DataBackend for Matrix.
Data is split into a (numerical) sparse part and an optional dense part.
These parts are automatically merged to a sparse format during $data().
Note that merging both parts potentially comes with a data loss, as all
dense columns are converted to numeric columns.
mlr3::DataBackend -> DataBackendMatrix
rownames(integer())
Returns vector of all distinct row identifiers, i.e. the contents of the primary key column.
colnames(character())
Returns vector of all column names, including the primary key column.
nrow(integer(1))
Number of rows (observations).
ncol(integer(1))
Number of columns (variables), including the primary key column.
Inherited methods
new()Creates a new instance of this R6 class.
DataBackendMatrix$new(data, dense, primary_key = NULL)dataMatrix::Matrix()
The input Matrix::Matrix().
densedata.frame().
Dense data, converted to data.table::data.table().
primary_key(character(1) | integer())
Name of the primary key column, or integer vector of row ids.
data()Returns a slice of the data as "data.table".
The rows must be addressed as vector of primary key values, columns must be referred to via column names.
Queries for rows with no matching row id and queries for columns with no matching column name are silently ignored.
Rows are guaranteed to be returned in the same order as rows, columns may be returned in an arbitrary order.
Duplicated row ids result in duplicated rows, duplicated column names lead to an exception.
DataBackendMatrix$data(rows, cols, data_format)rows(positive integer())
Vector or row indices.
Always refers to the complete data set, even after filtering.
cols(character())
Vector of column names.
data_format(character(1))
Deprecated. Ignored, and will be removed in the future.
n(integer(1))
Number of rows.
data.table::data.table() of the first n rows.
distinct()Returns a named list of vectors of distinct values for each column
specified. If na_rm is TRUE, missing values are removed from the
returned vectors of distinct values. Non-existing rows and columns are
silently ignored.
DataBackendMatrix$distinct(rows, cols, na_rm = TRUE)rows(positive integer())
Vector or row indices.
Always refers to the complete data set, even after filtering.
cols(character())
Vector of column names.
na_rmlogical(1)
Whether to remove NAs or not.
Named list() of distinct values.
missings()Returns the number of missing values per column in the specified slice of data. Non-existing rows and columns are silently ignored.
DataBackendMatrix$missings(rows, cols)rows(positive integer())
Vector or row indices.
Always refers to the complete data set, even after filtering.
cols(character())
Vector of column names.
Total of missing values per column (named numeric()).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-backends
Package mlr3db to interface out-of-memory data, e.g. SQL servers or duckdb.
Other DataBackend:
DataBackend,
DataBackendDataTable,
as_data_backend.Matrix()
requireNamespace("Matrix")
data = Matrix::Matrix(sample(0:1, 20, replace = TRUE), ncol = 2)
colnames(data) = c("x1", "x2")
dense = data.frame(
..row_id = 1:10,
num = runif(10),
fact = factor(sample(c("a", "b"), 10, replace = TRUE), levels = c("a", "b"))
)
b = as_data_backend(data, dense = dense, primary_key = "..row_id")
b$head()
b$data(1:3, b$colnames)
Run the code above in your browser using DataLab