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توضیحات

Python wrapper for DPMMSubClusters julia package

ویژگی	مقدار
سیستم عامل	-
نام فایل	dpmmpython-trax-0.1.5
نام	dpmmpython-trax
نسخه کتابخانه	0.1.5
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Or Dinari
ایمیل نویسنده	dinari@post.bgu.ac.il
آدرس صفحه اصلی	https://github.com/dinarior/dpmmpython_trax
آدرس اینترنتی	https://pypi.org/project/dpmmpython-trax/
مجوز	MIT

<img src="https://www.cs.bgu.ac.il/~dinari/images/clusters_low_slow.gif" alt="DPGMM SubClusters 2d example"> ## DPMMSubClusters This package is a Python wrapper for the [DPMMSubClusters.jl](https://github.com/BGU-CS-VIL/DPMMSubClusters.jl) Julia package. ### 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: | 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 | ### 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. 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** 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. 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) [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} } ```

نیازمندی

مقدار	نام
-	julia
-	wget
-	numpy
-	numpy
-	ipython
>=4.4	pytest
-	mock

نحوه نصب

نصب پکیج whl dpmmpython-trax-0.1.5:

pip install dpmmpython-trax-0.1.5.whl

نصب پکیج tar.gz dpmmpython-trax-0.1.5:

pip install dpmmpython-trax-0.1.5.tar.gz