def discover_organizational_roles(log: Union[EventLog, pd.DataFrame]):
"""
Mines the organizational roles
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
roles
Organizational roles. List where each role is a sublist with two elements:
- The first element of the sublist is the list of activities belonging to a role.
Each activity belongs to a single role
- The second element of the sublist is a dictionary containing the resources of the role
and the number of times they executed activities belonging to the role.
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]: raise Exception("the method can be applied only to a traditional event log!")
from pm4py.algo.organizational_mining.roles import algorithm as roles
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
return roles.apply(log, variant=roles.Variants.PANDAS, parameters=get_properties(log))
else:
return roles.apply(log, variant=roles.Variants.LOG, parameters=get_properties(log))
def discover_organizational_roles(log: Union[EventLog, pd.DataFrame]):
"""
Mines the organizational roles
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
roles
Organizational roles. List where each role is a sublist with two elements:
- The first element of the sublist is the list of activities belonging to a role.
Each activity belongs to a single role
- The second element of the sublist is a dictionary containing the resources of the role
and the number of times they executed activities belonging to the role.
"""
from pm4py.algo.organizational_mining.roles import algorithm as roles
if check_is_dataframe(log):
check_dataframe_columns(log)
return roles.apply(log, variant=roles.Variants.PANDAS)
else:
return roles.apply(log, variant=roles.Variants.LOG)
def test_log_orgmining_local_roles(self):
from pm4py.algo.organizational_mining.local_diagnostics import algorithm
from pm4py.algo.organizational_mining.roles import algorithm as roles_detection
log = xes_importer.apply(os.path.join("input_data", "receipt.xes"))
roles = roles_detection.apply(log)
algorithm.apply_from_clustering_or_roles(log, roles)
def test_role_receipt_xes(self):
log = xes_importer.apply(os.path.join("..", "tests", "input_data", "receipt.xes"))
roles = role_mining.apply(log)
def test_role_receipt_csv(self):
df = pd.read_csv(os.path.join("input_data", "receipt.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
roles = role_mining.apply(df)
def test_role_running_xes(self):
log = xes_importer.apply(os.path.join("..", "tests", "input_data", "running-example.xes"))
roles = role_mining.apply(log)