def run_rule_number_experiment_cfg3():
"""Experiment with what is the minimum amount of rules possible to get max
fitness, with cfg set 3."""
exp_title = "Rule base size 3"
varied_param = "chromosome_size"
min_number_of_rules = 1
max_number_of_rules = 11
min_chromosome_size = min_number_of_rules * BASE_RULE_SIZE
max_chromosome_size = max_number_of_rules * BASE_RULE_SIZE
# Increment two rules at a time in the rule
inc = BASE_RULE_SIZE
exp_settings = dict(BASE_SETTINGS)
exp_settings["population_size"] = 900
exp_settings["generations"] = 250
exp_settings["runs"] = 5
exp_settings["mutation_rate_percent"] = 0.4
exp_settings["chromosome_size"] = numpy.arange(min_chromosome_size,
max_chromosome_size,
inc)
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title,
exp_settings,
varied_param)
return fittest_individual, stats
def run_mutation_creep_experiment():
"""Experiment with changing the creep value"""
exp_title = "Mutation creep experiment"
varied_param = "indiv_additional_args"
arg_sets = []
for creep_val in numpy.arange(0.05, 0.3, 0.05):
arg_sets.append([
CONDITION_SIZE,
RESULT_SIZE,
creep_val,
TRAINING_FILE,
BASE_TRAIN_FROM_RULE,
BASE_TRAIN_TO_RULE,
TRAINING_FILE,
BASE_TEST_FROM_RULE,
BASE_TEST_TO_RULE
])
exp_settings = dict(BASE_SETTINGS)
exp_settings["runs"] = 5
exp_settings["indiv_additional_args"] = arg_sets
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title,
exp_settings,
varied_param)
return fittest_individual, stats
def run_population_size_experiment_with_0_mutation_per():
"""Experiment with the affects of changing population size when every
other setting is set to base except mutation which is set 0% of the
typical range"""
exp_title = "Population size parameter when mutation is 0% in range"
varied_param = "population_size"
num_of_rules = 10
settings = {
"runs": 10,
"generations": 100,
"population_size": numpy.arange(20, 1000, 20),
"chromosome_size":
num_of_rules * (BASE_CONDITION_SIZE + BASE_RESULT_SIZE),
"tournament_size": 2,
"single_point_crossover_point": 0.5,
"mutation_rate_percent": 0.0,
"indiv_class": BinaryRuleSet,
"indiv_additional_args": BASE_INDIV_ADD_STD_ARGS
}
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, settings, varied_param)
return fittest_individual, stats
def run_roulette_experiment():
"""Experiment with using roulette wheel selection over tournament"""
exp_title = "Using roulette wheel selection"
varied_param = "selection_method"
exp_settings = dict(BASE_SETTINGS)
exp_settings["selection_method"] = ["roulette", "tournament"]
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, exp_settings, varied_param)
return fittest_individual, stats
def run_tournament_size_experiment():
"""Experiment with changing the tournament size"""
exp_title = "Tournament size variation"
varied_param = "tournament_size"
exp_settings = dict(BASE_SETTINGS)
exp_settings["tournament_size"] = [2, 3, 4, 5, 6, 7, 8]
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, exp_settings, varied_param)
return fittest_individual, stats
def run_population_size_experiment_with_base_cfg():
"""Experiment with the affects of changing population size when every
other setting is set to base"""
exp_title = "Population size parameter"
varied_param = "population_size"
exp_settings = dict(BASE_SETTINGS)
exp_settings["population_size"] = numpy.arange(20, 1000, 20)
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, exp_settings, varied_param)
return fittest_individual, stats
def run_mutation_only_experiment():
"""Experiment with the affects of changing mutation rate"""
exp_title = "Mutation rate parameter"
varied_param = "mutation_rate_percent"
