Experiment: 'R6' class representing a simulation experiment.
In Yu-Group/simChef: Intensive Computational Experiments Made Easy
Experiment R Documentation
R6 class representing a simulation experiment.
Description
A simulation experiment with any number of DGP,
Method, Evaluator, and Visualizer objects.
Generally speaking, users won't directly interact with the Experiment R6
class, but instead indirectly through create_experiment() and the tidy
Experiment helpers listed in below in the See also section.
Details
When run, an Experiment seamlessly combines DGPs and
Methods, computing results in parallel. Those results can then be
evaluated using Evaluators and visualized using Visualizers.
Public fields
nameThe name of the Experiment.
Methods
Public methods
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Method new()
Initialize a new Experiment object.
Usage
Experiment$new(
name = "experiment",
dgp_list = list(),
method_list = list(),
evaluator_list = list(),
visualizer_list = list(),
future.globals = TRUE,
future.packages = NULL,
clone_from = NULL,
save_dir = NULL,
...
)
Arguments
nameThe name of the Experiment.
dgp_listAn optional list of DGP objects.
method_listAn optional list of Method objects.
evaluator_listAn optional list of Evaluator objects.
visualizer_listAn optional list of Visualizer objects.
future.globalsCharacter vector of names in the global environment to
pass to parallel workers. Passed as the argument of the same name to
future.apply::future_lapply() and related functions. To set for a
specific run of the experiment, use the same argument in
run_experiment().
future.packagesCharacter vector of packages required by parallel
workers. Passed as the argument of the same name to
future.apply::future_lapply() and related functions. To set for a
specific run of the experiment, use the same argument in
run_experiment().
clone_fromAn optional Experiment object to use as a base for
this one.
save_dirAn optional directory in which to save the experiment's
results. If NULL, results are saved in the current working directory
in a directory called "results" with a sub-directory named after
Experiment$name when using run_experiment() or fit_experiment()
with save=TRUE.
...Not used.
Returns
A new instance of Experiment.
Method run()
Run the full Experiment pipeline (fitting, evaluating,
and visualizing).
Usage
Experiment$run(
n_reps = 1,
parallel_strategy = "reps",
future.globals = NULL,
future.packages = NULL,
future.seed = TRUE,
use_cached = FALSE,
return_all_cached_reps = FALSE,
save = FALSE,
checkpoint_n_reps = 0,
verbose = 1,
...
)
Arguments
n_repsThe number of replicates of the Experiment for this run.
parallel_strategyA vector with some combination of "reps", "dgps", or
"methods". Determines how computation will be distributed across available
resources. Currently only the default, "reps", is supported.
future.globalsCharacter vector of names in the global environment to
pass to parallel workers. Passed as the argument of the same name to
future.apply::future_lapply and related functions. To set for all runs of
the experiment, use the same argument during initialization.
future.packagesCharacter vector of packages required by parallel
workers. Passed as the argument of the same name to
future.apply::future_lapply and related functions. To set for all runs of
the experiment, use the same argument during initialization.
future.seedPassed as the argument of the same name in
future.apply::future_apply.
use_cachedLogical. If TRUE, find and return previously saved
results. If cached results cannot be found, continue as if use_cached was
FALSE.
return_all_cached_repsLogical. If FALSE (default), returns
only the fit results for the requested n_reps. If TRUE,
returns fit results for the requested n_reps plus any additional
cached replicates from the (DGP, Method) combinations in the
Experiment. Note that even if return_all_cached_reps = TRUE,
only the n_reps replicates are used when evaluating and visualizing
the Experiment.
saveIf TRUE, save outputs to disk.
checkpoint_n_repsThe number of experiment replicates to compute
before saving results to disk. If 0 (the default), no checkpoints are
saved.
verboseLevel of verbosity. Default is 1, which prints out messages
after major checkpoints in the experiment. If 2, prints additional
debugging information for warnings and messages from user-defined functions
(in addition to error debugging information). If 0, no messages are printed
other than user-defined function error debugging information.
...Not used.
Returns
A named list of results from the simulation experiment with the
following entries:
- fit_results
A tibble containing results from the fit
method. In addition to results columns, has columns named '.rep', '.dgp_name',
'.method_name', and the vary_across parameter names if applicable.
- eval_results
A list of tibbles containing results from the
evaluate method, which evaluates each Evaluator in
the Experiment. Length of list is equivalent to the number of
Evaluators.
- viz_results
A list of tibbles containing results from the
visualize method, which visualizes each Visualizer in
the Experiment. Length of list is equivalent to the number of
Visualizers.
Method generate_data()
Generate sample data from all DGP objects that were added
to the Experiment, including their varied params. Primarily useful
for debugging. Note that results are not generated in parallel.
Usage
Experiment$generate_data(n_reps = 1, ...)
Arguments
n_repsThe number of datasets to generate per DGP.
...Not used.
