1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
|
%global _empty_manifest_terminate_build 0
Name: python-dpmmpython-trax
Version: 0.1.5
Release: 1
Summary: Python wrapper for DPMMSubClusters julia package
License: MIT
URL: https://github.com/dinarior/dpmmpython_trax
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/95/55/34b8dd6097c6c466d6b71f07a556bcbe997bb49a74b5e38424b211c4ba3d/dpmmpython_trax-0.1.5.tar.gz
BuildArch: noarch
Requires: python3-julia
Requires: python3-wget
Requires: python3-numpy
Requires: python3-numpy
Requires: python3-ipython
Requires: python3-pytest
Requires: python3-mock
%description
<br>
<p align="center">
<img src="https://www.cs.bgu.ac.il/~dinari/images/clusters_low_slow.gif" alt="DPGMM SubClusters 2d example">
</p>
## DPMMSubClusters
This package is a Python wrapper for the [DPMMSubClusters.jl](https://github.com/BGU-CS-VIL/DPMMSubClusters.jl) Julia package.<br>
### Motivation
Working on a subset of 100K images from ImageNet, containing 79 classes, we have created embeddings using [SWAV](https://github.com/facebookresearch/swav), and reduced the dimension to 128 using PCA. We have compared our method with the popular scikit-learn [GMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html) and [DPGMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html) with the following results:
<p align="center">
| Method | Timing (sec) | NMI (higher is better) |
|-----------------------------------------------------|--------------|------------------------|
| *Scikit-learn's GMM* (using EM, and given the True K) | 2523 | 0.695 |
| *Scikit-learn's DPGMM* | 6108 | 0.683 |
| DPMMpython | 475 | 0.705 |
</p>
### Installation
```
pip install dpmmpython
```
If you already have Julia installed, install [PyJulia](https://github.com/JuliaPy/pyjulia) and add the package `DPMMSubClusters` to your julia installation. <p>
<p>
Make sure Julia path is configured correctly, e.g. you should be able to run julia by typing `julia` from the terminal, unless configured properly, PyJulia wont work.
**Installation Shortcut for Ubuntu distributions** <br>
If you do not have Julia installed, or wish to create a clean installation for the purpose of using this package. after installing (with pip), do the following:
```
import dpmmpython
dpmmpython.install()
```
Optional arguments are `install(julia_download_path = 'https://julialang-s3.julialang.org/bin/linux/x64/1.4/julia-1.4.0-linux-x86_64.tar.gz', julia_target_path = None)`, where the former specify the julia download file, and the latter the installation path, if the installation path is not specified, `$HOME$/julia` will be used.<br>
As the `install()` command edit your `.bashrc` path, before using the pacakge, the terminal should either be reset, or modify the current environment according to the julia path you specified (`$HOME$/julia/julia-1.4.0/bin` by default).
### Usage Example:
```
from dpmmpython.dpmmwrapper import DPMMPython
from dpmmpython.priors import niw
import numpy as np
data,gt = DPMMPython.generate_gaussian_data(10000, 2, 10, 100.0)
prior = niw(1,np.zeros(2),4,np.eye(2))
labels,_,sub_labels= DPMMPython.fit(data,100,prior = prior,verbose = True, gt = gt)
```
```
Iteration: 1 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.004499912261962891 || Total time:0.004499912261962891
Iteration: 2 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.0038819313049316406 || Total time:0.008381843566894531
...
Iteration: 98 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.015907764434814453 || Total time:0.5749104022979736
Iteration: 99 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.01072382926940918 || Total time:0.5856342315673828
Iteration: 100 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.010260820388793945 || Total time:0.5958950519561768
```
You can modify the number of processes by using `DPMMPython.add_procs(procs_count)`, note that you can only scale it upwards.
#### Additional Examples:
[Clustering](https://nbviewer.jupyter.org/github/BGU-CS-VIL/dpmmpython/blob/master/examples/clustering_example.ipynb)
<br>
[Multi-Process](https://nbviewer.jupyter.org/github/BGU-CS-VIL/dpmmpython/blob/master/examples/multi_process.ipynb)
#### Python 3.8
Due to recent issue with the package used as interface between Julia and Python https://github.com/JuliaPy/pyjulia/issues/425 , there might be problems working with Python >= 3.8.
### Misc
For any questions: dinari@post.bgu.ac.il
Contributions, feature requests, suggestion etc.. are welcomed.
