def apply(log, parameters=None):
"""
Apply the IMDF algorithm to a log obtaining a Petri net along with an initial and final marking
Parameters
-----------
log
Log
parameters
Parameters of the algorithm, including:
pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> attribute of the log to use as activity name
(default concept:name)
Returns
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
"""
if parameters is None:
parameters = {}
if pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters:
parameters[pmutil.constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] = xes_util.DEFAULT_NAME_KEY
if pmutil.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY not in parameters:
parameters[
pmutil.constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] = xes_util.DEFAULT_TIMESTAMP_KEY
if pmutil.constants.PARAMETER_CONSTANT_CASEID_KEY not in parameters:
parameters[
pmutil.constants.
PARAMETER_CONSTANT_CASEID_KEY] = pmutil.constants.CASE_ATTRIBUTE_GLUE
if type(log) is pd.DataFrame:
return apply_variants(variants_get.get_variants_set(
log, parameters=parameters),
parameters=parameters)
else:
log = log_conversion.apply(log, parameters,
log_conversion.TO_EVENT_LOG)
tree = apply_tree(log, parameters=parameters)
net, initial_marking, final_marking = tree_to_petri.apply(tree)
return net, initial_marking, final_marking
def apply_tree(log, parameters=None):
"""
Apply the IMDF algorithm to a log obtaining a process tree
Parameters
----------
log
Log
parameters
Parameters of the algorithm, including:
pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> attribute of the log to use as activity name
(default concept:name)
Returns
----------
tree
Process tree
"""
if parameters is None:
parameters = {}
if pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters:
parameters[pmutil.constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] = xes_util.DEFAULT_NAME_KEY
if pmutil.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY not in parameters:
parameters[
pmutil.constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] = xes_util.DEFAULT_TIMESTAMP_KEY
if pmutil.constants.PARAMETER_CONSTANT_CASEID_KEY not in parameters:
parameters[
pmutil.constants.
PARAMETER_CONSTANT_CASEID_KEY] = pmutil.constants.CASE_ATTRIBUTE_GLUE
if type(log) is pd.DataFrame:
return apply_tree_variants(variants_get.get_variants_set(
log, parameters=parameters),
parameters=parameters)
else:
activity_key = parameters[
pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY]
traces = Counter([
tuple(y[activity_key] for y in x if activity_key in y) for x in log
])
return apply_internal(traces)
def test_df_tree_variants_dfg_based(self):
df = pd.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
tree = inductive_miner.apply_tree_variants(
pd_variants.get_variants_set(df),
variant=inductive_miner.DFG_BASED)
def test_df_tree_variants_dfg_based_old(self):
df = csv_importer.import_dataframe_from_path(
os.path.join("input_data", "running-example.csv"))
tree = inductive_miner.apply_tree_variants(
pd_variants.get_variants_set(df),
variant=inductive_miner.DFG_BASED_OLD_VERSION)
def test_df_variants_dfg_based(self):
df = csv_importer.import_dataframe_from_path(
os.path.join("input_data", "running-example.csv"))
net, im, fm = inductive_miner.apply_variants(
pd_variants.get_variants_set(df),
variant=inductive_miner.DFG_BASED)
