Note
Click here to download the full example code
Merge moves with HDP-HMM¶
How to try merge moves efficiently for time-series datasets.
This example reviews three possible ways to plan and execute merge proposals.
try merging all pairs of clusters
pick fewer merge pairs (at most 5 per cluster) in a size-biased way
pick fewer merge pairs (at most 5 per cluster) in objective-driven way
# sphinx_gallery_thumbnail_number = 2
import bnpy
import numpy as np
import os
from matplotlib import pylab
import seaborn as sns
FIG_SIZE = (10, 5)
pylab.rcParams['figure.figsize'] = FIG_SIZE
Setup: Load data¶
# Read bnpy's built-in "Mocap6" 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: Initialization hyperparameters¶
init_kwargs = dict(
K=20,
initname='randexamples',
)
alg_kwargs = dict(
nLap=29,
nTask=1, nBatch=1, convergeThr=0.0001,
)
Setup: HDP-HMM hyperparameters¶
hdphmm_kwargs = dict(
gamma = 5.0, # top-level Dirichlet concentration parameter
transAlpha = 0.5, # trans-level Dirichlet concentration parameter
)
Setup: Gaussian observation model hyperparameters¶
gauss_kwargs = dict(
sF = 1.0, # Set prior so E[covariance] = identity
ECovMat = 'eye',
)
All-Pairs : Try all possible pairs of merges every 10 laps¶
This is expensive, but a good exhaustive test.
allpairs_merge_kwargs = dict(
m_startLap = 10,
# Set limits to number of merges attempted each lap.
# This value specifies max number of tries for each cluster
# Setting this very high (to 50) effectively means try all pairs
m_maxNumPairsContainingComp = 50,
# Set "reactivation" limits
# So that each cluster is eligible again after 10 passes thru dataset
# Or when it's size changes by 400%
m_nLapToReactivate = 10,
m_minPercChangeInNumAtomsToReactivate = 400 * 0.01,
# Specify how to rank pairs (determines order in which merges are tried)
# 'total_size' and 'descending' means try largest combined clusters first
m_pair_ranking_procedure = 'total_size',
m_pair_ranking_direction = 'descending',
)
allpairs_trained_model, allpairs_info_dict = bnpy.run(
dataset, 'HDPHMM', 'DiagGauss', 'memoVB',
output_path='/tmp/mocap6/trymerge-K=20-model=HDPHMM+DiagGauss-ECovMat=1*eye-merge_strategy=all_pairs/',
moves='merge,shuffle',
**dict(
sum(map(list, [alg_kwargs.items(),
init_kwargs.items(),
hdphmm_kwargs.items(),
gauss_kwargs.items(),
allpairs_merge_kwargs.items()]),[]))
)
Dataset Summary:
GroupXData
total size: 6 units
batch size: 6 units
num. batches: 1
Allocation Model: None
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 = 20 (number of clusters)
seed = 1607680
elapsed_time: 0.0 sec
Learn Alg: memoVB | task 1/1 | alg. seed: 1607680 | data order seed: 8541952
task_output_path: /tmp/mocap6/trymerge-K=20-model=HDPHMM+DiagGauss-ECovMat=1*eye-merge_strategy=all_pairs/1
MERGE @ lap 1.00: Disabled. Cannot plan merge on first lap. Need valid SS that represent whole dataset.
1.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.716756017e+00 |
MERGE @ lap 2.00: Disabled. Waiting for lap >= 10 (--m_startLap).
2.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.611824567e+00 | Ndiff 30.418
MERGE @ lap 3.00: Disabled. Waiting for lap >= 10 (--m_startLap).
3.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.579928854e+00 | Ndiff 18.080
MERGE @ lap 4.00: Disabled. Waiting for lap >= 10 (--m_startLap).
4.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.565367190e+00 | Ndiff 19.077
MERGE @ lap 5.00: Disabled. Waiting for lap >= 10 (--m_startLap).
5.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.552412231e+00 | Ndiff 18.396
MERGE @ lap 6.00: Disabled. Waiting for lap >= 10 (--m_startLap).
6.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.547245682e+00 | Ndiff 9.353
MERGE @ lap 7.00: Disabled. Waiting for lap >= 10 (--m_startLap).
