Source code for pm4py.analysis

'''
    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 typing import List, Optional, Tuple, Dict, Union

from pm4py.objects.log.obj import Trace, EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.utils import get_properties
from pm4py.util.pandas_utils import check_is_pandas_dataframe, check_pandas_dataframe_columns

import pandas as pd


[docs]def construct_synchronous_product_net(trace: Trace, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking) -> Tuple[PetriNet, Marking, Marking]: """ constructs the synchronous product net between a trace and a Petri net process model. Parameters ---------------- trace Trace of an event log petri_net Petri net initial_marking Initial marking final_marking Final marking Returns ---------------- sync_net Synchronous product net sync_im Initial marking of the sync net sync_fm Final marking of the sync net """ from pm4py.objects.petri_net.utils.petri_utils import construct_trace_net from pm4py.objects.petri_net.utils.synchronous_product import construct from pm4py.objects.petri_net.utils.align_utils import SKIP trace_net, trace_im, trace_fm = construct_trace_net(trace) sync_net, sync_im, sync_fm = construct(trace_net, trace_im, trace_fm, petri_net, initial_marking, final_marking, SKIP) return sync_net, sync_im, sync_fm
[docs]def solve_marking_equation(petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, cost_function: Dict[PetriNet.Transition, float] = None) -> float: """ Solves the marking equation of a Petri net. The marking equation is solved as an ILP problem. An optional transition-based cost function to minimize can be provided as well. Parameters --------------- petri_net Petri net initial_marking Initial marking final_marking Final marking cost_function optional cost function to use when solving the marking equation. Returns ---------------- h_value Heuristics value calculated resolving the marking equation """ from pm4py.algo.analysis.marking_equation import algorithm as marking_equation if cost_function is None: cost_function = dict() for t in petri_net.transitions: cost_function[t] = 1 me = marking_equation.build( petri_net, initial_marking, final_marking, parameters={'costs': cost_function}) return marking_equation.get_h_value(me)
[docs]def solve_extended_marking_equation(trace: Trace, sync_net: PetriNet, sync_im: Marking, sync_fm: Marking, split_points: Optional[List[int]] = None) -> float: """ Gets an heuristics value (underestimation of the cost of an alignment) between a trace and a synchronous product net using the extended marking equation with the standard cost function (e.g. sync moves get cost equal to 0, invisible moves get cost equal to 1, other move on model / move on log get cost equal to 10000), with an optimal provisioning of the split points Parameters ---------------- trace Trace sync_net Synchronous product net sync_im Initial marking (of the sync net) sync_fm Final marking (of the sync net) split_points If specified, the indexes of the events of the trace to be used as split points. If not specified, the split points are identified automatically Returns ---------------- h_value Heuristics value calculated resolving the marking equation """ from pm4py.algo.analysis.extended_marking_equation import algorithm as extended_marking_equation parameters = {} if split_points is not None: parameters[extended_marking_equation.Variants.CLASSIC.value.Parameters.SPLIT_IDX] = split_points me = extended_marking_equation.build( trace, sync_net, sync_im, sync_fm, parameters=parameters) return extended_marking_equation.get_h_value(me)
[docs]def check_soundness(petri_net: PetriNet, initial_marking: Marking, final_marking: Marking) -> bool: """ Check if a given Petri net is a sound WF-net. A Petri net is a WF-net iff: - it has a unique source place - it has a unique end place - every element in the WF-net is on a path from the source to the sink place A WF-net is sound iff: - it contains no live-locks - it contains no deadlocks - we are able to always reach the final marking For a formal definition of sound WF-net, consider: http://www.padsweb.rwth-aachen.de/wvdaalst/publications/p628.pdf Parameters --------------- petri_net Petri net initial_marking Initial marking final_marking Final marking Returns -------------- boolean Soundness """ from pm4py.algo.analysis.woflan import algorithm as woflan return woflan.apply(petri_net, initial_marking, final_marking)
[docs]def insert_artificial_start_end(log: Union[EventLog, pd.DataFrame]) -> Union[EventLog, pd.DataFrame]: """ Inserts the artificial start/end activities in an event log / Pandas dataframe Parameters ------------------ log Event log / Pandas dataframe Returns ------------------ log Event log / Pandas dataframe with artificial start / end activities """ properties = get_properties(log) if check_is_pandas_dataframe(log): check_pandas_dataframe_columns(log) from pm4py.objects.log.util import dataframe_utils return dataframe_utils.insert_artificial_start_end(log, parameters=properties) else: from pm4py.objects.log.util import artificial return artificial.insert_artificial_start_end(log, parameters=properties)
[docs]def check_is_workflow_net(net: PetriNet) -> bool: """ Checks if the input Petri net satisfies the WF-net conditions: 1. unique source place 2. unique sink place 3. every node is on a path from the source to the sink Parameters ------------------ net PetriNet Returns ------------------ True iff the input net is a WF-net. """ from pm4py.algo.analysis.workflow_net import algorithm return algorithm.apply(net)