def perform_alignments(log, petri_string, parameters=None):
"""
Perform alignments
Parameters
------------
log
Log
net
Petri net
parameters
Parameters of the algorithm
Returns
-------------
petri
SVG of the decorated Petri
table
SVG of the decorated table
"""
if parameters is None:
parameters = {}
net, im, fm = pnml.import_petri_from_string(petri_string,
parameters=parameters)
parameters_align = copy(parameters)
parameters_align["ret_tuple_as_trans_desc"] = True
alignments = align_factory.apply(log,
net,
im,
fm,
parameters=parameters_align)
decorations = alignments_decoration.get_alignments_decoration(
net, im, fm, aligned_traces=alignments, parameters=parameters)
gviz_on_petri = pn_vis_factory.apply(net,
im,
fm,
aggregated_statistics=decorations,
variant="alignments",
parameters={"format": "svg"})
svg_on_petri = get_base64_from_gviz(gviz_on_petri)
parameters_table = copy(parameters)
parameters_table["format"] = "svg"
gviz_table = align_table_factory.apply(log,
alignments,
parameters=parameters_table)
svg_table = get_base64_from_gviz(gviz_table)
return svg_on_petri, svg_table
def inductive_miner_petrinet_no_decor(log_file):
net, i_m, f_m = inductive_miner.apply(log_file)
gviz = pn_vis_factory.apply(net,
i_m,
f_m,
parameters={
"format": "png",
"debug": False
})
pn_vis_factory.save(gviz, "static/inductive_miner_petnet_no_decor.png")
return net, i_m, f_m
def execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "compressed_input_data",
"09_a32f0n00.xes.gz"))
heu_net = heuristics_miner.apply_heu(
log, parameters={"dependency_thresh": 0.99})
gviz = hn_vis_factory.apply(heu_net, parameters={"format": "svg"})
hn_vis_factory.view(gviz)
net, im, fm = heuristics_miner.apply(
log, parameters={"dependency_thresh": 0.99})
gviz2 = petri_vis_factory.apply(net, im, fm, parameters={"format": "svg"})
petri_vis_factory.view(gviz2)
def SPAlphaMiner(context):
args = context.args
log = convert_df_pm_format(args.inputData)
net, initial_marking, final_marking = alpha_miner.apply(log)
gviz = pn_vis_factory.apply(net, initial_marking, final_marking)
pn_vis_factory.save(gviz, os.path.join(args.outputData, "alpha-miner.png"))
pnml_exporter.export_net(
net,
initial_marking,
os.path.join(args.outputData, "petri_final.pnml"),
final_marking=final_marking,
)
return args.outputData
def explore_process(name, csv, miner, miner_name):
event_log = csv_importer.import_log_from_string(csv)
net, initial_marking, final_marking = miner.apply(event_log)
gviz = pn_vis_factory.apply(net, initial_marking, final_marking)
pn_vis_factory.save(gviz, join('solutions', name + '_' + miner_name + '_petri_net.png'))
pn_vis_factory.save(gviz, join('solutions', name + '_' + miner_name + '_petri_net.pdf'))
bpmn_graph, elements_correspondence, inv_elements_correspondence, el_corr_keys_map = bpmn_converter.apply(net, initial_marking, final_marking)
bpmn_exporter.export_bpmn(bpmn_graph, join('solutions', name + '_' + miner_name + '.bpmn'))
bpmn_figure = bpmn_vis_factory.apply(bpmn_graph)
bpmn_vis_factory.save(bpmn_figure, join('solutions', name + '_' + miner_name + '_bpmn.png'))
bpmn_vis_factory.save(bpmn_figure, join('solutions', name + '_' + miner_name + '_bpmn.pdf'))
def visualize_as_petri_net(net, initial_marking, final_marking, path=''):
if len(path) > 0:
parameters = {
pn_visualizer.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"
}
gviz = pn_visualizer.apply(net,
initial_marking,
final_marking,
parameters=parameters)
pn_visualizer.save(gviz, path)
else:
gviz = pn_vis_factory.apply(net, initial_marking, final_marking)
pn_vis_factory.view(gviz)
return net, initial_marking, final_marking
def inductive_miner_petrinet_frequency(log_file):
net, i_m, f_m = inductive_miner.apply(log_file)
gviz = pn_vis_factory.apply(net,
i_m,
f_m,
parameters={
"format": "png",
"debug": False
},
variant="frequency",
log=log_file)
pn_vis_factory.save(gviz, "static/inductive_miner_petnet_frequency.png")
return "success!"
