abydos/distance/_flexmetric.py
# Copyright 2019-2020 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Abydos is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Abydos. If not, see <http://www.gnu.org/licenses/>.
"""abydos.distance._flexmetric.
FlexMetric distance
"""
from typing import (
Any,
Callable,
FrozenSet,
List,
Optional,
Sequence,
Set,
Tuple,
Union,
cast,
)
from numpy import float_ as np_float
from numpy import zeros as np_zeros
from ._distance import _Distance
__all__ = ['FlexMetric']
class FlexMetric(_Distance):
r"""FlexMetric distance.
FlexMetric distance :cite:`Kempken:2005`
.. versionadded:: 0.4.0
"""
def __init__(
self,
normalizer: Callable[[List[float]], float] = max,
indel_costs: Optional[
List[Tuple[Union[Sequence[str], Set[str], FrozenSet[str]], float]]
] = None,
subst_costs: Optional[
List[Tuple[Union[Sequence[str], Set[str], FrozenSet[str]], float]]
] = None,
**kwargs: Any
) -> None:
"""Initialize FlexMetric instance.
Parameters
----------
normalizer : function
A function that takes an list and computes a normalization term
by which the edit distance is divided (max by default). Another
good option is the sum function.
indel_costs : list of tuples
A list of insertion and deletion costs. Each list element should
be a tuple consisting of an iterable (sets are best) and a float
value. The iterable consists of those letters whose insertion
or deletion has a cost equal to the float value.
subst_costs : list of tuples
A list of substitution costs. Each list element should
be a tuple consisting of an iterable (sets are best) and a float
value. The iterable consists of the letters in each letter class,
which may be substituted for each other at cost equal to the float
value.
**kwargs
Arbitrary keyword arguments
.. versionadded:: 0.4.0
"""
super(FlexMetric, self).__init__(**kwargs)
self._normalizer = normalizer
def _get_second(
s: Tuple[Union[Sequence[str], Set[str], FrozenSet[str]], float]
) -> float:
return s[1]
if indel_costs is None:
self._indel_costs = [
(frozenset('dtch'), 0.4),
(frozenset('e'), 0.5),
(frozenset('u'), 0.9),
(frozenset('rpn'), 0.95),
] # type: List[Tuple[Union[Sequence[str], Set[str], FrozenSet[str]], float]] # noqa: E501
else:
self._indel_costs = sorted(indel_costs, key=_get_second)
if subst_costs is None:
self._subst_costs = [
(frozenset('szß'), 0.1),
(frozenset('dt'), 0.1),
(frozenset('iy'), 0.1),
(frozenset('ckq'), 0.1),
(frozenset('eä'), 0.1),
(frozenset('uüv'), 0.1),
(frozenset('iü'), 0.1),
(frozenset('fv'), 0.1),
(frozenset('zc'), 0.1),
(frozenset('ij'), 0.1),
(frozenset('bp'), 0.1),
(frozenset('eoö'), 0.2),
(frozenset('aä'), 0.2),
(frozenset('mbp'), 0.4),
(frozenset('uw'), 0.4),
(frozenset('uo'), 0.8),
(frozenset('aeiouy'), 0.9),
] # type: List[Tuple[Union[Sequence[str], Set[str], FrozenSet[str]], float]] # noqa: E501
else:
self._subst_costs = sorted(subst_costs, key=_get_second)
def _cost(self, src: str, s_pos: int, tar: str, t_pos: int) -> float:
if s_pos == -1:
if t_pos > 0 and tar[t_pos - 1] == tar[t_pos]:
return 0.0
for letter_set in self._indel_costs:
if tar[t_pos] in letter_set[0]:
return letter_set[1]
else:
return 1.0
elif t_pos == -1:
if s_pos > 0 and src[s_pos - 1] == src[s_pos]:
return 0.0
for letter_set in self._indel_costs:
if src[s_pos] in letter_set[0]:
return letter_set[1]
else:
return 1.0
for letter_set in self._subst_costs:
if src[s_pos] in letter_set[0] and tar[t_pos] in letter_set[0]:
return letter_set[1]
else:
return 1.0
def dist_abs(self, src: str, tar: str) -> float:
"""Return the FlexMetric distance of two strings.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
Returns
-------
float
FlexMetric distance
Examples
--------
>>> cmp = FlexMetric()
>>> cmp.dist_abs('cat', 'hat')
0.8
>>> cmp.dist_abs('Niall', 'Neil')
1.5
>>> cmp.dist_abs('aluminum', 'Catalan')
6.7
>>> cmp.dist_abs('ATCG', 'TAGC')
2.1999999999999997
.. versionadded:: 0.4.0
"""
src_len = len(src)
tar_len = len(tar)
if src == tar:
return 0
if not src:
return sum(self._cost('', -1, tar, j) for j in range(len(tar)))
if not tar:
return sum(self._cost(src, i, '', -1) for i in range(len(src)))
d_mat = np_zeros((src_len + 1, tar_len + 1), dtype=np_float)
for i in range(1, src_len + 1):
d_mat[i, 0] = d_mat[i - 1, 0] + self._cost(src, i - 1, '', -1)
for j in range(1, tar_len + 1):
d_mat[0, j] = d_mat[0, j - 1] + self._cost('', -1, tar, j - 1)
src_lc = src.lower()
tar_lc = tar.lower()
for i in range(src_len):
for j in range(tar_len):
d_mat[i + 1, j + 1] = min(
d_mat[i + 1, j] + self._cost('', -1, tar_lc, j), # ins
d_mat[i, j + 1] + self._cost(src_lc, i, '', -1), # del
d_mat[i, j]
+ (
self._cost(src_lc, i, tar_lc, j)
if src[i] != tar[j]
else 0
), # sub/==
)
return cast(float, d_mat[src_len, tar_len])
def dist(self, src: str, tar: str) -> float:
"""Return the normalized FlexMetric distance of two strings.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
Returns
-------
float
Normalized FlexMetric distance
Examples
--------
>>> cmp = FlexMetric()
>>> cmp.dist('cat', 'hat')
0.26666666666666666
>>> cmp.dist('Niall', 'Neil')
0.3
>>> cmp.dist('aluminum', 'Catalan')
0.8375
>>> cmp.dist('ATCG', 'TAGC')
0.5499999999999999
.. versionadded:: 0.4.0
"""
score = self.dist_abs(src, tar)
if score:
return score / self._normalizer([len(src), len(tar)])
return 0.0
if __name__ == '__main__':
import doctest
doctest.testmod()