Task: Task Class
In five-dots/ml4e: Machine Learning for Everyone
Description
Active bindings
Methods
Description
A base class describe a machine learning task specification. A task is
defined by a Options and keys which is named character vector specifiyng
option's key. The main functionality is evaluate quoted call arguments and
assign the acutal values to be called in the later task.
Active bindings
optionsA Options object.
keysA named character vector of keys.
formulasA Formulas object.
datasetsA Datasets object.
seedsA Seeds object.
modelsA Models object.
measureA Measure object.
labelsA vector of labels (response variable).
label_varA label (response) variable name.
label_levelsLevels of label (response) variable.
task_typeA task type.
show_progressWheather to show progress messages.
keep_dataWheather to keep data.
parallelWheather to calculate parallely.
formulaA tasks' formula.
datasetA task's dataset.
trainA task's train data.
testA task's test data.
resample_callA task's resample call.
seedA task's seed.
modelA task's model.
search_seedA task's search seed for resampling.
cv_seedA task's cv seed for resampling.
model_seedA task's model seed.
id_colA task's datasets' id_col.
test_idsA task's datasets' test_ids.
preproc_callA task's preproc call.
data_callA task's data call.
fit_callA task's fit call.
pred_callA task's prediction call.
has_test_labelsWheather the dataset has test labels.
resultA task result.
doneWheather the task is finished.
Methods
Public methods
Method new()
Construct a new Task class object.
Usage
Task$new(options = NULL, keys = NULL)
Arguments
optionsA Options class object.
keysA named character vector of keys. Names must be "formula",
"dataset", "resample", "seed", "model", "preproc", "fit_param" and
values must be key of the each options.
Returns
A new Task class object
Method print()
Print object.
Usage
Task$print()
Method print_task()
Print task object.
Usage
Task$print_task(title = " Task ", key_width = 14L)
Arguments
titleA character scalar of printed title.
key_widthA integer scalar of key column width.
Method eval_call_args()
Evaluate call arguments in the task environment.
Usage
Task$eval_call_args(call = NULL)
Arguments
callcall object.
Returns
A call object.
Method clone()
The objects of this class are cloneable with this method.
Usage
Task$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
five-dots/ml4e documentation built on June 19, 2020, 4:26 p.m.
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Description Active bindings Methods
Description
A base class describe a machine learning task specification. A task is
defined by a Options and keys which is named character vector specifiyng
option's key. The main functionality is evaluate quoted call arguments and
assign the acutal values to be called in the later task.
Active bindings
optionsA Options object.
keysA named character vector of keys.
formulasA Formulas object.
datasetsA Datasets object.
seedsA Seeds object.
modelsA Models object.
measureA Measure object.
labelsA vector of labels (response variable).
label_varA label (response) variable name.
label_levelsLevels of label (response) variable.
task_typeA task type.
show_progressWheather to show progress messages.
keep_dataWheather to keep data.
parallelWheather to calculate parallely.
formulaA tasks' formula.
datasetA task's dataset.
trainA task's train data.
testA task's test data.
resample_callA task's resample call.
seedA task's seed.
modelA task's model.
search_seedA task's search seed for resampling.
cv_seedA task's cv seed for resampling.
model_seedA task's model seed.
id_colA task's datasets' id_col.
test_idsA task's datasets' test_ids.
preproc_callA task's preproc call.
data_callA task's data call.
fit_callA task's fit call.
pred_callA task's prediction call.
has_test_labelsWheather the dataset has test labels.
resultA task result.
doneWheather the task is finished.
Methods
Public methods
Method new()
Construct a new Task class object.
Usage
Task$new(options = NULL, keys = NULL)
Arguments
optionsA Options class object.
keysA named character vector of keys. Names must be "formula", "dataset", "resample", "seed", "model", "preproc", "fit_param" and values must be key of the each options.
Returns
A new Task class object
Method print()
Print object.
Usage
Task$print()
Method print_task()
Print task object.
Usage
Task$print_task(title = " Task ", key_width = 14L)
Arguments
titleA character scalar of printed title.
key_widthA integer scalar of key column width.
Method eval_call_args()
Evaluate call arguments in the task environment.
Usage
Task$eval_call_args(call = NULL)
Arguments
callcallobject.
Returns
A call object.
Method clone()
The objects of this class are cloneable with this method.
Usage
Task$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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