def __init__(self, anomaly, api=None):
self.resource_id = None
self.sample_size = None
self.input_fields = None
self.mean_depth = None
self.expected_mean_depth = None
self.iforest = None
self.top_anomalies = None
self.id_fields = []
if not (isinstance(anomaly, dict) and 'resource' in anomaly and
anomaly['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_anomaly_id(anomaly)
if self.resource_id is None:
raise Exception(api.error_message(anomaly,
resource_type='anomaly',
method='get'))
query_string = ONLY_MODEL
anomaly = retrieve_resource(api, self.resource_id,
query_string=query_string)
else:
self.resource_id = get_anomaly_id(anomaly)
if 'object' in anomaly and isinstance(anomaly['object'], dict):
anomaly = anomaly['object']
self.sample_size = anomaly.get('sample_size')
self.input_fields = anomaly.get('input_fields')
self.id_fields = anomaly.get('id_fields', [])
if 'model' in anomaly and isinstance(anomaly['model'], dict):
ModelFields.__init__(self, anomaly['model'].get('fields'))
if ('top_anomalies' in anomaly['model'] and
isinstance(anomaly['model']['top_anomalies'], list)):
self.mean_depth = anomaly['model'].get('mean_depth')
status = get_status(anomaly)
if 'code' in status and status['code'] == FINISHED:
self.expected_mean_depth = None
if self.mean_depth is None or self.sample_size is None:
raise Exception("The anomaly data is not complete. "
"Score will"
" not be available")
else:
default_depth = (
2 * (DEPTH_FACTOR + \
math.log(self.sample_size - 1) - \
(float(self.sample_size - 1) / self.sample_size)))
self.expected_mean_depth = min(self.mean_depth,
default_depth)
iforest = anomaly['model'].get('trees', [])
if iforest:
self.iforest = [
AnomalyTree(anomaly_tree['root'], self.fields)
for anomaly_tree in iforest]
self.top_anomalies = anomaly['model']['top_anomalies']
else:
raise Exception("The anomaly isn't finished yet")
else:
raise Exception("Cannot create the Anomaly instance. Could not"
" find the 'top_anomalies' key in the"
" resource:\n\n%s" % anomaly['model'].keys())
def __init__(self, anomaly, api=None):
self.resource_id = None
self.sample_size = None
self.input_fields = None
self.mean_depth = None
self.expected_mean_depth = None
self.iforest = None
self.top_anomalies = None
self.id_fields = []
self.resource_id, anomaly = get_resource_dict( \
anomaly, "anomaly", api=api)
if 'object' in anomaly and isinstance(anomaly['object'], dict):
anomaly = anomaly['object']
self.sample_size = anomaly.get('sample_size')
self.input_fields = anomaly.get('input_fields')
self.id_fields = anomaly.get('id_fields', [])
if 'model' in anomaly and isinstance(anomaly['model'], dict):
ModelFields.__init__( \
self, anomaly['model'].get('fields'), \
missing_tokens=anomaly['model'].get('missing_tokens'))
if ('top_anomalies' in anomaly['model']
and isinstance(anomaly['model']['top_anomalies'], list)):
self.mean_depth = anomaly['model'].get('mean_depth')
status = get_status(anomaly)
if 'code' in status and status['code'] == FINISHED:
self.expected_mean_depth = None
if self.mean_depth is None or self.sample_size is None:
raise Exception("The anomaly data is not complete. "
"Score will"
" not be available")
else:
default_depth = (
2 * (DEPTH_FACTOR + \
math.log(self.sample_size - 1) - \
(float(self.sample_size - 1) / self.sample_size)))
self.expected_mean_depth = min(self.mean_depth,
default_depth)
iforest = anomaly['model'].get('trees', [])
if iforest:
self.iforest = [
AnomalyTree(anomaly_tree['root'], self.fields)
for anomaly_tree in iforest
]
self.top_anomalies = anomaly['model']['top_anomalies']
else:
raise Exception("The anomaly isn't finished yet")
else:
raise Exception("Cannot create the Anomaly instance. Could not"
" find the 'top_anomalies' key in the"
" resource:\n\n%s" % anomaly['model'].keys())