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Updated __init__ and descriptors for inheritance.

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Avoided dataclass overriding methods by customizing the __init__ and moving default methods to the descriptors.
This commit is contained in:
SimpleArt
2021-07-08 10:43:46 -04:00
parent f50e723981
commit e2970c0099
1 changed files with 100 additions and 40 deletions
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140
EasyGA/attributes.py
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@ -2,7 +2,7 @@ from __future__ import annotations
from inspect import signature
from typing import Any, Callable, Dict, Iterable, Iterator, Optional
from math import sqrt, ceil
from dataclasses import dataclass, field
from dataclasses import dataclass, field, _MISSING_TYPE
from types import MethodType
import random
@ -117,12 +117,14 @@ def simple_linear(self: Attributes, weight: float) -> float:
class AttributesData:
"""
Attributes class which stores all attributes in a dataclass.
This includes type-hints/annotations and default values.
This includes type-hints/annotations and default values, except for methods.
Additionally gains dataclass features, including an __init__ and __repr__ to avoid boilerplate code.
Developer Note:
See the Attributes class for default methods.
Override this class to set default attributes. See help(Attributes) for more information.
"""
@ -159,25 +161,30 @@ class AttributesData:
max_gene_mutation_rate: float = 0.15
min_gene_mutation_rate: float = 0.01
fitness_function_impl: Callable[["Attributes", Chromosome], float] = Fitness.is_it_5
make_gene: Callable[[Any], Gene] = Gene
make_chromosome: Callable[[Iterable[Any]], Chromosome] = Chromosome
make_population: Callable[[Iterable[Iterable[Any]]], Population] = Population
#=================================#
# Default methods are implemented #
# in the Attributes descriptors: #
#=================================#
gene_impl: Callable[[], Any] = rand_1_to_10
chromosome_impl: Callable[[], Iterable[Any]] = use_genes
population_impl: Callable[[], Iterable[Iterable[Any]]] = use_chromosomes
fitness_function_impl: Callable[["Attributes", Chromosome], float] = None
make_gene: Callable[[Any], Gene] = None
make_chromosome: Callable[[Iterable[Any]], Chromosome] = None
make_population: Callable[[Iterable[Iterable[Any]]], Population] = None
weighted_random: Callable[[float], float] = simple_linear
dist: Callable[["Attributes", Chromosome, Chromosome], None] = dist_fitness
gene_impl: Callable[[], Any] = None
chromosome_impl: Callable[[], Iterable[Any]] = None
population_impl: Callable[[], Iterable[Iterable[Any]]] = None
parent_selection_impl: Callable[["Attributes"], None] = Parent.Rank.tournament
crossover_individual_impl: Callable[["Attributes"], None] = Crossover.Individual.single_point
crossover_population_impl: Callable[["Attributes", Chromosome, Chromosome], None] = Crossover.Population.sequential
survivor_selection_impl: Callable[["Attributes"], None] = Survivor.fill_in_best
mutation_individual_impl: Callable[["Attributes", Chromosome], None] = Mutation.Individual.individual_genes
mutation_population_impl: Callable[["Attributes"], None] = Mutation.Population.random_avoid_best
termination_impl: Callable[["Attributes"], bool] = Termination.fitness_generation_tolerance
weighted_random: Callable[[float], float] = None
dist: Callable[["Attributes", Chromosome, Chromosome], None] = None
parent_selection_impl: Callable[["Attributes"], None] = None
crossover_individual_impl: Callable[["Attributes"], None] = None
crossover_population_impl: Callable[["Attributes", Chromosome, Chromosome], None] = None
survivor_selection_impl: Callable[["Attributes"], None] = None
mutation_individual_impl: Callable[["Attributes", Chromosome], None] = None
mutation_population_impl: Callable[["Attributes"], None] = None
termination_impl: Callable[["Attributes"], bool] = None
database: Database = field(default_factory=sql_database.SQL_Database)
database_name: str = "database.db"
@ -194,19 +201,69 @@ class AttributesData:
graph: Callable[[Database], Graph] = matplotlib_graph.Matplotlib_Graph
def __init__(self: AttributesData, *args: Any, **kwargs: Any) -> None:
"""
Generated AttributesData.__init__, accepts dataclass fields as parameters.
Ignores parameters whose values are None if something is already set.
"""
# Extract the default value from a dataclass field.
def get_default(default_field):
if type(default_field.default) is not _MISSING_TYPE:
return default_field.default
else:
return default_field.default_factory()
# Extract the default values from all fields.
defaults = {name: get_default(default_field) for name, default_field in self.__dataclass_fields__.items() if default_field.init}
# Verify not too many args are passed in.
if len(args) > len(defaults):
raise TypeError(f"__init__ takes {len(defaults)} positional arguments but {len(args)} were given")
# Convert the args to kwargs.
args = dict(zip(defaults, args))
# Verify a parameter is not in both the args and kwargs.
for name in args.keys() & kwargs.keys():
raise TypeError(f"__init__() got multiple values for '{name}'")
# Verify all kwargs are in the fields.
for name in kwargs.keys() - self.__dataclass_fields__.keys():
raise TypeError(f"__init__ got an unexpected keyword argument '{name}'")
# Merge the defaults, args, and kwargs.
for name, value in {**defaults, **args, **kwargs}.items():
# Ignore None values if self already has that attribute.
if value is not None or name not in dir(self):
setattr(self, name, value)
# Run the __post_init__.
if hasattr(self, "__post_init__"):
self.__post_init__()
class AsMethod:
"""A descriptor for converting function attributes into bound methods."""
