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 test_record_evaulation(self):
# record evaluation
results = []
evaluate(self.population.individuals,
self.functions,
self.config,
results,
recorder=self.json_store)
# assert
record = self.json_store.generation_record
# import pprint
# pprint.pprint(record)
self.assertEquals(record["evaluation"]["cache_size"], 10)
self.assertEquals(record["evaluation"]["match_cached"], 0)
def test_record_evaulation(self):
# record evaluation
results = []
evaluate(
self.population.individuals,
self.functions,
self.config,
results,
recorder=self.json_store
)
# assert
record = self.json_store.generation_record
# import pprint
# pprint.pprint(record)
self.assertEquals(record["evaluation"]["cache_size"], 10)
self.assertEquals(record["evaluation"]["match_cached"], 0)
def test_evaluate(self):
population = self.generator.init()
results = []
start_time = time.time()
evaluator.evaluate(
population.individuals,
self.functions,
self.config,
results
)
end_time = time.time()
print("GP run took: %2.2fsecs\n" % (end_time - start_time))
population.individuals = results
# assert
for individual in population.individuals:
self.assertTrue(individual.score is not None)
self.assertTrue(individual.score > 0)
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)
"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": "+",
# "SUB": "-",
# "MUL": "*",
# "DIV": "/",
# "POW": "**",
# "SIN": "math.sin",
# "COS": "math.cos",
# "RAD": "math.radians",
],
"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"
# TREE EVALUTOR 2
functions = {
"ADD": "+",
"SUB": "-",
"MUL": "*",
"DIV": "/",
"POW": "**",
"SIN": "math.sin",
"COS": "math.cos",
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" : 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)