%global _empty_manifest_terminate_build 0 Name: python-GPyM-TM Version: 3.0.1 Release: 1 Summary: The following package enables users to perform text modelling License: MIT License URL: https://github.com/jrmazarura/GPM Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4f/63/1468a4e7e5e6890ddc3cf3879d2edbad7b35b7dc563c0df3280afb406644/GPyM_TM-3.0.1.tar.gz BuildArch: noarch %description # [GPyM_TM](https://github.com/jrmazarura/GPM) **GPyM_TM** is a Python package to perform topic modelling, either through the use of a Dirichlet multinomial mixture model, or a Poisson model. Each of the above models is available within the package in a separate class, namely GSDMM utilizes the Dirichlet multinomial mixture model, while GPM makes use of the Poisson model to perform the text clustering respectively. The package is also available on [Pypi](https://pypi.org/project/GPyM-TM/3.0.0/). ## Preamble The aim of topic modelling is to extract latent topics from large corpora. GSDMM [1] assumes each document belongs to a single topic, which is a suitable assumption for some short texts. Given an initial number of topics, K, this algorithm clusters documents and extracts the topical structures present within the corpus. If K is set to a high value, then the model will also automatically learn the number of clusters. [1] Yin, J. and Wang, J., 2014, August. A Dirichlet multinomial mixture model-based approach for short text clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 233-242). ## Getting Started: The package is available [online](https://pypi.org/project/GPyM-TM/) for use within Python 3 enviroments. The installation can be performed through the use of a standard 'pip' install command, as provided below: `pip install GPyM-TM` ## Prerequisites: The package has several dependencies, namely: * numpy * random * math * pandas * re * nltk * gensim * scipy # GSDMM ## Function and class description: The class is named **GSDMM**, while the function itself is named **DMM**. The function can take 6 possible arguments, two of which are required, and the remaining 4 being optional. ### The required arguments are: * **corpus** - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed. * **nTopics** - the number of topics. ### The optional requirements are: * **alpha**, **beta** - these are the distribution specific parameters.(**The defaults for both of these parameters are 0.1.**) * **nTopWords** - number of top words per a topic.(**The default is 10.**) * **iters** - number of Gibbs sampler iterations.(**The default is 15.**) ## Output: The function provides several components of output, namely: * **psi** - topic x word matrix. * **theta** - document x topic matrix. * **topics** - the top words per topic. * **assignments** - the topic numbers of selected topics only, as well as the final topic assignments. * **Final k** - the final number of selected topics. * **coherence** - the coherence score, which is a performance measure. * **selected_theta** * **selected_psi** # GPM ## Function and class description: The class is named **GPM**, while the function itself is named **GPM**. The function can take 8 possible arguments, two of which are required, and the remaining 6 being optional. ### The required arguments are: * **corpus** - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed. * **nTopics** - the number of topics. ### The optional requirements are: * **alpha**, **beta** and **gam** - these are the distribution specific parameters.(**The defaults for these parameters are alpha = 0.001, beta = 0.001 and gam = 0.1 respectively.**) * **nTopWords** - number of top words per a topic.(**The default is 10.**) * **iters** - number of Gibbs sampler iterations.(**The default is 15.