biosustain / cameo / 963

Committed 15 Feb 2016 - 14:59 coverage decreased (-4.3%) to 62.885%

Build # 963

Build Type

Pull #41

travis-ci

Committed by

phantomas1234

Commit Message

Merge branch 'devel' into structural_analysis_2

Pull Request Pull Request #41: Second try on implementing the enumeration of shortest elementary flux modes and MCS

Run Details

431 of 908 new or added lines in 38 files covered. (47.47%)

678 existing lines in 28 files now uncovered.

3143 of 4998 relevant lines covered (62.89%)

1.23 hits per line

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87.37

/cameo/strain_design/heuristic/evolutionary/variators.py

# Copyright 2014 Novo Nordisk Foundation Center for Biosustainability, DTU.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import, print_function

__all__ = ['set_mutation', 'set_indel']

from six.moves import range

from inspyred.ec.variators import mutator, crossover
from ordered_set import OrderedSet
from cameo.strain_design.heuristic.evolutionary.genomes import MultipleChromosomeGenome
from numpy import float32 as float

import logging
logger = logging.getLogger(__name__)


def _do_set_n_point_crossover(representation, mom, dad, points, random, candidate_size):
    chunks = []
    i = 0
    for point in points:
        chunks.append(representation[i:point])
        i = point
    chunks.append(representation[i:])

    bro = OrderedSet()
    sis = OrderedSet()

    cross = True
    for variables in chunks:
        for v in variables:
            if v in mom:
                bro.append(v) if cross else sis.append(v)
            if v in dad:
                sis.append(v) if cross else bro.append(v)
        cross = not cross

    if len(bro) > candidate_size:
        bro = random.sample(bro, candidate_size)

    if len(sis) > candidate_size:
        sis = random.sample(sis, candidate_size)
    return bro, sis


@crossover
def set_n_point_crossover(random, mom, dad, args):
    representation = list(set(mom).union(set(dad)))
    crossover_rate = args.setdefault('crossover_rate', 1.0)
    num_crossover_points = args.setdefault('num_crossover_points', 1)
    candidate_size = args.setdefault('candidate_size', 9)
    children = []
    if random.random() <= crossover_rate:
        points = random.sample(representation, num_crossover_points)
        bro, sis = _do_set_n_point_crossover(representation, mom, dad, points, random, candidate_size)

        # ensure number of knockouts > 0 or do not add individual
        if len(bro) > 0:
            children.append(bro)
        if len(sis) > 0:
            children.append(sis)
    else:
        children.append(mom)
        children.append(dad)
    return children


@mutator
def set_mutation(random, individual, args):
    """
    Mutates a given set based on the entries available on the representation.

    Parameters
    ----------

    random: Random
    individual: list
        with unique integers
    args: dict
        must contain the representation

    Returns
    -------
    list
        created based on an ordered set

    """
    representation = args.get('representation')
    new_individual = []
    mutation_rate = float(args.get('mutation_rate', .1))
    for index in individual:
        if random.random() < mutation_rate:
            new_individual.append(random.randint(0, len(representation) - 1))
        else:
            new_individual.append(index)

    return list(OrderedSet(new_individual))


@mutator
def set_indel(random, individual, args):
    """
    Creates a random insertion or deletion in the individual.

    Parameters
    ----------

    random: Random
    individual: list
        with unique integers
    args: dict
        must contain the representation

    Returns
    -------
    list
        created based on an ordered set

    """
    max_size = args.get("max_size", 9)
    representation = args.get('representation')
    indel_rate = float(args.get('indel_rate', .1))
    new_individual = list(individual)
    if random.random() < indel_rate:
        logger.info("Applying indel mutation")
        if random.random() > 0.5 and len(new_individual) < max_size:
            new_individual.append(random.sample(range(len(representation)), 1)[0])
        else:
            if len(new_individual) > 1:
                new_individual = random.sample(new_individual, len(new_individual) - 1)

    return list(OrderedSet(new_individual))


@mutator
def multiple_chromosome_set_mutation(random, individual, args):
    """
    Mutates a given set based on the entries available on the representation.

