Source code for pm4py.statistics.traces.generic.pandas.case_arrival

'''
    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/>.
'''
import pandas as pd

from pm4py.util.xes_constants import DEFAULT_TIMESTAMP_KEY
from pm4py.util.constants import CASE_CONCEPT_NAME
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


[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"
[docs]def get_case_arrival_avg(df: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> float: """ Gets the average time interlapsed between case starts Parameters -------------- df Pandas dataframe parameters Parameters of the algorithm, including: Parameters.TIMESTAMP_KEY -> attribute of the log to be used as timestamp Returns -------------- case_arrival_avg Average time interlapsed between case starts """ if parameters is None: parameters = {} caseid_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, CASE_CONCEPT_NAME) timest_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, DEFAULT_TIMESTAMP_KEY) first_df = df.groupby(caseid_glue).first() first_df = first_df.sort_values(timest_key) first_df_shift = first_df.shift(-1) first_df_shift.columns = [str(col) + '_2' for col in first_df_shift.columns] df_successive_rows = pd.concat([first_df, first_df_shift], axis=1) df_successive_rows['interlapsed_time'] = ( df_successive_rows[timest_key + '_2'] - df_successive_rows[timest_key]).astype('timedelta64[s]') df_successive_rows = df_successive_rows.dropna(subset=['interlapsed_time']) return df_successive_rows['interlapsed_time'].mean()
[docs]def get_case_dispersion_avg(df, parameters=None): """ Gets the average time interlapsed between case ends Parameters -------------- df Pandas dataframe parameters Parameters of the algorithm, including: Parameters.TIMESTAMP_KEY -> attribute of the log to be used as timestamp Returns -------------- case_dispersion_avg Average time interlapsed between the completion of cases """ if parameters is None: parameters = {} caseid_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, CASE_CONCEPT_NAME) timest_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, DEFAULT_TIMESTAMP_KEY) first_df = df.groupby(caseid_glue).last() first_df = first_df.sort_values(timest_key) first_df_shift = first_df.shift(-1) first_df_shift.columns = [str(col) + '_2' for col in first_df_shift.columns] df_successive_rows = pd.concat([first_df, first_df_shift], axis=1) df_successive_rows['interlapsed_time'] = ( df_successive_rows[timest_key + '_2'] - df_successive_rows[timest_key]).astype('timedelta64[s]') df_successive_rows = df_successive_rows.dropna(subset=['interlapsed_time']) return df_successive_rows['interlapsed_time'].mean()