def suggest_via_metalearning(meta_base, dataset_name, metric, task, sparse,
num_initial_configurations):
logger = get_logger('autosklearn.metalearning.mismbo')
if task == MULTILABEL_CLASSIFICATION:
task = MULTICLASS_CLASSIFICATION
task = TASK_TYPES_TO_STRING[task]
logger.warning(task)
start = time.time()
ml = MetaLearningOptimizer(
dataset_name=dataset_name,
configuration_space=meta_base.configuration_space,
meta_base=meta_base,
distance='l1',
seed=1,
)
logger.info('Reading meta-data took %5.2f seconds', time.time() - start)
runs = ml.metalearning_suggest_all(exclude_double_configurations=True)
return runs[:num_initial_configurations]
def suggest_via_metalearning(
meta_base, dataset_name, metric, task, sparse,
num_initial_configurations):
logger = get_logger('autosklearn.metalearning.mismbo')
if task == MULTILABEL_CLASSIFICATION:
task = MULTICLASS_CLASSIFICATION
task = TASK_TYPES_TO_STRING[task]
logger.warning(task)
start = time.time()
ml = MetaLearningOptimizer(
dataset_name=dataset_name,
configuration_space=meta_base.configuration_space,
meta_base=meta_base,
distance='l1',
seed=1,)
logger.info('Reading meta-data took %5.2f seconds',
time.time() - start)
runs = ml.metalearning_suggest_all(exclude_double_configurations=True)
return runs[:num_initial_configurations]
def create_metalearning_string_for_smac_call(metafeatures_labels,
metafeatures_encoded_labels,
configuration_space, dataset_name,
metric, task, sparse,
num_initial_configurations,
metadata_directory):
"""
:param metafeatures_labels:
:param metafeatures_encoded_labels:
:param configuration_space:
:param dataset_name:
:param metric:
:param task:
:param sparse:
:param num_initial_configurations:
:param metadata_directory:
:return:
"""
logger = get_logger('autosklearn.metalearning.mismbo')
task = task if task != MULTILABEL_CLASSIFICATION else MULTICLASS_CLASSIFICATION
task = TASK_TYPES_TO_STRING[task]
if metafeatures_encoded_labels is None or \
metafeatures_labels is None:
raise ValueError('Please call '
'calculate_metafeatures_encoded_labels and '
'calculate_metafeatures_with_labels first!')
logger.warning(task)
current_directory = os.path.dirname(__file__)
if metadata_directory is None:
metadata_directory = os.path.join(
current_directory, 'files',
'%s_%s_%s' % (METRIC_TO_STRING[metric], task,
'sparse' if sparse is True else 'dense'))
logger.warning(metadata_directory)
# Concatenate the metafeatures!
mf = metafeatures_labels
mf.metafeature_values.update(
metafeatures_encoded_labels.metafeature_values)
metafeatures_subset = subsets['all']
metafeatures_subset.difference_update(EXCLUDE_META_FUTURES)
metafeatures_subset = list(metafeatures_subset)
start = time.time()
ml = MetaLearningOptimizer(dataset_name=dataset_name + SENTINEL,
configuration_space=configuration_space,
aslib_directory=metadata_directory,
distance='l1',
seed=1,
use_features=metafeatures_subset,
subset='all')
logger.info('Reading meta-data took %5.2f seconds', time.time() - start)
# TODO This is hacky, I must find a different way of adding a new
# dataset!
ml.meta_base.add_dataset(dataset_name + SENTINEL, mf)
runs = ml.metalearning_suggest_all(exclude_double_configurations=True)
# = Convert these configurations into the SMAC CLI configuration format
smac_initial_configuration_strings = []
for configuration in runs[:num_initial_configurations]:
smac_initial_configuration_strings.append(
convert_conf2smac_string(configuration))
return smac_initial_configuration_strings
def create_metalearning_string_for_smac_call(
metafeatures_labels, metafeatures_encoded_labels,
configuration_space, dataset_name, metric, task, sparse,
num_initial_configurations, metadata_directory):
"""
:param metafeatures_labels:
:param metafeatures_encoded_labels:
:param configuration_space:
:param dataset_name:
:param metric:
:param task:
:param sparse:
:param num_initial_configurations:
:param metadata_directory:
:return:
"""
logger = get_logger('autosklearn.metalearning.mismbo')
task = TASK_TYPES_TO_STRING[task]
if metafeatures_encoded_labels is None or \
metafeatures_labels is None:
raise ValueError('Please call '
'calculate_metafeatures_encoded_labels and '
'calculate_metafeatures_with_labels first!')
current_directory = os.path.dirname(__file__)
if metadata_directory is None:
metadata_directory = os.path.join(
current_directory, 'files',
'%s_%s_%s' % (METRIC_TO_STRING[metric], task,
'sparse' if sparse is True else 'dense'))
# Concatenate the metafeatures!
mf = metafeatures_labels
mf.metafeature_values.update(
metafeatures_encoded_labels.metafeature_values)
metafeatures_subset = subsets['all']
metafeatures_subset.difference_update(EXCLUDE_META_FUTURES)
metafeatures_subset = list(metafeatures_subset)
start = time.time()
ml = MetaLearningOptimizer(
dataset_name=dataset_name + SENTINEL,
configuration_space=configuration_space,
aslib_directory=metadata_directory,
distance='l1',
seed=1,
use_features=metafeatures_subset,
subset='all')
logger.info('Reading meta-data took %5.2f seconds',
time.time() - start)
# TODO This is hacky, I must find a different way of adding a new
# dataset!
ml.meta_base.add_dataset(dataset_name + SENTINEL, mf)
runs = ml.metalearning_suggest_all(exclude_double_configurations=True)
# = Convert these configurations into the SMAC CLI configuration format
smac_initial_configuration_strings = []
for configuration in runs[:num_initial_configurations]:
smac_initial_configuration_strings.append(
convert_conf2smac_string(configuration))
return smac_initial_configuration_strings
