Inheriting from Numpy¶
creator allows to inherit from
so that individuals can have the properties of the powerful
Numpy library. As with any other
base class, inheriting from a
numpy.ndarray is no more complicated
than putting it as a base class.
import numpy from deap import base, creator creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMax)
What You Should be Concerned With!¶
numpy.ndarray is an appealing feature, but some care
must be taken regarding validity of the data and performance of the system.
Copy and Slicing¶
numpy.ndarray should be done with care. The returned
element is a
numpy.ndarray.view() of the original object. This leads to
bug prone code when swapping data from one array to another. For example, the
two points crossover use the following for swapping data between two lists.
>>> a = [1,2,3,4] >>> b = [5,6,7,8] >>> a[1:3], b[1:3] = b[1:3], a[1:3] >>> print(a) [1, 6, 7, 4] >>> print(b) [5, 2, 3, 8]
numpy.array, the same operation leads to a single resulting
individual being changed.
>>> import numpy >>> a = numpy.array([1,2,3,4]) >>> b = numpy.array([5,6,7,8]) >>> a[1:3], b[1:3] = b[1:3], a[1:3] >>> print(a) [1 6 7 4] >>> print(b) [5 6 7 8]
The problem is that, first, the elements in
a are replaced by the
elements of the view returned by
b and the element of
b are replaced
by the element in the view of
a which are now the one intially in
leading to the wrong final result. One way of to circumvent this problem is
to explicitely copy the view returned by the
>>> import numpy >>> a = numpy.array([1,2,3,4]) >>> b = numpy.array([5,6,7,8]) >>> a[1:3], b[1:3] = b[1:3].copy(), a[1:3].copy() >>> print(a) [1 6 7 4] >>> print(b) [5 2 3 8]
Thus, care must be taken when inheriting from
none of the operators in the
tools module implement such
copying. See the One Max with Numpy example for the complete two points
When one wants to use a
hall-of-fame. The similar function should be changed to a compare all function. Using
operator.eq() function will result in a vector of comparisons
>>> a = numpy.array([1, 2, 3]) >>> b = numpy.array([1, 2, 3]) >>> operator.eq(a, b) array([ True, True, True], dtype=bool)
This cannot be used as a condition
>>> if operator.eq(a, b): ... print "Gosh!" ... Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
hof = tools.HallOfFame(1, similar=numpy.array_equal)
Now the condition can be computed and the hall-of-fame will be happy.
>>> if numpy.array_equal(a, b): ... print "Yeah!" "Yeah!"
What You Don’t Need to Know¶
The creator replaces systematically several functions of the basic
numpy.ndarray so that
- array instances can be created from an iterable;
- it deep copies the attributes added in the
__dict__of the object;
- pickling includes the dictionary of attributes.
See the implementation of
_numpy_array in the
creator module for more details.