Laurae.xgb.dmat: xgboost DMatrix generation
In Laurae2/LauraeDS: Laurae's Data Science Package

Description Usage Arguments Value Examples

Geneartes a (list of) xgb.DMatrix. Supported for clusters. Requires Matrix and xgboost packages.

1 2	Laurae.xgb.dmat(data, label, missing = NA, save_names = NULL, save_keep = TRUE, clean_mem = FALSE, progress_bar = TRUE, ...)

`data`	Type: matrix or dgCMatrix or data.frame or data.table or filename, or potentially a list of any of them. When a list is provided, it generates the appropriate `xgb.DMatrix` for all the sets. The data to convert to `xgb.DMatrix`. RAM usage required is 2x the current `data` input RAM usage, and 3x for `data.frame` and `data.table` due to internal matrix conversion added before binary matrix generation.
`label`	Type: numeric, or a list of numeric. The label of associated rows in `data`. Use `NULL` for passing no labels.
`missing`	Type: numeric. The value used to represent missing values in `data`. Defaults to `NA` (and missing values for `dgCMatrix`).
`save_names`	Type: character or NULL, or a list of characters. If names are provided, the generated `xgb.DMatrix` are stored physically to the drive. When a list is provided (along with a list of `data` and `labels`), it stores files sequentially by name if a list is provided for `data` but not for `save_names`. Defaults to `NA`.
`save_keep`	Type: logical, or a list of logicals. When names are provided, `save_keep` allows to selectively choose the `xgb.DMatrix` to retain for returning to the user. Useful when generating a list of `xgb.DMatrix` but choosing to keep only a part of them. When `FALSE`, it returns a `NULL` instead of the `xgb.DMatrix`. Defaults to `TRUE`.
`clean_mem`	Type: logical. Whether the force garbage collection at the end of each matrix construction in order to reclaim RAM. Defaults to `FALSE`.
`progress_bar`	Type: logical. Whether to print a progress bar in case of list inputs. Defaults to `TRUE`.
`...`	More arguments to pass to `xgboost::xgb.DMatrix`.

The xgb.DMatrix

library(Matrix)
library(xgboost)

set.seed(0)

# Generate xgb.DMatrix from matrix
random_mat <- matrix(runif(10000, 0, 1), nrow = 1000)
random_labels <- runif(1000, 0, 1)
xgb_from_mat <- Laurae.xgb.dmat(data = random_mat, label = random_labels, missing = NA)

# Generate xgb.DMatrix from data.frame
random_df <- data.frame(random_mat)
random_labels_2 <- runif(1000, 0, 1)
xgb_from_df <- Laurae.xgb.dmat(data = random_df, label = random_labels, missing = NA)

# Generate xgb.DMatrix from respective elements of a list with progress bar
# while keeping memory usage as low as theoretically possible
random_list <- list(random_mat, random_df)
random_labels_3 <- list(random_labels, random_labels_2)
xgb_from_list <- Laurae.xgb.dmat(data = random_list,
                                 label = random_labels_3,
                                 missing = NA,
                                 progress_bar = TRUE,
                                 clean_mem = TRUE)

# Generate xgb.DMatrix from respective elements of a list and keep only first
# while keeping memory usage as low as theoretically possible
xgb_from_list <- Laurae.xgb.dmat(data = random_list,
                                 label = random_labels_3,
                                 missing = NA,
                                 save_keep = c(TRUE, FALSE),
                                 clean_mem = TRUE)