Metadata-Version: 2.1
Name: tmplot
Version: 0.0.3
Summary: Visualization of Topic Modeling Results
Home-page: https://github.com/maximtrp/tmplot
Author: maximtrp
Author-email: maximtrp@gmail.com
License: MIT
Description: # tmplot
        
        [![Documentation Status](https://readthedocs.org/projects/tmplot/badge/?version=latest)](https://tmplot.readthedocs.io/en/latest/?badge=latest)
        [![Downloads](https://pepy.tech/badge/tmplot)](https://pepy.tech/project/tmplot)
        ![PyPI](https://img.shields.io/pypi/v/tmplot)
        
        **tmplot** is a Python package for visualizing topic modeling results. It provides the interactive report interface that borrows much from LDAvis/pyLDAvis and builds upon it offering a number of metrics for calculating topics distances and a number of algorithms for calculating scatter coordinates of topics.
        
        ![Plots](https://raw.githubusercontent.com/maximtrp/tmplot/main/images/topics_terms_plots.png)
        
        ## Features
        
        * Supported models:
        
          * [tomotopy](https://bab2min.github.io/tomotopy/): `LDAModel`, `LLDAModel`, `CTModel`, `DMRModel`, `HDPModel`, `PTModel`, `SLDAModel`, `GDMRModel`
          * [gensim](https://radimrehurek.com/gensim/): `LdaModel`, `LdaMulticore`
          * [bitermplus](https://github.com/maximtrp/bitermplus): `BTM`
        
        * Supported distance metrics:
        
          * Kullback-Leibler (symmetric and non-symmetric) divergence
          * Jenson-Shannon divergence
          * Jeffrey's divergence
          * Hellinger distance
          * Bhattacharyya distance
          * Total variation distance
          * Jaccard inversed index
        
        * Supported [algorithms](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold) for calculating topics scatter coordinates:
        
          * t-SNE
          * SpectralEmbedding
          * MDS
          * LocallyLinearEmbedding
          * Isomap
        
        ## Installation
        
        The package can be installed from PyPi:
        
        ```bash
        pip install tmplot
        ```
        
        Or directly from this repository:
        
        ```bash
        pip install git+https://github.com/maximtrp/tmplot.git
        ```
        
        ## Dependencies
        
        * `numpy`
        * `scipy`
        * `scikit-learn`
        * `pandas`
        * `altair`
        * `ipywidgets`
        * `tomotopy`, `gensim`, and `bitermplus`
        
        ## Quick example
        
        ```python
        # Importing packages
        import tmplot as tmp
        import pickle as pkl
        import pandas as pd
        
        # Reading a model from a file
        with open('data/model.pkl', 'rb') as file:
            model = pkl.load(file)
        
        # Reading documents from a file
        docs = pd.read_csv('data/docs.txt.gz', header=None).values.ravel()
        
        # Plotting topics as a scatter plot
        topics_coords = tmp.prepare_coords(model)
        tmp.plot_scatter_topics(topics_coords, size_col='size', label_col='label')
        
        # Plotting terms probabilities
        terms_probs = tmp.calc_terms_probs_ratio(phi, topic=0, lambda_=1)
        tmp.plot_terms(terms_probs)
        
        # Running report interface
        tmp.report(model, docs=docs, width=250)
        ```
        
        You can find more examples in the [tutorial](https://tmplot.readthedocs.io/en/latest/tutorial.html).
        
Keywords: data science,data analytics
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: General
Description-Content-Type: text/markdown
