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 apply(log, parameters=None): """ Gets the frequency DFG 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) 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) parameters["format"] = "svg" parameters["start_activities"] = start_activities parameters["end_activities"] = end_activities gviz = dfg_vis_factory.apply(dfg, log=filtered_log, variant="frequency", parameters=parameters) gviz_base64 = base64.b64encode(str(gviz).encode('utf-8')) ret_graph = get_graph.get_graph_from_dfg(dfg, start_activities, end_activities) net, im, fm = dfg_conv_factory.apply(dfg, parameters={ "start_activities": start_activities, "end_activities": end_activities }) return get_base64_from_gviz(gviz), export_petri_as_string( net, im, fm ), ".pnml", "xes", activities, start_activities, end_activities, gviz_base64, ret_graph, "dfg", "freq", None, "", activity_key
def apply(log, parameters=None, classic_output=False): """ Gets a simple model out of a log Parameters ------------- log Trace log parameters Parameters of the algorithm, including: maximum_number_activities -> Maximum number of activities to keep discovery_algorithm -> Discovery algorithm to use (alpha, inductive) desidered_output -> Desidered output of the algorithm (default: Petri) include_filtered_log -> Include the filtered log in the output include_dfg_frequency -> Include the DFG of frequencies in the output include_dfg_performance -> Include the DFG of performance in the output include_filtered_dfg_frequency -> Include the filtered DFG of frequencies in the output include_filtered_dfg_performance -> Include the filtered DFG of performance in the output classic_output Determine if the output shall contains directly the objects (e.g. net, initial_marking, final_marking) or can return a more detailed dictionary """ if parameters is None: parameters = {} returned_dictionary = {} net = None initial_marking = None final_marking = None bpmn_graph = None dfg_frequency = None dfg_performance = None filtered_dfg_frequency = None filtered_dfg_performance = None maximum_number_activities = parameters[ "maximum_number_activities"] if "maximum_number_activities" in parameters else 20 discovery_algorithm = parameters["discovery_algorithm"] if "discovery_algorithm" in parameters else "alpha" desidered_output = parameters["desidered_output"] if "desidered_output" in parameters else "petri" include_filtered_log = parameters["include_filtered_log"] if "include_filtered_log" in parameters else True include_dfg_frequency = parameters["include_dfg_frequency"] if "include_dfg_frequency" in parameters else True include_dfg_performance = parameters[ "include_dfg_performance"] if "include_dfg_performance" in parameters else False include_filtered_dfg_frequency = parameters[ "include_filtered_dfg_frequency"] if "include_filtered_dfg_frequency" in parameters else True include_filtered_dfg_performance = parameters[ "include_filtered_dfg_performance"] if "include_filtered_dfg_performance" in parameters else False if PARAMETER_CONSTANT_ATTRIBUTE_KEY in parameters: activity_key = parameters[ PARAMETER_CONSTANT_ATTRIBUTE_KEY] if PARAMETER_CONSTANT_ATTRIBUTE_KEY in parameters else DEFAULT_NAME_KEY parameters[PARAMETER_CONSTANT_ATTRIBUTE_KEY] = activity_key else: log, activity_key = insert_classifier.search_act_class_attr(log) if activity_key is None: activity_key = DEFAULT_NAME_KEY parameters[PARAMETER_CONSTANT_ATTRIBUTE_KEY] = activity_key if PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters: parameters[PARAMETER_CONSTANT_ACTIVITY_KEY] = parameters[PARAMETER_CONSTANT_ATTRIBUTE_KEY] activities_count_dictio = attributes_filter.get_attribute_values(log, activity_key) activities_count_list = [] for activity in activities_count_dictio: activities_count_list.append([activity, activities_count_dictio[activity]]) activities_count_list = sorted(activities_count_list, key=lambda x: x[1], reverse=True) activities_count_list = activities_count_list[:min(len(activities_count_list), maximum_number_activities)] activities_keep_list = [x[0] for x in activities_count_list] log = attributes_filter.apply(log, activities_keep_list, parameters=parameters) filtered_log = None if "alpha" in discovery_algorithm: # parameters_sa = deepcopy(parameters) # parameters_sa["decreasingFactor"] = 1.0 filtered_log = start_activities_filter.apply_auto_filter(log, parameters=parameters) filtered_log = end_activities_filter.apply_auto_filter(filtered_log, parameters=parameters) filtered_log = filter_topvariants_soundmodel.apply(filtered_log, parameters=parameters) elif "dfg_mining" in discovery_algorithm: filtered_log = start_activities_filter.apply_auto_filter(log, parameters=parameters) filtered_log = end_activities_filter.apply_auto_filter(filtered_log, parameters=parameters) filtered_log = auto_filter.apply_auto_filter(filtered_log, parameters=parameters) if include_dfg_frequency or "dfg_mining" in discovery_algorithm: dfg_frequency = dfg_factory.apply(log, parameters=parameters, variant="frequency") if include_dfg_performance: dfg_performance = dfg_factory.apply(log, parameters=parameters, variant="performance") if include_filtered_dfg_frequency: filtered_dfg_frequency = dfg_factory.apply(filtered_log, parameters=parameters, variant="frequency") if include_filtered_dfg_performance: filtered_dfg_performance = dfg_factory.apply(filtered_log, parameters=parameters, variant="performance") if "alpha" in discovery_algorithm: net, initial_marking, final_marking = alpha_miner.apply(filtered_log, parameters=parameters) elif "dfg_mining" in discovery_algorithm: start_activities = start_activities_filter.get_start_activities(filtered_log, parameters=parameters) end_activities = end_activities_filter.get_end_activities(filtered_log, parameters=parameters) parameters_conv = {} parameters_conv["start_activities"] = start_activities parameters_conv["end_activities"] = end_activities net, initial_marking, final_marking = dfg_conv_factory.apply(dfg_frequency, parameters=parameters_conv) if filtered_log is not None and include_filtered_log: returned_dictionary["filtered_log"] = filtered_log if net is not None and desidered_output == "petri": returned_dictionary["net"] = net if initial_marking is not None and desidered_output == "petri": returned_dictionary["initial_marking"] = initial_marking if final_marking is not None and desidered_output == "petri": returned_dictionary["final_marking"] = final_marking if bpmn_graph is not None and desidered_output == "bpmn": returned_dictionary["bpmn_graph"] = bpmn_graph if dfg_frequency is not None and include_dfg_frequency: returned_dictionary["dfg_frequency"] = dfg_frequency if dfg_performance is not None and include_dfg_performance: returned_dictionary["dfg_performance"] = dfg_performance if filtered_dfg_frequency is not None and include_filtered_dfg_frequency: returned_dictionary["filtered_dfg_frequency"] = filtered_dfg_frequency if filtered_dfg_performance is not None and include_filtered_dfg_performance: returned_dictionary["filtered_dfg_performance"] = filtered_dfg_performance if classic_output: if net is not None and desidered_output == "petri": return net, initial_marking, final_marking return returned_dictionary
def apply(log, parameters=None): """ Gets the process tree using Inductive Miner Directly-Follows 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) 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) tree = inductive_miner.apply_tree_dfg(dfg, parameters=parameters, activities=activities, start_activities=start_activities, end_activities=end_activities) parameters["format"] = "svg" gviz = pt_vis_factory.apply(tree, parameters=parameters) gviz_base64 = base64.b64encode(str(gviz).encode('utf-8')) return get_base64_from_gviz(gviz), None, "", "xes", activities, start_activities, end_activities, gviz_base64, [], "tree", "freq", None, "", 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(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