Returns
A list of length equal to the number of DGPs in the
Experiment. If the Experiment does not have a
vary_across component, then each element in the list is a list
of n_reps datasets generated by the given DGP. If the
Experiment does have a vary_across component, then each
element in the outermost list is a list of lists. The second layer of
lists corresponds to a specific parameter setting within the
vary_across scheme, and the innermost layer of lists is of
length n_reps with the dataset replicates, generated by the
DGP.
Method fit()
Fit Methods in the Experiment across all
DGPs for n_reps repetitions and return results from fits.
Usage
Experiment$fit(
n_reps = 1,
parallel_strategy = "reps",
future.globals = NULL,
future.packages = NULL,
future.seed = TRUE,
use_cached = FALSE,
return_all_cached_reps = FALSE,
save = FALSE,
checkpoint_n_reps = 0,
verbose = 1,
...
)
Arguments
n_repsThe number of replicates of the Experiment for this run.
parallel_strategyA vector with some combination of "reps", "dgps", or
"methods". Determines how computation will be distributed across available
resources. Currently only the default, "reps", is supported.
future.globalsCharacter vector of names in the global environment to
pass to parallel workers. Passed as the argument of the same name to
future.apply::future_lapply and related functions. To set for all runs of
the experiment, use the same argument during initialization.
future.packagesCharacter vector of packages required by parallel
workers. Passed as the argument of the same name to
future.apply::future_lapply and related functions. To set for all runs of
the experiment, use the same argument during initialization.
future.seedPassed as the argument of the same name in
future.apply::future_apply.
use_cachedLogical. If TRUE, find and return previously saved
results. If cached results cannot be found, continue as if use_cached was
FALSE.
return_all_cached_repsLogical. If FALSE (default), returns
only the fit results for the requested n_reps. If TRUE,
returns fit results for the requested n_reps plus any additional
cached replicates from the (DGP, Method) combinations in the
Experiment.
saveIf TRUE, save outputs to disk.
checkpoint_n_repsThe number of experiment replicates to compute
before saving results to disk. If 0 (the default), no checkpoints are
saved.
verboseLevel of verbosity. Default is 1, which prints out messages
after major checkpoints in the experiment. If 2, prints additional
debugging information for warnings and messages from user-defined functions
(in addition to error debugging information). If 0, no messages are printed
other than user-defined function error debugging information.
...Additional future.* arguments to pass to future.apply
functions. See future.apply::future_lapply() and
future.apply::future_mapply().
Returns
A tibble containing the results from fitting all Methods
across all DGPs for n_reps repetitions. In addition to
results columns, has columns named '.rep', '.dgp_name', '.method_name', and the
vary_across parameter names if applicable.
Method evaluate()
Evaluate the performance of method(s) across all
Evaluator objects in the Experiment and return results.
Usage
Experiment$evaluate(
fit_results,
use_cached = FALSE,
save = FALSE,
verbose = 1,
...
)
Arguments
fit_resultsA tibble, as returned by fit_experiment().
use_cachedLogical. If TRUE, find and return previously saved
results. If cached results cannot be found, continue as if use_cached was
FALSE.
saveIf TRUE, save outputs to disk.
verboseLevel of verbosity. Default is 1, which prints out messages
after major checkpoints in the experiment. If 2, prints additional
debugging information for warnings and messages from user-defined functions
(in addition to error debugging information). If 0, no messages are printed
other than user-defined function error debugging information.
...Not used.
Returns
A list of evaluation result tibbles, one for each
Evaluator.
Method visualize()
Visualize the performance of methods and/or its evaluation metrics
using all Visualizer objects in the Experiment and return
visualization results.
Usage
Experiment$visualize(
fit_results,
eval_results = NULL,
use_cached = FALSE,
save = FALSE,
verbose = 1,
...
)
Arguments
fit_resultsA tibble, as returned by fit_experiment().
eval_resultsA list of result tibbles, as returned by
evaluate_experiment().
use_cachedLogical. If TRUE, find and return previously saved
results. If cached results cannot be found, continue as if use_cached was
FALSE.
saveIf TRUE, save outputs to disk.
verboseLevel of verbosity. Default is 1, which prints out messages
after major checkpoints in the experiment. If 2, prints additional
debugging information for warnings and messages from user-defined functions
(in addition to error debugging information). If 0, no messages are printed
other than user-defined function error debugging information.
...Not used.
Returns
A list of visualizations, one for each Visualizer.
Method add_dgp()
Add a DGP object to the Experiment.
Usage
Experiment$add_dgp(dgp, name = NULL, ...)
Arguments
dgpA DGP object.
nameA name to identify the DGP.
...Not used.
Returns
The Experiment object, invisibly.
Method update_dgp()
Update a DGP object in the Experiment.
Usage
Experiment$update_dgp(dgp, name, ...)
Arguments
dgpA DGP object.
nameAn existing name identifying the DGP to be updated.
