abydos.distance._needleman_wunsch.NeedlemanWunsch.dist_abs() - Code Metrics - Inspection of "started new entry in HISTORY for 0.4.0" - chrislit/abydos - Measure and Improve Code Quality continuously with Scrutinizer

Test Failed

Push — master ( 23810f...afe14d )

by Chris

created 2019-06-01 00:50 UTC

NeedlemanWunsch.dist_abs() A

↳ Parent: abydos.distance._needleman_wunsch

Complexity

Conditions

Size

Total Lines	46
Code Lines	13

Duplication

Lines	38
Ratio	82.61 %

Code Coverage

Tests	13
CRAP Score	5

Importance

Changes

Metric	Value
cc	5
eloc	13
nop	5
dl	38
loc	46
ccs	13
cts	13
cp	1
crap	5
rs	9.2833
c	0
b	0
f	0

# -*- coding: utf-8 -*-

# Copyright 2014-2018 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._needleman_wunsch.

Needleman-Wunsch score
"""

from __future__ import (
    absolute_import,
    division,
    print_function,
    unicode_literals,
)

from numpy import float32 as np_float32
from numpy import zeros as np_zeros

from six.moves import range

from ._distance import _Distance
from ._ident import sim_ident

__all__ = ['NeedlemanWunsch', 'needleman_wunsch']


class NeedlemanWunsch(_Distance):
    """Needleman-Wunsch score.

    The Needleman-Wunsch score :cite:`Needleman:1970` is a standard edit
    distance measure.
    """

    @staticmethod
    def sim_matrix(
        src,
        tar,
        mat=None,
        mismatch_cost=0,
        match_cost=1,
        symmetric=True,
        alphabet=None,
    ):
        """Return the matrix similarity of two strings.

        With the default parameters, this is identical to sim_ident.
        It is possible for sim_matrix to return values outside of the range
        :math:`[0, 1]`, if values outside that range are present in mat,
        mismatch_cost, or match_cost.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        mat : dict
            A dict mapping tuples to costs; the tuples are (src, tar) pairs of
            symbols from the alphabet parameter
        mismatch_cost : float
            The value returned if (src, tar) is absent from mat when src does
            not equal tar
        match_cost : float
            The value returned if (src, tar) is absent from mat when src equals
            tar
        symmetric : bool
            True if the cost of src not matching tar is identical to the cost
            of tar not matching src; in this case, the values in mat need only
            contain (src, tar) or (tar, src), not both
        alphabet : str
            A collection of tokens from which src and tar are drawn; if this is
            defined a ValueError is raised if either tar or src is not found in
            alphabet

        Returns
        -------
        float
            Matrix similarity

        Raises
        ------
        ValueError
            src value not in alphabet
        ValueError
            tar value not in alphabet

        Examples
        --------
        >>> NeedlemanWunsch.sim_matrix('cat', 'hat')
        0
        >>> NeedlemanWunsch.sim_matrix('hat', 'hat')
        1

        """
        if alphabet:
            alphabet = tuple(alphabet)
            for i in src:
                if i not in alphabet:
                    raise ValueError('src value not in alphabet')
            for i in tar:
                if i not in alphabet:
                    raise ValueError('tar value not in alphabet')

        if src == tar:
            if mat and (src, src) in mat:
                return mat[(src, src)]
            return match_cost
        if mat and (src, tar) in mat:
            return mat[(src, tar)]
        elif symmetric and mat and (tar, src) in mat:
            return mat[(tar, src)]
        return mismatch_cost

    def dist_abs(self, src, tar, gap_cost=1, sim_func=sim_ident):

        """Return the Needleman-Wunsch score of two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        gap_cost : float
            The cost of an alignment gap (1 by default)
        sim_func : function
            A function that returns the similarity of two characters (identity
            similarity by default)

        Returns
        -------
        float
            Needleman-Wunsch score

        Examples
        --------
        >>> cmp = NeedlemanWunsch()
        >>> cmp.dist_abs('cat', 'hat')
        2.0
        >>> cmp.dist_abs('Niall', 'Neil')
        1.0
        >>> cmp.dist_abs('aluminum', 'Catalan')
        -1.0
        >>> cmp.dist_abs('ATCG', 'TAGC')
        0.0

