几种包映射算法实现
聚类中心 C = { c 1 , . . . , c k } C = {c_1,…,c_k}C ={c 1 ,…,c k }, 包 B i = { b i 1 , . . . , b i m } \mathbf{B}i={b{i1},…,b_{im}}B i ={b i 1 ,…,b i m }
一、最短距离映射
映射方式如下
h i = m a x e x p j ( − ∥ b i j − c i ∥ σ 2 ) h_i=\underset{j}{\rm{max}\,\rm{exp}}(- \frac{\|b_{ij}-c_i \|}{\sigma^2})h i =j m a x e x p (−σ2 ∥b i j −c i ∥)
B i \mathbf{B}_i B i 最终的映射向量是H ( B i ) = [ h 1 , . . . , h k ] H(\mathbf{B}_i) = [h_1,…,h_k]H (B i )=[h 1 ,…,h k ]
ret_vec = np.ones(self.k_m)
index = 0
for item in centers:
ret_vec[index] = np.linalg.norm(item - self.bags[idx, 0][:, :self.dimensions-1], axis=1).min()
index += 1
ret_vec = [math.exp(-x) for x in ret_vec]
return ret_vec / dis_euclidean(ret_vec, np.zeros_like(ret_vec))
二、去中心化映射
B i \mathbf{B}i B i 里面的实例根据 C C C 被分为了K K K类，K ≤ k K\le k K ≤k, 每一类为一个集合S i = { s 1 , . . . , s l } S_i={s_1,…,s_l}S i ={s 1 ,…,s l }, 映射方式如下
h i = ∑ a = 1 l s a − c i h_i = \sum^l{a=1}s_a-c_i h i =a =1 ∑l s a −c i
h i h_i h i 是与实例同维的向量，最终B i \mathbf{B}_i B i 映射为H ( B i ) = [ h 1 , . . . , h k ] H(\mathbf{B}_i) = [h_1,…,h_k]H (B i )=[h 1 ,…,h k ]，如果没有c i c_i c i 这一类的实例，则对应的 h i h_i h i 缺失值补零
ret_vec = np.zeros((len(centers), self.dimensions-1))
idx_ins = 0
for ins in self.bags[idx, 0][:, :self.dimensions-1]:
ret_vec[labels[idx_ins]] += ins - centers[labels[idx_ins]]
idx_ins += 1
ret_vec = np.resize(ret_vec, self.k_m * self.dimensions-1)
ret_vec = np.sign(ret_vec) * np.sqrt(np.abs(ret_vec))
return ret_vec / dis_euclidean(ret_vec, np.zeros_like(ret_vec))
三、均值映射
B i \mathbf{B}i B i 里面的实例根据 C C C 被分为了K K K类，K ≤ k K\le k K ≤k, 每一类为一个集合S i = { s 1 , . . . , s l } S_i={s_1,…,s_l}S i ={s 1 ,…,s l }, 映射方式如下
h i = 1 ∣ S i ∣ ∑ a = 1 l s a h_i = \frac{1}{|S_i|}\sum^l{a=1}s_a h i =∣S i ∣1 a =1 ∑l s a
h i h_i h i 是与实例同维的向量，最终B i \mathbf{B}_i B i 映射为H ( B i ) = [ h 1 , . . . , h k ] H(\mathbf{B}_i) = [h_1,…,h_k]H (B i )=[h 1 ,…,h k ]，如果没有c i c_i c i 这一类的实例，则对应的 h i h_i h i 缺失值补零
ret_vec = np.zeros((self.k_m, self.dimensions-1))
idx_ins = 0
for ins in self.bags[idx, 0][:, :self.dimensions-1]:
ret_vec[labels[idx_ins]] += ins
idx_ins += 1
unique, count = np.unique(labels, return_counts=True)
data_count = dict(zip(unique, count))
for key in data_count.keys():
ret_vec[key] /= data_count[key]
ret_vec = np.resize(ret_vec, self.k_m * self.dimensions-1)
ret_vec = np.sign(ret_vec) * np.sqrt(np.abs(ret_vec))
return ret_vec / dis_euclidean(ret_vec, np.zeros_like(ret_vec))
四、按比例映射
B i \mathbf{B}_i B i 里面的实例根据 C C C 被分为了K K K类，K ≤ k K\le k K ≤k, 每一类为一个集合S i = { s 1 , . . . , s l } S_i={s_1,…,s_l}S i ={s 1 ,…,s l }, 映射方式如下
h i = ∣ S i ∣ ∣ B i ∣ h_i = \frac{|S_i|}{|\mathbf{B}_i|}h i =∣B i ∣∣S i ∣
最终B i \mathbf{B}_i B i 映射为H ( B i ) = [ h 1 , . . . , h k ] H(\mathbf{B}_i) = [h_1,…,h_k]H (B i )=[h 1 ,…,h k ]，如果没有c i c_i c i 这一类的实例，则对应的 h i h_i h i 缺失值补零
ret_vec = np.zeros(self.k_m)
bag_size = len(self.bags_size[idx])
unique, count = np.unique(labels, return_counts=True)
data_count = dict(zip(unique, count))
for key in data_count.keys():
ret_vec[key] = data_count[key]/bag_size
return ret_vec
测试效果
一、最短距离映射
bag-level classify result:
confusion:
[[282 2]
[ 2 650]]
precision: 0.9969325153374233
recall: 0.9969325153374233
f1-score: 0.9969325153374233
accuracy: 0.9957264957264957
start training single-instance model----------------
model trainig complete!
Finally instance result:
confusion:
[[ 337 3]
[ 1 9019]]
precision: 0.9996674794945688
recall: 0.9998891352549889
f1-score: 0.9997782950892363
accuracy: 0.9995726495726496
class-time 27.534499883651733
二、去中心化映射
bag-level classify result:
confusion:
[[290 2]
[ 17 627]]
precision: 0.9968203497615262
recall: 0.9736024844720497
f1-score: 0.9850746268656716
accuracy: 0.9797008547008547
start training single-instance model----------------
model trainig complete!
Finally instance result:
confusion:
[[ 342 4]
[ 7 9007]]
precision: 0.9995560981023194
recall: 0.9992234302196583
f1-score: 0.9993897364771152
accuracy: 0.9988247863247863
class-time 31.642945766448975
三、均值映射
bag-level classify result:
confusion:
[[307 4]
[ 10 615]]
precision: 0.9935379644588045
recall: 0.984
f1-score: 0.9887459807073955
accuracy: 0.9850427350427351
start training single-instance model----------------
model trainig complete!
Finally instance result:
confusion:
[[ 368 5]
[ 1 8986]]
precision: 0.9994438883327772
recall: 0.999888728162902
f1-score: 0.9996662587607075
accuracy: 0.9993589743589744
class-time 32.479421615600586
四、按比例映射
bag-level classify result:
confusion:
[[272 25]
[ 9 630]]
precision: 0.9618320610687023
recall: 0.9859154929577465
f1-score: 0.973724884080371
accuracy: 0.9636752136752137
start training single-instance model----------------
model trainig complete!
Finally instance result:
confusion:
[[ 333 28]
[ 1 8998]]
precision: 0.9968978506536672
recall: 0.999888876541838
f1-score: 0.9983911234396671
accuracy: 0.9969017094017094
class-time 27.800466299057007
Original: https://blog.csdn.net/weixin_40872714/article/details/124544986
Author: swpu_jx_1998
Title: 日报2022-05-02
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