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ENH: avoid weird-looking float approximations when computing alpha(s). Clarify code. #603

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Jan 22, 2025
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ENH: avoid weird-looking float approximations when computing alpha(s). Clarify code. #603

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16 changes: 12 additions & 4 deletions mapie_v1/_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,16 +4,24 @@
from numpy import array
from mapie._typing import ArrayLike, NDArray
from sklearn.model_selection import BaseCrossValidator
from decimal import Decimal


def transform_confidence_level_to_alpha_list(
confidence_level: Union[float, List[float]]
) -> List[float]:
if isinstance(confidence_level, float):
confidence_levels = [confidence_level]
else:
if isinstance(confidence_level, list):
confidence_levels = confidence_level
return [1 - level for level in confidence_levels]
else:
confidence_levels = [confidence_level]

# Using decimals to avoid weird-looking float approximations
# when computing alpha = 1 - confidence_level
# Such approximations arise even with simple confidence levels like 0.9
confidence_levels_d = [Decimal(str(conf_level)) for conf_level in confidence_levels]
alphas_d = [Decimal("1") - conf_level_d for conf_level_d in confidence_levels_d]

return [float(alpha_d) for alpha_d in alphas_d]


def check_if_param_in_allowed_values(
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