def test_generate_func_node(self):
# SYMBOLIC REGRESSION TREES
for i in range(100):
node = self.generator.generate_func_node()
self.assertEquals(node.node_type, NodeType.FUNCTION)
# CLASSIFICATION TREES
self.config["tree_generation"]["tree_type"] = "CLASSIFICATION_TREE"
self.config["function_nodes"] = [{
"type": "CLASS_FUNCTION",
"name": "GREATER_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 1
}
}]
self.config["class_attributes"] = [
"attrubte_1", "attrubte_2", "attrubte_3"
]
generator = TreeGenerator(self.config)
for i in range(100):
node = generator.generate_func_node()
class_attribute = node.class_attribute
self.assertEquals(node.node_type, NodeType.CLASS_FUNCTION)
self.assertTrue(class_attribute in self.config["class_attributes"])
def evaluate_trees():
results = []
response = {}
# parse incomming data
if request.data is not None:
incomming = json.loads(request.data)
config = incomming["config"]
individuals = incomming["individuals"]
# convert dict to trees
parser = TreeGenerator(config)
for individual in list(individuals):
tree = parser.generate_tree_from_dict(individual)
individuals.append(tree)
individuals.remove(individual)
evaluate(individuals, functions, config, results)
# jsonify results
response["results"] = []
for individual in results:
result = {
"id": individual.tree_id,
"score": individual.score,
}
response["results"].append(result)
else:
response = {"status": PlayNodeStatus.ERROR}
return jsonify(response)
def __init__(self, config, **kwargs):
self.config = config
self.recorder = kwargs.get("recorder", None)
self.generator = TreeGenerator(self.config)
# mutation stats
self.method = None
self.index = None
self.mutation_probability = None
self.random_probability = None
self.mutated = False
self.before_mutation = None
self.after_mutation = None
def test_greedy_over_selection(self):
print "GREEDY-OVER SELECTION"
# create population of size 1000
self.config["max_population"] = 1000
generator = TreeGenerator(self.config)
population = generator.init()
# greedy over selection
old_pop_size = self.print_population("OLD", population)
self.selection.greedy_over_selection(population)
new_pop_size = self.print_population("NEW", population)
self.assertEquals(old_pop_size, new_pop_size)
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)
"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 = {
# "ADD": "+",
def setUp(self):
self.config = {
"tree_generation": {
"method": "GROW_METHOD",
"initial_max_depth": 4
},
"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": "INPUT",
"name": "x"
}],
"input_variables": [{
"type": "INPUT",
"name": "x"
}]
}
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.parser = TreeParser()
self.mutation = TreeMutation(self.config)
# create nodes
left_node = Node(NodeType.CONSTANT, value=1.0)
right_node = Node(NodeType.INPUT, name="x")
cos_func = Node(NodeType.FUNCTION,
name="COS",
arity=1,
branches=[left_node])
sin_func = Node(NodeType.FUNCTION,
name="SIN",
arity=1,
branches=[right_node])
add_func = Node(NodeType.FUNCTION,
name="ADD",
arity=2,
branches=[cos_func, sin_func])
# create tree
self.tree = Tree()
self.tree.root = add_func
self.tree.update_program()
self.tree.update_func_nodes()
self.tree.update_term_nodes()
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_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 test_evaluate(self):
random.seed(10)
# solution = {
# "results":
# [
# {"score": 15726642.002161335},
# {"score": 359.25843589015597},
# {"score": 92155571.22132382},
# {"score": 26186.46142920347},
# {"score": 15649304.847552022},
# {"score": 188.86069156360125},
# {"score": 23439.33097274221},
# ]
# }
# setup
config = {
"max_population" : 10,
"max_generation" : 5,
"tree_generation" : {
"method" : "GROW_METHOD",
