Note

Click here to download the full example code

This demo illustrates “sparse responsibilities” for mixture models.

In this example, we show how BNPy makes it easy to vary per-example inference, so that for a 3-cluster model we can manually try each of the following:

each data point assigned to up to 1 cluster

each data point assigned to up to 2 clusters

each data point assigned to up to 3 clusters

In our variational approximation, when we fit
a K-cluster mixture model to a specific data point,
we learn a vector of posterior **responsibilities** –
we often call these “resp” for short in the code –
indicating how much of the unit probability mass for this example
is explained by each of the K clusters.

For example, for a specific data point we might have a resp vector of:

[ 0. 0.9262 0.0738]

which we interpret as saying this point is 92% explained by the 2nd cluster, 7% by the 3rd cluster, and 0% by the first.

This short demo just shows how we can deliberately enforce sparsity in learned responsibilities, so that each example may have only L non-zero entries in its resp vector. For example, we’ll fit a K=3 model below, and show how we can enforce L=1 or L=2 sparsity.

For a technical introduction, see our ArXiv paper:

Michael C. Hughes and Erik B. Sudderth (2016) “Fast Learning of Clusters and Topics via Sparse Posteriors.” https://arxiv.org/abs/1609.07521

```
from __future__ import print_function
import bnpy
import numpy as np
import os
from matplotlib import pylab
import seaborn as sns
SMALL_FIG_SIZE = (2.5, 2.5)
FIG_SIZE = (5, 5)
pylab.rcParams['figure.figsize'] = FIG_SIZE
```

Load dataset from file

```
dataset_path = os.path.join(bnpy.DATASET_PATH, 'faithful')
dataset = bnpy.data.XData.read_csv(
os.path.join(dataset_path, 'faithful.csv'))
# Identify "target" example ids to focus on
target_example_ids = np.asarray(
[ 83, 46, 152, 173, 23, 164, 243, 239, 154, 100])
targeted_dataset = bnpy.data.XData(
dataset.X[target_example_ids])
```

Make a simple plot of the raw data

```
pylab.plot(dataset.X[:, 0], dataset.X[:, 1], 'k.')
pylab.xlabel(dataset.column_names[0])
pylab.ylabel(dataset.column_names[1])
pylab.tight_layout()
pylab.title('Complete Dataset')
data_ax_h = pylab.gca()
```

```
def pprint_sparse_or_dense_resp(LP, target_example_ids=target_example_ids):
np.set_printoptions(suppress=True, precision=4)
if 'spR' in LP:
# sparse case
resp_NK = LP['spR'][target_example_ids].toarray()
else:
resp_NK = LP['resp'][target_example_ids]
print(resp_NK)
```

Assumes exactly 3 clusters

Assumes diagonal covariances.

