mlr_backends_lazy: Lazy Data Backend

In mlr3torch: Deep Learning with 'mlr3'

Lazy Data Backend

Description

This lazy data backend wraps a constructor that lazily creates another backend, e.g. by downloading (and caching) some data from the internet. This backend should be used, when some metadata of the backend is known in advance and should be accessible before downloading the actual data. When the backend is first constructed, it is verified that the provided metadata was correct, otherwise an informative error message is thrown. After the construction of the lazily constructed backend, calls like ⁠$data()⁠, ⁠$missings()⁠, ⁠$distinct()⁠, or ⁠$hash()⁠ are redirected to it.

Information that is available before the backend is constructed is:

• nrow - The number of rows (set as the length of the rownames).

• ncol - The number of columns (provided via the id column of col_info).

• colnames - The column names.

• rownames - The row names.

• col_info - The column information, which can be obtained via mlr3::col_info().

Beware that accessing the backend's hash also contructs the backend.

Note that while in most cases the data contains lazy_tensor columns, this is not necessary and the naming of this class has nothing to do with the lazy_tensor data type.

Important

When the constructor generates factor() variables it is important that the ordering of the levels in data corresponds to the ordering of the levels in the col_info argument.

Super class

mlr3::DataBackend -> DataBackendLazy

Active bindings

backend

(DataBackend)
The wrapped backend that is lazily constructed when first accessed.

nrow

(integer(1))
Number of rows (observations).

ncol

(integer(1))
Number of columns (variables), including the primary key column.

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.

is_constructed

(logical(1))
Whether the backend has already been constructed.

Methods

Public methods

• DataBackendLazy$new()

• DataBackendLazy$data()

• DataBackendLazy$head()

• DataBackendLazy$distinct()

• DataBackendLazy$missings()

• DataBackendLazy$print()

Inherited methods

• mlr3::DataBackend$format()

---

Method new()

Creates a new instance of this R6 class.

Usage

DataBackendLazy$new(constructor, rownames, col_info, primary_key)

Arguments

constructor

(function)
A function with argument backend (the lazy backend), whose return value must be the actual backend. This function is called the first time the field ⁠$backend⁠ is accessed.

rownames

(integer())
The row names. Must be a permutation of the rownames of the lazily constructed backend.

col_info

(data.table::data.table())
A data.table with columns id, type and levels containing the column id, type and levels. Note that the levels must be provided in the correct order.

primary_key

(character(1))
Name of the primary key column.

---

Method data()

Returns a slice of the data in the specified format. 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.

Accessing the data triggers the construction of the backend.

Usage

DataBackendLazy$data(rows, cols)

Arguments

rows

(integer())
Row indices.

cols

(character())
Column names.

---

Method head()

Retrieve the first n rows. This triggers the construction of the backend.

Usage

DataBackendLazy$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.

This triggers the construction of the backend.

Usage

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

Arguments

rows

(integer())
Row indices.

cols

(character())
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.

This triggers the construction of the backend.

Usage

DataBackendLazy$missings(rows, cols)

Arguments

rows

(integer())
Row indices.

cols

(character())
Column names.

Returns

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

---

Method print()

Printer.

Usage

DataBackendLazy$print()

Examples

# We first define a backend constructor
constructor = function(backend) {
  cat("Data is constructed!\n")
  DataBackendDataTable$new(
    data.table(x = rnorm(10), y = rnorm(10), row_id = 1:10),
    primary_key = "row_id"
  )
}

# to wrap this backend constructor in a lazy backend, we need to provide the correct metadata for it
column_info = data.table(
  id = c("x", "y", "row_id"),
  type = c("numeric", "numeric", "integer"),
  levels = list(NULL, NULL, NULL)
)
backend_lazy = DataBackendLazy$new(
  constructor = constructor,
  rownames = 1:10,
  col_info = column_info,
  primary_key = "row_id"
)

# Note that the constructor is not called for the calls below
# as they can be read from the metadata
backend_lazy$nrow
backend_lazy$rownames
backend_lazy$ncol
backend_lazy$colnames
col_info(backend_lazy)

# Only now the backend is constructed
backend_lazy$data(1, "x")
# Is the same as:
backend_lazy$backend$data(1, "x")

mlr3torch documentation built on April 4, 2025, 3:03 a.m.