**) * **N** - this is a parameter used to normalize the document lengths, which is required for the Poisson model. ## Output: The function provides several components of output, namely: * **psi** - topic x word matrix. * **theta** - document x topic matrix. * **topics** - the top words per topic. * **assignments** - the topic numbers of selected topics only, as well as the final topic assignments. * **Final k** - the final number of selected topics. * **coherence** - the coherence score, which is a performance measure. * **selected_theta** * **selected_psi** # Example Usage: A more comprehensive [tutorial](https://github.com/CAIR-ZA/GPyM_TM/blob/master/Tutorial.ipynb) is also available. ### Installation; Run the following command within a Python command window: `pip install GPym_TM` ### Implementation; Import the package into the relevant python script, with the following: `from GSDMM import DMM` `from GPM import GPM` > Call the class: #### Possible examples of calling the GSDMM function are as follows: `data_DMM = GSDMM.DMM(corpus, nTopics)` `data_DMM = GSDMM.DMM(corpus, nTopics, alpha = 0.25, beta = 0.15, nTopWords = 12, iters =5)` #### Possible examples of calling the GPM function are as follows: `data_GPM = GPM.GPM(corpus, nTopics)` `data_GPM = GPM.GPM(corpus, nTopics, alpha = 0.002, beta = 0.03, gam = 0.06, nTopWords = 12, iters = 7, N = 8)` ### Results; The output obtained for the Dirichlet multinomial mixture model appears as follows: ![Post](/Images/Post.png) While, the output obtained for the Poisson model appears as follows: ![poisson](/Images/poisson.png) ## Built With: [Google Collab](https://colab.research.google.com/notebooks/intro.ipynb) - Web framework [Python](https://www.python.org/) - Programming language of choice [Pypi](https://pypi.org/) - Distribution ## Authors: [Jocelyn Mazarura](https://github.com/jrmazarura/GPM) ## Co-Authors: [Alta de Waal](https://github.com/altadewaal) [Ricardo Marques](https://github.com/RicSalgado) ## License: This project is licensed under the MIT License - see the LICENSE file for details. ## Acknowledgments: University of Pretoria ![Tuks Logo](/Images/UPlogohighres.jpg) %package -n python3-GPyM-TM Summary: The following package enables users to perform text modelling Provides: python-GPyM-TM BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-GPyM-TM # [GPyM_TM](https://github.com/jrmazarura/GPM) **GPyM_TM** is a Python package to perform topic modelling, either through the use of a Dirichlet multinomial mixture model, or a Poisson model. Each of the above models is available within the package in a separate class, namely GSDMM utilizes the Dirichlet multinomial mixture model, while GPM makes use of the Poisson model to perform the text clustering respectively. The package is also available on [Pypi](https://pypi.org/project/GPyM-TM/3.0.0/). ## Preamble The aim of topic modelling is to extract latent topics from large corpora. GSDMM [1] assumes each document belongs to a single topic, which is a suitable assumption for some short texts. Given an initial number of topics, K, this algorithm clusters documents and extracts the topical structures present within the corpus. If K is set to a high value, then the model will also automatically learn the number of clusters. [1] Yin, J. and Wang, J., 2014, August. A Dirichlet multinomial mixture model-based approach for short text clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 233-242). ## Getting Started: The package is available [online](https://pypi.org/project/GPyM-TM/) for use within Python 3 enviroments. The installation can be performed through the use of a standard 'pip' install command, as provided below: `pip install GPyM-TM` ## Prerequisites: The package has several dependencies, namely: * numpy * random * math * pandas * re * nltk * gensim * scipy # GSDMM ## Function and class description: The class is named **GSDMM**, while the function itself is named **DMM**. The function can take 6 possible arguments, two of which are required, and the remaining 4 being optional. ### The required arguments are: * **corpus** - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed. * **nTopics** - the number of topics. ### The optional requirements are: * **alpha**, **beta** - these are the distribution specific parameters.(**The defaults for both of these parameters are 0.1.**) * **nTopWords** - number of top words per a topic.(**The default is 10.**) * **iters** - number of Gibbs sampler iterations.(**The default is 15.