def test_pathsfilter(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
input_log = os.path.join(INPUT_DATA_DIR, "running-example.xes")
log = xes_importer.apply(input_log)
log1 = paths_filter.apply(log, [("examine casually", "check ticket")], {paths_filter.Parameters.POSITIVE: True})
log2 = paths_filter.apply(log, [("examine casually", "check ticket")], {paths_filter.Parameters.POSITIVE: False})
del log1
del log2
def apply(dataframe, filter, parameters=None):
"""
Apply a filter to the current log (paths filter)
Parameters
------------
log
Event log
filter
Filter to apply
parameters
Parameters of the algorithm
Returns
------------
log
Event log
"""
if parameters is None:
parameters = {}
parameters[constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY] = filter[1][0]
parameters["positive"] = True
paths_to_filter = []
for p in filter[1][1]:
paths_to_filter.append(tuple(p.split("@@")))
return paths_filter.apply(dataframe,
paths_to_filter,
parameters=parameters)
def execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "receipt.xes"))
throughput_time = case_statistics.get_median_caseduration(log)
variants, variants_times = variants_filter.get_variants_along_with_case_durations(
log)
dfg = dfg_discovery.apply(log)
filtered_log = variants_filter.apply_auto_filter(deepcopy(log))
# filtered_log = log
tree = inductive_miner.apply_tree(filtered_log)
fp_log = fp_discovery.apply(log,
variant=fp_discovery.Variants.ENTIRE_EVENT_LOG)
fp_model = fp_discovery.apply(tree)
conf = fp_conformance.apply(fp_log, fp_model)
conf_occ = sorted([(x, dfg[x]) for x in conf],
key=lambda y: (y[1], y[0][0], y[0][1]),
reverse=True)
print(
"source activity\t\ttarget activity\t\toccurrences\t\tthroughput time log\t\tthroughput time traces with path"
)
for i in range(min(10, len(conf_occ))):
path = conf_occ[i][0]
occ = conf_occ[i][1]
red_log = paths_filter.apply(log, [path])
red_throughput_time = case_statistics.get_median_caseduration(red_log)
print("%s\t\t%s\t\t%d\t\t%s\t\t%s" %
(path[0], path[1], occ, human_readable_stat(throughput_time),
human_readable_stat(red_throughput_time)))
variants_length = sorted([(x, len(variants[x])) for x in variants.keys()],
key=lambda y: (y[1], y[0]),
reverse=True)
print(
"\nvariant\t\toccurrences\t\tthroughput time log\t\tthroughput time traces with path"
)
for i in range(min(10, len(variants_length))):
var = variants_length[i][0]
vark = str(var)
if len(vark) > 10:
vark = vark[:10]
occ = variants_length[i][1]
fp_log_var = fp_discovery.apply(
variants[var], variant=fp_discovery.Variants.ENTIRE_EVENT_LOG)
conf_var = fp_conformance.apply(fp_log_var, fp_model)
is_fit = str(len(conf_var) == 0)
var_throughput = case_statistics.get_median_caseduration(variants[var])
print("%s\t\t%d\t\t%s\t\t%s\t\t%s" %
(vark, occ, is_fit, throughput_time,
human_readable_stat(var_throughput)))
# print(conf_occ)
conf_colors = tree_visualization.apply(tree, conf)
if True:
gviz = pt_visualizer.apply(
tree,
parameters={
"format":
"svg",
pt_visualizer.Variants.WO_DECORATION.value.Parameters.COLOR_MAP:
conf_colors,
pt_visualizer.Variants.WO_DECORATION.value.Parameters.ENABLE_DEEPCOPY:
False
})
pt_visualizer.view(gviz)