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Fixed methods-dataclass problem

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Type-hinted methods as if they were attributes will cause them to be treated as attributes for the dataclass. The type-hints were simply removed.
This commit is contained in:
SimpleArt
2021-06-10 22:03:02 -04:00
parent cd0054b3d0
commit a925eb96c8
1 changed files with 102 additions and 99 deletions
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201
EasyGA/attributes.py
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@ -21,105 +21,9 @@ from crossover import Crossover
from mutation import Mutation
from database import sql_database, matplotlib_graph
@dataclass
class Attributes:
"""
Attributes class which stores all attributes in a dataclass.
Contains default attributes for each attribute.
"""
properties: Dict[str, Any] = field(default_factory=dict, init=False, repr=False, compare=False)
run: int = 0
chromosome_length: int = 10
population_size: int = 10
population: Optional[Population] = None
target_fitness_type: str = 'max'
update_fitness: bool = False
parent_ratio: float = 0.1
selection_probability: float = 0.5
tournament_size_ratio: float = 0.1
current_generation: int = 0
generation_goal: int = 100
fitness_goal: Optional[float] = None
tolerance_goal: Optional[float] = None
percent_converged: float = 0.5
chromosome_mutation_rate: float = 0.15
gene_mutation_rate: float = 0.05
adapt_rate: float = 0.05
adapt_probability_rate: float = 0.05
adapt_population_flag: bool = True
max_selection_probability: float = 0.75
min_selection_probability: float = 0.25
max_chromosome_mutation_rate: float = None
min_chromosome_mutation_rate: float = None
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_population: Callable[[Iterable[Iterable[Any]]], Population] = Population
make_chromosome: Callable[[Iterable[Any]], Chromosome] = Chromosome
make_gene: Callable[[Any], Gene] = Gene
gene_impl: Callable[[Attributes], Any] = field(default_factory=lambda: rand_1_to_10)
chromosome_impl: Optional[[Attributes], Iterable[Any]] = field(default_factory=lambda: use_genes)
population_impl: Optional[[Attributes], Iterable[Iterable[Any]]] = field(default_factory=lambda: use_chromosomes)
weighted_random: Callable[[Attributes, float], float] = field(default_factory=lambda: simple_linear)
dist: Callable[[Attributes, Chromosome, Chromosome], float] = field(default_factory=lambda: dist_fitness)
parent_selection_impl: Callable[[Attributes], None] = Parent.Rank.tournament
crossover_individual_impl: Callable[[Attributes], None] = Crossover.Individual.single_point
crossover_population_impl: Callable[[Attributes], None] = Crossover.Population.sequential
survivor_selection_impl: Callable[[Attributes], None] = Survivor.fill_in_best
mutation_individual_impl: Callable[[Attributes], None] = Mutation.Individual.individual_genes
mutation_population_impl: Callable[[Attributes], None] = Mutation.Population.random_avoid_best
termination_impl: Callable[[Attributes], None] = Termination.fitness_generation_tolerance
database: Database = field(default_factory=sql_database.SQL_Database)
database_name: str = 'database.db'
sql_create_data_structure: str = """
CREATE TABLE IF NOT EXISTS data (
id INTEGER PRIMARY KEY,
config_id INTEGER DEFAULT NULL,
generation INTEGER NOT NULL,
fitness REAL,
chromosome TEXT
);
"""
graph: Callable[[Database], Graph] = matplotlib_graph.Matplotlib_Graph
#============================#
# Built-in database methods: #
#============================#
def save_population(self: Attributes) -> None:
"""Saves the current population to the database."""
self.database.insert_current_population(self)
def save_chromosome(self: Attributes, chromosome: Chromosome) -> None:
"""
Saves a chromosome to the database.
Parameters
----------
chromosome : Chromosome
The chromosome to be saved.
"""
self.database.insert_current_chromosome(self.current_generation, chromosome)
#========================================#
# Default methods not defined elsewhere. #
#========================================#
def rand_1_to_10(self: Attributes) -> int:
"""
@ -212,6 +116,105 @@ def simple_linear(self: Attributes, weight: float) -> float:
return 1 - (1-rand) * weight / (1-weight)
@dataclass
class Attributes:
"""
Attributes class which stores all attributes in a dataclass.
Contains default attributes for each attribute.
"""
properties: Dict[str, Any] = field(default_factory=dict, init=False, repr=False, compare=False)
run: int = 0
chromosome_length: int = 10
population_size: int = 10
population: Optional[Population] = None
target_fitness_type: str = 'max'
update_fitness: bool = False
parent_ratio: float = 0.1
selection_probability: float = 0.5
tournament_size_ratio: float = 0.1
current_generation: int = 0
generation_goal: int = 100
fitness_goal: Optional[float] = None
tolerance_goal: Optional[float] = None
percent_converged: float = 0.5
chromosome_mutation_rate: float = 0.15
gene_mutation_rate: float = 0.05
adapt_rate: float = 0.05
adapt_probability_rate: float = 0.05
adapt_population_flag: bool = True
max_selection_probability: float = 0.75
min_selection_probability: float = 0.25
max_chromosome_mutation_rate: float = None
min_chromosome_mutation_rate: float = None
max_gene_mutation_rate: float = 0.15
min_gene_mutation_rate: float = 0.01
fitness_function_impl = Fitness.is_it_5
make_population = Population
make_chromosome = Chromosome
make_gene = Gene
gene_impl = rand_1_to_10
chromosome_impl = use_genes
population_impl = use_chromosomes
weighted_random = simple_linear
dist = dist_fitness
parent_selection_impl = Parent.Rank.tournament
crossover_individual_impl = Crossover.Individual.single_point
crossover_population_impl = Crossover.Population.sequential
survivor_selection_impl = Survivor.fill_in_best
mutation_individual_impl = Mutation.Individual.individual_genes
mutation_population_impl = Mutation.Population.random_avoid_best
termination_impl = Termination.fitness_generation_tolerance
database: Database = field(default_factory=sql_database.SQL_Database)
database_name: str = 'database.db'
sql_create_data_structure: str = """
CREATE TABLE IF NOT EXISTS data (
id INTEGER PRIMARY KEY,
config_id INTEGER DEFAULT NULL,
generation INTEGER NOT NULL,
fitness REAL,
chromosome TEXT
);
"""
graph: Callable[[Database], Graph] = matplotlib_graph.Matplotlib_Graph
#============================#
# Built-in database methods: #
#============================#
def save_population(self: Attributes) -> None:
"""Saves the current population to the database."""
self.database.insert_current_population(self)
def save_chromosome(self: Attributes, chromosome: Chromosome) -> None:
"""
Saves a chromosome to the database.
Parameters
----------
chromosome : Chromosome
The chromosome to be saved.
"""
self.database.insert_current_chromosome(self.current_generation, chromosome)
#==================================================#
# Properties for attributes behaving like methods. #
#==================================================#