...Not used.
Returns
The Experiment object, invisibly.
Method remove_dgp()
Remove a DGP object from the Experiment.
Usage
Experiment$remove_dgp(name = NULL, ...)
Arguments
nameAn existing name identifying the DGP to be removed.
...Not used
Returns
The Experiment object, invisibly.
Method get_dgps()
Retrieve the DGP objects associated with the Experiment.
Usage
Experiment$get_dgps()
Returns
A named list of the DGP objects in the Experiment.
Method add_method()
Add a Method object to the Experiment.
Usage
Experiment$add_method(method, name = NULL, ...)
Arguments
methodA Method object.
nameA name to identify the Method to be updated.
...Not used.
Returns
The Experiment object, invisibly.
Method update_method()
Update a Method object in the Experiment.
Usage
Experiment$update_method(method, name, ...)
Arguments
methodA Method object.
nameAn existing name identifying the Method to be updated.
...Not used.
Returns
The Experiment object, invisibly.
Method remove_method()
Remove a Method object from the Experiment.
Usage
Experiment$remove_method(name = NULL, ...)
Arguments
nameAn existing name identifying the Method to be removed.
...Not used
Returns
The Experiment object, invisibly.
Method get_methods()
Retrieve the Method objects associated with the Experiment.
Usage
Experiment$get_methods()
Returns
A named list of the Method objects in the Experiment.
Method add_evaluator()
Add an Evaluator object to the Experiment.
Usage
Experiment$add_evaluator(evaluator, name = NULL, ...)
Arguments
evaluatorAn Evaluator object.
nameA name to identify the Evaluator.
...Not used.
Returns
The Experiment object, invisibly.
Method update_evaluator()
Update an Evaluator object in the Experiment.
Usage
Experiment$update_evaluator(evaluator, name, ...)
Arguments
evaluatorAn Evaluator object.
nameAn existing name identifying the Evaluator to be updated.
...Not used.
Returns
The Experiment object, invisibly.
Method remove_evaluator()
Remove an Evaluator object from the Experiment.
Usage
Experiment$remove_evaluator(name = NULL, ...)
Arguments
nameAn existing name identifying the Evaluator to be removed.
...Not used
Returns
The Experiment object, invisibly.
Method get_evaluators()
Retrieve the Evaluator objects associated with the Experiment.
Usage
Experiment$get_evaluators()
Returns
A named list of the Evaluator objects in the Experiment.
Method add_visualizer()
Add a Visualizer object to the Experiment.
Usage
Experiment$add_visualizer(visualizer, name = NULL, ...)
Arguments
visualizerA Visualizer object.
nameA name to identify the Visualizer.
...Not used.
Returns
The Experiment object, invisibly.
Method update_visualizer()
Update a Visualizer object in the Experiment.
Usage
Experiment$update_visualizer(visualizer, name, ...)
Arguments
visualizerA Visualizer object.
nameAn existing name identifying the Visualizer to be updated.
...Not used.
Returns
The Experiment object, invisibly.
Method remove_visualizer()
Remove a Visualizer object from the Experiment.
Usage
Experiment$remove_visualizer(name = NULL, ...)
Arguments
nameAn existing name identifying the Visualizer to be removed.
...Not used
Returns
The Experiment object, invisibly.
Method get_visualizers()
Retrieve the Visualizer objects associated with the Experiment.
Usage
Experiment$get_visualizers()
Returns
A named list of the Visualizer objects in the Experiment.
vary_across component is added and the Experiment is run, the
Experiment is systematically varied across values of the specified
parameter in the DGP or Method while all other parameters are
held constant.
Method add_vary_across()
Usage
Experiment$add_vary_across(.dgp, .method, ...)
Arguments
.dgpName of DGP to vary in the Experiment. Can also be a
DGP object that matches one in the Experiment or even a
vector/list of DGP names/objects, assuming they all support the
target arguments provided via ....
.methodName of Method to vary in the Experiment. Can also be a
Method object that matches one in the Experiment or even a
vector/list of Method names/objects, assuming they all support the
target arguments provided via ....
...Any number of named arguments where names match an argument in the
user-specified DGP or Method function and values are vectors (for
scalar parameters) or lists (for arbitrary parameters).
Details
One of the .dgp or .method arguments (but not both) must
be provided.
Returns
The Experiment object, invisibly.
Method update_vary_across()
Update a vary_across component in the Experiment.
Usage
Experiment$update_vary_across(.dgp, .method, ...)
Arguments
.dgpName of DGP to vary in the Experiment. Can also be a
DGP object that matches one in the Experiment or even a
vector/list of DGP names/objects, assuming they all support the
target arguments provided via ....
.methodName of Method to vary in the Experiment. Can also be a
Method object that matches one in the Experiment or even a
vector/list of Method names/objects, assuming they all support the
target arguments provided via ....
