with 17 digits
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# numeric -> character -> numeric
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@@ -121,7 +185,7 @@ test_that("xgb.model.dt.tree works with and without feature names", {
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names.dt.trees <- c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
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dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
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expect_equal(names.dt.trees, names(dt.tree))
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expect_equal(dim(dt.tree), c(162, 10))
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expect_equal(dim(dt.tree), c(188, 10))
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expect_output(str(dt.tree), 'Feature.*\\"Age\\"')
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dt.tree.0 <- xgb.model.dt.tree(model = bst.Tree)
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