Source code for

from bisect import bisect_right
from collections import defaultdict
from copy import deepcopy
from functools import partial
from itertools import chain
from operator import eq

def identity(obj):
    """Returns directly the argument *obj*.
    return obj

[docs]class History(object): """The :class:`History` class helps to build a genealogy of all the individuals produced in the evolution. It contains two attributes, the :attr:`genealogy_tree` that is a dictionary of lists indexed by individual, the list contain the indices of the parents. The second attribute :attr:`genealogy_history` contains every individual indexed by their individual number as in the genealogy tree. The produced genealogy tree is compatible with `NetworkX <>`_, here is how to plot the genealogy tree :: history = History() # Decorate the variation operators toolbox.decorate("mate", history.decorator) toolbox.decorate("mutate", history.decorator) # Create the population and populate the history population = toolbox.population(n=POPSIZE) history.update(population) # Do the evolution, the decorators will take care of updating the # history # [...] import matplotlib.pyplot as plt import networkx graph = networkx.DiGraph(history.genealogy_tree) graph = graph.reverse() # Make the graph top-down colors = [toolbox.evaluate(history.genealogy_history[i])[0] for i in graph] networkx.draw(graph, node_color=colors) Using NetworkX in combination with `pygraphviz <>`_ (dot layout) this amazing genealogy tree can be obtained from the OneMax example with a population size of 20 and 5 generations, where the color of the nodes indicate their fitness, blue is low and red is high. .. image:: /_images/genealogy.png :width: 67% .. note:: The genealogy tree might get very big if your population and/or the number of generation is large. """ def __init__(self): self.genealogy_index = 0 self.genealogy_history = dict() self.genealogy_tree = dict()
[docs] def update(self, individuals): """Update the history with the new *individuals*. The index present in their :attr:`history_index` attribute will be used to locate their parents, it is then modified to a unique one to keep track of those new individuals. This method should be called on the individuals after each variation. :param individuals: The list of modified individuals that shall be inserted in the history. If the *individuals* do not have a :attr:`history_index` attribute, the attribute is added and this individual is considered as having no parent. This method should be called with the initial population to initialize the history. Modifying the internal :attr:`genealogy_index` of the history or the :attr:`history_index` of an individual may lead to unpredictable results and corruption of the history. """ try: parent_indices = tuple(ind.history_index for ind in individuals) except AttributeError: parent_indices = tuple() for ind in individuals: self.genealogy_index += 1 ind.history_index = self.genealogy_index self.genealogy_history[self.genealogy_index] = deepcopy(ind) self.genealogy_tree[self.genealogy_index] = parent_indices
@property def decorator(self): """Property that returns an appropriate decorator to enhance the operators of the toolbox. The returned decorator assumes that the individuals are returned by the operator. First the decorator calls the underlying operation and then calls the :func:`update` function with what has been returned by the operator. Finally, it returns the individuals with their history parameters modified according to the update function. """ def decFunc(func): def wrapFunc(*args, **kargs): individuals = func(*args, **kargs) self.update(individuals) return individuals return wrapFunc return decFunc
[docs] def getGenealogy(self, individual, max_depth=float("inf")): """Provide the genealogy tree of an *individual*. The individual must have an attribute :attr:`history_index` as defined by :func:`` in order to retrieve its associated genealogy tree. The returned graph contains the parents up to *max_depth* variations before this individual. If not provided the maximum depth is up to the beginning of the evolution. :param individual: The individual at the root of the genealogy tree. :param max_depth: The approximate maximum distance between the root (individual) and the leaves (parents), optional. :returns: A dictionary where each key is an individual index and the values are a tuple corresponding to the index of the parents. """ gtree = {} visited = set() # Adds memory to the breadth first search def genealogy(index, depth): if index not in self.genealogy_tree: return depth += 1 if depth > max_depth: return parent_indices = self.genealogy_tree[index] gtree[index] = parent_indices for ind in parent_indices: if ind not in visited: genealogy(ind, depth) visited.add(ind) genealogy(individual.history_index, 0) return gtree