...Any number of named arguments where names match an argument in the
user-specified DGP or Method function and values are vectors (for
scalar parameters) or lists (for arbitrary parameters).
Details
One of the .dgp or .method arguments (but not both) must
be provided.
Returns
The Experiment object, invisibly.
Method remove_vary_across()
Remove a vary_across component in the Experiment.
Usage
Experiment$remove_vary_across(dgp, method, param_names = NULL)
Arguments
dgpName of DGP to vary in the Experiment. Can also be a
DGP object that matches one in the Experiment or even a
vector/list of DGP names/objects.
methodName of Method to vary in the Experiment. Can also be a
Method object that matches one in the Experiment or even a
vector/list of Method names/objects.
param_namesA character vector of parameter names to remove. If not
provided, the entire set of vary_across parameters will be removed for
the specified DGP/Method.
Details
If both the dgp and method arguments are not provided,
then all vary_across parameters from the experiment are removed.
Returns
The Experiment object, invisibly.
Method get_vary_across()
Retrieve the parameters to vary across for each DGP and
Method in the Experiment.
Usage
Experiment$get_vary_across()
Returns
a nested list with entries "dgp" and "method".
Method clear_cache()
Clear (or delete) cached results from an Experiment to
start the experiment fresh/from scratch.
Usage
Experiment$clear_cache()
Returns
The Experiment object, invisibly.
Method get_cached_results()
Read in cached results from disk from a previously saved
Experiment run.
Usage
Experiment$get_cached_results(results_type, verbose = 0)
Arguments
results_typeCharacter string indicating the type of results to read
in. Must be one of "experiment", "experiment_cached_params", "fit", "eval",
or "viz".
verboseLevel of verbosity. Default is 1, which prints out messages
after major checkpoints in the experiment. If 2, prints additional
debugging information for warnings and messages from user-defined functions
(in addition to error debugging information). If 0, no messages are printed
other than user-defined function error debugging information.
Returns
The cached results, specifically the cached Experiment object
if results_type = "experiment", the cached fit results if
results_type = "fit", the cached evaluation results if
results_type = "eval", the cached visualization results if
results_type = "viz", and the experiment parameters used in
the cache if results_type = "experiment_cached_params".
Method set_doc_options()
Set R Markdown options for Evaluator or Visualizer
outputs in the summary report. Some options include the height/width of
plots and number of digits to show in tables.
Usage
Experiment$set_doc_options(field_name, name, show = NULL, nrows, ...)
Arguments
field_nameOne of "evaluator" or "visualizer".
nameName of Evaluator or Visualizer to set R Markdown
options.
showIf TRUE, show output; if FALSE, hide output in
R Markdown report. Default NULL does not change the "doc_show" field
in Evaluator/Visualizer.
nrowsMaximum number of rows to show in the Evaluator's results
table in the R Markdown report. If NULL, shows all rows. Default does
not change the "doc_nrows" field in the Evaluator. Argument is
ignored if field_name = "visualizer".
...Named R Markdown options to set. If field_name = "visualizer",
options are "height" and "width". If field_name = "evaluator",
see options for vthemes::pretty_DT().
Returns
The Experiment object, invisibly.
Method get_save_dir()
Get the directory in which the Experiment's results and
visualizations are saved.
Usage
Experiment$get_save_dir()
Returns
The relative path to where the Experiment's results and
visualizations are saved.
Method set_save_dir()
Set the directory in which the Experiment's results and
visualizations are saved.
Usage
Experiment$set_save_dir(save_dir)
Arguments
save_dirThe directory in which the Experiment's results
will be saved.
Returns
The Experiment object, invisibly.
Method save()
Save the Experiment object to a .rds file under the
Experiment's results directory (see get_save_dir()).
Usage
Experiment$save()
Returns
The Experiment object, invisibly.
Method export_visualizers()
Export all cached Visualizer results from an
Experiment to images in the viz_results/ directory under the
Experiment's results directory (see get_save_dir()).
Usage
Experiment$export_visualizers(
device = "pdf",
width = "auto",
height = "auto",
...
)
Arguments
deviceSee device argument of ggplot2::ggsave().
widthSee width argument of ggplot2::ggsave().
heightSee height argument of ggplot2::ggsave().
...Additional arguments to pass to ggplot2::ggsave().
Returns
The Experiment object, invisibly.
Method print()
Print the Experiment in a nice format, showing the
DGP, Method, Evaluator, Visualizers, and varied parameters
involved in the Experiment.
Usage
Experiment$print()
Returns
The original Experiment object, invisibly.
Method clone()
The objects of this class are cloneable with this method.
Usage
Experiment$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
The following tidy helpers take an Experiment object as their
first argument: create_experiment(), generate_data(),
fit_experiment(), evaluate_experiment(), visualize_experiment(),
run_experiment(), clear_cache(), get_cached_results(),
get_save_dir(), set_save_dir(), save_experiment(),
export_visualizers(), add_*(),
update_*(), remove_*(),
get_*(), and *_vary_across().
