如何在sklearn GMM中获得每次迭代的对数似然？答案

【问题标题】：How to get log-likelihood for each iteration in sklearn GMM?如何在sklearn GMM中获得每次迭代的对数似然？
【发布时间】：2021-03-10 15:05:11
【问题描述】：

我正在尝试在 sklearn 中拟合 GMM，并且我看到模型在第 3 个时期左右收敛，但我似乎无法访问在每个时期计算的对数似然分数。

from sklearn.mixture import GaussianMixture
gmm = GaussianMixture(n_components=4, tol=1e-8).fit(data)

有没有办法以某种方式访问每个时期的对数似然分数？

【问题讨论】：

标签： python scikit-learn cluster-analysis data-analysis gmm

【解决方案1】：

如果你只想看loglik分数，你可以设置verbose=2打印loglik的变化，verbose_interval=1捕捉每一步的变化：

from sklearn.mixture import GaussianMixture
gmm = GaussianMixture(n_components=3, tol=1e-8,verbose=2,verbose_interval=1)
gmm.fit(data)

Initialization 0
  Iteration 1    time lapse 0.00560s     ll change inf
  Iteration 2    time lapse 0.00134s     ll change 0.03655
  Iteration 3    time lapse 0.00119s     ll change 0.00867
  Iteration 4    time lapse 0.00118s     ll change 0.00619
  Iteration 5    time lapse 0.00116s     ll change 0.00612
  Iteration 6    time lapse 0.00125s     ll change 0.00647
  Iteration 7    time lapse 0.00128s     ll change 0.00700
  Iteration 8    time lapse 0.00127s     ll change 0.00727
  Iteration 9    time lapse 0.00126s     ll change 0.00673
  Iteration 10   time lapse 0.00117s     ll change 0.00604
  Iteration 11   time lapse 0.00109s     ll change 0.00530
  Iteration 12   time lapse 0.00125s     ll change 0.00431
  Iteration 13   time lapse 0.00121s     ll change 0.00366
  Iteration 14   time lapse 0.00123s     ll change 0.00404
  Iteration 15   time lapse 0.00130s     ll change 0.00361
  Iteration 16   time lapse 0.00118s     ll change 0.00157
  Iteration 17   time lapse 0.00124s     ll change 0.00048
  Iteration 18   time lapse 0.00126s     ll change 0.00015
  Iteration 19   time lapse 0.00115s     ll change 0.00005
  Iteration 20   time lapse 0.00116s     ll change 0.00001
  Iteration 21   time lapse 0.00124s     ll change 0.00000
  Iteration 22   time lapse 0.00122s     ll change 0.00000
  Iteration 23   time lapse 0.00142s     ll change 0.00000
  Iteration 24   time lapse 0.00126s     ll change 0.00000
  Iteration 25   time lapse 0.00124s     ll change 0.00000
  Iteration 26   time lapse 0.00122s     ll change 0.00000
  Iteration 27   time lapse 0.00120s     ll change 0.00000
Initialization converged: True   time lapse 0.03765s     ll -1.20124

要实际捕获此值，具体取决于您使用的内容，您可以使用 logging 将其写入日志，或者例如在下面的 jupyter 笔记本中，这可能有效：

%%capture cap --no-stderr
gmm.fit(data)

然后我们将其传递到数据帧中并尝试反向计算似然度：

res = pd.DataFrame([i.split() for i in cap.stdout.split("\n")]).iloc[:,[1,7]]
res.columns = ['iteration','change']
res.change = res.change.astype('float64')
res = res[np.isfinite(res.change)]
res['logLik'] = res['change'].values[-1]
res.loc[:len(res),['logLik']] = -res.change[:-1][::-1].cumsum()[::-1] + res.change.values[-1]
res


    iteration   change  logLik
2   2   0.03655 -1.31546
3   3   0.00867 -1.27891
4   4   0.00619 -1.27024
5   5   0.00612 -1.26405
6   6   0.00647 -1.25793
7   7   0.00700 -1.25146
8   8   0.00727 -1.24446
9   9   0.00673 -1.23719
10  10  0.00604 -1.23046
11  11  0.00530 -1.22442
12  12  0.00431 -1.21912
13  13  0.00366 -1.21481
14  14  0.00404 -1.21115
15  15  0.00361 -1.20711
16  16  0.00157 -1.20350
17  17  0.00048 -1.20193
18  18  0.00015 -1.20145
19  19  0.00005 -1.20130
20  20  0.00001 -1.20125
21  21  0.00000 -1.20124
22  22  0.00000 -1.20124
23  23  0.00000 -1.20124
24  24  0.00000 -1.20124
25  25  0.00000 -1.20124
26  26  0.00000 -1.20124
27  27  0.00000 -1.20124
28  converged:  -1.20124    -1.20124

【讨论】：