[docs]class Statistics(object): """Object that compiles statistics on a list of arbitrary objects. When created the statistics object receives a *key* argument that is used to get the values on which the function will be computed. If not provided the *key* argument defaults to the identity function. The value returned by the key may be a multi-dimensional object, i.e.: a tuple or a list, as long as the statistical function registered support it. So for example, statistics can be computed directly on multi-objective fitnesses when using numpy statistical function. :param key: A function to access the values on which to compute the statistics, optional. :: >>> import numpy >>> s = Statistics() >>> s.register("mean", numpy.mean) >>> s.register("max", max) >>> s.compile([1, 2, 3, 4]) # doctest: +SKIP {'max': 4, 'mean': 2.5} >>> s.compile([5, 6, 7, 8]) # doctest: +SKIP {'mean': 6.5, 'max': 8} """ def __init__(self, key=identity): self.key = key self.functions = dict() self.fields = []
[docs] def register(self, name, function, *args, **kargs): """Register a *function* that will be applied on the sequence each time :meth:`record` is called. :param name: The name of the statistics function as it would appear in the dictionary of the statistics object. :param function: A function that will compute the desired statistics on the data as preprocessed by the key. :param argument: One or more argument (and keyword argument) to pass automatically to the registered function when called, optional. """ self.functions[name] = partial(function, *args, **kargs) self.fields.append(name)
[docs] def compile(self, data): """Apply to the input sequence *data* each registered function and return the results as a dictionary. :param data: Sequence of objects on which the statistics are computed. """ values = tuple(self.key(elem) for elem in data) entry = dict() for key, func in self.functions.items(): entry[key] = func(values) return entry
[docs]class MultiStatistics(dict): """Dictionary of :class:`Statistics` object allowing to compute statistics on multiple keys using a single call to :meth:`compile`. It takes a set of key-value pairs associating a statistics object to a unique name. This name can then be used to retrieve the statistics object. The following code computes statistics simultaneously on the length and the first value of the provided objects. :: >>> from operator import itemgetter >>> import numpy >>> len_stats = Statistics(key=len) >>> itm0_stats = Statistics(key=itemgetter(0)) >>> mstats = MultiStatistics(length=len_stats, item=itm0_stats) >>> mstats.register("mean", numpy.mean, axis=0) >>> mstats.register("max", numpy.max, axis=0) >>> mstats.compile([[0.0, 1.0, 1.0, 5.0], [2.0, 5.0]]) # doctest: +SKIP {'length': {'mean': 3.0, 'max': 4}, 'item': {'mean': 1.0, 'max': 2.0}} """
[docs] def compile(self, data): """Calls :meth:`Statistics.compile` with *data* of each :class:`Statistics` object. :param data: Sequence of objects on which the statistics are computed. """ record = {} for name, stats in self.items(): record[name] = stats.compile(data) return record
@property def fields(self): return sorted(self.keys())
[docs] def register(self, name, function, *args, **kargs): """Register a *function* in each :class:`Statistics` object. :param name: The name of the statistics function as it would appear in the dictionary of the statistics object. :param function: A function that will compute the desired statistics on the data as preprocessed by the key. :param argument: One or more argument (and keyword argument) to pass automatically to the registered function when called, optional. """ for stats in self.values(): stats.register(name, function, *args, **kargs)
[docs]class Logbook(list): """Evolution records as a chronological list of dictionaries. Data can be retrieved via the :meth:`select` method given the appropriate names. The :class:`Logbook` class may also contain other logbooks referred to as chapters. Chapters are used to store information associated to a specific part of the evolution. For example when computing statistics on different components of individuals (namely :class:`MultiStatistics`), chapters can be used to distinguish the average fitness and the average size. """ def __init__(self): self.buffindex = 0 self.chapters = defaultdict(Logbook) """Dictionary containing the sub-sections of the logbook which are also :class:`Logbook`. Chapters are automatically created when the right hand side of a keyworded argument, provided to the *record* function, is a dictionary. The keyword determines the chapter's name. For example, the following line adds a new chapter "size" that will contain the fields "max" and "mean". :: logbook.record(gen=0, size={'max' : 10.0, 'mean' : 7.5}) To access a specific chapter, use the name of the chapter as a dictionary key. For example, to access the size chapter and select the mean use :: logbook.chapters["size"].select("mean") Compiling a :class:`MultiStatistics` object returns a dictionary containing dictionaries, therefore when recording such an object in a logbook using the keyword argument unpacking operator (**), chapters will be automatically added to the logbook. :: >>> fit_stats = Statistics(key=attrgetter("fitness.values")) >>> size_stats = Statistics(key=len) >>> mstats = MultiStatistics(fitness=fit_stats, size=size_stats) >>> # [...] >>> record = mstats.compile(population) >>> logbook.record(**record) >>> print logbook fitness length ------------ ------------ max mean max mean 2 1 4 3 """ self.columns_len = None self.header = None """Order of the columns to print when using the :data:`stream` and :meth:`__str__` methods. The syntax is a single iterable containing string elements. For example, with the previously defined statistics class, one can print the generation and the fitness average, and maximum with :: logbook.header = ("gen", "mean", "max") If not set the header is built with all fields, in arbitrary order on insertion of the first data. The header can be removed by setting it to :data:`None`. """ self.log_header = True """Tells the log book to output or not the header when streaming the first line or getting its entire string representation. This defaults :data:`True`. """