No sparsity assumptions during training

```
K = 3 # n clusters
gamma = 50.0 # DP concentration param
sF = 0.1 # scale of expected covariance
full_trained_model, full_info_dict = bnpy.run(
dataset, 'DPMixtureModel', 'DiagGauss', 'VB',
output_path='/tmp/faithful/demo_sparse_resp-K=3-lik=Gauss-ECovMat=5*eye/',
nLap=1000, nTask=5, nBatch=1, convergeThr=0.0001,
gamma0=gamma, sF=sF, ECovMat='eye',
K=K, initname='randexamples',
)
# Add this model into the current plot
bnpy.viz.PlotComps.plotCompsFromHModel(
full_trained_model,
)
```

```
WARNING: Found unrecognized keyword args. These are ignored.
--nBatch
Dataset Summary:
X Data
num examples: 272
num dims: 2
Allocation Model: DP mixture with K=0. Concentration gamma0= 50.00
Obs. Data Model: Gaussian with diagonal covariance.
Obs. Data Prior: independent Gauss-Wishart prior on each dimension
Wishart params
nu = 4
beta = [ 0.2 0.2]
Expectations
E[ mean[k]] =
[ 0 0]
E[ covar[k]] =
[[0.1 0. ]
[0. 0.1]]
Initialization:
initname = randexamples
K = 3 (number of clusters)
seed = 1607680
elapsed_time: 0.0 sec
Learn Alg: VB | task 1/5 | alg. seed: 1607680 | data order seed: 8541952
task_output_path: /tmp/faithful/demo_sparse_resp-K=3-lik=Gauss-ECovMat=5*eye/1
1/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.024985920e+00 |
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250/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.477968146e+00 | Ndiff 0.061
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256/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.477962687e+00 | Ndiff 0.070
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300/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.477330143e+00 | Ndiff 0.537
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327/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465626565e+00 | Ndiff 0.094
328/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465615098e+00 | Ndiff 0.031
329/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465613516e+00 | Ndiff 0.014
330/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465613125e+00 | Ndiff 0.008
331/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612997e+00 | Ndiff 0.005
332/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612952e+00 | Ndiff 0.003
333/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612936e+00 | Ndiff 0.002
334/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612930e+00 | Ndiff 0.001
335/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612928e+00 | Ndiff 0.001
336/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612927e+00 | Ndiff 0.000
337/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612927e+00 | Ndiff 0.000
338/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612927e+00 | Ndiff 0.000
339/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.465612927e+00 | Ndiff 0.000
... done. converged.
Learn Alg: VB | task 2/5 | alg. seed: 6454144 | data order seed: 7673856
task_output_path: /tmp/faithful/demo_sparse_resp-K=3-lik=Gauss-ECovMat=5*eye/2
1/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.144280649e+00 |
2/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.999427978e+00 | Ndiff 16.339
3/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.889092617e+00 | Ndiff 29.328
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86/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480138808e+00 | Ndiff 0.100
87/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480137409e+00 | Ndiff 0.096
88/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480136127e+00 | Ndiff 0.091
89/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480134953e+00 | Ndiff 0.087
90/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480133878e+00 | Ndiff 0.084
91/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480132893e+00 | Ndiff 0.080
92/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480131990e+00 | Ndiff 0.077
93/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480131162e+00 | Ndiff 0.073
94/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480130403e+00 | Ndiff 0.070
95/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480129708e+00 | Ndiff 0.067
96/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480129070e+00 | Ndiff 0.064
97/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480128485e+00 | Ndiff 0.061
98/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480127949e+00 | Ndiff 0.059
99/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480127457e+00 | Ndiff 0.056
100/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480127005e+00 | Ndiff 0.054
101/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480126591e+00 | Ndiff 0.052
102/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480126211e+00 | Ndiff 0.049
103/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480125862e+00 | Ndiff 0.047
104/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480125542e+00 | Ndiff 0.045
105/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480125248e+00 | Ndiff 0.043
106/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480124978e+00 | Ndiff 0.042
107/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480124731e+00 | Ndiff 0.040
108/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480124503e+00 | Ndiff 0.038
109/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480124294e+00 | Ndiff 0.037
110/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480124102e+00 | Ndiff 0.035
111/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123926e+00 | Ndiff 0.034
112/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123764e+00 | Ndiff 0.032
113/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123615e+00 | Ndiff 0.031
114/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123478e+00 | Ndiff 0.030
115/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123353e+00 | Ndiff 0.028
116/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123237e+00 | Ndiff 0.027
117/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123131e+00 | Ndiff 0.026
118/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480123034e+00 | Ndiff 0.025
119/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122944e+00 | Ndiff 0.024
120/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122862e+00 | Ndiff 0.023
121/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122786e+00 | Ndiff 0.022
122/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122716e+00 | Ndiff 0.021
123/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122652e+00 | Ndiff 0.020
124/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122593e+00 | Ndiff 0.019
125/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122539e+00 | Ndiff 0.019
126/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122489e+00 | Ndiff 0.018
127/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122444e+00 | Ndiff 0.017
128/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122401e+00 | Ndiff 0.016
129/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122363e+00 | Ndiff 0.016
130/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122327e+00 | Ndiff 0.015
131/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122294e+00 | Ndiff 0.014
132/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122264e+00 | Ndiff 0.014
133/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122236e+00 | Ndiff 0.013
134/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122211e+00 | Ndiff 0.013
135/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122187e+00 | Ndiff 0.012
136/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122166e+00 | Ndiff 0.012
137/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122146e+00 | Ndiff 0.011
138/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122128e+00 | Ndiff 0.011
139/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122111e+00 | Ndiff 0.010
140/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122095e+00 | Ndiff 0.010
141/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122081e+00 | Ndiff 0.009
