def main(args=sys.argv[1:]):
"""Runs the main GASTOp script, from the command line.
Reads and parses user input from command line, runs the code,
and prints and plots the resulting best truss.
"""
args = parse_args(args)
config = utilities.init_file_parser(args.config_path)
if args.display:
progress_fitness = True
progress_truss = True
elif args.quiet:
progress_fitness = False
progress_truss = False
else:
progress_fitness = config['monitor_params']['progress_fitness']
progress_truss = config['monitor_params']['progress_truss']
if args.num_threads:
num_threads = args.num_threads
else:
num_threads = config['ga_params']['num_threads']
if args.num_gens:
num_generations = args.num_gens
else:
num_generations = config['ga_params']['num_generations']
if args.pop_size:
pop_size = args.pop_size
else:
pop_size = config['ga_params']['pop_size']
# Create the Genetic Algorithm Object
ga = GenAlg(config)
ga.initialize_population(pop_size)
best, progress_history = ga.run(num_generations=num_generations,
progress_fitness=progress_fitness,
progress_truss=progress_truss,
num_threads=num_threads)
print(best)
if args.output:
utilities.save_progress_history(progress_history,
path_progress_history=args.output)
if progress_fitness or progress_truss:
best.plot(
domain=config['random_params']['domain'],
loads=config['evaluator_params']['boundary_conditions']['loads'],
fixtures=config['evaluator_params']['boundary_conditions']
['fixtures'],
deflection=True)
def testInvalidCrossover(self):
"""Tests genalg when % crossover + % mutation >1"""
config = utilities.init_file_parser(init_file_path)
config['ga_params']['percent_crossover'] = .6
config['ga_params']['percent_mutation'] = .6
ga = GenAlg(config)
ga.initialize_population(100)
for t in ga.population:
t.fitness_score = np.random.random()
self.assertRaises(RuntimeError, ga.update_population)
bad_path = 'gastop-config/error_config.txt'
self.assertRaises(RuntimeError, utilities.init_file_parser, bad_path)
def testSaveLoadPopProgress(self):
'''Tests that the pop_progress dictionary is correctly saved to and
loaded from a JSON file.
'''
# Parse input paramters from init.txt file
init_file_path = 'gastop-config/struct_making_test_init.txt'
config = utilities.init_file_parser(init_file_path)
progress_fitness = False
progress_truss = False
num_threads = 1
num_generations = 3
pop_size = 5
# Create the Genetic Algorithm Object
ga = GenAlg(config)
ga.initialize_population(pop_size)
best, progress_history = ga.run(num_generations=num_generations,
progress_fitness=progress_fitness,
progress_truss=progress_truss,
num_threads=num_threads)
# Save and load pop_progress to/from JSON file
utilities.save_progress_history(progress_history)
loaded_progress_history = utilities.load_progress_history()
print(loaded_progress_history)
for gen in loaded_progress_history.keys():
self.assertTrue(
isinstance(loaded_progress_history[gen]['Generation'], int))
self.assertTrue(
isinstance(loaded_progress_history[gen]['Best Truss'], Truss))
self.assertTrue(
isinstance(loaded_progress_history[gen]['Best Fitness Score'],
float))
self.assertTrue(
isinstance(
loaded_progress_history[gen]
['Population Median Fitness Score'], float))
self.assertTrue(
isinstance(
loaded_progress_history[gen]
['Population Fitness Score Range'], float))
def __init__(self, config):
"""Creates a GenAlg object
Once created, the object will store all of the relavant information
about a population. The object also contains the necessary functions to
modify itself, evaluate its 'goodness', and then create new members for
the next generation.
Args:
Either:
config (dict): Configuration dictionary with parameters, such as one
created by :meth:`gastop.utilities.init_file_parser`
config (str): File path to config file to be parsed. Used
instead of passing config dictionary directly.
Returns:
GenAlg callable object
"""
if isinstance(config, str):
config = utilities.init_file_parser(config)
self.config = config
self.ga_params = config['ga_params']
self.mutator_params = config['mutator_params']
self.random_params = config['random_params']
self.crossover_params = config['crossover_params']
self.selector_params = config['selector_params']
self.population = []
self.evaluator = Evaluator(**config['evaluator_params'])
self.fitness_function = FitnessFunction(**config['fitness_params'])
# progress monitor stuff
self.pop_progress = [] # initialize as empty array, DELETE?
