class ValueLastMatchFeature(object):
"""Extract transient feature from previous event"""
def __init__(self, field_name, entity_field, feature_name):
self.field_name = field_name
self.entity_field = entity_field
self.feature_name = feature_name
self.state_manager = StateManager()
def initialise_state(self, entity_id):
"""Sum and count state initiated as 0. and 0."""
self.state_manager.write(entity_id, None)
def get_feature_names(self):
"""Return feature name"""
return [self.feature_name]
def pre_extraction_update(self, event):
pass
def extract_feature_dict(self, event):
# if state missing, initialise state
entity_id = event[self.entity_field]
if self.state_manager.entity_missing(entity_id):
self.initialise_state(entity_id)
return {self.feature_name: self.state_manager.read(entity_id)}
def post_extraction_update(self, event):
self.state_manager.write(event[self.entity_field],
event[self.field_name])
class DecayFeature(object):
def __init__(self, feature_name, entity_field, sum_value_field, incl_count,
incl_sum, incl_mean, decay_rate):
"""Instance initiated with state_id and sample_decay_rate"""
self.feature_name = feature_name
self.entity_field = entity_field
self.sum_value_field = sum_value_field
self.incl_count = incl_count
self.incl_sum = incl_sum
self.incl_mean = incl_mean
self.decay_rate = decay_rate
self.state_manager = StateManager()
def initialise_state(self, entity_id):
raise Exception("Implementation missing")
def get_feature_names(self):
feature_names = []
if self.incl_count:
feature_names.append(self.feature_name + "_count")
if self.incl_sum:
feature_names.append(self.feature_name + "_sum")
if self.incl_mean:
feature_names.append(self.feature_name + "_mean")
return feature_names
def pre_extraction_update(self, event):
raise Exception("Implementation missing")
def extract_feature_dict(self, event):
"""Return feature dict state"""
# read state using state manager
state = self.state_manager.read(event[self.entity_field])
feature_dict = {}
if self.incl_count:
feature_dict[self.feature_name + "_count"] = state['count']
if self.incl_sum:
feature_dict[self.feature_name + "_sum"] = state['sum']
if self.incl_mean:
feature_dict[self.feature_name + "_mean"] = safe_division(
state['sum'], state['count'], 1.)
return feature_dict
