def run(self, graph, trainset, validset, train_config, device, verbose=False):
assert isinstance(graph, ProgramGraph)
current = copy.deepcopy(graph.root_node)
current_avg_f_score = float('inf')
best_program = None
best_total_cost = float('inf')
best_programs_list = []
num_children_trained = 0
start_time = time.time()
while not graph.is_fully_symbolic(current.program):
log_and_print("CURRENT program has avg fscore {:.4f}: {}".format(
current_avg_f_score, print_program(current.program, ignore_constants=(not verbose))))
children = graph.get_all_children(current, in_enumeration=True)
children_mapping = { print_program(child.program, ignore_constants=True) : child for child in children }
children_scores = { key : [] for key in children_mapping.keys() }
costs = [child.cost for child in children]
for i in range(self.num_mc_samples):
child = random.choices(children, weights=costs)[0]
sample = self.mc_sample(graph, child)
assert graph.is_fully_symbolic(sample.program)
log_and_print("Training sample program: {}".format(print_program(sample.program, ignore_constants=(not verbose))))
sample_score = execute_and_train(sample.program, validset, trainset, train_config,
graph.output_type, graph.output_size, neural=False, device=device)
num_children_trained += 1
log_and_print("{} total children trained".format(num_children_trained))
sample_f_score = sample.cost + sample_score
children_scores[print_program(child.program, ignore_constants=True)].append(sample_f_score)
if sample_f_score < best_total_cost:
best_program = copy.deepcopy(sample.program)
best_total_cost = sample_f_score
best_programs_list.append({
"program" : best_program,
"struct_cost" : sample.cost,
"score" : sample_score,
"path_cost" : sample_f_score,
"time" : time.time()-start_time
})
log_and_print("New BEST program found:")
print_program_dict(best_programs_list[-1])
# (Naive) selection operation
children_scores = { key : sum(val)/len(val) if len(val) > 0 else float('inf') for key,val in children_scores.items() }
best_child_name = min(children_scores, key=children_scores.get)
current = children_mapping[best_child_name]
current_avg_f_score = children_scores[best_child_name]
for key,val in children_scores.items():
log_and_print("Avg score {:.4f} for child {}".format(val,key))
log_and_print("SELECTING {} as best child node\n".format(best_child_name))
log_and_print("DEBUG: time since start is {:.3f}\n".format(time.time()-start_time))
return best_programs_list
def run(self, graph, trainset, validset, train_config, device, verbose=False):
assert isinstance(graph, ProgramGraph)
symbolic_programs = []
enum_depth = 1
while len(symbolic_programs) < self.max_num_programs:
print("DEBUG: starting enumerative synthesis with depth {}".format(enum_depth))
symbolic_programs = self.enumerate2depth(graph, enum_depth)
print("DEBUG: {} programs found".format(len(symbolic_programs)))
enum_depth += 1
if enum_depth > graph.max_depth:
break
log_and_print("Symbolic Synthesis: generated {}/{} symbolic programs from candidate program.".format(
len(symbolic_programs), self.max_num_programs))
total_eval = min(self.max_num_programs, len(symbolic_programs))
symbolic_programs.sort(key=lambda x: x["struct_cost"])
symbolic_programs = symbolic_programs[:total_eval]
best_program = None
best_total_cost = float('inf')
best_programs_list = []
start_time = time.time()
num_programs_trained = 1
for prog_dict in symbolic_programs:
child_start_time = time.time()
candidate = prog_dict["program"]
log_and_print("Training candidate program ({}/{}) {}".format(
num_programs_trained, total_eval, print_program(candidate, ignore_constants=(not verbose))))
num_programs_trained += 1
score = execute_and_train(candidate, validset, trainset, train_config,
graph.output_type, graph.output_size, neural=False, device=device)
total_cost = score + prog_dict["struct_cost"]
