DataBackendMatrix: DataBackend for Matrix
In mlr3: Machine Learning in R - Next Generation
DataBackendMatrix R Documentation
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 $data().
Note that merging both parts potentially comes with a data loss, as all
dense columns are converted to numeric columns.
Super class
mlr3::DataBackend -> DataBackendMatrix
Active bindings
rownames(integer())
Returns vector of all distinct row identifiers, i.e. the contents of the primary key column.
colnames(character())
Returns vector of all column names, including the primary key column.
nrow(integer(1))
Number of rows (observations).
ncol(integer(1))
Number of columns (variables), including the primary key column.
Methods
Public methods
-
-
-
-
-
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
DataBackendMatrix$new(data, dense, primary_key = NULL)
Arguments
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.
Method 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.
Usage
DataBackendMatrix$data(rows, cols, data_format)
Arguments
rows(positive integer())
Vector or row indices.
Always refers to the complete data set, even after filtering.
cols(character())
Vector of column names.
data_format(character(1))
Deprecated. Ignored, and will be removed in the future.
Method head()
Retrieve the first n rows.
Usage
DataBackendMatrix$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$distinct(rows, cols, na_rm = TRUE)
Arguments
rows(positive integer())
Vector or row indices.
Always refers to the complete data set, even after filtering.
cols(character())
Vector of column names.
na_rmlogical(1)
Whether to remove NAs or not.
Returns
Named list() of distinct values.
Method missings()
Returns the number of missing values per column in the specified slice
of data. Non-existing rows and columns are silently ignored.
Usage
DataBackendMatrix$missings(rows, cols)
Arguments
rows(positive integer())
Vector or row indices.
Always refers to the complete data set, even after filtering.
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)
mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| DataBackendMatrix | R Documentation |
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 $data().
Note that merging both parts potentially comes with a data loss, as all
dense columns are converted to numeric columns.
Super class
mlr3::DataBackend -> DataBackendMatrix
Active bindings
rownames(
integer())
Returns vector of all distinct row identifiers, i.e. the contents of the primary key column.colnames(
character())
Returns vector of all column names, including the primary key column.nrow(
integer(1))
Number of rows (observations).ncol(
integer(1))
Number of columns (variables), including the primary key column.
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
DataBackendMatrix$new(data, dense, primary_key = NULL)
Arguments
dataMatrix::Matrix()
The inputMatrix::Matrix().densedata.frame(). Dense data, converted todata.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 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.
Usage
DataBackendMatrix$data(rows, cols, data_format)
Arguments
rows(positive
integer())
Vector or row indices. Always refers to the complete data set, even after filtering.cols(
character())
Vector of column names.data_format(
character(1))
Deprecated. Ignored, and will be removed in the future.
Method head()
Retrieve the first n rows.
Usage
DataBackendMatrix$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$distinct(rows, cols, na_rm = TRUE)
Arguments
rows(positive
integer())
Vector or row indices. Always refers to the complete data set, even after filtering.cols(
character())
Vector of column names.na_rmlogical(1)
Whether to remove NAs or not.
Returns
Named list() of distinct values.
Method missings()
Returns the number of missing values per column in the specified slice of data. Non-existing rows and columns are silently ignored.
Usage
DataBackendMatrix$missings(rows, cols)
Arguments
rows(positive
integer())
Vector or row indices. Always refers to the complete data set, even after filtering.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)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.