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[R] Replace xgboost() with xgb.train() in most tests and examples (#9941)

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This commit is contained in:
david-cortes
2024-01-02 14:20:01 +01:00
committed by GitHub
parent 32cbab1cc0
commit 9e33a10202
27 changed files with 156 additions and 150 deletions
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4
R-package/demo/create_sparse_matrix.R
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@@ -81,8 +81,8 @@ output_vector <- df[, Y := 0][Improved == "Marked", Y := 1][, Y]
# Following is the same process as other demo
cat("Learning...\n")
bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 9,
eta = 1, nthread = 2, nrounds = 10, objective = "binary:logistic")
bst <- xgb.train(data = xgb.DMatrix(sparse_matrix, label = output_vector), max_depth = 9,
eta = 1, nthread = 2, nrounds = 10, objective = "binary:logistic")
importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)
print(importance)

18
R-package/demo/interaction_constraints.R
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@@ -74,26 +74,26 @@ cols2ids <- function(object, col_names) {
interaction_list_fid <- cols2ids(interaction_list, colnames(train))
# Fit model with interaction constraints
bst <- xgboost(data = train, label = y, max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000,
interaction_constraints = interaction_list_fid)
bst <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000,
interaction_constraints = interaction_list_fid)
bst_tree <- xgb.model.dt.tree(colnames(train), bst)
bst_interactions <- treeInteractions(bst_tree, 4)
# interactions constrained to combinations of V1*V2 and V3*V4*V5
# Fit model without interaction constraints
bst2 <- xgboost(data = train, label = y, max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000)
bst2 <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000)
bst2_tree <- xgb.model.dt.tree(colnames(train), bst2)
bst2_interactions <- treeInteractions(bst2_tree, 4) # much more interactions
# Fit model with both interaction and monotonicity constraints
bst3 <- xgboost(data = train, label = y, max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000,
interaction_constraints = interaction_list_fid,
monotone_constraints = c(-1, 0, 0, 0, 0, 0, 0, 0, 0, 0))
bst3 <- xgb.train(data = xgb.DMatrix(train, label = y), max_depth = 4,
eta = 0.1, nthread = 2, nrounds = 1000,
interaction_constraints = interaction_list_fid,
monotone_constraints = c(-1, 0, 0, 0, 0, 0, 0, 0, 0, 0))
bst3_tree <- xgb.model.dt.tree(colnames(train), bst3)
bst3_interactions <- treeInteractions(bst3_tree, 4)

4
R-package/demo/poisson_regression.R
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@@ -1,6 +1,6 @@
data(mtcars)
head(mtcars)
bst <- xgboost(data = as.matrix(mtcars[, -11]), label = mtcars[, 11],
objective = 'count:poisson', nrounds = 5)
bst <- xgb.train(data = xgb.DMatrix(as.matrix(mtcars[, -11]), label = mtcars[, 11]),
objective = 'count:poisson', nrounds = 5)
pred <- predict(bst, as.matrix(mtcars[, -11]))
sqrt(mean((pred - mtcars[, 11]) ^ 2))