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 __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 __init__(self,
instruction_set: Union[InstructionSet, str] = "core",
reset_on_run: bool = True):
self.reset_on_run = reset_on_run
# If no instruction set given, create one and register all instructions.
if instruction_set == "core":
self.instruction_set = InstructionSet(register_core=True)
else:
self.instruction_set = instruction_set
self.type_library = self.instruction_set.type_library
# Initialize the PushState and status
self.state: PushState = None
self.status: PushInterpreterStatus = None
self._validate()
def run_ga_on_odd_test(parallelism):
X = np.arange(-10, 10).reshape(-1, 1)
y = [[bool(x[0] % 2)] for x in X]
instruction_set = (InstructionSet().register_core_by_stack(
{"int"}, exclude_stacks={"str", "exec", "code"}))
spawner = GeneSpawner(n_inputs=1,
instruction_set=instruction_set,
literals=[],
erc_generators=[
partial(random.randint, 0, 10),
])
est = PushEstimator(spawner=spawner,
population_size=30,
max_generations=3,
simplification_steps=10,
parallelism=parallelism)
est.fit(X, y)
assert isinstance(est.solution, Individual)
assert len(est.solution.program.code) > 0
path = "tmp.push"
solution = est.solution.copy(deep=True)
est.save(path)
est.load(path)
assert solution == est.solution
os.remove(path)
def __init__(self,
instruction_set: Union[InstructionSet, str] = "core",
config: PushInterpreterConfig = None,
verbosity_config: VerbosityConfig = "default"):
# If no instruction set given, create one and register all instructions.
if instruction_set == "core":
self.instruction_set = InstructionSet(register_core=True)
else:
self.instruction_set = instruction_set
self.type_library = self.instruction_set.type_library
if config is None:
self.config = PushInterpreterConfig()
else:
self.config = config
if verbosity_config == "default":
self.verbosity_config = DEFAULT_VERBOSITY_LEVELS[0]
else:
self.verbosity_config = verbosity_config
# Initialize the PushState and status
self._validate()
self.reset()
def __init__(self,
instruction_set: Union[InstructionSet, str] = "core",
config: PushInterpreterConfig = None):
# If no instruction set given, create one and register all instructions.
if instruction_set == "core":
self.instruction_set = InstructionSet(register_all=True)
else:
self.instruction_set = instruction_set
self._supported_types = self.instruction_set.supported_types()
if config is None:
self.config = PushInterpreterConfig()
else:
self.config = config
# Initialize the PushState and status
self.reset()
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_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 simple_test_spawner():
instruction_set = InstructionSet().register_core_by_stack({"int"}, exclude_stacks={"str", "exec", "code"})
spawner = GeneSpawner(
n_inputs=1,
instruction_set=instruction_set,
literals=[],
erc_generators=[
partial(random.randint, 0, 10),
]
)
return spawner
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 __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_ga_on_odd():
X = np.arange(-10, 10).reshape(-1, 1)
y = [[bool(x[0] % 2)] for x in X]
instruction_set = (InstructionSet().register_by_type(
["int"], exclude=["str", "exec",
"code"]).register_n_inputs(X.shape[1]))
spawner = GeneSpawner(instruction_set=instruction_set,
literals=[],
erc_generators=[
lambda: random.randint(0, 10),
])
est = PushEstimator(spawner=spawner,
population_size=20,
max_generations=10,
simplification_steps=100)
est.fit(X, y)
assert isinstance(est._result.program, CodeBlock)
assert len(est._result.program) > 0
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()
def test_register_list(self, instr_set):
i_set = InstructionSet()
i_set.register_list([instr_set["int_add"], instr_set["int_sub"]])
assert len(i_set) == 2
import sys
from pyshgp.gp.estimators import PushEstimator
from pyshgp.gp.genome import GeneSpawner
from pyshgp.gp.selection import Lexicase
from pyshgp.push.instruction_set import InstructionSet
def target_function(x: float) -> (float, float):
return (max(0.0, x), max(0.1 * x, x))
X = np.arange(-1.0, 1.0, 0.15).reshape([-1, 1])
y = np.array([target_function(x[0]) for x in X])
instruction_set = (InstructionSet().register_by_type(
["float", "bool"]).register_n_inputs(X.shape[1]))
