# -*- coding: utf-8 -*-
# Copyright 2018 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.typo.
The distance.typo module implements typo edit distance functions.
"""
from __future__ import division, unicode_literals
from math import log
from numpy import float32 as np_float32
from numpy import zeros as np_zeros
from six.moves import range
__all__ = ['dist_typo', 'sim_typo', 'typo']
[docs]def typo(src, tar, metric='euclidean', cost=(1, 1, 0.5, 0.5), layout='QWERTY'):
"""Return the typo distance between two strings.
This is inspired by Typo-Distance :cite:`Song:2011`, and a fair bit of
this was copied from that module. Compared to the original, this supports
different metrics for substitution.
:param str src: source string for comparison
:param str tar: target string for comparison
:param str metric: supported values include: 'euclidean', 'manhattan',
'log-euclidean', and 'log-manhattan'
:param tuple cost: a 4-tuple representing the cost of the four possible
edits: inserts, deletes, substitutions, and shift, respectively (by
default: (1, 1, 0.5, 0.5)) The substitution & shift costs should be
significantly less than the cost of an insertion & deletion unless
a log metric is used.
:param str layout: name of the keyboard layout to use (Currently supported:
QWERTY, Dvorak, AZERTY, QWERTZ)
:returns: typo distance
:rtype: float
>>> typo('cat', 'hat')
1.5811388
>>> typo('Niall', 'Neil')
2.8251407
>>> typo('Colin', 'Cuilen')
3.4142137
>>> typo('ATCG', 'TAGC')
2.5
>>> typo('cat', 'hat', metric='manhattan')
2.0
>>> typo('Niall', 'Neil', metric='manhattan')
3.0
>>> typo('Colin', 'Cuilen', metric='manhattan')
3.5
>>> typo('ATCG', 'TAGC', metric='manhattan')
2.5
>>> typo('cat', 'hat', metric='log-manhattan')
0.804719
>>> typo('Niall', 'Neil', metric='log-manhattan')
2.2424533
>>> typo('Colin', 'Cuilen', metric='log-manhattan')
2.2424533
>>> typo('ATCG', 'TAGC', metric='log-manhattan')
2.3465736
"""
ins_cost, del_cost, sub_cost, shift_cost = cost
if src == tar:
return 0.0
if not src:
return len(tar) * ins_cost
if not tar:
return len(src) * del_cost
# fmt: off
kbs = {'QWERTY': (
(('`', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '-', '='),
('', 'q', 'w', 'e', 'r', 't', 'y', 'u', 'i', 'o', 'p', '[', ']',
'\\'),
('', 'a', 's', 'd', 'f', 'g', 'h', 'j', 'k', 'l', ';', '\''),
('', 'z', 'x', 'c', 'v', 'b', 'n', 'm', ',', '.', '/')),
(('~', '!', '@', '#', '$', '%', '^', '&', '*', '(', ')', '_', '+'),
('', 'Q', 'W', 'E', 'R', 'T', 'Y', 'U', 'I', 'O', 'P', '{', '}', '|'),
('', 'A', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L', ':', '"'),
('', 'Z', 'X', 'C', 'V', 'B', 'N', 'M', '<', '>', '?'))
), 'Dvorak': (
(('`', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '[', ']'),
('', '\'', ',', '.', 'p', 'y', 'f', 'g', 'c', 'r', 'l', '/', '=',
'\\'),
('', 'a', 'o', 'e', 'u', 'i', 'd', 'h', 't', 'n', 's', '-'),
('', ';', 'q', 'j', 'k', 'x', 'b', 'm', 'w', 'v', 'z')),
(('~', '!', '@', '#', '$', '%', '^', '&', '*', '(', ')', '{', '}'),
('', '"', '<', '>', 'P', 'Y', 'F', 'G', 'C', 'R', 'L', '?', '+', '|'),
('', 'A', 'O', 'E', 'U', 'I', 'D', 'H', 'T', 'N', 'S', '_'),
('', ':', 'Q', 'J', 'K', 'X', 'B', 'M', 'W', 'V', 'Z'))
), 'AZERTY': (
(('²', '&', 'é', '"', '\'', '(', '-', 'è', '_', 'ç', 'à', ')', '='),
('', 'a', 'z', 'e', 'r', 't', 'y', 'u', 'i', 'o', 'p', '', '$'),
('', 'q', 's', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'm', 'ù', '*'),
('<', 'w', 'x', 'c', 'v', 'b', 'n', ',', ';', ':', '!')),
(('~', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '°', '+'),
('', 'A', 'W', 'E', 'R', 'T', 'Y', 'U', 'I', 'O', 'P', '', '£'),
('', 'Q', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'Ù', 'μ'),
('>', 'W', 'X', 'C', 'V', 'B', 'N', '?', '.', '/', '§'))
), 'QWERTZ': (
(('', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', 'ß', ''),
('', 'q', 'w', 'e', 'r', 't', 'z', 'u', 'i', 'o', 'p', ' ü', '+',
'\\'),
('', 'a', 's', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'ö', 'ä', '#'),
('<', 'y', 'x', 'c', 'v', 'b', 'n', 'm', ',', '.', '-')),
(('°', '!', '"', '§', '$', '%', '&', '/', '(', ')', '=', '?', ''),
('', 'Q', 'W', 'E', 'R', 'T', 'Z', 'U', 'I', 'O', 'P', 'Ü', '*', ''),
('', 'A', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'Ö', 'Ä', '\''),
('>', 'Y', 'X', 'C', 'V', 'B', 'N', 'M', ';', ':', '_'))
)}
# fmt: on
keyboard = kbs[layout]
lowercase = {item for sublist in keyboard[0] for item in sublist}
uppercase = {item for sublist in keyboard[1] for item in sublist}
def _kb_array_for_char(char):
"""Return the keyboard layout that contains ch."""
