def setUp(self):
self.config = {
"max_population": 50,
"tree_generation": {
"method": "FULL_METHOD",
"initial_max_depth": 4
},
"evaluator": {
"use_cache": True
},
"function_nodes": [
{"type": "FUNCTION", "name": "ADD", "arity": 2},
{"type": "FUNCTION", "name": "SUB", "arity": 2},
{"type": "FUNCTION", "name": "MUL", "arity": 2},
{"type": "FUNCTION", "name": "DIV", "arity": 2},
{"type": "FUNCTION", "name": "COS", "arity": 1},
{"type": "FUNCTION", "name": "SIN", "arity": 1}
],
"terminal_nodes": [
{"type": "CONSTANT", "value": 1.0},
],
"input_variables": [
{"type": "INPUT", "name": "x"}
],
"data_file": "tests/data/sine.dat",
"response_variables": [{"name": "y"}]
}
config.load_data(self.config)
self.functions = {
"ADD": "+",
"SUB": "-",
"MUL": "*",
"DIV": "/",
"POW": "**",
"SIN": "math.sin",
"COS": "math.cos",
"RAD": "math.radians",
"LN": "math.ln",
"LOG": "math.log"
}
self.generator = TreeGenerator(self.config)
def setUp(self):
self.config = {
"max_population": 50,
"tree_generation": {
"method": "FULL_METHOD",
"initial_max_depth": 4
},
"evaluator": {
"use_cache": True
},
"function_nodes": [
{"type": "FUNCTION", "name": "ADD", "arity": 2},
{"type": "FUNCTION", "name": "SUB", "arity": 2},
{"type": "FUNCTION", "name": "MUL", "arity": 2},
{"type": "FUNCTION", "name": "DIV", "arity": 2},
{"type": "FUNCTION", "name": "COS", "arity": 1},
{"type": "FUNCTION", "name": "SIN", "arity": 1}
],
"terminal_nodes": [
{"type": "CONSTANT", "value": 1.0},
],
"input_variables": [
{"type": "INPUT", "name": "x"}
],
"data_file": "tests/data/sine.dat",
"response_variables": [{"name": "y"}]
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
"data_file":
"data/iris.dat",
"input_variables": [{
"name": "sepal_length"
}, {
"name": "sepal_width"
}, {
"name": "petal_length"
}, {
"name": "petal_width"
}],
"response_variables": [{
"name": "species"
}]
}
load_data(config, script_path)
functions = GPFunctionRegistry("CLASSIFICATION")
generator = TreeGenerator(config)
# genetic operators
selection = Selection(config)
crossover = TreeCrossover(config)
mutation = TreeMutation(config)
# run symbolic regression
population = generator.init()
start_time = time.time()
details = play.play_details(
population=population,
evaluate=evaluate,
def test_load_data(self):
config.load_data(self.config)
self.config["data"].pop("rows")
self.assertEquals(self.config["data"], self.solution)
def setUp(self):
self.config = {
"cartesian": {
"rows": 1,
"columns": 4,
"levels_back": 4,
"num_inputs": 12,
"num_outputs": 1
},
"input_variables": [
{"type": "INPUT", "name": "x"},
{"type": "CONSTANT", "name": "0.0"},
{"type": "CONSTANT", "name": "1.0"},
{"type": "CONSTANT", "name": "2.0"},
{"type": "CONSTANT", "name": "3.0"},
{"type": "CONSTANT", "name": "4.0"},
{"type": "CONSTANT", "name": "5.0"},
{"type": "CONSTANT", "name": "6.0"},
{"type": "CONSTANT", "name": "7.0"},
{"type": "CONSTANT", "name": "8.0"},
{"type": "CONSTANT", "name": "9.0"},
{"type": "CONSTANT", "name": "10.0"}
],
"function_nodes": [
{"type": "FUNCTION", "name": "ADD", "arity": 2},
{"type": "FUNCTION", "name": "SUB", "arity": 2},
{"type": "FUNCTION", "name": "MUL", "arity": 2},
{"type": "FUNCTION", "name": "DIV", "arity": 2},
{"type": "FUNCTION", "name": "SIN", "arity": 1},
{"type": "FUNCTION", "name": "COS", "arity": 1},
{"type": "FUNCTION", "name": "RAD", "arity": 1}
],
"response_variables" : [{"name": "y"}],
"data_file": "../../data/sine.dat",
}
script_path = os.path.dirname(os.path.realpath(sys.argv[0]))
load_data(self.config, script_path)
# add constants
rows = len(self.config["data"]["y"])
for i in range(11):
