salamander-learn 0.4.0

Salamander is a non-negative matrix factorization framework for signature analysis

Salamander

Salamander is a non-negative matrix factorization (NMF) framework for signature analysis. It implements multiple NMF algorithms, common visualizations, and can be easily customized & expanded.

---

Installation

PyPI:

pip install salamander-learn

Usage

The following example illustrates the basic syntax:

import pandas as pd
import salamander

# samples and features have to be named appropriately
data_path = "..."
data = pd.read_csv(data_path, index_col=0)

# NMF with a Poisson noise model
model = salamander.KLNMF(n_signatures=5)
model.fit(data)

# barplot
model.plot_signatures()

# stacked barplot
model.plot_exposures()

# signature correlation
model.plot_correlation()

# sample_correlation
model.plot_correlation(data="samples")

# dimensionality reduction of the exposures
# method: umap, pca or tsne
model.plot_embeddings(method="umap")

For examples of how to customize any NMF algorithm and the plots, check out the tutorial. The following algorithms are currently available:

• NMF with KL-divergence loss
• minimum-volume NMF
• a variant of correlated NMF

License

MIT

Changelog

Consult the CHANGELOG file for enhancements and fixes of each version.