def evaluate_1stlayer_bandit(algorithms,
mode,
dataset='credit',
trial_num=200,
seed=1):
_start_time = time.time()
raw_data = load_data(dataset, datanode_returned=True)
bandit = FirstLayerBandit(trial_num,
algorithms,
raw_data,
output_dir='logs',
per_run_time_limit=per_run_time_limit,
dataset_name=dataset,
share_feature=mode,
seed=seed)
bandit.optimize()
print(bandit.final_rewards)
print(bandit.action_sequence)
time_cost = time.time() - _start_time
save_path = project_dir + 'data/shared_hmab_%d_%s_%d_%d_%d.pkl' % (
mode, dataset, trial_num, len(algorithms), seed)
with open(save_path, 'wb') as f:
data = [
bandit.final_rewards, bandit.time_records, bandit.action_sequence,
time_cost
]
pickle.dump(data, f)
return time_cost
def evaluate_hmab(algorithms, run_id, dataset='credit', trial_num=200, seed=1, eval_type='holdout', enable_ens=False):
task_id = '%s-hmab-%d-%d' % (dataset, len(algorithms), trial_num)
_start_time = time.time()
raw_data, test_raw_data = load_train_test_data(dataset)
bandit = FirstLayerBandit(trial_num, algorithms, raw_data,
output_dir='logs/%s/' % task_id,
per_run_time_limit=per_run_time_limit,
dataset_name='%s-%d' % (dataset, run_id),
seed=seed,
eval_type=eval_type)
bandit.optimize()
time_cost = int(time.time() - _start_time)
print(bandit.final_rewards)
print(bandit.action_sequence)
validation_accuracy = np.max(bandit.final_rewards)
test_accuracy = bandit.score(test_raw_data, metric_func=balanced_accuracy)
test_accuracy_with_ens = EnsembleBuilder(bandit).score(test_raw_data, metric_func=balanced_accuracy)
print('Dataset : %s' % dataset)
print('Validation/Test score : %f - %f' % (validation_accuracy, test_accuracy))
print('Test score with ensem : %f' % test_accuracy_with_ens)
save_path = save_dir + '%s-%d.pkl' % (task_id, run_id)
with open(save_path, 'wb') as f:
stats = [time_cost, test_accuracy_with_ens, bandit.time_records, bandit.final_rewards]
pickle.dump([validation_accuracy, test_accuracy, stats], f)
return time_cost
def evaluate_1stlayer_bandit(run_id, B, algorithms, dataset='credit', trial_num=200, seed=1):
_start_time = time.time()
raw_data = load_data(dataset, datanode_returned=True)
bandit = FirstLayerBandit(trial_num, algorithms, raw_data,
output_dir='logs',
per_run_time_limit=per_run_time_limit,
dataset_name=dataset,
eval_type='holdout',
seed=seed)
bandit.B = B
bandit.optimize(strategy='discounted_ucb')
print(bandit.final_rewards)
print(bandit.action_sequence)
time_cost = time.time() - _start_time
save_folder = project_dir + 'data/1stlayer-mab/'
if not os.path.exists(save_folder):
os.makedirs(save_folder)
save_path = save_folder + 'eval_ducb_%.4f_%s_%d_%d_%d.pkl' % (
B, dataset, run_id, trial_num, len(algorithms))
with open(save_path, 'wb') as f:
data = [bandit.final_rewards, bandit.time_records, bandit.action_sequence, time_cost]
pickle.dump(data, f)
return time_cost
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_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 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 evaluate_hmab(algorithms,
run_id,
dataset='credit',
trial_num=200,
n_jobs=1,
meta_configs=0,
seed=1,
eval_type='holdout'):
task_id = '%s-hmab-%d-%d' % (dataset, len(algorithms), trial_num)
_start_time = time.time()
raw_data, test_raw_data = load_train_test_data(dataset)
bandit = FirstLayerBandit(trial_num,
algorithms,
raw_data,
output_dir='logs/%s/' % task_id,
per_run_time_limit=per_run_time_limit,
dataset_name='%s-%d' % (dataset, run_id),
n_jobs=n_jobs,
meta_configs=meta_configs,
seed=seed,
eval_type=eval_type)
bandit.optimize()
time_cost = int(time.time() - _start_time)
print(bandit.final_rewards)
print(bandit.action_sequence)
validation_accuracy = np.max(bandit.final_rewards)