def bot_hm(chat_id, dependency_threshold=0.99):
log = get_current_log(chat_id)
heu_net = heuristics_miner.apply_heu(
log, parameters={"dependency_thresh": dependency_threshold})
gviz = hn_vis_factory.apply(heu_net)
new_file, filename = tempfile.mkstemp(suffix="png")
hn_vis_factory.save(gviz, filename)
net, im, fm = heuristics_miner.apply(
log, parameters={"dependency_thresh": dependency_threshold})
gviz = pn_vis_factory.apply(net, im, fm)
new_file2, filename2 = tempfile.mkstemp(suffix="png")
pn_vis_factory.save(gviz, filename2)
return [filename, filename2]
def generate_petri_net_visual(net, initial_marking, final_marking, xes_log):
try:
# visualizing the petri net using graphviz library, by passing net initial and final marking and viewing it
parameters = {"format": "png"}
gviz = pn_vis_factory.apply(net,
initial_marking,
final_marking,
parameters=parameters,
variant="frequency",
log=xes_log)
pn_vis_factory.view(gviz)
print("By what name do you want to save the petri net image?")
filename = str(input())
pn_vis_factory.save(gviz, filename + ".png")
except TypeError:
print("Please check input values")
def execute_script():
# import the log
log_path = os.path.join("..", "tests", "input_data", "receipt.xes")
log = xes_importer.import_log(log_path)
# apply Inductive Miner
net, initial_marking, final_marking = inductive_miner.apply(log)
# get visualization
variant = "performance"
parameters_viz = {"aggregationMeasure": "mean", "format": "svg"}
gviz = pn_vis_factory.apply(net,
initial_marking,
final_marking,
log=log,
variant=variant,
parameters=parameters_viz)
pn_vis_factory.view(gviz)
def show_log(self, log):
# Miner application log file
net, initial_marking, final_marking = alpha_miner.apply(log)
# Visual interface application analysis results
gviz = visualizer.apply(net, initial_marking, final_marking)
file, _ = os.path.splitext(os.path.basename(self.fileName))
output_full_name = "output_time_filtered_" + file + ".png"
output_file = global_util.get_full_path_output_file(output_full_name)
print(output_file)
visualizer.save(gviz, output_file)
return output_full_name
def execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "receipt.xes"))
log = sorting.sort_timestamp(log)
net, im, fm = inductive_miner.apply(log)
log1 = EventLog(log[:500])
log2 = EventLog(log[len(log) - 500:])
statistics = element_usage_comparison.compare_element_usage_two_logs(
net, im, fm, log1, log2)
gviz = pn_vis_factory.apply(net,
im,
fm,
variant="frequency",
aggregated_statistics=statistics,
parameters={"format": "svg"})
pn_vis_factory.view(gviz)
def import_csv_file(filename):
filename = os.path.basename(filename)
file_path = global_util.get_full_path_input_file(filename)
event_stream = csv_importer.import_event_stream(file_path)
# dataframe = csv_import_adapter.import_dataframe_from_path(file_path, sep=",")
log = conversion_factory.apply(
event_stream,
parameters={constants.PARAMETER_CONSTANT_TIMESTAMP_KEY: "日期和时间"})
net, initial_marking, final_marking = alpha_miner.apply(log)
gviz = visualizer.apply(net, initial_marking, final_marking)
visualizer.view(gviz)
def SPHeuristicsMiner(context):
args = context.args
inputFile = os.path.join(args.inputData, os.listdir(args.inputData)[0])
log = xes_importer.import_log(inputFile)
net, im, fm = heuristics_miner.apply(
log,
parameters={
"dependency_thresh": args.dependencyThresh,
"and_measure_thresh": args.andMeasureThresh,
"min_act_count": args.minActCount,
"min_dfg_occurrences": args.minDfgOccurrences,
"dfg_pre_cleaning_noise_thresh": args.dfgPreCleaningNoiseThresh,
},
)
gviz = pn_vis_factory.apply(net, im, fm)
pn_vis_factory.save(gviz,
os.path.join(args.outputData, "heuristics-miner.png"))
return args.outputData
def import_csv_file(filename):
filename = os.path.basename(filename)
# 修改日志文件路径
file_path = global_util.get_full_path_input_file(filename)
# 将文件按 csv 文件格式导入，得到事件流结构
event_stream = csv_importer.import_event_stream(file_path)
