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()