Yu-Group/simChef documentation built on March 25, 2024, 3:22 a.m.
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| Experiment | R Documentation |
R6 class representing a simulation experiment.
Description
A simulation experiment with any number of DGP, Method, Evaluator, and Visualizer objects.
Generally speaking, users won't directly interact with the Experiment R6
class, but instead indirectly through create_experiment() and the tidy
Experiment helpers listed in below in the See also section.
Details
When run, an Experiment seamlessly combines DGPs and
Methods, computing results in parallel. Those results can then be
evaluated using Evaluators and visualized using Visualizers.
Public fields
nameThe name of the
Experiment.
Methods
Public methods
Method new()
Initialize a new Experiment object.
Usage
Experiment$new( name = "experiment", dgp_list = list(), method_list = list(), evaluator_list = list(), visualizer_list = list(), future.globals = TRUE, future.packages = NULL, clone_from = NULL, save_dir = NULL, ... )
Arguments
nameThe name of the
Experiment.dgp_listAn optional list of DGP objects.
method_listAn optional list of Method objects.
evaluator_listAn optional list of Evaluator objects.
visualizer_listAn optional list of Visualizer objects.
future.globalsCharacter vector of names in the global environment to pass to parallel workers. Passed as the argument of the same name to
future.apply::future_lapply()and related functions. To set for a specific run of the experiment, use the same argument inrun_experiment().future.packagesCharacter vector of packages required by parallel workers. Passed as the argument of the same name to
future.apply::future_lapply()and related functions. To set for a specific run of the experiment, use the same argument inrun_experiment().clone_fromAn optional
Experimentobject to use as a base for this one.save_dirAn optional directory in which to save the experiment's results. If
NULL, results are saved in the current working directory in a directory called "results" with a sub-directory named afterExperiment$namewhen usingrun_experiment()orfit_experiment()withsave=TRUE....Not used.
Returns
A new instance of Experiment.
Method run()
Run the full Experiment pipeline (fitting, evaluating,
and visualizing).
Usage
Experiment$run( n_reps = 1, parallel_strategy = "reps", future.globals = NULL, future.packages = NULL, future.seed = TRUE, use_cached = FALSE, return_all_cached_reps = FALSE, save = FALSE, checkpoint_n_reps = 0, verbose = 1, ... )
Arguments
n_repsThe number of replicates of the
Experimentfor this run.parallel_strategyA vector with some combination of "reps", "dgps", or "methods". Determines how computation will be distributed across available resources. Currently only the default, "reps", is supported.
future.globalsCharacter vector of names in the global environment to pass to parallel workers. Passed as the argument of the same name to
future.apply::future_lapplyand related functions. To set for all runs of the experiment, use the same argument during initialization.future.packagesCharacter vector of packages required by parallel workers. Passed as the argument of the same name to
future.apply::future_lapplyand related functions. To set for all runs of the experiment, use the same argument during initialization.future.seedPassed as the argument of the same name in
future.apply::future_apply.use_cachedLogical. If
TRUE, find and return previously saved results. If cached results cannot be found, continue as ifuse_cachedwasFALSE.return_all_cached_repsLogical. If
FALSE(default), returns only the fit results for the requestedn_reps. IfTRUE, returns fit results for the requestedn_repsplus any additional cached replicates from the (DGP,Method) combinations in theExperiment. Note that even ifreturn_all_cached_reps = TRUE, only then_repsreplicates are used when evaluating and visualizing theExperiment.saveIf
TRUE, save outputs to disk.checkpoint_n_repsThe number of experiment replicates to compute before saving results to disk. If 0 (the default), no checkpoints are saved.
verboseLevel of verbosity. Default is 1, which prints out messages after major checkpoints in the experiment. If 2, prints additional debugging information for warnings and messages from user-defined functions (in addition to error debugging information). If 0, no messages are printed other than user-defined function error debugging information.
...Not used.
Returns
A named list of results from the simulation experiment with the following entries:
- fit_results
A tibble containing results from the
fitmethod. In addition to results columns, has columns named '.rep', '.dgp_name', '.method_name', and thevary_acrossparameter names if applicable.- eval_results
A list of tibbles containing results from the
evaluatemethod, which evaluates eachEvaluatorin theExperiment. Length of list is equivalent to the number ofEvaluators.- viz_results
A list of tibbles containing results from the
visualizemethod, which visualizes eachVisualizerin theExperiment. Length of list is equivalent to the number ofVisualizers.
Method generate_data()
Generate sample data from all DGP objects that were added
to the Experiment, including their varied params. Primarily useful
for debugging. Note that results are not generated in parallel.
Usage
Experiment$generate_data(n_reps = 1, ...)
Arguments
n_repsThe number of datasets to generate per
DGP....Not used.
