src.utils package

Subpackages

Submodules

src.utils.django_orm module

src.utils.django_orm.duplicate_orm_row(obj)

src.utils.event_attributes module

src.utils.event_attributes.get_additional_columns(log)

src.utils.event_attributes.get_event_attributes(log)

Get log event attributes that are not name or time

As log file is a list, it has no global event attributes. Getting from first event of first trace. This may be bad.

src.utils.event_attributes.get_global_event_attributes(log)

Get log event attributes that are not name or time

src.utils.event_attributes.get_global_trace_attributes(log)

src.utils.event_attributes.unique_events(log)

List of unique events using event concept:name

Adds all events into a list and removes duplicates while keeping order.

src.utils.event_attributes.unique_events2(training_log, test_log)

Combines unique events from two logs into one list.

Renamed to 2 because Python doesn’t allow functions with same names. Python is objectively the worst language.

src.utils.file_service module

src.utils.file_service.create_unique_name(name)

Return type:str

src.utils.file_service.get_log(log)

Read in event log from disk

Uses xes_importer to parse log.

Return type:EventLog

src.utils.file_service.save_result(results, job, start_time)

src.utils.log_metrics module

src.utils.log_metrics.avg_events_in_log(log)

Returns the average number of events in any trace

:return 3

Return type:int

src.utils.log_metrics.event_executions(log)

Creates dict of event execution count

Return {‘Event A’:  7, ‘2011-01-06’: 8} Return type:OrderedDict

src.utils.log_metrics.events_by_date(log)

Creates dict of events by date ordered by date

Return {‘2010-12-30’:  7, ‘2011-01-06’: 8} Return type:OrderedDict

src.utils.log_metrics.events_in_trace(log)

Creates dict of number of events in trace

Return {‘4’:11, ‘3’: 8} Return type:OrderedDict

src.utils.log_metrics.max_events_in_log(log)

Returns the maximum number of events in any trace

:return 3

Return type:int

src.utils.log_metrics.new_trace_start(log)

Creates dict of new traces by date

Return {‘2010-12-30’:  1, ‘2011-01-06’: 2} Return type:OrderedDict

src.utils.log_metrics.resources_by_date(log)

Creates dict of used unique resources ordered by date

Resource and timestamp delimited by &&. If this is in resources name, bad stuff will happen. Returns a dict with a date and the number of unique resources used on that day. :return {‘2010-12-30’: 7, ‘2011-01-06’: 8}

Return type:OrderedDict

src.utils.log_metrics.std_var_events_in_log(log)

Returns the standard variation of the average number of events in any trace

:return 3

Return type:int

src.utils.log_metrics.trace_attributes(log)

Creates an array of dicts that describe trace attributes. Only looks at first trace. Filters out concept:name.

Return [{name:‘name’, type: ‘string’, example: 34}] Return type:list

src.utils.result_metrics module

src.utils.result_metrics.calculate_auc(actual, scores, auc)

Return type:float

src.utils.result_metrics.calculate_nlevenshtein(actual, predicted)

Return type:float

src.utils.result_metrics.calculate_results_classification(actual, predicted)

Return type:dict

src.utils.result_metrics.calculate_results_regression(input_df, label)

Return type:dict

src.utils.result_metrics.calculate_results_time_series_prediction(actual, predicted)

Return type:dict

src.utils.result_metrics.get_auc(actual, scores)

Return type:float

src.utils.result_metrics.get_confusion_matrix(actual, predicted)

Return type:dict

src.utils.tests_utils module

src.utils.tests_utils.create_test_clustering(clustering_type='noCluster', configuration={})

Return type:Clustering

src.utils.tests_utils.create_test_encoding(prefix_length=1, padding=False, value_encoding='simpleIndex', add_elapsed_time=False, add_remaining_time=False, add_resources_used=False, add_new_traces=False, add_executed_events=False, task_generation_type='only')

Return type:Encoding

src.utils.tests_utils.create_test_hyperparameter_optimizer(hyperoptim_type='hyperopt', performance_metric='acc', max_evals=10)

src.utils.tests_utils.create_test_job(split=None, encoding=None, labelling=None, clustering=None, predictive_model=None, job_type='prediction', hyperparameter_optimizer=None)

src.utils.tests_utils.create_test_labelling(label_type='next_activity', attribute_name=None, threshold_type='threshold_mean', threshold=0.0)

Return type:Labelling

src.utils.tests_utils.create_test_log(log_name='general_example.xes', log_path='cache/log_cache/test_logs/general_example.xes')

Return type:Log

src.utils.tests_utils.create_test_predictive_model(predictive_model='classification', prediction_method='randomForest')

Return type:PredictiveModel

src.utils.tests_utils.create_test_split(split_type='single', split_ordering_method='sequential', test_size=0.2, original_log=None, train_log=None, test_log=None)

src.utils.time_metrics module

src.utils.time_metrics.count_on_event_day(trace, date_dict, event_id)

Finds the date of event and returns the value from date_dict :param date_dict one of the dicts from log_metrics.py :param event_id Event id :param trace Log trace

src.utils.time_metrics.duration(trace)

Calculate the duration of a trace

src.utils.time_metrics.elapsed_time(trace, event)

Calculate elapsed time by event in trace

src.utils.time_metrics.elapsed_time_id(trace, event_index)

Calculate elapsed time by event index in trace

src.utils.time_metrics.remaining_time(trace, event)

Calculate remaining time by event in trace

src.utils.time_metrics.remaining_time_id(trace, event_index)

Calculate remaining time by event index in trace

Module contents