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Commit 698969b7 authored by Anthony Larcher's avatar Anthony Larcher
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Deactivate ILP for dependency issue

parent c0e803f5
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s4d/clustering/ilp_iv.py
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......@@ -23,249 +23,249 @@
Copyright 2014-2020 Sylvain Meignier and Anthony Larcher
"""
import matplotlib.pyplot as plt
import time
import os
from ecyglpki import Problem, SimplexControls
from scipy.cluster import hierarchy as hac
from .hac_utils import *
class ILP_IV:
"""
"""
def __init__(self, diar, scores, threshold=0.0):
"""
:param diar:
:param scores:
:param threshold:
"""
self.sep = '!!'
self.dic = dict()
self.diar = copy.deepcopy(diar)
self.thr = threshold
self.scores = copy.deepcopy(scores)
def plot(self, link, cluster_list):
"""
:param link:
:param cluster_list:
:return:
"""
#print(link)
hac.dendrogram(link, color_threshold=self.thr, labels=cluster_list)
plt.show()
def perform(self, save_ilp=False):
"""
Creates an LP problem and solves it using GLPK library
(with ecyglpki Cython interface)
:param save_ilp: if True, saves the generated problem
in CPLEX LP format (problem.ilp), and the solution of
the problem (solution.ilp).
:return: a Diar object with the new clusters and a
dictionary of clusters (old clusters as key, new ones
as value in an array).
"""
distances, t = scores2distance(self.scores, self.thr)
cluster_list = sorted(self.scores.modelset.tolist())
lp = Problem()
lp.set_obj_dir('minimize')
# columns
cols = []
for idx, cluster in enumerate(cluster_list):
col = '{}{}{}'.format(cluster, self.sep, cluster)
cols.append(col)
lp.add_named_cols(col)
lp.set_obj_coef(col, 1)
lp.set_col_bnds(col, 0, None)
# sum of dist > thr in the lower triangular part od the distance matrix
mask = (np.tril(distances, -1) > t)
threshold = np.tril(distances, -1).copy()
threshold[mask] = 0
s = np.sum(threshold) + 1
l = len(cluster_list)
for i in range(l):
cluster_i = cluster_list[i]
for j in range(i + 1, l):
if distances[i, j] < t:
cluster_j = cluster_list[j]
v = distances[i, j] / s
col = '{}{}{}'.format(cluster_i, self.sep, cluster_j)
cols.append(col)
lp.add_named_cols(col)
lp.set_obj_coef(col, v)
lp.set_col_bnds(col, 0, None)
# rows
for i, cluster_i in enumerate(cluster_list):
r_cols = {}
row = 'S{}'.format(i)
lp.add_named_rows(row)
r_cols['{}{}{}'.format(cluster_i, self.sep, cluster_i)] = 1
for j, cluster_j in enumerate(cluster_list):
if i != j and distances[i, j] < t:
col = '{}{}{}'.format(cluster_i, self.sep, cluster_j)
if col not in cols:
cols.append(col)
lp.add_named_cols(col)
lp.set_col_bnds(col, 0, None)
r_cols[col] = 1
#boundaries <= 0
col = '{}{}{}'.format(cluster_i, self.sep, cluster_j)
idx = lp.add_rows(1)
lp.set_mat_row(idx, {'{}{}{}'.format(cluster_j, self.sep, cluster_j):-1, col:1})
lp.set_row_bnds(idx, None, 0)
lp.set_mat_row(row, r_cols)
lp.set_row_bnds(row, 1, 1)
if save_ilp:
lp.write_lp('problem.ilp')
# solving problem
ctrl = SimplexControls()
ctrl.presolve = True
lp.simplex(ctrl)
if save_ilp:
lp.print_sol('solution.ilp')
cluster_dict = dict()
for i in range(lp.get_num_cols()):
names = lp.get_col_name(i+1).split(self.sep)
activity = lp.get_col_prim(i+1)
if activity == 1 and names[0] != names[1]:
if names[1] not in cluster_dict:
cluster_dict[names[1]] = []
cluster_dict[names[1]].append(names[0])
table = copy.deepcopy(self.diar)
for idx in cluster_dict:
table.rename('cluster', cluster_dict[idx], idx)
return table, cluster_dict
def _perform(self, filename='tmp.ilp', rm_tmp=True):
"""
Same as perform(), using glpk solver directly
instead of the Cython interface (slower).