    Parameters
    ----------

    random: Random
    individual: MultipleChromosomeGenome
        with unique integers in each chromosome
    args: dict
        must contain the representation of each chromosome

    Returns
    -------
    MultipleChromosomeGenome
        A mutated individual

    """
    new_individual = MultipleChromosomeGenome(keys=individual.keys)

    for key in individual.keys:
        representation = args.get('%s_representation' % key)
        mutation_rate = args.get('%s_mutation_rate' % key, .1)
        for index in individual[key]:
            if random.random() < mutation_rate:
                new_individual[key].append(random.randint(0, len(representation) - 1))
            else:
                new_individual[key].append(index)

    return new_individual


@mutator
def multiple_chromosome_set_indel(random, individual, args):
    """
    Creates a random insertion or deletion in the individual.

    Parameters
    ----------

    random: Random
    individual: MultipleChromosomeGenome
        with unique integers in each chromosome
    args: dict
        must contain the representation of each chromosome

    Returns
    -------
    MultipleChromosomeGenome
        A mutated individual
    """
    new_individual = individual.copy()
    max_size = args.get("max_size", 9)
    for key in individual.keys:
        representation = args.get('%s_representation' % key)
        indel_rate = args.get('%s_indel_rate' % key, .1)
        if random.random() < indel_rate:
            if random.random() > 0.5 and len(new_individual[key]) < max_size:
                new_individual[key].append(random.sample(range(len(representation)), 1)[0])
            else:
                if len(individual[key]) > 1:
                    new_individual[key] = random.sample(new_individual[key], len(new_individual[key]) - 1)

    return new_individual


@crossover
def multiple_chromosome_n_point_crossover(random, mom, dad, args):
    children = MultipleChromosomeGenome(keys=mom.keys)
    for key in children.keys:
        children[key] = set_n_point_crossover(random, [mom[key], dad[key]], args)