Returns
A list of length equal to the number of DGPs in the
Experiment. If the Experiment does not have a
vary_across component, then each element in the list is a list
of n_reps datasets generated by the given DGP. If the
Experiment does have a vary_across component, then each
element in the outermost list is a list of lists. The second layer of
lists corresponds to a specific parameter setting within the
vary_across scheme, and the innermost layer of lists is of
length n_reps with the dataset replicates, generated by the
DGP.
Method fit()
Fit Methods in the Experiment across all
DGPs for n_reps repetitions and return results from fits.
Usage
Experiment$fit( n_reps = 1, parallel_strategy = "reps", future.globals = NULL, future.packages = NULL, future.seed = TRUE, use_cached = FALSE, return_all_cached_reps = FALSE, save = FALSE, checkpoint_n_reps = 0, verbose = 1, ... )
Arguments
n_repsThe number of replicates of the
Experimentfor this run.parallel_strategyA vector with some combination of "reps", "dgps", or "methods". Determines how computation will be distributed across available resources. Currently only the default, "reps", is supported.
future.globalsCharacter vector of names in the global environment to pass to parallel workers. Passed as the argument of the same name to
future.apply::future_lapplyand related functions. To set for all runs of the experiment, use the same argument during initialization.future.packagesCharacter vector of packages required by parallel workers. Passed as the argument of the same name to
future.apply::future_lapplyand related functions. To set for all runs of the experiment, use the same argument during initialization.future.seedPassed as the argument of the same name in
future.apply::future_apply.use_cachedLogical. If
TRUE, find and return previously saved results. If cached results cannot be found, continue as ifuse_cachedwasFALSE.return_all_cached_repsLogical. If
FALSE(default), returns only the fit results for the requestedn_reps. IfTRUE, returns fit results for the requestedn_repsplus any additional cached replicates from the (DGP,Method) combinations in theExperiment.saveIf
TRUE, save outputs to disk.checkpoint_n_repsThe number of experiment replicates to compute before saving results to disk. If 0 (the default), no checkpoints are saved.
verboseLevel of verbosity. Default is 1, which prints out messages after major checkpoints in the experiment. If 2, prints additional debugging information for warnings and messages from user-defined functions (in addition to error debugging information). If 0, no messages are printed other than user-defined function error debugging information.
...Additional
future.*arguments to pass to future.apply functions. Seefuture.apply::future_lapply()andfuture.apply::future_mapply().
Returns
A tibble containing the results from fitting all Methods
across all DGPs for n_reps repetitions. In addition to
results columns, has columns named '.rep', '.dgp_name', '.method_name', and the
vary_across parameter names if applicable.
Method evaluate()
Evaluate the performance of method(s) across all
Evaluator objects in the Experiment and return results.
Usage
Experiment$evaluate( fit_results, use_cached = FALSE, save = FALSE, verbose = 1, ... )
Arguments
fit_resultsA tibble, as returned by
fit_experiment().use_cachedLogical. If
TRUE, find and return previously saved results. If cached results cannot be found, continue as ifuse_cachedwasFALSE.saveIf
TRUE, save outputs to disk.verboseLevel of verbosity. Default is 1, which prints out messages after major checkpoints in the experiment. If 2, prints additional debugging information for warnings and messages from user-defined functions (in addition to error debugging information). If 0, no messages are printed other than user-defined function error debugging information.
...Not used.
Returns
A list of evaluation result tibbles, one for each
Evaluator.
Method visualize()
Visualize the performance of methods and/or its evaluation metrics
using all Visualizer objects in the Experiment and return
visualization results.
Usage
Experiment$visualize( fit_results, eval_results = NULL, use_cached = FALSE, save = FALSE, verbose = 1, ... )
Arguments
fit_resultsA tibble, as returned by
fit_experiment().eval_resultsA list of result tibbles, as returned by
evaluate_experiment().use_cachedLogical. If
TRUE, find and return previously saved results. If cached results cannot be found, continue as ifuse_cachedwasFALSE.saveIf
TRUE, save outputs to disk.verboseLevel of verbosity. Default is 1, which prints out messages after major checkpoints in the experiment. If 2, prints additional debugging information for warnings and messages from user-defined functions (in addition to error debugging information). If 0, no messages are printed other than user-defined function error debugging information.
...Not used.
Returns
A list of visualizations, one for each Visualizer.
Method add_dgp()
Add a DGP object to the Experiment.
Usage
Experiment$add_dgp(dgp, name = NULL, ...)
Arguments
dgpA
DGPobject.nameA name to identify the
DGP....Not used.
Returns
The Experiment object, invisibly.
Method update_dgp()
Update a DGP object in the Experiment.
Usage
Experiment$update_dgp(dgp, name, ...)
Arguments
dgpA
DGPobject.nameAn existing name identifying the
DGPto be updated....Not used.
Returns
The Experiment object, invisibly.
Method remove_dgp()
Remove a DGP object from the Experiment.
