def test_register_by_name(self):
i_set = InstructionSet()
i_set.register_by_name(".*_mult")
print(i_set)
assert len(i_set) == 2
assert set([i.name
for i in i_set.values()]) == {"int_mult", "float_mult"}
def test_unregister(self, instr_set):
i_set = InstructionSet()
i_set.register(instr_set["int_add"])
i_set.register(instr_set["int_sub"])
i_set.unregister("int_add")
assert len(i_set) == 1
assert list(i_set.values())[0].name == "int_sub"
def test_unregister(self, atoms):
i_set = InstructionSet()
i_set.register(atoms["add"])
i_set.register(atoms["sub"])
i_set.unregister("int_add")
assert len(i_set) == 1
assert list(i_set.values())[0].name == "int_sub"
def test_register_core_by_stack_with_exclude(self, core_type_lib):
foo = common.instructions(core_type_lib)
print([i for i in foo
if i.name == "exec_dup_times"][0].required_stacks())
i_set = InstructionSet(register_core=False)
i_set.register_core_by_stack({"int"},
exclude_stacks={"str", "exec", "code"})
for i in i_set.values():
if len(i.required_stacks()) > 0:
print(i.name, i.required_stacks())
assert i.name not in {
"exec_pop", "exec_dup", "exec_dup_times", "exec_swap",
"exec_rot", "exec_flush", "exec_stack_depth", "exec_yank",
"exec_yank_dup", "exec_shove", "exec_shove_dup"
}
assert "int" in i.required_stacks()
assert "exec" not in i.required_stacks()
def __init__(self,
instruction_set: InstructionSet,
literals: Sequence[Union[Literal, Any]],
erc_generators: Sequence[Callable],
distribution: DiscreteProbDistrib = "proportional"):
self.instruction_set = instruction_set
self.type_library = instruction_set.type_library
self.literals = [lit if isinstance(lit, Literal) else infer_literal(lit, self.type_library) for lit in literals]
self.erc_generators = erc_generators
if distribution == "proportional":
self.distribution = (
DiscreteProbDistrib()
.add("instruction", len(instruction_set))
.add("close", sum([i.code_blocks for i in instruction_set.values()]))
.add("literal", len(literals))
.add("erc", len(erc_generators))
)
else:
self.distribution = distribution
def __init__(self,
instruction_set: InstructionSet,
literals: Sequence[Union[Literal, Any]],
erc_generators: Sequence[Callable],
distribution: DiscreteProbDistrib = "proportional"):
self.instruction_set = instruction_set
self.literals = [
lit if isinstance(lit, Literal) else Literal(lit)
for lit in literals
]
self.erc_generators = erc_generators
if distribution == "proportional":
self.distribution = (DiscreteProbDistrib().add(
"instruction", len(instruction_set)).add(
"close",
sum([i.code_blocks for i in instruction_set.values()
])).add("literal",
len(literals)).add("erc",
len(erc_generators)))
else:
self.distribution = distribution
def test_register_core(self, all_core_instructions):
i_set = InstructionSet().register_core()
assert set(i_set.values()) == all_core_instructions
def test_register_core_by_stack(self):
i_set = InstructionSet()
i_set.register_core_by_stack({"int"})
for i in i_set.values():
if len(i.required_stacks()) > 0:
assert "int" in i.required_stacks()
class GeneSpawner:
"""A factory of random Genes (Atoms) and Genomes.
When spawning a random gene, the result can be one of three types of Atoms.
An Instruction, a Closer, or a Literal. If the Atom is a Literal, it may
be one of the supplied Literals, or it may be the result of running one of
the Ephemeral Random Constant generators.
Reference for ERCs:
"A field guide to genetic programming", Section 3.1
Riccardo Poli and William B. Langdon and Nicholas Freitag McPhee,
http://www.gp-field-guide.org.uk/
Attributes
----------
n_input : int
Number of input instructions that could appear the genomes.
instruction_set : pyshgp.push.instruction_set.InstructionSet
InstructionSet containing instructions to use when spawning genes and
genomes.
literals : Sequence[pyshgp.push.instruction_set.atoms.Literal]
A list of Literal objects to pull from when spawning genes and genomes.
erc_generator : Sequence[Callable]
A list of functions (aka Ephemeral Random Constant generators). When one
of these functions is called, the output is placed in a Literal and
returned as the spawned gene.
distribution : pyshgp.utils.DiscreteProbDistrib
A probability distribution describing how frequently to produce
Instructions, Closers, Literals, and ERCs.
