Note
Click here to download the full example code
Topic models with Gaussian likelihoods¶
Quick demonstration that you can easily use bnpy to perform mixed membership modeling of grouped data with any likelihood.
The basic idea is that we use the same Gaussian mixture model for each group of data, but the appearance probabilities are allowed to be learned in a customized way for each group.
Here, we’ll analyze motion capture data from 6 different sequences of an individual actor peforming different exercises.
Although this data is inherently sequential in nature and it is smart to use a model that accounts for time, we’ll ignore that for now and focus on the dataset’s grouped nature.
That is, we can compare the following two models:
Baseline: Gaussian mixture model that pools all observations from all sequences
Smarter alternative: Latent Dirichlet Allocation with a Gaussian likelihood
That is, we treat each sequence as a separate collection of data examples, modeled by group specific appearance probabilities but shared cluster means and covariances.
# sphinx_gallery_thumbnail_number = 1
import bnpy
import numpy as np
import os
from matplotlib import pylab
import seaborn as sns
FIG_SIZE = (5, 5)
LANDSCAPE_FIG_SIZE = (15, 5)
pylab.rcParams['figure.figsize'] = FIG_SIZE
np.set_printoptions(precision=3, suppress=1, linewidth=200)
Load dataset from file
dataset_path = os.path.join(bnpy.DATASET_PATH, 'mocap6')
dataset = bnpy.data.GroupXData.read_npz(
os.path.join(dataset_path, 'dataset.npz'))
Setup: Function to make a simple plot of the raw data¶
def show_single_sequence(seq_id):
start = dataset.doc_range[seq_id]
stop = dataset.doc_range[seq_id + 1]
pylab.figure(figsize=LANDSCAPE_FIG_SIZE)
for dim in range(12):
X_seq = dataset.X[start:stop]
pylab.plot(X_seq[:, dim], '.-')
pylab.xlabel('time')
pylab.ylabel('angle')
pylab.tight_layout()
Visualization of the first sequence¶
show_single_sequence(0)
Setup: hyperparameters¶
K = 10 # Number of clusters/states
alpha = 0.25 # group-level Dirichlet concentration parameter
gamma = 5.0 # top-level Dirichlet concentration parameter (used by HDP only)
sF = 1.0 # Set observation model prior so E[covariance] = identity
ECovMat = 'eye'
nLap = 200
Baseline: Mixture model with DiagGauss observation model¶
We’ll take the best of 3 independent inits (‘tasks’)
mix_model, mix_info_dict = bnpy.run(
dataset, 'FiniteMixtureModel', 'DiagGauss', 'memoVB',
output_path='/tmp/mocap6/test-model=FiniteMixtureModel+DiagGauss-ECovMat=1*eye/',
nLap=nLap, nTask=3, nBatch=1, convergeThr=0.0001,
gamma=1.0,
sF=sF, ECovMat=ECovMat,
K=K, initname='randexamples',
)
Dataset Summary:
GroupXData
total size: 6 units
batch size: 6 units
num. batches: 1
Allocation Model: Finite mixture model. Dir prior param 1.00
Obs. Data Model: Gaussian with diagonal covariance.
Obs. Data Prior: independent Gauss-Wishart prior on each dimension
Wishart params
nu = 14 ...
beta = [ 12 12] ...
Expectations
E[ mean[k]] =
[ 0 0] ...
E[ covar[k]] =
[[1. 0.]
[0. 1.]] ...
Initialization:
initname = randexamples
K = 10 (number of clusters)
seed = 1607680
elapsed_time: 0.0 sec
Learn Alg: memoVB | task 1/3 | alg. seed: 1607680 | data order seed: 8541952
task_output_path: /tmp/mocap6/test-model=FiniteMixtureModel+DiagGauss-ECovMat=1*eye/1
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.892221921e+00 |
2.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.793128859e+00 | Ndiff 70.741
3.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.744872834e+00 | Ndiff 54.945
4.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.728928355e+00 | Ndiff 36.565
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6.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.720839494e+00 | Ndiff 19.301
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8.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.712988461e+00 | Ndiff 29.509
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66.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090564e+00 | Ndiff 0.009
67.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.006
68.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.005
69.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.003
70.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.002
71.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.002
72.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.001
73.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.001
74.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.001
75.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.000
76.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.000
77.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.000
78.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.000
79.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.000
80.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.684090563e+00 | Ndiff 0.000
... done. converged.
