def evaluate_hmab(algorithms,
dataset,
run_id,
trial_num,
seed,
time_limit=1200):
print('%s-%s-%d: %d' % (hmab_flag, dataset, run_id, time_limit))
_start_time = time.time()
train_data, test_data = load_train_test_data(dataset,
task_type=MULTICLASS_CLS)
cls_task_type = BINARY_CLS if len(set(
train_data.data[1])) == 2 else MULTICLASS_CLS
balanced_acc_metric = make_scorer(balanced_accuracy)
if is_unbalanced_dataset(train_data):
from solnml.components.feature_engineering.transformations.preprocessor.smote_balancer import DataBalancer
train_data = DataBalancer().operate(train_data)
bandit = FirstLayerBandit(cls_task_type,
trial_num,
algorithms,
train_data,
output_dir='logs',
per_run_time_limit=per_run_time_limit,
dataset_name=dataset,
ensemble_size=50,
inner_opt_algorithm=opt_algo,
metric=balanced_acc_metric,
fe_algo='bo',
seed=seed,
time_limit=time_limit,
eval_type='holdout')
bandit.optimize()
time_taken = time.time() - _start_time
model_desc = [
bandit.nbest_algo_ids, bandit.optimal_algo_id, bandit.final_rewards,
bandit.action_sequence
]
validation_accuracy = np.max(bandit.final_rewards)
best_pred = bandit._best_predict(test_data)
test_accuracy = balanced_accuracy(test_data.data[1], best_pred)
bandit.refit()
es_pred = bandit._es_predict(test_data)
test_accuracy_with_ens = balanced_accuracy(test_data.data[1], es_pred)
data = [
dataset, validation_accuracy, test_accuracy, test_accuracy_with_ens,
time_taken, model_desc
]
print(model_desc)
print(data)
save_path = project_dir + '%s_%s_%s_%d_%d_%d_%d_%d.pkl' % (
hmab_flag, opt_algo, dataset, trial_num, len(algorithms), seed, run_id,
time_limit)
with open(save_path, 'wb') as f:
pickle.dump(data, f)
def operate(self, input_datanode, target_fields=None):
output_datanode = input_datanode.copy_()
output_datanode.trans_hist.append(self.type)
if is_unbalanced_dataset(input_datanode):
output_datanode.data_balance = 1
return output_datanode
def evaluate_hmab(algorithms, dataset, run_id, trial_num, seed, time_limit=1200):
print('%s-%s-%d: %d' % (hmab_flag, dataset, run_id, time_limit))
exclude_datasets = ['gina_prior2', 'pc2', 'abalone', 'wind', 'waveform-5000(2)',
'page-blocks(1)', 'winequality_white', 'pollen']
alad = AlgorithmAdvisor(task_type=MULTICLASS_CLS, n_algorithm=9,
metric='bal_acc', exclude_datasets=exclude_datasets)
n_algo = 5
assert dataset in exclude_datasets
meta_infos = alad.fit_meta_learner()
assert dataset not in meta_infos
model_candidates = alad.fetch_algorithm_set(dataset)
include_models = list()
print(model_candidates)
for algo in model_candidates:
if algo in algorithms and len(include_models) < n_algo:
include_models.append(algo)
print('After algorithm recommendation', include_models)
_start_time = time.time()
train_data, test_data = load_train_test_data(dataset, task_type=MULTICLASS_CLS)
cls_task_type = BINARY_CLS if len(set(train_data.data[1])) == 2 else MULTICLASS_CLS
balanced_acc_metric = make_scorer(balanced_accuracy)
if is_unbalanced_dataset(train_data):
from solnml.components.feature_engineering.transformations.balancer.smote_balancer import DataBalancer
train_data = DataBalancer().operate(train_data)
bandit = FirstLayerBandit(cls_task_type, trial_num, include_models, train_data,
output_dir='logs',
per_run_time_limit=per_run_time_limit,
dataset_name=dataset,
ensemble_size=50,
inner_opt_algorithm=opt_algo,
metric=balanced_acc_metric,
fe_algo='bo',
seed=seed,
time_limit=time_limit,
eval_type='holdout')
bandit.optimize()
time_taken = time.time() - _start_time
model_desc = [bandit.nbest_algo_ids, bandit.optimal_algo_id, bandit.final_rewards, bandit.action_sequence]
validation_accuracy = np.max(bandit.final_rewards)
best_pred = bandit._best_predict(test_data)
test_accuracy = balanced_accuracy(test_data.data[1], best_pred)
bandit.refit()
es_pred = bandit._es_predict(test_data)
test_accuracy_with_ens = balanced_accuracy(test_data.data[1], es_pred)
data = [dataset, validation_accuracy, test_accuracy, test_accuracy_with_ens, time_taken, model_desc]
print(model_desc)
print(data)
save_path = project_dir + '%s_%s_%s_%d_%d_%d_%d_%d.pkl' % (
hmab_flag, opt_algo, dataset, trial_num, len(algorithms), seed, run_id, time_limit)
with open(save_path, 'wb') as f:
pickle.dump(data, f)
def fit(self, train_data: DataNode, dataset_id=None):
"""
this function includes this following two procedures.