142/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122068e+00 | Ndiff 0.009
143/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122056e+00 | Ndiff 0.009
144/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122045e+00 | Ndiff 0.008
145/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122034e+00 | Ndiff 0.008
146/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122025e+00 | Ndiff 0.008
147/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122016e+00 | Ndiff 0.007
148/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122008e+00 | Ndiff 0.007
149/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480122001e+00 | Ndiff 0.007
150/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.480121994e+00 | Ndiff 0.007
151/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121988e+00 | Ndiff 0.006
152/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121982e+00 | Ndiff 0.006
153/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121977e+00 | Ndiff 0.006
154/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121972e+00 | Ndiff 0.006
155/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121968e+00 | Ndiff 0.005
156/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121964e+00 | Ndiff 0.005
157/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121960e+00 | Ndiff 0.005
158/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121956e+00 | Ndiff 0.005
159/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121953e+00 | Ndiff 0.004
160/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121950e+00 | Ndiff 0.004
161/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121948e+00 | Ndiff 0.004
162/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121945e+00 | Ndiff 0.004
163/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121943e+00 | Ndiff 0.004
164/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121941e+00 | Ndiff 0.004
165/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121939e+00 | Ndiff 0.004
166/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121937e+00 | Ndiff 0.003
167/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121935e+00 | Ndiff 0.003
168/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121934e+00 | Ndiff 0.003
169/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121932e+00 | Ndiff 0.003
170/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121931e+00 | Ndiff 0.003
171/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121930e+00 | Ndiff 0.003
172/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121929e+00 | Ndiff 0.003
173/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121928e+00 | Ndiff 0.003
174/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121927e+00 | Ndiff 0.002
175/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121926e+00 | Ndiff 0.002
176/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121925e+00 | Ndiff 0.002
177/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121924e+00 | Ndiff 0.002
178/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121924e+00 | Ndiff 0.002
179/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121923e+00 | Ndiff 0.002
180/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121923e+00 | Ndiff 0.002
181/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121922e+00 | Ndiff 0.002
182/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121922e+00 | Ndiff 0.002
183/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121921e+00 | Ndiff 0.002
184/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121921e+00 | Ndiff 0.002
185/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121920e+00 | Ndiff 0.002
186/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121920e+00 | Ndiff 0.001
187/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121920e+00 | Ndiff 0.001
188/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121919e+00 | Ndiff 0.001
189/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121919e+00 | Ndiff 0.001
190/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121919e+00 | Ndiff 0.001
191/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121919e+00 | Ndiff 0.001
192/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121918e+00 | Ndiff 0.001
193/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121918e+00 | Ndiff 0.001
194/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121918e+00 | Ndiff 0.001
195/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121918e+00 | Ndiff 0.001
196/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121918e+00 | Ndiff 0.001
197/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121918e+00 | Ndiff 0.001
198/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
199/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
200/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
201/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
202/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
203/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
204/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
205/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
206/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
207/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
208/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
209/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121917e+00 | Ndiff 0.001
210/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.001
211/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.001
212/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.001
213/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
214/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
215/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
216/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
217/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
218/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
219/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
220/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
221/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
222/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
223/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
224/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
225/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
226/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
227/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
228/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
229/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
230/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
231/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
232/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
233/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
234/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
235/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
236/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
237/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
238/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
239/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
240/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
241/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
242/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
243/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
244/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
245/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
246/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
247/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
248/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
249/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
250/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
251/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
252/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.480121916e+00 | Ndiff 0.000
... done. converged.
Learn Alg: VB | task 3/5 | alg. seed: 6168832 | data order seed: 7360256
