def localOptimization1(PopInfors,matrixs,talents_pool):
class_size = PopInfors.class_size
min_num_classifier = class_size - 1
max_num_classifier = class_size * 2
matrixs = sortMatrix1(matrixs)
record_matrixs = copy.deepcopy(matrixs)
record_matrixs1 = copy.deepcopy(matrixs)
good = record_matrixs[0]
good_coding_matrix = good.coding_matrix
good_segment_accuracies = good.segment_accuracies
good_segment_confusion_matrixs = good.segment_confusion_matrixs
#找到最优个体分类最差的segment
worst_segment_accuracy = float("inf")
worst_segment_accuracy_index = 0
for i in range(len(good_segment_accuracies)):
if good_segment_accuracies[i] < worst_segment_accuracy:
worst_segment_accuracy = good_segment_accuracies[i]
worst_segment_accuracy_index = i
#找到最优个体分类最差的segment最差的一类
worst_confusion_matrix = good_segment_confusion_matrixs[worst_segment_accuracy_index]
worst_class_accuracy = float("inf")
worst_class_accuracy_index = 0
for i in range(len(worst_confusion_matrix)):
if worst_confusion_matrix[i] < worst_class_accuracy:
worst_class_accuracy_index = i
worst_class_accuracy = worst_confusion_matrix[i]
#找到其他矩阵中该类分类准确率最好的segment
best_segment_accuracy = -1
best_matrix_index = 1
best_matrix_segment_index = 0
for i in range(1,len(matrixs)):
segment_confusion_matrixs = matrixs[i].segment_confusion_matrixs
for j in range(len(segment_confusion_matrixs)):
if segment_confusion_matrixs[j][worst_class_accuracy_index] > best_segment_accuracy:
best_segment_accuracy = segment_confusion_matrixs[j][worst_class_accuracy_index]
best_matrix_index = i
best_matrix_segment_index = j
find_matrix = matrixs[best_matrix_index]
find_coding_segment = find_matrix.coding_matrix[best_matrix_segment_index]
find_feature_segment = find_matrix.feature_matrix[best_matrix_segment_index]
find_base_learner = find_matrix.base_learner_matrix[best_matrix_segment_index]
# '''局部优化操作'''
chromes = []
chrome = Initialization.coding(good.coding_matrix,good.feature_matrix,good.base_learner_matrix)
chromes.append(chrome)
#find列替换该列
temp_matrix1 = copy.deepcopy(good)
temp_matrix1.coding_matrix[worst_segment_accuracy_index] = find_coding_segment
temp_matrix1.feature_matrix[worst_segment_accuracy_index] = find_feature_segment
temp_matrix1.base_learner_matrix[worst_segment_accuracy_index] = find_base_learner
chrome1 = Initialization.coding(temp_matrix1.coding_matrix,temp_matrix1.feature_matrix,temp_matrix1.base_learner_matrix)
chromes.append(chrome1)
#突变基分类器,后来添加
temp_matrix_add = copy.deepcopy(good)
num_base_learners = len(PopInfors.base_learners)
random_index = random.randint(0,num_base_learners-1)
learner0 = temp_matrix_add.base_learner_matrix[worst_segment_accuracy_index]
while learner0 == random_index:
random_index = random.randint(0,num_base_learners-1)
temp_matrix_add.base_learner_matrix[worst_segment_accuracy_index] = random_index
chrome_add = Initialization.coding(temp_matrix_add.coding_matrix,temp_matrix_add.feature_matrix,temp_matrix_add.base_learner_matrix)
chromes.append(chrome_add)
#添加find列或者精英池中添加一segment
if len(good_coding_matrix)+1 < max_num_classifier:
#添加find列
temp_matrix2 = copy.deepcopy(good)
temp_matrix2.coding_matrix.append(find_coding_segment)
temp_matrix2.feature_matrix.append(find_feature_segment)
temp_matrix2.base_learner_matrix.append(find_base_learner)
chrome2 = Initialization.coding(temp_matrix2.coding_matrix,temp_matrix2.feature_matrix,temp_matrix2.base_learner_matrix)
chromes.append(chrome2)
if len(talents_pool) != 0 :
#精英池中选择
random_index = random.randint(0,len(talents_pool)-1)
choose_pool = talents_pool[random_index]
temp_matrix4 = copy.deepcopy(good)
temp_matrix4.coding_matrix.append(choose_pool.code_segment)
temp_matrix4.feature_matrix.append(choose_pool.feature_segment)
temp_matrix4.base_learner_matrix.append(choose_pool.estimator)