"""
table = copy.deepcopy(self.diar)
logging.debug('ilp filename: %s', filename)
f = open(filename, 'w')
self._ilp_write(f)
f.close()
while not os.path.exists(filename):
time.sleep(1)
cmd = 'glpsol --lp {} -o {} &> {}'.format(filename, filename + '.out', filename + '.err')
# print(cmd)
if os.path.exists(filename + '.out'):
os.remove(filename + '.out')
while os.path.exists(filename + '.out'):
time.sleep(1)
os.system(cmd)
time.sleep(1)
while not os.path.exists(filename + '.out'):
time.sleep(1)
f = open(filename + '.out', 'r')
cluster_dict = self._ilp_read(f)
f.close()
for idx in cluster_dict:
table.rename('cluster', cluster_dict[idx], idx)
if rm_tmp:
os.remove(filename)
os.remove(filename+ '.out')
os.remove(filename+ '.err')
while os.path.exists(filename) or os.path.exists(filename+ '.out') or os.path.exists(filename+ '.err'):
time.sleep(1)
return table, cluster_dict
def _ilp_write(self, f):
distances, t = scores2distance(self.scores, self.thr)
cluster_list = self.scores.modelset.tolist()
f.write("Minimize\n")
f.write("problem : \n")
for idx, cluster in enumerate(cluster_list):
f.write(' + {}{}{}'.format(cluster, self.sep, cluster))
# sum of dist > thr in the lower triangular part od the distance matrix
mask = (np.tril(distances, -1)>t)
threshold = np.tril(distances, -1).copy()
threshold[mask] = 0
s = np.sum(threshold) + 1
# s = np.sum(stats.threshold(np.tril(distances, -1), threshmax=t, newval=0)) + 1
logging.debug('ilp sum scores: '+str(s))
l = len(cluster_list)
for i in range(l):
cluster_i = cluster_list[i]
for j in range(i+1, l):
if distances[i, j] < t:
cluster_j = cluster_list[j]
v = distances[i, j] / s
f.write(
' +{} {}{}{} '.format(v, cluster_i, self.sep,
cluster_j))
f.write("\nSubject to\n")
# x_i,i is a centre or not
for i, cluster_i in enumerate(cluster_list):
f.write('S{}: {}{}{}'.format(i, cluster_i, self.sep, cluster_i))
for j, cluster_j in enumerate(cluster_list):
if i != j and distances[i, j] < t:
f.write(" + {}{}{}".format(cluster_i, self.sep, cluster_j))
f.write(' = 1\n')
# center
for i, cluster_i in enumerate(cluster_list):
for j, cluster_j in enumerate(cluster_list):
if i != j and distances[i, j] < t:
f.write("{}{}{} - {}{}{} <=0\n".format(cluster_i, self.sep, cluster_j,
cluster_j, self.sep, cluster_j))
f.write('End')
def _ilp_read(self, f):
"""
:param f:
:return:
"""
cluster_dict = dict()
for line in f:
line.strip()
if line.find(self.sep) != -1:
lst = line.split()
if len(lst) < 3:
line += f.read()
lst = line.split()
names = lst[1].split(self.sep)
if lst[3] == '1' and names[0] != names[1]:
logging.debug('merge %s in %s', names[0], names[1])
if names[1] not in cluster_dict:
cluster_dict[names[1]] = []
cluster_dict[names[1]].append(names[0])
return cluster_dict
# import matplotlib.pyplot as plt
# import time
# import os
#
# from ecyglpki import Problem, SimplexControls
# from scipy.cluster import hierarchy as hac
# from .hac_utils import *
def ilp_iv(diar, scores, threshold=0.0):
"""
:param diar:
:param scores:
:param threshold:
:return:
"""
ilp = ILP_IV(diar, scores, threshold)
return ilp.perform()