i = float(i)
self.config["data"][str(i) + ".0"] = [i for j in range(rows)]
self.functions = [
functions.add_function,
functions.sub_function,
functions.mul_function,
functions.div_function,
functions.sin_function,
functions.cos_function,
functions.rad_function
]
self.input_nodes = [
"x",
"0.0", "1.0", "2.0", "3.0", "4.0", "5.0",
"6.0", "7.0", "8.0", "9.0", "10.0"
]
self.func_nodes = [
[2, 11, 11], # 12
[6, 0], # 13
[2, 12, 13], # 14
[4, 14], # 15
]
self.output_nodes = [15]
self.cartesian = Cartesian(
config={},
rows=1,
columns=4,
levels_back=4,
func_nodes=self.func_nodes,
input_nodes=self.input_nodes,
output_nodes=self.output_nodes
)
def setUp(self):
self.config = {
"max_population":
10,
"tree_generation": {
"method": "FULL_METHOD",
"initial_max_depth": 4
},
"evaluator": {
"use_cache": True
},
"selection": {
"method": "TOURNAMENT_SELECTION",
"tournament_size": 2
},
"crossover": {
"method": "POINT_CROSSOVER",
"probability": 0.6
},
"mutation": {
"methods": ["POINT_MUTATION"],
"probability": 0.8
},
"function_nodes": [{
"type": "FUNCTION",
"name": "ADD",
"arity": 2
}, {
"type": "FUNCTION",
"name": "SUB",
"arity": 2
}],
"terminal_nodes": [
{
"type": "CONSTANT",
"value": 1.0
},
],
"input_variables": [{
"type": "INPUT",
"name": "x"
}],
"data_file":
"tests/data/sine.dat",
"response_variables": [{
"name": "y"
}],
"recorder": {
"store_file": "json_store_test.json",
"compress": True
}
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.json_store = JSONStore(self.config)
self.json_store.setup_store()
self.population = self.generator.init()
results = []
cache = {}
evaluate(self.population.individuals, self.functions, self.config,
results, cache, self.json_store)
self.population.sort_individuals()
self.selection = Selection(self.config, recorder=self.json_store)
self.crossover = TreeCrossover(self.config, recorder=self.json_store)
self.mutation = TreeMutation(self.config, recorder=self.json_store)
def setUp(self):
self.config = {
"max_population": 5,
"tree_generation": {
"tree_type": "CLASSIFICATION_TREE",
"method": "FULL_METHOD",
"initial_max_depth": 2
},
"evaluator": {
"use_cache": True
},
"function_nodes": [
{
"type": "CLASS_FUNCTION",
"name": "GREATER_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
},
{
"type": "CLASS_FUNCTION",
"name": "LESS_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
},
{
"type": "CLASS_FUNCTION",
"name": "EQUALS",
"arity": 2,
"decimal_precision": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}
],
"terminal_nodes": [
{
"type": "RANDOM_CONSTANT",
"name": "species",
"range": [
1.0,
2.0,
3.0
]
},
],
"input_variables": [
{"name": "sepal_length"},
{"name": "sepal_width"},
{"name": "petal_length"},
{"name": "petal_width"}
],
"class_attributes": [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width"
],
"data_file": "tests/data/iris.dat",
"response_variables": [{"name": "species"}]
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("CLASSIFICATION")
self.generator = TreeGenerator(self.config)
self.population = self.generator.init()
def setUp(self):
self.config = {
"max_population":
5,
"tree_generation": {
"tree_type": "CLASSIFICATION_TREE",
"method": "FULL_METHOD",
"initial_max_depth": 2
},
"evaluator": {
"use_cache": True
},
"function_nodes": [{
"type": "CLASS_FUNCTION",
"name": "GREATER_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}, {
"type": "CLASS_FUNCTION",
"name": "LESS_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}, {
"type": "CLASS_FUNCTION",
"name": "EQUALS",
"arity": 2,
"decimal_precision": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}],
