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

'''
    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/>.
'''
from enum import Enum

import numpy as np
import pandas as pd

from pm4py.util import constants, points_subset
from pm4py.util import exec_utils, pandas_utils
from pm4py.util import xes_constants as xes
from typing import Optional, Dict, Any, Union, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd


[docs]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 PARAMETER_SAMPLE_SIZE = "sample_size" SORT_LOG_REQUIRED = "sort_log_required"
[docs]def gen_patterns(pattern, length): return ["".join(pattern[i:i + length]) for i in range(len(pattern) - (length - 1))]
[docs]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 disconnected 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 = {} case_id_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.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 = 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 all_patterns = [(len(list_activities) - i, gen_patterns(list_activities, len(list_activities) - i)) for i in range(len(list_activities) - 1)] def key(k, n): return k + str(n) def to_points(match, l): return {'case_id': match[key(case_id_glue, 0)], 'points': [(match[key(activity_key, i)], match[key(timestamp_key, i)]) for i in range(l)]} points = [] for l, patterns in all_patterns: # concat shifted and suffixed dataframes to get a dataframe that allows to check for the patterns dfs = [dataframe.add_suffix(str(i)).shift(-i) for i in range(l)] df_merged = pd.concat(dfs, axis=1) indices = [shift_index(dfs[i].index, i) for i in range(len(dfs))] mindex = pd.MultiIndex.from_arrays(indices) df_merged = df_merged.set_index(mindex) for i in range(l - 1): df_merged = df_merged[df_merged[key(case_id_glue, i)] == df_merged[key(case_id_glue, i + 1)]] column_list = [key(activity_key, i) for i in range(l)] matches = df_merged[np.isin(df_merged[column_list].sum(axis=1), patterns)] points.extend([to_points(m, l) for m in matches.to_dict('records')]) # drop rows of this match to not discover subsets of this match again dataframe = dataframe.drop([int(i) for indices in matches.index for i in indices[:-1]]) pass points = sorted(points, key=lambda x: min(x['points'], key=lambda x: x[1])[1]) if len(points) > sample_size: points = points_subset.pick_chosen_points_list(sample_size, points) return points
[docs]def shift_index(index, n): if n == 0: return list(index) nones = [None for _ in range(n)] return list(index[n:]) + nones