def save_results(R_dict, individuals_fitness_per_generation):
meta_data_file = os.path.join(os.environ['FCM'], 'Examples', 'compute',
'genetic_algorithm_multinode', 'results',
'metadata.json')
id = str(random.randint(0, 1e16))
results_loc = os.path.join(
'Examples/compute/genetic_algorithm_multinode/results',
main.args.dataset, id)
comments = main.args.comment
meta_data_result = manager_results.metadata_template(
id, main.args.dataset, results_loc, comments)
params = main.args.__dict__
# Save dictionary of ensemble results
manager_results.save_results(meta_data_file, meta_data_result, params,
R_dict)
# Additionally save ensemble results per generation (list)
io.save_pickle(
os.path.join(os.environ['FCM'], results_loc,
'individuals_fitness_per_generation.pkl'),
individuals_fitness_per_generation)
for th0 in np.arange(0, 1+step_th, step_th):
for ic1 in range(ic0+1, len(models_paths)):
c1 = models_paths[ic1]
#for th2 in np.arange(0.2, 1, step_th):
# for ic2 in range(ic1+1, len(models)):
# c2 = models[ic2]
r = build_evaluate_chain([c0, c1], [th0])
sysid = "%s-%f-%s" % (c0.split('/')[-1], th0, c1.split('/')[-1])
print(sysid)
records[sysid] = r
# Crear la meta_data
meta_data_file = os.path.join(os.environ['FCM'],
'Examples',
'compute',
'fully_connected_chain',
'results',
'metadata.json')
id = str(random.randint(0, 1e16))
results_loc = os.path.join('Examples/compute/fully_connected_chain/results', dataset, id)
meta_data_result = manager_results.metadata_template(id, dataset, results_loc, args.comment)
# Obtenir el diccionari de params
params = args.__dict__
# Guardar els resultats en la carpeta del dataset
manager_results.save_results(meta_data_file, meta_data_result, params, records)
records = {}
hvolume_current = compute_hvolume(obj)
# Info about current generation
print("Generation %d" % iteration)
print("Hyper-volume %f" % hvolume_current)
print("TIME: Seconds per generation: %f " % (time.time() - start))
# Save the results
import Examples.metadata_manager_results as manager_results
meta_data_file = os.path.join(os.environ['FCM'], 'Examples', 'compute',
'bagging_boosting_of_chains_GA', 'results',
'metadata.json')
id = str(random.randint(0, 1e16))
results_loc = os.path.join(
'Examples/compute/bagging_boosting_of_chains_GA/results',
main.args.dataset, id)
comments = main.args.comment
meta_data_result = manager_results.metadata_template(
id, main.args.dataset, results_loc, comments)
# Save the ensemble evaluation results
R_dict_old.update(R_dict)
params = main.args.__dict__
manager_results.save_results(meta_data_file, meta_data_result, params,
R_dict_old)
io.save_pickle(
os.path.join(os.environ['FCM'], results_loc,
'individuals_fitness_per_generation.pkl'),
individuals_fitness_per_generation)
file = m
model = make.make_classifier(m, file)
sys.add_classifier(model)
classifiers_ids.append(model.id)
merger = make.make_merger("MERGER", classifiers_ids, merge_type=protocol)
sys.add_merger(merger)
sys.set_start(merger.id)
r = eval.evaluate(sys, sys.get_start(), phases=["test"])
results[generate_system_id(sys)] = r
# Save the evaluation results
import Examples.metadata_manager_results as manager_results
meta_data_file = os.path.join(os.environ['FCM'],
'Examples',
'compute',
'merger_combinations',
'results',
'metadata.json')
id = str(random.randint(0, 1e16))
results_loc = os.path.join('Examples/compute/merger_combinations/results', dataset, id)
meta_data_result = manager_results.metadata_template(id, dataset, results_loc, "")
# Save the ensemble evaluation results
params = args.__dict__
manager_results.save_results(meta_data_file, meta_data_result, params, results)