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.. _creating-types:

Creating Types
==============
This tutorial shows how types are created using the creator and initialized
using the toolbox.

Fitness
-------
The provided :class:`~deap.base.Fitness` class is an abstract class that needs a
:attr:`~deap.base.Fitness.weights` attribute in order to be functional. A
minimizing fitness is built using negatives weights, while a maximizing fitness
has positive weights. For example, the following line creates, in the
:mod:`~deap.creator`, a ready to use single objective minimizing fitness named
:class:`FitnessMin`.

.. literalinclude:: /code/tutorials/part_2/2_1_fitness.py
   :lines: 6

The :func:`~deap.creator.create` function takes at least two arguments, a name for the newly created class and a base class. Any subsequent argument becomes an attribute of the class.
As specified in the :class:`~deap.base.Fitness` documentation, the :attr:`~deap.base.Fitness.weights` attribute must be a tuple so that multi-objective and single objective fitnesses can be treated the same way. A
:class:`FitnessMulti` would be created the same way but using:

.. literalinclude:: /code/tutorials/part_2/2_1_fitness.py
   :lines: 9

This code produces a fitness that minimizes the first objective and maximize the
second one. The weights can also be used to vary the importance of each
objective one against another. This means that the weights can be any real
number and only the sign is used to determine if a maximization or minimization
is done. An example of where the weights can be useful is in the crowding
distance sort made in the NSGA-II selection algorithm.

Individual
----------
Simply by thinking about the different flavors of evolutionary algorithms (GA,
GP, ES, PSO, DE, ...), we notice that an extremely large variety of individuals
are possible, reinforcing the assumption that all types cannot be made available
by developers. Here is a guide on how to create some of those individuals using
the :mod:`~deap.creator` and initializing them using a
:class:`~deap.base.Toolbox`.

.. warning::

   Before inheriting from :class:`numpy.ndarray` you should **absolutely** read
   the :doc:`/tutorials/advanced/numpy` tutorial and have a look at the
   :doc:`/examples/ga_onemax_numpy` example!

.. _list-of-floats:

List of Floats
++++++++++++++
The first individual created will be a simple list containing floats. In order
to produce this kind of individual, we need to create an :class:`Individual`
class, using the creator, that will inherit from the standard :class:`list` type
and have a :attr:`fitness` attribute.

.. Then, we will initialize this list using
.. the :func:`~deap.tools.initRepeat` helper function that will repeat ``n`` times
.. the float generator that has been registered under the :func:`attr_float` alias
.. of the toolbox. Note that the :func:`attr_float` is a direct reference to the
.. :func:`~random.random` function.

.. literalinclude:: /code/tutorials/part_2/2_2_1_list_of_floats.py
   :lines: 2,5-18

The newly introduced :meth:`~deap.base.Toolbox.register` method takes at least two arguments; an alias and a function assigned to this alias. Any subsequent argument is passed to the function when called (à la :func:`functools.partial`).
Thus, the preceding code creates two aliases in the toolbox; ``attr_float`` and ``individual``. The first one redirects to the :func:`random.random` function. The second one is a shortcut to the :func:`~deap.tools.initRepeat` function, fixing its :data:`container` argument to the :class:`creator.Individual` class, its :data:`func` argument to the :func:`toolbox.attr_float` function, and its number of repetitions argument to ``IND_SIZE``.

Now, calling :func:`toolbox.individual` will call :func:`~deap.tools.initRepeat` with the fixed arguments and return a complete :class:`creator.Individual`
composed of ``IND_SIZE`` floating point numbers with a maximizing single
objective :attr:`fitness` attribute.

Variations of this type are possible by inheriting from :class:`array.array`
or :class:`numpy.ndarray` as following.

.. literalinclude:: /code/tutorials/part_2/2_2_1_list_of_floats.py
   :lines: 20,21

Type inheriting from arrays needs a *typecode* on initialization, just as the
original class.

