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.
Passes all arguments to the constructor. See documentation for Constructor below.
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.table containing the underlying data
A character vector of variable names that define the set of covariates
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.
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.
A vector of levels expected for the outcome variable. If
outcome_type is a character, this will be used to construct an appropriate
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.
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.
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.
A list of character vectors as nodes. This will override the
offset arguments if specified, and is an alternative way to specify those arguments.
A named list of characters that maps between column names in
data and how those variables are referenced in
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
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
Returns a task with rows subsetted using the
row_index index vector
row_index: An index vector defining the subset
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.
Returns true if the node is defined in the task
node_name: The name of the node to look for
Returns a ddta.table with the requested node's data
node_name: The name of the node to look for
function(node_name, n) that can generate the node if it was not specified in the task.
Internal representation of the data
Formatted task data
Number of observations
A list of node variables
a data.table containing the covariates
a data.table containing the covariates and an intercept term
a vector containing the outcomes
a vector containing the offset. Will return an error if the offset wasn't specified on construction
a vector containing the observation weights. If weights aren't specified on construction, weights will default to 1
a vector containing the observation units. If the ids aren't specified on construction, id will return seq_len(nrow)
An origami fold object, as generated by
make_folds, specifying a cross-validation scheme
A unique identifier of this task
The named list mapping variable names to internal column names
variable_type object specifying the type of the outcome