DataBackend
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:
DataBackendDataTable which stores the data as data.table::data.table().
DataBackendMatrix which stores the data as sparse Matrix::sparseMatrix().
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).
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".
hash(character(1))
Hash (unique identifier) for this object.
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().
DataBackend$new(data, primary_key, data_formats = "data.table")
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 $predict(),
e.g. "data.table".
format()
Helper for print outputs.
DataBackend$format()
print()
Printer.
DataBackend$print()
Extension Packages: mlr3db
Other DataBackend:
DataBackendDataTable,
DataBackendMatrix,
as_data_backend.Matrix()
data = data.table::data.table(id = 1:5, x = runif(5), y = sample(letters[1:3], 5, replace = TRUE)) b = DataBackendDataTable$new(data, primary_key = "id") print(b) b$head(2) b$data(rows = 1:2, cols = "x") b$distinct(rows = b$rownames, "y") b$missings(rows = b$rownames, cols = names(data))
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