# validation_accuracy_without_ens = bandit.validate()
# assert np.isclose(validation_accuracy, validation_accuracy_without_ens)
test_accuracy_with_ens = EnsembleBuilder(
bandit, n_jobs=n_jobs).score(test_raw_data)
print('Dataset : %s' % dataset)
print('Validation score without ens: %f' % validation_accuracy)
print("Test score with ensemble : %f" % test_accuracy_with_ens)
save_path = save_dir + '%s-%d.pkl' % (task_id, run_id)
with open(save_path, 'wb') as f:
stats = [time_cost, 0., bandit.time_records, bandit.final_rewards]
pickle.dump([validation_accuracy, test_accuracy_with_ens, stats], f)
del bandit
return time_cost
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 ensemble_implementation_examples(bandit: FirstLayerBandit, test_data: DataNode):
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import accuracy_score
from autosklearn.metrics import accuracy
n_best = 20
stats = bandit.fetch_ensemble_members(test_data)
seed = stats['split_seed']
test_size = 0.2
train_predictions = []
test_predictions = []
for algo_id in bandit.nbest_algo_ids:
X, y = stats[algo_id]['train_dataset'].data
sss = StratifiedShuffleSplit(n_splits=1, test_size=test_size, random_state=1)
for train_index, test_index in sss.split(X, y):
X_train, X_valid = X[train_index], X[test_index]
y_train, y_valid = y[train_index], y[test_index]
X_test, y_test = stats[algo_id]['test_dataset'].data
configs = stats[algo_id]['configurations']
performance = stats[algo_id]['performance']
best_index = np.argsort(-np.array(performance))
best_configs = [configs[i] for i in best_index[:n_best]]
for config in best_configs:
try:
# Build the ML estimator.
_, estimator = get_estimator(config)
# print(X_train.shape, X_test.shape)
estimator.fit(X_train, y_train)
y_valid_pred = estimator.predict_proba(X_valid)
y_test_pred = estimator.predict_proba(X_test)
train_predictions.append(y_valid_pred)
test_predictions.append(y_test_pred)
except Exception as e:
print(str(e))
es = EnsembleSelection(ensemble_size=50, task_type=1,
metric=accuracy, random_state=np.random.RandomState(seed))
assert len(train_predictions) == len(test_predictions)
es.fit(train_predictions, y_valid, identifiers=None)
y_pred = es.predict(test_predictions)
y_pred = np.argmax(y_pred, axis=-1)
test_score = accuracy_score(y_test, y_pred)
return test_score
def evaluate_imbalanced(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)
cls_task_type = BINARY_CLS if len(set(
train_data.data[1])) == 2 else MULTICLASS_CLS
# ACC or Balanced_ACC
balanced_acc_metric = make_scorer(balanced_accuracy)
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,
opt_algo=opt_algo,
metric=balanced_acc_metric,
fe_algo='bo',
seed=seed)
bandit.optimize()
model_desc = [
bandit.nbest_algo_ids, bandit.optimal_algo_id, bandit.final_rewards,
bandit.action_sequence
]
time_taken = time.time() - _start_time
validation_accuracy = np.max(bandit.final_rewards)
best_pred = bandit._best_predict(test_data)
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, test_accuracy_with_ens,
time_taken, model_desc
]
print(model_desc)
print(data[:4])
save_path = project_dir + 'data/%s_%s_%s_%d_%d_%d_%d.pkl' % (
hmab_flag, opt_algo, dataset, trial_num, len(algorithms), seed, run_id)
with open(save_path, 'wb') as f:
pickle.dump(data, f)
def evaluate_1stlayer_bandit(algorithms, run_id, dataset='credit', trial_num=200, n_jobs=1, meta_configs=0, seed=1):
task_id = '%s-hmab-%d-%d' % (dataset, len(algorithms), trial_num)
_start_time = time.time()
raw_data, test_raw_data = load_train_test_data(dataset, random_state=seed)
bandit = FirstLayerBandit(trial_num, algorithms, raw_data,
output_dir='logs/%s/' % task_id,
per_run_time_limit=per_run_time_limit,