spawner = GeneSpawner(instruction_set=instruction_set,
literals=[0.1, 0.0],
erc_generators=[
lambda: random.randint(0, 10),
])
ep_lex_sel = Lexicase(epsilon=True)
est = PushEstimator(population_size=500,
max_generations=50,
spawner=spawner,
selector=ep_lex_sel,
verbose=2)
X_train_synthetic = [mirror_vectors() for _ in range(10)] + \
[equal_vectors() for _ in range(10)] + \
[random_vectors() for _ in range(10)]
X_train = X_train_edge + X_train_synthetic
y_train = [[target_function(x[0], x[1])] for x in X_train]
X_test = [mirror_vectors() for _ in range(100)] + \
[equal_vectors() for _ in range(100)] + \
[random_vectors() for _ in range(100)]
y_test = [[target_function(x[0], x[1])] for x in X_test]
spawner = GeneSpawner(
n_inputs=2,
instruction_set=InstructionSet().register_core_by_stack(
{"int", "bool", "vector_int", "exec"}),
literals=[" ", "\n"],
erc_generators=[
lambda: random.random() < 0.5,
],
)
if __name__ == "__main__":
est = PushEstimator(search="GA",
population_size=500,
max_generations=150,
spawner=spawner,
simplification_steps=100,
verbose=2)
start = time.time()
def check_unary_fn_translation(program_name: str):
interpreter = PushInterpreter(InstructionSet(register_core=True))
check_translation(program_name, interpreter)
def simple_gene_spawner(atoms):
i_set = InstructionSet()
i_set.register_list([atoms["add"], atoms["sub"], atoms["if"]])
l_set = [5, 1.2, True]
return GeneSpawner(i_set, l_set, [random.random])
PushTypeLibrary(register_core=False)
.register(PushFloat)
.create_and_register("point", (Point, ), coercion_func=to_point)
)
# An instruction set which will register all core instructions that can be supported
# using only exec, code, stdout, float, and point types.
#
# For example, the instruction int_from_float will NOT be registered because
# our type library does not define a type that would support the "int" stack.
#
# Our two custom instructions as well as the input instructions are also defined.
instruction_set = (
InstructionSet(type_library=type_library, register_core=True)
.register(point_distance_insrt)
.register(point_from_floats_instr)
)
print(instruction_set.keys())
spawner = GeneSpawner(
n_inputs=2,
instruction_set=instruction_set,
literals=[2.0],
erc_generators=[]
)
# Our estimator with a custom interpreter defined.
est = PushEstimator(
def test_register_list(self, atoms):
i_set = InstructionSet()
i_set.register_list([atoms["add"], atoms["sub"]])
assert len(i_set) == 2
def simple_gene_spawner(instr_set):
i_set = InstructionSet()
i_set.register_list([instr_set["int_add"], instr_set["int_sub"], instr_set["exec_if"]])
l_set = [5, 1.2, True]
return GeneSpawner(1, i_set, l_set, [random.random])
def instr_set():
return InstructionSet(register_core=True)
def point_instr_set(point_type_library, point_instructions):
return (
InstructionSet(type_library=point_type_library, register_core=True)
.register_list(point_instructions)
)
return Rectangle(float(value[0]), float(value[1]))
type_library = (PushTypeLibrary(
register_core=False).register(PushFloat).register(RectangleType()))
"""
Next we define out instruction set using the type library and the two instructions we created.
Our two custom instructions as well as the input instructions are defined.
The instruction set will register all core instructions that can be supported
using only exec, code, float, and rectangle types because the only core PushType we registered was "PushInt"
For example, the instruction int_from_float will NOT be registered because
our type library does not define a type that would support the "int" stack.
"""
instruction_set = (InstructionSet(
type_library=type_library,
register_core=True).register(rectangle_areas_instruction).register(
rectangle_from_floats_instruction))
print("Stacks: ", instruction_set.required_stacks())
print("Types: ", type_library.supported_stacks())
print("Instruction Set: ", instruction_set.keys())
print()
"""
Next we have to declare our "GeneSpawner."
We pass to it our instruction_set.
n_inputs=2 because we would like the genome to possibly include 2 input instructions.
literals=[2.0] because it will detect that 2.0 is a float,
and the spawner will pull floats when spawning genes and genomes.
"""
"""The goal of the Odd problem is to evolve a program that will produce a True if
the input integer is odd, and a False if its even.
