Source code for pm4py.algo.discovery.performance_spectrum.algorithm

from pm4py.algo.discovery.performance_spectrum.variants import (
    dataframe,
    log,
    dataframe_disconnected,
    log_disconnected,
)
from pm4py.util import exec_utils
from enum import Enum
from pm4py.util import constants, pandas_utils
from typing import Optional, Dict, Any, Union, List
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
    ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY
    PARAMETER_SAMPLE_SIZE = "sample_size"





class Outputs(Enum):
    LIST_ACTIVITIES = "list_activities"
    POINTS = "points"





class Variants(Enum):
    DATAFRAME = dataframe
    LOG = log
    DATAFRAME_DISCONNECTED = dataframe_disconnected
    LOG_DISCONNECTED = log_disconnected





def apply(
    log: Union[EventLog, EventStream, pd.DataFrame],
    list_activities: List[str],
    variant=None,
    parameters: Optional[Dict[Any, Any]] = None,
) -> Dict[str, Any]:
    """
    Finds the performance spectrum provided a log/dataframe
    and a list of activities

    Parameters
    -------------
    log
        Event log/Dataframe
    list_activities
        List of activities interesting for the performance spectrum (at least two)
    variant
        Variant to be used (see Variants Enum)
    parameters
        Parameters of the algorithm, including:
            - Parameters.ACTIVITY_KEY
            - Parameters.TIMESTAMP_KEY

    Returns
    -------------
    ps
        Performance spectrum object (dictionary)
    """
    from pm4py.objects.conversion.log import converter as log_conversion

    if parameters is None:
        parameters = {}

    sample_size = exec_utils.get_param_value(
        Parameters.PARAMETER_SAMPLE_SIZE, parameters, 10000
    )

    if len(list_activities) < 2:
        raise Exception(
            "performance spectrum can be applied providing at least two activities!"
        )

    points = None

    if pandas_utils.check_is_pandas_dataframe(log):
        if variant is None:
            variant = Variants.DATAFRAME

        points = exec_utils.get_variant(variant).apply(
            log, list_activities, sample_size, parameters
        )

    if points is None:
        if variant is None:
            variant = Variants.LOG

        points = exec_utils.get_variant(variant).apply(
            log_conversion.apply(
                log,
                variant=log_conversion.Variants.TO_EVENT_LOG,
                parameters=parameters,
            ),
            list_activities,
            sample_size,
            parameters,
        )

    ps = {
        Outputs.LIST_ACTIVITIES.value: list_activities,
        Outputs.POINTS.value: points,
    }

    return ps