        """
        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 * gap_cost)
        for j in range(len(tar) + 1):
            d_mat[0, j] = -(j * gap_cost)
        for i in range(1, len(src) + 1):
            for j in range(1, len(tar) + 1):
                match = d_mat[i - 1, j - 1] + sim_func(src[i - 1], tar[j - 1])
                delete = d_mat[i - 1, j] - gap_cost
                insert = d_mat[i, j - 1] - gap_cost
                d_mat[i, j] = max(match, delete, insert)
        return d_mat[d_mat.shape[0] - 1, d_mat.shape[1] - 1]


def needleman_wunsch(src, tar, gap_cost=1, sim_func=sim_ident):
    """Return the Needleman-Wunsch score of two strings.

    This is a wrapper for :py:meth:`NeedlemanWunsch.dist_abs`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    gap_cost : float
        The cost of an alignment gap (1 by default)
    sim_func : function
        A function that returns the similarity of two characters (identity
        similarity by default)

    Returns
    -------
    float
        Needleman-Wunsch score

    Examples
    --------
    >>> needleman_wunsch('cat', 'hat')
    2.0
    >>> needleman_wunsch('Niall', 'Neil')
    1.0
    >>> needleman_wunsch('aluminum', 'Catalan')
    -1.0
    >>> needleman_wunsch('ATCG', 'TAGC')
    0.0

    """
    return NeedlemanWunsch().dist_abs(src, tar, gap_cost, sim_func)


if __name__ == '__main__':
    import doctest

    doctest.testmod()