"""
import logging
import numpy as np
import random
import sys
from pyshgp.gp.estimators import PushEstimator
from pyshgp.gp.genome import GeneSpawner
from pyshgp.push.instruction_set import InstructionSet
X = np.arange(-10, 10).reshape(-1, 1)
y = [[bool(x % 2)] for x in X]
instruction_set = (InstructionSet(register_all=True).register_n_inputs(
X.shape[1]))
spawner = GeneSpawner(instruction_set=instruction_set,
literals=[],
erc_generators=[lambda: random.randint(0, 10)])
est = PushEstimator(spawner=spawner,
population_size=500,
max_generations=200,
verbose=2)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO,
format="%(asctime)s %(levelname)s %(message)s",
stream=sys.stdout)
est.fit(X, y)
class PushInterpreter:
"""An interpreter capable of running Push programs.
Parameters
----------
instruction_set : Union[InstructionSet, str], optional
The InstructionSet to use for executing programs. Default is "core"
which instansiates an InstructionSet using all the core instructions.
config : PushInterpreterConfig, optional
A PushInterpreterConfig specifying limits and early termination
conditions. Default is None, which creates a config will all default
values.
verbosity_config : VerbosityConfig, optional
A VerbosityConfig controling what is logged during the execution
of the program. Default is no verbosity.
Attributes
----------
instruction_set : InstructionSet
The InstructionSet to use for executing programs.
config : PushInterpreterConfig
A PushInterpreterConfig specifying limits and early termination
conditions.
verbosity_config : VerbosityConfig, optional
A VerbosityConfig controling what is logged during the execution
of the program. Default is no verbosity.
state : PushState
The current PushState. Contains one stack for each PushType utilized
mentioned by the instructions in the instruction set.
status : PushInterpreterStatus
A string denoting if the Interpreter has enountered a situation
where non-standard termination was required.
"""
def __init__(self,
instruction_set: Union[InstructionSet, str] = "core",
config: PushInterpreterConfig = None,
verbosity_config: VerbosityConfig = "default"):
# If no instruction set given, create one and register all instructions.
if instruction_set == "core":
self.instruction_set = InstructionSet(register_core=True)
else:
self.instruction_set = instruction_set
self.type_library = self.instruction_set.type_library
if config is None:
self.config = PushInterpreterConfig()
else:
self.config = config
if verbosity_config == "default":
self.verbosity_config = DEFAULT_VERBOSITY_LEVELS[0]
else:
self.verbosity_config = verbosity_config
# Initialize the PushState and status
self._validate()
self.reset()
def _validate(self):
library_type_names = set(self.type_library.keys())
required_stacks = self.instruction_set.required_stacks() - {"stdout", "exec", "untyped"}
if not required_stacks <= library_type_names:
raise ValueError(
"PushInterpreter instruction_set and type_library are incompatible. {iset} vs {tlib}. Diff: {d}".format(
iset=required_stacks,
tlib=library_type_names,
d=required_stacks - library_type_names,
))
def reset(self):
"""Reset the interpreter status and PushState."""
self.state = PushState(self.type_library)
self.status = PushInterpreterStatus.normal
self._verbose_trace = self.verbosity_config.program_trace
self._log_fn_for_trace = log_function(self._verbose_trace)
def _log_trace(self, msg=None, log_state=False):
if msg is not None:
self._log_fn_for_trace(msg)
if log_state:
self.state.pretty_print(self._log_fn_for_trace)
def _evaluate_instruction(self, instruction: Union[Instruction, JitInstructionRef]):
self.state = instruction.evaluate(self.state, self.config)
def untyped_to_typed(self):
"""Infers PushType of items on state's untyped queue and pushes to corresponding stacks."""
while len(self.state.untyped) > 0:
el = self.state.untyped.popleft()
push_type = self.type_library.push_type_of(el, error_on_not_found=True)
self.state[push_type.name].push(el)
def evaluate_atom(self, atom: Atom):
"""Evaluate an Atom.
Parameters
----------
atom : Atom
The Atom (Literal, Instruction, JitInstructionRef, or CodeBlock) to
evaluate against the current PushState.
