Source code for pm4py.algo.discovery.performance_spectrum.variants.dataframe

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from enum import Enum

from pm4py.util import constants
from pm4py.util import exec_utils, pandas_utils
from pm4py.util import xes_constants as xes
from pm4py.util.constants import CASE_CONCEPT_NAME
from typing import Optional, Dict, Any, Union, List
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"
    SORT_LOG_REQUIRED = "sort_log_required"





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

    Parameters
    -------------
    dataframe
        Dataframe
    list_activities
        List of activities interesting for the performance spectrum (at least two)
    sample_size
        Size of the sample
    parameters
        Parameters of the algorithm,  including:
            - Parameters.ACTIVITY_KEY
            - Parameters.TIMESTAMP_KEY
            - Parameters.CASE_ID_KEY

    Returns
    -------------
    points
        Points of the performance spectrum
    """
    if parameters is None:
        parameters = {}

    import pandas as pd
    import numpy as np

    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, xes.DEFAULT_NAME_KEY
    )
    timestamp_key = exec_utils.get_param_value(
        Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY
    )
    sort_log_required = exec_utils.get_param_value(
        Parameters.SORT_LOG_REQUIRED, parameters, True
    )

    dataframe = dataframe[[case_id_glue, activity_key, timestamp_key]]
    dataframe[activity_key] = dataframe[activity_key].astype("string")
    dataframe = dataframe[dataframe[activity_key].isin(list_activities)]
    dataframe = pandas_utils.insert_index(
        dataframe, constants.DEFAULT_EVENT_INDEX_KEY
    )
    if sort_log_required:
        dataframe = dataframe.sort_values(
            [case_id_glue, timestamp_key, constants.DEFAULT_EVENT_INDEX_KEY]
        )
    dataframe[timestamp_key] = (
        dataframe[timestamp_key].astype(np.int64) / 10**9
    )

    def key(k, n):
        return k + str(n)

    # create a dataframe with all needed columns to check for the activities
    # pattern
    dfs = [
        dataframe.add_suffix(str(i)).shift(-i)
        for i in range(len(list_activities))
    ]
    dataframe = pandas_utils.concat(dfs, axis=1)
    # keep only rows that belong to exactly one case
    for i in range(len(list_activities) - 1):
        dataframe = dataframe[
            dataframe[key(case_id_glue, i)]
            == dataframe[key(case_id_glue, i + 1)]
        ]

    column_list = [key(activity_key, i) for i in range(len(list_activities))]
    pattern = "".join(list_activities)
    # keep only rows that have the desired activities pattern

    matches = dataframe[
        np.equal(dataframe[column_list].agg("".join, axis=1), pattern)
    ]
    if len(matches) > sample_size:
        matches = matches.sample(n=sample_size)

    filt_col_names = [
        timestamp_key + str(i) for i in range(len(list_activities))
    ]
    points = pandas_utils.to_dict_records(matches)
    points = [[p[tk] for tk in filt_col_names] for p in points]
    points = sorted(points, key=lambda x: x[0])

    return points