[docs] def record(self, **infos): """Enter a record of event in the logbook as a list of key-value pairs. The information are appended chronologically to a list as a dictionary. When the value part of a pair is a dictionary, the information contained in the dictionary are recorded in a chapter entitled as the name of the key part of the pair. Chapters are also Logbook. """ apply_to_all = {k: v for k, v in infos.items() if not isinstance(v, dict)} for key, value in list(infos.items()): if isinstance(value, dict): chapter_infos = value.copy() chapter_infos.update(apply_to_all) self.chapters[key].record(**chapter_infos) del infos[key] self.append(infos)
[docs] def select(self, *names): """Return a list of values associated to the *names* provided in argument in each dictionary of the Statistics object list. One list per name is returned in order. :: >>> log = Logbook() >>> log.record(gen=0, mean=5.4, max=10.0) >>> log.record(gen=1, mean=9.4, max=15.0) >>>"mean") [5.4, 9.4] >>>"gen", "max") ([0, 1], [10.0, 15.0]) With a :class:`MultiStatistics` object, the statistics for each measurement can be retrieved using the :data:`chapters` member : :: >>> log = Logbook() >>> log.record(**{'gen': 0, 'fit': {'mean': 0.8, 'max': 1.5}, ... 'size': {'mean': 25.4, 'max': 67}}) >>> log.record(**{'gen': 1, 'fit': {'mean': 0.95, 'max': 1.7}, ... 'size': {'mean': 28.1, 'max': 71}}) >>> log.chapters['size'].select("mean") [25.4, 28.1] >>> log.chapters['fit'].select("gen", "max") ([0, 1], [1.5, 1.7]) """ if len(names) == 1: return [entry.get(names[0], None) for entry in self] return tuple([entry.get(name, None) for entry in self] for name in names)
@property def stream(self): """Retrieve the formatted not streamed yet entries of the database including the headers. :: >>> log = Logbook() >>> log.append({'gen' : 0}) >>> print # doctest: +NORMALIZE_WHITESPACE gen 0 >>> log.append({'gen' : 1}) >>> print # doctest: +NORMALIZE_WHITESPACE 1 """ startindex, self.buffindex = self.buffindex, len(self) return self.__str__(startindex) def __delitem__(self, key): if isinstance(key, slice): for i, in range(*key.indices(len(self))): self.pop(i) for chapter in self.chapters.values(): chapter.pop(i) else: self.pop(key) for chapter in self.chapters.values(): chapter.pop(key)
[docs] def pop(self, index=0): """Retrieve and delete element *index*. The header and stream will be adjusted to follow the modification. :param item: The index of the element to remove, optional. It defaults to the first element. You can also use the following syntax to delete elements. :: del log[0] del log[1::5] """ if index < self.buffindex: self.buffindex -= 1 return super(self.__class__, self).pop(index)
def __txt__(self, startindex): columns = self.header if not columns: columns = sorted(self[0].keys()) + sorted(self.chapters.keys()) if not self.columns_len or len(self.columns_len) != len(columns): self.columns_len = [len(c) for c in columns] chapters_txt = {} offsets = defaultdict(int) for name, chapter in self.chapters.items(): chapters_txt[name] = chapter.__txt__(startindex) if startindex == 0: offsets[name] = len(chapters_txt[name]) - len(self) str_matrix = [] for i, line in enumerate(self[startindex:]): str_line = [] for j, name in enumerate(columns): if name in chapters_txt: column = chapters_txt[name][i + offsets[name]] else: value = line.get(name, "") string = "{0:n}" if isinstance(value, float) else "{0}" column = string.format(value) self.columns_len[j] = max(self.columns_len[j], len(column)) str_line.append(column) str_matrix.append(str_line) if startindex == 0 and self.log_header: header = [] nlines = 1 if len(self.chapters) > 0: nlines += max(map(len, chapters_txt.values())) - len(self) + 1 header = [[] for i in range(nlines)] for j, name in enumerate(columns): if name in chapters_txt: length = max(len(line.expandtabs()) for line in chapters_txt[name]) blanks = nlines - 2 - offsets[name] for i in range(blanks): header[i].append(" " * length) header[blanks].append( header[blanks + 1].append("-" * length) for i in range(offsets[name]): header[blanks + 2 + i].append(chapters_txt[name][i]) else: length = max(len(line[j].expandtabs()) for line in str_matrix) for line in header[:-1]: line.append(" " * length) header[-1].append(name) str_matrix = chain(header, str_matrix) template = "\t".join("{%i:<%i}" % (i, k) for i, k in enumerate(self.columns_len)) text = [template.format(*line) for line in str_matrix] return text def __str__(self, startindex=0): text = self.__txt__(startindex) return "\n".join(text)