# dataframe = csv_import_adapter.import_dataframe_from_path(file_path, sep=",")
# 将事件流转换成 xes 结构
log = conversion_factory.apply(event_stream, parameters={constants.PARAMETER_CONSTANT_TIMESTAMP_KEY: "日期和时间"})
# 矿工应用日志文件，这里简单的应用阿尔法矿工
net, initial_marking, final_marking = alpha_miner.apply(log)
# 可视化界面应用分析结果
gviz = visualizer.apply(net, initial_marking, final_marking)
# 显示结果
visualizer.view(gviz)
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
log = xes_importer.import_log(log_path)
net, marking, final_marking = alpha_factory.apply(log)
for place in marking:
print("initial marking " + place.name)
for place in final_marking:
print("final marking " + place.name)
gviz = pn_vis_factory.apply(net,
marking,
final_marking,
parameters={"format": "svg"})
pn_vis_factory.view(gviz)
print("started token replay")
aligned_traces = token_replay.apply(log, net, marking, final_marking)
fit_traces = [x for x in aligned_traces if x['trace_is_fit']]
perc_fitness = 0.00
if len(aligned_traces) > 0:
perc_fitness = len(fit_traces) / len(aligned_traces)
print("perc_fitness=", perc_fitness)
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
log = xes_importer.import_log(log_path)
net, marking, final_marking = inductive_factory.apply(log)
for place in marking:
print("initial marking " + place.name)
for place in final_marking:
print("final marking " + place.name)
gviz = pn_vis_factory.apply(net, marking, final_marking, parameters={"format": "svg", "debug": True})
pn_vis_factory.view(gviz)
if True:
fit_traces = []
for i in range(0, len(log)):
try:
print("\n", i, [x["concept:name"] for x in log[i]])
cf_result = pm4py.algo.conformance.alignments.versions.state_equation_a_star.apply(log[i], net, marking,
final_marking)[
'alignment']
if cf_result is None:
print("alignment is none!")
else:
is_fit = True
for couple in cf_result:
print(couple)
if not (couple[0] == couple[1] or couple[0] == ">>" and couple[1] is None):
is_fit = False
print("isFit = " + str(is_fit))
if is_fit:
fit_traces.append(log[i])
except TypeError:
print("EXCEPTION ", i)
traceback.print_exc()
print(fit_traces)
print(len(fit_traces))
def startSimulator(env, df2, tns):
#Using iloc to access can also use iat or at to access elements as matrix
sfc_sim = str(df2.iloc[0]['SFC'])[:6]
sfc_act = int(sfc_sim)
sfc_gen = int(sfc_generator())
print(sfc_act)
print(sfc_gen)
merged_sfc = str(sfc_act) + str(sfc_gen)
print(f'Starting simulation for the SFC {merged_sfc}')
#print(f'Processed at the resources {resource_list}')
df_construct = {}
new_df = pd.DataFrame()
#print(new_df)
for index, row in df2.iterrows():
#print(index, row['OPERATION'],row['PROCESSING_TIME_SECS'],row['WAITING_TIME_SECS'])
if index + 1 > tns:
break
row['SFC'] = merged_sfc
row['case:concept:name'] = row['WORK_CENTER']
row['concept:name'] = row['OPERATION']
row['org:resource'] = row['RESRCE']
#row['DATE_TIME'] = f"{datetime.now():%d-%m-%Y %H:%M:%S}"
row['DATE_TIME'] = f"{datetime.fromtimestamp(env.now):%d-%m-%Y %H:%M:%S}"
row['time:timestamp'] = row['DATE_TIME']
curr_row = pd.DataFrame([row])
pt_time = int(row['PROCESSING_TIME_SECS'])
print(pt_time)
yield env.timeout(pt_time)
new_df = new_df.append([curr_row], ignore_index=True)
#print(new_df)
# Implementation of Petrin nets representation will be added
log = conversion_factory.apply(new_df)
print(log)
net, initial_marking, final_marking = alpha_miner.apply(log)
gviz = vis_factory.apply(net, initial_marking, final_marking)
vis_factory.view(gviz)
def execute_script(variant="frequency"):
# read the log using the nonstandard importer (faster)
log_path = os.path.join("..", "tests", "input_data", "receipt.xes")
log = xes_importer.import_log(log_path, variant="nonstandard")
# applies Inductive Miner on the log
net, initial_marking, final_marking = inductive_miner.apply(log)
# find shortest paths in the net
spaths = get_shortest_paths(net)
