def __init__(self, parameters=None, **kwargs):
"""Constructor
Parameters
----------
parameters : dict
Extraction parameters, extractor label as key and parameters as value.
"""
if parameters is None:
parameters = {}
kwargs.update(
{
'parameters': parameters
}
)
# Run ProcessorMixin init
ProcessorMixin.__init__(self, **kwargs)
# Run super init to call init of mixins too
super(RepositoryFeatureExtractorProcessor, self).__init__(**kwargs)
self.parameters = kwargs.get('parameters', {})
self.label_to_class = {}
for processor in get_class_inheritors(FeatureExtractorProcessor):
self.label_to_class[processor.label] = processor
def feature_extractor_factory(feature_extractor_label, **kwargs):
"""Function to get correct feature extractor class instance based on extractor label or class name.
Parameters
----------
feature_extractor_label : str
Class name or extractor label
Raises
------
NameError
Class does not exists
Returns
-------
Feature extractor class instance
"""
try:
feature_extractor_class = None
# Get all classes inherited from FeatureExtractor
class_list = get_class_inheritors(FeatureExtractor)
# Search correct feature extractor
for item in class_list:
if str(item.__name__) == feature_extractor_label:
feature_extractor_class = getattr(
importlib.import_module(str(item.__module__)),
feature_extractor_label)
break
elif hasattr(
item, 'label'
) and item.label == feature_extractor_label and item.__name__.endswith(
'Extractor'):
feature_extractor_class = getattr(
importlib.import_module(str(item.__module__)),
item.__name__)
break
# Valid feature extractor class not found, raise error
if not feature_extractor_class:
raise AttributeError
except AttributeError:
message = 'Invalid FeatureExtractor class name or extractor label given [{label}].'.format(
label=feature_extractor_label)
logger = logging.getLogger(__name__)
if not logger.handlers:
setup_logging()
logger.exception(message)
raise AttributeError(message)
return feature_extractor_class(**dict(kwargs))
def __setstate__(self, d):
self.parameters = d['parameters']
self.label_to_class = {}
for processor in get_class_inheritors(FeatureExtractorProcessor):
self.label_to_class[processor.label] = processor