Source code for pm4py.algo.discovery.dfg.variants.case_attributes

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

from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import exec_utils, constants, xes_constants


[docs]class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY CASE_ATTRIBUTES = "case_attributes" RETURN_NODES_ATTRIBUTES = "return_nodes_attributes"
[docs]def apply(log: EventLog, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Union[ Tuple[Dict[Tuple[str, str], Dict[str, Dict[str, Any]]], Dict[str, Dict[str, Dict[str, Any]]]], Dict[ Tuple[str, str], Dict[str, Dict[str, Any]]]]: """ Discovers a directly-follows graph from an event log, with the edges that are annotated with the different values for the given case attributes. Parameters ----------------- log Event log parameters Parameters of the variant, including: - Parameters.ACTIVITY_KEY => the attribute to use as activity - Parameters.CASE_ATTRIBUTES => the case attributes that are used to annotate the edges (default: the case ID) - Parameters.RETURN_NODES_ATTRIBUTES => (optional) returns also a dictionary with the values of the attributes for each activity of the graph (default: False) Returns ----------------- dfg Directly-follows graph (with the edges annotated with the specified case attributes), e.g.: {('register request', 'examine casually'): {'creator': {'Fluxicon Nitro': 3}, 'concept:name': {'3': 1, '6': 1, '5': 1}} ... nodes (Optional) dictionary of activities (annotated with the specified case attributes), e.g.: {'register request': {'creator': {'Fluxicon Nitro': 6}, 'concept:name': {'3': 1, '2': 1, '1': 1, '6': 1, '5': 1, '4': 1}} ... """ if parameters is None: parameters = {} log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters) activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY) case_attributes = exec_utils.get_param_value(Parameters.CASE_ATTRIBUTES, parameters, list([xes_constants.DEFAULT_TRACEID_KEY])) return_nodes_attributes = exec_utils.get_param_value(Parameters.RETURN_NODES_ATTRIBUTES, parameters, False) dfg = {} for trace in log: for attr in set(case_attributes).intersection(set(trace.attributes)): attr_value = trace.attributes[attr] for i in range(len(trace) - 1): ev_couple = (trace[i][activity_key], trace[i + 1][activity_key]) if ev_couple not in dfg: dfg[ev_couple] = {} if attr not in dfg[ev_couple]: dfg[ev_couple][attr] = {} if attr_value not in dfg[ev_couple][attr]: dfg[ev_couple][attr][attr_value] = 0 dfg[ev_couple][attr][attr_value] += 1 if return_nodes_attributes: nodes = {} for trace in log: for attr in set(case_attributes).intersection(set(trace.attributes)): attr_value = trace.attributes[attr] for i in range(len(trace)): act = trace[i][activity_key] if act not in nodes: nodes[act] = {} if attr not in nodes[act]: nodes[act][attr] = {} if attr_value not in nodes[act][attr]: nodes[act][attr][attr_value] = 0 nodes[act][attr][attr_value] += 1 return dfg, nodes return dfg