# then we start to decorate the net
# we decide if we should decorate it with frequency or performance
# we decide the aggregation measure (sum, min, max, mean, median, stdev)
aggregation_measure = "mean"
if variant == "frequency":
aggregation_measure = "sum"
# we find the DFG
dfg = dfg_factory.apply(log, variant=variant)
# we find the number of activities occurrences in the trace log
activities_count = attributes_filter.get_attribute_values(
log, "concept:name")
# we calculate the statistics on the Petri net applying the greedy algorithm
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net,
dfg,
spaths,
activities_count,
variant=variant,
aggregation_measure=aggregation_measure)
# we find the gviz
gviz = pn_vis_factory.apply(net,
initial_marking,
final_marking,
variant=variant,
aggregated_statistics=aggregated_statistics,
parameters={"format": "svg"})
# we show the viz on screen
pn_vis_factory.view(gviz)
def show(model, tel, file_name, parameters):
'''
Show model and its quality measures
:param model: model type (transition system, state based region, DFG miner, alpha miner)
:param tel: input log
:param file_name: img file name to show model
:param parameters: parmater for transition system (afreq, sfreq)
:return:
'''
tel_flag = False
if isinstance(tel[0][0], tel_event):
tel_flag = True
if model in ['ts', 'sbr']:
if tel_flag:
output_file_path = os.path.join(
"static", "images", file_name[:file_name.find('.')] + '_' +
model + '_' + str(parameters['afreq_thresh']) + '_' +
str(parameters['sfreq_thresh']) + ".png")
else:
output_file_path = os.path.join(
"static", "images",
"2" + "_" + file_name[:file_name.find('.')] + '_' + model +
'_' + str(parameters[PARAM_KEY_DIRECTION]) + '_' +
str(parameters[PARAM_KEY_WINDOW]) + "_" +
str(parameters[PARAM_KEY_VIEW]) + ".png")
auto = utils.discover_annotated_automaton(tel, parameters=parameters)
max_thresh = {}
max_afreq = 0
max_sfreq = 0
if tel_flag:
for trans in auto.transitions:
max_afreq = max(max_afreq, trans.afreq)
for state in auto.states:
max_sfreq = max(max_sfreq, state.sfreq)
max_thresh['afreq'] = max_afreq
max_thresh['sfreq'] = max_sfreq
if model == 'ts':
result = {}
gviz = vis_factory.apply(auto)
vis_factory.save(gviz, output_file_path)
result['num of transitions'] = len(auto.transitions)
result['num of states'] = len(auto.states)
else:
net, im, fm = sb.petri_net_synthesis(auto)
gviz = petri_vis_factory.apply(net, im, fm)
petri_vis_factory.save(gviz, output_file_path)
result = evaluation(net, im, fm, tel)
else:
if tel_flag:
output_file_path = os.path.join(
"static", "images",
file_name[:file_name.find('.')] + '_' + model + '_' + ".png")
else:
output_file_path = os.path.join(
"static", "images", "2" + file_name[:file_name.find('.')] +
'_' + model + '_' + ".png")
if model == 'alpha':
if isinstance(tel[0][0], Event):
net, im, fm = trans_alpha(tel)
else:
net, im, fm = alpha_miner.apply(tel)
gviz = petri_vis_factory.apply(net, im, fm)
petri_vis_factory.save(gviz, output_file_path)
result = evaluation(net, im, fm, tel)
else:
dfg = dfg_factory.apply(tel)
if tel_flag:
dfg_tel = inductive_revise.get_dfg_graph_trans(tel)
#dfg = dfg_tel + dfg
dfg = dfg_tel
gviz = dfg_vis_factory.apply(dfg, log=tel)
dfg_vis_factory.save(gviz, output_file_path)
result = dict(
sorted(dfg.items(), key=operator.itemgetter(1), reverse=True))
max_thresh = None
return output_file_path, result, max_thresh
#Topic:
#-----------------------------
#libraries
from pm4py.algo.discovery.alpha import factory as alpha_miner
from pm4py.objects.log.importer.xes import factory as importer
from pm4py.visualization.petrinet import factory as visualizer
from pm4py.objects.log.importer.csv import factory as csv_importer
event_stream = csv_importer.import_event_stream(
os.path.join("pmdata/", "running-example.csv"))
event_stream
event_stream_length = len(event_stream)
print(event_stream_length)
for event in event_stream:
print(event)
from pm4py.objects.conversion.log import factory as conversion_factory