1. tune each algorithm's hyperparameters.
2. engineer each algorithm's features automatically.
:param train_data:
:return:
"""
if self.enable_meta_algorithm_selection:
try:
alad = AlgorithmAdvisor(task_type=self.task_type,
n_algorithm=9,
metric=self.metric_id)
n_algo = 5
model_candidates = alad.fetch_algorithm_set(
train_data, dataset_id=dataset_id)
include_models = list()
for algo in model_candidates:
if algo in self.include_algorithms and len(
include_models) < n_algo:
include_models.append(algo)
self.include_algorithms = include_models
self.logger.info(
'Executing meta-learning based algorithm recommendation!')
self.logger.info('Algorithms recommended: %s' %
','.join(self.include_algorithms))
except Exception as e:
self.logger.error("Meta-learning failed!")
# Check whether this dataset is balanced or not.
if self.task_type in CLS_TASKS and is_unbalanced_dataset(train_data):
# self.include_algorithms = imb_classication_algorithms
self.logger.info('Input dataset is imbalanced!')
train_data = DataBalancer().operate(train_data)
if self.amount_of_resource is None:
trial_num = len(self.include_algorithms) * 30
else:
trial_num = self.amount_of_resource
self.solver = FirstLayerBandit(
self.task_type,
trial_num,
self.include_algorithms,
train_data,
per_run_time_limit=self.per_run_time_limit,
dataset_name=self.dataset_name,
ensemble_method=self.ensemble_method,
ensemble_size=self.ensemble_size,
inner_opt_algorithm='fixed',
metric=self.metric,
fe_algo='bo',
seed=self.seed,
time_limit=self.time_limit,
eval_type=self.evaluation_type,
output_dir=self.output_dir)
self.solver.optimize()
def evaluate_autosklearn(algorithms, dataset, run_id, trial_num, seed, time_limit=1200):
print('%s-%s-%d: %d' % (hmab_flag, dataset, run_id, time_limit))
_start_time = time.time()
train_data, test_data = load_train_test_data(dataset, task_type=MULTICLASS_CLS)
cls_task_type = BINARY_CLS if len(set(train_data.data[1])) == 2 else MULTICLASS_CLS
balanced_acc_metric = make_scorer(balanced_accuracy)
if is_unbalanced_dataset(train_data):
from solnml.components.feature_engineering.transformations.balancer.smote_balancer import DataBalancer
train_data = DataBalancer().operate(train_data)
bandit = FirstLayerBandit(cls_task_type, trial_num, algorithms, train_data,
output_dir='logs',
per_run_time_limit=per_run_time_limit,
dataset_name=dataset,
ensemble_size=50,
inner_opt_algorithm=opt_algo,
metric=balanced_acc_metric,
fe_algo='bo',
seed=seed,
time_limit=time_limit,
eval_type='holdout')
while time.time() - _start_time < time_limit:
bandit.sub_bandits['random_forest'].optimizer['hpo'].iterate()
# bandit.optimize()
# fe_exp_output = bandit.sub_bandits['random_forest'].exp_output['fe']
# hpo_exp_output = bandit.sub_bandits['random_forest'].exp_output['hpo']
fe_exp_output = dict()
hpo_exp_output = bandit.sub_bandits['random_forest'].optimizer['hpo'].exp_output
inc_config = bandit.sub_bandits['random_forest'].optimizer['hpo'].incumbent_config.get_dictionary()
inc_config.pop('estimator')
from solnml.components.models.classification.random_forest import RandomForest
rf = RandomForest(**inc_config)
rf.fit(train_data.data[0], train_data.data[1])
