def apply(log: EventLog, activity: str, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]: """ Gets the time passed to each succeeding activity Parameters ------------- log Log activity Activity that we are considering parameters Possible parameters of the algorithm Returns ------------- dictio Dictionary containing a 'post' key with the list of aggregates times from the given activity to each succeeding activity """ if parameters is None: parameters = {} log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters) dfg_frequency = native.native(log, parameters=parameters) dfg_performance = performance.performance(log, parameters=parameters) post = [] sum_perf_post = 0.0 sum_acti_post = 0.0 for entry in dfg_performance.keys(): if entry[0] == activity: post.append([ entry[1], float(dfg_performance[entry]), int(dfg_frequency[entry]) ]) sum_perf_post = sum_perf_post + float( dfg_performance[entry]) * float(dfg_frequency[entry]) sum_acti_post = sum_acti_post + float(dfg_frequency[entry]) perf_acti_post = 0.0 if sum_acti_post > 0: perf_acti_post = sum_perf_post / sum_acti_post return {"post": post, "post_avg_perf": perf_acti_post}
def get_decorated_net(net, initial_marking, final_marking, log, parameters=None, variant="frequency"): """ Get a decorated net according to the specified variant (decorate Petri net based on DFG) Parameters ------------ net Petri net initial_marking Initial marking final_marking Final marking log Log to use to decorate the Petri net parameters Algorithm parameters variant Specify if the decoration should take into account the frequency or the performance Returns ------------ gviz GraphViz object """ if parameters is None: parameters = {} aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE, parameters, "sum" if "frequency" in variant else "mean") activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY) stat_locale = exec_utils.get_param_value(Parameters.STAT_LOCALE, parameters, {}) # we find the DFG if variant == "performance": dfg = performance.performance(log, parameters=parameters) else: dfg = native.native(log, parameters=parameters) # we find shortest paths spaths = get_shortest_paths(net) # we find the number of activities occurrences in the log activities_count = attr_get.get_attribute_values(log, activity_key, parameters=parameters) aggregated_statistics = get_decorations_from_dfg_spaths_acticount(net, dfg, spaths, activities_count, variant=variant, aggregation_measure=aggregation_measure, stat_locale=stat_locale) return visualize.apply(net, initial_marking, final_marking, parameters=parameters, decorations=aggregated_statistics)
def apply(log, activity, parameters=None): """ Gets the time passed from each preceding activity Parameters ------------- log Log activity Activity that we are considering parameters Possible parameters of the algorithm Returns ------------- dictio Dictionary containing a 'pre' key with the list of aggregates times from each preceding activity to the given activity """ if parameters is None: parameters = {} dfg_frequency = native.native(log, parameters=parameters) dfg_performance = performance.performance(log, parameters=parameters) pre = [] sum_perf_pre = 0.0 sum_acti_pre = 0.0 for entry in dfg_performance.keys(): if entry[1] == activity: pre.append([ entry[0], float(dfg_performance[entry]), int(dfg_frequency[entry]) ]) sum_perf_pre = sum_perf_pre + float( dfg_performance[entry]) * float(dfg_frequency[entry]) sum_acti_pre = sum_acti_pre + float(dfg_frequency[entry]) perf_acti_pre = 0.0 if sum_acti_pre > 0: perf_acti_pre = sum_perf_pre / sum_acti_pre return {"pre": pre, "pre_avg_perf": perf_acti_pre}