7.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.545361199e+00 | Ndiff 3.043
MERGE @ lap 8.00: Disabled. Waiting for lap >= 10 (--m_startLap).
8.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.542436507e+00 | Ndiff 8.684
MERGE @ lap 9.00: Disabled. Waiting for lap >= 10 (--m_startLap).
9.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.533845903e+00 | Ndiff 8.233
MERGE @ lap 10.00 : 5/136 accepted. Ndiff 213.29. 54 skipped.
10.000/29 after 20 sec. | 229.7 MiB | K 15 | loss 3.520225074e+00 | Ndiff 8.233
MERGE @ lap 11.00 : 0/34 accepted. Ndiff 0.00. 0 skipped.
11.000/29 after 25 sec. | 229.7 MiB | K 15 | loss 3.512417678e+00 | Ndiff 9.249
MERGE @ lap 12.00: No promising candidates, so no attempts.
12.000/29 after 25 sec. | 229.7 MiB | K 15 | loss 3.510801853e+00 | Ndiff 9.939
MERGE @ lap 13.00: No promising candidates, so no attempts.
13.000/29 after 25 sec. | 229.7 MiB | K 15 | loss 3.510128661e+00 | Ndiff 12.145
MERGE @ lap 14.00: No promising candidates, so no attempts.
14.000/29 after 25 sec. | 229.7 MiB | K 15 | loss 3.509861771e+00 | Ndiff 6.725
MERGE @ lap 15.00: No promising candidates, so no attempts.
15.000/29 after 25 sec. | 229.7 MiB | K 15 | loss 3.509386291e+00 | Ndiff 3.376
MERGE @ lap 16.00: No promising candidates, so no attempts.
16.000/29 after 25 sec. | 229.7 MiB | K 15 | loss 3.509363787e+00 | Ndiff 1.866
MERGE @ lap 17.00: No promising candidates, so no attempts.
17.000/29 after 25 sec. | 229.7 MiB | K 15 | loss 3.509368704e+00 | Ndiff 1.466
MERGE @ lap 18.00: No promising candidates, so no attempts.
18.000/29 after 26 sec. | 229.7 MiB | K 15 | loss 3.509322290e+00 | Ndiff 1.627
MERGE @ lap 19.00: No promising candidates, so no attempts.
19.000/29 after 26 sec. | 229.7 MiB | K 15 | loss 3.509288542e+00 | Ndiff 0.863
MERGE @ lap 20.00 : 0/71 accepted. Ndiff 0.00. 0 skipped.
20.000/29 after 34 sec. | 229.7 MiB | K 15 | loss 3.509285892e+00 | Ndiff 0.429
MERGE @ lap 21.00 : 0/34 accepted. Ndiff 0.00. 0 skipped.
21.000/29 after 38 sec. | 229.7 MiB | K 15 | loss 3.509285238e+00 | Ndiff 0.273
MERGE @ lap 22.00: No promising candidates, so no attempts.
22.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509285001e+00 | Ndiff 0.176
MERGE @ lap 23.00: No promising candidates, so no attempts.
23.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509284909e+00 | Ndiff 0.114
MERGE @ lap 24.00: No promising candidates, so no attempts.
24.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509284870e+00 | Ndiff 0.075
MERGE @ lap 25.00: No promising candidates, so no attempts.
25.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509284852e+00 | Ndiff 0.054
MERGE @ lap 26.00: No promising candidates, so no attempts.
26.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509284844e+00 | Ndiff 0.039
MERGE @ lap 27.00: No promising candidates, so no attempts.
27.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509284840e+00 | Ndiff 0.028
MERGE @ lap 28.00: No promising candidates, so no attempts.
28.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509284838e+00 | Ndiff 0.020
MERGE @ lap 29.00: No promising candidates, so no attempts.
29.000/29 after 39 sec. | 229.7 MiB | K 15 | loss 3.509284837e+00 | Ndiff 0.014
... done. not converged. max laps thru data exceeded.