validation_accuracy = bandit.sub_bandits['random_forest'].optimizer['hpo'].incumbent_perf
best_pred = rf.predict(test_data.data[0])
test_accuracy = balanced_accuracy(test_data.data[1], best_pred)
# es_pred = bandit._es_predict(test_data)
# test_accuracy_with_ens = balanced_accuracy(test_data.data[1], es_pred)
data = [dataset, validation_accuracy, test_accuracy, fe_exp_output, hpo_exp_output,
_start_time]
save_path = project_dir + '%s_%s_%s_%d_%d_%d_%d_%d.pkl' % (
ausk_flag, opt_algo, dataset, trial_num, len(algorithms), seed, run_id, time_limit)
with open(save_path, 'wb') as f:
pickle.dump(data, f)
del_path = './logs/'
for i in os.listdir(del_path):
file_data = del_path + "/" + i
if os.path.isfile(file_data):
os.remove(file_data)
def __init__(self, name, task_type, datanode, seed=1):
self.name = name
self._seed = seed
self.root_node = datanode.copy_()
self.incumbent = self.root_node
self.task_type = task_type
self.graph = TransformationGraph()
self.graph.add_node(self.root_node)
self.time_budget = None
self.maximum_evaluation_num = None
self.logger = get_logger(self.__module__ + "." +
self.__class__.__name__)
self.if_bal = False if is_unbalanced_dataset(
data_node=datanode) else True
def evaluate_hmab(algorithms,
dataset,
run_id,
trial_num,
seed,
time_limit=1200):
print('%s-%s-%d: %d' % (hmab_flag, dataset, run_id, time_limit))
_start_time = time.time()
train_data, test_data = load_train_test_data(dataset,
task_type=MULTICLASS_CLS)
cls_task_type = BINARY_CLS if len(set(
train_data.data[1])) == 2 else MULTICLASS_CLS
balanced_acc_metric = make_scorer(balanced_accuracy)
if is_unbalanced_dataset(train_data):
from solnml.components.feature_engineering.transformations.preprocessor.smote_balancer import DataBalancer
train_data = DataBalancer().operate(train_data)
bandit = FirstLayerBandit(cls_task_type,
trial_num,
algorithms,
train_data,
output_dir='logs',
per_run_time_limit=per_run_time_limit,
dataset_name=dataset,
ensemble_size=50,
inner_opt_algorithm=opt_algo,
metric=balanced_acc_metric,
fe_algo='bo',
seed=seed,
time_limit=time_limit,
eval_type='partial')
# while time.time()-_start_time<time_limit:
# bandit.sub_bandits['random_forest'].optimizer['fe'].iterate()
# # print(bandit.sub_bandits['random_forest'].optimizer['hpo'].exp_output)
bandit.optimize()
fe_exp_output = bandit.sub_bandits['random_forest'].optimizer[
'fe'].exp_output
hpo_exp_output = bandit.sub_bandits['random_forest'].optimizer[
'hpo'].exp_output
validation_accuracy = np.max(bandit.final_rewards)
best_pred = bandit._best_predict(test_data)
test_accuracy = balanced_accuracy(test_data.data[1], best_pred)
bandit.refit()
es_pred = bandit._es_predict(test_data)
test_accuracy_with_ens = balanced_accuracy(test_data.data[1], es_pred)
data = [
dataset, validation_accuracy, test_accuracy, test_accuracy_with_ens,
fe_exp_output, hpo_exp_output, _start_time
]
save_path = project_dir + '%s_%s_%s_%d_%d_%d_%d_%d.pkl' % (
hmab_flag, opt_algo, dataset, trial_num, len(algorithms), seed, run_id,
time_limit)
with open(save_path, 'wb') as f:
pickle.dump(data, f)
del_path = './logs/'
for i in os.listdir(del_path):
file_data = del_path + "/" + i
if os.path.isfile(file_data):
os.remove(file_data)