cocopp.algportfolio.DataSet

class documentation

class DataSet(pp.DataSet):

Constructor: DataSet(dslist)

View In Hierarchy

Unit element of algorithm portfolio data set.

Modified class attributes:
  • comment -- comments for the setting (list)
  • algId -- algorithm name (list)
Method __init__ Instantiate one algorithm portfolio data set.
Instance Variable algId Undocumented
Instance Variable comment Undocumented
Instance Variable dim Undocumented
Instance Variable finalfunvals Undocumented
Instance Variable funcId Undocumented
Instance Variable funvals Undocumented
Instance Variable instancenumbers 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

Inherited from DataSet:

Method __eq__ Compare indexEntry instances.
Method __ne__ Undocumented
Method __repr__ Undocumented
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 createDictInstance Returns a dictionary of the instances.
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 detERT Determine the expected running time (ERT) to reach target values. The value is numpy.inf, if the target was never reached.
Method detEvals return len(targets) data rows self.evals[i, 1:].
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 pickle Save this instance to a pickle file.
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 algs Undocumented
Instance Variable dataFiles Undocumented
Instance Variable indexFiles Undocumented
Instance Variable isFinalized Undocumented
Instance Variable pickleFile Undocumented
Instance Variable readfinalFminusFtarget Undocumented
Instance Variable readmaxevals maxevals as read from the info files
Instance Variable reference_values Undocumented
Instance Variable success_ratio 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 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
Instance Variable _target 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 __init__(self, dslist):
overrides cocopp.pproc.DataSet.__init__

Instantiate one algorithm portfolio data set.

Parameters
dslistUndocumented
dict dslistlist of pproc.DataSetList instances.
algId =

Undocumented

comment =
overrides cocopp.pproc.DataSet.comment

Undocumented

dim =

Undocumented

finalfunvals =

Undocumented

funcId =

Undocumented

funvals =
overrides cocopp.pproc.DataSet.funvals

Undocumented

instancenumbers =
overrides cocopp.pproc.DataSet.instancenumbers

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