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**Note**: This package is still work in progress and scientific papers on some of the novel methods (e.g., decomposition-based methods) are currently undergoing peer-review. If you use this package and you encounter any problem, let us know by opening relevant issues.
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## Roadmap
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## Feature Roadmap
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- [x] Model Implementation
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- [x] Pretty Printing
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- [x] Implement visualization utilites for these models in topicwizard
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- [x] Thorough documentation
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- [x] Dynamic modeling (currently `GMM` and `ClusteringTopicModel` others might follow)
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- [x] Dynamic modeling (`GMM`, `ClusteringTopicModel` and `KeyNMF`)
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- [ ] Publish papers :hourglass_flowing_sand: (in progress..)
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- [ ] High-level topic descriptions with LLMs.
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- [ ] Contextualized evaluation metrics.
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#### New in version 0.3.0: Dynamic KeyNMF
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KeyNMF can now be used for dynamic topic modeling.
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```python
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from datetime import datetime
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from turftopic import KeyNMF
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corpus: list[str] = [...]
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timestamps = list[datetime] = [...]
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model = KeyNMF(10)
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doc_topic_matrix = model.fit_transform_dynamic(corpus, timestamps=timestamps, bins=10)
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model.print_topics_over_time()
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# This needs Plotly: pip install plotly
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model.plot_topics_over_time()
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```
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## Basics [(Documentation)](https://x-tabdeveloping.github.io/turftopic/)
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[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/x-tabdeveloping/turftopic/blob/main/examples/basic_example_20newsgroups.ipynb)
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