This is the abstract base class for task objects like TaskClassif and TaskRegr.

Tasks serve two purposes:

  1. Tasks wrap a DataBackend, an object to transparently interface different data storage types.

  2. Tasks store meta-information, such as the role of the individual columns in the DataBackend. For example, for a classification task a single column must be marked as target column, and others as features.

Predefined (toy) tasks are stored in the dictionary mlr_tasks, e.g. iris or boston_housing.

S3 methods

Task mutators

The following methods change the task in-place:

  • Any modification to $col_roles and $row_roles. This provides a different "view" on the data without altering the data itself.

  • $filter() and $select() subset the set of active rows or features in $row_roles or $col_roles, respectively. This provides a different "view" on the data without altering the data itself.

  • rbind() and cbind() change the task in-place by binding rows or columns to the data, but without modifying the original DataBackend. Instead, the methods first create a new DataBackendDataTable from the provided new data, and then merge both backends into an abstract DataBackend which merges the results on-demand.

  • rename() wraps the DataBackend of the Task in an additional DataBackend which deals with the renaming. Also updates $col_roles and $col_info.

See also

Public fields

id

(character(1))
Identifier of the object. Used in tables, plot and text output.

task_type

(character(1))
Task type, e.g. "classif" or "regr".For a complete list of possible task types (depending on the loaded packages), see mlr_reflections$task_types$type.

backend

(DataBackend)
Abstract interface to the data of the task.

col_info

(data.table::data.table())
Table with with 3 columns:

  • "id" (character()) stores the name of the column.

  • "type" (character()) holds the storage type of the variable, e.g. integer, numeric or character. See mlr_reflections$task_feature_types for a complete list of allowed types.

  • "levels" stores a vector of distinct values (levels) for ordered and unordered factor variables.

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. Defaults to NA, but can be set by child classes.

Active bindings

hash

(character(1))
Hash (unique identifier) for this object.

row_ids

(integer())
Returns the row ids of the DataBackend for observations with role "use".

row_names

(data.table::data.table())
Returns a table with two columns:

feature_names

(character())
Returns all column names with role == "feature".

target_names

(character())
Returns all column names with role "target".

properties

(character())
Set of task properties. Possible properties are are stored in mlr_reflections$task_properties. The following properties are currently standardized and understood by tasks in mlr3:

  • "strata": The task is resampled using one or more stratification variables (role "stratum").

  • "groups": The task comes with grouping/blocking information (role "group").

  • "weights": The task comes with observation weights (role "weight").

Note that above listed properties are calculated from the $col_roles and may not be set explicitly.

row_roles

(named list())
Each row (observation) can have an arbitrary number of roles in the learning task:

  • "use": Use in train / predict / resampling.

  • "validation": Hold the observations back unless explicitly requested. Validation sets are not yet completely integrated into the package.

row_roles keeps track of the roles with a named list, elements are named by row role and each element is a integer() vector of row ids. To alter the roles, just modify the list, e.g. with R's set functions (intersect(), setdiff(), union(), ...).

col_roles

(named list())
Each column (feature) can have an arbitrary number of the following roles:

  • "feature": Regular feature used in the model fitting process.

  • "target": Target variable.

  • "name": Row names / observation labels. To be used in plots. Can be queried with $row_names.

  • "order": Data returned by $data() is ordered by this column (or these columns).

  • "group": During resampling, observations with the same value of the variable with role "group" are marked as "belonging together". They will be exclusively assigned to be either in the training set or in the test set for each resampling iteration. Only up to one column may have this role.

  • "stratum": Stratification variables. Multiple discrete columns may have this role.