Large-Pairs : Try 5-largest-size pairs of merges every 10 laps¶
This is much cheaper than all pairs. Let’s see how well it does.
largepairs_merge_kwargs = dict(
m_startLap = 10,
# Set limits to number of merges attempted each lap.
# This value specifies max number of tries for each cluster
m_maxNumPairsContainingComp = 5,
# Set "reactivation" limits
# So that each cluster is eligible again after 10 passes thru dataset
# Or when it's size changes by 400%
m_nLapToReactivate = 10,
m_minPercChangeInNumAtomsToReactivate = 400 * 0.01,
# Specify how to rank pairs (determines order in which merges are tried)
# 'total_size' and 'descending' means try largest size clusters first
m_pair_ranking_procedure = 'total_size',
m_pair_ranking_direction = 'descending',
)
largepairs_trained_model, largepairs_info_dict = bnpy.run(
dataset, 'HDPHMM', 'DiagGauss', 'memoVB',
output_path='/tmp/mocap6/trymerge-K=20-model=HDPHMM+DiagGauss-ECovMat=1*eye-merge_strategy=large_pairs/',
moves='merge,shuffle',
**dict(
sum(map(list, [alg_kwargs.items(),
init_kwargs.items(),
hdphmm_kwargs.items(),
gauss_kwargs.items(),
largepairs_merge_kwargs.items()]),[])))
Dataset Summary:
GroupXData
total size: 6 units
batch size: 6 units
num. batches: 1
Allocation Model: None
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 = 20 (number of clusters)
seed = 1607680
elapsed_time: 0.0 sec
Learn Alg: memoVB | task 1/1 | alg. seed: 1607680 | data order seed: 8541952
task_output_path: /tmp/mocap6/trymerge-K=20-model=HDPHMM+DiagGauss-ECovMat=1*eye-merge_strategy=large_pairs/1
MERGE @ lap 1.00: Disabled. Cannot plan merge on first lap. Need valid SS that represent whole dataset.
1.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.716756017e+00 |
MERGE @ lap 2.00: Disabled. Waiting for lap >= 10 (--m_startLap).
2.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.611824567e+00 | Ndiff 30.418
MERGE @ lap 3.00: Disabled. Waiting for lap >= 10 (--m_startLap).
3.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.579928854e+00 | Ndiff 18.080
MERGE @ lap 4.00: Disabled. Waiting for lap >= 10 (--m_startLap).
4.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.565367190e+00 | Ndiff 19.077
MERGE @ lap 5.00: Disabled. Waiting for lap >= 10 (--m_startLap).
5.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.552412231e+00 | Ndiff 18.396
MERGE @ lap 6.00: Disabled. Waiting for lap >= 10 (--m_startLap).
6.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.547245682e+00 | Ndiff 9.353
MERGE @ lap 7.00: Disabled. Waiting for lap >= 10 (--m_startLap).
7.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.545361199e+00 | Ndiff 3.043
MERGE @ lap 8.00: Disabled. Waiting for lap >= 10 (--m_startLap).
8.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.542436507e+00 | Ndiff 8.684
MERGE @ lap 9.00: Disabled. Waiting for lap >= 10 (--m_startLap).
9.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.533845903e+00 | Ndiff 8.233
MERGE @ lap 10.00 : 1/42 accepted. Ndiff 41.88. 4 skipped.
10.000/29 after 7 sec. | 229.7 MiB | K 19 | loss 3.528709925e+00 | Ndiff 8.233
MERGE @ lap 11.00 : 1/43 accepted. Ndiff 5.72. 3 skipped.
11.000/29 after 13 sec. | 229.7 MiB | K 18 | loss 3.524443059e+00 | Ndiff 8.233
MERGE @ lap 12.00 : 1/40 accepted. Ndiff 63.51. 2 skipped.
12.000/29 after 19 sec. | 229.7 MiB | K 17 | loss 3.514142988e+00 | Ndiff 8.233
MERGE @ lap 13.00 : 0/25 accepted. Ndiff 0.00. 0 skipped.
13.000/29 after 22 sec. | 229.7 MiB | K 17 | loss 3.510973714e+00 | Ndiff 11.426
MERGE @ lap 14.00 : 1/4 accepted. Ndiff 26.72. 1 skipped.