Learn Alg: memoVB | task 2/3 | alg. seed: 6454144 | data order seed: 7673856
task_output_path: /tmp/mocap6/test-model=FiniteMixtureModel+DiagGauss-ECovMat=1*eye/2
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.890179326e+00 |
2.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.777472898e+00 | Ndiff 83.721
3.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.750123362e+00 | Ndiff 62.267
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108.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.699871581e+00 | Ndiff 0.125
109.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699871088e+00 | Ndiff 0.131
110.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699870566e+00 | Ndiff 0.134
111.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699870026e+00 | Ndiff 0.137
112.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699869484e+00 | Ndiff 0.137
113.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699868955e+00 | Ndiff 0.134
114.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699868454e+00 | Ndiff 0.130
115.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699867995e+00 | Ndiff 0.123
116.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699867586e+00 | Ndiff 0.114
117.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699867230e+00 | Ndiff 0.104
118.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699866929e+00 | Ndiff 0.098
119.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699866679e+00 | Ndiff 0.091
120.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699866474e+00 | Ndiff 0.084
121.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699866308e+00 | Ndiff 0.077
122.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699866176e+00 | Ndiff 0.070
123.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699866071e+00 | Ndiff 0.065
124.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865987e+00 | Ndiff 0.061
125.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865922e+00 | Ndiff 0.057
126.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865870e+00 | Ndiff 0.053
127.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865830e+00 | Ndiff 0.049
128.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865799e+00 | Ndiff 0.045
129.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865775e+00 | Ndiff 0.041
130.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865756e+00 | Ndiff 0.037
131.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865741e+00 | Ndiff 0.034
132.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865730e+00 | Ndiff 0.031
133.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865721e+00 | Ndiff 0.027
134.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865715e+00 | Ndiff 0.025
135.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865709e+00 | Ndiff 0.022
136.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865706e+00 | Ndiff 0.020
137.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865703e+00 | Ndiff 0.017
138.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865700e+00 | Ndiff 0.015
139.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865699e+00 | Ndiff 0.014
140.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865697e+00 | Ndiff 0.012
141.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865696e+00 | Ndiff 0.011
142.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865696e+00 | Ndiff 0.009
143.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865695e+00 | Ndiff 0.008
144.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865695e+00 | Ndiff 0.007
145.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.006
146.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.005
147.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.005
148.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.004
149.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.004
150.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.003
151.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.003
152.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.002
153.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.002
154.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.002
155.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.002
156.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.001
157.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865694e+00 | Ndiff 0.001
158.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.001
159.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.001
160.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.001
161.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.001
162.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.001
163.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.001
164.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
165.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
166.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
167.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
168.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
169.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
170.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
171.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
172.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
173.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
174.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
175.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.699865693e+00 | Ndiff 0.000
... done. converged.
Learn Alg: memoVB | task 3/3 | alg. seed: 6168832 | data order seed: 7360256
task_output_path: /tmp/mocap6/test-model=FiniteMixtureModel+DiagGauss-ECovMat=1*eye/3
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.967149643e+00 |
2.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.828051356e+00 | Ndiff 79.117
3.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.796678874e+00 | Ndiff 51.355
4.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.782958585e+00 | Ndiff 34.942
5.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.772865919e+00 | Ndiff 33.222
6.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.768379291e+00 | Ndiff 18.402
7.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.765922644e+00 | Ndiff 9.243
8.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.763522049e+00 | Ndiff 12.443
9.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.759687240e+00 | Ndiff 18.742
10.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.758329562e+00 | Ndiff 15.307
11.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.757284595e+00 | Ndiff 14.151
12.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.755332671e+00 | Ndiff 16.539
13.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.751936044e+00 | Ndiff 19.374
14.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.749232109e+00 | Ndiff 18.939
15.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.745412591e+00 | Ndiff 18.269
16.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.741711477e+00 | Ndiff 20.402
17.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.739209544e+00 | Ndiff 18.430
18.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.737744024e+00 | Ndiff 13.082
19.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.737042157e+00 | Ndiff 10.524
20.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.736410038e+00 | Ndiff 8.500
21.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.735755836e+00 | Ndiff 6.634
22.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.734903519e+00 | Ndiff 5.964
23.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.733948260e+00 | Ndiff 5.787
24.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.732926027e+00 | Ndiff 5.839
25.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.731979439e+00 | Ndiff 5.735
26.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.731382239e+00 | Ndiff 5.591
27.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.731262368e+00 | Ndiff 5.144
28.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.731165872e+00 | Ndiff 4.593
29.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.731086681e+00 | Ndiff 4.064
30.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.731027897e+00 | Ndiff 3.407
31.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730989046e+00 | Ndiff 2.691
32.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730964762e+00 | Ndiff 2.058
33.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730949343e+00 | Ndiff 1.571
34.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730938956e+00 | Ndiff 1.216