.. _permutation:

Permutation
+++++++++++
An individual for the permutation representation is almost similar to the
general list individual. In fact they both inherit from the basic :class:`list`
type. The only difference is that instead of filling the list with a series of
floats, we need to generate a random permutation and provide that permutation to
the individual.

.. First, the individual class is created the exact same way as the
.. previous one. Then, an :func:`indices` function is added to the toolbox
.. referring to the :func:`~random.sample` function. Sample is used instead of
.. :func:`~random.shuffle` because the latter does not return the shuffled list.
.. The indices function returns a complete permutation of the numbers between ``0``
.. and ``IND_SIZE - 1``. Finally, the individual is initialized with the
.. :func:`~deap.tools.initIterate` function which gives to the individual an
.. iterable of what is produced by the call to the indices function.

.. literalinclude:: /code/tutorials/part_2/2_2_2_permutation.py
   :lines: 2-

The first registered function ``indices`` redirects to the :func:`random.sample` function with its arguments fixed to sample ``IND_SIZE`` numbers from the given range. The second registered function ``individual`` is a shortcut to the :func:`~deap.tools.initIterate` function, with its :data:`container` argument set to the :class:`creator.Individual` class and its :data:`generator` argument to the :func:`toolbox.indices` alias.

Calling :func:`toolbox.individual` will call :func:`~deap.tools.initIterate` with the fixed arguments and return a complete :class:`creator.Individual`
composed of a permutation with a minimizing single
objective :attr:`fitness` attribute.

.. _arithmetic-expr:

Arithmetic Expression
+++++++++++++++++++++
The next individual that is commonly used is a prefix tree of mathematical
expressions. This time, a :class:`~deap.gp.PrimitiveSet` must be defined
containing all possible mathematical operators that our individual can use.
Here, the set is called ``MAIN`` and has a single variable defined by the
arity_.  Operators :func:`~operator.add`, :func:`~operator.sub`, and
:func:`~operator.mul` are added to the primitive set with each an arity of 2.
Next, the :class:`Individual` class is created as before with the addition of a
static attribute :attr:`pset` to remember the global primitive set. This time,
the content of the individuals will be generated by the
:func:`~deap.gp.genHalfAndHalf` function that generates trees in a list format based
on a ramped procedure. Once again, the individual is initialized using the
:func:`~deap.tools.initIterate` function to give the complete generated iterable
to the individual class.

.. literalinclude:: /code/tutorials/part_2/2_2_3_arithmetic_expression.py
   :lines: 2-

.. _arity: http://en.wikipedia.org/wiki/Arity

Calling :func:`toolbox.individual` will readily return a complete individual
that is an arithmetic expression in the form of a prefix tree with a
minimizing single objective fitness attribute.

Evolution Strategy
++++++++++++++++++
Evolution strategies individuals are slightly different as they contain
generally two lists, one for the actual individual and one for its mutation
parameters. This time, instead of using the list base class, we will inherit from
an :class:`array.array` for both the individual and the strategy. Since there is
no helper function to generate two different vectors in a single object, we must
define this function ourselves. The :func:`initES` function receives two classes
and instantiates them generating itself the random numbers in the ranges
provided for individuals of a given size.

.. literalinclude:: /code/tutorials/part_2/2_2_4_evolution_strategy.py
   :lines: 2-

Calling :func:`toolbox.individual` will readily return a complete evolution
strategy with a strategy vector and a minimizing single objective fitness
attribute.

Particle
++++++++
A particle is another special type of individual as it usually has a speed and
generally remembers its best position. This type of individual is created (once
again) the same way as inheriting from a list. This time, :attr:`speed`,
:attr:`best` and speed limits (:attr:`smin`, :attr:`smax`) attributes are added to the object. Again, an
initialization function :func:`initParticle` is also registered to produce the
individual receiving the particle class, size, domain, and speed limits as
arguments.

.. literalinclude:: /code/tutorials/part_2/2_2_5_particle.py
   :lines: 2-

Calling :func:`toolbox.particle` will readily return a complete particle with
a speed vector and a fitness attribute for maximizing two objectives.

