DataBackend: DataBackend

In mlr3: Machine Learning in R - Next Generation

DataBackend

Description

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.

Details

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).

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.

col_hashes

(named character)
Hash (unique identifier) for all columns except the primary_key: A character vector, named by the columns that each element refers to.
Columns of different Tasks or DataBackends that have agreeing col_hashes always represent the same data, given that the same rows are selected. The reverse is not necessarily true: There can be columns with the same content that have different col_hashes.

Methods

Public methods

• DataBackend$new()

• DataBackend$format()

• DataBackend$print()

---

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(...)

Arguments

...

(ignored).

---

Method print()

Printer.

Usage

DataBackend$print()

See Also

• 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: DataBackendDataTable, DataBackendMatrix, as_data_backend.Matrix()

Examples

data = data.table::data.table(id = 1:5, x = runif(5),
  y = sample(letters[1:3], 5, replace = TRUE))

b = DataBackendDataTable$new(data, primary_key = "id")
print(b)
b$head(2)
b$data(rows = 1:2, cols = "x")
b$distinct(rows = b$rownames, "y")
b$missings(rows = b$rownames, cols = names(data))

mlr3 documentation built on Nov. 17, 2023, 5:07 p.m.