"terminal_nodes": [
{
"type": "RANDOM_CONSTANT",
"name": "species",
"range": [1.0, 2.0, 3.0]
},
],
"input_variables": [{
"name": "sepal_length"
}, {
"name": "sepal_width"
}, {
"name": "petal_length"
}, {
"name": "petal_width"
}],
"class_attributes":
["sepal_length", "sepal_width", "petal_length", "petal_width"],
"data_file":
"tests/data/iris.dat",
"response_variables": [{
"name": "species"
}]
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("CLASSIFICATION")
self.generator = TreeGenerator(self.config)
self.population = self.generator.init()
"type": "CONSTANT",
"value": 10.0
},
],
"input_variables": [{
"type": "INPUT",
"name": "var1"
}],
"response_variables": [{
"name": "answer"
}],
"data_file":
"arabas_et_al-f1.dat"
}
config["max_population"] = 100000
load_data(config, data_dir)
generator = TreeGenerator(config)
population = generator.init()
results = []
# TREE EVALUTOR 1
start_time = time.time()
tree_eval_1.evaluate(copy.deepcopy(population.individuals),
GPFunctionRegistry("SYMBOLIC_REGRESSION"), config,
results)
end_time = time.time()
time_taken = end_time - start_time
print "Evaluator 1 took:", str(round(time_taken, 2)) + "s"
# TREE EVALUTOR 2
# functions = {
def setUp(self):
self.config = {
"max_population" : 10,
"tree_generation" : {
"method" : "FULL_METHOD",
"initial_max_depth" : 4
},
"evaluator" : {
"use_cache": True
},
"selection" : {
"method" : "TOURNAMENT_SELECTION",
"tournament_size": 2
},
"crossover" : {
"method" : "POINT_CROSSOVER",
"probability" : 0.6
},
"mutation" : {
"methods": ["POINT_MUTATION"],
"probability" : 0.8
},
"function_nodes" : [
{"type": "FUNCTION", "name": "ADD", "arity": 2},
{"type": "FUNCTION", "name": "SUB", "arity": 2}
],
"terminal_nodes" : [
{"type": "CONSTANT", "value": 1.0},
],
"input_variables" : [
{"type": "INPUT", "name": "x"}
],
"data_file" : "tests/data/sine.dat",
"response_variables" : [{"name": "y"}],
"recorder" : {
"store_file": "json_store_test.json",
"compress": True
}
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.json_store = JSONStore(self.config)
self.json_store.setup_store()
self.population = self.generator.init()
results = []
cache = {}
evaluate(
self.population.individuals,
self.functions,
self.config,
results,
cache,
self.json_store
)
self.population.sort_individuals()
self.selection = Selection(self.config, recorder=self.json_store)
self.crossover = TreeCrossover(self.config, recorder=self.json_store)
self.mutation = TreeMutation(self.config, recorder=self.json_store)
def gp_benchmark_loop(config):
try:
# setup
random.seed(config["random_seed"]) # VERY IMPORTANT!
load_data(config, config["call_path"])
json_store = JSONStore(config)
# functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
generator = TreeGenerator(config)
# genetic operators
selection = Selection(config, recorder=json_store)
crossover = TreeCrossover(config, recorder=json_store)
mutation = TreeMutation(config, recorder=json_store)
# setup the initial random population
population = generator.init()
# create play details
details = play.play_details(
population=population,
functions=config["functions"],
evaluate=evaluate,
selection=selection,
crossover=crossover,
mutation=mutation,
editor=edit_trees,
stop_func=default_stop_func,
# print_func=print_func,
config=config,
recorder=json_store)
# run symbolic regression
start_time = time.time()
play.play(details)
end_time = time.time()
time_taken = end_time - start_time
# print msg
print("DONE -> pop: {0} cross: {1} mut: {2} seed: {3} [{4}s]".format(
config["max_population"], config["crossover"]["probability"],