35.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730931280e+00 | Ndiff 0.956
36.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730924798e+00 | Ndiff 0.760
37.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730918563e+00 | Ndiff 0.606
38.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730912654e+00 | Ndiff 0.476
39.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730908305e+00 | Ndiff 0.360
40.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730905994e+00 | Ndiff 0.260
41.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730904916e+00 | Ndiff 0.181
42.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730904381e+00 | Ndiff 0.122
43.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730904090e+00 | Ndiff 0.080
44.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903919e+00 | Ndiff 0.057
45.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903813e+00 | Ndiff 0.050
46.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903744e+00 | Ndiff 0.044
47.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903698e+00 | Ndiff 0.038
48.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903665e+00 | Ndiff 0.033
49.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903641e+00 | Ndiff 0.029
50.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903623e+00 | Ndiff 0.025
51.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903609e+00 | Ndiff 0.022
52.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903598e+00 | Ndiff 0.019
53.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903589e+00 | Ndiff 0.016
54.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903582e+00 | Ndiff 0.014
55.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903575e+00 | Ndiff 0.012
56.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903570e+00 | Ndiff 0.011
57.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903565e+00 | Ndiff 0.010
58.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903561e+00 | Ndiff 0.009
59.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903557e+00 | Ndiff 0.008
60.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903554e+00 | Ndiff 0.008
61.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903550e+00 | Ndiff 0.007
62.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903547e+00 | Ndiff 0.007
63.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903544e+00 | Ndiff 0.007
64.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.730903541e+00 | Ndiff 0.006
65.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903538e+00 | Ndiff 0.006
66.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903535e+00 | Ndiff 0.006
67.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903532e+00 | Ndiff 0.006
68.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903528e+00 | Ndiff 0.006
69.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903525e+00 | Ndiff 0.006
70.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903521e+00 | Ndiff 0.006
71.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903517e+00 | Ndiff 0.007
72.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903513e+00 | Ndiff 0.007
73.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903508e+00 | Ndiff 0.007
74.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903502e+00 | Ndiff 0.008
75.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903495e+00 | Ndiff 0.009
76.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903488e+00 | Ndiff 0.009
77.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903478e+00 | Ndiff 0.010
78.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903466e+00 | Ndiff 0.011
79.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903451e+00 | Ndiff 0.013
80.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903431e+00 | Ndiff 0.015
81.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903405e+00 | Ndiff 0.017
82.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903367e+00 | Ndiff 0.020
83.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903312e+00 | Ndiff 0.025
84.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903227e+00 | Ndiff 0.030
85.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730903088e+00 | Ndiff 0.039
86.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730902847e+00 | Ndiff 0.051
87.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730902397e+00 | Ndiff 0.068
88.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730901499e+00 | Ndiff 0.096
89.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730899644e+00 | Ndiff 0.136
90.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730896100e+00 | Ndiff 0.185
91.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730891244e+00 | Ndiff 0.217
92.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730887713e+00 | Ndiff 0.198
93.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730886264e+00 | Ndiff 0.145
94.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885689e+00 | Ndiff 0.098
95.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885409e+00 | Ndiff 0.086
96.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885256e+00 | Ndiff 0.074
97.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885166e+00 | Ndiff 0.064
98.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885111e+00 | Ndiff 0.054
99.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885076e+00 | Ndiff 0.045
100.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885053e+00 | Ndiff 0.038
101.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885038e+00 | Ndiff 0.031
102.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885028e+00 | Ndiff 0.026
103.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885022e+00 | Ndiff 0.022
104.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885017e+00 | Ndiff 0.018
105.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885014e+00 | Ndiff 0.015
106.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885012e+00 | Ndiff 0.012
107.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.730885010e+00 | Ndiff 0.010
108.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885009e+00 | Ndiff 0.008
109.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885008e+00 | Ndiff 0.007
110.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885007e+00 | Ndiff 0.006
111.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885007e+00 | Ndiff 0.005
112.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885007e+00 | Ndiff 0.004
113.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885007e+00 | Ndiff 0.003
114.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.003
115.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.002
116.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.002
117.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.002
118.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.001
119.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.001
120.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.001
121.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.001
122.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.001
123.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.001
124.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
125.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
126.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
127.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
128.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
129.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
130.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
131.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
132.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
133.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.730885006e+00 | Ndiff 0.000
... done. converged.
FiniteTopicModel with DiagGauss observation model¶
We’ll take the best of 3 independent inits (‘tasks’)
finite_model, finite_info_dict = bnpy.run(
dataset, 'FiniteTopicModel', 'DiagGauss', 'memoVB',
output_path='/tmp/mocap6/test-model=FiniteTopicModel+DiagGauss-ECovMat=1*eye/',
nLap=nLap, nTask=3, nBatch=1, convergeThr=0.0001,
alpha=alpha,
sF=sF, ECovMat=ECovMat,
K=K, initname='randexamples',
)
Dataset Summary:
GroupXData
total size: 6 units
batch size: 6 units
num. batches: 1
Allocation Model: Finite LDA model with K=0 comps. alpha=0.25
Obs. Data Model: Gaussian with diagonal covariance.
Obs. Data Prior: independent Gauss-Wishart prior on each dimension
Wishart params
nu = 14 ...
beta = [ 12 12] ...
Expectations
E[ mean[k]] =
[ 0 0] ...
E[ covar[k]] =
[[1. 0.]
[0. 1.]] ...
