TournamentResults

class negmas.tournaments.TournamentResults(scores, total_scores, winners, winners_scores, ttest=None, kstest=None, stats=None, agg_stats=None, score_stats=None, path=None, world_stats=None, type_stats=None, agent_stats=None, params=None, extra_scores=None)[source]

Bases: object

Attributes Summary

`agent_stats`	Some statistics about each agent
`agg_stats`	Aggregated stats per world
`extra_scores`	Extra scores returned from the scoring function.
`kstest`	Results of the nonparametric kstest
`params`	Parameters of the tournament
`path`	Path at which tournament results are stored
`score_stats`	Score statistics for different competitor types
`stats`	Stats of all worlds
`ttest`	Results of ttest analysis of the scores
`type_stats`	Some statistics about each type
`world_stats`	Some statistics about each world run

Attributes Documentation

agent_stats: DataFrame | None = None: Some statistics about each agent

agg_stats: DataFrame | None = None: Aggregated stats per world

extra_scores: dict[str, DataFrame] | None = None: Extra scores returned from the scoring function. This can be used to have multi-dimensional scoring

kstest: DataFrame | None = None: Results of the nonparametric kstest

params: dict[str, Any] | None = None: Parameters of the tournament

path: str | Path | None = None: Path at which tournament results are stored

score_stats: DataFrame | None = None: Score statistics for different competitor types

stats: DataFrame | None = None: Stats of all worlds

ttest: DataFrame | None = None: Results of ttest analysis of the scores

type_stats: DataFrame | None = None: Some statistics about each type

world_stats: DataFrame | None = None: Some statistics about each world run