from sklearn.decomposition import NMF
nmf = NMF(n_components=15, random_state=1234, max_iter=500)
doc_emb = nmf.fit_transform(dtm)
doc_emb[0]
array([0.04495691, 0.00993037, 0.00414172, 0. , 0.08992402,
0.01568343, 0. , 0.20330137, 0.00417763, 0. ,
0. , 0. , 0.10685757, 0. , 0.18545175])
(doc_emb[0] ** 2).sum()
0.09762898174810467
from sklearn.preprocessing import normalize
norm_doc_emb = normalize(doc_emb)
norm_doc_emb[0]
array([0.1438822 , 0.03178161, 0.01325536, 0. , 0.28779706,
0.05019399, 0. , 0.65065523, 0.0133703 , 0. ,
0. , 0. , 0.34199196, 0. , 0.59352848])
(norm_doc_emb[0] ** 2).sum()
0.9999999999999999
from sklearn.cluster import KMeans
kms = {}
inertia = []
ks = list(range(2, 30))
for k in ks:
km = KMeans(n_clusters=k, n_init='auto', random_state=1234)
km.fit(norm_doc_emb)
inertia.append(km.inertia_)
kms[k] = km
import matplotlib.pyplot as plt
plt.plot(ks, inertia)
[<matplotlib.lines.Line2D at 0x246c68391e0>]
n_cluster = 7
km = kms[n_cluster]
cluster = km.fit_predict(norm_doc_emb)
cluster
array([2, 5, 1, 2, 2, 2, 3, 2, 5, 1, 0, 3, 5, 4, 0, 0, 1, 2, 1, 2, 2, 0,
0, 3, 0, 5, 3, 6, 5, 6, 3, 0, 2, 1, 5, 2, 3, 0, 2, 0, 0, 4, 2, 2,
0, 2, 6, 2, 5, 0, 1, 3, 4, 0, 5, 0, 3, 2, 1, 1, 2, 2, 1, 1, 1, 2,
2, 3, 5, 2, 6, 6, 2, 2, 4, 3, 2, 2, 2, 5, 2, 5, 2, 1, 2, 6, 3, 3,
2, 4, 5, 2, 4, 3, 2, 2, 1, 6, 1, 2, 1, 2, 3, 2, 5, 5, 4, 3, 3, 0,
5, 3, 2, 5, 5, 2, 1, 0, 3, 2, 2, 3, 1, 0, 3, 3, 1, 2, 0, 5, 5, 2,
1, 1, 0, 5, 4, 4, 2, 2, 2, 2, 2, 2, 0, 3, 1, 2, 3, 1, 5, 2, 3, 6,
5, 4, 6, 2, 5, 2, 5, 2, 0, 6, 0, 3, 1, 0, 0, 3, 5, 0, 2, 3, 0, 3,
2, 0, 0, 3, 0, 0, 4, 3, 3, 6, 3, 1, 2, 3, 2, 1, 5, 3, 6, 6, 5, 5,
2, 5, 3, 0, 0, 2, 6, 2, 3, 3, 0, 3, 2, 2, 1, 0, 5, 1, 2, 1, 5, 1,
2, 0, 5, 5, 2, 2, 5, 2, 4, 3, 5, 2, 3, 4, 4, 2, 5, 6, 0, 4, 2, 4,
4, 2, 0, 0, 2, 4, 0, 0, 2, 2, 2, 2, 6, 2, 2, 6, 5, 2, 2, 6, 4, 2,
3, 2, 5, 1, 4, 3, 0, 6, 3, 3, 2, 0, 0, 2, 4, 3, 5, 1, 3, 3, 6, 3,
4, 6, 2, 0, 3, 2, 4, 0, 6, 4, 0, 2, 6, 4, 4, 6, 0, 0, 0, 0, 4, 0,
0, 3, 0, 4, 0, 2, 4, 4, 2, 6, 5, 2, 2, 6, 4, 3, 2, 2, 0, 3, 4, 0,
6, 6, 2, 2, 0, 2, 6, 6, 4, 1, 1, 2, 4, 6, 4, 2, 2, 4, 4, 3, 6, 5,
4, 3, 2, 6, 3, 0, 6, 0, 2, 2, 2, 2, 2, 3, 0, 4, 3, 2, 5, 0, 3, 2,
2, 5, 4, 5, 6, 2, 1, 2, 2, 2, 5, 1, 6, 5, 6, 5, 5, 4, 3, 2, 6, 6,
0, 6, 6, 6, 2, 2, 3, 0, 6, 6, 6, 6, 6, 2, 6, 6, 2, 0, 5, 2, 3, 0,
6, 0, 6, 1, 6, 6, 2, 4, 5, 1, 2, 3, 5, 2, 1, 2, 2, 1, 2, 0, 5])
df[cluster == 0].head()
| status | ko_title | en_title | abstract |
---|
10 | 등록 | 탈모방지 및 발모촉진용 헤어샴푸 조성물과 이의 제조방법 | hair shampoo composition for preventing hair l... | 본 발명은 샴푸 전동 의자에 관한 것으로, 회전암대 회동수단; 일측이 회전암대 회동... |
---|
14 | 등록 | 영유아 샴푸 보조 기구 | SHAMPOO ASSIST DEVICE FOR INFANT AND TODDLER | 본 발명은 직립형 샴푸 캡에 관한 것으로, 사용자의 머리에 착용 가능하도록 형성되고... |
---|
15 | 등록 | 컨디셔닝 샴푸 조성물 | Conditioning shampoo composition | 본 발명의 실시예에 따른 영유아 샴푸 보조 기구는 아기의 머리를 지지하는 상단 돌출... |
---|
21 | 등록 | 샴푸 용기용 펌프 디스펜서 | PUMP DISPENSER FOR SHAMPOO VESSEL | 본 발명은 샴푸 용기용 펌프 디스펜서에 관한 것으로서, 승강수단은 상기 피스톤(23... |
---|
22 | 등록 | 석창포 추출물을 함유하는 발모촉진과 탈모방지 및 비듬방지를 위한 샴푸 조성물과 조성... | Acoris gramineus Sol shampoo composition and m... | 본 발명은 샴푸 용기용 펌프 디스펜서에 관한 것으로서, 승강수단은 상기 피스톤(23... |
---|
from sklearn.metrics import silhouette_samples, silhouette_score
slh_avg = silhouette_score(norm_doc_emb, cluster)
slh_avg
0.2648528175529998
slh_values = silhouette_samples(norm_doc_emb, cluster)
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
y_lower = 10
for i in range(n_cluster):
cluster_slh = slh_values[cluster == i]
cluster_slh.sort()
size = cluster_slh.shape[0]
y_upper = y_lower + size
color = cm.nipy_spectral(float(i) / n_cluster)
plt.fill_betweenx(
np.arange(y_lower, y_upper),
0,
cluster_slh,
facecolor=color,
edgecolor=color,
alpha=0.7,
)
plt.text(-0.05, y_lower + 0.5 * size, str(i))
y_lower = y_upper + 10
plt.axvline(slh_avg, linestyle='--', color='gray')
plt.yticks([])
([], [])
![](data:image/png;base64, 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)