log = conversion_factory.apply(event_stream)
from pm4py.objects.log.exporter.csv import factory as csv_exporter
csv_exporter.export(event_stream, "data/outputFile1.csv")
#log = importer.apply('pmdata/running-example.xes')
net, initial_marking, final_marking = alpha_miner.apply(log)
gviz = visualizer.apply(net, initial_marking, final_marking)
visualizer.view(gviz)
inv1_p9, d_p10, h_p1, b_p11
]
for arc in arcs:
arc.source.out_arcs.add(arc)
arc.target.in_arcs.add(arc)
net.transitions.update(trans)
net.places.update(places)
net.arcs.update(arcs)
init_marking = petri.Marking([p0])
final_marking = petri.Marking([p11])
out_fp = './net.dot'
gviz = vis_factory.apply(net, init_marking, final_marking)
gviz.save(out_fp)
# out_fp = './test-net.pnml'
# exporter.pnml.export_net(net, init_marking, out_fp, final_marking=final_marking)
# build the reachability graph
is_inv = lambda t: t.label is None
rg, inv_states = build_reachability_graph(net, init_marking, is_inv)
collapse_inv_trans(rg, inv_states)
# out_fp = './image/test-rg.dot'
# dot_gr = util.visualize_graphviz.visualize(rg)
# dot_gr.save(out_fp, '.')
def model(request, log_id, cube_id):
values = request.GET.get("values")
if (values == None):
values = "{}"
values = json.loads(values)
def convert(value, dtype):
if (dtype == 'float'):
return float(value)
elif (dtype == 'int'):
return int(value)
elif (dtype == 'date'):
return convert_date(value)
elif (dtype == 'bool'):
return bool(value)
else:
return value
def convert_date(value):
# Construct datetime object to filter with pymongo
time_format = "%Y-%m-%dT%H:%M:%S.%f"
time_format = "%Y-%m-%d %H:%M:%S.%f"
if ("." not in value):
time_format = time_format[:-3]
return datetime.strptime(value, time_format)
algo = request.GET.get("algorithm")
values_ = {}
# Convert to attribute id to name like it is in the events.
values_ = {}
for key in values:
if (key != 'log'):
attribute = Attribute.objects.get(pk=key)
if (":" in attribute.parent):
parent = attribute.parent.split(':')[0]
d_name = attribute.parent.split(':')[1]
name = attribute.name
# Query for elements of dictionary
queryname = parent + ":" + d_name + ".children." + name
else:
queryname = attribute.name
if (attribute.parent == "trace"):
queryname = 'trace:' + queryname
if ("to" in values[key]):
lower = values[key].split("to")[0].strip()
upper = values[key].split('to')[1].strip()
lower = convert(lower, attribute.dtype)
upper = convert(upper, attribute.dtype)
values_[queryname] = {'$gt': lower, '$lt': upper}
else:
value = convert(values[key], attribute.dtype)
values_[queryname] = value
values_['log'] = log_id
values = values_
client = MongoClient(host=DATABASES['default']['HOST'])
db = client[DATABASES['default']['NAME']]
trace_collection = db['traces']
event_collection = db['events']
t1 = time()
all_events = event_collection.find(values)
t2 = time()
print("Time to get events: {}".format(t2 - t1))
t1 = time()
traces = groupby(all_events, key=lambda e: e['trace:_id'])
t2 = time()
print("Time to get traces: {}".format(t2 - t1))
t1 = time()
traces = [log_lib.log.Trace(g) for k, g in traces]
t2 = time()
print("Time to make list: {}".format(t2 - t1))
# log_list = [log_lib.log.Trace([log_lib.log.Event(t_e) for t_e in all_events if t_e['trace:_id'] == db_trace['_id']]) for db_trace in db_traces]
t1 = time()
log = log_lib.log.EventLog(traces)
t2 = time()
print("Time to make event log: {}".format(t2 - t1))
# for db_trace in db_traces:
# pm4py_trace = log_lib.log.Trace([log_lib.log.Event(t_e) for t_e in all_events if t_e['trace:_id'] == db_trace['_id']])
# log.append(pm4py_trace)
# print(len(log))
# pm_events = []
# traces = {str(e['trace:_id']): log_lib.log.Trace() for e in events}
# for event in events:
# trace = trace_collection.find_one({"_id": event['trace:_id']})
# t = traces[str(event['trace:_id'])]
# del event['_id']
# del event['trace:_id']
# e = log_lib.log.Event(event)
# t.append(e)
# log.append(traces.items())
# for trace in traces:
# log.append(traces[trace])
parameters = {"format": "svg"}