"initial_max_depth" : 3
},
"evaluator": {
"use_cache" : True
},
"selection" : {
"method" : "TOURNAMENT_SELECTION",
"tournament_size": 5
},
"crossover" : {
"method" : "POINT_CROSSOVER",
"probability" : 0.8
},
"mutation" : {
"methods": [
"POINT_MUTATION",
"HOIST_MUTATION",
"SUBTREE_MUTATION",
"SHRINK_MUTATION",
"EXPAND_MUTATION"
],
"probability" : 0.9
},
"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},
{"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}
],
"data_file" : "tests/data/sine.dat",
"input_variables" : [{"type": "INPUT", "name": "x"}],
"response_variables" : [{"name": "y"}]
}
parser = TreeParser()
population = TreeGenerator(config).init()
# create a dictionary of trees
data = {"config": config, "individuals": []}
for individual in population.individuals:
tree_json = parser.tree_to_dict(individual, individual.root)
data["individuals"].append(tree_json)
# make sure population size is equals to number of trees
population_size = len(population.individuals)
individuals = len(data["individuals"])
self.assertEquals(population_size, individuals)
# evaluating individuals
data = json.dumps(data)
host = "localhost"
port = 8080
req_type = "POST"
path = "evaluate"
response = self.transmit(host, port, req_type, path, data)
response = json.loads(response)
print response
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(10)
self.config = {
"max_population": 10,
"tree_generation": {
"method": "FULL_METHOD",
"initial_max_depth": 4
},
"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},
{"type": "INPUT", "name": "x"},
{"type": "INPUT", "name": "y"},
{"type": "INPUT", "name": "z"}
],
"input_variables": [
{"name": "x"},
{"name": "y"},
{"name": "z"}
]
}
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.parser = TreeParser()
# create nodes
left_node = Node(NodeType.CONSTANT, value=1.0)
right_node = Node(NodeType.CONSTANT, value=2.0)
cos_func = Node(
NodeType.FUNCTION,
name="COS",
arity=1,
branches=[left_node]
)
sin_func = Node(
NodeType.FUNCTION,
name="SIN",
arity=1,
branches=[right_node]
)
add_func = Node(
NodeType.FUNCTION,
name="ADD",
arity=2,
branches=[cos_func, sin_func]
)
# create tree
self.tree = Tree()
self.tree.root = add_func
self.tree.update_program()
self.tree.update_func_nodes()
self.tree.update_term_nodes()
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):
self.config = {
"max_population":
10,
"tree_generation": {
"method": "FULL_METHOD",
"initial_max_depth": 3
},
"selection": {
"method": "ROULETTE_SELECTION"
},
"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.selection = Selection(self.config)
self.population = self.generator.init()
# give population random scores
for inidividual in self.population.individuals:
inidividual.score = random.triangular(1, 100)
def setUp(self):
self.config = {
"max_population":
10,
"tree_generation": {
"tree_type": "SYMBOLIC_REGRESSION",
"method": "RAMPED_HALF_AND_HALF_METHOD",
"initial_max_depth": 3
},
"function_nodes": [{
"type": "FUNCTION",
"arity": 2,
"name": "ADD"
}, {
"type": "FUNCTION",
"arity": 2,
"name": "SUB"
}, {
"type": "FUNCTION",
"arity": 2,
"name": "MUL"
}, {
"type": "FUNCTION",
"arity": 2,
"name": "DIV"
}, {
"type": "FUNCTION",
"arity": 1,
"name": "COS"
}, {
"type": "FUNCTION",
"arity": 1,
"name": "SIN"
}],
"terminal_nodes": [{
"type": "CONSTANT",
"value": 1.0
}, {
"type": "INPUT",
"name": "x"
}, {
"type": "INPUT",
"name": "y"
}, {
"type": "RANDOM_CONSTANT",
"data_range": {
"upper_bound": 10.0,
"lower_bound": -10.0,
"decimal_places": 1
}
}],
"input_variables": [{
"name": "x"
}, {
"name": "y"
}]
}
self.functions = GPFunctionRegistry("SYMBOLIC_REGRESSION")
self.generator = TreeGenerator(self.config)
self.parser = TreeParser()
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()