Initialization:
initname = randexamples
K = 10 (number of clusters)
seed = 1607680
elapsed_time: 0.0 sec
Learn Alg: memoVB | task 1/3 | alg. seed: 1607680 | data order seed: 8541952
task_output_path: /tmp/mocap6/test-model=FiniteTopicModel+DiagGauss-ECovMat=1*eye/1
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.891536742e+00 |
2.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.786835062e+00 | Ndiff 100.168
3.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.737768323e+00 | Ndiff 54.412
4.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.727530929e+00 | Ndiff 36.160
5.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.723599603e+00 | Ndiff 26.974
6.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.720184656e+00 | Ndiff 22.499
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169.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.658907895e+00 | Ndiff 0.000
170.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.658907895e+00 | Ndiff 0.000
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172.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.658907895e+00 | Ndiff 0.000
173.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.658907895e+00 | Ndiff 0.000
... done. converged.
Learn Alg: memoVB | task 2/3 | alg. seed: 6454144 | data order seed: 7673856
task_output_path: /tmp/mocap6/test-model=FiniteTopicModel+DiagGauss-ECovMat=1*eye/2
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.880184431e+00 |
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50.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.675204290e+00 | Ndiff 5.243
51.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.675093495e+00 | Ndiff 7.577
52.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.674877831e+00 | Ndiff 11.020
53.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.674494204e+00 | Ndiff 15.319
54.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.674002187e+00 | Ndiff 18.736
55.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.673459444e+00 | Ndiff 20.095
56.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.672940405e+00 | Ndiff 18.174
57.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.672466743e+00 | Ndiff 15.318
58.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.671825477e+00 | Ndiff 14.597
59.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.671050065e+00 | Ndiff 13.869
60.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.670432981e+00 | Ndiff 12.032
61.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.669861634e+00 | Ndiff 10.912
62.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.669261301e+00 | Ndiff 10.844
63.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667301180e+00 | Ndiff 11.473
64.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667137918e+00 | Ndiff 3.900
65.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667105076e+00 | Ndiff 2.261
66.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667096981e+00 | Ndiff 1.481
67.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667094652e+00 | Ndiff 1.299
68.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667093899e+00 | Ndiff 1.140
69.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667093665e+00 | Ndiff 0.996
70.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667093628e+00 | Ndiff 0.868
71.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667093673e+00 | Ndiff 0.756
72.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667093757e+00 | Ndiff 0.658
73.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667093860e+00 | Ndiff 0.573
74.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667093971e+00 | Ndiff 0.499
75.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667094084e+00 | Ndiff 0.436
76.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667094196e+00 | Ndiff 0.381
77.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667094304e+00 | Ndiff 0.333
78.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094408e+00 | Ndiff 0.291
79.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094505e+00 | Ndiff 0.255
80.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094596e+00 | Ndiff 0.224
81.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094680e+00 | Ndiff 0.197
82.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094757e+00 | Ndiff 0.173
83.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094829e+00 | Ndiff 0.152
84.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094894e+00 | Ndiff 0.134
85.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667094953e+00 | Ndiff 0.118
86.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095007e+00 | Ndiff 0.104
87.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095056e+00 | Ndiff 0.091
88.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095100e+00 | Ndiff 0.081
89.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095140e+00 | Ndiff 0.071
90.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095176e+00 | Ndiff 0.063
91.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095209e+00 | Ndiff 0.056
92.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095238e+00 | Ndiff 0.049
93.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095264e+00 | Ndiff 0.043
94.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095287e+00 | Ndiff 0.038
95.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095309e+00 | Ndiff 0.034
96.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095327e+00 | Ndiff 0.030
97.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095344e+00 | Ndiff 0.026
98.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095359e+00 | Ndiff 0.023
99.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095373e+00 | Ndiff 0.021
100.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095385e+00 | Ndiff 0.018
101.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095396e+00 | Ndiff 0.016
102.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095406e+00 | Ndiff 0.014
103.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095414e+00 | Ndiff 0.013
104.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095422e+00 | Ndiff 0.011
105.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095429e+00 | Ndiff 0.010
106.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095435e+00 | Ndiff 0.009
107.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095440e+00 | Ndiff 0.008
108.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095445e+00 | Ndiff 0.007
109.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095449e+00 | Ndiff 0.006
110.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667095453e+00 | Ndiff 0.005
111.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095457e+00 | Ndiff 0.005
112.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095460e+00 | Ndiff 0.004
113.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095463e+00 | Ndiff 0.004
114.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095465e+00 | Ndiff 0.003
115.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095467e+00 | Ndiff 0.003
116.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095469e+00 | Ndiff 0.003
117.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095471e+00 | Ndiff 0.002
118.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095472e+00 | Ndiff 0.002
119.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095474e+00 | Ndiff 0.002
120.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095475e+00 | Ndiff 0.002
121.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095476e+00 | Ndiff 0.001
122.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095477e+00 | Ndiff 0.001
123.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095478e+00 | Ndiff 0.001
124.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095479e+00 | Ndiff 0.001
125.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095479e+00 | Ndiff 0.001
126.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095480e+00 | Ndiff 0.001
127.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095480e+00 | Ndiff 0.001
128.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095481e+00 | Ndiff 0.001
129.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095481e+00 | Ndiff 0.001
130.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095482e+00 | Ndiff 0.000
131.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095482e+00 | Ndiff 0.000
132.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095482e+00 | Ndiff 0.000
133.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095482e+00 | Ndiff 0.000
134.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095483e+00 | Ndiff 0.000
135.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095483e+00 | Ndiff 0.000
136.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095483e+00 | Ndiff 0.000
137.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095483e+00 | Ndiff 0.000
138.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095483e+00 | Ndiff 0.000
139.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095484e+00 | Ndiff 0.000
140.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095484e+00 | Ndiff 0.000
141.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095484e+00 | Ndiff 0.000
142.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095484e+00 | Ndiff 0.000
143.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667095484e+00 | Ndiff 0.000
... done. converged.