14.000/29 after 23 sec. | 229.7 MiB | K 16 | loss 3.506988279e+00 | Ndiff 11.426
MERGE @ lap 15.00: No promising candidates, so no attempts.
15.000/29 after 23 sec. | 229.7 MiB | K 16 | loss 3.505872299e+00 | Ndiff 5.842
MERGE @ lap 16.00: No promising candidates, so no attempts.
16.000/29 after 23 sec. | 229.7 MiB | K 16 | loss 3.505589201e+00 | Ndiff 2.350
MERGE @ lap 17.00: No promising candidates, so no attempts.
17.000/29 after 23 sec. | 229.7 MiB | K 16 | loss 3.505217921e+00 | Ndiff 3.323
MERGE @ lap 18.00: No promising candidates, so no attempts.
18.000/29 after 23 sec. | 229.7 MiB | K 16 | loss 3.505132466e+00 | Ndiff 2.900
MERGE @ lap 19.00: No promising candidates, so no attempts.
19.000/29 after 23 sec. | 229.7 MiB | K 16 | loss 3.505086590e+00 | Ndiff 1.972
MERGE @ lap 20.00 : 0/34 accepted. Ndiff 0.00. 0 skipped.
20.000/29 after 28 sec. | 229.7 MiB | K 16 | loss 3.504352544e+00 | Ndiff 12.591
MERGE @ lap 21.00 : 0/33 accepted. Ndiff 0.00. 0 skipped.
21.000/29 after 32 sec. | 229.7 MiB | K 16 | loss 3.502844332e+00 | Ndiff 4.034
MERGE @ lap 22.00 : 0/31 accepted. Ndiff 0.00. 0 skipped.
22.000/29 after 36 sec. | 229.7 MiB | K 16 | loss 3.502177856e+00 | Ndiff 8.703
MERGE @ lap 23.00 : 0/20 accepted. Ndiff 0.00. 0 skipped.
23.000/29 after 38 sec. | 229.7 MiB | K 16 | loss 3.501968229e+00 | Ndiff 2.968
MERGE @ lap 24.00 : 0/2 accepted. Ndiff 0.00. 0 skipped.
24.000/29 after 39 sec. | 229.7 MiB | K 16 | loss 3.501918723e+00 | Ndiff 1.676
MERGE @ lap 25.00: No promising candidates, so no attempts.
25.000/29 after 39 sec. | 229.7 MiB | K 16 | loss 3.501881600e+00 | Ndiff 1.590
MERGE @ lap 26.00: No promising candidates, so no attempts.
26.000/29 after 39 sec. | 229.7 MiB | K 16 | loss 3.501870374e+00 | Ndiff 1.171
MERGE @ lap 27.00: No promising candidates, so no attempts.
27.000/29 after 39 sec. | 229.7 MiB | K 16 | loss 3.501865781e+00 | Ndiff 0.869
MERGE @ lap 28.00: No promising candidates, so no attempts.
28.000/29 after 39 sec. | 229.7 MiB | K 16 | loss 3.501867113e+00 | Ndiff 0.638
MERGE @ lap 29.00: No promising candidates, so no attempts.
29.000/29 after 40 sec. | 229.7 MiB | K 16 | loss 3.501863089e+00 | Ndiff 0.468
... done. not converged. max laps thru data exceeded.
Good-ELBO-Pairs : Rank pairs of merges by improvement to observation model¶
This is much cheaper than all pairs and perhaps more principled. Let’s see how well it does.