log_and_print("Structural cost is {} with structural penalty {}".format(prog_dict["struct_cost"], graph.penalty))
log_and_print("Time to train child {:.3f}".format(time.time()-child_start_time))
log_and_print("Total time elapsed is: {:.3f}".format(time.time()-start_time))
if total_cost < best_total_cost:
best_program = copy.deepcopy(prog_dict["program"])
best_total_cost = total_cost
prog_dict["score"] = score
prog_dict["path_cost"] = total_cost
prog_dict["time"] = time.time()-start_time
best_programs_list.append(prog_dict)
log_and_print("New BEST program found:")
print_program_dict(best_programs_list[-1])
return best_programs_list
def run_near(self, model, num_iter):
train_config = {
'lr': self.learning_rate,
'neural_epochs': self.neural_epochs,
'symbolic_epochs': self.symbolic_epochs,
'optimizer': optim.Adam,
'lossfxn': nn.CrossEntropyLoss(), #todo
'evalfxn': label_correctness,
'num_labels': self.num_labels
}
# Initialize program graph starting from trained NN
program_graph = ProgramGraph(model,
DSL_DICT,
CUSTOM_EDGE_COSTS,
self.input_type,
self.output_type,
self.input_size,
self.output_size,
self.max_num_units,
self.min_num_units,
self.max_num_children,
self.max_depth,
self.penalty,
ite_beta=self.ite_beta)
# Initialize algorithm
algorithm = ASTAR_NEAR(frontier_capacity=self.frontier_capacity)
best_programs = algorithm.run(program_graph, self.batched_trainset,
self.validset, train_config, self.device)
if self.algorithm == "rnn":
# special case for RNN baseline
best_program = best_programs
else:
# Print all best programs found
log_and_print("\n")
log_and_print("BEST programs found:")
for item in best_programs:
print_program_dict(item)
best_program = best_programs[-1]["program"]
# Save best program
self.program_path = os.path.join(self.save_path,
"program_%d.p" % num_iter)
pickle.dump(best_program, open(self.program_path, "wb"))
def run(self,
graph,
trainset,
validset,
train_config,
device,
verbose=False):
assert isinstance(graph, ProgramGraph)
self.input_size = graph.input_size
self.output_size = graph.output_size
best_program = None
best_score = float('inf')
best_total = float('inf')
best_programs_list = []
start_time = time.time()
log_and_print("Generating all programs up to specified depth.")
all_generated_programs = self.enumerative_synthesis(
graph, self.max_enum_depth)
random.shuffle(all_generated_programs)
current_population = all_generated_programs[:self.population_size]
total_trained_programs = 0
def crossover_operation(listofprogramdicts):
new_prog_dict = {}
parents = random.choices(listofprogramdicts, k=2)
prog_dict1, prog_dict2 = parents[0], parents[1]
xover1 = random.randint(1, prog_dict1['depth'])
xover2 = random.randint(2, prog_dict2['depth'])
print('DEBUG: trying new crossover')
new_program, crossed_over = self.crossover_helper(
prog_dict1['program'], prog_dict2['program'], xover1, xover2)
if crossed_over:
log_and_print("Crossing over the following programs:")
log_and_print(
print_program(prog_dict1['program'],
ignore_constants=True))
log_and_print(
print_program(prog_dict2['program'],
ignore_constants=True))
new_prog_dict["program"] = new_program
new_prog_dict["struct_cost"], new_prog_dict[
"depth"] = graph.compute_program_cost(new_program)
log_and_print("Result has structural cost {:.4f}:".format(
new_prog_dict["struct_cost"]))
log_and_print(
print_program(new_prog_dict['program'],
ignore_constants=True))
return new_prog_dict
else:
return None
def mutation_operation(mod_prog_dict):
new_prog_dict = {}
mutation_point = random.randrange(1, mod_prog_dict['depth'])
new_prog_dict['program'] = self.mutation_helper(
graph,
mod_prog_dict['program'],
mod_prog_dict['depth'],
mutation_point,
max_enumeration_depth=self.max_enum_depth)
new_prog_dict["struct_cost"], new_prog_dict[
"depth"] = graph.compute_program_cost(new_prog_dict['program'])
return new_prog_dict
for gen_idx in range(self.num_gens):
#evaluation operation: train each program and evaluate it.