  • "weight": Observation weights. Only up to one column (assumed to be discrete) may have this role.

col_roles keeps track of the roles with a named list, the elements are named by column role and each element is a character vector of column names. To alter the roles, just modify the list, e.g. with R's set functions (intersect(), setdiff(), union(), ...).

nrow

(integer(1))
Returns the total number of rows with role "use".

ncol

(integer(1))
Returns the total number of columns with role "target" or "feature".

feature_types

(data.table::data.table())
Returns a table with columns id and type where id are the column names of "active" features of the task and type is the storage type.

data_formats

character()
Vector of supported data output formats. A specific format can be chosen in the $data() method.

strata

(data.table::data.table())
If the task has columns designated with role "stratum", returns a table with one subpopulation per row and two columns:

  • N (integer()) with the number of observations in the subpopulation, and

  • row_id (list of integer()) as list column with the row ids in the respective subpopulation. Returns NULL if there are is no stratification variable. See Resampling for more information on stratification.

groups

(data.table::data.table())
If the task has a column with designated role "group", a table with two columns:

Returns NULL if there are is no grouping column. See Resampling for more information on grouping.

order

(data.table::data.table())
If the task has at least one column with designated role "order", a table with two columns:

Returns NULL if there are is no order column.

weights

(data.table::data.table())
If the task has a column with designated role "weight", a table with two columns:

Returns NULL if there are is no weight column.

Methods

Public methods


Method new()

Creates a new instance of this R6 class.

Note that this object is typically constructed via a derived classes, e.g. TaskClassif or TaskRegr.

Usage

Task$new(id, task_type, backend)

Arguments

id

(character(1))
Identifier for the new instance.

task_type

(character(1))
Type of task, e.g. "regr" or "classif". Must be an element of mlr_reflections$task_types$type.

backend

(DataBackend)
Either a DataBackend, or any object which is convertible to a DataBackend with as_data_backend(). E.g., a data.frame() will be converted to a DataBackendDataTable.


Method help()

Opens the corresponding help page referenced by field $man.

Usage

Task$help()


Method format()

Helper for print outputs.

Usage

Task$format()


Method print()

Printer.

Usage

Task$print(...)

Arguments

...

(ignored).


Method data()

Returns a slice of the data from the DataBackend in the data format specified by data_format. Rows are additionally subsetted to only contain observations with role "use", and columns are filtered to only contain features with roles "target" and "feature". If invalid rows or cols are specified, an exception is raised.

Usage

Task$data(rows = NULL, cols = NULL, data_format = "data.table", ordered = TRUE)

Arguments

rows

integer()
Row indices.

cols

character()
Column names.

data_format

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

ordered

(logical(1))
If TRUE (default), data is ordered according to the columns with column role "order".

Returns

Depending on the DataBackend, but usually a data.table::data.table().


Method formula()

Constructs a formula(), e.g. [target] ~ [feature_1] + [feature_2] + ... + [feature_k], using the features provided in argument rhs (defaults to all columns with role "feature", symbolized by ".").

Usage

Task$formula(rhs = ".")

Arguments

rhs

(character(1))
Right hand side of the formula. Defaults to "." (all features of the task).

Returns

formula().


Method head()

Get the first n observations with role "use" of all columns with role "target" or "feature".

Usage

Task$head(n = 6L)

Arguments

Returns

data.table::data.table() with n rows.


Method levels()

Returns the distinct values for columns referenced in cols with storage type "factor" or "ordered". Argument cols defaults to all such columns with role "target" or "feature".

Note that this function ignores the row roles, it returns all levels available in the DataBackend. To update the stored level information, e.g. after subsetting a task with $filter(), call $droplevels().

Usage

Task$levels(cols = NULL)

Arguments

cols

character()
Column names.

Returns

named list().


Method missings()

Returns the number of missing observations for columns referenced in cols. Considers only active rows with row role "use". Argument cols defaults to all columns with role "target" or "feature".

Usage

Task$missings(cols = NULL)

Arguments

cols

character()
Column names.

Returns

Named integer().


Method filter()

Subsets the task, keeping only the rows specified via row ids rows.

