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Initial evaluation for removing matplotlib and pytest as dependencies.

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This commit is contained in:
Thomas Paulin
2023-01-02 18:44:41 +01:00
parent e620321ef4
commit a107bdf69e
4 changed files with 121 additions and 102 deletions
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78
EasyGA/EasyGA.py
View File

@ -7,7 +7,7 @@ import decorators
# Import all the data structure prebuilt modules
from structure import Population as make_population
from structure import Chromosome as make_chromosome
from structure import Gene as make_gene
from structure import Gene as make_gene
from structure import Population
from structure import Chromosome
from structure import Gene
@ -17,23 +17,23 @@ from examples import Fitness
from termination import Termination
# Parent/Survivor Selection Methods
from parent import Parent
from parent import Parent
from survivor import Survivor
# Genetic Operator Methods
from crossover import Crossover
from mutation import Mutation
from mutation import Mutation
# Default Attributes for the GA
from attributes import Attributes
# Database class
from database import SQLDatabase
from sqlite3 import Error
# from database import SQLDatabase
# from sqlite3 import Error
# Graphing package
from database import MatplotlibGraph
import matplotlib.pyplot as plt
# from database import MatplotlibGraph
# import matplotlib.pyplot as plt
class GA(Attributes):
@ -46,7 +46,6 @@ class GA(Attributes):
https://github.com/danielwilczak101/EasyGA/wiki
"""
def evolve(self: GA, number_of_generations: float = float('inf'), consider_termination: bool = True) -> None:
"""
Evolves the ga until the ga is no longer active.
@ -63,9 +62,12 @@ class GA(Attributes):
if self.population is None:
self.initialize_population()
cond1 = lambda: number_of_generations > 0 # Evolve the specified number of generations.
cond2 = lambda: not consider_termination # If consider_termination flag is set:
cond3 = lambda: cond2() or self.active() # check termination conditions.
# Evolve the specified number of generations.
def cond1(): return number_of_generations > 0
# If consider_termination flag is set:
def cond2(): return not consider_termination
# check termination conditions.
def cond3(): return cond2() or self.active()
while cond1() and cond3():
@ -74,10 +76,11 @@ class GA(Attributes):
# Create the database here to allow the user to change the
# database name and structure before running the function.
self.database.create_all_tables(self)
# self.database.create_all_tables(self)
# Add the current configuration to the config table
self.database.insert_config(self)
# self.database.insert_config(self)
pass
# Otherwise evolve the population.
else:
@ -102,10 +105,9 @@ class GA(Attributes):
if int(adapt_counter) < int(adapt_counter + self.adapt_rate):
self.adapt()
number_of_generations -= 1
number_of_generations -= 1
self.current_generation += 1
def update_population(self: GA) -> None:
"""
Updates the population to the new population
@ -113,7 +115,6 @@ class GA(Attributes):
"""
self.population.update()
def reset_run(self: GA) -> None:
"""
Resets a run by re-initializing the
@ -123,7 +124,6 @@ class GA(Attributes):
self.current_generation = 0
self.run += 1
def adapt(self: GA) -> None:
"""Adapts the ga to hopefully get better results."""
@ -134,7 +134,6 @@ class GA(Attributes):
self.set_all_fitness()
self.sort_by_best_fitness()
def adapt_probabilities(self: GA) -> None:
"""
Modifies the parent ratio and mutation rates based on the adapt
@ -154,7 +153,7 @@ class GA(Attributes):
# Difference between best and i-th chromosomes
best_chromosome = self.population[0]
tol = lambda i: self.dist(best_chromosome, self.population[i])