If you use this code for your work, please cite the following:
```
@inproceedings{dinari2019distributed,
title={Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia},
author={Dinari, Or and Yu, Angel and Freifeld, Oren and Fisher III, John W},
booktitle={2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)},
pages={518--525},
year={2019}
}
```
%package -n python3-dpmmpython-trax
Summary: Python wrapper for DPMMSubClusters julia package
Provides: python-dpmmpython-trax
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-dpmmpython-trax
<br>
<p align="center">
<img src="https://www.cs.bgu.ac.il/~dinari/images/clusters_low_slow.gif" alt="DPGMM SubClusters 2d example">
</p>
## DPMMSubClusters
This package is a Python wrapper for the [DPMMSubClusters.jl](https://github.com/BGU-CS-VIL/DPMMSubClusters.jl) Julia package.<br>
### Motivation
Working on a subset of 100K images from ImageNet, containing 79 classes, we have created embeddings using [SWAV](https://github.com/facebookresearch/swav), and reduced the dimension to 128 using PCA. We have compared our method with the popular scikit-learn [GMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html) and [DPGMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html) with the following results:
<p align="center">
| Method | Timing (sec) | NMI (higher is better) |
|-----------------------------------------------------|--------------|------------------------|
| *Scikit-learn's GMM* (using EM, and given the True K) | 2523 | 0.695 |
| *Scikit-learn's DPGMM* | 6108 | 0.683 |
| DPMMpython | 475 | 0.705 |
</p>
### Installation
```
pip install dpmmpython
```
If you already have Julia installed, install [PyJulia](https://github.com/JuliaPy/pyjulia) and add the package `DPMMSubClusters` to your julia installation. <p>
<p>
Make sure Julia path is configured correctly, e.g. you should be able to run julia by typing `julia` from the terminal, unless configured properly, PyJulia wont work.
**Installation Shortcut for Ubuntu distributions** <br>
If you do not have Julia installed, or wish to create a clean installation for the purpose of using this package. after installing (with pip), do the following:
```
import dpmmpython
dpmmpython.install()
```
Optional arguments are `install(julia_download_path = 'https://julialang-s3.julialang.org/bin/linux/x64/1.4/julia-1.4.0-linux-x86_64.tar.gz', julia_target_path = None)`, where the former specify the julia download file, and the latter the installation path, if the installation path is not specified, `$HOME$/julia` will be used.<br>
As the `install()` command edit your `.bashrc` path, before using the pacakge, the terminal should either be reset, or modify the current environment according to the julia path you specified (`$HOME$/julia/julia-1.4.0/bin` by default).
### Usage Example:
```
from dpmmpython.dpmmwrapper import DPMMPython
from dpmmpython.priors import niw
import numpy as np
data,gt = DPMMPython.generate_gaussian_data(10000, 2, 10, 100.0)
prior = niw(1,np.zeros(2),4,np.eye(2))
labels,_,sub_labels= DPMMPython.fit(data,100,prior = prior,verbose = True, gt = gt)
```
```
Iteration: 1 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.004499912261962891 || Total time:0.004499912261962891
Iteration: 2 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.0038819313049316406 || Total time:0.008381843566894531
...
Iteration: 98 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.015907764434814453 || Total time:0.5749104022979736
Iteration: 99 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.01072382926940918 || Total time:0.5856342315673828
Iteration: 100 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.010260820388793945 || Total time:0.5958950519561768
```
You can modify the number of processes by using `DPMMPython.add_procs(procs_count)`, note that you can only scale it upwards.
#### Additional Examples:
[Clustering](https://nbviewer.jupyter.org/github/BGU-CS-VIL/dpmmpython/blob/master/examples/clustering_example.ipynb)
<br>
[Multi-Process](https://nbviewer.jupyter.org/github/BGU-CS-VIL/dpmmpython/blob/master/examples/multi_process.ipynb)
#### Python 3.8
Due to recent issue with the package used as interface between Julia and Python https://github.com/JuliaPy/pyjulia/issues/425 , there might be problems working with Python >= 3.8.
### Misc
For any questions: dinari@post.bgu.ac.il
Contributions, feature requests, suggestion etc.. are welcomed.
