An increasingly less thin wrapper around a data.table containing the data. Contains metadata about the particular machine learning problem, including which variables are to be used as covariates and outcomes.

make_sl3_Task(...)

Arguments

...

Passes all arguments to the constructor. See documentation for Constructor below.

Format

R6Class object.

Value

sl3_Task object

Constructor

make_sl3_Task(data, covariates, outcome = NULL, outcome_type = NULL, outcome_levels = NULL, id = NULL, weights = NULL, offset = NULL, nodes = NULL, column_names = NULL, row_index = NULL, folds = NULL)

data

A data.frame or data.table containing the underlying data

covariates

A character vector of variable names that define the set of covariates

outcome

A character vector of variable names that define the set of outcomes. Usually just one variable, although some learners support multivariate outcomes. Use sl3_list_learners("multivariate_outcome") to find such learners.

outcome_type

A Variable_type object that defines the variable type of the outcome. Alternatively, a character specifying such a type. See variable_type for details on defining variable types.

outcome_levels

A vector of levels expected for the outcome variable. If outcome_type is a character, this will be used to construct an appropriate variable_type object.

id

A character indicating which variable (if any) to be used as an observation "id", for learners that support clustered observations. Use sl3_list_learners("id") to find such learners.

weights

A character indicating which variable (if any) to be used as observation weights, for learners that support that. Use sl3_list_learners("weights") to find such learners.

offset

A character indicating which variable (if any) to be used as an observation "id", for methods that support clustered observations. Use sl3_list_learners("offset") to find such learners.

nodes

A list of character vectors as nodes. This will override the covariates, outcome, id, weights, and offset arguments if specified, and is an alternative way to specify those arguments.

column_names

A named list of characters that maps between column names in data and how those variables are referenced in sl3_Task functions.

Methods

add_interactions(interactions, warn_on_existing = TRUE)

Adds interaction terms to task, returns a task with interaction terms added to covariate list.

  • interactions: A list of lists, where each sublist describes one interaction term, listing the variables that comprise it

  • warn_on_existing: If TRUE, produce a warning if there is already a column with a name matching this interaction term

add_columns(fit_uuid, new_data, global_cols=FALSE)

Add columns to internal data, returning an updated vector of column_names

  • fit_uuid: A uuid character that is used to generate unique internal column names. This prevents two added columns with the same name overwriting each other, provided they have different fit_uuid.

  • new_data: A data.table containing the columns to add

  • global_cols: If true, don't use the fit_uuid to make unique column names

next_in_chain(covariates=NULL, outcome=NULL, id=NULL, weights=NULL, offset=NULL, column_names=NULL, new_nodes=NULL, ...)

Used by learner$chain methods to generate a task with the same underlying data, but redefined nodes. Most of the parameter values are passed to the sl3_Task constructor, documented above.

  • covariates: An updated covariates character vector

  • outcome: An updated outcome character vector

  • id: An updated id character value

  • weights: An updated weights character value

  • offset: An updated offset character value

  • column_names: An updated column_names character vector

  • new_nodes: An updated list of node names

  • ...: Other arguments passed to the sl3_Task constructor for the new task

subset_task(row_index)

Returns a task with rows subsetted using the row_index index vector

  • row_index: An index vector defining the subset

get_data(rows, columns)

Returns a data.table containing a subset of task data.

  • rows: An index vector defining the rows to return

  • columns: A character vector of columns to return.

has_node(node_name)

Returns true if the node is defined in the task

  • node_name: The name of the node to look for

get_node(node_name, generator_fun=NULL)

Returns a ddta.table with the requested node's data

  • node_name: The name of the node to look for

  • generator_fun: A function(node_name, n) that can generate the node if it was not specified in the task.

Fields

raw_data

Internal representation of the data

data

Formatted task data

nrow

Number of observations

nodes

A list of node variables

X

a data.table containing the covariates

X

a data.table containing the covariates and an intercept term

Y

a vector containing the outcomes

offsets

a vector containing the offset. Will return an error if the offset wasn't specified on construction

weights

a vector containing the observation weights. If weights aren't specified on construction, weights will default to 1

id

a vector containing the observation units. If the ids aren't specified on construction, id will return seq_len(nrow)

folds

An origami fold object, as generated by make_folds, specifying a cross-validation scheme

uuid

A unique identifier of this task

column_names

The named list mapping variable names to internal column names

outcome_type

A variable_type object specifying the type of the outcome