mlr3 (version 0.20.2)

DataBackendMatrix: DataBackend for Matrix

Description

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.

Super class

mlr3::DataBackend -> DataBackendMatrix

Active bindings

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.

Methods

Public methods

• DataBackendMatrix$new()

• DataBackendMatrix$data()

• DataBackendMatrix$head()

• DataBackendMatrix$distinct()

• DataBackendMatrix$missings()

Inherited methods

• mlr3::DataBackend$format()
• mlr3::DataBackend$print()

---

Method new()

Creates a new instance of this R6 class.

Usage

DataBackendMatrix$new(data, dense, primary_key = NULL)

Arguments

data

Matrix::Matrix()
The input Matrix::Matrix().

dense

data.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.

---

Method data()

Returns a slice of the data in the specified format. Currently, the only supported formats are "data.table" and "Matrix". 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.

Usage

DataBackendMatrix$data(rows, cols, data_format = "data.table")

Arguments

rows

(positive integer())
Vector or row indices.

cols

(character())
Vector of column names.

data_format

(character(1))
Desired data format, e.g. "data.table" or "Matrix".

---

Method head()

Retrieve the first n rows.

Usage

DataBackendMatrix$head(n = 6L)

Arguments

n

(integer(1))
Number of rows.

Returns

data.table::data.table() of the first n rows.

---

Method 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.

Usage

DataBackendMatrix$distinct(rows, cols, na_rm = TRUE)

Arguments

rows

(positive integer())
Vector or row indices.

cols

(character())
Vector of column names.

na_rm

logical(1)
Whether to remove NAs or not.

Returns

Named list() of distinct values.

---

Method missings()

Returns the number of missing values per column in the specified slice of data. Non-existing rows and columns are silently ignored.

Usage

DataBackendMatrix$missings(rows, cols)

Arguments

rows

(positive integer())
Vector or row indices.

cols

(character())
Vector of column names.

Returns

Total of missing values per column (named numeric()).

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: DataBackend, DataBackendDataTable, as_data_backend.Matrix()

Examples

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$colnames, data_format = "Matrix")
b$data(1:3, b$colnames, data_format = "data.table")