Source code for pm4py.algo.discovery.dfg.variants.clean

import time
from enum import Enum
from typing import Optional, Dict, Any

import numpy as np
import pandas as pd

from pm4py.objects.dfg.obj import DFG
from pm4py.util import constants, exec_utils
from pm4py.util import xes_constants as xes_util




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY



CONST_AUX_ACT_START = "aux_act_start"
CONST_PROCESS_START = "#!$#PROCESS_START#!$#"
CONST_AUX_ACT_END = "aux_act_end"
CONST_PROCESS_END = "#!$#PROCESS_END#!$#"




def apply(
    log: pd.DataFrame, parameters: Optional[Dict[str, Any]] = None
) -> DFG:
    parameters = {} if parameters is None else parameters
    act_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes_util.DEFAULT_NAME_KEY
    )
    cid_key = exec_utils.get_param_value(
        Parameters.CASE_ID_KEY, parameters, constants.CASE_ATTRIBUTE_GLUE
    )
    time_key = exec_utils.get_param_value(
        Parameters.TIMESTAMP_KEY, parameters, xes_util.DEFAULT_TIMESTAMP_KEY
    )

    df = (
        log.sort_values([cid_key, time_key])
        .loc[:, [cid_key, act_key]]
        .reset_index()
    )

    aux_act_start = CONST_AUX_ACT_START + str(time.time())
    aux_act_end = CONST_AUX_ACT_END + str(time.time())
    df[aux_act_start] = (
        df.groupby(cid_key)[act_key]
        .shift(1)
        .replace(np.nan, CONST_PROCESS_START)
    )
    df[aux_act_end] = (
        df.groupby(cid_key)[act_key]
        .shift(-1)
        .replace(np.nan, CONST_PROCESS_END)
    )

    starters = df[(df[aux_act_start] == CONST_PROCESS_START)]
    borders = df[(df[aux_act_end] == CONST_PROCESS_END)]
    connections = df[
        (
            (df[aux_act_start] != CONST_PROCESS_START)
            & (df[aux_act_end] != CONST_PROCESS_END)
        )
    ]

    dfg = DFG()

    for a, b, f in list(
        connections.groupby([act_key, aux_act_end])
        .size()
        .reset_index()
        .itertuples(index=False, name=None)
    ):
        dfg.graph[(a, b)] += f

    for a, f in list(
        starters.groupby([act_key])
        .size()
        .reset_index()
        .itertuples(index=False, name=None)
    ):
        dfg.start_activities[a] += f

    for a, f in list(
        borders.groupby([act_key])
        .size()
        .reset_index()
        .itertuples(index=False, name=None)
    ):
        dfg.end_activities[a] += f

    return dfg