    return children

1	# Copyright 2014 Novo Nordisk Foundation Center for Biosustainability, DTU.
2	#
3	# Licensed under the Apache License, Version 2.0 (the "License");
4	# you may not use this file except in compliance with the License.
5	# You may obtain a copy of the License at
6	#
7	# http://www.apache.org/licenses/LICENSE-2.0
8	#
9	# Unless required by applicable law or agreed to in writing, software
10	# distributed under the License is distributed on an "AS IS" BASIS,
11	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12	# See the License for the specific language governing permissions and
13	# limitations under the License.
14
15	from __future__ import absolute_import, print_function	2×
16
17	__all__ = ['set_mutation', 'set_indel']	2×
18
19	from six.moves import range	2×
20
21	from inspyred.ec.variators import mutator, crossover	2×
22	from ordered_set import OrderedSet	2×
23	from cameo.strain_design.heuristic.evolutionary.genomes import MultipleChromosomeGenome	2×
24	from numpy import float32 as float	2×
25
26	import logging	2×
27	logger = logging.getLogger(__name__)	2×
28
29
30	def _do_set_n_point_crossover(representation, mom, dad, points, random, candidate_size):	2×
31	chunks = []	2×
32	i = 0	2×
33	for point in points:	2×
34	chunks.append(representation[i:point])	2×
35	i = point	2×
36	chunks.append(representation[i:])	2×
37
38	bro = OrderedSet()	2×
39	sis = OrderedSet()	2×
40
41	cross = True	2×
42	for variables in chunks:	2×
43	for v in variables:	2×
44	if v in mom:	2×
45	bro.append(v) if cross else sis.append(v)	2×
46	if v in dad:	2×
47	sis.append(v) if cross else bro.append(v)	2×
48	cross = not cross	2×
49
50	if len(bro) > candidate_size:	2×
UNCOV 51	bro = random.sample(bro, candidate_size)	!
52
53	if len(sis) > candidate_size:	2×
UNCOV 54	sis = random.sample(sis, candidate_size)	!
55	return bro, sis	2×
56
57
58	@crossover	2×
59	def set_n_point_crossover(random, mom, dad, args):
60	representation = list(set(mom).union(set(dad)))	2×
61	crossover_rate = args.setdefault('crossover_rate', 1.0)	2×
62	num_crossover_points = args.setdefault('num_crossover_points', 1)	2×
63	candidate_size = args.setdefault('candidate_size', 9)	2×
64	children = []	2×
65	if random.random() <= crossover_rate:	2×
66	points = random.sample(representation, num_crossover_points)	2×
67	bro, sis = _do_set_n_point_crossover(representation, mom, dad, points, random, candidate_size)	2×
68
69	# ensure number of knockouts > 0 or do not add individual
70	if len(bro) > 0:	2×
71	children.append(bro)	2×
72	if len(sis) > 0:	2×
73	children.append(sis)	2×
74	else:
UNCOV 75	children.append(mom)	!
76	children.append(dad)	!
77	return children	2×
78
79
80	@mutator	2×
81	def set_mutation(random, individual, args):
82	"""
83	Mutates a given set based on the entries available on the representation.
84
85	Parameters
86	----------
87
88	random: Random
89	individual: list
90	with unique integers
91	args: dict
92	must contain the representation
93
94	Returns
95	-------
96	list
97	created based on an ordered set
98
99	"""
100	representation = args.get('representation')	2×
101	new_individual = []	2×
102	mutation_rate = float(args.get('mutation_rate', .1))	2×
103	for index in individual:	2×
104	if random.random() < mutation_rate:	2×
105	new_individual.append(random.randint(0, len(representation) - 1))	2×
106	else:
UNCOV 107	new_individual.append(index)	!
108
109	return list(OrderedSet(new_individual))	2×
110
111
112	@mutator	2×
113	def set_indel(random, individual, args):
114	"""
115	Creates a random insertion or deletion in the individual.
116
117	Parameters
118	----------
119
120	random: Random
121	individual: list
122	with unique integers
123	args: dict
124	must contain the representation
125
126	Returns
127	-------
128	list
129	created based on an ordered set
130
131	"""
132	max_size = args.get("max_size", 9)	2×
133	representation = args.get('representation')	2×
134	indel_rate = float(args.get('indel_rate', .1))	2×
135	new_individual = list(individual)	2×
136	if random.random() < indel_rate:	2×
137	logger.info("Applying indel mutation")	2×
138	if random.random() > 0.5 and len(new_individual) < max_size:	2×
139	new_individual.append(random.sample(range(len(representation)), 1)[0])	2×
140	else:
UNCOV 141	if len(new_individual) > 1:	!
142	new_individual = random.sample(new_individual, len(new_individual) - 1)	!
143
144	return list(OrderedSet(new_individual))	2×
145
146
147	@mutator	2×
148	def multiple_chromosome_set_mutation(random, individual, args):
149	"""
150	Mutates a given set based on the entries available on the representation.
151
152	Parameters
153	----------
154
155	random: Random
156	individual: MultipleChromosomeGenome
157	with unique integers in each chromosome
158	args: dict
159	must contain the representation of each chromosome
160
161	Returns
162	-------
163	MultipleChromosomeGenome
164	A mutated individual
165
166	"""
167	new_individual = MultipleChromosomeGenome(keys=individual.keys)	2×
168
169	for key in individual.keys:	2×
170	representation = args.get('%s_representation' % key)	2×
171	mutation_rate = args.get('%s_mutation_rate' % key, .1)	2×
172	for index in individual[key]:	2×
173	if random.random() < mutation_rate:	2×
174	new_individual[key].append(random.randint(0, len(representation) - 1))	2×
175	else:
UNCOV 176	new_individual[key].append(index)	!
177
178	return new_individual	2×
179
180
181	@mutator	2×
182	def multiple_chromosome_set_indel(random, individual, args):
183	"""
184	Creates a random insertion or deletion in the individual.
185
186	Parameters
187	----------
188
189	random: Random
190	individual: MultipleChromosomeGenome
191	with unique integers in each chromosome
192	args: dict
193	must contain the representation of each chromosome
194
195	Returns
196	-------
197	MultipleChromosomeGenome
198	A mutated individual
199	"""
200	new_individual = individual.copy()	2×
201	max_size = args.get("max_size", 9)	2×
202	for key in individual.keys:	2×
203	representation = args.get('%s_representation' % key)	2×
204	indel_rate = args.get('%s_indel_rate' % key, .1)	2×
205	if random.random() < indel_rate:	2×
206	if random.random() > 0.5 and len(new_individual[key]) < max_size:	2×
207	new_individual[key].append(random.sample(range(len(representation)), 1)[0])	2×
208	else:
209	if len(individual[key]) > 1:	2×
210	new_individual[key] = random.sample(new_individual[key], len(new_individual[key]) - 1)	2×
211
212	return new_individual	2×
213
214
215	@crossover	2×
216	def multiple_chromosome_n_point_crossover(random, mom, dad, args):
UNCOV 217	children = MultipleChromosomeGenome(keys=mom.keys)	!
218	for key in children.keys:	!
219	children[key] = set_n_point_crossover(random, [mom[key], dad[key]], args)	!
220
UNCOV 221	return children	!

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