def test_reproduce(self):
tests = 1
for i in range(tests):
population = self.generator.init()
res = []
evaluate(population.individuals, self.functions, self.config, res)
population.individuals = res
# print "POPULATION"
# for i in population.individuals:
# print i, i.score
# print "\n"
self.selection.select(population)
# print "SELECTION"
# for i in population.individuals:
# print i, i.score
# print "\n"
# reproduce
play_details = play.play_details(
population=population,
selection=self.selection,
crossover=self.crossover,
mutation=self.mutation,
evaluate=None,
config=self.config
)
play.play_ga_reproduce(play_details)
# print "REPRODUCE"
# for i in population.individuals:
# print i, i.score
# assert
max_pop = self.config["max_population"]
self.assertEquals(len(population.individuals), max_pop)
self.assertTrue(population.config is self.config)
def test_reproduce(self):
tests = 1
for i in range(tests):
population = self.generator.init()
res = []
evaluate(population.individuals, self.functions, self.config, res)
population.individuals = res
# print "POPULATION"
# for i in population.individuals:
# print i, i.score
# print "\n"
self.selection.select(population)
# print "SELECTION"
# for i in population.individuals:
# print i, i.score
# print "\n"
# reproduce
play_details = play.play_details(population=population,
selection=self.selection,
crossover=self.crossover,
mutation=self.mutation,
evaluate=None,
config=self.config)
play.play_ga_reproduce(play_details)
# print "REPRODUCE"
# for i in population.individuals:
# print i, i.score
# assert
max_pop = self.config["max_population"]
self.assertEquals(len(population.individuals), max_pop)
self.assertTrue(population.config is self.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,
functions=functions,
selection=selection,
crossover=crossover,
mutation=mutation,
print_func=print_func,
stop_func=default_stop_func,
config=config,
editor=edit_trees,
)
play.play(details)
import networkx as nx
import matplotlib.pyplot as plt
best = population.best_individuals[0]
graph = nx.DiGraph()
traverse_tree(best.root, graph)
labels = dict((n, d["label"]) for n, d in graph.nodes(data=True))
# 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,
functions=functions,
selection=selection,
crossover=crossover,
mutation=mutation,
print_func=print_func,
stop_func=default_stop_func,
config=config,
editor=edit_trees,
)
play.play(details)
import networkx as nx
import matplotlib.pyplot as plt
best = population.best_individuals[0]
graph = nx.DiGraph()
traverse_tree(best.root, graph)
labels = dict((n, d["label"]) for n, d in graph.nodes(data=True))
def gp_predict(train_data, test_data, train_cat, xx, yy):
# setup
config = {
"max_population": 800,
"max_generation": 30,
"stale_limit": 10,
"tree_generation": {
"tree_type": "CLASSIFICATION_TREE",
"method": "RAMPED_HALF_AND_HALF_METHOD",
"depth_ranges": [
{"size": 1, "percentage": 1.0}
]
},
"evaluator": {"use_cache": True},
"selection": {
"method": "TOURNAMENT_SELECTION",
"tournament_size": 100
},
"crossover": {
"method": "POINT_CROSSOVER",
"probability": 0.8
},
"mutation": {
"methods": [
"SUBTREE_MUTATION"
],
"probability": 0.8
},
"function_nodes": [
{
"type": "CLASS_FUNCTION",
"name": "GREATER_THAN",
"arity": 2,
"data_range": {
"lower_bound": -1.0,
"upper_bound": 1.0,
"decimal_places": 2,
}
},
{
"type": "CLASS_FUNCTION",
"name": "LESS_THAN",
"arity": 2,
"data_range": {
"lower_bound": -1.0,
"upper_bound": 1.0,
"decimal_places": 2,
}
},
{
"type": "CLASS_FUNCTION",
"name": "EQUALS",
"arity": 2,
"data_range": {
"lower_bound": -1.0,