event_log = EventLog.objects.get(pk=log_id)
filename = str(event_log.pk) + algo + ".svg"
t1 = time()
if (algo == "alpha"):
net, initial_marking, final_marking = alpha_miner.apply(log)
gviz = pn_vis_factory.apply(net,
initial_marking,
final_marking,
parameters=parameters)
pn_vis_factory.save(gviz, filename)
elif (algo == "inductive"):
mine_tree = request.GET.get("mine_tree")
if (mine_tree == 'true'):
tree = inductive_miner.apply_tree(log)
gviz = pt_vis_factory.apply(tree, parameters=parameters)
pt_vis_factory.save(gviz, filename)
else:
net, initial_marking, final_marking = inductive_miner.apply(log)
gviz = pn_vis_factory.apply(net,
initial_marking,
final_marking,
parameters=parameters)
pn_vis_factory.save(gviz, filename)
elif (algo == "heuristic"):
dependency_thresh = float(request.GET.get("dependency_thresh"))
and_measure_thresh = float(request.GET.get("and_measure_thresh"))
min_act_count = float(request.GET.get("min_act_count"))
min_dfg_occurrences = float(request.GET.get("min_dfg_occurrences"))
dfg_pre_cleaning_noise_thresh = float(
request.GET.get("dfg_pre_cleaning_noise_thresh"))
h_params = {
'dependency_thresh': dependency_thresh,
'and_measure_thresh': and_measure_thresh,
'min_act_count': min_act_count,
'min_dfg_occurrences': min_dfg_occurrences,
'dfg_pre_cleaning_noise_thresh': dfg_pre_cleaning_noise_thresh,
}
net, im, fm = heuristics_miner.apply(log, parameters=h_params)
gviz = pn_vis_factory.apply(net, im, fm, parameters=parameters)
pn_vis_factory.save(gviz, filename)
t2 = time()
print("Time pm4py: {}".format(t2 - t1))
svg = open(filename, "rb")
svg_content = svg.read()
svg.close()
# Tdelete file, it's not required anymore
os.remove(svg.name)
return HttpResponse(svg_content, content_type="image/svg+xml")
#-----------------
from pm4py.objects.log.importer.csv import factory as csv_importer
excellentLog1A = csv_importer.import_event_stream('Excellent1A_fixed.csv')
from pm4py.objects.conversion.log import factory as conversion_factory
log1 = conversion_factory.apply(excellentLog1A)
from pm4py.visualization.dfg import factory as dfg_vis_factory
gviz = dfg_vis_factory.apply(dfg1, log=log1, variant="frequency")
dfg_vis_factory.view(gviz)
from pm4py.objects.conversion.dfg import factory as dfg_mining_factory
net, im, fm = dfg_mining_factory.apply(dfg1)
from pm4py.visualization.petrinet import factory as pn_vis_factory
gviz = pn_vis_factory.apply(net, im, fm)
pn_vis_factory.view(gviz)
from pm4py.evaluation.replay_fitness import factory as replay_factory
fitness_alpha = replay_factory.apply(log1, net, im, fm)
from pm4py.algo.conformance.alignments import factory as align_factory
alignments = align_factory.apply(log1, net, im, fm)
print(alignments)
#excellentLog1A = excellentLog1A.sort_values(by=['org:resource','case','time:timestamp'])
def apply(dataframe, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by frequency metric
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
dfg = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
net, im, fm = inductive_miner.apply_dfg(dfg,
parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
spaths = get_shortest_paths(net)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="frequency")
gviz = pn_vis_factory.apply(net,
im,
fm,
parameters={"format": "svg"},
variant="frequency",
aggregated_statistics=aggregated_statistics)
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_petri(net, im, fm)
return get_base64_from_gviz(gviz), export_petri_as_string(
net, im, fm
), ".pnml", "parquet", activities, start_activities, end_activities, gviz_base64, ret_graph, "inductive", "freq", None, "", activity_key
import os
from pm4py.objects.log.importer.csv import factory as csv_importer
from pm4py.objects.conversion.log import factory as conversion_factory
from pm4py.algo.discovery.alpha import factory as alpha_miner