**) ## Output: The function provides several components of output, namely: * **psi** - topic x word matrix. * **theta** - document x topic matrix. * **topics** - the top words per topic. * **assignments** - the topic numbers of selected topics only, as well as the final topic assignments. * **Final k** - the final number of selected topics. * **coherence** - the coherence score, which is a performance measure. * **selected_theta** * **selected_psi** # GPM ## Function and class description: The class is named **GPM**, while the function itself is named **GPM**. The function can take 8 possible arguments, two of which are required, and the remaining 6 being optional. ### The required arguments are: * **corpus** - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed. * **nTopics** - the number of topics. ### The optional requirements are: * **alpha**, **beta** and **gam** - these are the distribution specific parameters.(**The defaults for these parameters are alpha = 0.001, beta = 0.001 and gam = 0.1 respectively.**) * **nTopWords** - number of top words per a topic.(**The default is 10.**) * **iters** - number of Gibbs sampler iterations.(**The default is 15.**) * **N** - this is a parameter used to normalize the document lengths, which is required for the Poisson model. ## Output: The function provides several components of output, namely: * **psi** - topic x word matrix. * **theta** - document x topic matrix. * **topics** - the top words per topic. * **assignments** - the topic numbers of selected topics only, as well as the final topic assignments. * **Final k** - the final number of selected topics. * **coherence** - the coherence score, which is a performance measure. * **selected_theta** * **selected_psi** # Example Usage: A more comprehensive [tutorial](https://github.com/CAIR-ZA/GPyM_TM/blob/master/Tutorial.ipynb) is also available. ### Installation; Run the following command within a Python command window: `pip install GPym_TM` ### Implementation; Import the package into the relevant python script, with the following: `from GSDMM import DMM` `from GPM import GPM` > Call the class: #### Possible examples of calling the GSDMM function are as follows: `data_DMM = GSDMM.DMM(corpus, nTopics)` `data_DMM = GSDMM.DMM(corpus, nTopics, alpha = 0.25, beta = 0.15, nTopWords = 12, iters =5)` #### Possible examples of calling the GPM function are as follows: `data_GPM = GPM.GPM(corpus, nTopics)` `data_GPM = GPM.GPM(corpus, nTopics, alpha = 0.002, beta = 0.03, gam = 0.06, nTopWords = 12, iters = 7, N = 8)` ### Results; The output obtained for the Dirichlet multinomial mixture model appears as follows: ![Post](/Images/Post.png) While, the output obtained for the Poisson model appears as follows: ![poisson](/Images/poisson.png) ## Built With: [Google Collab](https://colab.research.google.com/notebooks/intro.ipynb) - Web framework [Python](https://www.python.org/) - Programming language of choice [Pypi](https://pypi.org/) - Distribution ## Authors: [Jocelyn Mazarura](https://github.com/jrmazarura/GPM) ## Co-Authors: [Alta de Waal](https://github.com/altadewaal) [Ricardo Marques](https://github.com/RicSalgado) ## License: This project is licensed under the MIT License - see the LICENSE file for details. ## Acknowledgments: University of Pretoria ![Tuks Logo](/Images/UPlogohighres.jpg) %package help Summary: Development documents and examples for GPyM-TM Provides: python3-GPyM-TM-doc %description help # [GPyM_TM](https://github.com/jrmazarura/GPM) **GPyM_TM** is a Python package to perform topic modelling, either through the use of a Dirichlet multinomial mixture model, or a Poisson model. Each of the above models is available within the package in a separate class, namely GSDMM utilizes the Dirichlet multinomial mixture model, while GPM makes use of the Poisson model to perform the text clustering respectively. The package is also available on [Pypi](https://pypi.org/project/GPyM-TM/3.0.0/). ## Preamble The aim of topic modelling is to extract latent topics from large corpora. GSDMM [1] assumes each document belongs to a single topic, which is a suitable assumption for some short texts. Given an initial number of topics, K, this algorithm clusters documents and extracts the topical structures present within the corpus. If K is set to a high value, then the model will also automatically learn the number of clusters. [1] Yin, J. and Wang, J., 2014, August. A Dirichlet multinomial mixture model-based approach for short text clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 233-242). ## Getting Started: The package is available [online](https://pypi.org/project/GPyM-TM/) for use within Python 3 enviroments. The installation can be performed through the use of a standard 'pip' install command, as provided below: `pip install GPyM-TM` ## Prerequisites: The package has several dependencies, namely: * numpy * random * math * pandas * re * nltk * gensim * scipy # GSDMM ## Function and class description: The class is named **GSDMM**, while the function itself is named **DMM**. The function can take 6 possible arguments, two of which are required, and the remaining 4 being optional. ### The required arguments are: * **corpus** - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed. * **nTopics** - the number of topics. ### The optional requirements are: * **alpha**, **beta** - these are the distribution specific parameters.