log_and_print(
"Training generation {}'s population of programs.".format(
gen_idx + 1))
for programdict in current_population:
total_trained_programs += 1
child_start_time = time.time()
log_and_print("Training candidate program ({}/{}) {}".format(
total_trained_programs, self.total_eval,
print_program(programdict['program'],
ignore_constants=(not verbose))))
score = execute_and_train(programdict['program'],
validset,
trainset,
train_config,
graph.output_type,
graph.output_size,
neural=False,
device=device)
log_and_print(
"Structural cost is {} with structural penalty {}".format(
programdict["struct_cost"], graph.penalty))
log_and_print(
"Time to train child {:.3f}".format(time.time() -
child_start_time))
log_and_print("Total time elapsed is: {}".format(time.time() -
start_time))
programdict["score"] = score
programdict["path_cost"] = score + programdict["struct_cost"]
programdict["time"] = time.time() - start_time
if programdict["path_cost"] < best_total:
best_program = copy.deepcopy(programdict["program"])
best_total = score + programdict["struct_cost"]
best_score = score
best_cost = programdict["struct_cost"]
best_programs_list.append(copy.deepcopy(programdict))
log_and_print("New BEST program found:")
print_program_dict(best_programs_list[-1])
if total_trained_programs > self.total_eval:
break
#select the best programs based on cost + score.
current_population.sort(key=lambda x: x["path_cost"])
selected_population = current_population[:self.selection_size]
#perform crossover on the selected population
crossed_population = []
log_and_print("Beginning crossover operation.")
while len(crossed_population) < self.population_size:
new_prog_dict = crossover_operation(selected_population)
if new_prog_dict is not None:
crossed_population.append(new_prog_dict)
#perform mutations on the crossed population
current_population = []
for crossed_prog_dict in crossed_population:
if random.random() < self.mutation_prob:
log_and_print("Mutating program.")
current_population.append(
mutation_operation(crossed_prog_dict))
else:
current_population.append(crossed_prog_dict)
return best_programs_list
def run_near(self):
# print(self.device)
train_config = {
'lr' : self.learning_rate,
'neural_epochs' : self.neural_epochs,
'symbolic_epochs' : self.symbolic_epochs,
'optimizer' : optim.Adam,
'lossfxn' : nn.CrossEntropyLoss(weight=self.loss_weight), #todo
'evalfxn' : label_correctness,
'num_labels' : self.num_labels
}
near_input_type = self.hole_node[0].input_type
near_output_type = self.hole_node[0].output_type
near_input_size = self.hole_node[0].input_size
near_output_size = self.hole_node[0].output_size
# Initialize program graph starting from trained NN
program_graph = ProgramGraph(DSL_DICT, CUSTOM_EDGE_COSTS, near_input_type, near_output_type, near_input_size, near_output_size,
self.max_num_units, self.min_num_units, self.max_num_children, self.max_depth, self.penalty, ite_beta=self.ite_beta)
# Initialize algorithm
algorithm = ASTAR_NEAR(frontier_capacity=self.frontier_capacity)
best_programs = algorithm.run(self.timestamp, self.base_program_name, self.hole_node_ind,
program_graph, self.batched_trainset, self.validset, train_config, self.device)
best_program_str = []
if self.algorithm == "rnn":
# special case for RNN baseline
best_program = best_programs
else:
# Print all best programs found
log_and_print("\n")
log_and_print("BEST programs found:")
for item in best_programs:
program_struct = print_program(item["program"], ignore_constants=True)
program_info = "struct_cost {:.4f} | score {:.4f} | path_cost {:.4f} | time {:.4f}".format(
item["struct_cost"], item["score"], item["path_cost"], item["time"])
best_program_str.append((program_struct, program_info))
print_program_dict(item)
best_program = best_programs[-1]["program"]
# Save best programs
f = open(os.path.join(self.save_path, "best_programs.txt"),"w")
f.write( str(best_program_str) )
f.close()
self.program_path = os.path.join(self.save_path, "subprogram.p")
pickle.dump(best_program, open(self.program_path, "wb"))
self.full_path = os.path.join(self.save_path, "fullprogram.p")
if self.device == 'cpu':
base_program = CPU_Unpickler(open("%s.p" % self.base_program_name, "rb")).load()
else:
base_program = pickle.load(open("%s.p" % self.base_program_name, "rb"))
curr_level = 0
l = []
traverse(base_program.submodules,l)
curr_program = base_program.submodules
change_key(base_program.submodules, [], self.hole_node_ind, best_program.submodules["program"])
pickle.dump(base_program, open(self.full_path, "wb"))