# Mutation rate typically between 1/pop size and 1/chromosome length.
exp_settings = dict(BASE_SETTINGS)
exp_settings["mutation_rate_percent"] = numpy.arange(0.0, 1, 0.1)
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, exp_settings, varied_param)
return fittest_individual, stats
def run_single_point_crossover_experiment():
"""Experiment with the affects of changing the single point crossover"""
exp_title = "Single point crossover variation"
varied_param = "single_point_crossover_point"
exp_settings = dict(BASE_SETTINGS)
exp_settings["single_point_crossover_point"] = [
BASE_RULE_SIZE, BASE_RULE_SIZE * 2, BASE_RULE_SIZE * 3,
BASE_RULE_SIZE * 4, BASE_RULE_SIZE * 5, BASE_RULE_SIZE * 6,
BASE_RULE_SIZE * 7, BASE_RULE_SIZE * 8, BASE_RULE_SIZE * 9
]
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, exp_settings, varied_param)
return fittest_individual, stats
def run_population_size_experiment_with_40_mutation_per():
"""Experiment with the affects of changing population size when every
other setting is set to base except mutation which is set 40% of the
typical range"""
exp_title = "Population size parameter when mutation is 40% in range"
varied_param = "population_size"
exp_settings = dict(BASE_SETTINGS)
exp_settings["population_size"] = numpy.arange(20, 1000, 20)
exp_settings["mutation_rate_percent"] = 0.4
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title,
exp_settings,
varied_param)
return fittest_individual, stats
def run_two_point_crossover_experiment():
"""Experiment with the affects of changing the two step crossover point"""
exp_title = "Two point crossover variation"
varied_param = "two_point_crossover_points"
exp_settings = dict(BASE_SETTINGS)
exp_settings["two_point_crossover_points"] = [
[BASE_RULE_SIZE * 1, BASE_RULE_SIZE * 9], # Crossover rule 2 to rule 9
[BASE_RULE_SIZE * 2, BASE_RULE_SIZE * 8], # Crossover rule 3 to rule 8
[BASE_RULE_SIZE * 3, BASE_RULE_SIZE * 7], # Crossover rule 4 to rule 7
[BASE_RULE_SIZE * 4, BASE_RULE_SIZE * 6], # Crossover rule 5 to rule 6
]
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, exp_settings, varied_param)
return fittest_individual, stats
def run_rule_number_experiment_cfg1():
"""Experiment with what is the minimum amount of rules possible to get max
fitness, with cfg set 1."""
exp_title = "Rule base size 1 cfg P1"
varied_param = "chromosome_size"
min_number_of_rules = 8
max_number_of_rules = 10
min_chromosome_size = min_number_of_rules * RULE_SIZE
max_chromosome_size = max_number_of_rules * RULE_SIZE
# Increment two rules at a time in the rule
inc = RULE_SIZE
mutation_creep = 0.45
args = [
CONDITION_SIZE,
RESULT_SIZE,
mutation_creep,
TRAINING_FILE,
BASE_TRAIN_FROM_RULE,
BASE_TRAIN_TO_RULE,
TRAINING_FILE,
BASE_TEST_FROM_RULE,
BASE_TEST_TO_RULE
]
exp_settings = dict(BASE_SETTINGS)
exp_settings["population_size"] = 500
exp_settings["generations"] = 150
exp_settings["runs"] = 3
exp_settings["mutation_rate_percent"] = 0.5
exp_settings["indiv_additional_args"] = args
exp_settings["chromosome_size"] = numpy.arange(min_chromosome_size,
max_chromosome_size,
inc)
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title,
exp_settings,
varied_param,
test_func=test_rule_set)
return fittest_individual, stats
def run_wildcard_experiment():
"""Experiment with how using a wildcard makes a difference to fitness"""
indiv_add_args_wc_disabled = [
BASE_CONDITION_SIZE, BASE_RESULT_SIZE, TRAINING_FILE, TRAIN_FROM_RULE,
TRAIN_TO_RULE, False
]
exp_title = "Wildcard Enabled Vs Disabled"
varied_param = "indiv_additional_args"
exp_settings = dict(BASE_SETTINGS)
# Include an arg set where wildcards are enabled ano another where they
# are not enabled.
exp_settings["indiv_additional_args"] = [
indiv_add_args_wc_disabled, BASE_INDIV_ADD_STD_ARGS
]
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title, exp_settings, varied_param)
return fittest_individual, stats
def run_training_rule_set_size():
exp_title = "Training and test data distribution"
varied_param = "indiv_additional_args"
arg_sets = []
# What ever rules aren't used for training are used for testing.
# Test size = maximum rules in file - training size
training_sizes = [600, 800, 1000, 1200, 1400, 1600]
# Create an argument set for each training and testing size
for training_size in training_sizes:
# Training rules will always start from 0 and go up to a set size
arg_sets.append([
CONDITION_SIZE,
RESULT_SIZE,
BASE_MUTATION_CREEP,
TRAINING_FILE,
BASE_TRAIN_FROM_RULE,
training_size,
TRAINING_FILE,
training_size+1,
BASE_TEST_TO_RULE
])
exp_settings = dict(BASE_SETTINGS)
exp_settings["indiv_additional_args"] = arg_sets
# exp_settings["population_size"] = 400
fittest_individual, best_rule_set_settings, stats = run_varied_setting_experiment(
exp_title,
exp_settings,
varied_param,
test_func=test_rule_set)
return fittest_individual, stats