.. _funky:

A Funky One
+++++++++++
Supposing your problem has very specific needs, it is also possible to build
custom individuals very easily. The next individual created is a list of
alternating integers and floating point numbers, using the
:func:`~deap.tools.initCycle` function.

.. literalinclude:: /code/tutorials/part_2/2_2_6_funky_one.py
   :lines: 2-

Calling :func:`toolbox.individual` will readily return a complete individual of
the form ``[int float int float ... int float]`` with a maximizing two
objectives fitness attribute.

.. _population:

Population
----------
Populations are much like individuals. Instead of being initialized with
attributes, they are filled with individuals, strategies or particles.

Bag
+++
A bag population is the most commonly used type. It has no particular ordering
although it is generally implemented using a list. Since the bag has no
particular attribute, it does not need any special class. The population is
initialized using the toolbox and the :func:`~deap.tools.initRepeat`
function directly.

.. literalinclude:: /code/tutorials/part_2/2_3_1_bag.py
   :lines: 17

Calling :func:`toolbox.population` will readily return a complete population in
a list, providing a number of times the repeat helper must be repeated as an
argument of the population function. The following example produces a population
with 100 individuals.

.. literalinclude:: /code/tutorials/part_2/2_3_1_bag.py
   :lines: 19

Grid
++++
A grid population is a special case of structured population where neighbouring
individuals have a direct effect on each other. The individuals are distributed
in the grid where each cell contains a single individual. However, its
implementation only differs from the list of the bag population, in that it is
composed of lists of individuals.

.. literalinclude:: /code/tutorials/part_2/2_3_2_grid.py
   :lines: 20-21

Calling :func:`toolbox.population` will readily return a complete population
where the individuals are accessible using two indices, for example
``pop[r][c]``. For the moment, there is no algorithm specialized for structured
populations, we are awaiting your submissions.

Swarm
+++++
A swarm is used in particle swarm optimization. It is different in the sense
that it contains a communication network. The simplest network is the completely
connected one, where each particle knows the best position that has ever been
visited by any particle. This is generally implemented by copying that global
best position to a :attr:`gbest` attribute and the global best fitness to a
:attr:`gbestfit` attribute.

.. literalinclude:: /code/tutorials/part_2/2_3_3_swarm.py
   :lines: 11,23

Calling :func:`toolbox.swarm` will readily return a complete swarm. After
each evaluation the :attr:`gbest` and :attr:`gbestfit` should be set by the
algorithm to reflect the best found position and fitness.

Demes
+++++
A deme is a sub-population that is contained in a population. It is similar to
an island in the island model. Demes, being only sub-populations, are in fact not
different from populations, aside from their names. Here, we create a population
containing 3 demes, each having a different number of individuals using the *n*
argument of the :func:`~deap.tools.initRepeat` function.

.. literalinclude:: /code/tutorials/part_2/2_3_4_demes.py
   :lines: 17-20

Seeding a Population
++++++++++++++++++++
Sometimes, a first guess population can be used to initialize an evolutionary
algorithm. The key idea to initialize a population with non-random individuals
is to have an individual initializer that takes a content as argument.

.. literalinclude:: /code/tutorials/part_2/2_3_5_seeding_a_population.py
   :lines: 2-

The population will be initialized from the file ``my_guess.json`` that shall
contain a list of first guess individuals. This initialization can be combined
with a regular initialization to have part random and part non-random
individuals. Note that the definition of :func:`initIndividual` and the
registration of :func:`individual_guess` are optional as the default constructor
of a list is similar. Removing those lines leads to the following: ::
    toolbox.register("population_guess", initPopulation, list, creator.Individual, "my_guess.json")

	.. _creating-types:

	Creating Types
	==============
	This tutorial shows how types are created using the creator and initialized
	using the toolbox.

