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https://github.com/jrmazarura/GPM


https://github.com/jrmazarura/GPM

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README 

        
# GPyM_TM



**GPyM_TM** is a Python package to perform topic modelling, either through the use of the Dirichlet multinomial mixture model (GSDMM) [1] or the [Gamma Poisson mixture model](https://www.hindawi.com/journals/mpe/2020/4728095/) (GPM) [2]. Each of the above models is available within the package in a separate class, namely GSDMM and GPM, respectively. The package is also available on [Pypi](https://pypi.org/project/GPyM-TM/3.0.1/).



## Preamble  

The aim of topic modelling is to extract latent topics from large corpora. GSDMM [1] and GPM [2] assume 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 models 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)](https://dl.acm.org/doi/abs/10.1145/2623330.2623715?casa_token=lSSGu4bHw6wAAAAA:iDc8SAzLNC-zOySLwkDJBe3L17Wht7WiQe5JXVd0sy7_dEBbU10C8y8mhcidwUu_9Dl4kMhEfvE)



[2] [Mazarura, J., de Waal, A. and de Villiers, P., 2020. A Gamma-Poisson Mixture Topic Model for Short Text. Mathematical Problems in Engineering, 2020](https://www.hindawi.com/journals/mpe/2020/4728095/)



Further details about the GPM can be found in my thesis [here](https://repository.up.ac.za/handle/2263/78519).



## 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 GPyM_TM import GSDMM`

`from GPyM_TM 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:



I would like to extend a special thank you to my colleagues [Alta de Waal](https://github.com/altadewaal) and [Ricardo Marques](https://github.com/RicSalgado). None of this would have been possible without either of you.



Thank you!



## License:



This project is licensed under the MIT License - see the LICENSE file for details.



## Acknowledgments:



University of Pretoria 

![Tuks Logo](/Images/UPlogohighres.jpg)