# Save parameters
f = open(os.path.join(self.save_path, "parameters.txt"),"w")
parameters = ['input_type', 'output_type', 'input_size', 'output_size', 'num_labels', 'neural_units', 'max_num_units',
'min_num_units', 'max_num_children', 'max_depth', 'penalty', 'ite_beta', 'train_valid_split', 'normalize', 'batch_size',
'learning_rate', 'neural_epochs', 'symbolic_epochs', 'lossfxn', 'class_weights', 'num_iter', 'num_f_epochs', 'algorithm',
'frontier_capacity', 'initial_depth', 'performance_multiplier', 'depth_bias', 'exponent_bias', 'num_mc_samples', 'max_num_programs',
'population_size', 'selection_size', 'num_gens', 'total_eval', 'mutation_prob', 'max_enum_depth', 'exp_id', 'base_program_name', 'hole_node_ind']
for p in parameters:
f.write( p + ': ' + str(self.__dict__[p]) + '\n' )
f.close()
def run(self,
graph,
trainset,
validset,
train_config,
device,
verbose=False):
assert isinstance(graph, ProgramGraph)
log_and_print("Training root program ...")
current = copy.deepcopy(graph.root_node)
initial_score = execute_and_train(current.program,
validset,
trainset,
train_config,
graph.output_type,
graph.output_size,
neural=True,
device=device)
log_and_print(
"Initial training complete. Score from program is {:.4f} \n".
format(1 - initial_score))
# Branch-and-bound search with iterative deepening
current_depth = self.initial_depth
current_f_score = float('inf')
order = 0
frontier = ProgramNodeFrontier(capacity=self.frontier_capacity)
next_frontier = ProgramNodeFrontier(capacity=self.frontier_capacity)
num_children_trained = 0
start_time = time.time()
best_program = None
best_total_cost = float('inf')
best_programs_list = []
log_and_print("Starting iterative deepening with depth {}\n".format(
current_depth))
while current_depth <= graph.max_depth:
log_and_print("CURRENT program has fscore {:.4f}: {}".format(
current_f_score,
print_program(current.program,
ignore_constants=(not verbose))))
log_and_print("Current depth of program is {}".format(
current.depth))
log_and_print("Creating children for current node/program")
log_and_print("Total time elapsed is {:.3f}".format(time.time() -
start_time))
children_nodes = graph.get_all_children(current)
# prune if more than self.max_num_children
if len(children_nodes) > graph.max_num_children:
log_and_print("Sampling {}/{} children".format(
graph.max_num_children, len(children_nodes)))
children_nodes = random.sample(
children_nodes,
k=graph.max_num_children) # sample without replacement
log_and_print("{} total children to train for current node".format(
len(children_nodes)))
child_tuples = []
for child_node in children_nodes:
child_start_time = time.time()
log_and_print("Training child program: {}".format(
print_program(child_node.program,
ignore_constants=(not verbose))))
is_neural = not graph.is_fully_symbolic(child_node.program)
child_node.score = execute_and_train(child_node.program,
validset,
trainset,
train_config,
graph.output_type,
graph.output_size,
neural=is_neural,
device=device)
log_and_print(
"Time to train child {:.3f}".format(time.time() -
child_start_time))
num_children_trained += 1
log_and_print(
"{} total children trained".format(num_children_trained))
child_node.parent = current
child_node.children = []
order -= 1
child_node.order = order # insert order of exploration as tiebreaker for equivalent f-scores
# computing path costs (f_scores)
child_f_score = child_node.cost + child_node.score # cost + heuristic
log_and_print("DEBUG: f-score {}".format(child_f_score))
current.children.append(child_node)
child_tuples.append((child_f_score, order, child_node))
if not is_neural and child_f_score < best_total_cost:
best_program = copy.deepcopy(child_node.program)
best_total_cost = child_f_score
best_programs_list.append({
"program": best_program,
"struct_cost": child_node.cost,
"score": child_node.score,
"path_cost": child_f_score,
"time": time.time() - start_time
})
log_and_print("New BEST program found:")
print_program_dict(best_programs_list[-1])
# find next current among children, from best to worst
nextfound = False
child_tuples.sort(key=lambda x: x[0])
for child_tuple in child_tuples:
child = child_tuple[2]
if graph.is_fully_symbolic(child.program):
continue # don't want to expand symbolic programs (no children)
elif child.depth >= current_depth:
next_frontier.add(child_tuple)
else:
if not nextfound:
nextfound = True # first child program that's not symbolic and within current_depth
current_f_score, current_order, current = child_tuple
log_and_print(
"Found program among children: {} with f_score {}".
