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

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

import polars as pl

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 = "aux_act_"
CONST_AUX_CASE = "aux_case_"
CONST_COUNT = "count_"




def apply(
    log: pl.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
    )
    aux_act = CONST_AUX_ACT + str(time.time())
    aux_case = CONST_AUX_CASE + str(time.time())
    df = log[[cid_key, act_key, time_key]].clone()
    df = df.sort([cid_key, time_key])
    df = df[[cid_key, act_key]]
    df = df.with_column(df[act_key].shift(-1).alias(aux_act))
    df = df.with_column(df[cid_key].shift(-1).alias(aux_case))
    dfg = DFG()

    excl_starter = df[0, act_key]
    borders = df.filter(df[cid_key] != df[aux_case])

    for d in filter(
        lambda d: d[aux_act] is not None,
        borders.groupby([aux_act]).count().to_dicts(),
    ):
        v = d["count"] + 1 if d[aux_act] == excl_starter else d["count"]
        dfg.start_activities[d[aux_act]] = v

    for d in filter(
        lambda d: d[act_key] is not None,
        borders.groupby([act_key]).count().to_dicts(),
    ):
        dfg.end_activities[d[act_key]] = d["count"]

    for d in (
        df.filter((df[cid_key] == df[aux_case]))
        .groupby([act_key, aux_act])
        .count()
        .to_dicts()
    ):
        dfg.graph[(d[act_key], d[aux_act])] = d["count"]

    return dfg