If you use this code for your work, please cite the following:
```
@inproceedings{dinari2019distributed,
title={Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia},
author={Dinari, Or and Yu, Angel and Freifeld, Oren and Fisher III, John W},
booktitle={2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)},
pages={518--525},
year={2019}
}
```
%package help
Summary: Development documents and examples for dpmmpython-trax
Provides: python3-dpmmpython-trax-doc
%description help
<br>
<p align="center">
<img src="https://www.cs.bgu.ac.il/~dinari/images/clusters_low_slow.gif" alt="DPGMM SubClusters 2d example">
</p>
## DPMMSubClusters
This package is a Python wrapper for the [DPMMSubClusters.jl](https://github.com/BGU-CS-VIL/DPMMSubClusters.jl) Julia package.<br>
### Motivation
Working on a subset of 100K images from ImageNet, containing 79 classes, we have created embeddings using [SWAV](https://github.com/facebookresearch/swav), and reduced the dimension to 128 using PCA. We have compared our method with the popular scikit-learn [GMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html) and [DPGMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html) with the following results:
<p align="center">
| Method | Timing (sec) | NMI (higher is better) |
|-----------------------------------------------------|--------------|------------------------|
| *Scikit-learn's GMM* (using EM, and given the True K) | 2523 | 0.695 |
| *Scikit-learn's DPGMM* | 6108 | 0.683 |
| DPMMpython | 475 | 0.705 |
</p>
### Installation
```
pip install dpmmpython
```
If you already have Julia installed, install [PyJulia](https://github.com/JuliaPy/pyjulia) and add the package `DPMMSubClusters` to your julia installation. <p>
<p>
Make sure Julia path is configured correctly, e.g. you should be able to run julia by typing `julia` from the terminal, unless configured properly, PyJulia wont work.
**Installation Shortcut for Ubuntu distributions** <br>
If you do not have Julia installed, or wish to create a clean installation for the purpose of using this package. after installing (with pip), do the following:
```
import dpmmpython
dpmmpython.install()
```
Optional arguments are `install(julia_download_path = 'https://julialang-s3.julialang.org/bin/linux/x64/1.4/julia-1.4.0-linux-x86_64.tar.gz', julia_target_path = None)`, where the former specify the julia download file, and the latter the installation path, if the installation path is not specified, `$HOME$/julia` will be used.<br>
As the `install()` command edit your `.bashrc` path, before using the pacakge, the terminal should either be reset, or modify the current environment according to the julia path you specified (`$HOME$/julia/julia-1.4.0/bin` by default).
### Usage Example:
```
from dpmmpython.dpmmwrapper import DPMMPython
from dpmmpython.priors import niw
import numpy as np
data,gt = DPMMPython.generate_gaussian_data(10000, 2, 10, 100.0)
prior = niw(1,np.zeros(2),4,np.eye(2))
labels,_,sub_labels= DPMMPython.fit(data,100,prior = prior,verbose = True, gt = gt)
```
```
Iteration: 1 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.004499912261962891 || Total time:0.004499912261962891
Iteration: 2 || Clusters count: 1 || Log posterior: -71190.14226686998 || Vi score: 1.990707323192506 || NMI score: 6.69243345834295e-16 || Iter Time:0.0038819313049316406 || Total time:0.008381843566894531
...
Iteration: 98 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.015907764434814453 || Total time:0.5749104022979736
Iteration: 99 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.01072382926940918 || Total time:0.5856342315673828
Iteration: 100 || Clusters count: 9 || Log posterior: -40607.39498126549 || Vi score: 0.11887067921133423 || NMI score: 0.9692247699387838 || Iter Time:0.010260820388793945 || Total time:0.5958950519561768
```
You can modify the number of processes by using `DPMMPython.add_procs(procs_count)`, note that you can only scale it upwards.
#### Additional Examples:
[Clustering](https://nbviewer.jupyter.org/github/BGU-CS-VIL/dpmmpython/blob/master/examples/clustering_example.ipynb)
<br>
[Multi-Process](https://nbviewer.jupyter.org/github/BGU-CS-VIL/dpmmpython/blob/master/examples/multi_process.ipynb)
#### Python 3.8
Due to recent issue with the package used as interface between Julia and Python https://github.com/JuliaPy/pyjulia/issues/425 , there might be problems working with Python >= 3.8.
### Misc
For any questions: dinari@post.bgu.ac.il
Contributions, feature requests, suggestion etc.. are welcomed.
If you use this code for your work, please cite the following:
```
@inproceedings{dinari2019distributed,
title={Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia},
author={Dinari, Or and Yu, Angel and Freifeld, Oren and Fisher III, John W},
booktitle={2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)},
pages={518--525},
year={2019}
}
```
%prep
%autosetup -n dpmmpython-trax-0.1.5
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-dpmmpython-trax -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.5-1
- Package Spec generated
|