task_output_path: /tmp/faithful/demo_sparse_resp-K=3-lik=Gauss-ECovMat=5*eye/3
1/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.597890645e+00 |
2/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.500334722e+00 | Ndiff 12.103
3/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.481732540e+00 | Ndiff 6.728
4/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.477128325e+00 | Ndiff 3.822
5/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.475214582e+00 | Ndiff 2.612
6/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.474025229e+00 | Ndiff 1.975
7/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.473158433e+00 | Ndiff 1.625
8/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.472487721e+00 | Ndiff 1.387
9/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.471960617e+00 | Ndiff 1.189
10/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.471546906e+00 | Ndiff 1.019
11/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.471223928e+00 | Ndiff 0.873
12/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470972980e+00 | Ndiff 0.748
13/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470778682e+00 | Ndiff 0.642
14/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470628726e+00 | Ndiff 0.551
15/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470513450e+00 | Ndiff 0.473
16/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470425312e+00 | Ndiff 0.405
17/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470358389e+00 | Ndiff 0.347
18/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470307985e+00 | Ndiff 0.297
19/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470270346e+00 | Ndiff 0.253
20/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470242474e+00 | Ndiff 0.215
21/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470221992e+00 | Ndiff 0.183
22/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470207040e+00 | Ndiff 0.155
23/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470196186e+00 | Ndiff 0.131
24/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470188343e+00 | Ndiff 0.111
25/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470182696e+00 | Ndiff 0.094
26/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470178643e+00 | Ndiff 0.079
27/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470175740e+00 | Ndiff 0.067
28/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470173665e+00 | Ndiff 0.056
29/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470172185e+00 | Ndiff 0.047
30/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470171130e+00 | Ndiff 0.040
31/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470170380e+00 | Ndiff 0.033
32/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470169846e+00 | Ndiff 0.028
33/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470169466e+00 | Ndiff 0.024
34/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470169197e+00 | Ndiff 0.020
35/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470169005e+00 | Ndiff 0.017
36/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168870e+00 | Ndiff 0.014
37/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168773e+00 | Ndiff 0.012
38/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168705e+00 | Ndiff 0.010
39/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168657e+00 | Ndiff 0.008
40/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168622e+00 | Ndiff 0.007
41/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168598e+00 | Ndiff 0.006
42/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168581e+00 | Ndiff 0.005
43/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168569e+00 | Ndiff 0.004
44/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168560e+00 | Ndiff 0.004
45/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168554e+00 | Ndiff 0.003
46/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168550e+00 | Ndiff 0.003
47/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168547e+00 | Ndiff 0.002
48/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168544e+00 | Ndiff 0.002
49/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168543e+00 | Ndiff 0.001
50/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.470168542e+00 | Ndiff 0.001
51/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168541e+00 | Ndiff 0.001
52/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168540e+00 | Ndiff 0.001
53/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168540e+00 | Ndiff 0.001
54/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168540e+00 | Ndiff 0.001
55/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168540e+00 | Ndiff 0.001
56/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
57/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
58/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
59/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
60/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
61/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
62/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
63/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
64/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
65/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.470168539e+00 | Ndiff 0.000
... done. converged.
Learn Alg: VB | task 4/5 | alg. seed: 8205184 | data order seed: 900864
task_output_path: /tmp/faithful/demo_sparse_resp-K=3-lik=Gauss-ECovMat=5*eye/4
1/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.625818566e+00 |
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233/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.001
234/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.001
235/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.000
236/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.000
237/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.000
238/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.000
239/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.000
240/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.000
241/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719144e+00 | Ndiff 0.000
242/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
243/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
244/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
245/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
246/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
247/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
248/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
249/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
250/1000 after 2 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
251/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
252/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
253/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
254/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
255/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
256/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
257/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
258/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
259/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
260/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
261/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
262/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
263/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
264/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
265/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
266/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
267/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
268/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
269/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
270/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
271/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
272/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