"""
A descriptor for converting function attributes into bound methods.
def __init__(self: AsMethod, name: str) -> None:
To support both inheritance and dataclasses, if the method is None,
then nothing is set.
"""
def __init__(self: AsMethod, name: str, default: Callable) -> None:
if not callable(default):
raise TypeError(f"'default' must be a method i.e. callable.")
self.name = name
self.default = default
def __get__(self: AsMethod, obj: "AttributesProperties", cls: type) -> MethodType:
return vars(obj)[self.name]
def __get__(self: AsMethod, obj: "AttributesProperties", cls: type) -> Callable:
# Already has the attribute on the object.
if self.name in vars(obj):
return vars(obj)[self.name]
# Otherwise use the default as a method.
if next(iter(signature(self.default).parameters), None) in ("self", "ga"):
return MethodType(self.default, obj)
# Otherwise use the default as a function.
return self.default
def __set__(self: AsMethod, obj: "AttributesProperties", method: Callable) -> None:
if not callable(method):
raise TypeError(f"{self.name} must be a method i.e. callable.")
def __set__(self: AsMethod, obj: "AttributesProperties", method: Optional[Callable]) -> None:
if method is None:
return
elif not callable(method):
raise TypeError(f"'{self.name}' must be a method i.e. callable.")
elif next(iter(signature(method).parameters), None) in ("self", "ga"):
method = MethodType(method, obj)
vars(obj)[self.name] = method
@ -264,19 +321,22 @@ class Attributes(AttributesData):
# functions into methods: #
#===========================#
fitness_function_impl = AsMethod("fitness_function_impl")
parent_selection_impl = AsMethod("parent_selection_impl")
crossover_individual_impl = AsMethod("crossover_individual_impl")
crossover_population_impl = AsMethod("crossover_population_impl")
survivor_selection_impl = AsMethod("survivor_selection_impl")
mutation_individual_impl = AsMethod("mutation_individual_impl")
mutation_population_impl = AsMethod("mutation_population_impl")
termination_impl = AsMethod("termination_impl")
dist = AsMethod("dist")
weighted_random = AsMethod("weighted_random")
gene_impl = AsMethod("gene_impl")
chromosome_impl = AsMethod("chromosome_impl")
population_impl = AsMethod("population_impl")
fitness_function_impl = AsMethod("fitness_function_impl", Fitness.is_it_5)
make_gene = AsMethod("make_gene", Gene)
make_chromosome = AsMethod("make_chromosome", Chromosome)
make_population = AsMethod("make_population", Population)
gene_impl = AsMethod("gene_impl", rand_1_to_10)
chromosome_impl = AsMethod("chromosome_impl", use_genes)
population_impl = AsMethod("population_impl", use_chromosomes)
dist = AsMethod("dist", dist_fitness)
weighted_random = AsMethod("weighted_random", simple_linear)
parent_selection_impl = AsMethod("parent_selection_impl", Parent.Rank.tournament)
crossover_individual_impl = AsMethod("crossover_individual_impl", Crossover.Individual.single_point)
crossover_population_impl = AsMethod("crossover_population_impl", Crossover.Population.sequential)
survivor_selection_impl = AsMethod("survivor_selection_impl", Survivor.fill_in_best)
mutation_individual_impl = AsMethod("mutation_individual_impl", Mutation.Individual.individual_genes)
mutation_population_impl = AsMethod("mutation_population_impl", Mutation.Population.random_avoid_best)
termination_impl = AsMethod("termination_impl", Termination.fitness_generation_tolerance)
#=============#
# Properties: #
@ -325,7 +385,7 @@ class Attributes(AttributesData):
@property
def max_chromosome_mutation_rate(self: AttributesProperties) -> float:
# Default value.
if vars(self)["max_chromosome_mutation_rate"] is None:
if vars(self).get("max_chromosome_mutation_rate", None) is None:
return min(self.chromosome_mutation_rate * 2, (self.chromosome_mutation_rate + 1) / 2)
# Set value.
return vars(self)["max_chromosome_mutation_rate"]
@ -341,7 +401,7 @@ class Attributes(AttributesData):
@property
def min_chromosome_mutation_rate(self: AttributesProperties) -> float:
# Default value.
if vars(self)["min_chromosome_mutation_rate"] is None:
if vars(self).get("min_chromosome_mutation_rate", None) is None:
return max(self.chromosome_mutation_rate / 2, self.chromosome_mutation_rate * 2 - 1)
# Set value.
return vars(self)["min_chromosome_mutation_rate"]