def generate_heuristics_miner_net(xes_log):
try:
heu_net = heuristics_miner.apply_heu(xes_log)
gviz = hn_vis_factory.apply(heu_net)
hn_vis_factory.view(gviz)
return {'heu_net': heu_net}
except AttributeError:
print("Please check your input values")
def test_heunet_running_example(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"
log = xes_importer.import_log(
os.path.join(INPUT_DATA_DIR, "running-example.xes"))
heu_net = heuristics_miner.apply_heu(log)
gviz = hn_vis_factory.apply(heu_net)
del gviz
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 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 SPHeuristicsMiner(context):
args = context.args
inputFile = os.path.join(args.inputData, os.listdir(args.inputData)[0])
log = xes_importer.import_log(inputFile)
heu_net = heuristics_miner.apply_heu(
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 = hn_vis_factory.apply(heu_net)
hn_vis_factory.save(gviz,
os.path.join(args.outputData, "heuristics-miner.png"))
return args.outputData
def find_input(log, timewindow=4, parameters=None):
'''
Finds input binding and output binding for each activity
:param log: input log
:param timewindow: size for binding (default = 4)
:param parameters: parameter for heuristic miner (same with heuristic miner's parameters)
:return: input_dict, (dictionary for input binding for each activity)
'''
heu_net = heuristics_miner.apply_heu(log, parameters)
input_cand = {}
for act, values in heu_net.nodes.items():
input_cand[act] = set()
for t in values.input_connections:
input_cand[act].add(t.node_name)
input_dict = {}
for act in heu_net.nodes:
input_dict[act] = set()
for act in input_cand:
input = set()
for trace in log:
for event_index, event in enumerate(trace):
if event["concept:name"] == act:
index = event_index
for ev in trace[max(index - timewindow, 0):index]:
if ev["concept:name"] in input_cand[act]:
input.add(ev["concept:name"])
if len(input) > 0:
input_dict[act].add(frozenset(input))
input = set()
for key, value in input_dict.items():
if key in heu_net.start_activities[0].keys():
value.add(frozenset())
return input_dict
def run_heuristic_net(log,parameters={"dependency_thresh": 0.99}):
heu_net = heuristics_miner.apply_heu(log, parameters)
#gviz = vis_heu.apply(heu_net)
#vis_heu.view(gviz)
return heu_net
def find_input_output(log, timewindow=4, parameters=None):
'''
Finds input binding and output binding for each activity
:param log: input log
:param timewindow: size for binding (default = 4)
:param parameters: parameter for heuristic miner (same with heuristic miner's parameters)
:return: input_dict, output_dict (dictionary for input binding and output binding for each activity)
'''
heu_net = heuristics_miner.apply_heu(log, parameters)
input_cand = {}
output_cand = {}
for act, values in heu_net.nodes.items():
input_cand[act] = set()
output_cand[act] = set()
for t in values.input_connections:
input_cand[act].add(t.node_name)
for t in values.output_connections:
output_cand[act].add(t.node_name)
input_dict = {}
output_dict = {}
input_exact = {}
output_exact = {}
for trace in log:
case_id = trace.attributes['concept:name']
output_exact[case_id] = []
input_exact[case_id] = []
for i in range(len(trace)):
output_exact[case_id].append(set())
input_exact[case_id].append(set())
for act in heu_net.nodes:
input_dict[act] = set()
output_dict[act] = set()
for act in input_cand:
input = set()
output = set()
for trace in log:
case_id = trace.attributes['concept:name']
for event_index, event in enumerate(trace):
if event["concept:name"] == act:
index = event_index
for ev in trace[index + 1:index + timewindow + 1]:
if ev["concept:name"] in output_cand[act]:
output.add(ev["concept:name"])
output_exact[case_id][event_index] = output
output_dict[act].add(frozenset(output))
output = set()
for ev in trace[max(index - timewindow, 0):index]:
if ev["concept:name"] in input_cand[act]:
input.add(ev["concept:name"])
input_exact[case_id][event_index] = input
input_dict[act].add(frozenset(input))
input = set()
return input_dict, output_dict, input_exact, output_exact
a2 = pcaDataWeeksFrequency[w].loc[
pcaDataWeeksFrequency[w]['result_exam_1'] == 1, ['pc2']]
b2 = pcaDataWeeksFrequency[w].loc[
pcaDataWeeksFrequency[w]['result_exam_1'] == 0, ['pc2']]
t2, p2 = stats.ttest_ind(a2, b2)
print('Week ' + str(w) + ':')
print('--PC1: ' + 't-value: ' + str(t1) + ' p-value: ' + str(p1))
print('-- Excellent: ' + str(a1.mean()[0]))
print('-- Weak: ' + str(b1.mean()[0]))
print('--PC2: ' + 't-value: ' + str(t2) + ' p-value: ' + str(p2))
print('-- Excellent: ' + str(a2.mean()[0]))
print('-- Weak: ' + str(b2.mean()[0]))
#Heuristic Miner
from pm4py.objects.conversion.log import factory as conversion_factory
Log = pd.concat(workingWeekLog)
ex1_personal_log_1_converted = conversion_factory.apply(
Log.loc[Log['org:resource'].isin(ex3_excellent.index)])
ex1_personal_log_2_converted = conversion_factory.apply(
Log.loc[Log['org:resource'].isin(ex3_weak.index)])
from pm4py.algo.discovery.heuristics import factory as heuristics_miner
from pm4py.visualization.heuristics_net import factory as hn_vis_factory
excellent_heu_net = heuristics_miner.apply_heu(
ex1_personal_log_2_converted, parameters={"dependency_thresh": 0.0})
gviz = hn_vis_factory.apply(excellent_heu_net)
hn_vis_factory.view(gviz)
from pm4py.algo.discovery.heuristics import factory as heuristics_miner
from pm4py.objects.log.importer.csv import factory as csv_importer
from pm4py.objects.log.adapters.pandas import csv_import_adapter
from pm4py.objects.conversion.log import factory as conversion_factory
from pm4py.visualization.petrinet import factory as pn_vis_factory
from pm4py.visualization.heuristics_net import factory as hn_vis_factory
from pm4py.util import constants
import os
os.environ["PATH"] += os.pathsep + 'C:/Archivos de Programa/GraphViz/bin/'
dataframe = csv_import_adapter.import_dataframe_from_path(
'cursos.csv', sep=",")
log = conversion_factory.apply(dataframe, parameters={constants.PARAMETER_CONSTANT_CASEID_KEY: "case:concept:name",
constants.PARAMETER_CONSTANT_ACTIVITY_KEY: "concept:name",
constants.PARAMETER_CONSTANT_TIMESTAMP_KEY: "time:timestamp"})
net = heuristics_miner.apply_heu(
log, parameters={"dependency_thresh": 0.99})
gviz = hn_vis_factory.apply(net)
hn_vis_factory.view(gviz)
def create_heuristics_miner(self):
heu_net = heuristics_miner.apply_heu(self._log, parameters={"dependency_thresh": 0.99})
gviz = hn_vis_factory.apply(heu_net, parameters={"format": "pdf"})
self._create_image(gviz, "heuristic_miner")
