src.pm_rank.model.utils

Attributes

AGGREGATE_FNS

Functions

get_logger([name])
forecaster_data_to_rankings(forecaster_data[, ...])	Convert the forecaster data to rankings.
spearman_correlation(→ float)	Compute the Spearman correlation between two rankings.
kendall_correlation(→ float)	Compute the Kendall correlation between two rankings.
log_ranking_table(logger, ranking_result[, max_rows])	Take in any model's ranking result, which might or might not include scores, and log the table.

Module Contents

src.pm_rank.model.utils.get_logger(name: str = 'pm_rank.model')

src.pm_rank.model.utils.AGGREGATE_FNS

src.pm_rank.model.utils.forecaster_data_to_rankings(forecaster_data: Dict[str, List[float]], include_scores: bool = True, ascending: bool = True, aggregate: Literal['mean', 'median', 'max', 'min'] = 'mean')

Convert the forecaster data to rankings. A forecaster data is a dictionary that maps forecaster name to a list of scores.

Args:

forecaster_data: a dictionary that maps forecaster name to a list of scores. include_scores: whether to include the scores in the rankings. ascending: if true, the score is smaller, the better; otherwise, the score is larger, the better.

Returns:

A dictionary that maps forecaster name to a list of rankings.

src.pm_rank.model.utils.spearman_correlation(rank_dict_a: Dict[str, int], rank_dict_b: Dict[str, int]) → float

Compute the Spearman correlation between two rankings. Reference: https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient

src.pm_rank.model.utils.kendall_correlation(rank_dict_a: Dict[str, int], rank_dict_b: Dict[str, int]) → float

Compute the Kendall correlation between two rankings. Reference: https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient

src.pm_rank.model.utils.log_ranking_table(logger: logging.Logger, ranking_result: Tuple[Dict[str, Any], Dict[str, int]] | Dict[str, int], max_rows: int = 20)

Take in any model’s ranking result, which might or might not include scores, and log the table.