dataset_name='%s-%d' % (dataset, run_id),
n_jobs=n_jobs,
meta_configs=meta_configs,
seed=seed,
eval_type='holdout')
bandit.optimize()
time_cost = int(time.time() - _start_time)
print(bandit.final_rewards)
print(bandit.action_sequence)
validation_accuracy_without_ens0 = np.max(bandit.final_rewards)
validation_accuracy_without_ens1 = bandit.validate()
assert np.isclose(validation_accuracy_without_ens0, validation_accuracy_without_ens1)
test_accuracy_without_ens = bandit.score(test_raw_data)
# For debug.
mode = True
if mode:
test_accuracy_with_ens0 = ensemble_implementation_examples(bandit, test_raw_data)
test_accuracy_with_ens1 = EnsembleBuilder(bandit).score(test_raw_data)
print('Dataset : %s' % dataset)
print('Validation score without ens: %f - %f' % (
validation_accuracy_without_ens0, validation_accuracy_without_ens1))
print("Test score without ensemble : %f" % test_accuracy_without_ens)
print("Test score with ensemble : %f - %f" % (test_accuracy_with_ens0, test_accuracy_with_ens1))
save_path = save_dir + '%s-%d.pkl' % (task_id, run_id)
with open(save_path, 'wb') as f:
stats = [time_cost, test_accuracy_with_ens0, test_accuracy_with_ens1, test_accuracy_without_ens]
pickle.dump([validation_accuracy_without_ens0, test_accuracy_with_ens1, stats], f)
del bandit
return time_cost
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)
class AutoML(object):
def __init__(self,
time_limit=300,
dataset_name='default_name',
amount_of_resource=None,
task_type=None,
metric='bal_acc',
include_algorithms=None,
ensemble_method='ensemble_selection',
enable_meta_algorithm_selection=True,
per_run_time_limit=150,
ensemble_size=50,
evaluation='holdout',
output_dir="logs",
logging_config=None,
random_state=1,
n_jobs=1):
self.metric_id = metric
self.metric = get_metric(self.metric_id)
self.dataset_name = dataset_name
self.time_limit = time_limit
self.seed = random_state
self.per_run_time_limit = per_run_time_limit
self.output_dir = output_dir
self.logging_config = logging_config
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
self.logger = self._get_logger(self.dataset_name)
self.evaluation_type = evaluation
self.amount_of_resource = amount_of_resource
self.ensemble_method = ensemble_method
self.ensemble_size = ensemble_size
self.enable_meta_algorithm_selection = enable_meta_algorithm_selection
self.task_type = task_type
self.n_jobs = n_jobs
self.solver = None
if include_algorithms is not None:
self.include_algorithms = include_algorithms
else:
if task_type in CLS_TASKS:
self.include_algorithms = list(classification_algorithms)
elif task_type in REG_TASKS:
self.include_algorithms = list(regression_algorithms)
else:
raise ValueError("Unknown task type %s" % task_type)
if ensemble_method is not None and ensemble_method not in ensemble_list:
raise ValueError("%s is not supported for ensemble!" %
ensemble_method)
def _get_logger(self, name):
logger_name = 'SolnML-%s(%d)' % (name, self.seed)
setup_logger(
os.path.join(self.output_dir, '%s.log' % str(logger_name)),
self.logging_config,
)
return get_logger(logger_name)
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 refit(self):
self.solver.refit()
def predict_proba(self, test_data: DataNode):
return self.solver.predict_proba(test_data)
def predict(self, test_data: DataNode):
return self.solver.predict(test_data)
def score(self, test_data: DataNode, metric_func=None):
if metric_func is None:
metric_func = self.metric
return metric_func(self, test_data, test_data.data[1])
def get_ens_model_info(self):
if self.ensemble_method is not None:
return self.solver.es.get_ens_model_info()
else:
return None
def fit(self, train_data: DataNode, **kwargs):
"""
This function includes this following two procedures.