"""
try:
if isinstance(atom, Instruction):
self._evaluate_instruction(atom)
elif isinstance(atom, JitInstructionRef):
self._evaluate_instruction(self.instruction_set[atom.name])
elif isinstance(atom, CodeBlock):
for a in atom[::-1]:
self.state["exec"].push(a)
elif isinstance(atom, Literal):
self.state[atom.push_type.name].push(atom.value)
elif isinstance(atom, Closer):
raise PushError("Closers should not be in push programs. Only genomes.")
else:
raise PushError("Cannont evaluate {t}, require a subclass of Atom".format(t=type(atom)))
self.untyped_to_typed()
except Exception as e:
err_type = type(e).__name__
err_msg = str(e)
raise PushError(
"{t} raised while evaluating {atom}. Origional mesage: \"{m}\"".format(
t=err_type,
atom=atom,
m=err_msg
))
def run(self,
program: CodeBlock,
inputs: Sequence,
output_types: Sequence[str]):
"""Run a Push program given some inputs and desired output PushTypes.
The general flow of this method is:
1. Create a new push state
2. Load the program and inputs.
3. If the exec stack is empty, return the outputs.
4. Else, pop the exec stack and process the atom.
5. Return to step 3.
Parameters
----------
program
Program to run.
inputs
A sequence of values to use as inputs to the push program.
output_types
A secence of values that denote the Pushtypes of the expected
outputs of the push program.
Returns
-------
Sequence
A sequence of values pulled from the final push state. May contain
pyshgp.utils.Token.no_stack_item if needed stacks are empty.
"""
if self.config.reset_on_run:
self.reset()
# Setup
self.state.load_program(program)
self.state.load_inputs(inputs)
stop_time = time.time() + self.config.runtime_limit
steps = 0
if self._verbose_trace >= self.verbosity_config.log_level:
self._log_trace("Initial State:", True)
# Iterate atom evaluation until entire program is evaluated.
while len(self.state["exec"]) > 0:
# Stopping conditions
if steps > self.config.atom_limit:
self.status = PushInterpreterStatus.atom_limit_exceeded
break
if time.time() > stop_time:
self.status = PushInterpreterStatus.runtime_limit_exceeded
break
# Next atom in the program to evaluate.
next_atom = self.state["exec"].pop()
if self._verbose_trace >= self.verbosity_config.log_level:
self._log_trace("Current Atom: " + str(next_atom))
# Evaluate atom.
old_size = len(self.state)
self.evaluate_atom(next_atom)
if len(self.state) > old_size + self.config.growth_cap:
self.status = PushInterpreterStatus.growth_cap_exceeded
break
if self._verbose_trace >= self.verbosity_config.log_level:
self._log_trace("Current State:", True)
steps += 1
if self._verbose_trace >= self.verbosity_config.log_level:
self._log_trace("Finished program evaluation.")
return self.state.observe_stacks(output_types)
["^_^ " * 5],
]
y_train_edge = [target_function(x[0]) for x in X_train_edge]
X_train_synthetic = [[synthetic_input()] for _ in range(70)]
y_train_synthetic = [target_function(x[0]) for x in X_train_synthetic]
X_train = X_train_edge + X_train_synthetic
y_train = y_train_edge + y_train_synthetic
X_test = [[synthetic_input()] for _ in range(100)]
y_test = [target_function(x[0]) for x in X_test]
# Spawner
instruction_set = (InstructionSet().register_by_type(
["int", "bool", "string", "char", "exec", "stdout"]).register_n_inputs(1))
spawner = GeneSpawner(
instruction_set=instruction_set,
literals=[Char(" "), Char("\n")],
erc_generators=[lambda: Char(choice(_possible_chars)), synthetic_input],
)
# Estimator
est = PushEstimator(search="GA",
population_size=1000,
max_generations=100,
spawner=spawner,
last_str_from_stdout=True,
verbose=2)
class PushInterpreter:
"""An interpreter capable of running Push programs.
Parameters
----------
instruction_set : Union[InstructionSet, str], optional
The ``InstructionSet`` to use for executing programs. Default is "core"
which instantiates an ``InstructionSet`` using all the core instructions.
Attributes
----------
instruction_set : InstructionSet
The ``InstructionSet`` to use for executing programs.
state : PushState
The current ``PushState``. Contains one stack for each ``PushType``
mentioned by the instructions in the instruction set.
status : PushInterpreterStatus
A string denoting if the interpreter has encountered a situation
where non-standard termination was required.