# class ILP_IV:
# """
#
# """
# def __init__(self, diar, scores, threshold=0.0):
# """
#
# :param diar:
# :param scores:
# :param threshold:
# """
# self.sep = '!!'
# self.dic = dict()
# self.diar = copy.deepcopy(diar)
#
# self.thr = threshold
# self.scores = copy.deepcopy(scores)
#
# def plot(self, link, cluster_list):
# """
#
# :param link:
# :param cluster_list:
# :return:
# """
# #print(link)
# hac.dendrogram(link, color_threshold=self.thr, labels=cluster_list)
# plt.show()
#
# def perform(self, save_ilp=False):
# """
# Creates an LP problem and solves it using GLPK library
# (with ecyglpki Cython interface)
# :param save_ilp: if True, saves the generated problem
# in CPLEX LP format (problem.ilp), and the solution of
# the problem (solution.ilp).
# :return: a Diar object with the new clusters and a
# dictionary of clusters (old clusters as key, new ones
# as value in an array).
# """
# distances, t = scores2distance(self.scores, self.thr)
# cluster_list = sorted(self.scores.modelset.tolist())
#
# lp = Problem()
# lp.set_obj_dir('minimize')
#
# # columns
# cols = []
# for idx, cluster in enumerate(cluster_list):
# col = '{}{}{}'.format(cluster, self.sep, cluster)
# cols.append(col)
# lp.add_named_cols(col)
# lp.set_obj_coef(col, 1)
# lp.set_col_bnds(col, 0, None)
#
# # sum of dist > thr in the lower triangular part od the distance matrix
# mask = (np.tril(distances, -1) > t)
# threshold = np.tril(distances, -1).copy()
# threshold[mask] = 0
# s = np.sum(threshold) + 1
# l = len(cluster_list)
# for i in range(l):
# cluster_i = cluster_list[i]
# for j in range(i + 1, l):
# if distances[i, j] < t:
# cluster_j = cluster_list[j]
# v = distances[i, j] / s
# col = '{}{}{}'.format(cluster_i, self.sep, cluster_j)
# cols.append(col)
# lp.add_named_cols(col)
# lp.set_obj_coef(col, v)
# lp.set_col_bnds(col, 0, None)
#
# # rows
# for i, cluster_i in enumerate(cluster_list):
# r_cols = {}
# row = 'S{}'.format(i)
# lp.add_named_rows(row)
# r_cols['{}{}{}'.format(cluster_i, self.sep, cluster_i)] = 1
#
# for j, cluster_j in enumerate(cluster_list):
# if i != j and distances[i, j] < t:
# col = '{}{}{}'.format(cluster_i, self.sep, cluster_j)
# if col not in cols:
# cols.append(col)
# lp.add_named_cols(col)
# lp.set_col_bnds(col, 0, None)
# r_cols[col] = 1
#
# #boundaries <= 0
# col = '{}{}{}'.format(cluster_i, self.sep, cluster_j)
# idx = lp.add_rows(1)
# lp.set_mat_row(idx, {'{}{}{}'.format(cluster_j, self.sep, cluster_j):-1, col:1})
# lp.set_row_bnds(idx, None, 0)
#
# lp.set_mat_row(row, r_cols)
# lp.set_row_bnds(row, 1, 1)
#
# if save_ilp:
# lp.write_lp('problem.ilp')
#
# # solving problem
# ctrl = SimplexControls()
# ctrl.presolve = True
# lp.simplex(ctrl)
#
# if save_ilp:
# lp.print_sol('solution.ilp')
#
# cluster_dict = dict()
# for i in range(lp.get_num_cols()):
# names = lp.get_col_name(i+1).split(self.sep)
# activity = lp.get_col_prim(i+1)
# if activity == 1 and names[0] != names[1]:
# if names[1] not in cluster_dict:
# cluster_dict[names[1]] = []
# cluster_dict[names[1]].append(names[0])
#
# table = copy.deepcopy(self.diar)
# for idx in cluster_dict:
# table.rename('cluster', cluster_dict[idx], idx)
# return table, cluster_dict
#
#
#
# def _perform(self, filename='tmp.ilp', rm_tmp=True):
# """
# Same as perform(), using glpk solver directly
# instead of the Cython interface (slower).
