Python 如何获得SKGMM中每次迭代的对数似然？

我试图在sklearn中拟合GMM，我看到模型在第3纪元左右收敛，但我似乎无法访问在每个纪元计算的对数似然分数

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

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是否有办法以某种方式访问每个历元的对数似然度分数？

如果您只想查看对数似然度分数，可以设置

verbose=2

以打印对数似然度的变化，并设置

verbose\u interval=1

以在每一步捕获它：

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，根据您使用的内容，您可以使用

日志记录
将其写入日志，或者例如，在下面的jupyter笔记本中，这可能会起作用：

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

然后，我们将其传递到数据帧中，并尝试反向计算可能性：

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