def set_state(state):
"""
Given a dictionary representing the state of an evolutionary run, set all
aspects of the system to re-create that state. The state includes the
current population, the current random state, the parameters dictionary,
the stats dictionary, and all lists in the utilities.stats.trackers module.
Sets all aspects of the system and then returns a population of
individuals at the current generation.
:param state: The complete state of a run.
:return: A population of individuals.
"""
from algorithm.parameters import params
from utilities.algorithm.initialise_run import set_param_imports
from stats.stats import stats
from utilities.stats import trackers
from time import time
# Set random state.
random.setstate(state['random_state'])
# Set stats.
for stat in state['stats']:
stats[stat] = state['stats'][stat]
# Set trackers.
for tracker in state['trackers']:
setattr(trackers, tracker, state['trackers'][tracker])
# Set parameters.
for param in state['params']:
params[param] = state['params'][param]
# Set correct param imports for specified function options, including
# error metrics and fitness functions.
set_param_imports()
# Set time adjustment to account for old time.
stats['time_adjust'] = time() - state['time']
return state['individuals']
def set_params(command_line_args, create_files=True):
"""
This function parses all command line arguments specified by the user.
If certain parameters are not set then defaults are used (e.g. random
seeds, elite size). Sets the correct imports given command line
arguments. Sets correct grammar file and fitness function. Also
initialises save folders and tracker lists in utilities.trackers.
:param command_line_args: Command line arguments specified by the user.
:return: Nothing.
"""
from utilities.algorithm.initialise_run import initialise_run_params
from utilities.algorithm.initialise_run import set_param_imports
from utilities.fitness.math_functions import return_one_percent
from utilities.algorithm.command_line_parser import parse_cmd_args
from utilities.stats import trackers, clean_stats
from representation import grammar
cmd_args, unknown = parse_cmd_args(command_line_args)
if unknown:
# We currently do not parse unknown parameters. Raise error.
s = "algorithm.parameters.set_params\nError: " \
"unknown parameters: %s\nYou may wish to check the spelling, " \
"add code to recognise this parameter, or use " \
"--extra_parameters" % str(unknown)
raise Exception(s)
# LOAD PARAMETERS FILE
# NOTE that the parameters file overwrites all previously set parameters.
if 'PARAMETERS' in cmd_args:
load_params(path.join("..", "parameters", cmd_args['PARAMETERS']))
# Join original params dictionary with command line specified arguments.
# NOTE that command line arguments overwrite all previously set parameters.
params.update(cmd_args)
if params['LOAD_STATE']:
# Load run from state.
from utilities.algorithm.state import load_state
# Load in state information.
individuals = load_state(params['LOAD_STATE'])
# Set correct search loop.
from algorithm.search_loop import search_loop_from_state
params['SEARCH_LOOP'] = search_loop_from_state
# Set population.
setattr(trackers, "state_individuals", individuals)
else:
if params['REPLACEMENT'].split(".")[-1] == "steady_state":
# Set steady state step and replacement.
params['STEP'] = "steady_state_step"
params['GENERATION_SIZE'] = 2
else:
# Elite size is set to either 1 or 1% of the population size,
# whichever is bigger if no elite size is previously set.
if params['ELITE_SIZE'] is None:
params['ELITE_SIZE'] = return_one_percent(
1, params['POPULATION_SIZE'])
# Set the size of a generation
params['GENERATION_SIZE'] = params['POPULATION_SIZE'] - \
params['ELITE_SIZE']
# Initialise run lists and folders before we set imports.r
initialise_run_params(create_files)
# Set correct param imports for specified function options, including
# error metrics and fitness functions.
set_param_imports()
# Clean the stats dict to remove unused stats.
clean_stats.clean_stats()
# Set GENOME_OPERATIONS automatically for faster linear operations.
if (params['CROSSOVER'].representation == "subtree"
or params['MUTATION'].representation == "subtree"):
params['GENOME_OPERATIONS'] = False
else:
params['GENOME_OPERATIONS'] = True
# Ensure correct operators are used if multiple fitness functions used.
if hasattr(params['FITNESS_FUNCTION'], 'multi_objective'):
# Check that multi-objective compatible selection is specified.
if not hasattr(params['SELECTION'], "multi_objective"):
s = "algorithm.parameters.set_params\n" \
"Error: multi-objective compatible selection " \
"operator not specified for use with multiple " \
"fitness functions."
