This is the abstract base class for data backends.

Data backends provide a layer of abstraction for various data storage systems. It is not recommended to work directly with the DataBackend. Instead, all data access is handled transparently via the Task.

This package comes with two implementations for backends:

To connect to out-of-memory database management systems such as SQL servers, see the extension package mlr3db.

The required set of fields and methods to implement a custom DataBackend is listed in the respective sections (see DataBackendDataTable or DataBackendMatrix for exemplary implementations of the interface).

See also

Extension Packages: mlr3db

Other DataBackend: DataBackendDataTable, DataBackendMatrix, as_data_backend.data.frame()

Public fields

primary_key

(character(1))
Column name of the primary key column of unique integer row ids.

data_formats

(character())
Set of supported formats, e.g. "data.table" or "Matrix".

Active bindings

hash

(character(1))
Hash (unique identifier) for this object.

Methods

Public methods


Method new()

Creates a new instance of this R6 class.

Note: This object is typically constructed via a derived classes, e.g. DataBackendDataTable or DataBackendMatrix, or via the S3 method as_data_backend().

Usage

DataBackend$new(data, primary_key, data_formats = "data.table")

Arguments

data

(any)
The format of the input data depends on the specialization. E.g., DataBackendDataTable expects a data.table::data.table() and DataBackendMatrix expects a Matrix::Matrix() from Matrix.

primary_key

(character(1))
Each DataBackend needs a way to address rows, which is done via a column of unique integer values, referenced here by primary_key. The use of this variable may differ between backends.

data_formats

(character())
Set of supported data formats which can be processed during $train() and $predict(), e.g. "data.table".


Method format()

Helper for print outputs.

Usage

DataBackend$format()


Method print()

Printer.

Usage

DataBackend$print()

Examples

data = data.table::data.table(id = 1:5, x = runif(5), y = sample(letters[1:3], 5, replace = TRUE)) b = DataBackendDataTable$new(data, primary_key = "id") print(b)
#> <DataBackendDataTable> (5x3) #> id x y #> 1 0.70066184 a #> 2 0.04373037 a #> 3 0.91952597 b #> 4 0.98109477 a #> 5 0.81599705 a
b$head(2)
#> id x y #> 1: 1 0.70066184 a #> 2: 2 0.04373037 a
b$data(rows = 1:2, cols = "x")
#> x #> 1: 0.70066184 #> 2: 0.04373037
b$distinct(rows = b$rownames, "y")
#> $y #> [1] "a" "b" #>
b$missings(rows = b$rownames, cols = names(data))
#> id x y #> 0 0 0