"""
def __init__(self,
instruction_set: Union[InstructionSet, str] = "core",
reset_on_run: bool = True):
self.reset_on_run = reset_on_run
# If no instruction set given, create one and register all instructions.
if instruction_set == "core":
self.instruction_set = InstructionSet(register_core=True)
else:
self.instruction_set = instruction_set
self.type_library = self.instruction_set.type_library
# Initialize the PushState and status
self.state: PushState = None
self.status: PushInterpreterStatus = None
self._validate()
def _validate(self):
library_type_names = set(self.type_library.keys())
required_stacks = self.instruction_set.required_stacks() - {
"stdout", "exec", "untyped"
}
if not required_stacks <= library_type_names:
raise ValueError(
"PushInterpreter instruction_set and type_library are incompatible. {iset} vs {tlib}. Diff: {d}"
.format(
iset=required_stacks,
tlib=library_type_names,
d=required_stacks - library_type_names,
))
def _evaluate_instruction(self, instruction: Instruction,
config: PushConfig):
self.state = instruction.evaluate(self.state, config)
def untyped_to_typed(self):
"""Infer ``PushType`` of items on state's untyped queue and push to corresponding stacks."""
while len(self.state.untyped) > 0:
el = self.state.untyped.popleft()
push_type = self.type_library.push_type_of(el,
error_on_not_found=True)
self.state[push_type.name].push(el)
@tap
def evaluate_atom(self, atom: Atom, config: PushConfig):
"""Evaluate an ``Atom``.
Parameters
----------
atom : Atom
The Atom (``Literal``, ``InstructionMeta``, ``Input``, or ``CodeBlock``) to
evaluate against the current ``PushState``.
config : PushConfig
The configuration of the Push program being run.
"""
try:
if isinstance(atom, InstructionMeta):
self._evaluate_instruction(self.instruction_set[atom.name],
config)
elif isinstance(atom, Input):
input_value = self.state.inputs[atom.input_index]
self.state.untyped.append(input_value)
elif isinstance(atom, CodeBlock):
for a in atom[::-1]:
self.state["exec"].push(a)
elif isinstance(atom, Literal):
self.state[atom.push_type.name].push(atom.value)
elif isinstance(atom, Closer):
raise PushError(
"Closers should not be in push programs. Only genomes.")
else:
raise PushError(
"Cannot evaluate {t}, require a subclass of Atom".format(
t=type(atom)))
self.untyped_to_typed()
except Exception as e:
err_type = type(e).__name__
err_msg = str(e)
raise PushError(
"{t} raised while evaluating {atom}. Original message: \"{m}\""
.format(t=err_type, atom=atom, m=err_msg))
@tap
def run(self,
program: Program,
inputs: list,
print_trace: bool = False) -> list:
"""Run a Push ``Program`` given some inputs and desired output ``PushTypes``.
The general flow of this method is:
1. Create a new push state
2. Load the program and inputs.
3. If the exec stack is empty, return the outputs.
4. Else, pop the exec stack and process the atom.
5. Return to step 3.
Parameters
----------
program : Program
Program to run.
inputs : list
A sequence of values to use as inputs to the push program.
print_trace : bool
If True, each step of program execution will be summarized in stdout.
Returns
-------
Sequence
A sequence of values pulled from the final push state. May contain
pyshgp.utils.Token.no_stack_item if output stacks are empty.
"""
push_config = program.signature.push_config
if self.reset_on_run or self.state is None:
self.state = PushState(self.type_library, push_config)
self.status = PushInterpreterStatus.normal
# Setup
self.state.load_code(program.code)
self.state.load_inputs(inputs)
stop_time = time.time() + push_config.runtime_limit
steps = 0
if print_trace:
print("Initial State:")
self.state.pretty_print()
# Iterate atom evaluation until entire program is evaluated.
while len(self.state["exec"]) > 0:
# Stopping conditions
if steps > push_config.step_limit:
self.status = PushInterpreterStatus.step_limit_exceeded
break
if time.time() > stop_time:
self.status = PushInterpreterStatus.runtime_limit_exceeded
break
# Next atom in the program to evaluate.
next_atom = self.state["exec"].pop()
if print_trace:
start = time.time()
print("\nCurrent Atom: " + str(next_atom))
# Evaluate atom.
old_size = self.state.size()
self.evaluate_atom(next_atom, push_config)
if self.state.size() > old_size + push_config.growth_cap:
self.status = PushInterpreterStatus.growth_cap_exceeded
break
if print_trace:
duration = time.time() - start
print("Current State (step {step}):".format(step=steps))
self.state.pretty_print()
print("Step duration:", duration)
steps += 1
if print_trace:
print("Finished program evaluation.")
