cocopp.bestalg.BestAlgSet

class documentation

class BestAlgSet(DataSet):

Constructor: BestAlgSet(dict_alg, algId)

Unit element of best algorithm data set.

Here unit element means for one function and one dimension. This class is derived from DataSet but it does not inherit from it.

Class attributes:

funcId -- function Id (integer)
dim -- dimension (integer)
comment -- comment for the setting (string)
algId -- algorithm name (string)
evals -- collected data aligned by function values (array)
maxevals -- maximum number of function evaluations (array)

evals and funvals are arrays of data collected from N data sets. Both have the same format: zero-th column is the value on which the data of a row is aligned, the N subsequent columns are either the numbers of function evaluations for evals or function values for funvals.

Known bug: algorithms where the ERT is NaN or Inf are not taken into account!?

Method	`__eq__`	Compare indexEntry instances.
Method	`__init__`	Instantiate one best algorithm data set with name algId.
Method	`__ne__`	Undocumented
Method	`__repr__`	Undocumented
Method	`createDictInstance`	Returns a dictionary of the instances
Method	`detERT`	Determine the expected running time to reach target values.
Method	`detEvals`	Determine the number of evaluations to reach target values.
Method	`get_success_ratio`	Undocumented
Method	`pickle`	Save instance to a pickle file.
Instance Variable	`algbestfinalfunvals`	Undocumented
Instance Variable	`algId`	Undocumented
Instance Variable	`algs`	Undocumented
Instance Variable	`best_algorithm_data`	Undocumented
Instance Variable	`bestfinalfunvals`	Undocumented
Instance Variable	`comment`	Undocumented
Instance Variable	`dim`	Undocumented
Instance Variable	`finalfunvals`	Undocumented
Instance Variable	`funcId`	Undocumented
Instance Variable	`funvalsnofail`	Undocumented
Instance Variable	`instances`	Undocumented
Instance Variable	`pickleFile`	Undocumented
Instance Variable	`precision`	Undocumented
Instance Variable	`success_ratio`	Undocumented
Instance Variable	`suite`	Undocumented
Instance Variable	`used_algorithms`	Undocumented
Instance Variable	`_ert`	Undocumented
Instance Variable	`_evals`	`_evals` are the central data and later accessed via the `evals` property. Each line `_evals[i]` has a (target) function value in `_evals[i][0]` and the function evaluation for which this target was reached the first time in trials 1,...
Instance Variable	`_maxevals`	Undocumented
Instance Variable	`_target`	Undocumented

Inherited from DataSet:

Method	`bootstrap_sample_size`	return minimum size not smaller than `sample_size` such that modulo self.nbRuns() == 0
Method	`computeERTfromEvals`	Sets the attributes ert and target from the attribute evals.
Method	`consistency_check`	checks consistency of data set according to - number of instances - instances used
Method	`createDictInstanceCount`	Returns a dictionary of the instances and their count.
Method	`detAverageEvals`	Determine the average number of f-evals for each target in `targets` list.
Method	`detEvals_by_instance`	return result of `detEvals` for each instance individually
Method	`detSuccesses`	return the number of successful runs for each target.
Method	`detSuccessRates`	return a np.array with the success rate for each target in targets, easiest target first.
Method	`evals_with_simulated_restarts`	Return a `len(targets)` `list` of `samplesize` "simulated runtimes"
Method	`generateRLData`	Determine the running lengths for reaching the target values.
Method	`get_data_format`	Undocumented
Method	`info`	print text info to stdout
Method	`info_str`	return print info as string
Method	`instance_index_lists`	return `OrderedDict` of index lists for each instance.
Method	`isBiobjective`	Undocumented
Method	`median_evals`	return median for each row in `self.evals`, unsuccessful runs count.
Method	`mMaxEvals`	Returns the maximum number of function evaluations over all runs (trials), obsolete and replaced by attribute `max_eval`
Method	`nbRuns`	Returns the number of runs depending on `genericsettings.balance_instances`.
Method	`plot`	plot all data from `evals` attribute and the median.
Method	`plot_funvals`	plot data of `funvals` attribute, versatile
Method	`splitByTrials`	Splits the post-processed data arrays by trials.
Method	`successes_by_instance`	return `OrderedDict` with number of successes for each instance
Instance Variable	`dataFiles`	Undocumented
Instance Variable	`funvals`	Undocumented
Instance Variable	`indexFiles`	Undocumented
Instance Variable	`instancenumbers`	Undocumented
Instance Variable	`isFinalized`	Undocumented
Instance Variable	`readfinalFminusFtarget`	Undocumented
Instance Variable	`readmaxevals`	maxevals as read from the info files
Instance Variable	`reference_values`	Undocumented
Property	`budget_effective_estimates`	return `OrderedDict` of `sum(maxevals) / max(1, #successes)`
Property	`ert`	expected runtimes for the targets in `target`.
Property	`evals`	`evals` contains the central data, number of evaluations.
Property	`evals_appended`	Is this abandoned?
Property	`evals_are_appended`	return `True` if `self.evals_appended` consist of appended trials (same instances are appended)
Property	`instance_multipliers`	number of repetitions per instance to balance a skewed instance distribution.
Property	`instancenumbers_balanced`	return instancenumbers extended with balancing_instancenumbers
Property	`max_eval`	maximum number of function evaluations over all runs (trials),
Property	`maxevals`	maxevals per instance data, i.e. the columns of `evals[:, 1:]`.
Property	`maxfgevals`	maximum of the weighted f+g sum per instance.
Property	`nbRuns_raw`	Undocumented
Property	`number_of_constraints`	number of constraints of the function/problem the `DataSet` is based upon.
Property	`suite_name`	Returns a string, with the name of the DataSet's underlying test suite.
Property	`target`	target values (`np.array`) corresponding to `ert` (which all have finite values)
Property	`trial_count_by_instance`	return `Counter` `dict` with number of trials (actually) done for each instance
Static Method	`_largest_finite_index`	return `i` such that `isfinite(ar[i]) and not isfinite(ar[i+1])`,
Method	`__parseHeader`	Extract data from a header line in an index entry.
Method	`_argsort`	return index array for a sorted order of trials.
Method	`_balanced_evals_row`	append evaluations to `evals_row` to achieve a balanced instance distribution.
Method	`_complement_data`	insert a line for each target value, never used (detEvals(targets) does the job on the fly)
Method	`_cut_data`	attributes `target`, `evals`, and `ert` are truncated to target values not much smaller than defined in attribute `precision` (typically `1e-8`). Attribute `maxevals` is recomputed for columns that reach the final target precision...
Method	`_data_differ`	return a list of targets for which `ds` differs from `self`
Method	`_detEvals2`	Determine the number of evaluations to reach target values.
Method	`_detMaxEvals`	computes for each data column of _evals the (maximal) evaluation until final_target was reached, or `self.maxevals` otherwise.
Method	`_evals_appended_compute`	create evals-array with appended instances.
Method	`_evals_with_simulated_restarts`	return simulated runtimes for each 1D-array in `evals_list`.
Method	`_number_of_better_runs`	return the number of `self.evals(target)` that are smaller
Method	`_old_plot`	plot data from `evals` attribute.
Method	`_update_evals_balanced`	update attribute `_evals_balanced` if necessary.
Method	`_WIP_number_of_better_runs`	return the number of `self.evals([target])` that are better
Class Variable	`_attributes`	Undocumented
Instance Variable	`_ert_nb_of_data`	Undocumented
Instance Variable	`_evals_appended`	Undocumented
Instance Variable	`_evals_balanced`	Undocumented
Instance Variable	`_evals_balanced_raw_data_columns`	Undocumented
Instance Variable	`_extra_attr`	Undocumented
Instance Variable	`_lasttdatfilelines`	Undocumented
Instance Variable	`_maxevals_appended`	Undocumented
Property	`_budget_estimates`	return `OrderedDict` of sum(maxevals) for each (raw data) instance.
Property	`_instance_repetitions`	return the number of runs that repeated a previous instance.
Property	`_need_balancing`	return True of gs.balance_instances and self.instance_multipliers are >1