format(
print_program(current.program,
ignore_constants=(not verbose)),
current_f_score))
else:
frontier.add(child_tuple) # put the rest onto frontier
# find next node in frontier
if not nextfound:
frontier.sort(tup_idx=1) # DFS order
log_and_print("Frontier length is: {}".format(len(frontier)))
original_depth = current.depth
while len(frontier) > 0 and not nextfound:
current_f_score, current_order, current = frontier.pop(
0, sort_fscores=False) # DFS order
if current_f_score < self.bound_modify(
best_total_cost, original_depth, current.depth):
nextfound = True
log_and_print(
"Found program in frontier: {} with f_score {}".
format(
print_program(current.program,
ignore_constants=(not verbose)),
current_f_score))
else:
log_and_print(
"PRUNE from frontier: {} with f_score {}".format(
print_program(current.program,
ignore_constants=(not verbose)),
current_f_score))
log_and_print("Frontier length is now {}".format(
len(frontier)))
# frontier is empty, go to next stage of iterative deepening
if not nextfound:
assert len(frontier) == 0
log_and_print("Empty frontier, moving to next depth level")
log_and_print(
"DEBUG: time since start is {:.3f}\n".format(time.time() -
start_time))
current_depth += 1
if current_depth > graph.max_depth:
log_and_print("Max depth {} reached. Exiting.\n".format(
graph.max_depth))
break
elif len(next_frontier) == 0:
log_and_print("Next frontier is empty. Exiting.\n")
break
else:
log_and_print(
"Starting iterative deepening with depth {}\n".format(
current_depth))
frontier = copy.deepcopy(next_frontier)
next_frontier = ProgramNodeFrontier(
capacity=self.frontier_capacity)
current_f_score, current_order, current = frontier.pop(0)
if best_program is None:
log_and_print("ERROR: no program found")
return best_programs_list
total_eval=args.total_eval,
mutation_prob=args.mutation_prob,
max_enum_depth=args.max_enum_depth)
elif args.algorithm == "rnn":
algorithm = RNN_BASELINE()
else:
raise NotImplementedError
# Run program learning algorithm
best_programs = algorithm.run(program_graph, batched_trainset, validset,
train_config, device)
if args.algorithm == "rnn":
# special case for RNN baseline
best_program = best_programs
else:
# Print all best programs found
log_and_print("\n")
log_and_print("BEST programs found:")
for item in best_programs:
print_program_dict(item)
best_program = best_programs[-1]["program"]
# Save best program
pickle.dump(best_program, open(os.path.join(save_path, "program.p"), "wb"))
# Evaluate best program on test set
test_set_eval(best_program, testset, args.output_type, args.output_size,
args.num_labels, device)
log_and_print("ALGORITHM END \n\n")
def run(self,
graph,
trainset,
validset,
train_config,
device,
verbose=False,
trainset_neural=None):
assert isinstance(graph, ProgramGraph)
log_and_print("Training root program ...")
current = copy.deepcopy(graph.root_node)
dataset = trainset_neural if trainset_neural is not None else trainset
initial_score = execute_and_train(current.program,
validset,
dataset,
train_config,
graph.output_type,
graph.output_size,
neural=True,
device=device)
log_and_print(
"Initial training complete. Score from program is {:.4f} \n".