	Fitness
	-------
	The provided :class:`~deap.base.Fitness` class is an abstract class that needs a
	:attr:`~deap.base.Fitness.weights` attribute in order to be functional. A
	minimizing fitness is built using negatives weights, while a maximizing fitness
	has positive weights. For example, the following line creates, in the
	:mod:`~deap.creator`, a ready to use single objective minimizing fitness named
	:class:`FitnessMin`.

	.. literalinclude:: /code/tutorials/part_2/2_1_fitness.py
	:lines: 6

	The :func:`~deap.creator.create` function takes at least two arguments, a name for the newly created class and a base class. Any subsequent argument becomes an attribute of the class.
	As specified in the :class:`~deap.base.Fitness` documentation, the :attr:`~deap.base.Fitness.weights` attribute must be a tuple so that multi-objective and single objective fitnesses can be treated the same way. A
	:class:`FitnessMulti` would be created the same way but using:

	.. literalinclude:: /code/tutorials/part_2/2_1_fitness.py
	:lines: 9

	This code produces a fitness that minimizes the first objective and maximize the
	second one. The weights can also be used to vary the importance of each
	objective one against another. This means that the weights can be any real
	number and only the sign is used to determine if a maximization or minimization
	is done. An example of where the weights can be useful is in the crowding
	distance sort made in the NSGA-II selection algorithm.

	Individual
	----------
	Simply by thinking about the different flavors of evolutionary algorithms (GA,
	GP, ES, PSO, DE, ...), we notice that an extremely large variety of individuals
	are possible, reinforcing the assumption that all types cannot be made available
	by developers. Here is a guide on how to create some of those individuals using
	the :mod:`~deap.creator` and initializing them using a
	:class:`~deap.base.Toolbox`.

	.. warning::

	Before inheriting from :class:`numpy.ndarray` you should absolutely read
	the :doc:`/tutorials/advanced/numpy` tutorial and have a look at the
	:doc:`/examples/ga_onemax_numpy` example!

	.. _list-of-floats:

	List of Floats
	++++++++++++++
	The first individual created will be a simple list containing floats. In order
	to produce this kind of individual, we need to create an :class:`Individual`
	class, using the creator, that will inherit from the standard :class:`list` type
	and have a :attr:`fitness` attribute.

	.. Then, we will initialize this list using
	.. the :func:`~deap.tools.initRepeat` helper function that will repeat ``n`` times
	.. the float generator that has been registered under the :func:`attr_float` alias
	.. of the toolbox. Note that the :func:`attr_float` is a direct reference to the
	.. :func:`~random.random` function.

	.. literalinclude:: /code/tutorials/part_2/2_2_1_list_of_floats.py
	:lines: 2,5-18

	The newly introduced :meth:`~deap.base.Toolbox.register` method takes at least two arguments; an alias and a function assigned to this alias. Any subsequent argument is passed to the function when called (à la :func:`functools.partial`).
	Thus, the preceding code creates two aliases in the toolbox; ``attr_float`` and ``individual``. The first one redirects to the :func:`random.random` function. The second one is a shortcut to the :func:`~deap.tools.initRepeat` function, fixing its :data:`container` argument to the :class:`creator.Individual` class, its :data:`func` argument to the :func:`toolbox.attr_float` function, and its number of repetitions argument to ``IND_SIZE``.

	Now, calling :func:`toolbox.individual` will call :func:`~deap.tools.initRepeat` with the fixed arguments and return a complete :class:`creator.Individual`
	composed of ``IND_SIZE`` floating point numbers with a maximizing single
	objective :attr:`fitness` attribute.

	Variations of this type are possible by inheriting from :class:`array.array`
	or :class:`numpy.ndarray` as following.

	.. literalinclude:: /code/tutorials/part_2/2_2_1_list_of_floats.py
	:lines: 20,21

	Type inheriting from arrays needs a typecode on initialization, just as the
	original class.

	.. _permutation:

	Permutation
	+++++++++++
	An individual for the permutation representation is almost similar to the
	general list individual. In fact they both inherit from the basic :class:`list`
	type. The only difference is that instead of filling the list with a series of
	floats, we need to generate a random permutation and provide that permutation to
	the individual.