raise Exception(s)
if not hasattr(params['REPLACEMENT'], "multi_objective"):
# Check that multi-objective compatible replacement is
# specified.
if not hasattr(params['REPLACEMENT'], "multi_objective"):
s = "algorithm.parameters.set_params\n" \
"Error: multi-objective compatible replacement " \
"operator not specified for use with multiple " \
"fitness functions."
raise Exception(s)
# Parse grammar file and set grammar class.
params['BNF_GRAMMAR'] = grammar.Grammar(
path.join("..", "grammars", params['GRAMMAR_FILE']))
# Population loading for seeding runs (if specified)
if params['TARGET_SEED_FOLDER']:
# Import population loading function.
from operators.initialisation import load_population
# A target folder containing seed individuals has been given.
params['SEED_INDIVIDUALS'] = load_population(
params['TARGET_SEED_FOLDER'])
elif params['REVERSE_MAPPING_TARGET']:
# A single seed phenotype has been given. Parse and run.
# Import GE LR Parser.
from scripts import GE_LR_parser
# Parse seed individual and store in params.
params['SEED_INDIVIDUALS'] = [GE_LR_parser.main()]
def set_params(command_line_args, create_files=True):
"""
This function parses all command line arguments specified by the user.
If certain parameters are not set then defaults are used (e.g. random
seeds, elite size). Sets the correct imports given command line
arguments. Sets correct grammar file and fitness function. Also
initialises save folders and tracker lists in utilities.trackers.
:param command_line_args: Command line arguments specified by the user.
:return: Nothing.
"""
from utilities.algorithm.initialise_run import initialise_run_params
from utilities.algorithm.initialise_run import set_param_imports
from utilities.fitness.math_functions import return_one_percent
from utilities.algorithm.command_line_parser import parse_cmd_args
from utilities.stats import trackers, clean_stats
from representation import grammar
cmd_args, unknown = parse_cmd_args(command_line_args)
if unknown:
# We currently do not parse unknown parameters. Raise error.
s = "algorithm.parameters.set_params\nError: " \
"unknown parameters: %s\nYou may wish to check the spelling, " \
"add code to recognise this parameter, or use " \
"--extra_parameters" % str(unknown)
raise Exception(s)
# LOAD PARAMETERS FILE
# NOTE that the parameters file overwrites all previously set parameters.
if 'PARAMETERS' in cmd_args:
load_params(path.join("..", "parameters", cmd_args['PARAMETERS']))
# Join original params dictionary with command line specified arguments.
# NOTE that command line arguments overwrite all previously set parameters.
params.update(cmd_args)
if params['LOAD_STATE']:
# Load run from state.
from utilities.algorithm.state import load_state
# Load in state information.
individuals = load_state(params['LOAD_STATE'])
# Set correct search loop.
from algorithm.search_loop import search_loop_from_state
params['SEARCH_LOOP'] = search_loop_from_state
# Set population.
setattr(trackers, "state_individuals", individuals)
else:
if params['REPLACEMENT'].split(".")[-1] == "steady_state":
# Set steady state step and replacement.
params['STEP'] = "steady_state_step"
params['GENERATION_SIZE'] = 2
else:
# Elite size is set to either 1 or 1% of the population size,
# whichever is bigger if no elite size is previously set.
if params['ELITE_SIZE'] is None:
params['ELITE_SIZE'] = return_one_percent(1, params[
'POPULATION_SIZE'])
# Set the size of a generation
params['GENERATION_SIZE'] = params['POPULATION_SIZE'] - \
params['ELITE_SIZE']
# Initialise run lists and folders before we set imports.r
initialise_run_params(create_files)
# Set correct param imports for specified function options, including
# error metrics and fitness functions.
set_param_imports()
# Clean the stats dict to remove unused stats.
clean_stats.clean_stats()
# Set GENOME_OPERATIONS automatically for faster linear operations.
if (params['CROSSOVER'].representation == "subtree" or
params['MUTATION'].representation == "subtree"):
params['GENOME_OPERATIONS'] = False
else:
params['GENOME_OPERATIONS'] = True
# Ensure correct operators are used if multiple fitness functions used.
if hasattr(params['FITNESS_FUNCTION'], 'multi_objective'):
# Check that multi-objective compatible selection is specified.
if not hasattr(params['SELECTION'], "multi_objective"):
s = "algorithm.parameters.set_params\n" \
"Error: multi-objective compatible selection " \
"operator not specified for use with multiple " \
"fitness functions."
raise Exception(s)
if not hasattr(params['REPLACEMENT'], "multi_objective"):
# Check that multi-objective compatible replacement is
# specified.
if not hasattr(params['REPLACEMENT'], "multi_objective"):
s = "algorithm.parameters.set_params\n" \
"Error: multi-objective compatible replacement " \
"operator not specified for use with multiple " \
"fitness functions."
raise Exception(s)
# Parse grammar file and set grammar class.
params['BNF_GRAMMAR'] = grammar.Grammar(path.join("..", "grammars",
params['GRAMMAR_FILE']))
# Population loading for seeding runs (if specified)
if params['TARGET_SEED_FOLDER']:
# Import population loading function.
from operators.initialisation import load_population
# A target folder containing seed individuals has been given.
params['SEED_INDIVIDUALS'] = load_population(
params['TARGET_SEED_FOLDER'])
elif params['REVERSE_MAPPING_TARGET']:
# A single seed phenotype has been given. Parse and run.
# Import GE LR Parser.
from scripts import GE_LR_parser
# Parse seed individual and store in params.
params['SEED_INDIVIDUALS'] = [GE_LR_parser.main()]
def set_params(command_line_args, create_files=True):
"""
This function parses all command line arguments specified by the user.
If certain parameters are not set then defaults are used (e.g. random
seeds, elite size). Sets the correct imports given command line
arguments. Sets correct grammar file and fitness function. Also
initialises save folders and tracker lists in utilities.trackers.
:param command_line_args: Command line arguments specified by the user.
:return: Nothing.
"""
from utilities.algorithm.initialise_run import initialise_run_params
from utilities.algorithm.initialise_run import set_param_imports
from utilities.fitness.math_functions import return_one_percent
from representation import grammar
import utilities.algorithm.command_line_parser as parser
from utilities.stats import trackers, clean_stats
cmd_args, unknown = parser.parse_cmd_args(command_line_args)
if unknown:
# We currently do not parse unknown parameters. Raise error.
s = "algorithm.parameters.set_params\nError: " \
"unknown parameters: %s\nYou may wish to check the spelling, " \
"add code to recognise this parameter, or use " \
"--extra_parameters" % str(unknown)
raise Exception(s)
# LOAD PARAMETERS FILE
# NOTE that the parameters file overwrites all previously set parameters.
if 'PARAMETERS' in cmd_args:
load_params(path.join("..", "parameters", cmd_args['PARAMETERS']))