1			# -- coding: utf-8 --
2
3			# Copyright 2014-2018 by Christopher C. Little.
4			# This file is part of Abydos.
5			#
6			# Abydos is free software: you can redistribute it and/or modify
7			# it under the terms of the GNU General Public License as published by
8			# the Free Software Foundation, either version 3 of the License, or
9			# (at your option) any later version.
10			#
11			# Abydos is distributed in the hope that it will be useful,
12			# but WITHOUT ANY WARRANTY; without even the implied warranty of
13			# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14			# GNU General Public License for more details.
15			#
16			# You should have received a copy of the GNU General Public License
17			# along with Abydos. If not, see <http://www.gnu.org/licenses/>.
18
19	1		"""abydos.distance._needleman_wunsch.
20
21			Needleman-Wunsch score
22			"""
23
24	1		from __future__ import (
25			absolute_import,
26			division,
27			print_function,
28			unicode_literals,
29			)
30
31	1		from numpy import float32 as np_float32
32	1		from numpy import zeros as np_zeros
33
34	1		from six.moves import range
35
36	1		from ._distance import _Distance
37	1		from ._ident import sim_ident
38
39	1		__all__ = ['NeedlemanWunsch', 'needleman_wunsch']
40
41
42	1		class NeedlemanWunsch(_Distance):
43			"""Needleman-Wunsch score.
44
45			The Needleman-Wunsch score :cite:`Needleman:1970` is a standard edit
46			distance measure.
47			"""
48
49	1		@staticmethod
50	1		def sim_matrix(
51			src,
52			tar,
53			mat=None,
54			mismatch_cost=0,
55			match_cost=1,
56			symmetric=True,
57			alphabet=None,
58			):
59			"""Return the matrix similarity of two strings.
60
61			With the default parameters, this is identical to sim_ident.
62			It is possible for sim_matrix to return values outside of the range
63			:math:`[0, 1]`, if values outside that range are present in mat,
64			mismatch_cost, or match_cost.
65
66			Parameters
67			----------
68			src : str
69			Source string for comparison
70			tar : str
71			Target string for comparison
72			mat : dict
73			A dict mapping tuples to costs; the tuples are (src, tar) pairs of
74			symbols from the alphabet parameter
75			mismatch_cost : float
76			The value returned if (src, tar) is absent from mat when src does
77			not equal tar
78			match_cost : float
79			The value returned if (src, tar) is absent from mat when src equals
80			tar
81			symmetric : bool
82			True if the cost of src not matching tar is identical to the cost
83			of tar not matching src; in this case, the values in mat need only
84			contain (src, tar) or (tar, src), not both
85			alphabet : str
86			A collection of tokens from which src and tar are drawn; if this is
87			defined a ValueError is raised if either tar or src is not found in
88			alphabet
89
90			Returns
91			-------
92			float
93			Matrix similarity
94
95			Raises
96			------
97			ValueError
98			src value not in alphabet
99			ValueError
100			tar value not in alphabet
101
102			Examples
103			--------
104			>>> NeedlemanWunsch.sim_matrix('cat', 'hat')
105			0
106			>>> NeedlemanWunsch.sim_matrix('hat', 'hat')
107			1
108
109			"""
110	1		if alphabet:
111	1		alphabet = tuple(alphabet)
112	1		for i in src:
113	1		if i not in alphabet:
114	1		raise ValueError('src value not in alphabet')
115	1		for i in tar:
116	1		if i not in alphabet:
117	1		raise ValueError('tar value not in alphabet')
118
119	1		if src == tar:
120	1		if mat and (src, src) in mat:
121	1		return mat[(src, src)]
122	1		return match_cost
123	1		if mat and (src, tar) in mat:
124	1		return mat[(src, tar)]
125	1		elif symmetric and mat and (tar, src) in mat:
126	1		return mat[(tar, src)]
127	1		return mismatch_cost
128
129	1	View Code Duplication	def dist_abs(self, src, tar, gap_cost=1, sim_func=sim_ident):
			0 ignored issues – show Duplication introduced 2018-11-04 08:02 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
130			"""Return the Needleman-Wunsch score of two strings.
131
132			Parameters
133			----------
134			src : str
135			Source string for comparison
136			tar : str
137			Target string for comparison
138			gap_cost : float
139			The cost of an alignment gap (1 by default)
140			sim_func : function
141			A function that returns the similarity of two characters (identity
142			similarity by default)
143
144			Returns
145			-------
146			float
147			Needleman-Wunsch score
148
149			Examples
150			--------
151			>>> cmp = NeedlemanWunsch()
152			>>> cmp.dist_abs('cat', 'hat')
153			2.0
154			>>> cmp.dist_abs('Niall', 'Neil')
155			1.0
156			>>> cmp.dist_abs('aluminum', 'Catalan')
157			-1.0
158			>>> cmp.dist_abs('ATCG', 'TAGC')
159			0.0
160
161			"""
162	1		d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
163
164	1		for i in range(len(src) + 1):
165	1		d_mat[i, 0] = -(i * gap_cost)
166	1		for j in range(len(tar) + 1):
167	1		d_mat[0, j] = -(j * gap_cost)
168	1		for i in range(1, len(src) + 1):
169	1		for j in range(1, len(tar) + 1):
170	1		match = d_mat[i - 1, j - 1] + sim_func(src[i - 1], tar[j - 1])
171	1		delete = d_mat[i - 1, j] - gap_cost
172	1		insert = d_mat[i, j - 1] - gap_cost
173	1		d_mat[i, j] = max(match, delete, insert)
174	1		return d_mat[d_mat.shape[0] - 1, d_mat.shape[1] - 1]
175
176
177	1		def needleman_wunsch(src, tar, gap_cost=1, sim_func=sim_ident):
178			"""Return the Needleman-Wunsch score of two strings.
179
180			This is a wrapper for :py:meth:`NeedlemanWunsch.dist_abs`.
181
182			Parameters
183			----------
184			src : str
185			Source string for comparison
186			tar : str
187			Target string for comparison
188			gap_cost : float
189			The cost of an alignment gap (1 by default)
190			sim_func : function
191			A function that returns the similarity of two characters (identity
192			similarity by default)
193
194			Returns
195			-------
196			float
197			Needleman-Wunsch score
198
199			Examples
200			--------
201			>>> needleman_wunsch('cat', 'hat')
202			2.0
203			>>> needleman_wunsch('Niall', 'Neil')
204			1.0
205			>>> needleman_wunsch('aluminum', 'Catalan')
206			-1.0
207			>>> needleman_wunsch('ATCG', 'TAGC')
208			0.0
209
210			"""
211	1		return NeedlemanWunsch().dist_abs(src, tar, gap_cost, sim_func)
212
213
214			if __name__ == '__main__':
215			import doctest
216
217			doctest.testmod()
218

chrislit / abydos

Push — master ( 23810f...afe14d )

NeedlemanWunsch.dist_abs() A

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