	.. First, the individual class is created the exact same way as the
	.. previous one. Then, an :func:`indices` function is added to the toolbox
	.. referring to the :func:`~random.sample` function. Sample is used instead of
	.. :func:`~random.shuffle` because the latter does not return the shuffled list.
	.. The indices function returns a complete permutation of the numbers between ``0``
	.. and ``IND_SIZE - 1``. Finally, the individual is initialized with the
	.. :func:`~deap.tools.initIterate` function which gives to the individual an
	.. iterable of what is produced by the call to the indices function.

	.. literalinclude:: /code/tutorials/part_2/2_2_2_permutation.py
	:lines: 2-

	The first registered function ``indices`` redirects to the :func:`random.sample` function with its arguments fixed to sample ``IND_SIZE`` numbers from the given range. The second registered function ``individual`` is a shortcut to the :func:`~deap.tools.initIterate` function, with its :data:`container` argument set to the :class:`creator.Individual` class and its :data:`generator` argument to the :func:`toolbox.indices` alias.

	Calling :func:`toolbox.individual` will call :func:`~deap.tools.initIterate` with the fixed arguments and return a complete :class:`creator.Individual`
	composed of a permutation with a minimizing single
	objective :attr:`fitness` attribute.

	.. _arithmetic-expr:

	Arithmetic Expression
	+++++++++++++++++++++
	The next individual that is commonly used is a prefix tree of mathematical
	expressions. This time, a :class:`~deap.gp.PrimitiveSet` must be defined
	containing all possible mathematical operators that our individual can use.
	Here, the set is called ``MAIN`` and has a single variable defined by the
	arity_. Operators :func:`~operator.add`, :func:`~operator.sub`, and
	:func:`~operator.mul` are added to the primitive set with each an arity of 2.
	Next, the :class:`Individual` class is created as before with the addition of a
	static attribute :attr:`pset` to remember the global primitive set. This time,
	the content of the individuals will be generated by the
	:func:`~deap.gp.genHalfAndHalf` function that generates trees in a list format based
	on a ramped procedure. Once again, the individual is initialized using the
	:func:`~deap.tools.initIterate` function to give the complete generated iterable
	to the individual class.

	.. literalinclude:: /code/tutorials/part_2/2_2_3_arithmetic_expression.py
	:lines: 2-

	.. _arity: http://en.wikipedia.org/wiki/Arity

	Calling :func:`toolbox.individual` will readily return a complete individual
	that is an arithmetic expression in the form of a prefix tree with a
	minimizing single objective fitness attribute.

	Evolution Strategy
	++++++++++++++++++
	Evolution strategies individuals are slightly different as they contain
	generally two lists, one for the actual individual and one for its mutation
	parameters. This time, instead of using the list base class, we will inherit from
	an :class:`array.array` for both the individual and the strategy. Since there is
	no helper function to generate two different vectors in a single object, we must
	define this function ourselves. The :func:`initES` function receives two classes
	and instantiates them generating itself the random numbers in the ranges
	provided for individuals of a given size.

	.. literalinclude:: /code/tutorials/part_2/2_2_4_evolution_strategy.py
	:lines: 2-

	Calling :func:`toolbox.individual` will readily return a complete evolution
	strategy with a strategy vector and a minimizing single objective fitness
	attribute.

	Particle
	++++++++
	A particle is another special type of individual as it usually has a speed and
	generally remembers its best position. This type of individual is created (once
	again) the same way as inheriting from a list. This time, :attr:`speed`,
	:attr:`best` and speed limits (:attr:`smin`, :attr:`smax`) attributes are added to the object. Again, an
	initialization function :func:`initParticle` is also registered to produce the
	individual receiving the particle class, size, domain, and speed limits as
	arguments.