Learn Alg: memoVB | task 3/3 | alg. seed: 6168832 | data order seed: 7360256
task_output_path: /tmp/mocap6/test-model=FiniteTopicModel+DiagGauss-ECovMat=1*eye/3
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.960889382e+00 |
2.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.812954811e+00 | Ndiff 106.901
3.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.775678774e+00 | Ndiff 68.748
4.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.760183281e+00 | Ndiff 29.861
5.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.752879371e+00 | Ndiff 25.952
6.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.749282461e+00 | Ndiff 24.923
7.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.741353570e+00 | Ndiff 30.374
8.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.734628562e+00 | Ndiff 12.783
9.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.733089112e+00 | Ndiff 9.680
10.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.729618996e+00 | Ndiff 11.796
11.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.728491286e+00 | Ndiff 3.954
12.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.728005826e+00 | Ndiff 4.496
13.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.727627474e+00 | Ndiff 2.491
14.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.727448628e+00 | Ndiff 1.857
15.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.727142224e+00 | Ndiff 2.028
16.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.726427129e+00 | Ndiff 5.899
17.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.724227916e+00 | Ndiff 9.490
18.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.723804571e+00 | Ndiff 3.002
19.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.723565436e+00 | Ndiff 2.800
20.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.723446675e+00 | Ndiff 2.095
21.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.723273245e+00 | Ndiff 2.483
22.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722684880e+00 | Ndiff 3.864
23.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721978561e+00 | Ndiff 2.712
24.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721938966e+00 | Ndiff 0.670
25.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721935848e+00 | Ndiff 0.248
26.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721934654e+00 | Ndiff 0.148
27.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721933859e+00 | Ndiff 0.112
28.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721932594e+00 | Ndiff 0.086
29.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721925386e+00 | Ndiff 0.125
30.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721787282e+00 | Ndiff 0.511
31.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721646191e+00 | Ndiff 0.232
32.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721534458e+00 | Ndiff 0.503
33.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721204789e+00 | Ndiff 0.529
34.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721199428e+00 | Ndiff 0.232
35.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721197505e+00 | Ndiff 0.160
36.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721196629e+00 | Ndiff 0.113
37.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721196193e+00 | Ndiff 0.092
38.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195962e+00 | Ndiff 0.076
39.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195834e+00 | Ndiff 0.064
40.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195760e+00 | Ndiff 0.053
41.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195715e+00 | Ndiff 0.045
42.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195687e+00 | Ndiff 0.038
43.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195668e+00 | Ndiff 0.032
44.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195656e+00 | Ndiff 0.028
45.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195648e+00 | Ndiff 0.026
46.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195643e+00 | Ndiff 0.023
47.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195639e+00 | Ndiff 0.020
48.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195636e+00 | Ndiff 0.018
49.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195634e+00 | Ndiff 0.016
50.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721195632e+00 | Ndiff 0.014
51.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195631e+00 | Ndiff 0.012
52.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195631e+00 | Ndiff 0.011
53.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195630e+00 | Ndiff 0.009
54.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195630e+00 | Ndiff 0.008
55.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195629e+00 | Ndiff 0.007
56.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195629e+00 | Ndiff 0.006
57.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195629e+00 | Ndiff 0.005
58.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195629e+00 | Ndiff 0.005
59.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195629e+00 | Ndiff 0.004
60.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195629e+00 | Ndiff 0.003
61.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195629e+00 | Ndiff 0.003
62.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.003
63.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.002
64.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.002
65.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.002
66.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
67.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
68.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
69.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
70.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
71.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
72.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
73.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.001
74.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
75.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
76.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
77.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
78.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
79.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
80.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
81.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
82.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
83.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
84.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
85.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721195628e+00 | Ndiff 0.000
... done. converged.
HDP-HMM with DiagGauss observation model¶
We’ll take the best of 3 independent inits (‘tasks’)
hdp_topic_model, hdp_info_dict = bnpy.run(
dataset, 'HDPTopicModel', 'DiagGauss', 'memoVB',
output_path='/tmp/mocap6/test-model=HDPTopicModel+DiagGauss-ECovMat=1*eye/',
nLap=nLap, nTask=3, nBatch=1, convergeThr=0.0001,
gamma=gamma, alpha=alpha,
sF=sF, ECovMat=ECovMat,
K=K, initname='randexamples',
moves='shuffle',
)
Dataset Summary:
GroupXData
total size: 6 units
batch size: 6 units
num. batches: 1
Allocation Model: HDP model with K=0 active comps. gamma=5.00. alpha=0.25
Obs. Data Model: Gaussian with diagonal covariance.