Usage
Experiment$remove_dgp(name = NULL, ...)
Arguments
nameAn existing name identifying the
DGPto be removed....Not used
Returns
The Experiment object, invisibly.
Method get_dgps()
Retrieve the DGP objects associated with the Experiment.
Usage
Experiment$get_dgps()
Returns
A named list of the DGP objects in the Experiment.
Method add_method()
Add a Method object to the Experiment.
Usage
Experiment$add_method(method, name = NULL, ...)
Arguments
methodA
Methodobject.nameA name to identify the
Methodto be updated....Not used.
Returns
The Experiment object, invisibly.
Method update_method()
Update a Method object in the Experiment.
Usage
Experiment$update_method(method, name, ...)
Arguments
methodA
Methodobject.nameAn existing name identifying the
Methodto be updated....Not used.
Returns
The Experiment object, invisibly.
Method remove_method()
Remove a Method object from the Experiment.
Usage
Experiment$remove_method(name = NULL, ...)
Arguments
nameAn existing name identifying the
Methodto be removed....Not used
Returns
The Experiment object, invisibly.
Method get_methods()
Retrieve the Method objects associated with the Experiment.
Usage
Experiment$get_methods()
Returns
A named list of the Method objects in the Experiment.
Method add_evaluator()
Add an Evaluator object to the Experiment.
Usage
Experiment$add_evaluator(evaluator, name = NULL, ...)
Arguments
evaluatorAn
Evaluatorobject.nameA name to identify the
Evaluator....Not used.
Returns
The Experiment object, invisibly.
Method update_evaluator()
Update an Evaluator object in the Experiment.
Usage
Experiment$update_evaluator(evaluator, name, ...)
Arguments
evaluatorAn
Evaluatorobject.nameAn existing name identifying the
Evaluatorto be updated....Not used.
Returns
The Experiment object, invisibly.
Method remove_evaluator()
Remove an Evaluator object from the Experiment.
Usage
Experiment$remove_evaluator(name = NULL, ...)
Arguments
nameAn existing name identifying the
Evaluatorto be removed....Not used
Returns
The Experiment object, invisibly.
Method get_evaluators()
Retrieve the Evaluator objects associated with the Experiment.
Usage
Experiment$get_evaluators()
Returns
A named list of the Evaluator objects in the Experiment.
Method add_visualizer()
Add a Visualizer object to the Experiment.
Usage
Experiment$add_visualizer(visualizer, name = NULL, ...)
Arguments
visualizerA
Visualizerobject.nameA name to identify the
Visualizer....Not used.
Returns
The Experiment object, invisibly.
Method update_visualizer()
Update a Visualizer object in the Experiment.
Usage
Experiment$update_visualizer(visualizer, name, ...)
Arguments
visualizerA
Visualizerobject.nameAn existing name identifying the
Visualizerto be updated....Not used.
Returns
The Experiment object, invisibly.
Method remove_visualizer()
Remove a Visualizer object from the Experiment.
Usage
Experiment$remove_visualizer(name = NULL, ...)
Arguments
nameAn existing name identifying the
Visualizerto be removed....Not used
Returns
The Experiment object, invisibly.
Method get_visualizers()
Retrieve the Visualizer objects associated with the Experiment.
Usage
Experiment$get_visualizers()
Returns
A named list of the Visualizer objects in the Experiment.
vary_across component is added and the Experiment is run, the
Experiment is systematically varied across values of the specified
parameter in the DGP or Method while all other parameters are
held constant.
Method add_vary_across()
Usage
Experiment$add_vary_across(.dgp, .method, ...)
Arguments
.dgpName of
DGPto vary in theExperiment. Can also be aDGPobject that matches one in theExperimentor even a vector/list ofDGPnames/objects, assuming they all support the target arguments provided via.....methodName of
Methodto vary in theExperiment. Can also be aMethodobject that matches one in theExperimentor even a vector/list ofMethodnames/objects, assuming they all support the target arguments provided via.......Any number of named arguments where names match an argument in the user-specified
DGPorMethodfunction and values are vectors (for scalar parameters) or lists (for arbitrary parameters).
Details
One of the .dgp or .method arguments (but not both) must
be provided.
Returns
The Experiment object, invisibly.
Method update_vary_across()
Update a vary_across component in the Experiment.
Usage
Experiment$update_vary_across(.dgp, .method, ...)
Arguments
.dgpName of
DGPto vary in theExperiment. Can also be aDGPobject that matches one in theExperimentor even a vector/list ofDGPnames/objects, assuming they all support the target arguments provided via.....methodName of
Methodto vary in theExperiment. Can also be aMethodobject that matches one in theExperimentor even a vector/list ofMethodnames/objects, assuming they all support the target arguments provided via.......Any number of named arguments where names match an argument in the user-specified
DGPorMethodfunction and values are vectors (for scalar parameters) or lists (for arbitrary parameters).
