| Name | TreeOrdination JSON |
| Version |
1.3.3
JSON |
| download |
| home_page | |
| Summary | Projection of High-Dimensional Data Using Multivariate Decision Trees and UMAP |
| upload_time | 2023-07-13 21:38:36 |
| maintainer | |
| docs_url | None |
| author | G. Brian Golding, Stefan C. Kremer |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2023 Josip Rudar, G.Brian Golding, Stefan C. Kremer, Mehrdad Hajibabaei Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
dimensionality reduction
ecology
multivariate statistics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
### TreeOrdination
[](https://github.com/jrudar/TreeOrdination/actions/workflows/ci.yml)
Implementation of a wrapper which creates unsupervised projections using LANDMark and UMAP.
### Install Dependencies
The LANDMark package is needed for TreeOrdination to work. It is available at: https://github.com/jrudar/LANDMark
### Install
From PyPI:
```
pip install TreeOrdination
```
From source:
```bash
git clone https://github.com/jrudar/TreeOrdination.git
cd TreeOrdination
pip install .
# or create a virtual environment
python -m venv venv
source venv/bin/activate
pip install .
```
### Example Usage
from TreeOrdination import TreeOrdination
from sklearn.datasets import make_classification
#Create the dataset
X, y = make_classification(n_samples = 200, n_informative = 20)
#Give features a name
f_names = ["Feature %s" %str(i) for i in range(X.shape[0])]
tree_ord = TreeOrdination(feature_names = f_names).fit(X, y)
#This is the LANDMark embedding of the dataset. This dataset is used to train the supervised model ('supervised_clf' parameter)
landmark_embedding = tree_ord.LM_emb
#This is the UMAP projection of the LANDMark embedding
umap_projection = tree_ord.UMAP_emb
#This is the PCA projetion of the UMAP embedding
pca_projection = tree_ord.PCA_emb
### Notebooks and Other Examples
Comming Soon.
When available, examples of how to use `TreeOrdination` will be found [here](notebooks/README.md).
### Interface
An overview of the API can be found [here](docs/API.md).
### Contributing
To contribute to the development of `TreeOrdination` please read our [contributing guide](docs/CONTRIBUTING.md)
### References
Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble
approach to the supervised selection of biomarkers in high-throughput sequencing data.
BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn:
Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–30.
Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3–42.
Rudar, J., Golding, G.B., Kremer, S.C., Hajibabaei, M. (2023). Decision Tree Ensembles Utilizing
Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon
Sequencing Data. Microbiology Spectrum e02065-22.
Raw data
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"description": "### TreeOrdination\n[](https://github.com/jrudar/TreeOrdination/actions/workflows/ci.yml)\n\nImplementation of a wrapper which creates unsupervised projections using LANDMark and UMAP.\n \n### Install Dependencies\nThe LANDMark package is needed for TreeOrdination to work. It is available at: https://github.com/jrudar/LANDMark\n\n### Install\nFrom PyPI:\n\n```\npip install TreeOrdination\n```\n\nFrom source:\n\n```bash\ngit clone https://github.com/jrudar/TreeOrdination.git\ncd TreeOrdination\npip install .\n# or create a virtual environment\npython -m venv venv\nsource venv/bin/activate\npip install .\n```\n \n### Example Usage\n from TreeOrdination import TreeOrdination\n from sklearn.datasets import make_classification\n \n #Create the dataset\n X, y = make_classification(n_samples = 200, n_informative = 20)\n \n #Give features a name\n f_names = [\"Feature %s\" %str(i) for i in range(X.shape[0])]\n \n tree_ord = TreeOrdination(feature_names = f_names).fit(X, y)\n\n #This is the LANDMark embedding of the dataset. This dataset is used to train the supervised model ('supervised_clf' parameter)\n landmark_embedding = tree_ord.LM_emb\n \n #This is the UMAP projection of the LANDMark embedding\n umap_projection = tree_ord.UMAP_emb\n \n #This is the PCA projetion of the UMAP embedding\n pca_projection = tree_ord.PCA_emb \n\n### Notebooks and Other Examples\nComming Soon.\nWhen available, examples of how to use `TreeOrdination` will be found [here](notebooks/README.md).\n\n### Interface\nAn overview of the API can be found [here](docs/API.md).\n\n### Contributing\nTo contribute to the development of `TreeOrdination` please read our [contributing guide](docs/CONTRIBUTING.md)\n\n### References\n\nRudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble \napproach to the supervised selection of biomarkers in high-throughput sequencing data. \nBMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z\n\nPedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: \nMachine Learning in Python. Journal of Machine Learning Research. 2011;12:2825\u201330. \n \nGeurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3\u201342.\n \nRudar, J., Golding, G.B., Kremer, S.C., Hajibabaei, M. (2023). Decision Tree Ensembles Utilizing \nMultivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon \nSequencing Data. Microbiology Spectrum e02065-22.\n\n",
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