format(1 - initial_score))
order = 0
frontier = ProgramNodeFrontier(capacity=self.frontier_capacity)
frontier.add((float('inf'), order, current))
num_children_trained = 0
start_time = time.time()
best_program = None
best_total_cost = float('inf')
best_programs_list = []
while len(frontier) != 0:
current_f_score, _, current = frontier.pop(0)
log_and_print("CURRENT program has fscore {:.4f}: {}".format(
current_f_score,
print_program(current.program,
ignore_constants=(not verbose))))
log_and_print("Current depth of program is {}".format(
current.depth))
log_and_print("Creating children for current node/program")
children_nodes = graph.get_all_children(current)
# prune if more than self.max_num_children
if len(children_nodes) > graph.max_num_children:
children_nodes = random.sample(
children_nodes,
k=graph.max_num_children) # sample without replacement
log_and_print("{} total children to train for current node".format(
len(children_nodes)))
for child_node in children_nodes:
child_start_time = time.time()
log_and_print("Training child program: {}".format(
print_program(child_node.program,
ignore_constants=(not verbose))))
is_neural = not graph.is_fully_symbolic(child_node.program)
dataset = trainset_neural if (
is_neural and trainset_neural is not None) else trainset
child_node.score = execute_and_train(child_node.program,
validset,
dataset,
train_config,
graph.output_type,
graph.output_size,
neural=is_neural,
device=device)
log_and_print(
"Time to train child {:.3f}".format(time.time() -
child_start_time))
num_children_trained += 1
log_and_print(
"{} total children trained".format(num_children_trained))
child_node.parent = current
child_node.children = []
order -= 1
child_node.order = order # insert order of exploration as tiebreaker for equivalent f-scores
current.children.append(child_node)
# computing path costs (f_scores)
child_f_score = child_node.cost + child_node.score # cost + heuristic
log_and_print("DEBUG: f-score {}".format(child_f_score))
if not is_neural and child_f_score < best_total_cost:
best_program = copy.deepcopy(child_node.program)
best_total_cost = child_f_score
best_programs_list.append({
"program": best_program,
"struct_cost": child_node.cost,
"score": child_node.score,
"path_cost": child_f_score,
"time": time.time() - start_time
})
log_and_print("New BEST program found:")
print_program_dict(best_programs_list[-1])
if is_neural:
assert child_node.depth < graph.max_depth
child_tuple = (child_f_score, order, child_node)
frontier.add(child_tuple)
# clean up frontier
frontier.sort(tup_idx=0)
while len(frontier) > 0 and frontier.peek(-1)[0] > best_total_cost:
frontier.pop(-1)
log_and_print("Frontier length is: {}".format(len(frontier)))
log_and_print("Total time elapsed is {:.3f}".format(time.time() -
start_time))
if best_program is None:
log_and_print("ERROR: no program found")
return best_programs_list
def run(self, timestamp, base_program_name, hole_node_ind, graph, trainset, validset, train_config, device, verbose=False):
assert isinstance(graph, ProgramGraph)
log_and_print("Training root program ...")