1. tune each algorithm's hyperparameters.
2. engineer each algorithm's features automatically.
:param train_data:
:return:
"""
# Check whether this dataset is balanced or not.
# if self.task_type in CLS_TASKS and is_imbalanced_dataset(train_data):
# self.logger.info('Input dataset is imbalanced!')
# train_data = DataBalancer().operate(train_data)
dataset_id = kwargs.get('dataset_id', None)
inner_opt_algorithm = kwargs.get('opt_strategy', 'alter_hpo')
self.logger.info('Optimization algorithm in 2rd bandit: %s' %
inner_opt_algorithm)
if self.enable_meta_algorithm_selection:
try:
n_algo_recommended = 5
meta_datasets = kwargs.get('meta_datasets', None)
self.logger.info(
'Executing Meta-Learning based Algorithm Recommendation.')
alad = RankNetAdvisor(task_type=self.task_type,
n_algorithm=n_algo_recommended,
metric=self.metric_id)
alad.fit()
model_candidates = alad.fetch_algorithm_set(
dataset_id, datanode=train_data)
include_models = list()
for algo in model_candidates:
if algo in self.include_algorithms and len(
include_models) < n_algo_recommended:
include_models.append(algo)
# if 'logistic_regression' in include_models:
# include_models.remove('logistic_regression')
# if 'adaboost' not in include_models:
# include_models.append('adaboost')
# include_models = ['extra_trees', 'adaboost', 'liblinear_svc', 'random_forest',
# 'libsvm_svc', 'lightgbm']
self.include_algorithms = include_models
self.logger.info('Final Algorithms Recommended: [%s]' %
','.join(self.include_algorithms))
except Exception as e:
self.logger.error(
"Meta-Learning based Algorithm Recommendation FAILED: %s."
% str(e))
traceback.print_exc(file=sys.stdout)
self.solver = FirstLayerBandit(
self.task_type,
self.amount_of_resource,
self.include_algorithms,
train_data,
include_preprocessors=self.include_preprocessors,
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=inner_opt_algorithm,
metric=self.metric,
enable_fe=self.enable_fe,
fe_algo='bo',
seed=self.seed,
time_limit=self.time_limit,
eval_type=self.evaluation_type,
output_dir=self.output_dir)
self.solver.optimize()
class AutoML(object):
def __init__(self,
time_limit=300,
dataset_name='default_name',
amount_of_resource=None,
task_type=None,
metric='bal_acc',
include_algorithms=None,
include_preprocessors=None,
ensemble_method='ensemble_selection',
enable_meta_algorithm_selection=True,
enable_fe=True,
per_run_time_limit=150,
ensemble_size=50,
evaluation='holdout',
output_dir="logs",
logging_config=None,
random_state=1,
n_jobs=1):
self.metric_id = metric
self.metric = get_metric(self.metric_id)
self.dataset_name = dataset_name
self.time_limit = time_limit
self.seed = random_state
self.per_run_time_limit = per_run_time_limit
self.output_dir = output_dir
self.logging_config = logging_config
self.logger = self._get_logger(self.dataset_name)
self.evaluation_type = evaluation
self.include_preprocessors = include_preprocessors
self.amount_of_resource = amount_of_resource
self.ensemble_method = ensemble_method
self.ensemble_size = ensemble_size
self.enable_meta_algorithm_selection = enable_meta_algorithm_selection
self.enable_fe = enable_fe
self.task_type = task_type
self.n_jobs = n_jobs
self.solver = None
# Disable meta learning
if self.include_preprocessors is not None:
self.enable_meta_algorithm_selection = False
if include_algorithms is not None:
self.include_algorithms = include_algorithms
else:
if task_type in CLS_TASKS:
if task_type in [IMG_CLS, TEXT_CLS]:
raise ValueError(
'Please use AutoDL module, instead of AutoML.')
