sklearn谱聚类Spectral Clustering(一)运行:以coco标签为例
背景：我们需要对多标签的问题，标签进行谱聚类，然后看相应的聚类结果。
官方API描述：https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering
目录
一、安装sklearn
1.1 scikit-learn概览
1.2 安装
二、简单谱聚类
2.1 谱聚类详解
2.2 简单运行
2.3 iris数据集
三、谱聚类函数
3.1 SpectralClustering
3.2 输入
3.3 输出
四、用coco标签进行谱聚类
4.1 coco互相关矩阵保存
4.3 加载
4.4 直接进行预测
4.5 affinity matrix有向图
4.6 调整获取最好参数
一、安装sklearn
1.1 scikit-learn概览
sklearn全称为scikit-learn
Scikit-learn(sklearn)的定位是通用机器学习库，而TensorFlow(tf)的定位主要是深度学习库。一个显而易见的不同：tf并未提供sklearn那种强大的特征工程，如维度压缩、特征选择等。究其根本，我认为是因为机器学习模型的两种不同的处理数据的方式：
- 传统机器学习：利用特征工程(feature engineering)，人为对数据进行提炼清洗
- 深度学习：利用表示学习(representation learning)，机器学习模型自身对数据进行提炼
1.2 安装
torch031环境下，pip install sklearn
(torch031) [[email protected] chun-ML_GCN]$ pip install sklearn
env环境下
env/bin/python -m pip install sklearn
Collecting sklearn
Using cached https://files.pythonhosted.org/packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz
Collecting scikit-learn (from sklearn)
Downloading https://files.pythonhosted.org/packages/5e/82/c0de5839d613b82bddd088599ac0bbfbbbcbd8ca470680658352d2c435bd/scikit_learn-0.20.3-cp36-cp36m-manylinux1_x86_64.whl (5.4MB)
100% |################################| 5.4MB 20kB/s
Requirement already satisfied: numpy>=1.8.2 in ./env/lib/python3.6/site-packages (from scikit-learn->sklearn) (1.16.3)
Requirement already satisfied: scipy>=0.13.3 in ./env/lib/python3.6/site-packages (from scikit-learn->sklearn) (1.2.1)
Building wheels for collected packages: sklearn
Running setup.py bdist_wheel for sklearn ... done
Stored in directory: /home/xingxiangrui/.cache/pip/wheels/76/03/bb/589d421d27431bcd2c6da284d5f2286c8e3b2ea3cf1594c074
Successfully built sklearn
Installing collected packages: scikit-learn, sklearn
Successfully installed scikit-learn-0.20.3 sklearn-0.0二、简单谱聚类
2.1 谱聚类详解
Graph特征提取方法:谱聚类(Spectral Clustering)详解
2.2 简单运行
参考样例
https://www.bbsmax.com/A/NMAzABqJ9p/
https://blog.csdn.net/fjssharpsword/article/details/79198054
https://blog.csdn.net/weixin_40042143/article/details/82462804
代码：
#-*-coding:utf-8 -*-
"""
created by xingxinangrui on 2019.5.5
this is the simple demo that use sk-learn to run spectral clustering
"""
#demo one use iris dataset
import numpy as np
from sklearn import datasets
X = datasets.load_iris()
# print(X)
# 查看默认的谱聚类效果
from sklearn.cluster import SpectralClustering
spectral = SpectralClustering()
pred_y = spectral.fit_predict(X.data)
from sklearn import metrics
print("Calinski-Harabasz Score", metrics.calinski_harabaz_score(X.data, pred_y))
"""
Calinski-Harabasz Score 438.286953256
"""
# 默认使用的是高斯核，需要对n_cluster和gamma进行调参，选择合适的参数
scores = []
s = dict()
for index, gamma in enumerate((0.01, 0.1, 1, 10)):
for index, k in enumerate((3, 4, 5, 6)):