# """
# table = copy.deepcopy(self.diar)
# logging.debug('ilp filename: %s', filename)
# f = open(filename, 'w')
# self._ilp_write(f)
# f.close()
# while not os.path.exists(filename):
# time.sleep(1)
# cmd = 'glpsol --lp {} -o {} &> {}'.format(filename, filename + '.out', filename + '.err')
# # print(cmd)
# if os.path.exists(filename + '.out'):
# os.remove(filename + '.out')
# while os.path.exists(filename + '.out'):
# time.sleep(1)
# os.system(cmd)
# time.sleep(1)
# while not os.path.exists(filename + '.out'):
# time.sleep(1)
# f = open(filename + '.out', 'r')
# cluster_dict = self._ilp_read(f)
# f.close()
# for idx in cluster_dict:
# table.rename('cluster', cluster_dict[idx], idx)
# if rm_tmp:
# os.remove(filename)
# os.remove(filename+ '.out')
# os.remove(filename+ '.err')
# while os.path.exists(filename) or os.path.exists(filename+ '.out') or os.path.exists(filename+ '.err'):
# time.sleep(1)
# return table, cluster_dict
#
# def _ilp_write(self, f):
# distances, t = scores2distance(self.scores, self.thr)
#
# cluster_list = self.scores.modelset.tolist()
# f.write("Minimize\n")
# f.write("problem : \n")
# for idx, cluster in enumerate(cluster_list):
# f.write(' + {}{}{}'.format(cluster, self.sep, cluster))
# # sum of dist > thr in the lower triangular part od the distance matrix
#
# mask = (np.tril(distances, -1)>t)
# threshold = np.tril(distances, -1).copy()
# threshold[mask] = 0
# s = np.sum(threshold) + 1
# # s = np.sum(stats.threshold(np.tril(distances, -1), threshmax=t, newval=0)) + 1
# logging.debug('ilp sum scores: '+str(s))
# l = len(cluster_list)
# for i in range(l):
# cluster_i = cluster_list[i]
# for j in range(i+1, l):
# if distances[i, j] < t:
# cluster_j = cluster_list[j]
# v = distances[i, j] / s
# f.write(
# ' +{} {}{}{} '.format(v, cluster_i, self.sep,
# cluster_j))
# f.write("\nSubject to\n")
#
# # x_i,i is a centre or not
# for i, cluster_i in enumerate(cluster_list):
# f.write('S{}: {}{}{}'.format(i, cluster_i, self.sep, cluster_i))
# for j, cluster_j in enumerate(cluster_list):
# if i != j and distances[i, j] < t:
# f.write(" + {}{}{}".format(cluster_i, self.sep, cluster_j))
# f.write(' = 1\n')
#
# # center
# for i, cluster_i in enumerate(cluster_list):
# for j, cluster_j in enumerate(cluster_list):
# if i != j and distances[i, j] < t:
# f.write("{}{}{} - {}{}{} <=0\n".format(cluster_i, self.sep, cluster_j,
# cluster_j, self.sep, cluster_j))
# f.write('End')
#
# def _ilp_read(self, f):
# """
#
# :param f:
# :return:
# """
# cluster_dict = dict()
# for line in f:
# line.strip()
# if line.find(self.sep) != -1:
# lst = line.split()
# if len(lst) < 3:
# line += f.read()
# lst = line.split()
# names = lst[1].split(self.sep)
# if lst[3] == '1' and names[0] != names[1]:
# logging.debug('merge %s in %s', names[0], names[1])
# if names[1] not in cluster_dict:
# cluster_dict[names[1]] = []
# cluster_dict[names[1]].append(names[0])
# return cluster_dict
#
#
# def ilp_iv(diar, scores, threshold=0.0):
# """
#
# :param diar:
# :param scores:
# :param threshold:
# :return:
# """
# ilp = ILP_IV(diar, scores, threshold)
# return ilp.perform()
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