"""
def __init__(self,
n_inputs: int,
instruction_set: Union[InstructionSet, str],
literals: Sequence[Any],
erc_generators: Sequence[Callable],
distribution: DiscreteProbDistrib = "proportional"):
self.n_inputs = n_inputs
self.erc_generators = erc_generators
self.instruction_set = instruction_set
if self.instruction_set == "core":
self.instruction_set = InstructionSet(register_core=True)
self.type_library = self.instruction_set.type_library
self.literals = [
lit if isinstance(lit, Literal) else infer_literal(
lit, self.type_library) for lit in literals
]
if distribution == "proportional":
self.distribution = (DiscreteProbDistrib().add(
GeneTypes.INPUT, self.n_inputs).add(
GeneTypes.INSTRUCTION, len(self.instruction_set)).add(
GeneTypes.CLOSE,
sum([
i.code_blocks
for i in self.instruction_set.values()
])).add(GeneTypes.LITERAL,
len(literals)).add(GeneTypes.ERC,
len(erc_generators)))
else:
self.distribution = distribution
def random_input(self) -> Input:
"""Return a random ``Input``.
Returns
-------
pyshgp.push.atoms.Input
"""
return Input(input_index=np.random.randint(self.n_inputs))
def random_instruction(self) -> InstructionMeta:
"""Return a random Instruction from the InstructionSet.
Returns
-------
pyshgp.push.atoms.InstructionMeta
A randomly selected Literal.
"""
i = np.random.choice(list(self.instruction_set.values()))
return InstructionMeta(name=i.name, code_blocks=i.code_blocks)
def random_literal(self) -> Literal:
"""Return a random Literal from the set of Literals.
Returns
-------
pyshgp.push.atoms.Literal
A randomly selected Literal.
"""
lit = np.random.choice(self.literals)
if not isinstance(lit, Literal):
lit = infer_literal(lit, self.type_library)
return lit
def random_erc(self) -> Literal:
"""Materialize a random ERC generator into a Literal and return it.
Returns
-------
pyshgp.push.atoms.Literal
A Literal whose value comes from running a ERC generator function.
"""
erc_value = np.random.choice(self.erc_generators)()
if not isinstance(erc_value, Literal):
erc_value = infer_literal(erc_value, self.type_library)
return erc_value
def random_gene(self) -> Atom:
"""Return a random Atom based on the GenomeSpawner's distribution.
Returns
-------
pyshgp.push.atoms.Atom
An random Atom. Either an Instruction, Closer, or Literal.
"""
atom_type = self.distribution.sample()
if atom_type is GeneTypes.INPUT:
return self.random_input()
elif atom_type is GeneTypes.INSTRUCTION:
return self.random_instruction()
elif atom_type is GeneTypes.CLOSE:
return Closer()
elif atom_type is GeneTypes.LITERAL:
return self.random_literal()
elif atom_type is GeneTypes.ERC:
return self.random_erc()
else:
raise ValueError(
"GenomeSpawner distribution bad atom type {t}".format(
t=str(atom_type)))
def spawn_genome(self, size: Union[int, Sequence[int]]) -> Genome:
"""Return a random Genome based on the GenomeSpawner's distribution.
The genome will contain the specified number of Atoms if size is an
integer. If size is a pair of integers, the genome will be of a random
size in the range of the two integers.
Parameters
----------
size
The resulting genome will contain this many Atoms if size is an
integer. If size is a pair of integers, the genome will be of a random
size in the range of the two integers.
Returns
-------
pyshgp.gp.genome.Genome
A Genome with random contents of a given size.
"""
if isinstance(size, Sequence):
size = np.random.randint(size[0], size[1]) + 1
return Genome([self.random_gene() for _ in range(size)])
def test_register_all(self):
i_set = InstructionSet().register_all()
# assert len(i_set) == len(_CORE_INSTRUCTIONS) # If fail, likely duplicated instr names.
assert set(i_set.values()) == set(_CORE_INSTRUCTIONS)
def test_register_by_type(self):
i_set = InstructionSet()
i_set.register_by_type(["int"])
for i in i_set.values():
assert "int" in i.relevant_types()
def test_register_core(self, all_core_instrucitons):
i_set = InstructionSet().register_core()
assert set(i_set.values()) == all_core_instrucitons
def test_register_core_by_name(self, core_type_lib):
i_set = InstructionSet()
i_set.register_core_by_name(".*_mult")
assert len(i_set) == 2
assert set([i.name for i in i_set.values()]) == {"int_mult", "float_mult"}
def test_register_core_by_stack(self, core_type_lib):
i_set = InstructionSet()
i_set.register_core_by_stack({"int"})
for i in i_set.values():
if len(i.required_stacks()) > 0:
assert "int" in i.required_stacks()