[docs]class HallOfFame(object): """The hall of fame contains the best individual that ever lived in the population during the evolution. It is lexicographically sorted at all time so that the first element of the hall of fame is the individual that has the best first fitness value ever seen, according to the weights provided to the fitness at creation time. The insertion is made so that old individuals have priority on new individuals. A single copy of each individual is kept at all time, the equivalence between two individuals is made by the operator passed to the *similar* argument. :param maxsize: The maximum number of individual to keep in the hall of fame. :param similar: An equivalence operator between two individuals, optional. It defaults to operator :func:`operator.eq`. The class :class:`HallOfFame` provides an interface similar to a list (without being one completely). It is possible to retrieve its length, to iterate on it forward and backward and to get an item or a slice from it. """ def __init__(self, maxsize, similar=eq): self.maxsize = maxsize self.keys = list() self.items = list() self.similar = similar
[docs] def update(self, population): """Update the hall of fame with the *population* by replacing the worst individuals in it by the best individuals present in *population* (if they are better). The size of the hall of fame is kept constant. :param population: A list of individual with a fitness attribute to update the hall of fame with. """ for ind in population: if len(self) == 0 and self.maxsize != 0: # Working on an empty hall of fame is problematic for the # "for else" self.insert(population[0]) continue if > self[-1].fitness or len(self) < self.maxsize: for hofer in self: # Loop through the hall of fame to check for any # similar individual if self.similar(ind, hofer): break else: # The individual is unique and strictly better than # the worst if len(self) >= self.maxsize: self.remove(-1) self.insert(ind)
[docs] def insert(self, item): """Insert a new individual in the hall of fame using the :func:`~bisect.bisect_right` function. The inserted individual is inserted on the right side of an equal individual. Inserting a new individual in the hall of fame also preserve the hall of fame's order. This method **does not** check for the size of the hall of fame, in a way that inserting a new individual in a full hall of fame will not remove the worst individual to maintain a constant size. :param item: The individual with a fitness attribute to insert in the hall of fame. """ item = deepcopy(item) i = bisect_right(self.keys, self.items.insert(len(self) - i, item) self.keys.insert(i,
[docs] def remove(self, index): """Remove the specified *index* from the hall of fame. :param index: An integer giving which item to remove. """ del self.keys[len(self) - (index % len(self) + 1)] del self.items[index]
[docs] def clear(self): """Clear the hall of fame.""" del self.items[:] del self.keys[:]
def __len__(self): return len(self.items) def __getitem__(self, i): return self.items[i] def __iter__(self): return iter(self.items) def __reversed__(self): return reversed(self.items) def __str__(self): return str(self.items)
[docs]class ParetoFront(HallOfFame): """The Pareto front hall of fame contains all the non-dominated individuals that ever lived in the population. That means that the Pareto front hall of fame can contain an infinity of different individuals. :param similar: A function that tells the Pareto front whether or not two individuals are similar, optional. The size of the front may become very large if it is used for example on a continuous function with a continuous domain. In order to limit the number of individuals, it is possible to specify a similarity function that will return :data:`True` if the genotype of two individuals are similar. In that case only one of the two individuals will be added to the hall of fame. By default the similarity function is :func:`operator.eq`. Since, the Pareto front hall of fame inherits from the :class:`HallOfFame`, it is sorted lexicographically at every moment. """ def __init__(self, similar=eq): HallOfFame.__init__(self, None, similar)
[docs] def update(self, population): """Update the Pareto front hall of fame with the *population* by adding the individuals from the population that are not dominated by the hall of fame. If any individual in the hall of fame is dominated it is removed. :param population: A list of individual with a fitness attribute to update the hall of fame with. """ for ind in population: is_dominated = False dominates_one = False has_twin = False to_remove = [] for i, hofer in enumerate(self): # hofer = hall of famer if not dominates_one and is_dominated = True break elif dominates_one = True to_remove.append(i) elif == and self.similar(ind, hofer): has_twin = True break for i in reversed(to_remove): # Remove the dominated hofer self.remove(i) if not is_dominated and not has_twin: self.insert(ind)
__all__ = ['HallOfFame', 'ParetoFront', 'History', 'Statistics', 'MultiStatistics', 'Logbook'] if __name__ == "__main__": import doctest doctest.run_docstring_examples(Statistics, globals()) doctest.run_docstring_examples(Statistics.register, globals()) doctest.run_docstring_examples(Statistics.compile, globals()) doctest.run_docstring_examples(MultiStatistics, globals()) doctest.run_docstring_examples(MultiStatistics.register, globals()) doctest.run_docstring_examples(MultiStatistics.compile, globals())