def apply(df, parameters=None):
"""
Gets the roles (group of different activities done by similar resources)
out of the log_skeleton
Parameters
-------------
df
Pandas dataframe
parameters
Possible parameters of the algorithm
Returns
------------
roles
List of different roles inside the log_skeleton
"""
if parameters is None:
parameters = {}
resource_key = exec_utils.get_param_value(Parameters.RESOURCE_KEY, parameters, xes.DEFAULT_RESOURCE_KEY)
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
activity_resource_couples = Counter(dict(df.groupby([resource_key, activity_key]).size()))
return algorithm.apply(activity_resource_couples, parameters=parameters)
def apply(log, parameters=None):
"""
Gets the roles (group of different activities done by similar resources)
out of the log
Parameters
-------------
log
Log object
parameters
Possible parameters of the algorithm
Returns
------------
roles
List of different roles inside the log
"""
if parameters is None:
parameters = {}
resource_key = parameters[
constants.
PARAMETER_CONSTANT_RESOURCE_KEY] if constants.PARAMETER_CONSTANT_RESOURCE_KEY in parameters else xes.DEFAULT_RESOURCE_KEY
activity_key = parameters[
constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
stream = log_conv_factory.apply(log,
variant=log_conv_factory.TO_EVENT_STREAM)
activity_resource_couples = Counter(
(event[resource_key], event[activity_key]) for event in stream)
return algorithm.apply(activity_resource_couples, parameters=parameters)
def apply(df, parameters=None):
"""
Gets the roles (group of different activities done by similar resources)
out of the log
Parameters
-------------
df
Pandas dataframe
parameters
Possible parameters of the algorithm
Returns
------------
roles
List of different roles inside the log
"""
if parameters is None:
parameters = {}
resource_key = parameters[
constants.
PARAMETER_CONSTANT_RESOURCE_KEY] if constants.PARAMETER_CONSTANT_RESOURCE_KEY in parameters else xes.DEFAULT_RESOURCE_KEY
activity_key = parameters[
constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
activity_resource_couples = Counter(
dict(df.groupby([resource_key, activity_key]).size()))
return algorithm.apply(activity_resource_couples, parameters=parameters)
def apply(log, parameters=None):
"""
Gets the roles (group of different activities done by similar resources)
out of the log_skeleton
Parameters
-------------
log
Log object
parameters
Possible parameters of the algorithm
Returns
------------
roles
List of different roles inside the log_skeleton
"""
if parameters is None:
parameters = {}
resource_key = exec_utils.get_param_value(Parameters.RESOURCE_KEY,
parameters,
xes.DEFAULT_RESOURCE_KEY)
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY,
parameters, xes.DEFAULT_NAME_KEY)
stream = log_converter.apply(log, variant=log_converter.TO_EVENT_STREAM)
activity_resource_couples = Counter(
(event[resource_key], event[activity_key]) for event in stream)
return algorithm.apply(activity_resource_couples, parameters=parameters)