# self.progress_display = progress_display #type of progress display
# [0,1,2] = [no display, terminal display, plot display], change to text later
if isinstance(self.random_params['rng_seed'], tuple):
np.random.set_state(self.random_params['rng_seed'])
elif isinstance(self.random_params['rng_seed'], int):
np.random.seed(self.random_params['rng_seed'])
else:
np.random.seed(1729)
def testOddNumCrossover(self):
"""Tests genalg when crossover number is odd
to make sure it still return the correct population size
"""
config = utilities.init_file_parser(init_file_path)
config['ga_params']['percent_crossover'] = 1
config['ga_params']['percent_mutation'] = 0
config['ga_params']['num_elite'] = 5
ga = GenAlg(config)
ga.initialize_population(100)
ga.run(num_generations=2)
self.assertAlmostEqual(len(ga.population), 100)
# test zero crossover
config['ga_params']['percent_crossover'] = 0
config['ga_params']['percent_mutation'] = 1
ga = GenAlg(config)
ga.initialize_population(100)
ga.run(num_generations=2)
self.assertAlmostEqual(len(ga.population), 100)
def testSaveLoadState(self):
'''Tests that config and population can be saved to and loaded from
JSON files.
'''
# Parse input parameters from init file
init_file_path = 'gastop-config/struct_making_test_init.txt'
config = utilities.init_file_parser(init_file_path)
# Create GenAlg object
pop_size = int(1e4)
ga = GenAlg(config)
ga.initialize_population(pop_size)
# Save and reload
ga.save_state()
ga_loaded = ga.load_state()
config = ga_loaded.config
population = ga_loaded.population
# Test config
self.assertTrue(isinstance(config['ga_params']['num_elite'], int))
self.assertTrue(
isinstance(config['ga_params']['percent_crossover'], float))
self.assertTrue(
type(config['mutator_params']['node_mutator_params']['boundaries'])
is type(np.array([1, 1])))
self.assertTrue(
isinstance(
config['mutator_params']['node_mutator_params']['int_flag'],
bool))
# Test population
for truss in population:
self.assertTrue(isinstance(truss, Truss))
self.assertTrue(isinstance(truss.user_spec_nodes, np.ndarray))
self.assertTrue(isinstance(truss.rand_nodes, np.ndarray))
self.assertTrue(isinstance(truss.edges, np.ndarray))
self.assertTrue(isinstance(truss.properties, np.ndarray))
def testInitFileParser(self):
"""Tests that the init file is parsed correctly"""
init_file_path = 'gastop-config/boolean_parse_test_init.txt'
config = utilities.init_file_parser(init_file_path)
self.assertTrue(isinstance(config['ga_params']['num_elite'], int))
self.assertTrue(
isinstance(config['ga_params']['percent_crossover'], float))
self.assertTrue(
type(config['mutator_params']['node_mutator_params']['boundaries'])
is type(np.array([1, 1])))
self.assertTrue(
isinstance(
config['mutator_params']['node_mutator_params']['int_flag'],
bool))
self.assertTrue(config['general']['bool0'] is False)
self.assertTrue(config['general']['bool1'] is True)
self.assertTrue(config['general']['boolnone'] is None)
self.assertEqual(config['ga_params']['config_save_name'],
'config.json')
self.assertEqual(config['ga_params']['pop_save_name'],
'population.json')
self.assertEqual(config['ga_params']['save_frequency'], 0)
import cProfile
import unittest
import numpy as np
import numpy.testing as npt
import matplotlib.pyplot as plt
from matplotlib import style
import json
import sys
sys.path.append('.')
from gastop import GenAlg, Truss, Evaluator, FitnessFunction, utilities
# Parse input paramters from init.txt file
init_file_path = 'gastop-config/struct_making_test_init.txt'
config = utilities.init_file_parser(init_file_path)
ga_params = config['ga_params']
random_params = config['random_params']
crossover_params = config['crossover_params']
mutator_params = config['mutator_params']
selector_params = config['selector_params']
evaluator_params = config['evaluator_params']
fitness_params = config['fitness_params']
# properties_df = 0
evaluator = 0
pop_size = int(1e6)
num_gens = int(1e2)