[R] Add data iterator, quantile dmatrix, external memory, and missing feature_types (#9913)
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david-cortes
GitHub
@@ -343,7 +343,7 @@ test_that("xgb.DMatrix: data.frame", {
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expect_equal(
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getinfo(m, "feature_type"), c("float", "float", "int", "i", "c", "c")
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)
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expect_error(xgb.DMatrix(df))
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expect_error(xgb.DMatrix(df, enable_categorical = FALSE))
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df <- data.frame(
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missing = c("a", "b", "d", NA),
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@@ -380,6 +380,261 @@ test_that("xgb.DMatrix: can take multi-dimensional 'base_margin'", {
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expect_equal(pred_only_x, pred_w_base - b, tolerance = 1e-5)
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})
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test_that("xgb.DMatrix: QuantileDMatrix produces same result as DMatrix", {
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data(mtcars)
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y <- mtcars[, 1]
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x <- mtcars[, -1]
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cast_matrix <- function(x) as.matrix(x)
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cast_df <- function(x) as.data.frame(x)
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cast_csr <- function(x) as(as.matrix(x), "RsparseMatrix")
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casting_funs <- list(cast_matrix, cast_df, cast_csr)
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for (casting_fun in casting_funs) {
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qdm <- xgb.QuantileDMatrix(
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data = casting_fun(x),
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label = y,
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nthread = n_threads,
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max_bin = 5
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)
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params <- list(
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tree_method = "hist",
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objective = "reg:squarederror",
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nthread = n_threads,
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max_bin = 5
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)
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model_qdm <- xgb.train(
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params = params,
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data = qdm,
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nrounds = 2
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)
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pred_qdm <- predict(model_qdm, x)
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dm <- xgb.DMatrix(
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data = x,
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label = y,
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nthread = n_threads
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)
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model_dm <- xgb.train(
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params = params,
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data = dm,
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nrounds = 2
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)
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pred_dm <- predict(model_dm, x)
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expect_equal(pred_qdm, pred_dm)
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}
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})
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test_that("xgb.DMatrix: QuantileDMatrix is not accepted by exact method", {
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data(mtcars)
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y <- mtcars[, 1]
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x <- as.matrix(mtcars[, -1])
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qdm <- xgb.QuantileDMatrix(
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data = x,
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label = y,
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nthread = n_threads
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)
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params <- list(
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tree_method = "exact",
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objective = "reg:squarederror",
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nthread = n_threads
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)
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expect_error({
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xgb.train(
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params = params,
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data = qdm,
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nrounds = 2
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)
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})
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})
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test_that("xgb.DMatrix: ExternalDMatrix produces the same results as regular DMatrix", {
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data(mtcars)
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y <- mtcars[, 1]
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x <- as.matrix(mtcars[, -1])
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set.seed(123)
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params <- list(
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objective = "reg:squarederror",
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nthread = n_threads
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)
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model <- xgb.train(
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data = xgb.DMatrix(x, label = y),
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params = params,
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nrounds = 5
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)
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pred <- predict(model, x)
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iterator_env <- as.environment(
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list(
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iter = 0,
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x = mtcars[, -1],
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y = mtcars[, 1]
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)
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)
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iterator_next <- function(iterator_env, proxy_handle) {
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curr_iter <- iterator_env[["iter"]]
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if (curr_iter >= 2) {
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return(NULL)
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}
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if (curr_iter == 0) {
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x_batch <- iterator_env[["x"]][1:16, ]
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y_batch <- iterator_env[["y"]][1:16]
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} else {
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x_batch <- iterator_env[["x"]][17:32, ]
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y_batch <- iterator_env[["y"]][17:32]
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}
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on.exit({
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iterator_env[["iter"]] <- curr_iter + 1
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})
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return(xgb.ProxyDMatrix(data = x_batch, label = y_batch))
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}
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iterator_reset <- function(iterator_env) {
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iterator_env[["iter"]] <- 0
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}
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data_iterator <- xgb.DataIter(
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env = iterator_env,
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f_next = iterator_next,
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f_reset = iterator_reset
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)
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cache_prefix <- tempdir()
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edm <- xgb.ExternalDMatrix(data_iterator, cache_prefix, nthread = 1)
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expect_true(inherits(edm, "xgb.ExternalDMatrix"))
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expect_true(inherits(edm, "xgb.DMatrix"))