pred_y = SpectralClustering(n_clusters=k).fit_predict(X.data)
print("Calinski-Harabasz Score with gamma=", gamma, "n_cluster=", k, "score=",
metrics.calinski_harabaz_score(X.data, pred_y))
tmp = dict()
tmp['gamma'] = gamma
tmp['n_cluster'] = k
tmp['score'] = metrics.calinski_harabaz_score(X.data, pred_y)
s[metrics.calinski_harabaz_score(X.data, pred_y)] = tmp
scores.append(metrics.calinski_harabaz_score(X.data, pred_y))
print(np.max(scores))
print("max score:")
print(s.get(np.max(scores)))运行结果
[[email protected] chun-ML_GCN]$ env/bin/python sk_spectral_cluster/demo_spectral_clustering.py
Calinski-Harabasz Score 412.6798087360221
Calinski-Harabasz Score with gamma= 0.01 n_cluster= 3 score= 556.1176919037501
Calinski-Harabasz Score with gamma= 0.01 n_cluster= 4 score= 467.76231583239655
Calinski-Harabasz Score with gamma= 0.01 n_cluster= 5 score= 432.63682714748126
Calinski-Harabasz Score with gamma= 0.01 n_cluster= 6 score= 410.18904618455855
Calinski-Harabasz Score with gamma= 0.1 n_cluster= 3 score= 556.11769190375
Calinski-Harabasz Score with gamma= 0.1 n_cluster= 4 score= 467.76231583239655
Calinski-Harabasz Score with gamma= 0.1 n_cluster= 5 score= 437.95873781534283
Calinski-Harabasz Score with gamma= 0.1 n_cluster= 6 score= 410.4013080804197
Calinski-Harabasz Score with gamma= 1 n_cluster= 3 score= 556.1176919037501
Calinski-Harabasz Score with gamma= 1 n_cluster= 4 score= 467.76231583239655
Calinski-Harabasz Score with gamma= 1 n_cluster= 5 score= 437.95873781534283
Calinski-Harabasz Score with gamma= 1 n_cluster= 6 score= 404.45914748471347
Calinski-Harabasz Score with gamma= 10 n_cluster= 3 score= 556.1176919037501
Calinski-Harabasz Score with gamma= 10 n_cluster= 4 score= 467.76231583239655
Calinski-Harabasz Score with gamma= 10 n_cluster= 5 score= 437.9587378153427
Calinski-Harabasz Score with gamma= 10 n_cluster= 6 score= 401.6775659923456
556.1176919037501
max score:
{'gamma': 10, 'n_cluster': 3, 'score': 556.1176919037501}2.3 iris数据集
用莺尾花数据集（IRIS数据集）
Iris数据集是常用的分类实验数据集，由Fisher, 1936收集整理。Iris也称鸢尾花卉数据集，是一类多重变量分析的数据集。数据集包含150个数据集，分为3类，每类50个数据，每个数据包含4个属性。可通过花萼长度，花萼宽度，花瓣长度，花瓣宽度4个属性预测鸢尾花卉属于（Setosa，Versicolour，Virginica）三个种类中的哪一类。
iris以鸢尾花的特征作为数据来源，常用在分类操作中。该数据集由3种不同类型的鸢尾花的50个样本数据构成。其中的一个种类与另外两个种类是线性可分离的，后两个种类是非线性可分离的。
该数据集包含了5个属性：
- & Sepal.Length（花萼长度），单位是cm;
- & Sepal.Width（花萼宽度），单位是cm;
- & Petal.Length（花瓣长度），单位是cm;
- & Petal.Width（花瓣宽度），单位是cm;
- & 种类：Iris Setosa（山鸢尾）、Iris Versicolour（杂色鸢尾），以及Iris Virginica（维吉尼亚鸢尾）。
数据集输入之后，我们将数据集print
dataset iris:
{'data': array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
。。。
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='打出纬度：
#demo one use iris dataset
import numpy as np
