def get_decorated_net(net, initial_marking, final_marking, log, parameters=None, variant="frequency"):
"""
Get a decorated net according to the specified variant (decorate Petri net based on DFG)
Parameters
------------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
Log to use to decorate the Petri net
parameters
Algorithm parameters
variant
Specify if the decoration should take into account the frequency or the performance
Returns
------------
gviz
GraphViz object
"""
if parameters is None:
parameters = {}
aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE, parameters,
"sum" if "frequency" in variant else "mean")
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
# we find the DFG
if variant == "performance":
dfg = log_retrieval.performance(log, parameters=parameters)
else:
dfg = log_retrieval.native(log, parameters=parameters)
# we find shortest paths
spaths = get_shortest_paths(net)
# we find the number of activities occurrences in the log
activities_count = attr_get.get_attribute_values(log, activity_key, parameters=parameters)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(net, dfg, spaths,
activities_count,
variant=variant,
aggregation_measure=aggregation_measure)
return visualize.apply(net, initial_marking, final_marking, parameters=parameters,
decorations=aggregated_statistics)
def apply(log, activity, parameters=None):
"""
Gets the time passed from each preceding activity
Parameters
-------------
log
Log
activity
Activity that we are considering
parameters
Possible parameters of the algorithm
Returns
-------------
dictio
Dictionary containing a 'pre' key with the
list of aggregates times from each preceding activity to the given activity
"""
if parameters is None:
parameters = {}
dfg_frequency = log_retrieval.native(log, parameters=parameters)
dfg_performance = log_retrieval.performance(log, parameters=parameters)
pre = []
sum_perf_pre = 0.0
sum_acti_pre = 0.0
for entry in dfg_performance.keys():
if entry[1] == activity:
pre.append([
entry[0],
float(dfg_performance[entry]),
int(dfg_frequency[entry])
])
sum_perf_pre = sum_perf_pre + float(
dfg_performance[entry]) * float(dfg_frequency[entry])
sum_acti_pre = sum_acti_pre + float(dfg_frequency[entry])
perf_acti_pre = 0.0
if sum_acti_pre > 0:
perf_acti_pre = sum_perf_pre / sum_acti_pre
return {"pre": pre, "pre_avg_perf": perf_acti_pre}
def apply(log, parameters=None):
"""
Measure performance between couples of attributes in the DFG graph
Parameters
----------
log
Log
parameters
Possible parameters passed to the algorithms:
aggregationMeasure -> performance aggregation measure (min, max, mean, median)
activity_key -> Attribute to use as activity
timestamp_key -> Attribute to use as timestamp
Returns
-------
dfg
DFG graph
"""
return log_calc.performance(log, parameters=parameters)
def apply(log, parameters=None):
"""
Measure performance between couples of attributes in the DFG graph
Parameters
----------
log
Log
parameters
Possible parameters passed to the algorithms:
Parameters.AGGREGATION_MEASURE -> performance aggregation measure (min, max, mean, median)
Parameters.ACTIVITY_KEY -> Attribute to use as activity
Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp
Returns
-------
dfg
DFG graph
"""
return log_calc.performance(log, parameters=parameters)