config["mutation"]["probability"], config["random_seed"],
round(time_taken, 2)))
# log on completion
if config.get("log_path", False):
config.pop("data")
msg = {
"timestamp": time.mktime(datetime.now().timetuple()),
"status": "DONE",
"config": config,
"runtime": time_taken,
"best_score": population.find_best_individuals()[0].score,
"best": str(population.find_best_individuals()[0])
}
log_path = os.path.expandvars(config["log_path"])
log_file = open(log_path, "a+")
log_file.write(json.dumps(msg) + "\n")
log_file.close()
except Exception as err_msg:
import traceback
traceback.print_exc()
# log exception
if config.get("log_path", False):
msg = {
"timestamp": time.mktime(datetime.now().timetuple()),
"status": "ERROR",
"config": config,
"error": err_msg
}
log_path = os.path.expandvars(config["log_path"])
log_file = open(log_path, "a+")
log_file.write(json.dumps(msg) + "\n")
log_file.close()
raise # raise the exception
return config
def setUp(self):
random.seed(0)
self.config = {
"max_population":
20,
"max_generation":
5,
"tree_generation": {
"method": "GROW_METHOD",
"initial_max_depth": 4
},
"evaluator": {
"use_cache": True
},
"selection": {
"method": "TOURNAMENT_SELECTION",
"tournament_size": 2
},
"crossover": {
"method": "POINT_CROSSOVER",
"probability": 0.8
},
"mutation": {
"methods": [
"POINT_MUTATION", "HOIST_MUTATION", "SUBTREE_MUTATION",
"SHRINK_MUTATION", "EXPAND_MUTATION"
],
"probability":
1.0
},
"function_nodes": [{
"type": "FUNCTION",
"name": "ADD",
"arity": 2
}, {
"type": "FUNCTION",
"name": "SUB",
"arity": 2
}, {
"type": "FUNCTION",
"name": "MUL",
"arity": 2
}, {
"type": "FUNCTION",
"name": "DIV",
"arity": 2
}, {
"type": "FUNCTION",
"name": "COS",
"arity": 1
}, {
"type": "FUNCTION",
"name": "SIN",
"arity": 1
}, {
"type": "FUNCTION",
"name": "RAD",
"arity": 1
}],
"terminal_nodes": [{
"type": "CONSTANT",
"value": 1.0
}, {
"type": "CONSTANT",
"value": 2.0
}, {
"type": "CONSTANT",
"value": 2.0
}, {
"type": "CONSTANT",
"value": 3.0
}, {
"type": "CONSTANT",
"value": 4.0
}, {
"type": "CONSTANT",
"value": 5.0
}, {
"type": "CONSTANT",
"value": 6.0
}, {
"type": "CONSTANT",
"value": 7.0
}, {
"type": "CONSTANT",
"value": 8.0
}, {
"type": "CONSTANT",
"value": 9.0
}, {
"type": "CONSTANT",
"value": 10.0
}],
"input_variables": [{
"type": "INPUT",
"name": "x"
}],
"data_file":
"tests/data/sine.dat",
"response_variables": [{
"name": "y"
}]
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.selection = Selection(self.config, recorder=None)
self.crossover = TreeCrossover(self.config, recorder=None)
self.mutation = TreeMutation(self.config, recorder=None)
def setUp(self):
self.config = {
"max_population":
50,
"tree_generation": {
"method": "FULL_METHOD",
"initial_max_depth": 4
},
"evaluator": {
"use_cache": True
},
"function_nodes": [{
"type": "FUNCTION",
"name": "ADD",
"arity": 2
}, {
"type": "FUNCTION",
"name": "SUB",
"arity": 2
}, {
"type": "FUNCTION",
"name": "MUL",
"arity": 2
}, {
"type": "FUNCTION",
"name": "DIV",
"arity": 2
}, {
"type": "FUNCTION",
"name": "COS",
"arity": 1
}, {
"type": "FUNCTION",
"name": "SIN",
"arity": 1
}],
"terminal_nodes": [
{
"type": "CONSTANT",
"value": 1.0
},
],
"input_variables": [{
"type": "INPUT",
"name": "x"
}],
"data_file":
"tests/data/sine.dat",
"response_variables": [{
"name": "y"
}]
}
config.load_data(self.config)
self.functions = {
"ADD": "+",
"SUB": "-",
"MUL": "*",
"DIV": "/",
"POW": "**",
"SIN": "math.sin",
"COS": "math.cos",
"RAD": "math.radians",
"LN": "math.ln",
"LOG": "math.log"
}
self.generator = TreeGenerator(self.config)
def setUp(self):
random.seed(0)
self.config = {
"max_population" : 20,
