def apply_petri(net,
initial_marking,
final_marking,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Visualize a BPMN graph from a Petri net, decorated with frequency, using the given parameters
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
(Optional) log where the replay technique should be applied
aggregated_statistics
(Optional) element-wise statistics calculated on the Petri net
parameters
Possible parameters of the algorithm, including:
format -> Format of the image to render (pdf, png, svg)
aggregationMeasure -> Measure to use to aggregate statistics
pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> Specification of the activity key
(if not concept:name)
pmutil.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY -> Specification of the timestamp key
(if not time:timestamp)
Returns
-----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
image_format = parameters["format"] if "format" in parameters else "png"
bpmn_graph, elements_correspondence, inv_el_corr, el_corr_keys_map = bpmn_converter.apply(
net, initial_marking, final_marking)
if aggregated_statistics is None and log is not None:
aggregated_statistics = alignments_decoration.get_alignments_decoration(
net, initial_marking, final_marking, log=log)
bpmn_aggreg_statistics = None
if aggregated_statistics is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_el_corr)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
def apply_through_conv_greedy(bpmn_graph,
dfg,
activities_count,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Decorate BPMN graph through conversion to Petri net, using shortest paths in the Petri net
Parameters
-------------
bpmn_graph
BPMN graph
dfg
Directly-Follows graph
activities_count
Count of occurrences of the activities
log
Log object
aggregated_statistics
Aggregated statistics object
parameters
Possible parameters of the algorithm
Returns
-------------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
del log
del aggregated_statistics
image_format = parameters["format"] if "format" in parameters else "png"
net, initial_marking, final_marking, elements_correspondence, inv_elements_correspondence, el_corr_keys_map = \
bpmn_to_petri.apply(bpmn_graph)
spaths = vis_trans_shortest_paths.get_shortest_paths(net,
enable_extension=True)
aggregated_statistics = vis_trans_shortest_paths.get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="performance")
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_elements_correspondence)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
def apply_through_conv(bpmn_graph,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Visualize a BPMN graph decorating it through conversion to a Petri net
Parameters
-----------
bpmn_graph
BPMN graph object
log
(Optional) log where the replay technique should be applied
aggregated_statistics
(Optional) element-wise statistics calculated on the Petri net
parameters
Possible parameters, of the algorithm, including:
format -> Format of the image to render (pdf, png, svg)
Returns
-----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
image_format = parameters["format"] if "format" in parameters else "png"
net, initial_marking, final_marking, elements_correspondence, inv_elements_correspondence, el_corr_keys_map = \
bpmn_to_petri.apply(bpmn_graph)
if aggregated_statistics is None and log is not None:
aggregated_statistics = token_decoration.get_decorations(
log,
net,
initial_marking,
final_marking,
parameters=parameters,
measure="performance",
ht_perf_method="first")
bpmn_aggreg_statistics = None
if aggregated_statistics is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_elements_correspondence)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
def apply_embedding_greedy(bpmn_graph,
dfg,
activities_count,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Embed decoration information inside the BPMN graph
Parameters
-----------
bpmn_graph
BPMN graph
dfg
Directly-Follows graph
activities_count
Count of occurrences of the activities
log
Log object
aggregated_statistics
Aggregated statistics object
parameters
Possible parameters of the algorithm
Returns
-----------
bpmn_graph
Annotated BPMN graph
"""
del parameters
del log
del aggregated_statistics
net, initial_marking, final_marking, elements_correspondence, inv_elements_correspondence, el_corr_keys_map = \
bpmn_to_petri.apply(bpmn_graph)
spaths = vis_trans_shortest_paths.get_shortest_paths(net,
enable_extension=True)
aggregated_statistics = vis_trans_shortest_paths.get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="performance")
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_elements_correspondence)
bpmn_graph = bpmn_embedding.embed_info_into_bpmn(bpmn_graph,
bpmn_aggreg_statistics,
"performance")
return bpmn_graph
def apply_embedding(bpmn_graph,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Embed decoration information inside the BPMN graph
Parameters
-----------
bpmn_graph
BPMN graph object
log
(Optional) log where the replay technique should be applied
aggregated_statistics
(Optional) element-wise statistics calculated on the Petri net
parameters
Possible parameters, of the algorithm
Returns
-----------
bpmn_graph
Annotated BPMN graph
"""
if parameters is None:
parameters = {}
net, initial_marking, final_marking, elements_correspondence, inv_elements_correspondence, el_corr_keys_map = \
bpmn_to_petri.apply(bpmn_graph)
if aggregated_statistics is None and log is not None:
aggregated_statistics = token_decoration.get_decorations(
log,
net,
initial_marking,
final_marking,
parameters=parameters,
measure="performance")
if aggregated_statistics is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_elements_correspondence)
bpmn_graph = bpmn_embedding.embed_info_into_bpmn(
bpmn_graph, bpmn_aggreg_statistics, "performance")
return bpmn_graph
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_petri_greedy(net,
initial_marking,
final_marking,
log=None,
aggr_stat=None,
parameters=None):
"""
Visualize a BPMN graph from a Petri net, decorated with performance, using the given parameters (greedy algorithm)
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
(Optional) log where the replay technique should be applied
aggr_stat
(Optional) element-wise statistics calculated on the Petri net
parameters
Possible parameters of the algorithm, including:
format -> Format of the image to render (pdf, png, svg)
aggregationMeasure -> Measure to use to aggregate statistics
pm4py.util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> Specification of the activity key
(if not concept:name)
pm4py.util.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY -> Specification of the timestamp key
(if not time:timestamp)
Returns
-----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
image_format = parameters["format"] if "format" in parameters else "png"
bpmn_graph, el_corr, inv_el_corr, el_corr_keys_map = bpmn_converter.apply(
net, initial_marking, final_marking)
activity_key = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY] if PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else DEFAULT_NAME_KEY
if aggr_stat is None and log is not None:
dfg = dfg_factory.apply(log, variant="performance")
activities_count = attributes_filter.get_attribute_values(
log, activity_key)
spaths = vis_trans_shortest_paths.get_shortest_paths(net)
aggr_stat = vis_trans_shortest_paths.get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="performance")
bpmn_aggreg_statistics = None
if aggr_stat is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggr_stat, inv_el_corr)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
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