def tol(i): return self.dist(best_chromosome, self.population[i])
# Too few converged: cross more and mutate less
if tol(amount_converged//2) > tol(amount_converged//4)*2:
@ -169,13 +168,14 @@ class GA(Attributes):
self.max_gene_mutation_rate)
# Weighted average of x and y
average = lambda x, y: weight * x + (1-weight) * y
def average(x, y): return weight * x + (1-weight) * y
# Adjust rates towards the bounds
self.selection_probability = average(bounds[0], self.selection_probability)
self.chromosome_mutation_rate = average(bounds[1], self.chromosome_mutation_rate)
self.gene_mutation_rate = average(bounds[2], self.gene_mutation_rate)
self.selection_probability = average(
bounds[0], self.selection_probability)
self.chromosome_mutation_rate = average(
bounds[1], self.chromosome_mutation_rate)
self.gene_mutation_rate = average(bounds[2], self.gene_mutation_rate)
def adapt_population(self: GA) -> None:
"""
@ -202,7 +202,7 @@ class GA(Attributes):
self.crossover_individual_impl(
self.population[n],
parent,
weight = -3/4,
weight=-3/4,
)
# If negative weights can't be used or division by 0, use positive weight
@ -210,7 +210,7 @@ class GA(Attributes):
self.crossover_individual_impl(
self.population[n],
parent,
weight = +1/4,
weight=+1/4,
)
# Stop if we've filled up an entire population
@ -218,20 +218,19 @@ class GA(Attributes):
break
# Replace worst chromosomes with new chromosomes, except for the previous best chromosome
min_len = min(len(self.population)-1, len(self.population.next_population))
min_len = min(len(self.population)-1,
len(self.population.next_population))
if min_len > 0:
self.population[-min_len:] = self.population.next_population[:min_len]
self.population.next_population = []
self.population.mating_pool = []
def initialize_population(self: GA) -> None:
"""
Sets self.population using the chromosome implementation and population size.
"""
self.population = self.make_population(self.population_impl())
def set_all_fitness(self: GA) -> None:
"""
Sets the fitness of each chromosome in the population.
@ -252,15 +251,14 @@ class GA(Attributes):
if chromosome.fitness is None or self.update_fitness:
chromosome.fitness = self.fitness_function_impl(chromosome)
def sort_by_best_fitness(
self: GA,
chromosome_list: Optional[
Union[MutableSequence[Chromosome],
Iterable[Chromosome]]
] = None,
in_place: bool = True,
) -> MutableSequence[Chromosome]:
self: GA,
chromosome_list: Optional[
Union[MutableSequence[Chromosome],
Iterable[Chromosome]]
] = None,
in_place: bool = True,
) -> MutableSequence[Chromosome]:
"""
Sorts the chromosome list by fitness based on fitness type.
1st element has best fitness.
@ -315,7 +313,6 @@ class GA(Attributes):
else:
return sorted(chromosome_list, key=key, reverse=reverse)
def get_chromosome_fitness(self: GA, index: int) -> float:
"""
Computes the converted fitness of a chromosome at an index.
@ -339,7 +336,6 @@ class GA(Attributes):
"""
return self.convert_fitness(self.population[index].fitness)
def convert_fitness(self: GA, fitness: float) -> float:
"""
Calculates a modified version of the fitness for various
@ -376,23 +372,19 @@ class GA(Attributes):
return max_fitness - fitness + min_fitness
def print_generation(self: GA) -> None:
"""Prints the current generation."""
print(f"Current Generation \t: {self.current_generation}")
def print_population(self: GA) -> None:
"""Prints the entire population."""
print(self.population)
def print_best_chromosome(self: GA) -> None:
"""Prints the best chromosome and its fitness."""
print(f"Best Chromosome \t: {self.population[0]}")
print(f"Best Fitness \t: {self.population[0].fitness}")
def print_worst_chromosome(self: GA) -> None:
"""Prints the worst chromosome and its fitness."""
print(f"Worst Chromosome \t: {self.population[-1]}")