Obs. Data Prior: independent Gauss-Wishart prior on each dimension
Wishart params
nu = 14 ...
beta = [ 12 12] ...
Expectations
E[ mean[k]] =
[ 0 0] ...
E[ covar[k]] =
[[1. 0.]
[0. 1.]] ...
Initialization:
initname = randexamples
K = 10 (number of clusters)
seed = 1607680
elapsed_time: 0.0 sec
Learn Alg: memoVB | task 1/3 | alg. seed: 1607680 | data order seed: 8541952
task_output_path: /tmp/mocap6/test-model=HDPTopicModel+DiagGauss-ECovMat=1*eye/1
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.892295970e+00 |
2.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.787474069e+00 | Ndiff 167.893
3.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.738420912e+00 | Ndiff 108.155
4.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.728232794e+00 | Ndiff 36.020
5.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.724249654e+00 | Ndiff 78.033
6.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.721009176e+00 | Ndiff 87.093
7.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.717815642e+00 | Ndiff 110.105
8.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.716133498e+00 | Ndiff 17.207
9.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.715152039e+00 | Ndiff 108.292
10.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.714666091e+00 | Ndiff 3.784
11.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.714242891e+00 | Ndiff 3.528
12.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.713537978e+00 | Ndiff 8.000
13.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.710603627e+00 | Ndiff 35.132
14.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.708700954e+00 | Ndiff 16.257
15.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.706776744e+00 | Ndiff 75.015
16.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.703666205e+00 | Ndiff 42.738
17.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.700832620e+00 | Ndiff 27.477
18.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.699218948e+00 | Ndiff 23.414
19.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.698514238e+00 | Ndiff 52.198
20.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.698213092e+00 | Ndiff 9.303
21.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.698031745e+00 | Ndiff 6.076
22.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.697593507e+00 | Ndiff 6.353
23.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.697312701e+00 | Ndiff 5.777
24.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.697000999e+00 | Ndiff 5.781
25.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.696799733e+00 | Ndiff 6.540
26.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.696450679e+00 | Ndiff 8.455
27.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.695590334e+00 | Ndiff 12.813
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176.000/200 after 6 sec. | 229.7 MiB | K 10 | loss 3.659487153e+00 | Ndiff 0.000
... done. converged.
Learn Alg: memoVB | task 2/3 | alg. seed: 6454144 | data order seed: 7673856
task_output_path: /tmp/mocap6/test-model=HDPTopicModel+DiagGauss-ECovMat=1*eye/2
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.880920404e+00 |
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63.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.667858640e+00 | Ndiff 59.079
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69.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667639812e+00 | Ndiff 1.005
70.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667640267e+00 | Ndiff 0.876
71.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667640685e+00 | Ndiff 0.763
72.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667641052e+00 | Ndiff 0.665
73.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667641372e+00 | Ndiff 0.579
74.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667641652e+00 | Ndiff 0.505
75.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667641898e+00 | Ndiff 0.441
76.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667642118e+00 | Ndiff 0.385
77.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667642313e+00 | Ndiff 0.337
78.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667642488e+00 | Ndiff 0.295
79.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667642644e+00 | Ndiff 0.259
80.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667642785e+00 | Ndiff 0.227
81.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667642911e+00 | Ndiff 0.199
82.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643024e+00 | Ndiff 0.175
83.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643126e+00 | Ndiff 0.154
84.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643218e+00 | Ndiff 0.136
85.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643300e+00 | Ndiff 0.119
86.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643374e+00 | Ndiff 313.637
87.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643527e+00 | Ndiff 0.101
88.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643638e+00 | Ndiff 284.695
89.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643784e+00 | Ndiff 0.082
90.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.667643880e+00 | Ndiff 0.071
91.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667643942e+00 | Ndiff 0.061
92.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667643990e+00 | Ndiff 0.054
93.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644030e+00 | Ndiff 0.047
94.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644064e+00 | Ndiff 0.042
95.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644095e+00 | Ndiff 0.037
96.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644121e+00 | Ndiff 0.033
97.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644145e+00 | Ndiff 0.029
98.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644166e+00 | Ndiff 0.025
99.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644184e+00 | Ndiff 0.023
100.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644201e+00 | Ndiff 0.020
101.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644215e+00 | Ndiff 0.018
102.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644228e+00 | Ndiff 0.016
103.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644240e+00 | Ndiff 0.014
104.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644250e+00 | Ndiff 0.012
105.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644259e+00 | Ndiff 0.011
106.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644267e+00 | Ndiff 0.010
107.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644274e+00 | Ndiff 0.009
108.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644281e+00 | Ndiff 0.008
109.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644286e+00 | Ndiff 0.007
110.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644292e+00 | Ndiff 0.006
111.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644296e+00 | Ndiff 0.005
112.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644300e+00 | Ndiff 0.005
113.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644304e+00 | Ndiff 0.004
114.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644307e+00 | Ndiff 0.004
115.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644310e+00 | Ndiff 0.003
116.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644312e+00 | Ndiff 0.003
117.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644314e+00 | Ndiff 0.003
118.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644316e+00 | Ndiff 0.002
119.000/200 after 4 sec. | 229.7 MiB | K 10 | loss 3.667644318e+00 | Ndiff 0.002
120.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644320e+00 | Ndiff 0.002
121.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644321e+00 | Ndiff 0.002
122.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644322e+00 | Ndiff 0.001
123.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644323e+00 | Ndiff 0.001
124.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644324e+00 | Ndiff 0.001
125.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644325e+00 | Ndiff 0.001
126.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644326e+00 | Ndiff 0.001
127.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644327e+00 | Ndiff 0.001
128.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644327e+00 | Ndiff 0.001
129.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644328e+00 | Ndiff 0.001
130.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644328e+00 | Ndiff 0.001
131.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644329e+00 | Ndiff 0.000
132.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644329e+00 | Ndiff 0.000
133.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644330e+00 | Ndiff 0.000
134.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644330e+00 | Ndiff 0.000
135.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644330e+00 | Ndiff 0.000
136.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644330e+00 | Ndiff 0.000
137.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
138.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
139.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
140.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
141.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
142.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
143.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
144.000/200 after 5 sec. | 229.7 MiB | K 10 | loss 3.667644331e+00 | Ndiff 0.000
... done. converged.