current = copy.deepcopy(graph.root_node)
initial_score, l, m = execute_and_train_with_full(base_program_name, hole_node_ind, current.program, validset, trainset, train_config,
graph.output_type, graph.output_size, neural=True, device=device)
# print("initial losses:")
# print(l)
# print("initial f1:")
# print(m)
log_and_print("Initial training complete. Score from program is {:.4f} \n".format(1 - initial_score))
order = 0
frontier = ProgramNodeFrontier(capacity=self.frontier_capacity) #mcheng priority queue
frontier.add((float('inf'), order, current))
num_children_trained = 0
start_time = time.time()
best_program = None
best_total_cost = float('inf')
best_programs_list = []
# if not os.path.exists(timestamp):
# os.makedirs(timestamp)
while len(frontier) != 0:
current_f_score, _, current = frontier.pop(0)
log_and_print("CURRENT program has fscore {:.4f}: {}".format(
current_f_score, print_program(current.program, ignore_constants=(not verbose))))
log_and_print("Current depth of program is {}".format(current.depth))
log_and_print("Creating children for current node/program")
children_nodes = graph.get_all_children(current)
# print(children_nodes)
# prune if more than self.max_num_children
truncated_children = []
symbolic_children = []
if len(children_nodes) > graph.max_num_children:
#keep all neural ones
for c in children_nodes:
is_neural = not graph.is_fully_symbolic(c.program)
if is_neural:
truncated_children.append(c)
else:
symbolic_children.append(c)
n = len(truncated_children)
if n < graph.max_num_children:
#get weights for each child
# if print_program(current.program).split('(')[-1].split(')')[0] == 'AtomToAtomModule':
# weights = []
# # print(weights_dict)
# for c in symbolic_children:
# diff_node = print_program(c.program).split('(')[-2]
# # print(print_program(c.program))
# # print(diff_node)
# # try:
# # weights.append(weights_dict[diff_node])
# # except IndexError:
# # print('tutu')
# # weights.append(0)
# else: #weight equally
# weights = [1] * len(symbolic_children)
# #make into probabilities
# sum_h = sum(weights)
# probs = [i/sum_h for i in weights]
# print(probs)
picked_children = np.random.choice(symbolic_children, graph.max_num_children - n)
#p=probs
truncated_children.extend(picked_children)
# truncated_children.extend(random.sample(symbolic_children, k=graph.max_num_children-n)) # sample without replacement
else:
print(truncated_children)
truncated_children = random.sample(truncated_children, k=graph.max_num_children)
children_nodes = truncated_children
print(children_nodes)
#todo if theres more neural children than alloewd....
log_and_print("{} total children to train for current node".format(len(children_nodes)))
for child_node in children_nodes:
child_start_time = time.time()
log_and_print("Training child program: {}".format(print_program(child_node.program, ignore_constants=(not verbose))))
is_neural = not graph.is_fully_symbolic(child_node.program) #mcheng is not complete
child_node.score, l, m = execute_and_train_with_full(base_program_name, hole_node_ind, child_node.program, validset, trainset, train_config,
graph.output_type, graph.output_size, neural=is_neural, device=device)
## print losses and 1-f1 score for training
# plt.close()
# plt.figure()
# plt.plot(l[2:])
# plt.title("losses %s" % print_program(child_node.program, ignore_constants=(not verbose)))
# plt.savefig("%s/losses_%s.png" % (timestamp,print_program(child_node.program, ignore_constants=(not verbose))))
# plt.close()
# plt.figure()
# plt.plot(m[2:])
# plt.title("f1 %s" %print_program(child_node.program, ignore_constants=(not verbose)))
# plt.savefig("%s/f1_%s.png" % (timestamp,print_program(child_node.program, ignore_constants=(not verbose))))
log_and_print("Time to train child {:.3f}".format(time.time() - child_start_time))
num_children_trained += 1
log_and_print("{} total children trained".format(num_children_trained))
child_node.parent = current
child_node.children = []
order -= 1
child_node.order = order # insert order of exploration as tiebreaker for equivalent f-scores
current.children.append(child_node)
# computing path costs (f_scores)
child_f_score = child_node.cost + child_node.score # cost + heuristic
log_and_print("DEBUG: f-score {}".format(child_f_score))
if not is_neural and child_f_score < best_total_cost:
best_program = copy.deepcopy(child_node.program)
best_total_cost = child_f_score
best_programs_list.append({
"program" : best_program,
"struct_cost" : child_node.cost,
"score" : child_node.score,
"path_cost" : child_f_score,
"time" : time.time()-start_time
})
log_and_print("New BEST program found:")
print_program_dict(best_programs_list[-1])
if is_neural:
assert child_node.depth < graph.max_depth
child_tuple = (child_f_score, order, child_node)
frontier.add(child_tuple)
# clean up frontier
frontier.sort(tup_idx=0)
while len(frontier) > 0 and frontier.peek(-1)[0] > best_total_cost:
frontier.pop(-1)
log_and_print("Frontier length is: {}".format(len(frontier)))
log_and_print("Total time elapsed is {:.3f}".format(time.time()-start_time))
if best_program is None:
log_and_print("ERROR: no program found")
return best_programs_list
#todo look at train fn, todo understand why some of the nodes are neural
#starts with a neural fn.