TreeOrdination 1.3.1

Projection of High-Dimensional Data Using Multivariate Decision Trees and UMAP

TreeOrdination

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:

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.

Interface

An overview of the API can be found here.

Contributing

To contribute to the development of TreeOrdination please read our contributing guide

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.

Klaise J., Van Looveren A., Vacanti G., and Coca A. Alibi Explain: Algorithms for Explaining Machine Learning Models. Journal of Machine Learning Research. 2021;22(181):1-7.