python3-deap 1.0.2.post2-2 / usr / lib / python3 / dist-packages / deap / tools / selection.py

This file is indexed.

/usr/lib/python3/dist-packages/deap/tools/selection.py is in python3-deap 1.0.2.post2-2.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below. 

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
import random

from functools import partial
from operator import attrgetter

######################################
# Selections                         #
######################################

def selRandom(individuals, k):
    """Select *k* individuals at random from the input *individuals* with
    replacement. The list returned contains references to the input
    *individuals*.
    
    :param individuals: A list of individuals to select from.
    :param k: The number of individuals to select.
    :returns: A list of selected individuals.
    
    This function uses the :func:`~random.choice` function from the
    python base :mod:`random` module.
    """
    return [random.choice(individuals) for i in range(k)]


def selBest(individuals, k):
    """Select the *k* best individuals among the input *individuals*. The
    list returned contains references to the input *individuals*.
    
    :param individuals: A list of individuals to select from.
    :param k: The number of individuals to select.
    :returns: A list containing the k best individuals.
    """
    return sorted(individuals, key=attrgetter("fitness"), reverse=True)[:k]


def selWorst(individuals, k):
    """Select the *k* worst individuals among the input *individuals*. The
    list returned contains references to the input *individuals*.
    
    :param individuals: A list of individuals to select from.
    :param k: The number of individuals to select.
    :returns: A list containing the k worst individuals.
    """
    return sorted(individuals, key=attrgetter("fitness"))[:k]


def selTournament(individuals, k, tournsize):
    """Select *k* individuals from the input *individuals* using *k*
    tournaments of *tournsize* individuals. The list returned contains
    references to the input *individuals*.
    
    :param individuals: A list of individuals to select from.
    :param k: The number of individuals to select.
    :param tournsize: The number of individuals participating in each tournament.
    :returns: A list of selected individuals.
    
    This function uses the :func:`~random.choice` function from the python base
    :mod:`random` module.
    """
    chosen = []
    for i in range(k):
        aspirants = selRandom(individuals, tournsize)
        chosen.append(max(aspirants, key=attrgetter("fitness")))
    return chosen

def selRoulette(individuals, k):
    """Select *k* individuals from the input *individuals* using *k*
    spins of a roulette. The selection is made by looking only at the first
    objective of each individual. The list returned contains references to
    the input *individuals*.
    
    :param individuals: A list of individuals to select from.
    :param k: The number of individuals to select.
    :returns: A list of selected individuals.
    
    This function uses the :func:`~random.random` function from the python base
    :mod:`random` module.
    
    .. warning::
       The roulette selection by definition cannot be used for minimization 
       or when the fitness can be smaller or equal to 0.
    """
    s_inds = sorted(individuals, key=attrgetter("fitness"), reverse=True)
    sum_fits = sum(ind.fitness.values[0] for ind in individuals)
    
    chosen = []
    for i in range(k):
        u = random.random() * sum_fits
        sum_ = 0
        for ind in s_inds:
            sum_ += ind.fitness.values[0]
            if sum_ > u:
                chosen.append(ind)
                break
    
    return chosen


def selDoubleTournament(individuals, k, fitness_size, parsimony_size, fitness_first):
    """Tournament selection which use the size of the individuals in order
    to discriminate good solutions. This kind of tournament is obviously
    useless with fixed-length representation, but has been shown to
    significantly reduce excessive growth of individuals, especially in GP,
    where it can be used as a bloat control technique (see 
    [Luke2002fighting]_). This selection operator implements the double 
    tournament technique presented in this paper.
    
    The core principle is to use a normal tournament selection, but using a
    special sample function to select aspirants, which is another tournament
    based on the size of the individuals. To ensure that the selection
    pressure is not too high, the size of the size tournament (the number
    of candidates evaluated) can be a real number between 1 and 2. In this
    case, the smaller individual among two will be selected with a probability
    *size_tourn_size*/2. For instance, if *size_tourn_size* is set to 1.4,
    then the smaller individual will have a 0.7 probability to be selected.
    
    .. note::
        In GP, it has been shown that this operator produces better results
        when it is combined with some kind of a depth limit.
    
    :param individuals: A list of individuals to select from.
    :param k: The number of individuals to select.
    :param fitness_size: The number of individuals participating in each \
    fitness tournament
    :param parsimony_size: The number of individuals participating in each \
    size tournament. This value has to be a real number\
    in the range [1,2], see above for details.
    :param fitness_first: Set this to True if the first tournament done should \
    be the fitness one (i.e. the fitness tournament producing aspirants for \
    the size tournament). Setting it to False will behaves as the opposite \
    (size tournament feeding fitness tournaments with candidates). It has been \
    shown that this parameter does not have a significant effect in most cases\
    (see [Luke2002fighting]_).
    :returns: A list of selected individuals.
    
    .. [Luke2002fighting] Luke and Panait, 2002, Fighting bloat with 
        nonparametric parsimony pressure
    """
    assert (1 <= parsimony_size <= 2), "Parsimony tournament size has to be in the range [1, 2]."

    def _sizeTournament(individuals, k, select):
        chosen = []
        for i in range(k):
            # Select two individuals from the population
            # The first individual has to be the shortest
            prob = parsimony_size / 2.
            ind1, ind2 = select(individuals, k=2)

            if len(ind1) > len(ind2):
                ind1, ind2 = ind2, ind1
            elif len(ind1) == len(ind2):
                # random selection in case of a tie
                prob = 0.5

            # Since size1 <= size2 then ind1 is selected
            # with a probability prob
            chosen.append(ind1 if random.random() < prob else ind2)

        return chosen
    
    def _fitTournament(individuals, k, select):
        chosen = []
        for i in range(k):
            aspirants = select(individuals, k=fitness_size)
            chosen.append(max(aspirants, key=attrgetter("fitness")))
        return chosen
    
    if fitness_first:
        tfit = partial(_fitTournament, select=selRandom)
        return _sizeTournament(individuals, k, tfit)
    else:
        tsize = partial(_sizeTournament, select=selRandom)
        return _fitTournament(individuals, k, tsize)

__all__ = ['selRandom', 'selBest', 'selWorst', 'selRoulette',
           'selTournament', 'selDoubleTournament']

APT Browse - Built by Thomas Orozco.