"SUB": "-",
"MUL": "*",
"DIV": "/",
"POW": "**",
"SIN": "math.sin",
"COS": "math.cos",
"RAD": "math.radians",
"LN": "math.ln",
"EXP": "math.exp",
"LOG": "math.log"
}
generator = TreeGenerator(config)
# genetic operators
selection = Selection(config, recorder=json_store)
crossover = TreeCrossover(config, recorder=json_store)
mutation = TreeMutation(config, recorder=json_store)
# 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,
plot_func=plot_func,
stop_func=default_stop_func,
}, {
"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,
functions=functions,
selection=selection,
crossover=crossover,
mutation=mutation,
print_func=print_func,
stop_func=default_stop_func,
def setUp(self):
self.config = {
"tree_generation": {
"initial_max_depth": 4
},
"crossover": {
"method": "POINT_CROSSOVER",
"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"
}]
}
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.crossover = TreeCrossover(self.config)
self.parser = TreeParser()
# create nodes
left_node_1 = Node(NodeType.INPUT, name="x")
right_node_1 = Node(NodeType.CONSTANT, value=2.0)
node = Node(NodeType.CONSTANT, value=2.0)
left_node_2 = Node(NodeType.CONSTANT, value=3.0)
right_node_2 = Node(NodeType.CONSTANT, value=4.0)
cos_func_1 = Node(NodeType.FUNCTION,
name="ADD",
arity=2,
branches=[left_node_1, right_node_1])
sin_func_1 = Node(NodeType.FUNCTION,
name="SIN",
arity=1,
branches=[node])
cos_func_2 = Node(NodeType.FUNCTION,
name="COS",
arity=1,
branches=[left_node_2])
sin_func_2 = Node(NodeType.FUNCTION,
name="SIN",
arity=1,
branches=[right_node_2])
add_func = Node(NodeType.FUNCTION,
name="ADD",
arity=2,
branches=[cos_func_1, sin_func_1])
sub_func = Node(NodeType.FUNCTION,
name="SUB",
arity=2,
branches=[sin_func_2, cos_func_2])
# create tree_1
self.tree_1 = Tree()
self.tree_1.root = add_func
self.tree_1.update()
print self.tree_1
# create tree_2
self.tree_2 = Tree()
self.tree_2.root = sub_func
self.tree_2.update()
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 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
