Source code for pm4py.algo.discovery.footprints.log.variants.entire_dataframe

'''
    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
from pm4py.util import xes_constants
from pm4py.util import constants
from pm4py.algo.discovery.dfg.adapters.pandas import df_statistics
from pm4py.util import exec_utils, pandas_utils
from pm4py.algo.discovery.causal import algorithm as causal_discovery
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
import pandas as pd


[docs]class Outputs(Enum): DFG = "dfg" SEQUENCE = "sequence" PARALLEL = "parallel" START_ACTIVITIES = "start_activities" END_ACTIVITIES = "end_activities" ACTIVITIES = "activities" SKIPPABLE = "skippable" ACTIVITIES_ALWAYS_HAPPENING = "activities_always_happening" MIN_TRACE_LENGTH = "min_trace_length" TRACE = "trace"
[docs]class Parameters(Enum): SORT_REQUIRED = "sort_required" 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 INDEX_KEY = "index_key"
DEFAULT_SORT_REQUIRED = True DEFAULT_INDEX_KEY = "@@index"
[docs]def apply(df: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]: """ Discovers a footprint object from a dataframe (the footprints of the dataframe are returned) Parameters -------------- df Dataframe parameters Parameters of the algorithm Returns -------------- footprints_obj Footprints object """ if parameters is None: parameters = {} activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY) caseid_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME) start_timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, None) timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes_constants.DEFAULT_TIMESTAMP_KEY) sort_required = exec_utils.get_param_value(Parameters.SORT_REQUIRED, parameters, DEFAULT_SORT_REQUIRED) index_key = exec_utils.get_param_value(Parameters.INDEX_KEY, parameters, DEFAULT_INDEX_KEY) df = df[[caseid_key, activity_key, timestamp_key]] if sort_required: df = pandas_utils.insert_index(df, index_key) if start_timestamp_key is not None: df = df.sort_values([caseid_key, start_timestamp_key, timestamp_key, index_key]) else: df = df.sort_values([caseid_key, timestamp_key, index_key]) grouped_df = df.groupby(caseid_key) dfg = df_statistics.get_dfg_graph(df, measure="frequency", activity_key=activity_key, case_id_glue=caseid_key, timestamp_key=timestamp_key, sort_caseid_required=False, sort_timestamp_along_case_id=False, start_timestamp_key=start_timestamp_key) activities = set(df[activity_key].unique()) start_activities = set(grouped_df.first()[activity_key].unique()) end_activities = set(grouped_df.last()[activity_key].unique()) parallel = {(x, y) for (x, y) in dfg if (y, x) in dfg} sequence = set(causal_discovery.apply(dfg, causal_discovery.Variants.CAUSAL_ALPHA)) ret = {} ret[Outputs.DFG.value] = dfg ret[Outputs.SEQUENCE.value] = sequence ret[Outputs.PARALLEL.value] = parallel ret[Outputs.ACTIVITIES.value] = activities ret[Outputs.START_ACTIVITIES.value] = start_activities ret[Outputs.END_ACTIVITIES.value] = end_activities ret[Outputs.MIN_TRACE_LENGTH.value] = int(grouped_df.size().min()) return ret