This operation mutates the task in-place. See the section on task mutators for more information.

Usage

Task$filter(rows)

Arguments

rows

integer()
Row indices.

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.


Method select()

Subsets the task, keeping only the features specified via column names cols. Note that you cannot deselect the target column, for obvious reasons.

This operation mutates the task in-place. See the section on task mutators for more information.

Usage

Task$select(cols)

Arguments

cols

character()
Column names.

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.


Method rbind()

Adds additional rows to the DataBackend stored in $backend. New row ids are automatically created, unless data has a column whose name matches the primary key of the DataBackend (task$backend$primary_key). In case of name clashes of row ids, rows in data have higher precedence and virtually overwrite the rows in the DataBackend.

All columns with the roles "target", "feature", "weight", "group", "stratum", and "order" must be present in data. Columns only present in data but not in the DataBackend of task will be discarded.

This operation mutates the task in-place. See the section on task mutators for more information.

Usage

Task$rbind(data)

Arguments

data

(data.frame()).

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.


Method cbind()

Adds additional columns to the DataBackend stored in $backend.

The row ids must be provided as column in data (with column name matching the primary key name of the DataBackend). If this column is missing, it is assumed that the rows are exactly in the order of $row_ids. In case of name clashes of column names in data and DataBackend, columns in data have higher precedence and virtually overwrite the columns in the DataBackend.

This operation mutates the task in-place. See the section on task mutators for more information.

Usage

Task$cbind(data)

Arguments

data

(data.frame()).


Method rename()

Renames columns by mapping column names in old to new column names in new (element-wise).

This operation mutates the task in-place. See the section on task mutators for more information.

Usage

Task$rename(old, new)

Arguments

old

(character())
Old names.

new

(character())
New names.

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.


Method set_row_role()

Adds the roles new_roles to rows referred to by row ids rows. If exclusive is TRUE, the referenced rows will be removed from all other roles.

This function is deprecated and will be removed in the next version in favor of directly modifying $row_roles.

Usage

Task$set_row_role(rows, new_roles, exclusive = TRUE)

Arguments

rows

integer()
Row indices.

new_roles

(character()).

exclusive

(logical(1)).

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.


Method set_col_role()

Adds the roles new_roles to columns referred to by column names cols. If exclusive is TRUE, the referenced columns will be removed from all other roles.

This function is deprecated and will be removed in the next version in favor of directly modifying $col_roles.

Usage

Task$set_col_role(cols, new_roles, exclusive = TRUE)

Arguments

cols

character()
Column names.

new_roles

(character()).

exclusive

(logical(1)).

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.


Method droplevels()

Updates the cache of stored factor levels, removing all levels not present in the current set of active rows. cols defaults to all columns with storage type "factor" or "ordered".

Usage

Task$droplevels(cols = NULL)

Arguments

cols

character()
Column names.

Returns

Modified self.


Method clone()

The objects of this class are cloneable with this method.

Usage

Task$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# we use the inherited class TaskClassif here, # Class Task is not intended for direct use task = TaskClassif$new("iris", iris, target = "Species") task$nrow
#> [1] 150
task$ncol
#> [1] 5
task$feature_names
#> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width"
task$formula()
#> Species ~ . #> NULL
# de-select "Petal.Width" task$select(setdiff(task$feature_names, "Petal.Width")) task$feature_names
#> [1] "Petal.Length" "Sepal.Length" "Sepal.Width"
# Add new column "foo" task$cbind(data.frame(foo = 1:150)) task$head()
#> Species Petal.Length Sepal.Length Sepal.Width foo #> 1: setosa 1.4 5.1 3.5 1 #> 2: setosa 1.4 4.9 3.0 2 #> 3: setosa 1.3 4.7 3.2 3 #> 4: setosa 1.5 4.6 3.1 4 #> 5: setosa 1.4 5.0 3.6 5 #> 6: setosa 1.7 5.4 3.9 6