Internal object to store results in list of data.tables, arranged in a star schema. It is advised to not directly work on this data structure as it may be changed in the future without further warnings.

The main motivation of this data structure is the necessity to avoid storing duplicated R6 objects. While this is usually no problem in a single R session, serialization via serialize() (which is used in save()/saveRDS() or during parallelization) leads to objects with unreasonable memory requirements.

## Public fields

data

(list())
List of data.tables(), arranged in a star schema. Do not operate directly on this list.

## Active bindings

task_type

(character(1))
Returns the task type of stored objects, e.g. "classif" or "regr". Returns NULL if the ResultData is empty.

## Methods

### Method new()

Creates a new instance of this R6 class. An alternative construction method is provided by as_result_data().

#### Arguments

view

character(1)
Single uhash to restrict the results to.

#### Returns

character().

### Method iterations()

Returns the number of recorded iterations / experiments.

#### Arguments

view

character(1)
Single uhash to restrict the results to.

#### Returns

data.table() with columns "task_hash" (character()) and "task" (Task).

### Method learners()

Returns a table of included Learners.

#### Arguments

view

character(1)
Single uhash to restrict the results to.

### Method resamplings()

Returns a table of included Resamplings.

#### Arguments

view

character(1)
Single uhash to restrict the results to.

predict_sets

(character())
Prediction sets to operate on, used in aggregate() to extract the matching predict_sets from the ResampleResult. Multiple predict sets are calculated by the respective Learner during resample()/benchmark(). Must be a non-empty subset of {"train", "test", "holdout"}. If multiple sets are provided, these are first combined to a single prediction object. Default is "test".

predict_sets

(character())
Prediction sets to operate on, used in aggregate() to extract the matching predict_sets from the ResampleResult. Multiple predict sets are calculated by the respective Learner during resample()/benchmark(). Must be a non-empty subset of {"train", "test", "holdout"}. If multiple sets are provided, these are first combined to a single prediction object. Default is "test".

predict_sets

(character())
Prediction sets to operate on, used in aggregate() to extract the matching predict_sets from the ResampleResult. Multiple predict sets are calculated by the respective Learner during resample()/benchmark(). Must be a non-empty subset of {"train", "test", "holdout"}. If multiple sets are provided, these are first combined to a single prediction object. Default is "test".

#### Returns

list() of Prediction.

### Method prediction()

Returns a combined Prediction objects.

#### Arguments

rdata

(ResultData).

#### Returns

self (invisibly).

### Method sweep()

Updates the ResultData object, removing rows from all tables which are not referenced by the fact table anymore. E.g., can be called after filtering/subsetting the fact table.

#### Arguments

backends

(logical(1))
If TRUE, the DataBackend is removed from all stored Tasks.

models

(logical(1))
If TRUE, the stored model is removed from all Learners.

#### Returns

Modified self (invisibly).

### Method as_data_table()

Combines internal tables into a single flat data.table().

#### Arguments

view

character(1)
Single uhash to restrict the results to.

condition

(character(1)) The condition to extract. One of "message", "warning" or "error".

#### Returns

data.table().

### Method clone()

The objects of this class are cloneable with this method.

ResultData$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples # table overview print(ResultData$new()$data) #>$fact
#> Empty data.table (0 rows and 8 cols): uhash,iteration,learner_state,prediction,task_hash,learner_hash...
#>
#> $uhashes #> Empty data.table (0 rows and 1 cols): uhash #> #>$tasks
#> $learners #> Empty data.table (0 rows and 2 cols): learner_phash,learner #> #>$resamplings