'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from pm4py.algo.conformance.tokenreplay import algorithm as tr_algorithm
from pm4py.util.colors import get_string_from_int_below_255
from collections import Counter
from copy import copy
import matplotlib as mpl
import matplotlib.cm as cm
import math
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.objects.conversion.log import converter as log_converter
import pandas as pd
[docs]
def give_color_to_direction_dynamic(dir):
"""
Assigns a color to the direction (dynamic-defined colors)
Parameters
--------------
dir
Direction
Returns
--------------
col
Color
"""
dir = 0.5 + 0.5 * dir
norm = mpl.colors.Normalize(vmin=0, vmax=1)
nodes = [0.0, 0.01, 0.25, 0.4, 0.45, 0.55, 0.75, 0.99, 1.0]
colors = [
"deepskyblue",
"skyblue",
"lightcyan",
"lightgray",
"gray",
"lightgray",
"mistyrose",
"salmon",
"tomato",
]
cmap = mpl.colors.LinearSegmentedColormap.from_list(
"mycmap2", list(zip(nodes, colors))
)
# cmap = cm.plasma
m = cm.ScalarMappable(norm=norm, cmap=cmap)
rgba = m.to_rgba(dir)
r = get_string_from_int_below_255(math.ceil(rgba[0] * 255.0))
g = get_string_from_int_below_255(math.ceil(rgba[1] * 255.0))
b = get_string_from_int_below_255(math.ceil(rgba[2] * 255.0))
return "#" + r + g + b
[docs]
def give_color_to_direction_static(dir):
"""
Assigns a color to the direction (static-defined colors)
Parameters
--------------
dir
Direction
Returns
--------------
col
Color
"""
direction_colors = [
[-0.5, "#4444FF"],
[-0.1, "#AAAAFF"],
[0.0, "#CCCCCC"],
[0.5, "#FFAAAA"],
[1.0, "#FF4444"],
]
for col in direction_colors:
if col[0] >= dir:
return col[1]
[docs]
def compare_element_usage_two_logs(
net: PetriNet,
im: Marking,
fm: Marking,
log1: Union[EventLog, pd.DataFrame],
log2: Union[EventLog, pd.DataFrame],
parameters: Optional[Dict[Any, Any]] = None,
) -> Dict[Any, Any]:
"""
Returns some statistics (also visual) about the comparison of the usage
of the elements in two logs given an accepting Petri net
Parameters
-------------
net
Petri net
im
Initial marking
fm
Final marking
log1
First log
log2
Second log
parameters
Parameters of the algorithm (to be passed to the token-based replay)
Returns
----------------
aggregated_statistics
Statistics about the usage of places, transitions and arcs in the net
"""
if parameters is None:
parameters = {}
log1 = log_converter.apply(
log1,
variant=log_converter.Variants.TO_EVENT_LOG,
parameters=parameters,
)
log2 = log_converter.apply(
log2,
variant=log_converter.Variants.TO_EVENT_LOG,
parameters=parameters,
)
tr_parameters = copy(parameters)
tr_parameters[
tr_algorithm.Variants.TOKEN_REPLAY.value.Parameters.ENABLE_PLTR_FITNESS
] = True
rep_traces1, pl_fit_trace1, tr_fit_trace1, ne_act_model1 = (
tr_algorithm.apply(log1, net, im, fm, parameters=tr_parameters)
)
rep_traces2, pl_fit_trace2, tr_fit_trace2, ne_act_model2 = (
tr_algorithm.apply(log2, net, im, fm, parameters=tr_parameters)
)
tr_occ1 = Counter(
[y for x in rep_traces1 for y in x["activated_transitions"]]
)
tr_occ2 = Counter(
[y for x in rep_traces2 for y in x["activated_transitions"]]
)
pl_occ1 = Counter(
{
p: pl_fit_trace1[p]["c"] + pl_fit_trace1[p]["r"]
for p in pl_fit_trace1
}
)
pl_occ2 = Counter(
{
p: pl_fit_trace2[p]["c"] + pl_fit_trace2[p]["r"]
for p in pl_fit_trace2
}
)
all_replayed_transitions = set(tr_occ1.keys()).union(set(tr_occ2.keys()))
all_replayed_places = set(pl_occ1.keys()).union(set(pl_occ2.keys()))
all_transitions = all_replayed_transitions.union(set(net.transitions))
all_places = all_replayed_places.union(set(net.places))
aggregated_statistics = {}
for place in all_places:
aggregated_statistics[place] = {
"log1_occ": pl_occ1[place],
"log2_occ": pl_occ2[place],
"total_occ": pl_occ1[place] + pl_occ2[place],
}
aggregated_statistics[place]["label"] = "(%d/%d/%d)" % (
pl_occ1[place],
pl_occ2[place],
pl_occ1[place] + pl_occ2[place],
)
dir = (
(pl_occ2[place] - pl_occ1[place])
/ (pl_occ1[place] + pl_occ2[place])
if (pl_occ1[place] + pl_occ2[place]) > 0
else 0
)
aggregated_statistics[place]["direction"] = dir
aggregated_statistics[place]["color"] = (
give_color_to_direction_dynamic(dir)
)
for trans in all_transitions:
aggregated_statistics[trans] = {
"log1_occ": tr_occ1[trans],
"log2_occ": tr_occ2[trans],
"total_occ": tr_occ1[trans] + tr_occ2[trans],
}
if trans.label is not None:
aggregated_statistics[trans]["label"] = trans.label + " "
else:
aggregated_statistics[trans]["label"] = ""
aggregated_statistics[trans]["label"] = aggregated_statistics[trans][
"label"
] + "(%d/%d/%d)" % (
tr_occ1[trans],
tr_occ2[trans],
tr_occ1[trans] + tr_occ2[trans],
)
dir = (
(tr_occ2[trans] - tr_occ1[trans])
/ (tr_occ1[trans] + tr_occ2[trans])
if (tr_occ1[trans] + tr_occ2[trans]) > 0
else 0
)
aggregated_statistics[trans]["direction"] = dir
aggregated_statistics[trans]["color"] = (
give_color_to_direction_dynamic(dir)
)
for arc in trans.in_arcs:
aggregated_statistics[arc] = aggregated_statistics[trans]
for arc in trans.out_arcs:
aggregated_statistics[arc] = aggregated_statistics[trans]
return aggregated_statistics