from sklearn import datasets
X = datasets.load_iris()
print('dataset iris .data shape:')
#print(X)
print(np.array(X.data).shape)
# 查看默认的谱聚类效果
from sklearn.cluster import SpectralClustering
spectral = SpectralClustering()
pred_y = spectral.fit_predict(X.data)
print("pred_y")
print(pred_y)
print("pred_y.shape")
print(np.array(pred_y).shape)
"""
dataset iris .data shape:
(150, 4)
pred_y
[6 0 0 0 6 6 0 0 0 0 6 0 0 0 6 6 6 6 6 6 6 6 0 6 0 0 6 6 6 0 0 6 6 6 0 0 6
6 0 6 6 0 0 6 6 0 6 0 6 0 3 3 3 5 3 5 3 2 3 5 2 5 5 3 5 3 5 5 3 5 7 5 7 3
3 3 3 3 3 5 5 5 5 7 5 3 3 3 5 5 5 3 5 2 5 5 5 3 2 5 4 7 4 7 4 1 5 4 4 4 4
7 4 7 7 4 4 1 1 7 4 7 1 7 4 4 7 7 4 4 4 1 4 7 7 1 4 4 7 4 4 4 7 4 4 4 7 4
4 7]
pred_y.shape
(150,)
"""即按每行元素进行聚类，聚类得到结果输出为一个list，每个元素对应一个list
三、谱聚类函数
3.1 SpectralClustering
函数参数及用法：
http://www.cnblogs.com/pinard/p/6235920.html
https://blog.csdn.net/fjssharpsword/article/details/79198054
https://blog.csdn.net/weixin_40042143/article/details/82462804
https://www.bbsmax.com/A/NMAzABqJ9p/
from sklearn.cluster import SpectralClustering
spectral = SpectralClustering()
pred_y = spectral.fit_predict(X.data)或者：
pred_y = SpectralClustering(n_clusters=k).fit_predict(X.data)
print("Calinski-Harabasz Score with gamma=", gamma, "n_cluster=", k, "score=",
metrics.calinski_harabaz_score(X.data, pred_y))3.2 输入
输入
#gengrate our dataset
X, y = datasets.make_blobs(n_samples=500, n_features=6, centers=5, cluster_std=[0.4, 0.3, 0.4, 0.3, 0.4], random_state=11)
#print dataset shape
print("X.shape and y.shape:")
print(np.array(X).shape)
print(np.array(y).shape)
print("X is", X)随机生成的X，500个数据点，每个数据点为6维
X.shape and y.shape:
(500, 6)
(500,)
X is [[-7.76324808 -8.84656238 4.27901582 2.24092088 -9.01179124 -0.38420006]
[-6.7332813 -9.41315963 -0.7936109 4.74079464 -1.07047174 -0.74584275]
[-6.95129282 -9.51077056 -0.84525704 4.10274755 -1.28878541 0.14329266]
...
[-3.52246694 -6.80041267 4.91296278 6.27507964 -3.01961225 -3.42770556]
[ 7.76663892 7.82404263 -6.8318606 2.88728932 -9.76742041 -7.50407371]
[ 8.40228838 7.63184647 -6.50528954 3.36423644 -9.48793872 -7.73365915]]例如对于iris数据集，输入为：
dataset iris:
{'data': array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
。。。
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='3.3 输出
与输入的维度有关，多少个样本点得到对应的输出。对于5维为 0到4, 对于3维为 0到2
Calinski-Harabasz Score with gamma= 0.01 n_clusters= 5 score: 23410.638949991386
(500,)
y_pred is [0 3 3 2 3 3 1 2 3 3 0 0 3 3 3 2 3 1 1 1 4 4 0 1 4 2 2 2 3 0 0 1 3 3 1 0 0
2 4 2 2 1 1 3 2 4 0 0 1 4 4 0 2 2 0 1 4 4 2 2 0 4 4 4 1 3 2 4 4 2 4 1 1 0
3 1 2 1 2 0 3 2 4 4 2 3 3 3 3 0 2 3 3 4 2 1 1 3 0 3 2 0 2 0 1 2 0 0 3 1 3
0 2 2 2 4 0 2 2 3 0 3 1 3 3 0 0 1 0 2 4 2 3 4 1 1 1 1 3 1 4 0 4 4 4 4 4 0
