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(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
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
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