if char in lowercase:
return keyboard[0]
elif char in uppercase:
return keyboard[1]
raise ValueError(char + ' not found in any keyboard layouts')
def _get_char_coord(char, kb_array):
"""Return the row & column of char in the keyboard."""
for row in kb_array: # pragma: no branch
if char in row:
return kb_array.index(row), row.index(char)
def _euclidean_keyboard_distance(char1, char2):
row1, col1 = _get_char_coord(char1, _kb_array_for_char(char1))
row2, col2 = _get_char_coord(char2, _kb_array_for_char(char2))
return ((row1 - row2) ** 2 + (col1 - col2) ** 2) ** 0.5
def _manhattan_keyboard_distance(char1, char2):
row1, col1 = _get_char_coord(char1, _kb_array_for_char(char1))
row2, col2 = _get_char_coord(char2, _kb_array_for_char(char2))
return abs(row1 - row2) + abs(col1 - col2)
def _log_euclidean_keyboard_distance(char1, char2):
return log(1 + _euclidean_keyboard_distance(char1, char2))
def _log_manhattan_keyboard_distance(char1, char2):
return log(1 + _manhattan_keyboard_distance(char1, char2))
metric_dict = {
'euclidean': _euclidean_keyboard_distance,
'manhattan': _manhattan_keyboard_distance,
'log-euclidean': _log_euclidean_keyboard_distance,
'log-manhattan': _log_manhattan_keyboard_distance,
}
def _substitution_cost(char1, char2):
cost = sub_cost
cost *= metric_dict[metric](char1, char2) + shift_cost * (
_kb_array_for_char(char1) != _kb_array_for_char(char2)
)
return cost
d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
for i in range(len(src) + 1):
d_mat[i, 0] = i * del_cost
for j in range(len(tar) + 1):
d_mat[0, j] = j * ins_cost
for i in range(len(src)):
for j in range(len(tar)):
d_mat[i + 1, j + 1] = min(
d_mat[i + 1, j] + ins_cost, # ins
d_mat[i, j + 1] + del_cost, # del
d_mat[i, j]
+ (
_substitution_cost(src[i], tar[j])
if src[i] != tar[j]
else 0
), # sub/==
)
return d_mat[len(src), len(tar)]
[docs]def dist_typo(src, tar, metric='euclidean', cost=(1, 1, 0.5, 0.5)):
"""Return the normalized typo distance between two strings.
This is typo distance, normalized to [0, 1].
:param str src: source string for comparison
:param str tar: target string for comparison
:param str metric: supported values include: 'euclidean', 'manhattan',
'log-euclidean', and 'log-manhattan'
:param tuple cost: a 4-tuple representing the cost of the four possible
edits: inserts, deletes, substitutions, and shift, respectively (by
default: (1, 1, 0.5, 0.5)) The substitution & shift costs should be
significantly less than the cost of an insertion & deletion unless
a log metric is used.
:returns: normalized typo distance
:rtype: float
>>> round(dist_typo('cat', 'hat'), 12)
0.527046283086
>>> round(dist_typo('Niall', 'Neil'), 12)
0.565028142929
>>> round(dist_typo('Colin', 'Cuilen'), 12)
0.569035609563
>>> dist_typo('ATCG', 'TAGC')
0.625
"""
if src == tar:
return 0
ins_cost, del_cost = cost[:2]
return typo(src, tar, metric, cost) / (
max(len(src) * del_cost, len(tar) * ins_cost)
)
[docs]def sim_typo(src, tar, metric='euclidean', cost=(1, 1, 0.5, 0.5)):
"""Return the normalized typo similarity between two strings.
Normalized typo similarity is the complement of normalized typo distance:
:math:`sim_{typo} = 1 - dist_{typo}`.
:param str src: source string for comparison
:param str tar: target string for comparison
:param str metric: supported values include: 'euclidean', 'manhattan',
'log-euclidean', and 'log-manhattan'
:param tuple cost: a 4-tuple representing the cost of the four possible
edits: inserts, deletes, substitutions, and shift, respectively (by
default: (1, 1, 0.5, 0.5)) The substitution & shift costs should be
significantly less than the cost of an insertion & deletion unless
a log metric is used.
:returns: normalized typo similarity
:rtype: float
>>> round(sim_typo('cat', 'hat'), 12)
0.472953716914
>>> round(sim_typo('Niall', 'Neil'), 12)
0.434971857071
>>> round(sim_typo('Colin', 'Cuilen'), 12)
0.430964390437
>>> sim_typo('ATCG', 'TAGC')
0.375
"""
return 1 - dist_typo(src, tar, metric, cost)
if __name__ == '__main__':
import doctest
doctest.testmod()