chrome4 = Initialization.coding(temp_matrix4.coding_matrix,temp_matrix4.feature_matrix,temp_matrix4.base_learner_matrix)
chromes.append(chrome4)
#删除该列
if len(good_coding_matrix)-1 > min_num_classifier and len(good_coding_matrix)-1 > 1:
temp_matrix3 = copy.deepcopy(good)
del temp_matrix3.coding_matrix[worst_segment_accuracy_index]
del temp_matrix3.feature_matrix[worst_segment_accuracy_index]
del temp_matrix3.base_learner_matrix[worst_segment_accuracy_index]
chrome3 = Initialization.coding(temp_matrix3.coding_matrix,temp_matrix3.feature_matrix,temp_matrix3.base_learner_matrix)
chromes.append(chrome3)
best_temp = localOptimization(PopInfors,record_matrixs1,talents_pool)
new_good = chooseGood(PopInfors, chromes, talents_pool)
new_good = [new_good]
#新矩阵个体评价
new_good = Valuate.calObjValue(PopInfors, new_good)
talents_pool, new_good = Valuate.addToTalentsPool(PopInfors, new_good, talents_pool)
record_matrixs[0] = new_good[0]
print(record_matrixs[0].f1score)
if record_matrixs[0].f1score < best_temp.f1score:
record_matrixs[0] = copy.deepcopy(best_temp)
return record_matrixs
def localOptimization2(PopInfors,matrixs,talents_pool):
class_size = PopInfors.class_size
min_num_classifier = class_size - 1
max_num_classifier = 2*class_size -1
matrixs = sortMatrix(matrixs)
record_matrixs = copy.deepcopy(matrixs)
good = record_matrixs[0]
good_coding_matrix = good.coding_matrix
good_segment_accuracies = good.segment_accuracies
good_segment_confusion_matrixs = good.segment_confusion_matrixs
#找到最优个体分类最差的segment
worst_segment_accuracy = float("inf")
worst_segment_accuracy_index = 0
for i in range(len(good_segment_accuracies)):
if good_segment_accuracies[i] < worst_segment_accuracy:
worst_segment_accuracy = good_segment_accuracies[i]
worst_segment_accuracy_index = i
#找到最优个体分类最差的segment最差的一类
worst_confusion_matrix = good_segment_confusion_matrixs[worst_segment_accuracy_index]
worst_class_accuracy = float("inf")
worst_class_accuracy_index = 0
for i in range(len(worst_confusion_matrix)):
if worst_confusion_matrix[i] < worst_class_accuracy:
worst_class_accuracy_index = i
worst_class_accuracy = worst_confusion_matrix[i]
#找到其他矩阵中该类分类准确率最好的segment
best_segment_accuracy = -1
best_matrix_index = 1
best_matrix_segment_index = 0
for i in range(1,len(matrixs)):
segment_confusion_matrixs = matrixs[i].segment_confusion_matrixs
for j in range(len(segment_confusion_matrixs)):
if segment_confusion_matrixs[j][worst_class_accuracy_index] > best_segment_accuracy:
best_segment_accuracy = segment_confusion_matrixs[j][worst_class_accuracy_index]
best_matrix_index = i
best_matrix_segment_index = j
find_matrix = matrixs[best_matrix_index]
find_coding_segment = find_matrix.coding_matrix[best_matrix_segment_index]
find_feature_segment = find_matrix.feature_matrix[best_matrix_segment_index]
find_base_learner = find_matrix.base_learner_matrix[best_matrix_segment_index]
'''局部优化操作'''
chromes = []
chrome = Initialization.coding(good.coding_matrix,good.feature_matrix,good.base_learner_matrix)
chromes.append(chrome)
#find列替换该列
temp_matrix1 = copy.deepcopy(good)
temp_matrix1.coding_matrix[worst_segment_accuracy_index] = find_coding_segment
temp_matrix1.feature_matrix[worst_segment_accuracy_index] = find_feature_segment
temp_matrix1.base_learner_matrix[worst_segment_accuracy_index] = find_base_learner
chrome1 = Initialization.coding(temp_matrix1.coding_matrix,temp_matrix1.feature_matrix,temp_matrix1.base_learner_matrix)
chromes.append(chrome1)
#添加find列或者精英池中添加一segment
if len(good_coding_matrix)+1 < max_num_classifier:
distance0 = matrixDistance(good,class_size)
#添加find列
temp_matrix2 = copy.deepcopy(good)
temp_matrix2.coding_matrix.append(find_coding_segment)
temp_matrix2.feature_matrix.append(find_feature_segment)
temp_matrix2.base_learner_matrix.append(find_base_learner)