goodelbopairs_merge_kwargs = dict(
m_startLap = 10,
# Set limits to number of merges attempted each lap.
# This value specifies max number of tries for each cluster
m_maxNumPairsContainingComp = 5,
# Set "reactivation" limits
# So that each cluster is eligible again after 10 passes thru dataset
# Or when it's size changes by 400%
m_nLapToReactivate = 10,
m_minPercChangeInNumAtomsToReactivate = 400 * 0.01,
# Specify how to rank pairs (determines order in which merges are tried)
# 'obsmodel_elbo' means rank pairs by improvement to observation model ELBO
m_pair_ranking_procedure = 'obsmodel_elbo',
m_pair_ranking_direction = 'descending',
)
goodelbopairs_trained_model, goodelbopairs_info_dict = bnpy.run(
dataset, 'HDPHMM', 'DiagGauss', 'memoVB',
output_path='/tmp/mocap6/trymerge-K=20-model=HDPHMM+DiagGauss-ECovMat=1*eye-merge_strategy=good_elbo_pairs/',
moves='merge,shuffle',
**dict(
sum(map(list, [alg_kwargs.items(),
init_kwargs.items(),
hdphmm_kwargs.items(),
gauss_kwargs.items(),
goodelbopairs_merge_kwargs.items()]),[])))
Dataset Summary:
GroupXData
total size: 6 units
batch size: 6 units
num. batches: 1
Allocation Model: None
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 = 20 (number of clusters)
seed = 1607680
elapsed_time: 0.0 sec
Learn Alg: memoVB | task 1/1 | alg. seed: 1607680 | data order seed: 8541952
task_output_path: /tmp/mocap6/trymerge-K=20-model=HDPHMM+DiagGauss-ECovMat=1*eye-merge_strategy=good_elbo_pairs/1
MERGE @ lap 1.00: Disabled. Cannot plan merge on first lap. Need valid SS that represent whole dataset.
1.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.716756017e+00 |
MERGE @ lap 2.00: Disabled. Waiting for lap >= 10 (--m_startLap).
2.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.611824567e+00 | Ndiff 30.418
MERGE @ lap 3.00: Disabled. Waiting for lap >= 10 (--m_startLap).
3.000/29 after 0 sec. | 229.7 MiB | K 20 | loss 3.579928854e+00 | Ndiff 18.080
MERGE @ lap 4.00: Disabled. Waiting for lap >= 10 (--m_startLap).
4.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.565367190e+00 | Ndiff 19.077
MERGE @ lap 5.00: Disabled. Waiting for lap >= 10 (--m_startLap).
5.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.552412231e+00 | Ndiff 18.396
MERGE @ lap 6.00: Disabled. Waiting for lap >= 10 (--m_startLap).
6.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.547245682e+00 | Ndiff 9.353
MERGE @ lap 7.00: Disabled. Waiting for lap >= 10 (--m_startLap).
7.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.545361199e+00 | Ndiff 3.043
MERGE @ lap 8.00: Disabled. Waiting for lap >= 10 (--m_startLap).
8.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.542436507e+00 | Ndiff 8.684
MERGE @ lap 9.00: Disabled. Waiting for lap >= 10 (--m_startLap).
9.000/29 after 1 sec. | 229.7 MiB | K 20 | loss 3.533845903e+00 | Ndiff 8.233
MERGE @ lap 10.00 : 4/24 accepted. Ndiff 186.96. 24 skipped.
10.000/29 after 5 sec. | 229.7 MiB | K 16 | loss 3.519121780e+00 | Ndiff 8.233
MERGE @ lap 11.00 : 1/27 accepted. Ndiff 26.46. 8 skipped.
11.000/29 after 8 sec. | 229.7 MiB | K 15 | loss 3.512205787e+00 | Ndiff 8.233
MERGE @ lap 12.00 : 0/35 accepted. Ndiff 0.00. 0 skipped.
12.000/29 after 13 sec. | 229.7 MiB | K 15 | loss 3.510583397e+00 | Ndiff 7.969
MERGE @ lap 13.00 : 0/20 accepted. Ndiff 0.00. 0 skipped.
13.000/29 after 15 sec. | 229.7 MiB | K 15 | loss 3.510164786e+00 | Ndiff 9.666
MERGE @ lap 14.00 : 0/6 accepted. Ndiff 0.00. 0 skipped.
14.000/29 after 16 sec. | 229.7 MiB | K 15 | loss 3.509795232e+00 | Ndiff 8.294
MERGE @ lap 15.00: No promising candidates, so no attempts.
15.000/29 after 16 sec. | 229.7 MiB | K 15 | loss 3.509320540e+00 | Ndiff 4.196
MERGE @ lap 16.00: No promising candidates, so no attempts.
