DiscreteCartesianOutcomeSpace

class negmas.outcomes.DiscreteCartesianOutcomeSpace(issues, name: str | None = None)[source]

Bases: CartesianOutcomeSpace

A discrete outcome-space that is generated by the cartesian product of a tuple of Issue s (i.e. with finite number of outcomes).

Attributes Summary

`cardinality`	The space cardinality = the number of outcomes
`issue_names`	Returns an ordered list of issue names
`issues`
`name`

Methods Summary

`are_types_ok`(outcome)	Checks if the type of each value in the outcome is correct for the given issue
`cardinality_if_discretized`(levels[, ...])
`contains_issue`(x)	Cheks that the given issue is in the tuple of issues constituting the outcome space (i.e. it is one of its dimensions).
`contains_os`(x)	Checks whether an outcome-space is contained in this outcome-space
`ensure_correct_types`(outcome)	Returns an outcome that is guaratneed to have correct types or raises an exception
`enumerate`()
`enumerate_or_sample`([levels, max_cardinality])	Enumerates all outcomes if possible (i.e. discrete space) or returns `max_cardinality` different outcomes otherwise.
`enumerate_or_sample_rational`(preferences[, ...])	Enumerates all outcomes if possible (i.e. discrete space) or returns `max_cardinality` different outcomes otherwise.
`from_dict`(d[, python_class_identifier])
`from_genius`(file_name, **kwargs)	Imports a utility function from a GENIUS XML file.
`from_outcomes`(outcomes[, numeric_as_ranges, ...])
`from_xml_str`(xml_str[, safe_parsing, name])	Imports a utility function from a GENIUS XML string.
`is_all_continuous`()	Checks whether all issues are discrete
`is_compact`()	Checks whether all issues are complete ranges
`is_discrete`()	Checks whether there are no continua components of the space
`is_finite`()	Checks whether the space is finite
`is_float`()	Checks whether all issues are real
`is_integer`()	Checks whether all issues are integer
`is_not_discrete`()	Checks whether all issues are discrete
`is_numeric`()	Checks whether all issues are numeric
`is_valid`(outcome)
`limit_cardinality`([max_cardinality, levels])	Limits the cardinality of the outcome space to the given maximum (or the number of levels for each issue to `Logging Levels`)
`random_outcome`()
`sample`(n_outcomes[, with_replacement, ...])
`to_dict`([python_class_identifier])
`to_discrete`([levels, max_cardinality])	Discretizes the outcome space by sampling `Logging Levels` values for each continuous issue.
`to_genius`(file_name, **kwargs)	Exports a utility function to a GENIUS XML file.
`to_largest_discrete`(levels[, max_cardinality])
`to_single_issue`([numeric, stringify, ...])	Creates a new outcome space that is a single-issue version of this one
`to_xml_str`(**kwargs)	Exports a utility function to a well formatted string

Attributes Documentation

cardinality

issue_names: Returns an ordered list of issue names

issues: tuple[Issue, ...]

name: str | None

Methods Documentation

are_types_ok(outcome: tuple) → bool: Checks if the type of each value in the outcome is correct for the given issue

cardinality_if_discretized(levels: int, max_cardinality: int | float = inf) → int[source]

contains_issue(x: Issue) → bool: Cheks that the given issue is in the tuple of issues constituting the outcome space (i.e. it is one of its dimensions)

contains_os(x: OutcomeSpace) → bool: Checks whether an outcome-space is contained in this outcome-space

ensure_correct_types(outcome: tuple) → tuple: Returns an outcome that is guaratneed to have correct types or raises an exception

enumerate() → Iterable[tuple][source]

enumerate_or_sample(levels: int | float = inf, max_cardinality: int | float = inf) → Iterable[tuple]: Enumerates all outcomes if possible (i.e. discrete space) or returns max_cardinality different outcomes otherwise

enumerate_or_sample_rational(preferences: Iterable[HasReservedValue | HasReservedOutcome], levels: int | float = inf, max_cardinality: int | float = inf, aggregator: Callable[[Iterable[bool]], bool] = <built-in function any>) → Iterable[Outcome]

Enumerates all outcomes if possible (i.e. discrete space) or returns max_cardinality different outcomes otherwise.

Parameters:

preferences – A list of Preferences that is used to judge outcomes
levels – The number of levels to use for discretization if needed
max_cardinality – The maximum cardinality allowed in case of discretization
aggregator – A predicate that takes an Iterable of booleans representing whether or not an outcome is rational for a given Preferences (i.e. better than reservation) and returns a single boolean representing the result for all preferences. Default is any but can be all.

classmethod from_dict(d, python_class_identifier='__python_class__')

classmethod from_genius(file_name: PathLike, **kwargs) → X

Imports a utility function from a GENIUS XML file.

Parameters:: file_name (str) – File name to import from
Returns:: A utility function object (depending on the input file)

static from_outcomes(outcomes: list[tuple], numeric_as_ranges: bool = False, issue_names: list[str] | None = None, name: str | None = None) → DiscreteCartesianOutcomeSpace

classmethod from_xml_str(xml_str: str, safe_parsing=True, name=None, **kwargs) → CartesianOutcomeSpace

Imports a utility function from a GENIUS XML string.

Parameters:: xml_str (str) – The string containing GENIUS style XML utility function definition
Returns:: A utility function object (depending on the input file)

is_all_continuous() → bool: Checks whether all issues are discrete

is_compact() → bool: Checks whether all issues are complete ranges

is_discrete() → bool[source]: Checks whether there are no continua components of the space

is_finite() → bool: Checks whether the space is finite

is_float() → bool: Checks whether all issues are real

is_integer() → bool: Checks whether all issues are integer

is_not_discrete() → bool: Checks whether all issues are discrete

is_numeric() → bool: Checks whether all issues are numeric

is_valid(outcome: tuple) → bool

limit_cardinality(max_cardinality: int | float = inf, levels: int | float = inf) → DiscreteCartesianOutcomeSpace[source]

Limits the cardinality of the outcome space to the given maximum (or the number of levels for each issue to Logging Levels)

Parameters:

max_cardinality – The maximum number of outcomes in the resulting space
levels – The maximum number of levels for each issue/subissue

random_outcome()

sample(n_outcomes: int, with_replacement: bool = True, fail_if_not_enough=True) → Iterable[tuple]

to_dict(python_class_identifier='__python_class__')

to_discrete(levels: int | float = 10, max_cardinality: int | float = inf) → DiscreteCartesianOutcomeSpace[source]

Discretizes the outcome space by sampling Logging Levels values for each continuous issue.

The result of the discretization is stable in the sense that repeated calls will return the same output.

to_genius(file_name: PathLike, **kwargs) → None

Exports a utility function to a GENIUS XML file.

Parameters:: file_name (str) – File name to export to
Returns:: None

Remarks:: See to_xml_str for all the parameters

to_largest_discrete(levels: int, max_cardinality: int | float = inf, **kwargs) → DiscreteCartesianOutcomeSpace[source]

to_single_issue(numeric=False, stringify=True, levels: int = 5, max_cardinality: int | float = inf) → DiscreteCartesianOutcomeSpace[source]

Creates a new outcome space that is a single-issue version of this one

Parameters:

numeric – If given, the output issue will be a ContiguousIssue otherwise it will be a CategoricalIssue
stringify – If given, the output issue will have string values. Checked only if Numeric and Mathematical Modules is False

Remarks:

maps the agenda and ufuns to work correctly together
Only works if the outcome space is finite

to_xml_str(**kwargs) → str: Exports a utility function to a well formatted string