def apply(log, parameters=None):
"""
Gets the performance HNet
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
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)
start_activities_count = start_activities_filter.get_start_activities(filtered_log, parameters=parameters)
end_activities_count = end_activities_filter.get_end_activities(filtered_log, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(start_activities_count.keys())
end_activities = list(end_activities_count.keys())
dfg_freq = dfg_factory.apply(filtered_log, parameters=parameters)
dfg_perf = dfg_factory.apply(filtered_log, variant="performance", parameters=parameters)
heu_net = HeuristicsNet(dfg_freq, performance_dfg=dfg_perf, activities=activities, start_activities=start_activities, end_activities=end_activities, activities_occurrences=activities_count)
heu_net.calculate(dfg_pre_cleaning_noise_thresh=constants.DEFAULT_DFG_CLEAN_MULTIPLIER * decreasingFactor)
vis = heu_vis_factory.apply(heu_net, parameters={"format": "svg"})
vis2 = heu_vis_factory.apply(heu_net, parameters={"format": "dot"})
gviz_base64 = get_base64_from_file(vis2.name)
return get_base64_from_file(vis.name), None, "", "xes", activities, start_activities, end_activities, gviz_base64, [], "heuristics", "perf", None, "", activity_key
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
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)
def apply(dataframe, parameters=None):
"""
Gets the performance HNet
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 ws_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=ws_constants.MAX_NO_ACTIVITIES)
dataframe, end_activities_count = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
start_activities_count = start_activities_filter.get_start_activities(
dataframe, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(start_activities_count.keys())
end_activities = list(end_activities_count.keys())
dfg_frequency, dfg_performance = df_statistics.get_dfg_graph(
dataframe,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
measure="both",
sort_caseid_required=False,
sort_timestamp_along_case_id=False)
heu_net = HeuristicsNet(dfg_frequency,
performance_dfg=dfg_performance,
activities=activities,
start_activities=start_activities,
end_activities=end_activities,
activities_occurrences=activities_count)
heu_net.calculate(dfg_pre_cleaning_noise_thresh=ws_constants.
DEFAULT_DFG_CLEAN_MULTIPLIER * decreasingFactor)
vis = heu_vis_factory.apply(heu_net, parameters={"format": "svg"})
vis2 = heu_vis_factory.apply(heu_net, parameters={"format": "dot"})
gviz_base64 = get_base64_from_file(vis2.name)
return get_base64_from_file(vis.name), None, "", "parquet", activities, start_activities, end_activities, gviz_base64, [], "heuristics", "perf", None, "", activity_key
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")
