Source code for pm4py.statistics.passed_time.pandas.variants.pre

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
from pm4py.util.xes_constants import DEFAULT_NAME_KEY, DEFAULT_TIMESTAMP_KEY
from pm4py.util.constants import CASE_CONCEPT_NAME
from pm4py.algo.discovery.dfg.adapters.pandas import df_statistics as pandas
from pm4py.util import exec_utils
from pm4py.util import constants
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple, List, Set
import pandas as pd


[docs]class Parameters(Enum): ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY MAX_NO_POINTS_SAMPLE = "max_no_of_points_to_sample" KEEP_ONCE_PER_CASE = "keep_once_per_case" BUSINESS_HOURS = "business_hours" WORKTIMING = "worktiming" WEEKENDS = "weekends" WORKCALENDAR = "workcalendar"
[docs]def apply(df: pd.DataFrame, activity: str, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]: """ Gets the time passed from each preceding activity Parameters ------------- df Dataframe 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 = {} case_id_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, CASE_CONCEPT_NAME) activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, DEFAULT_NAME_KEY) timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, DEFAULT_TIMESTAMP_KEY) start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters, None) business_hours = exec_utils.get_param_value(Parameters.BUSINESS_HOURS, parameters, False) worktiming = exec_utils.get_param_value(Parameters.WORKTIMING, parameters, [7, 17]) weekends = exec_utils.get_param_value(Parameters.WEEKENDS, parameters, [6, 7]) workcalendar = exec_utils.get_param_value(Parameters.WORKCALENDAR, parameters, constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR) [dfg_frequency, dfg_performance] = pandas.get_dfg_graph(df, measure="both", activity_key=activity_key, case_id_glue=case_id_glue, timestamp_key=timestamp_key, start_timestamp_key=start_timestamp_key, business_hours=business_hours, worktiming=worktiming, weekends=weekends, workcalendar=workcalendar) 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}