"max_generation" : 5,
"tree_generation" : {
"method" : "GROW_METHOD",
"initial_max_depth" : 4
},
"evaluator": {
"use_cache" : True
},
"selection" : {
"method" : "TOURNAMENT_SELECTION",
"tournament_size": 2
},
"crossover" : {
"method" : "POINT_CROSSOVER",
"probability" : 0.8
},
"mutation" : {
"methods": [
"POINT_MUTATION",
"HOIST_MUTATION",
"SUBTREE_MUTATION",
"SHRINK_MUTATION",
"EXPAND_MUTATION"
],
"probability" : 1.0
},
"function_nodes" : [
{"type": "FUNCTION", "name": "ADD", "arity": 2},
{"type": "FUNCTION", "name": "SUB", "arity": 2},
{"type": "FUNCTION", "name": "MUL", "arity": 2},
{"type": "FUNCTION", "name": "DIV", "arity": 2},
{"type": "FUNCTION", "name": "COS", "arity": 1},
{"type": "FUNCTION", "name": "SIN", "arity": 1},
{"type": "FUNCTION", "name": "RAD", "arity": 1}
],
"terminal_nodes" : [
{"type": "CONSTANT", "value": 1.0},
{"type": "CONSTANT", "value": 2.0},
{"type": "CONSTANT", "value": 2.0},
{"type": "CONSTANT", "value": 3.0},
{"type": "CONSTANT", "value": 4.0},
{"type": "CONSTANT", "value": 5.0},
{"type": "CONSTANT", "value": 6.0},
{"type": "CONSTANT", "value": 7.0},
{"type": "CONSTANT", "value": 8.0},
{"type": "CONSTANT", "value": 9.0},
{"type": "CONSTANT", "value": 10.0}
],
"input_variables" : [
{"type": "INPUT", "name": "x"}
],
"data_file" : "tests/data/sine.dat",
"response_variables" : [{"name": "y"}]
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.selection = Selection(self.config, recorder=None)
self.crossover = TreeCrossover(self.config, recorder=None)
self.mutation = TreeMutation(self.config, recorder=None)
{"type": "CONSTANT", "value": 2.0},
{"type": "CONSTANT", "value": 3.0},
{"type": "CONSTANT", "value": 4.0},
{"type": "CONSTANT", "value": 5.0},
{"type": "CONSTANT", "value": 6.0},
{"type": "CONSTANT", "value": 7.0},
{"type": "CONSTANT", "value": 8.0},
{"type": "CONSTANT", "value": 9.0},
{"type": "CONSTANT", "value": 10.0},
],
"input_variables": [{"type": "INPUT", "name": "var1"}],
"response_variables": [{"name": "answer"}],
"data_file": "arabas_et_al-f1.dat"
}
config["max_population"] = 10000
load_data(config, data_dir)
generator = TreeGenerator(config)
population = generator.init()
results = []
# TREE EVALUTOR 1
start_time = time.time()
tree_eval_1.evaluate(
copy.deepcopy(population.individuals),
GPFunctionRegistry("SYMBOLIC_REGRESSION"),
config,
results
)
end_time = time.time()
time_taken = end_time - start_time
print "Evaluator 1 took:", str(round(time_taken, 2)) + "s"
def test_load_data(self):
config.load_data(self.config)
self.config["data"].pop("rows")
self.assertEquals(self.config["data"], self.solution)
def setUp(self):
self.config = {
"cartesian": {
"rows": 1,
"columns": 4,
"levels_back": 4,
"num_inputs": 12,
"num_outputs": 1
},
"input_variables": [{
"type": "INPUT",
"name": "x"
}, {
"type": "CONSTANT",
"name": "0.0"
}, {
"type": "CONSTANT",
"name": "1.0"
}, {
"type": "CONSTANT",
"name": "2.0"
}, {
"type": "CONSTANT",
"name": "3.0"
}, {
"type": "CONSTANT",
"name": "4.0"
}, {
"type": "CONSTANT",
"name": "5.0"
}, {
"type": "CONSTANT",
"name": "6.0"
}, {
"type": "CONSTANT",
"name": "7.0"
}, {
"type": "CONSTANT",
"name": "8.0"
}, {
"type": "CONSTANT",
"name": "9.0"
}, {
"type": "CONSTANT",
"name": "10.0"
}],
"function_nodes": [{
"type": "FUNCTION",
"name": "ADD",
"arity": 2
}, {
"type": "FUNCTION",
"name": "SUB",
"arity": 2
}, {
"type": "FUNCTION",
"name": "MUL",
"arity": 2
}, {
"type": "FUNCTION",
"name": "DIV",
"arity": 2
}, {
"type": "FUNCTION",
"name": "SIN",
"arity": 1
}, {
"type": "FUNCTION",
"name": "COS",
"arity": 1
}, {
"type": "FUNCTION",
"name": "RAD",