Learn Alg: memoVB | task 3/3 | alg. seed: 6168832 | data order seed: 7360256
task_output_path: /tmp/mocap6/test-model=HDPTopicModel+DiagGauss-ECovMat=1*eye/3
1.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.961512682e+00 |
2.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.813561471e+00 | Ndiff 163.068
3.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.776258956e+00 | Ndiff 240.881
4.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.760790644e+00 | Ndiff 282.557
5.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.753457390e+00 | Ndiff 25.948
6.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.749857849e+00 | Ndiff 24.919
7.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.741928990e+00 | Ndiff 30.360
8.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.735207406e+00 | Ndiff 12.788
9.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.733663979e+00 | Ndiff 9.696
10.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.730193088e+00 | Ndiff 14.819
11.000/200 after 0 sec. | 229.7 MiB | K 10 | loss 3.729061704e+00 | Ndiff 3.956
12.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.728573893e+00 | Ndiff 4.488
13.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.728201595e+00 | Ndiff 2.416
14.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.728022593e+00 | Ndiff 1.867
15.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.727711958e+00 | Ndiff 2.028
16.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.726978910e+00 | Ndiff 6.024
17.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.724785894e+00 | Ndiff 13.289
18.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.724377310e+00 | Ndiff 2.981
19.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.724107513e+00 | Ndiff 103.930
20.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.723983831e+00 | Ndiff 2.098
21.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.723805772e+00 | Ndiff 2.504
22.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.723203621e+00 | Ndiff 3.888
23.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722513644e+00 | Ndiff 2.643
24.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722476542e+00 | Ndiff 0.654
25.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722473498e+00 | Ndiff 0.245
26.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722472317e+00 | Ndiff 0.147
27.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722471519e+00 | Ndiff 0.111
28.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722470216e+00 | Ndiff 0.086
29.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722462530e+00 | Ndiff 0.129
30.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722315355e+00 | Ndiff 0.523
31.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722183071e+00 | Ndiff 0.218
32.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.722065842e+00 | Ndiff 0.515
33.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721742168e+00 | Ndiff 0.515
34.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721736951e+00 | Ndiff 0.230
35.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721735062e+00 | Ndiff 0.159
36.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721734200e+00 | Ndiff 0.112
37.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721733770e+00 | Ndiff 0.091
38.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721733543e+00 | Ndiff 0.076
39.000/200 after 1 sec. | 229.7 MiB | K 10 | loss 3.721733417e+00 | Ndiff 0.063
40.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733344e+00 | Ndiff 0.053
41.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733299e+00 | Ndiff 0.044
42.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733271e+00 | Ndiff 0.037
43.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733253e+00 | Ndiff 0.031
44.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733242e+00 | Ndiff 0.028
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48.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733221e+00 | Ndiff 0.018
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50.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733218e+00 | Ndiff 0.014
51.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733217e+00 | Ndiff 0.012
52.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733216e+00 | Ndiff 0.010
53.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733215e+00 | Ndiff 0.009
54.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733215e+00 | Ndiff 0.008
55.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733215e+00 | Ndiff 0.007
56.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733215e+00 | Ndiff 0.006
57.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.005
58.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.005
59.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.004
60.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.003
61.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.003
62.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.003
63.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.002
64.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.002
65.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.002
66.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
67.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
68.000/200 after 2 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
69.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
70.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
71.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
72.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
73.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.001
74.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
75.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
76.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
77.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
78.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
79.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
80.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
81.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
82.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
83.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
84.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
85.000/200 after 3 sec. | 229.7 MiB | K 10 | loss 3.721733214e+00 | Ndiff 0.000
... done. converged.
