def _get_initial_configuration(meta_features,
meta_features_encoded, basename, metric,
configuration_space,
task, metadata_directory,
initial_configurations_via_metalearning,
is_sparse,
watcher, logger):
task_name = 'InitialConfigurations'
watcher.start_task(task_name)
try:
initial_configurations = create_metalearning_string_for_smac_call(
meta_features,
meta_features_encoded,
configuration_space, basename, metric,
task,
is_sparse == 1,
initial_configurations_via_metalearning,
metadata_directory
)
except Exception as e:
logger.error(str(e))
logger.error(traceback.format_exc())
initial_configurations = []
watcher.stop_task(task_name)
return initial_configurations
def _get_initial_configuration(meta_features,
meta_features_encoded, basename, metric,
configuration_space,
task, metadata_directory,
initial_configurations_via_metalearning,
is_sparse,
watcher, logger):
task_name = 'InitialConfigurations'
watcher.start_task(task_name)
try:
initial_configurations = create_metalearning_string_for_smac_call(
meta_features,
meta_features_encoded,
configuration_space, basename, metric,
task,
is_sparse == 1,
initial_configurations_via_metalearning,
metadata_directory
)
except Exception as e:
logger.error(str(e))
logger.error(traceback.format_exc())
initial_configurations = []
watcher.stop_task(task_name)
return initial_configurations
def test_metalearning(self):
dataset_name = 'digits'
initial_challengers = {
ACC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
AUC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'none' "
"-classifier:__choice__ 'random_forest'",
BAC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
F1_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
PAC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'none' "
"-classifier:__choice__ 'random_forest'"
}
for metric in initial_challengers:
configuration_space = get_configuration_space(
{
'metric': metric,
'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False
},
include_preprocessors=['no_preprocessing'])
X_train, Y_train, X_test, Y_test = get_dataset(dataset_name)
categorical = [False] * X_train.shape[1]
meta_features_label = calc_meta_features(X_train, Y_train,
categorical, dataset_name)
meta_features_encoded_label = calc_meta_features_encoded(X_train,
Y_train,
categorical,
dataset_name)
initial_configuration_strings_for_smac = \
create_metalearning_string_for_smac_call(
meta_features_label,
meta_features_encoded_label,
configuration_space, dataset_name, metric,
MULTICLASS_CLASSIFICATION, False, 1, None)
print(metric)
print(initial_configuration_strings_for_smac[0])
self.assertTrue(initial_configuration_strings_for_smac[
0].startswith(initial_challengers[metric]))
def test_metalearning(self):
dataset_name = 'digits'
initial_challengers = {
ACC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
AUC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'none' "
"-classifier:__choice__ 'random_forest'",
BAC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
F1_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
PAC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'none' "
"-classifier:__choice__ 'random_forest'"
}
for metric in initial_challengers:
configuration_space = get_configuration_space(
{
'metric': metric,
'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False
},
include_preprocessors=['no_preprocessing'])
X_train, Y_train, X_test, Y_test = get_dataset(dataset_name)
categorical = [False] * X_train.shape[1]
meta_features_label = calc_meta_features(X_train, Y_train,
categorical, dataset_name)
meta_features_encoded_label = calc_meta_features_encoded(X_train,
Y_train,
categorical,
dataset_name)
initial_configuration_strings_for_smac = \
create_metalearning_string_for_smac_call(
meta_features_label,
meta_features_encoded_label,
configuration_space, dataset_name, metric,
MULTICLASS_CLASSIFICATION, False, 1, None)
print(metric)
print(initial_configuration_strings_for_smac[0])
self.assertTrue(initial_configuration_strings_for_smac[
0].startswith(initial_challengers[metric]))
