def main(command_line_args):
"""
Given a specified grammar file, parse the grammar using the Grammar class
and print out the number of unique permutations and combinations of
distinct phenotypes that this grammar is capable of generating at a
number of given depths.
:return: Nothing.
"""
# Parse command line args (we only want the grammar file)
cmd_args, unknown = parser.parse_cmd_args(command_line_args)
# Join original params dictionary with command line specified arguments.
# NOTE that command line arguments overwrite all previously set parameters.
params.update(cmd_args)
# Parse grammar file and set grammar class.
grammar = Grammar(os.path.join("..", "grammars", params['GRAMMAR_FILE']))
print("\nSpecified grammar:", params['GRAMMAR_FILE'])
# Initialise zero maximum branching factor for grammar
max_b_factor = 0
print("\nBranching factor for each non-terminal:")
for NT in sorted(grammar.non_terminals.keys()):
# Get branching factor for current NT.
b_factor = grammar.non_terminals[NT]['b_factor']
# Print info.
print("", NT, " \t:", b_factor)
# Set maximum branching factor.
if b_factor > max_b_factor:
max_b_factor = b_factor
print("\nMaximum branching factor of the grammar:", max_b_factor)
# Initialise counter for the total number of solutions.
total_solutions = 0
print("\nNumber of unique possible solutions for a range of depths:\n")
for depth in grammar.permutations:
# Get number of solutions possible at current depth
solutions = grammar.permutations[depth]
print(" Depth: %d \t Number of unique solutions: %s" %
(depth, sci_notation(solutions)))
# Increment total number of solutions.
total_solutions += solutions
print("\nTotal number of unique possible solutions that can be Generated"
"up to and including a depth of %d: %s" %
(depth, sci_notation(total_solutions)))
def main(command_line_args):
"""
Given a specified grammar file, parse the grammar using the Grammar class
and print out the number of unique permutations and combinations of
distinct phenotypes that this grammar is capable of generating at a
number of given depths.
:return: Nothing.
"""
# Parse command line args (we only want the grammar file)
cmd_args, unknown = parser.parse_cmd_args(command_line_args)
# Join original params dictionary with command line specified arguments.
# NOTE that command line arguments overwrite all previously set parameters.
params.update(cmd_args)
# Parse grammar file and set grammar class.
grammar = Grammar(os.path.join("..", "grammars", params['GRAMMAR_FILE']))
print(
"\nUnique permutations and combinations of possible solutions"
" for a range of depths for specified grammar:",
params['GRAMMAR_FILE'], "\n")
for depth in grammar.permutations:
print(" Depth: %d \t Permutations: %d" %
(depth, grammar.permutations[depth]))
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, 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()]