def __eq__(self, other): ¶

overrides cocopp.pproc.DataSet.__eq__

Compare indexEntry instances.

def __init__(self, dict_alg, algId='Virtual Best Algorithm'): ¶

overrides cocopp.pproc.DataSet.__init__

Instantiate one best algorithm data set with name algId.

Parameters
dict_alg	dictionary of datasets, keys are algorithm names, values are 1-element `DataSetList`.
algId	name of the to-be-constructed algorithm as string

def __ne__(self, other): ¶

overrides cocopp.pproc.DataSet.__ne__

Undocumented

def __repr__(self): ¶

overrides cocopp.pproc.DataSet.__repr__

Undocumented

def createDictInstance(self): ¶

overrides cocopp.pproc.DataSet.createDictInstance

Returns a dictionary of the instances

The key is the instance id, the value is a list of index.

def detERT(self, targets): ¶

overrides cocopp.pproc.DataSet.detERT

Determine the expected running time to reach target values.

Parameters
targets	Undocumented
list targets	target function values of interest
Returns
list of expected running times corresponding to the targets.

def detEvals(self, targets): ¶

overrides cocopp.pproc.DataSet.detEvals

Determine the number of evaluations to reach target values.

Parameters
targets	Undocumented
seq targets	target precisions
Returns
list of arrays each corresponding to one value in targets and the list of the corresponding algorithms

def get_success_ratio(self, target): ¶

Undocumented

def pickle(self, outputdir=None): ¶

overrides cocopp.pproc.DataSet.pickle

Save instance to a pickle file.

Saves the instance to a pickle file. If not specified by argument outputdir, the location of the pickle is given by the location of the first index file associated.

algbestfinalfunvals = ¶

Undocumented

algId = ¶

Undocumented

algs = ¶

overrides cocopp.pproc.DataSet.algs

Undocumented

best_algorithm_data = ¶

Undocumented

bestfinalfunvals = ¶

Undocumented

comment = ¶

overrides cocopp.pproc.DataSet.comment

Undocumented

dim = ¶

Undocumented

finalfunvals = ¶

Undocumented

funcId = ¶

Undocumented

funvalsnofail = ¶

Undocumented

instances = ¶

Undocumented

pickleFile = ¶

overrides cocopp.pproc.DataSet.pickleFile

Undocumented

precision = ¶

Undocumented

success_ratio = ¶

overrides cocopp.pproc.DataSet.success_ratio

Undocumented

suite = ¶

Undocumented

used_algorithms = ¶

Undocumented

_ert = ¶

overrides cocopp.pproc.DataSet._ert

Undocumented

_evals = ¶

overrides cocopp.pproc.DataSet._evals

_evals are the central data and later accessed via the evals property. Each line _evals[i] has a (target) function value in _evals[i][0] and the function evaluation for which this target was reached the first time in trials 1,... in _evals[i][1:].

_maxevals = ¶

overrides cocopp.pproc.DataSet._maxevals

Undocumented

_target = ¶

overrides cocopp.pproc.DataSet._target

Undocumented