def apply_dfg(
dfg: Dict[Tuple[str, str], int],
activities=None,
activities_occurrences=None,
start_activities=None,
end_activities=None,
parameters: Optional[Dict[Any, Any]] = None
) -> Tuple[PetriNet, Marking, Marking]:
"""
Discovers a Petri net using Heuristics Miner
Parameters
------------
dfg
Directly-Follows Graph
activities
(If provided) list of activities of the log
activities_occurrences
(If provided) dictionary of activities occurrences
start_activities
(If provided) dictionary of start activities occurrences
end_activities
(If provided) dictionary of end activities occurrences
parameters
Possible parameters of the algorithm,
including:
- Parameters.ACTIVITY_KEY
- Parameters.TIMESTAMP_KEY
- Parameters.CASE_ID_KEY
- Parameters.DEPENDENCY_THRESH
- Parameters.AND_MEASURE_THRESH
- Parameters.MIN_ACT_COUNT
- Parameters.MIN_DFG_OCCURRENCES
- Parameters.DFG_PRE_CLEANING_NOISE_THRESH
- Parameters.LOOP_LENGTH_TWO_THRESH
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
heu_net = apply_heu_dfg(dfg,
activities=activities,
activities_occurrences=activities_occurrences,
start_activities=start_activities,
end_activities=end_activities,
parameters=parameters)
net, im, fm = hn_conv_alg.apply(heu_net, parameters=parameters)
return net, im, fm
def apply_pandas(
df: pd.DataFrame,
parameters: Optional[Dict[Any, Any]] = None
) -> Tuple[PetriNet, Marking, Marking]:
"""
Discovers a Petri net using the Heuristics Miner ++ algorithm
Implements the approach described in
Burattin, Andrea, and Alessandro Sperduti. "Heuristics Miner for Time Intervals." ESANN. 2010.
https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf
Parameters
--------------
df
Dataframe
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY
- Parameters.START_TIMESTAMP_KEY
- Parameters.TIMESTAMP_KEY
- Parameters.CASE_ID_KEY
- Parameters.DEPENDENCY_THRESH
- Parameters.AND_MEASURE_THRESH
- Parameters.MIN_ACT_COUNT
- Parameters.MIN_DFG_OCCURRENCES
- Parameters.HEU_NET_DECORATION
Returns
--------------
net
Petri net
im
Initial marking
fm
Final marking
"""
heu_net = apply_heu_pandas(df, parameters=parameters)
net, im, fm = hn_conv_alg.apply(heu_net, parameters=parameters)
return net, im, fm
def apply(
log: EventLog,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None
) -> Tuple[PetriNet, Marking, Marking]:
"""
Discovers a Petri net using Heuristics Miner
Parameters
------------
log
Event log
parameters
Possible parameters of the algorithm,
including:
- Parameters.ACTIVITY_KEY
- Parameters.TIMESTAMP_KEY
- Parameters.CASE_ID_KEY
- Parameters.DEPENDENCY_THRESH
- Parameters.AND_MEASURE_THRESH
- Parameters.MIN_ACT_COUNT
- Parameters.MIN_DFG_OCCURRENCES
- Parameters.DFG_PRE_CLEANING_NOISE_THRESH
- Parameters.LOOP_LENGTH_TWO_THRESH
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
heu_net = apply_heu(log, parameters=parameters)
net, im, fm = hn_conv_alg.apply(heu_net, parameters=parameters)
return net, im, fm
def apply_pandas(df, parameters=None):
"""
Discovers a Petri net using Heuristics Miner
Parameters
------------
df
Pandas dataframe
parameters
Possible parameters of the algorithm,
including: activity_key, case_id_glue, timestamp_key,
dependency_thresh, and_measure_thresh, min_act_count, min_dfg_occurrences, dfg_pre_cleaning_noise_thresh,
loops_length_two_thresh
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
if pkgutil.find_loader("pandas"):
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
case_id_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME)
start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters,
None)
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY)
from pm4py.algo.discovery.dfg.adapters.pandas import df_statistics, freq_triples as get_freq_triples
from pm4py.statistics.attributes.pandas import get as pd_attributes
from pm4py.statistics.start_activities.pandas import get as pd_sa_filter
from pm4py.statistics.end_activities.pandas import get as pd_ea_filter
start_activities = pd_sa_filter.get_start_activities(df, parameters=parameters)
