Spaces:
Sleeping
Sleeping
| import pm4py | |
| from pm4py.objects.conversion.log import converter as log_converter | |
| from typing import Union, Optional, Dict, Any | |
| from pm4py.objects.log.obj import EventLog, EventStream | |
| from enum import Enum | |
| from pm4py.util import exec_utils, constants, xes_constants | |
| from pm4py.statistics.service_time.pandas import get as service_time_get | |
| from pm4py.statistics.eventually_follows.pandas import get as eventually_follows | |
| import pandas as pd | |
| class Parameters(Enum): | |
| MAX_LEN = "max_len" | |
| RESPONSE_HEADER = "response_header" | |
| PERFORMANCE_AGGREGATION = "performance_aggregation" | |
| ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY | |
| TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY | |
| START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY | |
| CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY | |
| def apply(log_obj: Union[EventLog, EventStream, pd.DataFrame], | |
| parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> str: | |
| """ | |
| Provides an abstraction of the activities' frequency and performance. | |
| Minimum Viable Example: | |
| import pm4py | |
| from pm4py.algo.querying.llm.abstractions import log_to_acti_summary | |
| log = pm4py.read_xes("tests/input_data/receipt.xes") | |
| print(log_to_acti_summary.apply(log)) | |
| Example output: | |
| Below you find the top activities of the event log, specified with their total number of occurrences, the number of cases in which they occur, an aggregation of the service time, an aggregation of the times from the preceding activities and to the succeeding activities. | |
| Confirmation of receipt [tot.occ=1434; num.cases=1434; service time=0.00 s; time from prec.=0.00 s; time to succ.=76014.05 s] | |
| T02 Check confirmation of receipt [tot.occ=1368; num.cases=1316; service time=0.00 s; time from prec.=100581.24 s; time to succ.=44701.62 s] | |
| T06 Determine necessity of stop advice [tot.occ=1416; num.cases=1309; service time=0.00 s; time from prec.=186313.29 s; time to succ.=44757.08 s] | |
| T04 Determine confirmation of receipt [tot.occ=1307; num.cases=1303; service time=0.00 s; time from prec.=36815.50 s; time to succ.=65668.55 s] | |
| Parameters | |
| --------------- | |
| log_obj | |
| Log object | |
| parameters | |
| Optional parameters of the algorithm, including: | |
| - Parameters.MAX_LEN => desidered length of the textual abstraction | |
| - Parameters.RESPONSE_HEADER => includes an header in the textual abstraction, which explains the context | |
| - Parameters.PERFORMANCE_AGGREGATION => performance metric to be used to express the performance (e.g., mean). Available options: mean, median, stdev, min, max, sum | |
| - Parameters.ACTIVITY_KEY => the attribute of the log to be used as activity | |
| - Parameters.TIMESTAMP_KEY => the attribute of the log to be used as timestamp | |
| - Parameters.CASE_ID_KEY => the attribute of the log to be used as case identifier | |
| Returns | |
| -------------- | |
| textual_abstraction | |
| Textual abstraction of the activities frequency and of their performance. | |
| """ | |
| if parameters is None: | |
| parameters = {} | |
| response_header = exec_utils.get_param_value(Parameters.RESPONSE_HEADER, parameters, True) | |
| max_len = exec_utils.get_param_value(Parameters.MAX_LEN, parameters, constants.OPENAI_MAX_LEN) | |
| activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY) | |
| timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, | |
| xes_constants.DEFAULT_TIMESTAMP_KEY) | |
| start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters, | |
| xes_constants.DEFAULT_TIMESTAMP_KEY) | |
| case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME) | |
| performance_aggregation = exec_utils.get_param_value(Parameters.PERFORMANCE_AGGREGATION, parameters, "mean") | |
| log_obj = log_converter.apply(log_obj, variant=log_converter.Variants.TO_DATA_FRAME, parameters=parameters) | |
| log_obj = log_obj[list({activity_key, timestamp_key, start_timestamp_key, case_id_key})] | |
| num_occ = log_obj[activity_key].value_counts().to_dict() | |
| num_cases = log_obj.groupby([case_id_key, activity_key]).first().reset_index()[ | |
| activity_key].value_counts().to_dict() | |
| parameters["aggregationMeasure"] = performance_aggregation | |
| service_times = service_time_get.apply(log_obj, parameters=parameters) | |
| dir_follo_dataframe = eventually_follows.get_partial_order_dataframe(log_obj.copy(), activity_key=activity_key, | |
| start_timestamp_key=start_timestamp_key, | |
| timestamp_key=timestamp_key, | |
| case_id_glue=case_id_key, | |
| sort_caseid_required=False, | |
| sort_timestamp_along_case_id=False, | |
| reduce_dataframe=False) | |
| post_times = dir_follo_dataframe.groupby(activity_key)[constants.DEFAULT_FLOW_TIME].agg( | |
| performance_aggregation).to_dict() | |
| pre_times = dir_follo_dataframe.groupby(activity_key + "_2")[constants.DEFAULT_FLOW_TIME].agg( | |
| performance_aggregation).to_dict() | |
| activities_list = [] | |
| for act in num_occ: | |
| activities_list.append({"activity": act, "num_occ": num_occ[act], "num_cases": num_cases[act], | |
| "agg_service_time": service_times[act] if act in service_times else 0.0, | |
| "agg_pre_times": pre_times[act] if act in pre_times else 0.0, | |
| "agg_post_times": post_times[act] if act in post_times else 0.0}) | |
| activities_list = sorted(activities_list, key=lambda x: (x["num_cases"], x["num_occ"], x["activity"]), reverse=True) | |
| ret = "\n\n" | |
| if response_header: | |
| ret += "Below you find the top activities of the event log, specified with their total number of occurrences, the number of cases in which they occur, an aggregation of the service time, an aggregation of the times from the preceding activities and to the succeeding activities.\n\n" | |
| for dct in activities_list: | |
| ths = "%s [tot.occ=%d; num.cases=%d; service time=%.2f s; time from prec.=%.2f s; time to succ.=%.2f s]\n" % ( | |
| dct["activity"], dct["num_occ"], dct["num_cases"], dct["agg_service_time"], dct["agg_pre_times"], | |
| dct["agg_post_times"]) | |
| if len(ret) + len(ths) < max_len: | |
| ret = ret + ths | |
| else: | |
| break | |
| ret += "\n\n" | |
| return ret | |
| if __name__ == "__main__": | |
| log = pm4py.read_xes("../../../tests/input_data/receipt.xes") | |
| textual_abstraction = apply(log) | |
| print(textual_abstraction) | |