	.. literalinclude:: /code/tutorials/part_2/2_2_5_particle.py
	:lines: 2-

	Calling :func:`toolbox.particle` will readily return a complete particle with
	a speed vector and a fitness attribute for maximizing two objectives.

	.. _funky:

	A Funky One
	+++++++++++
	Supposing your problem has very specific needs, it is also possible to build
	custom individuals very easily. The next individual created is a list of
	alternating integers and floating point numbers, using the
	:func:`~deap.tools.initCycle` function.

	.. literalinclude:: /code/tutorials/part_2/2_2_6_funky_one.py
	:lines: 2-

	Calling :func:`toolbox.individual` will readily return a complete individual of
	the form ``[int float int float ... int float]`` with a maximizing two
	objectives fitness attribute.

	.. _population:

	Population
	----------
	Populations are much like individuals. Instead of being initialized with
	attributes, they are filled with individuals, strategies or particles.

	Bag
	+++
	A bag population is the most commonly used type. It has no particular ordering
	although it is generally implemented using a list. Since the bag has no
	particular attribute, it does not need any special class. The population is
	initialized using the toolbox and the :func:`~deap.tools.initRepeat`
	function directly.

	.. literalinclude:: /code/tutorials/part_2/2_3_1_bag.py
	:lines: 17

	Calling :func:`toolbox.population` will readily return a complete population in
	a list, providing a number of times the repeat helper must be repeated as an
	argument of the population function. The following example produces a population
	with 100 individuals.

	.. literalinclude:: /code/tutorials/part_2/2_3_1_bag.py
	:lines: 19

	Grid
	++++
	A grid population is a special case of structured population where neighbouring
	individuals have a direct effect on each other. The individuals are distributed
	in the grid where each cell contains a single individual. However, its
	implementation only differs from the list of the bag population, in that it is
	composed of lists of individuals.

	.. literalinclude:: /code/tutorials/part_2/2_3_2_grid.py
	:lines: 20-21

	Calling :func:`toolbox.population` will readily return a complete population
	where the individuals are accessible using two indices, for example
	``pop[r][c]``. For the moment, there is no algorithm specialized for structured
	populations, we are awaiting your submissions.

	Swarm
	+++++
	A swarm is used in particle swarm optimization. It is different in the sense
	that it contains a communication network. The simplest network is the completely
	connected one, where each particle knows the best position that has ever been
	visited by any particle. This is generally implemented by copying that global
	best position to a :attr:`gbest` attribute and the global best fitness to a
	:attr:`gbestfit` attribute.

	.. literalinclude:: /code/tutorials/part_2/2_3_3_swarm.py
	:lines: 11,23

	Calling :func:`toolbox.swarm` will readily return a complete swarm. After
	each evaluation the :attr:`gbest` and :attr:`gbestfit` should be set by the
	algorithm to reflect the best found position and fitness.

	Demes
	+++++
	A deme is a sub-population that is contained in a population. It is similar to
	an island in the island model. Demes, being only sub-populations, are in fact not
	different from populations, aside from their names. Here, we create a population
	containing 3 demes, each having a different number of individuals using the n
	argument of the :func:`~deap.tools.initRepeat` function.

	.. literalinclude:: /code/tutorials/part_2/2_3_4_demes.py
	:lines: 17-20

	Seeding a Population
	++++++++++++++++++++
	Sometimes, a first guess population can be used to initialize an evolutionary
	algorithm. The key idea to initialize a population with non-random individuals
	is to have an individual initializer that takes a content as argument.

	.. literalinclude:: /code/tutorials/part_2/2_3_5_seeding_a_population.py
	:lines: 2-

	The population will be initialized from the file ``my_guess.json`` that shall
	contain a list of first guess individuals. This initialization can be combined
	with a regular initialization to have part random and part non-random
	individuals. Note that the definition of :func:`initIndividual` and the
	registration of :func:`individual_guess` are optional as the default constructor
	of a list is similar. Removing those lines leads to the following: ::

	toolbox.register("population_guess", initPopulation, list, creator.Individual, "my_guess.json")