end_activities = pd_ea_filter.get_end_activities(df, parameters=parameters)
activities_occurrences = pd_attributes.get_attribute_values(df, activity_key, parameters=parameters)
activities = list(activities_occurrences.keys())
heu_net_decoration = exec_utils.get_param_value(Parameters.HEU_NET_DECORATION, parameters, "frequency")
if timestamp_key in df:
dfg = df_statistics.get_dfg_graph(df, case_id_glue=case_id_glue,
activity_key=activity_key, timestamp_key=timestamp_key,
start_timestamp_key=start_timestamp_key)
dfg_window_2 = df_statistics.get_dfg_graph(df, case_id_glue=case_id_glue,
activity_key=activity_key, timestamp_key=timestamp_key, window=2,
start_timestamp_key=start_timestamp_key)
frequency_triples = get_freq_triples.get_freq_triples(df, case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key)
else:
dfg = df_statistics.get_dfg_graph(df, case_id_glue=case_id_glue,
activity_key=activity_key, sort_timestamp_along_case_id=False)
dfg_window_2 = df_statistics.get_dfg_graph(df, case_id_glue=case_id_glue,
activity_key=activity_key, sort_timestamp_along_case_id=False,
window=2)
frequency_triples = get_freq_triples.get_freq_triples(df, case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
sort_timestamp_along_case_id=False)
performance_dfg = None
if heu_net_decoration == "performance":
performance_dfg = df_statistics.get_dfg_graph(df, case_id_glue=case_id_glue,
activity_key=activity_key, timestamp_key=timestamp_key,
start_timestamp_key=start_timestamp_key,
measure="performance")
heu_net = apply_heu_dfg(dfg, activities=activities, activities_occurrences=activities_occurrences,
start_activities=start_activities, end_activities=end_activities,
dfg_window_2=dfg_window_2,
freq_triples=frequency_triples, performance_dfg=performance_dfg, parameters=parameters)
net, im, fm = hn_conv_alg.apply(heu_net, parameters=parameters)
return net, im, fm
def apply_pandas(df, parameters=None):
"""
Discovers a Petri net using Heuristics Miner
Parameters
------------
df
Pandas dataframe
parameters
Possible parameters of the algorithm,
including: activity_key, case_id_glue, timestamp_key,
dependency_thresh, and_measure_thresh, min_act_count, min_dfg_occurrences, dfg_pre_cleaning_noise_thresh,
loops_length_two_thresh
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY,
parameters, xes.DEFAULT_NAME_KEY)
case_id_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY,
parameters,
constants.CASE_CONCEPT_NAME)
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY,
parameters,
xes.DEFAULT_TIMESTAMP_KEY)
start_activities = pd_sa_filter.get_start_activities(df,
parameters=parameters)
end_activities = pd_ea_filter.get_end_activities(df, parameters=parameters)
activities_occurrences = pd_attributes.get_attribute_values(
df, activity_key, parameters=parameters)
activities = list(activities_occurrences.keys())
if timestamp_key in df:
dfg = df_statistics.get_dfg_graph(df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key)
dfg_window_2 = df_statistics.get_dfg_graph(df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
window=2)
frequency_triples = get_freq_triples.get_freq_triples(
df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key)
else:
dfg = df_statistics.get_dfg_graph(df,
case_id_glue=case_id_glue,
activity_key=activity_key,
sort_timestamp_along_case_id=False)
dfg_window_2 = df_statistics.get_dfg_graph(
df,
case_id_glue=case_id_glue,
activity_key=activity_key,
sort_timestamp_along_case_id=False,
window=2)
frequency_triples = get_freq_triples.get_freq_triples(
df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
sort_timestamp_along_case_id=False)
heu_net = apply_heu_dfg(dfg,
activities=activities,
activities_occurrences=activities_occurrences,
start_activities=start_activities,
end_activities=end_activities,
dfg_window_2=dfg_window_2,
freq_triples=frequency_triples,
parameters=parameters)
net, im, fm = hn_conv_alg.apply(heu_net, parameters=parameters)
return net, im, fm