temp_matrix2 = Valuate.calObjValue(PopInfors, [temp_matrix2])[0]
distance1 = matrixDistance(temp_matrix2,class_size)
flag = 0
change_f1score = 0
p_matrix= []
if distance1 > distance0:#继续添加column,直至distance开始减少
temp_matrix4 = copy.deepcopy(temp_matrix2)
while change_f1score >= 0 and len(temp_matrix4.coding_matrix)+1 < max_num_classifier:
flag = 0
if len(talents_pool) != 0 :
#精英池中选择
for talent in talents_pool:
if not talent.code_segment in temp_matrix4.coding_matrix:
flag = 1
break
if flag == 0:
break
else:
random_index = random.randint(0,len(talents_pool)-1)
choose_pool = talents_pool[random_index]
p_distance = matrixDistance(temp_matrix4,class_size)
p_matrix = copy.deepcopy(temp_matrix4)
while choose_pool.code_segment in temp_matrix4.coding_matrix:
random_index = random.randint(0,len(talents_pool)-1)
choose_pool = talents_pool[random_index]
temp_matrix4.coding_matrix.append(choose_pool.code_segment)
temp_matrix4.feature_matrix.append(choose_pool.feature_segment)
temp_matrix4.base_learner_matrix.append(choose_pool.estimator)
chrome_ = Initialization.coding(temp_matrix4.coding_matrix,temp_matrix4.feature_matrix,temp_matrix4.base_learner_matrix)
temp_matrix4 = PopLegality.checkLegality([chrome_],PopInfors)
temp_matrix4 = Valuate.calObjValue(PopInfors, temp_matrix4)[0]
distance2 = matrixDistance(temp_matrix4,class_size)
print(distance2)
change_f1score = distance2 - p_distance
else:
break
#若连续三个column添加进来distance均减少，则终止
count = 0
temp_matrix5 = copy.deepcopy(temp_matrix4)
temp_matrix6 = copy.deepcopy(temp_matrix4)
if not flag == 0:
temp_matrix4 = p_matrix
temp_matrix5 = copy.deepcopy(temp_matrix4)
temp_matrix6 = copy.deepcopy(temp_matrix4)
while count < 3 and len(temp_matrix5.coding_matrix)+1 < max_num_classifier:
if len(talents_pool) != 0 :
#精英池中选择
random_index = random.randint(0,len(talents_pool)-1)
choose_pool = talents_pool[random_index]
if not choose_pool.code_segment in temp_matrix5.coding_matrix:
temp_matrix5.coding_matrix.append(choose_pool.code_segment)
temp_matrix5.feature_matrix.append(choose_pool.feature_segment)
temp_matrix5.base_learner_matrix.append(choose_pool.estimator)
chrome_ = Initialization.coding(temp_matrix5.coding_matrix,temp_matrix5.feature_matrix,temp_matrix5.base_learner_matrix)
temp_matrix5 = PopLegality.checkLegality([chrome_],PopInfors)
temp_matrix5 = Valuate.calObjValue(PopInfors, temp_matrix5)[0]
distance3 = matrixDistance(temp_matrix5,class_size)
if distance3 < distance2:
count += 1
print('局部优化:::',count)
else:
count = 0
temp_matrix6 = copy.deepcopy(temp_matrix5)
#精英池里面的column全部包含于该coding_matrix中，停止循环
f = 0
for talent in talents_pool:
if not talent.code_segment in temp_matrix5.coding_matrix:
f = 1
break
if f == 0:
break
else:
break
chrome6 = Initialization.coding(temp_matrix6.coding_matrix,temp_matrix6.feature_matrix,temp_matrix6.base_learner_matrix)
chromes.append(chrome6)
else:
chrome2 = Initialization.coding(temp_matrix2.coding_matrix,temp_matrix2.feature_matrix,temp_matrix2.base_learner_matrix)
chromes.append(chrome2)
#删除该列
if len(good_coding_matrix)-1 > min_num_classifier and len(good_coding_matrix)-1 > 1:
temp_matrix3 = copy.deepcopy(good)
del temp_matrix3.coding_matrix[worst_segment_accuracy_index]
del temp_matrix3.feature_matrix[worst_segment_accuracy_index]
del temp_matrix3.base_learner_matrix[worst_segment_accuracy_index]
chrome3 = Initialization.coding(temp_matrix3.coding_matrix,temp_matrix3.feature_matrix,temp_matrix3.base_learner_matrix)
chromes.append(chrome3)
new_good = chooseGood(PopInfors, chromes, talents_pool)
new_good = [new_good]
#新矩阵个体评价
new_good = Valuate.calObjValue(PopInfors, new_good)
talents_pool, new_good = Valuate.addToTalentsPool(PopInfors, new_good, talents_pool)
record_matrixs[0] = new_good[0]
return record_matrixs