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set.seed(123)
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model_ext <- xgb.train(
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data = edm,
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params = params,
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nrounds = 5
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)
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pred_model1_edm <- predict(model, edm)
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pred_model2_mat <- predict(model_ext, x)
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pred_model2_edm <- predict(model_ext, edm)
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expect_equal(pred_model1_edm, pred)
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expect_equal(pred_model2_mat, pred)
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expect_equal(pred_model2_edm, pred)
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})
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test_that("xgb.DMatrix: External QDM produces same results as regular QDM", {
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data(mtcars)
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y <- mtcars[, 1]
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x <- as.matrix(mtcars[, -1])
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set.seed(123)
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params <- list(
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objective = "reg:squarederror",
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nthread = n_threads,
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max_bin = 3
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)
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model <- xgb.train(
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data = xgb.QuantileDMatrix(
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x,
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label = y,
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nthread = 1,
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max_bin = 3
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),
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params = params,
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nrounds = 5
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)
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pred <- predict(model, x)
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iterator_env <- as.environment(
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list(
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iter = 0,
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x = mtcars[, -1],
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y = mtcars[, 1]
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)
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)
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iterator_next <- function(iterator_env, proxy_handle) {
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curr_iter <- iterator_env[["iter"]]
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if (curr_iter >= 2) {
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return(NULL)
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}
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if (curr_iter == 0) {
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x_batch <- iterator_env[["x"]][1:16, ]
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y_batch <- iterator_env[["y"]][1:16]
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} else {
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x_batch <- iterator_env[["x"]][17:32, ]
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y_batch <- iterator_env[["y"]][17:32]
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}
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on.exit({
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iterator_env[["iter"]] <- curr_iter + 1
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})
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return(xgb.ProxyDMatrix(data = x_batch, label = y_batch))
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}
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iterator_reset <- function(iterator_env) {
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iterator_env[["iter"]] <- 0
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}
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data_iterator <- xgb.DataIter(
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env = iterator_env,
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f_next = iterator_next,
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f_reset = iterator_reset
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)
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cache_prefix <- tempdir()
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qdm <- xgb.QuantileDMatrix.from_iterator(
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data_iterator,
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max_bin = 3,
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nthread = 1
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)
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expect_true(inherits(qdm, "xgb.QuantileDMatrix"))
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expect_true(inherits(qdm, "xgb.DMatrix"))
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set.seed(123)
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model_ext <- xgb.train(
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data = qdm,
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params = params,
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nrounds = 5
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)
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pred_model1_qdm <- predict(model, qdm)
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pred_model2_mat <- predict(model_ext, x)
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pred_model2_qdm <- predict(model_ext, qdm)
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expect_equal(pred_model1_qdm, pred)
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expect_equal(pred_model2_mat, pred)
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expect_equal(pred_model2_qdm, pred)
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})
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test_that("xgb.DMatrix: R errors thrown on DataIterator are thrown back to the user", {
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data(mtcars)
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iterator_env <- as.environment(
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list(
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iter = 0,
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x = mtcars[, -1],
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y = mtcars[, 1]
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)
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)
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iterator_next <- function(iterator_env, proxy_handle) {
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curr_iter <- iterator_env[["iter"]]
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if (curr_iter >= 2) {
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return(0)
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}
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if (curr_iter == 0) {
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x_batch <- iterator_env[["x"]][1:16, ]
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y_batch <- iterator_env[["y"]][1:16]
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} else {
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stop("custom error")
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}
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on.exit({
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iterator_env[["iter"]] <- curr_iter + 1
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})
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return(xgb.ProxyDMatrix(data = x_batch, label = y_batch))
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}
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iterator_reset <- function(iterator_env) {
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iterator_env[["iter"]] <- 0
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}
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data_iterator <- xgb.DataIter(
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env = iterator_env,
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f_next = iterator_next,
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f_reset = iterator_reset
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)
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expect_error(
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{xgb.ExternalDMatrix(data_iterator, nthread = 1)},
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"custom error"
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)
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})
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test_that("xgb.DMatrix: number of non-missing matches data", {
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x <- matrix(1:10, nrow = 5)
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dm1 <- xgb.DMatrix(x)
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