'''
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 scipy.spatial.distance import squareform
import numpy as np
from pm4py.algo.clustering.trace_attribute_driven.variants import act_dist_calc
from pm4py.algo.clustering.trace_attribute_driven.variants import suc_dist_calc
from pm4py.algo.clustering.trace_attribute_driven.leven_dist import (
leven_dist_calc,
)
from pm4py.algo.clustering.trace_attribute_driven.dfg import dfg_dist
[docs]
def dfg_dis(loglist, percent, alpha):
size = len(loglist)
dist_mat = np.zeros((size, size))
for i in range(0, size - 1):
for j in range(i + 1, size):
(dist_act, dist_dfg) = dfg_dist.dfg_dist_calc(
loglist[i], loglist[j]
)
dist_mat[i][j] = dist_act * alpha + dist_dfg * (1 - alpha)
dist_mat[j][i] = dist_mat[i][j]
y = squareform(dist_mat)
return y
[docs]
def eval_avg_variant(loglist, percent, alpha):
size = len(loglist)
dist_mat = np.zeros((size, size))
for i in range(0, size - 1):
for j in range(i + 1, size):
dist_act = act_dist_calc.act_sim_percent_avg(
loglist[i], loglist[j], percent, percent
)
dist_suc = suc_dist_calc.suc_sim_percent_avg(
loglist[i], loglist[j], percent, percent
)
dist_mat[i][j] = dist_act * alpha + dist_suc * (1 - alpha)
dist_mat[j][i] = dist_mat[i][j]
y = squareform(dist_mat)
return y
[docs]
def eval_DMM_variant(loglist, percent, alpha):
size = len(loglist)
dist_mat = np.zeros((size, size))
for i in range(0, size - 1):
for j in range(i + 1, size):
dist_act = act_dist_calc.act_sim_percent(
loglist[i], loglist[j], percent, percent
)
dist_suc = suc_dist_calc.suc_sim_percent(
loglist[i], loglist[j], percent, percent
)
dist_mat[i][j] = dist_act * alpha + dist_suc * (1 - alpha)
dist_mat[j][i] = dist_mat[i][j]
y = squareform(dist_mat)
return y
[docs]
def eval_avg_leven(loglist, percent, alpha):
size = len(loglist)
dist_mat = np.zeros((size, size))
for i in range(0, size - 1):
for j in range(i + 1, size):
dist_mat[i][j] = leven_dist_calc.leven_dist_avg(
loglist[i], loglist[j], percent, percent
)
dist_mat[j][i] = dist_mat[i][j]
y = squareform(dist_mat)
return y
[docs]
def eval_DMM_leven(loglist, percent, alpha):
size = len(loglist)
dist_mat = np.zeros((size, size))
for i in range(0, size - 1):
for j in range(i + 1, size):
dist_mat[i][j] = leven_dist_calc.leven_dist(
loglist[i], loglist[j], percent, percent
)
dist_mat[j][i] = dist_mat[i][j]
y = squareform(dist_mat)
return y