2 1 4 0 1 0 2 4 1 1 1 4 3 1 0 2 4 0 2 2 1 4 4 3 1 2 2 1 2 4 4 2 2 3 2 3 1
3 0 3 4 1 4 1 4 3 2 0 3 2 0 1 4 4 0 4 0 2 4 3 2 4 1 0 1 3 2 0 4 4 2 4 1 1
0 1 0 0 3 0 3 1 3 1 0 4 1 2 3 1 3 3 4 3 1 1 3 2 1 0 2 2 0 3 2 4 3 1 4 1 1
0 1 0 1 1 1 4 0 0 2 3 4 4 2 0 1 3 1 0 3 0 2 4 3 1 0 1 4 2 0 4 0 4 4 1 4 4
4 2 1 0 0 1 0 3 0 1 1 0 3 2 3 4 3 2 1 4 1 1 1 3 0 2 1 3 1 0 0 1 2 4 3 2 1
3 3 2 0 2 2 0 0 3 3 0 4 2 1 4 2 4 0 3 1 4 3 3 0 1 2 4 1 0 4 2 2 2 0 4 3 2
4 0 3 0 4 2 4 0 0 0 0 0 3 3 4 1 4 2 0 4 3 1 3 2 4 3 3 1 0 1 1 4 4 3 0 4 0
3 3 2 3 4 3 1 0 4 2 2 4 4 4 2 2 3 0 4 1 3 0 0 1 2 0 3 0 1 2 0 3 0 3 4 1 1
3 4 0 4 4 0 2 3 3 4 3 2 2 3 0 1 2 4 1 1 3 4 2 2 2 2 3 2 3 2 4 4 2 2 0 1 0
0 1 3 3 3 1 1 0 1 4 4 2 2 0 4 1 4 2 2]
Calinski-Harabasz Score with gamma= 0.1 n_clusters= 3 score: 1979.7709609161868
(500,)
y_pred is [1 1 1 2 1 1 0 2 1 1 1 1 1 1 1 2 1 0 0 0 1 1 1 0 1 2 2 2 1 1 1 0 1 1 0 1 1
2 1 2 2 0 0 1 2 1 1 1 0 1 1 1 2 2 1 0 1 1 2 2 1 1 1 1 0 1 2 1 1 2 1 0 0 1
1 0 2 0 2 1 1 2 1 1 2 1 1 1 1 1 2 1 1 1 2 0 0 1 1 1 2 1 2 1 0 2 1 1 1 0 1
1 2 2 2 1 1 2 2 1 1 1 0 1 1 1 1 0 1 2 1 2 1 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1
2 0 1 1 0 1 2 1 0 0 0 1 1 0 1 2 1 1 2 2 0 1 1 1 0 2 2 0 2 1 1 2 2 1 2 1 0
1 1 1 1 0 1 0 1 1 2 1 1 2 1 0 1 1 1 1 1 2 1 1 2 1 0 1 0 1 2 1 1 1 2 1 0 0
1 0 1 1 1 1 1 0 1 0 1 1 0 2 1 0 1 1 1 1 0 0 1 2 0 1 2 2 1 1 2 1 1 0 1 0 0
1 0 1 0 0 0 1 1 1 2 1 1 1 2 1 0 1 0 1 1 1 2 1 1 0 1 0 1 2 1 1 1 1 1 0 1 1
1 2 0 1 1 0 1 1 1 0 0 1 1 2 1 1 1 2 0 1 0 0 0 1 1 2 0 1 0 1 1 0 2 1 1 2 0
1 1 2 1 2 2 1 1 1 1 1 1 2 0 1 2 1 1 1 0 1 1 1 1 0 2 1 0 1 1 2 2 2 1 1 1 2
1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 0 1 2 1 1 1 0 1 2 1 1 1 0 1 0 0 1 1 1 1 1 1
1 1 2 1 1 1 0 1 1 2 2 1 1 1 2 2 1 1 1 0 1 1 1 0 2 1 1 1 0 2 1 1 1 1 1 0 0
1 1 1 1 1 1 2 1 1 1 1 2 2 1 1 0 2 1 0 0 1 1 2 2 2 2 1 2 1 2 1 1 2 2 1 0 1
1 0 1 1 1 0 0 1 0 1 1 2 2 1 1 0 1 2 2]
四、用coco标签进行谱聚类
4.1 coco互相关矩阵保存
将coco的互相关矩阵存下来，用pickle存入
correlations = {}
correlations.update(pp=A_B) #p(A/B)
correlations.update(fp=notA_B) # P(not A/B)
correlations.update(pf=A_notB)
correlations.update(ff=notA_notB)
with open('sk_spectral_cluster/coco_correlations.pkl', 'wb') as f:
print("write correlations in sk_spectral_cluster/coco_correlations.pkl")
pickle.dump(correlations, f)
with open('sk_spectral_cluster/coco_names.pkl','wb') as name_file:
print("write correlations in sk_spectral_cluster/coco_names.pkl")
pickle.dump(names, name_file)4.3 加载
加载出来
# ----------------------load coco_correlations.pkl and load coco_names.pkl--------
with open('sk_spectral_cluster/coco_correlations.pkl', 'rb') as f:
print("loading coco_correlations.pkl ")
correlations= pickle.load(f)
with open('sk_spectral_cluster/coco_names.pkl', 'rb') as f:
print("loading coco_names.pkl")
