Inheriting from Numpy¶

DEAP’s creator allows to inherit from numpy.ndarray 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!¶

Inheriting from 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¶

Slicing a 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]


With 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 b leading to the wrong final result. One way of to circumvent this problem is to explicitely copy the view returned by the __getitem__.

>>> 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 numpy.ndarray; none of the operators in the tools module implement such copying. See the One Max with Numpy example for the complete two points crossover.

Comparing Individuals¶

When one wants to use a HallOfFame or ParetoFront hall-of-fame. The similar function should be changed to a compare all function. Using the regular 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()


One must replace the similar operator by a numpy function like numpy.array_equal() or numpy.allclose().

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!"


Performance¶

If your intent is performance, DEAP Speed reveals that using an array.array should be prefered to numpy.ndarray. This is mostly because the creation (also required by the deep copy) of new arrays is longer for the numpy.array than for array.array.

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.