112
EasyGA/attributes.py
View File

@ -6,8 +6,8 @@ from dataclasses import dataclass, field, _MISSING_TYPE
from types import MethodType
import random
import sqlite3
import matplotlib.pyplot as plt
# import sqlite3
# import matplotlib.pyplot as plt
from structure import Population
from structure import Chromosome
@ -19,12 +19,13 @@ from parent import Parent
from survivor import Survivor
from crossover import Crossover
from mutation import Mutation
from database import SQLDatabase, MatplotlibGraph, SQLDatabase as Database, MatplotlibGraph as Graph
# from database import SQLDatabase, MatplotlibGraph, SQLDatabase as Database, MatplotlibGraph as Graph
#========================================#
# Default methods not defined elsewhere. #
#========================================#
def rand_1_to_10(self: Attributes) -> int:
"""
Default gene_impl, returning a random integer from 1 to 10.
@ -36,6 +37,7 @@ def rand_1_to_10(self: Attributes) -> int:
"""
return random.randint(1, 10)
def use_genes(self: Attributes) -> Iterator[Any]:
"""
Default chromosome_impl, generates a chromosome using the gene_impl and chromosome length.
@ -55,6 +57,7 @@ def use_genes(self: Attributes) -> Iterator[Any]:
for _ in range(self.chromosome_length):
yield self.gene_impl()
def use_chromosomes(self: Attributes) -> Iterator[Iterable[Any]]:
"""
Default population_impl, generates a population using the chromosome_impl and population size.
@ -74,6 +77,7 @@ def use_chromosomes(self: Attributes) -> Iterator[Iterable[Any]]:
for _ in range(self.population_size):
yield self.chromosome_impl()
def dist_fitness(self: Attributes, chromosome_1: Chromosome, chromosome_2: Chromosome) -> float:
"""
Measures the distance between two chromosomes based on their fitnesses.
@ -90,6 +94,7 @@ def dist_fitness(self: Attributes, chromosome_1: Chromosome, chromosome_2: Chrom
"""
return sqrt(abs(chromosome_1.fitness - chromosome_2.fitness))
def simple_linear(self: Attributes, weight: float) -> float:
"""
Returns a random value between 0 and 1, with increased probability
@ -181,26 +186,27 @@ class AttributesData:
parent_selection_impl: Callable[["Attributes"], None] = None
crossover_individual_impl: Callable[["Attributes"], None] = None
crossover_population_impl: Callable[["Attributes", Chromosome, Chromosome], 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=SQLDatabase)
database_name: str = "database.db"
save_data: bool = True
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
);
"""
# database: Database = field(default_factory=SQLDatabase)
#database_name: str = "database.db"
#save_data: bool = True
# 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] = MatplotlibGraph
# graph: Callable[[Database], Graph] = MatplotlibGraph
def __post_init__(self: AttributesData) -> None:
"""
@ -311,7 +317,8 @@ class Attributes(AttributesData):
chromosome : Chromosome
The chromosome to be saved.
"""
self.database.insert_current_chromosome(self.current_generation, chromosome)
self.database.insert_current_chromosome(
self.current_generation, chromosome)
#===========================#
# Descriptors which convert #
@ -327,13 +334,20 @@ class Attributes(AttributesData):
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)
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: #
@ -346,7 +360,8 @@ class Attributes(AttributesData):
@run.setter
def run(self: AttributesProperties, value: int) -> None:
if not isinstance(value, int) or value < 0:
raise ValueError("ga.run counter must be an integer greater than or equal to 0.")
raise ValueError(
"ga.run counter must be an integer greater than or equal to 0.")
vars(self)["run"] = value
@property
@ -356,7 +371,8 @@ class Attributes(AttributesData):
@current_generation.setter
def current_generation(self: AttributesProperties, value: int) -> None:
if not isinstance(value, int) or value < 0:
raise ValueError("ga.current_generation must be an integer greater than or equal to 0")
raise ValueError(
"ga.current_generation must be an integer greater than or equal to 0")
vars(self)["current_generation"] = value
@property
@ -366,7 +382,8 @@ class Attributes(AttributesData):
@chromosome_length.setter
def chromosome_length(self: AttributesProperties, value: int) -> None:
if not isinstance(value, int) or value <= 0:
raise ValueError("ga.chromosome_length must be an integer greater than and not equal to 0.")
raise ValueError(
"ga.chromosome_length must be an integer greater than and not equal to 0.")
vars(self)["chromosome_length"] = value
@property
@ -376,7 +393,8 @@ class Attributes(AttributesData):
@population_size.setter
def population_size(self: AttributesProperties, value: int) -> None:
if not isinstance(value, int) or value <= 0:
raise ValueError("ga.population_size must be an integer greater than and not equal to 0.")
raise ValueError(
"ga.population_size must be an integer greater than and not equal to 0.")
vars(self)["population_size"] = value
@property
@ -393,7 +411,8 @@ class Attributes(AttributesData):
if value is None or (isinstance(value, (float, int)) and 0 <= value <= 1):
vars(self)["max_chromosome_mutation_rate"] = value
else:
raise ValueError("Max chromosome mutation rate must be between 0 and 1")
raise ValueError(
"Max chromosome mutation rate must be between 0 and 1")
@property
def min_chromosome_mutation_rate(self: AttributesProperties) -> float:
@ -409,26 +428,27 @@ class Attributes(AttributesData):
if value is None or (isinstance(value, (float, int)) and 0 <= value <= 1):
vars(self)["min_chromosome_mutation_rate"] = value
else:
raise ValueError("Min chromosome mutation rate must be between 0 and 1")
raise ValueError(
"Min chromosome mutation rate must be between 0 and 1")
@property
def database_name(self: AttributesProperties) -> str:
return vars(self)["database_name"]
# @property
# def database_name(self: AttributesProperties) -> str:
# return vars(self)["database_name"]
@database_name.setter
def database_name(self: AttributesProperties, name: str) -> None:
# Update the database's name.
self.database._database_name = name
# Set the attribute for itself.
vars(self)["database_name"] = name
# @database_name.setter
# def database_name(self: AttributesProperties, name: str) -> None:
# # Update the database's name.
# self.database._database_name = name
# # Set the attribute for itself.
# vars(self)["database_name"] = name
@property
def graph(self: AttributesProperties) -> Graph:
return vars(self)["graph"]
# @property
# def graph(self: AttributesProperties) -> Graph:
# return vars(self)["graph"]
@graph.setter
def graph(self: AttributesProperties, graph: Callable[[Database], Graph]) -> None:
vars(self)["graph"] = graph(self.database)
# @graph.setter
# def graph(self: AttributesProperties, graph: Callable[[Database], Graph]) -> None:
# vars(self)["graph"] = graph(self.database)
@property
def active(self: AttributesProperties) -> Callable[[], bool]:

8
README.md
View File

@ -15,7 +15,9 @@ pip3 install EasyGA
```
## Getting started with EasyGA(Basic Example):
The goal of the basic example is to get all 5's in the chromosome.
```Python
import EasyGA
@ -31,6 +33,7 @@ ga.print_population()
```
### Output:
```bash
Current Generation : 15
Current population:
@ -47,6 +50,7 @@ Chromosome - 9 [7][2][8][10][3][5][5][8][1][7] / Fitness = 2
```
## Getting started with EasyGA (Password Cracker Example):
```Python
import EasyGA
import random
@ -94,6 +98,7 @@ ga.graph.show()
```
## Ouput:
```
Please enter a word:
EasyGA
@ -113,8 +118,8 @@ Chromosome - 9 [E][a][s][Y][G][A] / Fitness = 5
<img width="500px" src="https://raw.githubusercontent.com/danielwilczak101/EasyGA/media/images/password_cracker_results.png" />
## Issues
We would love to know if your having any issues. Please start a new issue on the [Issues Page](https://github.com/danielwilczak101/EasyGA/issues).
We would love to know if your having any issues. Please start a new issue on the [Issues Page](https://github.com/danielwilczak101/EasyGA/issues).
## Local System Approach
@ -123,6 +128,7 @@ Download the repository to some folder on your computer.
```
https://github.com/danielwilczak101/EasyGA/archive/master.zip
```
Use the run.py file inside the EasyGA folder to run your code. This is a local version of the package.
## Check out our [wiki](https://github.com/danielwilczak101/EasyGA/wiki) for more information.

19
setup.py
View File

@ -13,16 +13,17 @@ setuptools.setup(
url="https://github.com/danielwilczak101/EasyGA",
author="Daniel Wilczak, Jack RyanNguyen, Ryley Griffith, Jared Curtis, Matthew Chase Oxamendi ",
author_email="danielwilczak101@gmail.com",
long_description = long_description,
long_description_content_type = "text/markdown",
long_description=long_description,
long_description_content_type="text/markdown",
classifier=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)",
"Operating System :: OS Independent",
],
install_requires = ["matplotlib ~= 3.3.2",
"pyserial ~= 3.4",
"pytest>=3.7",
"tabulate >=0.8.7"
],
)
],
install_requires=[
# "matplotlib ~= 3.3.2",
# "pyserial ~= 3.4",
"pytest>=3.7",
"tabulate >=0.8.7"
],
)