"upper_bound": 1.0,
"decimal_places": 2
}
}
],
"terminal_nodes": [
{
"type": "RANDOM_CONSTANT",
"name": "category",
"range": [
0.0,
1.0
]
},
],
"class_attributes": [
"x",
"y"
],
"input_variables": [
{"name": "x"},
{"name": "y"}
],
"response_variables": [{"name": "category"}]
}
# load data
config["data"] = {}
config["data"]["rows"] = len(train_data)
config["data"]["x"] = []
config["data"]["y"] = []
config["data"]["category"] = train_cat
for row in train_data:
config["data"]["x"].append(row[0])
config["data"]["y"].append(row[1])
functions = GPFunctionRegistry("CLASSIFICATION")
generator = TreeGenerator(config)
# genetic operators
selection = Selection(config)
crossover = TreeCrossover(config)
mutation = TreeMutation(config)
# run symbolic regression
population = generator.init()
details = play.play_details(
population=population,
evaluate=evaluate,
functions=functions,
selection=selection,
crossover=crossover,
mutation=mutation,
print_func=print_func,
stop_func=default_stop_func,
config=config,
editor=edit_trees,
)
play.play(details)
best_tree = population.best_individuals[0]
# gp_plot_dt(best_tree, True)
# load test data
config["data"] = {}
config["data"]["rows"] = len(test_data)
config["data"]["x"] = []
config["data"]["y"] = []
for row in test_data:
config["data"]["x"].append(row[0])
config["data"]["y"].append(row[1])
# predict
predicted = gp_eval.predict_tree(best_tree, functions, config)
# load test data
config["data"] = {}
config["data"]["rows"] = xx.shape[0] * xx.shape[1]
config["data"]["x"] = np.reshape(xx, xx.shape[0] * xx.shape[1])
config["data"]["y"] = np.reshape(yy, yy.shape[0] * yy.shape[1])
contour = gp_eval.predict_tree(best_tree, functions, config)
contour = np.array(contour)
contour = contour.reshape(xx.shape)
return predicted, contour
]
generator = CartesianGenerator(config)
# genetic operators
selection = Selection(config, recorder=json_store)
mutation = CartesianMutation(config)
# run symbolic regression
population = generator.init()
start_time = time.time()
details = play.play_details(
population=population,
evaluate=evaluate,
functions=functions,
selection=selection,
mutation=mutation,
print_func=print_func,
plot_func=plot_func,
stop_func=default_stop_func,
config=config
)
# play.play_evolution_strategy(details)
play.play(details)
end_time = time.time()
print "GP run took: %2.2fsecs\n" % (end_time - start_time)
except Exception as err:
print err
print traceback.print_exc()
}
generator = BitStringGenerator(config)
# genetic operators
selection = Selection(config)
crossover = BitStringCrossover(config)
mutation = BitStringMutation(config)
# run GA
population = generator.init()
details = play.play_details(
population=population,
evaluate=evaluate,
functions=None,
selection=selection,
crossover=crossover,
mutation=mutation,
print_func=print_func,
stop_func=stop_func,
config=config,
)
start_time = time.time()
play.play(details)
end_time = time.time()
print "GA run took: %2.2fsecs\n" % (end_time - start_time)
except Exception as err:
raise
}
generator = BitStringGenerator(config)
# genetic operators
selection = Selection(config)
crossover = BitStringCrossover(config)
mutation = BitStringMutation(config)
# run GA
population = generator.init()
details = play.play_details(
population=population,
evaluate=evaluate,
functions=None,
selection=selection,
crossover=crossover,
mutation=mutation,
print_func=print_func,
stop_func=stop_func,
config=config,
)
start_time = time.time()
play.play(details)
end_time = time.time()
print "GA run took: %2.2fsecs\n" % (end_time - start_time)
except Exception as err:
raise
funcs.div_function, funcs.sin_function, funcs.cos_function,
funcs.rad_function
]
generator = CartesianGenerator(config)
# genetic operators
selection = Selection(config, recorder=json_store)
mutation = CartesianMutation(config)
# run symbolic regression
population = generator.init()
start_time = time.time()
details = play.play_details(population=population,
evaluate=evaluate,
functions=functions,
selection=selection,
mutation=mutation,
print_func=print_func,
plot_func=plot_func,
stop_func=default_stop_func,
config=config)
# play.play_evolution_strategy(details)
play.play(details)
end_time = time.time()
print "GP run took: %2.2fsecs\n" % (end_time - start_time)
except Exception as err:
print err
print traceback.print_exc()
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 gp_predict(train_data, test_data, train_cat, xx, yy):
# setup
config = {
"max_population":
800,
"max_generation":
30,
"stale_limit":
10,
"tree_generation": {
"tree_type": "CLASSIFICATION_TREE",
"method": "RAMPED_HALF_AND_HALF_METHOD",
"depth_ranges": [{
"size": 1,
"percentage": 1.0
}]
},
"evaluator": {
"use_cache": True
},
"selection": {
"method": "TOURNAMENT_SELECTION",
"tournament_size": 100
},
"crossover": {
"method": "POINT_CROSSOVER",
"probability": 0.8
},
"mutation": {
"methods": ["SUBTREE_MUTATION"],
"probability": 0.8
},
"function_nodes": [{
"type": "CLASS_FUNCTION",
"name": "GREATER_THAN",
"arity": 2,
"data_range": {
"lower_bound": -1.0,
"upper_bound": 1.0,
"decimal_places": 2,
}
}, {
"type": "CLASS_FUNCTION",
"name": "LESS_THAN",
"arity": 2,
"data_range": {
"lower_bound": -1.0,
"upper_bound": 1.0,
"decimal_places": 2,
}
}, {
"type": "CLASS_FUNCTION",
"name": "EQUALS",
"arity": 2,
"data_range": {
"lower_bound": -1.0,
"upper_bound": 1.0,
"decimal_places": 2
}
}],
"terminal_nodes": [
{
"type": "RANDOM_CONSTANT",
"name": "category",
"range": [0.0, 1.0]
},
],
"class_attributes": ["x", "y"],
"input_variables": [{
"name": "x"
}, {
"name": "y"
}],
"response_variables": [{
"name": "category"
}]
}
# load data
config["data"] = {}
config["data"]["rows"] = len(train_data)
config["data"]["x"] = []
config["data"]["y"] = []
config["data"]["category"] = train_cat
for row in train_data:
config["data"]["x"].append(row[0])
config["data"]["y"].append(row[1])
functions = GPFunctionRegistry("CLASSIFICATION")
generator = TreeGenerator(config)
# genetic operators
selection = Selection(config)
crossover = TreeCrossover(config)
mutation = TreeMutation(config)
# run symbolic regression
population = generator.init()
details = play.play_details(
population=population,
evaluate=evaluate,
functions=functions,
selection=selection,
crossover=crossover,
mutation=mutation,
print_func=print_func,
stop_func=default_stop_func,
config=config,
editor=edit_trees,
)
play.play(details)
best_tree = population.best_individuals[0]
# gp_plot_dt(best_tree, True)
# load test data
config["data"] = {}
config["data"]["rows"] = len(test_data)
config["data"]["x"] = []
config["data"]["y"] = []
for row in test_data:
config["data"]["x"].append(row[0])
config["data"]["y"].append(row[1])
# predict
predicted = gp_eval.predict_tree(best_tree, functions, config)
# load test data
config["data"] = {}
config["data"]["rows"] = xx.shape[0] * xx.shape[1]
config["data"]["x"] = np.reshape(xx, xx.shape[0] * xx.shape[1])
config["data"]["y"] = np.reshape(yy, yy.shape[0] * yy.shape[1])
contour = gp_eval.predict_tree(best_tree, functions, config)
contour = np.array(contour)
contour = contour.reshape(xx.shape)
return predicted, contour
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