names=pickle.load(f)
coco_correlation_A_B=correlations['pp']
print('coco label corrrelation matrix (80*80) : \n' , coco_correlation_A_B)4.4 直接进行预测
运用前面所讲的SpectralClustering 函数
#-----------------------spectral clustering----------------------------------------
y_pred = SpectralClustering().fit_predict(coco_correlation_A_B)
print ("Calinski-Harabasz Score:")
print(metrics.calinski_harabaz_score(coco_correlation_A_B, y_pred))
print("y_pred",y_pred)但是会出现一定的warning
/env/lib/python3.6/site-packages/sklearn/cluster/spectral.py:462:
UserWarning: The spectral clustering API has changed. ``fit``now constructs an affinity matrix from data.
To use a custom affinity matrix, set ``affinity=precomputed``.
warnings.warn("The spectral clustering API has changed. ``fit``"聚类结果
Calinski-Harabasz Score:
8.367635511918138
y_pred [1 2 6 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 2 7 7 2
2 2 4 0 4 0 0 0 4 2 2 0 2 2 2 2 0 2 0 4 0 0 2 4 2 5 5 5 0 5 2 3 3 2 3 3 0
2 0 2 2 2 2]4.5 affinity matrix有向图
此warning非常有用，给出了如果想要使用affinity matrix，直接代码中添加程序，affinity=precomputed
adjacent matrix和affinity matrix之间的区别:
https://blog.csdn.net/songkun123/article/details/80720938
adjacent表示点与点之间的关系，affinity可以理解为点与边之间的关系。
更改代码
y_pred = SpectralClustering(affinity=precomputed).fit_predict(coco_correlation_A_B)不可行，precomputed未定义不可用，可能需要将affinity matrix定义出来才可以运用。还是需要理解谱聚类中参数细节才可以运用这个。
4.6 调整获取最好参数
根据 calinski_harabaz_score 来获取最佳参数。
# ---------------choose parameters by calinski_harabaz_score
scores = []
best_parameters = dict()
for index, gamma in enumerate((0.01, 0.1, 1, 10)):
for index, k in enumerate((3, 4, 5, 6, 7, 8, 9, 10)):
pred_y = SpectralClustering(n_clusters=k,gamma=gamma).fit_predict(coco_correlation_A_B)
print("Calinski-Harabasz Score with gamma=", gamma, "n_cluster=", k, "score=",
metrics.calinski_harabaz_score(coco_correlation_A_B, pred_y))
tmp = dict()
tmp['gamma'] = gamma
tmp['n_cluster'] = k
tmp['score'] = metrics.calinski_harabaz_score(coco_correlation_A_B, pred_y)
best_parameters[metrics.calinski_harabaz_score(coco_correlation_A_B, pred_y)] = tmp
scores.append(metrics.calinski_harabaz_score(coco_correlation_A_B, pred_y))
print("pred_y:",pred_y)
print(np.max(scores))
print("max score:")
print(best_parameters.get(np.max(scores)))运行结果
env/bin/python sk_spectral_cluster/coco_spectral_cluster.py
loading coco_correlations.pkl
loading coco_names.pkl
coco label corrrelation matrix (80*80) :
[[1.00000000e+00 8.26410144e-01 7.04392284e-01 ... 4.03311258e-01
4.45312500e-01 5.40000000e-01]
[4.18382255e-02 1.00000000e+00 1.02719033e-01 ... 1.12582781e-02
0.00000000e+00 5.71428571e-03]
[1.34192234e-01 3.86532575e-01 1.00000000e+00 ... 3.84105960e-02
7.81250000e-03 8.57142857e-03]
...
