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'''
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 pm4py.objects.ocel.obj import OCEL
from typing import Optional, Dict, Any
from pm4py.algo.discovery.ocel.ocdfg import algorithm as ocdfg_disc
import numpy as np
from enum import Enum
from pm4py.util import exec_utils, constants
class Parameters(Enum):
MAX_LEN = "max_len"
INCLUDE_HEADER = "include_header"
INCLUDE_PERFORMANCE = "include_performance"
def __get_descr(curr, include_performance):
stru = " \"%s\" -> \"%s\" (frequency (number of events) = %d, frequency (number of objects) = %d" % (
curr[1][0], curr[1][1], curr[2], curr[3])
if include_performance:
stru += ", duration = %.2f" % curr[5]
stru += ")\n"
return stru
def apply(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None) -> str:
if parameters is None:
parameters = {}
max_len = exec_utils.get_param_value(Parameters.MAX_LEN, parameters, constants.OPENAI_MAX_LEN)
include_header = exec_utils.get_param_value(Parameters.INCLUDE_HEADER, parameters, True)
include_performance = exec_utils.get_param_value(Parameters.INCLUDE_PERFORMANCE, parameters, True)
ocdfg = ocdfg_disc.apply(ocel, parameters=parameters)
object_types = sorted(list(ocdfg["edges"]["total_objects"].keys()))
edges = set()
for ot in object_types:
for e in ocdfg["edges"]["event_couples"][ot]:
edges.add((ot, e))
edges_values = []
for obj in edges:
ot = obj[0]
e = obj[1]
edges_values.append([obj[0], obj[1],
len(ocdfg["edges"]["event_couples"][ot][e]),
len(ocdfg["edges"]["unique_objects"][ot][e]),
len(ocdfg["edges"]["total_objects"][ot][e]),
float(np.average(ocdfg["edges_performance"]["event_couples"][ot][e])),
float(np.average(ocdfg["edges_performance"]["total_objects"][ot][e]))
])
edges_values = sorted(edges_values, key=lambda x: (x[2], x[5], x[0], x[1]), reverse=True)
i = 0
curr_len = 0
while i < len(edges_values):
if curr_len >= max_len:
break
stru = __get_descr(edges_values[i], include_performance)
curr_len += len(stru)
i = i + 1
edges_values = edges_values[:i]
ot_edges = {}
for edg in edges_values:
if not edg[0] in ot_edges:
ot_edges[edg[0]] = []
ot_edges[edg[0]].append(edg)
ret = ["\n"]
if include_header:
ret.append("If I have an object-centric event log with the following directly follows graph (split between the different object types):\n")
for ot in ot_edges:
ret.append("\nObject type: %s\n" % (ot))
for edg in ot_edges[ot]:
ret.append(__get_descr(edg, include_performance))
ret.append("\n")
return "".join(ret)
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