Compare loss function traces for all methods¶
We’ll notice that the simple mixture performs noticeably worse than the more flexible models that allow group-specific cluster weights
pylab.figure()
pylab.plot(
mix_info_dict['lap_history'],
mix_info_dict['loss_history'], 'k--',
label='mix + diag gauss')
pylab.plot(
finite_info_dict['lap_history'],
finite_info_dict['loss_history'], 'm.-',
label='LDA + diag gauss')
pylab.plot(
hdp_info_dict['lap_history'],
hdp_info_dict['loss_history'], 'r.-',
label='HDP + diag gauss')
pylab.legend(loc='upper right')
pylab.xlabel('num. laps')
pylab.ylabel('loss')
pylab.xlim([0, 200]) # avoid early iterations
pylab.ylim([3.5, 3.7]) # handpicked
pylab.draw()
pylab.tight_layout()
Show the baseline per-sequence appearances¶
np.set_printoptions(precision=3, suppress=1, linewidth=200)
## Compute approx. posterior parameter 'resp' for each example
# resp : 2D array, n_examples x n_clusters
# resp[n] defines Discrete probability of using clusters to explain each example
LP = mix_model.calc_local_params(dataset)
resp_NK = LP['resp']
## Compute the per-sequence average usage.
avg_resp_DK = np.zeros((dataset.nDoc, K))
for d in range(dataset.nDoc):
start = dataset.doc_range[d]
stop = dataset.doc_range[d+1]
avg_resp_DK[d] = np.mean(resp_NK[start:stop], axis=0)
print("Baseline mixture model: per-sequence average cluster usage")
print(avg_resp_DK)
nnz = np.sum(avg_resp_DK < 0.001)
print("Sparsity level: %d/%d entries close-to-zero" % (
nnz, avg_resp_DK.size))
print("")
Baseline mixture model: per-sequence average cluster usage
[[0.045 0.14 0.193 0.086 0.097 0.088 0.085 0.057 0.14 0.07 ]
[0.014 0.245 0.081 0.114 0.098 0.169 0. 0.035 0.166 0.077]
[0.085 0.125 0.232 0.071 0. 0.143 0.037 0.056 0.149 0.103]
[0.065 0.053 0.045 0.085 0.051 0.102 0.123 0.109 0.316 0.052]
[0.097 0.001 0.283 0.094 0.07 0.125 0.088 0.049 0.133 0.06 ]
[0.211 0.004 0.216 0.069 0. 0.068 0.034 0.048 0.234 0.115]]
Sparsity level: 4/60 entries close-to-zero
Show the learned group-specific mixture weights¶
# Compute approx. posterior parameter 'theta' for each document
LP = finite_model.calc_local_params(dataset)
resp_NK = LP['resp']
avg_resp_DK = np.zeros((dataset.nDoc, K))
for d in range(dataset.nDoc):
start = dataset.doc_range[d]
stop = dataset.doc_range[d+1]
avg_resp_DK[d] = np.mean(resp_NK[start:stop], axis=0)
print("LDA mixed membership model: per-sequence average cluster usage")
print(avg_resp_DK)
nnz = np.sum(avg_resp_DK < 0.001)
print("Sparsity level: %d/%d entries close-to-zero" % (
nnz, avg_resp_DK.size))
print("")
# theta : 2D array, n_docs x n_clusters
# theta[d] defines Dirichlet parameters for d-th document
theta_DK = LP['theta']
# Compute expected probabilities
E_pi_DK = theta_DK / np.sum(theta_DK, axis=1)[:,np.newaxis]
print("LDA mixed membership model: per-sequence cluster probabilities")
print(E_pi_DK)
LDA mixed membership model: per-sequence average cluster usage
[[0.126 0.168 0.181 0.085 0.106 0.083 0.05 0.048 0. 0.154]
[0.063 0.296 0.057 0.111 0.117 0.158 0. 0.024 0. 0.174]
[0.161 0.144 0.224 0.063 0.008 0.123 0. 0.036 0. 0.241]
[0. 0.043 0.027 0.081 0.058 0.101 0.068 0.043 0.502 0.078]
[0. 0. 0.283 0.091 0.077 0.124 0.077 0.045 0.201 0.103]
[0.175 0. 0.206 0.067 0. 0.067 0. 0.041 0.22 0.223]]
Sparsity level: 11/60 entries close-to-zero
LDA mixed membership model: per-sequence cluster probabilities
[[0.126 0.168 0.181 0.085 0.106 0.083 0.05 0.048 0. 0.154]
[0.063 0.296 0.057 0.111 0.117 0.158 0. 0.024 0. 0.174]
[0.161 0.144 0.224 0.063 0.008 0.123 0. 0.036 0. 0.241]
[0. 0.043 0.027 0.081 0.058 0.101 0.068 0.043 0.502 0.078]
[0. 0. 0.282 0.091 0.077 0.124 0.077 0.045 0.201 0.103]
[0.175 0. 0.206 0.067 0. 0.067 0. 0.041 0.22 0.223]]
Total running time of the script: ( 0 minutes 36.906 seconds)