Details
One of the .dgp or .method arguments (but not both) must
be provided.
Returns
The Experiment object, invisibly.
Method remove_vary_across()
Remove a vary_across component in the Experiment.
Usage
Experiment$remove_vary_across(dgp, method, param_names = NULL)
Arguments
dgpName of
DGPto vary in theExperiment. Can also be aDGPobject that matches one in theExperimentor even a vector/list ofDGPnames/objects.methodName of
Methodto vary in theExperiment. Can also be aMethodobject that matches one in theExperimentor even a vector/list ofMethodnames/objects.param_namesA character vector of parameter names to remove. If not provided, the entire set of
vary_acrossparameters will be removed for the specifiedDGP/Method.
Details
If both the dgp and method arguments are not provided,
then all vary_across parameters from the experiment are removed.
Returns
The Experiment object, invisibly.
Method get_vary_across()
Retrieve the parameters to vary across for each DGP and
Method in the Experiment.
Usage
Experiment$get_vary_across()
Returns
a nested list with entries "dgp" and "method".
Method clear_cache()
Clear (or delete) cached results from an Experiment to
start the experiment fresh/from scratch.
Usage
Experiment$clear_cache()
Returns
The Experiment object, invisibly.
Method get_cached_results()
Read in cached results from disk from a previously saved
Experiment run.
Usage
Experiment$get_cached_results(results_type, verbose = 0)
Arguments
results_typeCharacter string indicating the type of results to read in. Must be one of "experiment", "experiment_cached_params", "fit", "eval", or "viz".
verboseLevel of verbosity. Default is 1, which prints out messages after major checkpoints in the experiment. If 2, prints additional debugging information for warnings and messages from user-defined functions (in addition to error debugging information). If 0, no messages are printed other than user-defined function error debugging information.
Returns
The cached results, specifically the cached Experiment object
if results_type = "experiment", the cached fit results if
results_type = "fit", the cached evaluation results if
results_type = "eval", the cached visualization results if
results_type = "viz", and the experiment parameters used in
the cache if results_type = "experiment_cached_params".
Method set_doc_options()
Set R Markdown options for Evaluator or Visualizer
outputs in the summary report. Some options include the height/width of
plots and number of digits to show in tables.
Usage
Experiment$set_doc_options(field_name, name, show = NULL, nrows, ...)
Arguments
field_nameOne of "evaluator" or "visualizer".
nameName of
EvaluatororVisualizerto set R Markdown options.showIf
TRUE, show output; ifFALSE, hide output in R Markdown report. DefaultNULLdoes not change the "doc_show" field inEvaluator/Visualizer.nrowsMaximum number of rows to show in the
Evaluator's results table in the R Markdown report. IfNULL, shows all rows. Default does not change the "doc_nrows" field in theEvaluator. Argument is ignored iffield_name = "visualizer"....Named R Markdown options to set. If
field_name = "visualizer", options are "height" and "width". Iffield_name = "evaluator", see options forvthemes::pretty_DT().
Returns
The Experiment object, invisibly.
Method get_save_dir()
Get the directory in which the Experiment's results and
visualizations are saved.
Usage
Experiment$get_save_dir()
Returns
The relative path to where the Experiment's results and
visualizations are saved.
Method set_save_dir()
Set the directory in which the Experiment's results and
visualizations are saved.
Usage
Experiment$set_save_dir(save_dir)
Arguments
save_dirThe directory in which the
Experiment's results will be saved.
Returns
The Experiment object, invisibly.
Method save()
Save the Experiment object to a .rds file under the
Experiment's results directory (see get_save_dir()).
Usage
Experiment$save()
Returns
The Experiment object, invisibly.
Method export_visualizers()
Export all cached Visualizer results from an
Experiment to images in the viz_results/ directory under the
Experiment's results directory (see get_save_dir()).
Usage
Experiment$export_visualizers( device = "pdf", width = "auto", height = "auto", ... )
Arguments
deviceSee
deviceargument ofggplot2::ggsave().widthSee
widthargument ofggplot2::ggsave().heightSee
heightargument ofggplot2::ggsave()....Additional arguments to pass to
ggplot2::ggsave().
Returns
The Experiment object, invisibly.
Method print()
Print the Experiment in a nice format, showing the
DGP, Method, Evaluator, Visualizers, and varied parameters
involved in the Experiment.
Usage
Experiment$print()
Returns
The original Experiment object, invisibly.
Method clone()
The objects of this class are cloneable with this method.
Usage
Experiment$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
The following tidy helpers take an Experiment object as their
first argument: create_experiment(), generate_data(),
fit_experiment(), evaluate_experiment(), visualize_experiment(),
run_experiment(), clear_cache(), get_cached_results(),
get_save_dir(), set_save_dir(), save_experiment(),
export_visualizers(), add_*(),
update_*(), remove_*(),
get_*(), and *_vary_across().
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