# Join original params dictionary with command line specified arguments.
# NOTE that command line arguments overwrite all previously set parameters.
params.update(cmd_args)
if params['LOAD_STATE']:
# Load run from state.
from utilities.algorithm.state import load_state
# Load in state information.
individuals = load_state(params['LOAD_STATE'])
# Set correct search loop.
from algorithm.search_loop import search_loop_from_state
params['SEARCH_LOOP'] = search_loop_from_state
# Set population.
setattr(trackers, "state_individuals", individuals)
else:
if params['REPLACEMENT'].split(".")[-1] == "steady_state":
# Set steady state step and replacement.
params['STEP'] = "steady_state_step"
params['GENERATION_SIZE'] = 2
else:
# Elite size is set to either 1 or 1% of the population size,
# whichever is bigger if no elite size is previously set.
if params['ELITE_SIZE'] is None:
params['ELITE_SIZE'] = return_one_percent(
1, params['POPULATION_SIZE'])
# Set the size of a generation
params['GENERATION_SIZE'] = params['POPULATION_SIZE'] - \
params['ELITE_SIZE']
# Set correct param imports for specified function options, including
# error metrics and fitness functions.
set_param_imports()
# Clean the stats dict to remove unused stats.
clean_stats.clean_stats()
# Initialise run lists and folders
initialise_run_params(create_files)
# Set GENOME_OPERATIONS automatically for faster linear operations.
if params['CROSSOVER'].representation == "linear" and \
params['MUTATION'].representation == "linear":
params['GENOME_OPERATIONS'] = True
else:
params['GENOME_OPERATIONS'] = False
# Parse grammar file and set grammar class.
params['BNF_GRAMMAR'] = grammar.Grammar(
path.join("..", "grammars", params['GRAMMAR_FILE']))
def set_params(command_line_args):
"""
This function parses all command line arguments specified by the user.
If certain parameters are not set then defaults are used (e.g. random
seeds, elite size). Sets the correct imports given command line
arguments. Sets correct grammar file and fitness function. Also
initialises save folders and tracker lists in utilities.trackers.
:param command_line_args: Command line arguments specified by the user.
:return: Nothing.
"""
from utilities.algorithm.initialise_run import initialise_run_params
from utilities.algorithm.initialise_run import set_param_imports
from utilities.fitness.math_functions import return_percent
from utilities.help_message import help_message
from representation import grammar
import getopt
try:
opts, args = getopt.getopt(command_line_args[1:], "",
["help", "debug", "population=",
"generations=", "initialisation=",
"max_init_depth=", "genome_init",
"max_tree_depth=", "codon_size=",
"selection=", "selection_proportion=",
"tournament_size=", "crossover=",
"crossover_probability=", "replacement=",
"mutation=", "mutation_events=",
"random_seed=", "bnf_grammar=",
"dataset=", "target_string=",
"verbose", "elite_size=", "save_all",
"save_plots", "cache", "lookup_fitness",
"lookup_bad_fitness", "mutate_duplicates",
"genome_length=",
"invalid_selection", "silent",
"dont_lookup_fitness", "experiment_name=",
"multicore", "cores=", "max_wraps=",
"error_metric=", "fitness_function=",
"parameters=", "step=", "search_loop="])
except getopt.GetoptError as err:
print("Most parameters need a value associated with them \n",
"Run python ponyge.py --help for more info")
print(str(err))
exit(2)
for opt, arg in opts:
if opt == "--help":
help_message()
exit()
# LOAD PARAMETERS FILE
elif opt == "--parameters":
load_params("../parameters/" + arg)
# LOAD STEP AND SEARCH LOOP FUNCTIONS
elif opt == "--search_loop":
params['SEARCH_LOOP'] = arg
elif opt == "--step":
params['STEP'] = arg
