Note
Click here to download the full example code
Demonstration of Sparse Responsibilities for Mixtures of Gaussians¶
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()
Setup: Func to print resp array nicely¶
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)
VB with DP mixture model and diagonal Gaussian observations¶
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
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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.
Do inference with L=1 sparsity¶
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.]]
Do inference with L=2 sparsity¶
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]]
Do inference with L=3 sparsity¶
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')
How to incorporate sparsity during training?¶
Just pass “nnzPerRowLP” kwarg to bnpy.run(…)
Total running time of the script: ( 0 minutes 10.619 seconds)