return self.state.observe_stacks(program.signature.output_stacks)
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_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()
y_train = y_train_edge + y_train_synthetic
X_test = [[synthetic_input()] for _ in range(100)]
y_test = [target_function(x[0]) for x in X_test]
# Spawner
def random_char():
"""Return a random character."""
return Char(choice(_possible_chars))
spawner = GeneSpawner(
n_inputs=1,
instruction_set=InstructionSet().register_core_by_stack(
{"int", "bool", "string", "char", "exec", "stdout"}),
literals=[" ", "\n"],
erc_generators=[
random_char,
],
)
if __name__ == "__main__":
est = PushEstimator(search="GA",
population_size=500,
max_generations=150,
spawner=spawner,
simplification_steps=100,
last_str_from_stdout=True,
parallelism=True,
verbose=2)
def test_register(self, instr_set):
i_set = InstructionSet()
i_set.register(instr_set["int_add"])
assert len(i_set) == 1
def test_register(self, atoms):
i_set = InstructionSet()
i_set.register(atoms["add"])
assert len(i_set) == 1
from pyshgp.gp.estimators import PushEstimator
from pyshgp.gp.genome import GeneSpawner
from pyshgp.gp.selection import Lexicase
from pyshgp.push.instruction_set import InstructionSet
def target_function(x: float) -> (float, float):
"""Generate a training data point."""
return max(0.0, x), max(0.1 * x, x)
X = np.arange(-1.0, 1.0, 0.05).reshape([-1, 1])
y = np.array([target_function(x[0]) for x in X])
spawner = GeneSpawner(n_inputs=1,
instruction_set=InstructionSet().register_core_by_stack(
{"float", "bool"}),
literals=[0.1, 0.0],
erc_generators=[
lambda: random.randint(0, 10),
])
ep_lex_sel = Lexicase(epsilon=True)
if __name__ == "__main__":
est = PushEstimator(population_size=300,
max_generations=50,
simplification_steps=500,
spawner=spawner,
selector=ep_lex_sel,
verbose=2)
def target_function(s):
"""Generate a training data point."""
return s[:-2] + s[:-2]
X = np.array([
"abcde", "", "E", "Hi", "Tom", "leprechaun", "zoomzoomzoom",
"qwertyuiopasd", "GallopTrotCanter", "Quinona", "_abc"
]).reshape(-1, 1)
y = np.array([[target_function(s[0])] for s in X])
spawner = GeneSpawner(
n_inputs=1,
instruction_set=InstructionSet().register_core_by_stack({"str", "int"}),
literals=[],
erc_generators=[
lambda: random.randint(0, 10),
]
)
if __name__ == "__main__":
est = PushEstimator(
spawner=spawner,
population_size=300,
max_generations=30,
initial_genome_size=(10, 50),
simplification_steps=500,
parallelism=False,
verbose=1
import sys
from pyshgp.gp.selection import Lexicase
from pyshgp.gp.estimators import PushEstimator
from pyshgp.gp.genome import GeneSpawner
from pyshgp.push.instruction_set import InstructionSet
def target_function(a, b):
return (2 * a * b) + (b * b)
X = np.arange(50).reshape(-1, 2)
y = np.array([[target_function(x[0], x[1])] for x in X])
instruction_set = (InstructionSet().register_by_type(
["int"], exclude=["str", "exec", "code"]).register_n_inputs(X.shape[1]))
spawner = GeneSpawner(instruction_set=instruction_set,
literals=[],
erc_generators=[
lambda: random.randint(0, 10),
])
ep_lex_sel = Lexicase(epsilon=True)
est = PushEstimator(population_size=200,
max_generations=50,
spawner=spawner,
selector=ep_lex_sel,
verbose=2)
class PushInterpreter:
"""An interpreter capable of running Push programs.
Parameters
----------
instruction_set : Union[InstructionSet, str], optional
The InstructionSet to use for executing programs. Default is "core"
which instansiates an InstructionSet using all the core instructions.
config : PushInterpreterConfig, optional
A PushInterpreterConfig specifying limits and early termination
conditions. Default is None, which creates a config will all default
values.