else:
self.include_algorithms = list(classification_algorithms)
elif task_type in RGS_TASKS:
self.include_algorithms = list(regression_algorithms)
else:
raise ValueError("Unknown task type %s" % task_type)
if ensemble_method is not None and ensemble_method not in ensemble_list:
raise ValueError("%s is not supported for ensemble!" %
ensemble_method)
def _get_logger(self, name):
logger_name = 'SolnML-%s(%d)' % (name, self.seed)
setup_logger(
os.path.join(self.output_dir, '%s.log' % str(logger_name)),
self.logging_config,
)
return get_logger(logger_name)
def fit(self, train_data: DataNode, **kwargs):
"""
This function includes this following two procedures.
1. tune each algorithm's hyperparameters.
2. engineer each algorithm's features automatically.
:param train_data:
:return:
"""
# Check whether this dataset is balanced or not.
# if self.task_type in CLS_TASKS and is_imbalanced_dataset(train_data):
# self.logger.info('Input dataset is imbalanced!')
# train_data = DataBalancer().operate(train_data)
dataset_id = kwargs.get('dataset_id', None)
inner_opt_algorithm = kwargs.get('opt_strategy', 'alter_hpo')
self.logger.info('Optimization algorithm in 2rd bandit: %s' %
inner_opt_algorithm)
if self.enable_meta_algorithm_selection:
try:
n_algo_recommended = 5
meta_datasets = kwargs.get('meta_datasets', None)
self.logger.info(
'Executing Meta-Learning based Algorithm Recommendation.')
alad = RankNetAdvisor(task_type=self.task_type,
n_algorithm=n_algo_recommended,
metric=self.metric_id)
alad.fit()
model_candidates = alad.fetch_algorithm_set(
dataset_id, datanode=train_data)
include_models = list()
for algo in model_candidates:
if algo in self.include_algorithms and len(
include_models) < n_algo_recommended:
include_models.append(algo)
# if 'logistic_regression' in include_models:
# include_models.remove('logistic_regression')
# if 'adaboost' not in include_models:
# include_models.append('adaboost')
# include_models = ['extra_trees', 'adaboost', 'liblinear_svc', 'random_forest',
# 'libsvm_svc', 'lightgbm']
self.include_algorithms = include_models
self.logger.info('Final Algorithms Recommended: [%s]' %
','.join(self.include_algorithms))
except Exception as e:
self.logger.error(
"Meta-Learning based Algorithm Recommendation FAILED: %s."
% str(e))
traceback.print_exc(file=sys.stdout)
self.solver = FirstLayerBandit(
self.task_type,
self.amount_of_resource,
self.include_algorithms,
train_data,
include_preprocessors=self.include_preprocessors,
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=inner_opt_algorithm,
metric=self.metric,
enable_fe=self.enable_fe,
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 refit(self):
self.solver.refit()
def predict_proba(self, test_data: DataNode):
return self.solver.predict_proba(test_data)
def predict(self, test_data: DataNode):
return self.solver.predict(test_data)
def score(self, test_data: DataNode, metric_func=None):
if metric_func is None:
metric_func = self.metric
return metric_func(self, test_data, test_data.data[1])
def get_ens_model_info(self):
if self.ensemble_method is not None:
return self.solver.es.get_ens_model_info()
else:
return None
def get_val_stats(self):
return self.solver.get_stats()