# POPULATION OPTIONS
elif opt == "--population":
check_int('POPULATION_SIZE', arg)
elif opt == "--generations":
check_int('GENERATIONS', arg)
# INDIVIDUAL SIZE
elif opt == "--max_tree_depth":
check_int('MAX_TREE_DEPTH', arg)
elif opt == "--codon_size":
check_int('CODON_SIZE', arg)
elif opt == "--genome_length":
check_int('GENOME_LENGTH', arg)
elif opt == "--max_wraps":
check_int('MAX_WRAPS', arg)
# INITIALISATION
elif opt == "--initialisation":
params['INITIALISATION'] = arg
elif opt == "--max_init_depth":
check_int('MAX_INIT_DEPTH', arg)
elif opt == "--genome_init":
params['GENOME_INIT'] = True
params['INITIALISATION'] = "operators.initialisation.random_init"
# SELECTION
elif opt == "--selection":
params['SELECTION'] = arg
elif opt == "--invalid_selection":
params['INVALID_SELECTION'] = arg
elif opt == "--tournament_size":
check_int('TOURNAMENT_SIZE', arg)
elif opt == "--selection_proportion":
check_float('SELECTION_PROPORTION', arg)
# EVALUATION
elif opt == "--multicore":
params['MULTIPCORE'] = True
elif opt == "--cores":
check_int('CORES', arg)
# CROSSOVER
elif opt == "--crossover":
params['CROSSOVER'] = arg
elif opt == "--crossover_probability":
check_float('CROSSOVER_PROBABILITY', arg)
# MUTATION
elif opt == "--mutation":
params['MUTATION'] = arg
elif opt == "--mutation_events":
check_int('MUTATION_EVENTS', arg)
elif opt == "--mutation_probability":
check_float('MUTATION_PROBABILITY', arg)
# REPLACEMENT
elif opt == "--replacement":
params['REPLACEMENT'] = arg
elif opt == "--elite_size":
check_int('ELITE_SIZE', arg)
# PROBLEM SPECIFICS
elif opt == "--bnf_grammar":
params['GRAMMAR_FILE'] = arg
elif opt == "--fitness_function":
params['FITNESS_FUNCTION'] = arg
elif opt == "--dataset":
params['DATASET'] = arg
elif opt == "--target_string":
params['STRING_MATCH_TARGET'] = arg
elif opt == "--experiment_name":
params['EXPERIMENT_NAME'] = arg
elif opt == "--error_metric":
params['ERROR_METRIC'] = arg
# OPTIONS
elif opt == "--random_seed":
check_int('RANDOM_SEED', arg)
elif opt == "--debug":
params['DEBUG'] = True
elif opt == "--verbose":
params['VERBOSE'] = True
elif opt == "--silent":
params['SILENT'] = True
elif opt == "--save_all":
params['SAVE_ALL'] = True
elif opt == "--save_plots":
params['SAVE_PLOTS'] = True
# CACHING
elif opt == "--cache":
params['CACHE'] = True
params['LOOKUP_FITNESS'] = True
elif opt == "--dont_lookup_fitness":
params['LOOKUP_FITNESS'] = False
elif opt == "--lookup_bad_fitness":
params['LOOKUP_FITNESS'] = False
params['LOOKUP_BAD_FITNESS'] = True
elif opt == "--mutate_duplicates":
params['LOOKUP_FITNESS'] = False
params['MUTATE_DUPLICATES'] = True
else:
assert False, "Unhandled Option, use --help for available params"
# Elite size is set to either 1 or 1% of the population size, whichever is
# bigger if no elite size is previously set.
if params['ELITE_SIZE'] is None:
params['ELITE_SIZE'] = return_percent(1, params['POPULATION_SIZE'])
# Set the size of a generation
params['GENERATION_SIZE'] = params['POPULATION_SIZE'] - params[
'ELITE_SIZE']
# Set GENOME_OPERATIONS automatically for faster linear operations.
if (params['MUTATION'] == 'operators.mutation.int_flip' or
params['MUTATION'] == 'int_flip') and \
(params['CROSSOVER'] == 'operators.crossover.onepoint' or
params['CROSSOVER'] == 'onepoint'):
params['GENOME_OPERATIONS'] = True
else:
params['GENOME_OPERATIONS'] = False
# Initialise run lists and folders
initialise_run_params()
# Set correct param imports for specified function options, including
# error metrics and fitness functions.
set_param_imports()
# Parse grammar file and set grammar class.
params['BNF_GRAMMAR'] = grammar.Grammar("../grammars/" +
params['GRAMMAR_FILE'])