Attributes
----------
instruction_set : InstructionSet
The InstructionSet to use for executing programs.
config : PushInterpreterConfig
A PushInterpreterConfig specifying limits and early termination
conditions.
state : PushState
The current PushState. Contains one stack for each PushType utilized
mentioned by the instructions in the instruction set.
status : PushInterpreterStatus
A string denoting if the Interpreter has enountered a situation
where non-standard termination was required.
"""
def __init__(self,
instruction_set: Union[InstructionSet, str] = "core",
config: PushInterpreterConfig = None):
# If no instruction set given, create one and register all instructions.
if instruction_set == "core":
self.instruction_set = InstructionSet(register_all=True)
else:
self.instruction_set = instruction_set
self._supported_types = self.instruction_set.supported_types()
if config is None:
self.config = PushInterpreterConfig()
else:
self.config = config
# Initialize the PushState and status
self.reset()
def reset(self):
"""Reset the interpreter status and PushState."""
self.state = PushState(self._supported_types)
self.status = PushInterpreterStatus.normal
def _evaluate_instruction(self, instruction: Union[Instruction,
JitInstructionRef]):
self.state = instruction.evaluate(self.state, self.config)
def evaluate_atom(self, atom: Atom):
"""Evaluate an Atom.
Parameters
----------
atom : Atom
The Atom (Literal, Instruction, JitInstructionRef, or CodeBlock) to
evaluate against the current PushState.
"""
try:
if isinstance(atom, Instruction):
self._evaluate_instruction(atom)
elif isinstance(atom, JitInstructionRef):
self._evaluate_instruction(self.instruction_set[atom.name])
elif isinstance(atom, CodeBlock):
for a in atom[::-1]:
self.state["exec"].push(a)
elif isinstance(atom, Literal):
self.state[atom.push_type.name].push(atom.value)
elif isinstance(atom, Closer):
raise PushError(
"Closers should not be in push programs. Only genomes.")
else:
raise PushError(
"Cannont evaluate {t}, require a subclass of Atom".format(
t=type(atom)))
except Exception as e:
err_type = type(e).__name__
err_msg = str(e)
raise PushError(
"{t} raised while evaluating {atom}. Origional mesage: \"{m}\""
.format(t=err_type, atom=atom, m=err_msg))
def run(self,
program: CodeBlock,
inputs: Sequence,
output_types: Sequence[str],
verbosity_config: VerbosityConfig = None):
"""Run a Push program given some inputs and desired output PushTypes.
The general flow of this method is:
1. Create a new push state
2. Load the program and inputs.
3. If the exec stack is empty, return the outputs.
4. Else, pop the exec stack and process the atom.
5. Return to step 3.
Parameters
----------
program
Program to run.
inputs
A sequence of values to use as inputs to the push program.
output_types
A secence of values that denote the Pushtypes of the expected
outputs of the push program.
verbosity_config : VerbosityConfig, optional
A VerbosityConfig controling what is logged during the execution
of the program. Default is no verbosity.
Returns
-------
Sequence
A sequence of values pulled from the final push state. May contain
pyshgp.utils.Token.no_stack_item if needed stacks are empty.
"""
if self.config.reset_on_run:
self.reset()
self.state.load_program(program)
self.state.load_inputs(inputs)
stop_time = time.time() + self.config.runtime_limit
steps = 0
if verbosity_config is None:
verbosity_config = DEFAULT_VERBOSITY_LEVELS[0]
verbose_trace = verbosity_config.program_trace
if verbose_trace:
verbose_trace("Initial State:")
self.state.pretty_print(verbose_trace)
while len(self.state["exec"]) > 0:
if steps > self.config.atom_limit:
self.status = PushInterpreterStatus.atom_limit_exceeded
break
if time.time() > stop_time:
self.status = PushInterpreterStatus.runtime_limit_exceeded
break
next_atom = self.state["exec"].pop()
if verbose_trace:
verbose_trace("Current Atom: " + str(next_atom))
old_size = len(self.state)
self.evaluate_atom(next_atom)
if len(self.state) > old_size + self.config.growth_cap:
self.status = PushInterpreterStatus.growth_cap_exceeded
break
if verbose_trace:
verbose_trace("Current State:")
self.state.pretty_print(verbose_trace)
steps += 1
if verbose_trace:
verbose_trace("Finished program evaluation.")
return self.state.observe_stacks(output_types)
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"}