(**The defaults for both of these parameters are 0.1.**) * **nTopWords** - number of top words per a topic.(**The default is 10.**) * **iters** - number of Gibbs sampler iterations.(**The default is 15.**) ## Output: The function provides several components of output, namely: * **psi** - topic x word matrix. * **theta** - document x topic matrix. * **topics** - the top words per topic. * **assignments** - the topic numbers of selected topics only, as well as the final topic assignments. * **Final k** - the final number of selected topics. * **coherence** - the coherence score, which is a performance measure. * **selected_theta** * **selected_psi** # GPM ## Function and class description: The class is named **GPM**, while the function itself is named **GPM**. The function can take 8 possible arguments, two of which are required, and the remaining 6 being optional. ### The required arguments are: * **corpus** - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed. * **nTopics** - the number of topics. ### The optional requirements are: * **alpha**, **beta** and **gam** - these are the distribution specific parameters.(**The defaults for these parameters are alpha = 0.001, beta = 0.001 and gam = 0.1 respectively.**) * **nTopWords** - number of top words per a topic.(**The default is 10.**) * **iters** - number of Gibbs sampler iterations.(**The default is 15.**) * **N** - this is a parameter used to normalize the document lengths, which is required for the Poisson model. ## Output: The function provides several components of output, namely: * **psi** - topic x word matrix. * **theta** - document x topic matrix. * **topics** - the top words per topic. * **assignments** - the topic numbers of selected topics only, as well as the final topic assignments. * **Final k** - the final number of selected topics. * **coherence** - the coherence score, which is a performance measure. * **selected_theta** * **selected_psi** # Example Usage: A more comprehensive [tutorial](https://github.com/CAIR-ZA/GPyM_TM/blob/master/Tutorial.ipynb) is also available. ### Installation; Run the following command within a Python command window: `pip install GPym_TM` ### Implementation; Import the package into the relevant python script, with the following: `from GSDMM import DMM` `from GPM import GPM` > Call the class: #### Possible examples of calling the GSDMM function are as follows: `data_DMM = GSDMM.DMM(corpus, nTopics)` `data_DMM = GSDMM.DMM(corpus, nTopics, alpha = 0.25, beta = 0.15, nTopWords = 12, iters =5)` #### Possible examples of calling the GPM function are as follows: `data_GPM = GPM.GPM(corpus, nTopics)` `data_GPM = GPM.GPM(corpus, nTopics, alpha = 0.002, beta = 0.03, gam = 0.06, nTopWords = 12, iters = 7, N = 8)` ### Results; The output obtained for the Dirichlet multinomial mixture model appears as follows: ![Post](/Images/Post.png) While, the output obtained for the Poisson model appears as follows: ![poisson](/Images/poisson.png) ## Built With: [Google Collab](https://colab.research.google.com/notebooks/intro.ipynb) - Web framework [Python](https://www.python.org/) - Programming language of choice [Pypi](https://pypi.org/) - Distribution ## Authors: [Jocelyn Mazarura](https://github.com/jrmazarura/GPM) ## Co-Authors: [Alta de Waal](https://github.com/altadewaal) [Ricardo Marques](https://github.com/RicSalgado) ## License: This project is licensed under the MIT License - see the LICENSE file for details. ## Acknowledgments: University of Pretoria ![Tuks Logo](/Images/UPlogohighres.jpg) %prep %autosetup -n GPyM-TM-3.0.1 %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-GPyM-TM -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 17 2023 Python_Bot - 3.0.1-1 - Package Spec generated