def get_content(self,
log_path,
encrypt_result=True,
frequency_threshold=0.0,
key="CHIAVECHIAVECHIA"):
pp = privacyPreserving(log_path.split("@@")[0])
if encrypt_result:
svg_output_file = "pm4py_enc.svg"
else:
svg_output_file = "pm4py_dec.svg"
aaa = pp.result_maker_pma(
log_path,
encrypt_result,
True,
True,
frequency_threshold,
svg_output_file,
activity_activity_matrix_path=tempfile.NamedTemporaryFile(
suffix='.csv').name,
key=key)
svg = get_base64_from_file(svg_output_file)
xml = get_base64_from_file(log_path)
ext = "image/svg"
return ext, svg, xml
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 get_case_duration_svg(dataframe, parameters=None):
"""
Gets the SVG of the case duration graph
Parameters
-------------
dataframe
Dataframe
parameters
Possible parameters of the algorithm
Returns
-------------
graph
Case duration graph
"""
if parameters is None:
parameters = {}
x, y = case_statistics.get_kde_caseduration(dataframe, parameters)
gviz = graphs_factory.apply_plot(x,
y,
variant="cases",
parameters={"format": "svg"})
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret = []
for i in range(len(x)):
ret.append((x[i], y[i]))
return get_base64_from_file(gviz), gviz_base64, ret
def get_events_per_time_svg(log, parameters=None):
"""
Gets the SVG of the events per time graph
Parameters
-------------
log
Log
parameters
Possible parameters of the algorithm
Returns
-------------
graph
Case duration graph
"""
if parameters is None:
parameters = {}
x, y = attributes_filter.get_kde_date_attribute(log, parameters=parameters)
gviz = graphs_factory.apply_plot(x,
y,
variant="dates",
parameters={"format": "svg"})
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret = []
for i in range(len(x)):
ret.append((x[i].replace(tzinfo=None).timestamp(), y[i]))
return get_base64_from_file(gviz), gviz_base64, ret
def get_numeric_attribute_distr_svg(dataframe, attribute_key, parameters=None):
"""
Gets the distribution of a numeric attribute values
Parameters
------------
dataframe
Dataframe
attribute_key
Attribute that we are interested to consider
parameters
Possible parameters of the algorithm
Returns
-------------
graph
Case duration graph
gviz_base64
Base64 representation of the dot
ret
List of points
"""
if parameters is None:
parameters = {}
x, y = attributes_filter.get_kde_numeric_attribute(dataframe,
attribute_key)
gviz = graphs_factory.apply_plot(x,
y,
variant="attributes",
parameters={"format": "svg"})
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret = []
for i in range(len(x)):
ret.append((x[i], y[i]))
return get_base64_from_file(gviz), gviz_base64, ret
def apply(dataframe, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by performance 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, dfg_perf
] = 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,
measure="both")
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
dfg_perf = {x: y for x, y in dfg_perf.items() if x in dfg}
net, im, fm = inductive_miner.apply_dfg(dfg,
parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
spaths = get_shortest_paths(net)
bpmn_graph, el_corr, inv_el_corr, el_corr_keys_map = petri_to_bpmn.apply(
net, im, fm)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net, dfg_perf, spaths, activities_count, variant="performance")
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_el_corr)
#bpmn_graph = bpmn_embedding.embed_info_into_bpmn(bpmn_graph, bpmn_aggreg_statistics, "performance")
bpmn_graph = bpmn_diagram_layouter.apply(bpmn_graph)
bpmn_string = bpmn_exporter.get_string_from_bpmn(bpmn_graph)
gviz = bpmn_vis_factory.apply_petri(
net,
im,
fm,
aggregated_statistics=aggregated_statistics,
variant="performance",
parameters={"format": "svg"})
gviz2 = bpmn_vis_factory.apply_petri(
net,
im,
fm,
aggregated_statistics=aggregated_statistics,
variant="performance",
parameters={"format": "dot"})
gviz_base64 = get_base64_from_file(gviz2.name)
ret_graph = get_graph.get_graph_from_petri(net, im, fm)
return get_base64_from_file(gviz.name), export_petri_as_string(
net, im, fm
), ".pnml", "parquet", activities, start_activities, end_activities, gviz_base64, ret_graph, "indbpmn", "perf", bpmn_string, ".bpmn", activity_key
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
def apply(log, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by performance 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=log, variant="performance", parameters=parameters)
bpmn_graph, el_corr, inv_el_corr, el_corr_keys_map = petri_to_bpmn.apply(
net, im, fm)
aggregated_statistics = token_decoration.get_decorations(
filtered_log,
net,
im,
fm,
parameters=parameters,
measure="performance")
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_el_corr)
#bpmn_graph = bpmn_embedding.embed_info_into_bpmn(bpmn_graph, bpmn_aggreg_statistics, "performance")
bpmn_graph = bpmn_diagram_layouter.apply(bpmn_graph)
bpmn_string = bpmn_exporter.get_string_from_bpmn(bpmn_graph)
gviz = bpmn_vis_factory.apply_petri(
net,
im,
fm,
aggregated_statistics=aggregated_statistics,
variant="performance",
parameters={"format": "svg"})
gviz2 = bpmn_vis_factory.apply_petri(
net,
im,
fm,
aggregated_statistics=aggregated_statistics,
variant="performance",
parameters={"format": "dot"})
svg = get_base64_from_file(gviz.name)
gviz_base64 = get_base64_from_file(gviz2.name)
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, "indbpmn", "perf", bpmn_string, ".bpmn", activity_key
