tsds: Time Series Data Segmentation Algorithm
This is a Python library for time series data segmentation, specifically developed for clinical data. It includes the following components:
- Dimensionality reduction using Non-negative Matrix Factorization (NMF)
- Optimal number of clusters calculation using Silhouette score, Calinski Harabasz score, and Davies Bouldin score.
- Predictive modeling using Multilayer Perceptron (MLP) classifier, Support Vector Machines (SVM), and Random Forest.
- Explanation of cluster groups using SHAP values.
- Analysis and simulation of disease progression using skip grams and Markov chains, with visual representation of group likelihood changes.
Usage
To use the library, simply import it into your project and follow the steps outlined in the components above. Detailed usage instructions and examples can be found in the library's documentation.
Dependencies
The library requires the following dependencies:
- NumPy
- Pandas
- Scikit-learn
- SHAP
- nltk
- Matplotlib (for visual representation)
Contribution
We welcome contributions to this library. If you have any suggestions or bug reports, please create a GitHub issue. If you would like to contribute code, please submit a pull request.
License
This library is available under the GNU General Public License Version 3.
1 maintainer
Metadata
1 maintainer
Release files for tsds 0.0.4
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| tsds-0.0.4.tar.gz | 20.9 kB | Without provenance | Details for tsds-0.0.4.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| tsds-0.0.4-py3-none-any.whl | Python 3 | none | any | Details for tsds-0.0.4-py3-none-any.whl |
Total release size: 42.1 kB
Release files / tsds-0.0.4.tar.gz
| Download URL | tsds-0.0.4.tar.gz |
|---|---|
| Size | 20.9 kB |
| Tags | Source |
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Release files / tsds-0.0.4-py3-none-any.whl
| Download URL | tsds-0.0.4-py3-none-any.whl |
|---|---|
| Size | 21.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/4.0.2 CPython/3.10.6
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