"arity": 1
}],
"response_variables": [{
"name": "y"
}],
"data_file":
"../../data/sine.dat",
}
script_path = os.path.dirname(os.path.realpath(sys.argv[0]))
load_data(self.config, script_path)
# add constants
rows = len(self.config["data"]["y"])
for i in range(11):
i = float(i)
self.config["data"][str(i) + ".0"] = [i for j in range(rows)]
self.functions = [
functions.add_function, functions.sub_function,
functions.mul_function, functions.div_function,
functions.sin_function, functions.cos_function,
functions.rad_function
]
self.input_nodes = [
"x", "0.0", "1.0", "2.0", "3.0", "4.0", "5.0", "6.0", "7.0", "8.0",
"9.0", "10.0"
]
self.func_nodes = [
[2, 11, 11], # 12
[6, 0], # 13
[2, 12, 13], # 14
[4, 14], # 15
]
self.output_nodes = [15]
self.cartesian = Cartesian(config={},
rows=1,
columns=4,
levels_back=4,
func_nodes=self.func_nodes,
input_nodes=self.input_nodes,
output_nodes=self.output_nodes)
"sepal_length",
"sepal_width",
"petal_length",
"petal_width"
],
"data_file": "data/iris.dat",
"input_variables": [
{"name": "sepal_length"},
{"name": "sepal_width"},
{"name": "petal_length"},
{"name": "petal_width"}
],
"response_variables": [{"name": "species"}]
}
load_data(config, script_path)
functions = GPFunctionRegistry("CLASSIFICATION")
generator = TreeGenerator(config)
# genetic operators
selection = Selection(config)
crossover = TreeCrossover(config)
mutation = TreeMutation(config)
# run symbolic regression
population = generator.init()
start_time = time.time()
details = play.play_details(
population=population,
evaluate=evaluate,
def gp_benchmark_loop(config):
try:
# setup
random.seed(config["random_seed"]) # VERY IMPORTANT!
load_data(config, config["call_path"])
json_store = JSONStore(config)
# functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
generator = TreeGenerator(config)
# genetic operators
selection = Selection(config, recorder=json_store)
crossover = TreeCrossover(config, recorder=json_store)
mutation = TreeMutation(config, recorder=json_store)
# setup the initial random population
population = generator.init()
# create play details
details = play.play_details(
population=population,
functions=config["functions"],
evaluate=evaluate,
selection=selection,
crossover=crossover,
mutation=mutation,
editor=edit_trees,
stop_func=default_stop_func,
# print_func=print_func,
config=config,
recorder=json_store
)
# run symbolic regression
start_time = time.time()
play.play(details)
end_time = time.time()
time_taken = end_time - start_time
# print msg
print(
"DONE -> pop: {0} cross: {1} mut: {2} seed: {3} [{4}s]".format(
config["max_population"],
config["crossover"]["probability"],
config["mutation"]["probability"],
config["random_seed"],
round(time_taken, 2)
)
)
# log on completion
if config.get("log_path", False):
config.pop("data")
msg = {
"timestamp": time.mktime(datetime.now().timetuple()),
"status": "DONE",
"config": config,
"runtime": time_taken,
"best_score": population.find_best_individuals()[0].score,
"best": str(population.find_best_individuals()[0])
}
log_path = os.path.expandvars(config["log_path"])
log_file = open(log_path, "a+")
log_file.write(json.dumps(msg) + "\n")
log_file.close()
except Exception as err_msg:
import traceback
traceback.print_exc()
# log exception
if config.get("log_path", False):
msg = {
"timestamp": time.mktime(datetime.now().timetuple()),
"status": "ERROR",
"config": config,
"error": err_msg
}
log_path = os.path.expandvars(config["log_path"])
log_file = open(log_path, "a+")
log_file.write(json.dumps(msg) + "\n")
log_file.close()
raise # raise the exception
return config