"""Functions which return a likeness score.
See Also:
The :class:`~.HeuristicScore` class.
"""
from typing import Iterable, Optional
from .types import CandidateType, ContextType, ValueType
VERBOSE: bool = False
"""If ``True`` enable optional DEBUG-level log messages on each score function invocation.
Notes:
Not all functions have verbose messages.
"""
[docs]def modified_hamming(
name: str,
candidates: Iterable[str],
context: Optional[ContextType],
add_length_ratio_term: bool = True,
) -> Iterable[float]:
"""Compute hamming distance modified by length ratio, from the back. Score range is ``[0, 1]``.
Keyword Args:
add_length_ratio_term: If ``True``, score is divided by ``abs(len(name) - len(candidate))``.
Examples:
>>> from .score_functions import modified_hamming
>>> print(list(modified_hamming('aa', ['aa', 'a', 'ab'], context=None)))
[1.0, 0.5, 0.5]
>>> print(list(modified_hamming('face', ['face', 'FAce', 'race', 'place'], context=None)))
[1.0, 0.5, 0.75, 0.375]
"""
def _apply(candidate: str) -> float:
sz = min(len(candidate), len(name))
same = sum([name[i] == candidate[i] for i in range(-sz, 0)])
ratio = (1 / (1 + abs(len(candidate) - len(name)))) if add_length_ratio_term else 1
normalized_hamming = same / sz
return ratio * normalized_hamming
yield from map(_apply, candidates)
[docs]def equality(value: ValueType, candidates: Iterable[CandidateType], context: Optional[ContextType]) -> Iterable[float]:
"""Return 1.0 if ``k == c_i``, 0.0 otherwise.
Examples:
>>> from .score_functions import equality
>>> print(list(equality('a', 'aAb', context=None)))
[1.0, 0.0, 0.0]
"""
yield from map(float, (value == c for c in candidates))