[1.34812060e-02 7.43331876e-03 6.73948408e-03 ... 1.00000000e+00
2.34375000e-02 8.57142857e-03]
[1.26178775e-03 0.00000000e+00 1.16198001e-04 ... 1.98675497e-03
1.00000000e+00 2.57142857e-02]
[8.36764511e-03 1.74901618e-03 6.97188008e-04 ... 3.97350993e-03
1.40625000e-01 1.00000000e+00]]
Calinski-Harabasz Score without affinity:
7.791262553375102
y_pred [2 4 1 4 4 1 4 1 4 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 0 4 0 0 4
4 4 6 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 4 3 4 7 7 7 5 7 4 6 6 4 6 6 5
4 5 4 4 4 4]
Calinski-Harabasz Score with n_cluster= 3 score= 9.492174347024601
pred_y: [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 2 0 0 1 0 2 2 2 0 2 0 0 0 0 0 0 2
0 0 0 0 0 0]
Calinski-Harabasz Score with n_cluster= 4 score= 8.725501530904658
pred_y: [1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 0 3 1 3 0 0 0 3 3 3 3 3 3 3 3 3 3 2 2 3 3 1 3 2 2 2 3 2 3 0 0 3 0 0 2
3 3 3 3 3 3]
Calinski-Harabasz Score with n_cluster= 5 score= 7.40234200110186
pred_y: [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 3 3 0
0 0 4 0 4 0 4 0 4 0 0 0 0 0 0 0 0 0 0 4 0 0 0 1 0 0 0 0 0 0 0 2 2 0 2 2 0
0 0 0 0 0 0]
Calinski-Harabasz Score with n_cluster= 6 score= 9.752144844448322
pred_y: [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 4 4 0
0 0 2 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 5 0 0 2 0 5 5 5 0 5 0 3 3 0 3 3 5
0 0 0 0 0 0]
Calinski-Harabasz Score with n_cluster= 7 score= 8.785937326387648
pred_y: [1 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 0
0 0 3 0 3 0 0 0 3 0 0 0 0 0 0 0 0 0 0 3 0 0 0 3 0 4 4 4 0 4 0 5 5 0 5 5 4
0 0 0 0 0 0]
Calinski-Harabasz Score with n_cluster= 8 score= 7.506167403594684
pred_y: [1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 6 6 0
0 0 4 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 0 2 0 5 5 5 7 5 0 4 4 0 4 4 7
0 7 0 0 0 0]
Calinski-Harabasz Score with n_cluster= 9 score= 7.752484557844312
pred_y: [1 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 7 7 0
0 0 3 0 2 5 5 5 5 0 0 0 0 0 0 0 0 0 0 4 4 4 0 2 0 8 8 8 4 8 0 3 3 0 3 3 4
0 4 0 0 0 0]
Calinski-Harabasz Score with n_cluster= 10 score= 7.382406625549057
pred_y: [1 2 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 6 2 6 6 2
2 2 8 7 3 7 7 7 7 9 9 2 9 2 2 2 2 2 7 0 0 0 2 3 2 4 4 4 0 4 2 8 8 2 8 8 0
2 0 2 2 2 2]
9.752144844448322
max score:
{'n_cluster': 6, 'score': 9.752144844448322}