16.000/29 after 16 sec. | 229.7 MiB | K 15 | loss 3.509297032e+00 | Ndiff 2.222
MERGE @ lap 17.00: No promising candidates, so no attempts.
17.000/29 after 16 sec. | 229.7 MiB | K 15 | loss 3.509289811e+00 | Ndiff 1.343
MERGE @ lap 18.00: No promising candidates, so no attempts.
18.000/29 after 17 sec. | 229.7 MiB | K 15 | loss 3.509286966e+00 | Ndiff 0.846
MERGE @ lap 19.00: No promising candidates, so no attempts.
19.000/29 after 17 sec. | 229.7 MiB | K 15 | loss 3.509285777e+00 | Ndiff 0.548
MERGE @ lap 20.00 : 0/18 accepted. Ndiff 0.00. 0 skipped.
20.000/29 after 19 sec. | 229.7 MiB | K 15 | loss 3.509285264e+00 | Ndiff 0.361
MERGE @ lap 21.00 : 0/26 accepted. Ndiff 0.00. 0 skipped.
21.000/29 after 22 sec. | 229.7 MiB | K 15 | loss 3.509285036e+00 | Ndiff 0.241
MERGE @ lap 22.00 : 0/35 accepted. Ndiff 0.00. 0 skipped.
22.000/29 after 27 sec. | 229.7 MiB | K 15 | loss 3.509284932e+00 | Ndiff 0.163
MERGE @ lap 23.00 : 0/20 accepted. Ndiff 0.00. 0 skipped.
23.000/29 after 29 sec. | 229.7 MiB | K 15 | loss 3.509284883e+00 | Ndiff 0.111
MERGE @ lap 24.00 : 0/6 accepted. Ndiff 0.00. 0 skipped.
24.000/29 after 30 sec. | 229.7 MiB | K 15 | loss 3.509284860e+00 | Ndiff 0.077
MERGE @ lap 25.00: No promising candidates, so no attempts.
25.000/29 after 30 sec. | 229.7 MiB | K 15 | loss 3.509284848e+00 | Ndiff 0.053
MERGE @ lap 26.00: No promising candidates, so no attempts.
26.000/29 after 30 sec. | 229.7 MiB | K 15 | loss 3.509284842e+00 | Ndiff 0.037
MERGE @ lap 27.00: No promising candidates, so no attempts.
27.000/29 after 30 sec. | 229.7 MiB | K 15 | loss 3.509284839e+00 | Ndiff 0.026
MERGE @ lap 28.00: No promising candidates, so no attempts.
28.000/29 after 31 sec. | 229.7 MiB | K 15 | loss 3.509284837e+00 | Ndiff 0.019
MERGE @ lap 29.00: No promising candidates, so no attempts.
29.000/29 after 31 sec. | 229.7 MiB | K 15 | loss 3.509284836e+00 | Ndiff 0.013
... done. not converged. max laps thru data exceeded.
Compare loss function vs wallclock time¶
pylab.figure()
for info_dict, color_str, label_str in [
(allpairs_info_dict, 'k', 'all_pairs'),
(largepairs_info_dict, 'g', 'large_pairs'),
(goodelbopairs_info_dict, 'b', 'good_elbo_pairs')]:
pylab.plot(
info_dict['elapsed_time_sec_history'],
info_dict['loss_history'],
'.-',
color=color_str,
label=label_str)
pylab.legend(loc='upper right')
pylab.xlabel('elapsed time (sec)')
pylab.ylabel('loss')
Text(78.97222222222221, 0.5, 'loss')
Compare number of active clusters vs wallclock time¶
pylab.figure()
for info_dict, color_str, label_str in [
(allpairs_info_dict, 'k', 'all_pairs'),
(largepairs_info_dict, 'g', 'large_pairs'),
(goodelbopairs_info_dict, 'b', 'good_elbo_pairs')]:
pylab.plot(
info_dict['elapsed_time_sec_history'],
info_dict['K_history'],
'.-',
color=color_str,
label=label_str)
pylab.legend(loc='upper right')
pylab.xlabel('elapsed time (sec)')
pylab.ylabel('num. clusters (K)')
pylab.show(block=False)
Total running time of the script: ( 1 minutes 49.904 seconds)