273/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
274/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
275/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
276/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
277/1000 after 3 sec. | 218.2 MiB | K 3 | loss 2.479719143e+00 | Ndiff 0.000
... done. converged.
Learn Alg: VB | task 5/5 | alg. seed: 2986368 | data order seed: 6479872
task_output_path: /tmp/faithful/demo_sparse_resp-K=3-lik=Gauss-ECovMat=5*eye/5
1/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.172454094e+00 |
2/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.141604650e+00 | Ndiff 2.137
3/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.132916761e+00 | Ndiff 2.207
4/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.116013242e+00 | Ndiff 5.951
5/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.079969466e+00 | Ndiff 11.990
6/1000 after 0 sec. | 218.2 MiB | K 3 | loss 3.017967278e+00 | Ndiff 19.686
7/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.936658417e+00 | Ndiff 25.772
8/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.854720770e+00 | Ndiff 26.020
9/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.778771855e+00 | Ndiff 21.413
10/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.688031379e+00 | Ndiff 17.921
11/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.590482847e+00 | Ndiff 12.677
12/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.538125017e+00 | Ndiff 5.219
13/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.514221187e+00 | Ndiff 3.415
14/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.497495257e+00 | Ndiff 2.846
15/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495853387e+00 | Ndiff 1.084
16/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495615725e+00 | Ndiff 0.686
17/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495466026e+00 | Ndiff 0.569
18/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495360868e+00 | Ndiff 0.492
19/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495282138e+00 | Ndiff 0.438
20/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495220122e+00 | Ndiff 0.397
21/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495169130e+00 | Ndiff 0.366
22/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495125609e+00 | Ndiff 0.342
23/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495087248e+00 | Ndiff 0.322
24/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495052510e+00 | Ndiff 0.305
25/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.495020376e+00 | Ndiff 0.290
26/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494990186e+00 | Ndiff 0.277
27/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494961538e+00 | Ndiff 0.265
28/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494934218e+00 | Ndiff 0.254
29/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494908140e+00 | Ndiff 0.243
30/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494883308e+00 | Ndiff 0.233
31/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494859775e+00 | Ndiff 0.223
32/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494837612e+00 | Ndiff 0.212
33/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494816893e+00 | Ndiff 0.202
34/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494797668e+00 | Ndiff 0.192
35/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494779964e+00 | Ndiff 0.183
36/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494763774e+00 | Ndiff 0.173
37/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494749060e+00 | Ndiff 0.163
38/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494735759e+00 | Ndiff 0.154
39/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494723785e+00 | Ndiff 0.145
40/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494713036e+00 | Ndiff 0.137
41/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494703405e+00 | Ndiff 0.129
42/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494694776e+00 | Ndiff 0.121
43/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494687038e+00 | Ndiff 0.114
44/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494680081e+00 | Ndiff 0.108
45/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494673802e+00 | Ndiff 0.102
46/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494668101e+00 | Ndiff 0.096
47/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494662889e+00 | Ndiff 0.091
48/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494658081e+00 | Ndiff 0.087
49/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494653596e+00 | Ndiff 0.083
50/1000 after 0 sec. | 218.2 MiB | K 3 | loss 2.494649357e+00 | Ndiff 0.079
51/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494645290e+00 | Ndiff 0.076
52/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494641317e+00 | Ndiff 0.074
53/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494637354e+00 | Ndiff 0.072
54/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494633308e+00 | Ndiff 0.071
55/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494629063e+00 | Ndiff 0.071
56/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494624470e+00 | Ndiff 0.072
57/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494619327e+00 | Ndiff 0.073
58/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494613338e+00 | Ndiff 0.076
59/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494606055e+00 | Ndiff 0.080
60/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494596763e+00 | Ndiff 0.086
61/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494584268e+00 | Ndiff 0.094
62/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494566496e+00 | Ndiff 0.107
63/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494539723e+00 | Ndiff 0.123
64/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494497190e+00 | Ndiff 0.146
65/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494427049e+00 | Ndiff 0.177
66/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494311214e+00 | Ndiff 0.217
67/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.494130236e+00 | Ndiff 0.262
68/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.493873502e+00 | Ndiff 0.313
69/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.493523728e+00 | Ndiff 0.371
70/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.492991715e+00 | Ndiff 0.441
71/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.492034383e+00 | Ndiff 0.514
72/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.490416803e+00 | Ndiff 0.538
73/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.489022467e+00 | Ndiff 0.469
74/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.488517958e+00 | Ndiff 0.454
75/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.488074913e+00 | Ndiff 0.435
76/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.487289742e+00 | Ndiff 0.375
77/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.486119349e+00 | Ndiff 0.236
78/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.485305514e+00 | Ndiff 0.082
79/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484928040e+00 | Ndiff 0.036
80/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484776037e+00 | Ndiff 0.007
81/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484732728e+00 | Ndiff 0.012
82/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484723997e+00 | Ndiff 0.015
83/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484722544e+00 | Ndiff 0.010
84/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484722316e+00 | Ndiff 0.005
85/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484722281e+00 | Ndiff 0.003
86/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484722275e+00 | Ndiff 0.001
87/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484722274e+00 | Ndiff 0.001
88/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484722274e+00 | Ndiff 0.000
89/1000 after 1 sec. | 218.2 MiB | K 3 | loss 2.484722274e+00 | Ndiff 0.000
... done. converged.
```