from pm4py.visualization.petrinet import factory as vis_factory
from tests.translucent_event_log_new.algo.discover_petrinet.alpha_revise import trans_alpha
event_stream = csv_importer.import_event_stream(
os.path.join("input_data", "sample.csv"))
log = conversion_factory.apply(event_stream)
net, im, fm = trans_alpha(log)
nett, imm, fmm = alpha_miner.apply(log)
gviz = vis_factory.apply(net, im, fm)
gvizz = vis_factory.apply(nett, imm, fmm)
vis_factory.view(gviz)
vis_factory.view(gvizz)
def export_petri_net(path, output):
net, i_m, f_m = output
viz_params = {"format": "svg", "debug": True}
gviz = pn_vis_factory.apply(net, i_m, f_m, parameters=viz_params)
pn_vis_factory.save(gviz, path)
def apply(log, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by frequency metric
Parameters
------------
log
Log
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
# reduce the depth of the search done by token-based replay
token_replay.MAX_REC_DEPTH = 1
token_replay.MAX_IT_FINAL1 = 1
token_replay.MAX_IT_FINAL2 = 1
token_replay.MAX_REC_DEPTH_HIDTRANSENABL = 1
log = attributes_filter.filter_log_on_max_no_activities(
log,
max_no_activities=constants.MAX_NO_ACTIVITIES,
parameters=parameters)
filtered_log = auto_filter.apply_auto_filter(log, parameters=parameters)
activities_count = attributes_filter.get_attribute_values(
filtered_log, activity_key)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
filtered_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(filtered_log,
parameters=parameters).keys())
dfg = dfg_factory.apply(filtered_log, parameters=parameters)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
net, im, fm = inductive_miner.apply_dfg(dfg,
parameters=parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
parameters["format"] = "svg"
gviz = pn_vis_factory.apply(net,
im,
fm,
log=filtered_log,
variant="frequency",
parameters=parameters)
svg = get_base64_from_gviz(gviz)
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_petri(net, im, fm)
return svg, export_petri_as_string(
net, im, fm
), ".pnml", "xes", activities, start_activities, end_activities, gviz_base64, ret_graph, "inductive", "freq", None, "", activity_key
(t2 - t1))
print(
"inductive is_sound_wfnet",
check_soundness.check_petri_wfnet_and_soundness(
inductive_model, debug=True))
parameters = {
pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY: activity_key,
pmutil.constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY:
activity_key,
"format": "png"
}
alpha_vis = petri_vis_factory.apply(alpha_model,
alpha_initial_marking,
alpha_final_marking,
log=log,
parameters=parameters,
variant="frequency")
vis_save(alpha_vis,
os.path.join(pngFolder, logNamePrefix + "_alpha.png"))
heuristics_vis = petri_vis_factory.apply(heu_model,
heu_initial_marking,
heu_final_marking,
log=log,
parameters=parameters,
variant="frequency")
vis_save(
heuristics_vis,
os.path.join(pngFolder, logNamePrefix + "_heuristics.png"))
inductive_vis = petri_vis_factory.apply(inductive_model,
inductive_im,
event_log = json_importer.import_log(os.path.join("form_json.json"),
parameters={"rename_map": rename})
event_log.sort()
trace_log = log_transform.transform_event_log_to_trace_log(event_log)
trace_log.sort()
trace_log.insert_trace_index_as_event_attribute()
net, marking, final_marking = alpha_factory.apply(trace_log)
for place in marking:
print("initial marking " + place.name)
for place in final_marking:
print("final marking " + place.name)
gviz = pn_vis_factory.apply(net,
marking,
final_marking,
parameters={"format": "svg"})
pn_vis_factory.view(gviz)
#xes_exporter.export_log(trace_log, "form_json.xes")
net, marking, final_marking = inductive_factory.apply(trace_log)
for place in marking:
print("initial marking " + place.name)
for place in final_marking:
print("final marking " + place.name)
gviz = pn_vis_factory.apply(net,
marking,
final_marking,
parameters={"format": "svg"})
pn_vis_factory.view(gviz)