Kwarg to set is “nnzPerRowLP = 1”

nnzPerRowLP is read as “number of non-zero entries per row of local parameters”

This enforces that each row of resp array has ___ non zero entries

```
print('SPARSITY LEVEL: 1 of 3')
LP1 = full_trained_model.calc_local_params(dataset, nnzPerRowLP=1)
pprint_sparse_or_dense_resp(LP1)
```

```
SPARSITY LEVEL: 1 of 3
[[0. 0. 1.]
[0. 0. 1.]
[0. 1. 0.]
[0. 0. 1.]
[0. 0. 1.]
[0. 0. 1.]
[0. 0. 1.]
[0. 1. 0.]
[1. 0. 0.]
[0. 1. 0.]]
```

```
print('SPARSITY LEVEL: 2 of 3')
LP2 = full_trained_model.calc_local_params(dataset, nnzPerRowLP=2)
pprint_sparse_or_dense_resp(LP2)
```

```
SPARSITY LEVEL: 2 of 3
[[0. 0.2424 0.7576]
[0.2281 0. 0.7719]
[0. 0.9262 0.0738]
[0.0427 0. 0.9573]
[0.0269 0. 0.9731]
[0.0247 0. 0.9753]
[0. 0.021 0.979 ]
[0. 0.9866 0.0134]
[0.9866 0. 0.0134]
[0. 0.9905 0.0095]]
```

```
print('SPARSITY LEVEL: 3 of 3 (DENSE)')
LP3 = full_trained_model.calc_local_params(dataset, nnzPerRowLP=3)
pprint_sparse_or_dense_resp(LP3)
# Show the model
pylab.figure(2)
bnpy.viz.PlotComps.plotCompsFromHModel(
full_trained_model,
dataset=targeted_dataset,
)
pylab.xlabel(dataset.column_names[0])
pylab.ylabel(dataset.column_names[1])
pylab.tight_layout()
pylab.title('Trained model with targeted examples')
```

```
SPARSITY LEVEL: 3 of 3 (DENSE)
[[0. 0.2424 0.7575]
[0.2281 0. 0.7719]
[0. 0.9262 0.0738]
[0.0427 0. 0.9573]
[0.0269 0.0002 0.9729]
[0.0247 0. 0.9753]
[0.0002 0.021 0.9788]
[0. 0.9866 0.0134]
[0.9866 0. 0.0134]
[0. 0.9905 0.0095]]
Text(0.5, 1.0, 'Trained model with targeted examples')
```

Just pass “nnzPerRowLP” kwarg to bnpy.run(…)

**Total running time of the script:** ( 0 minutes 10.619 seconds)