I've been working on a scikit-learn compatible Self Organizing Map (dbgsom).

I started this project when I learned programming and machine learning at university. Recently I did some benchmarks at it actually does really well against other scikit learn Estimators. It can be used for clustering, classification, visualization and non linear projection in two dimensions.

Might be interesting for a broader Data science audience?

Install:

  pip install dbgsom

Example:

    from dbgsom import SomClassifier
    from sklearn.datasets import load_digits

    X, y = load_digits(return_X_y=True)

    clf = SomClassifier(sigma_end=1, lambda_=30, random_state=42)
    labels = clf.fit(X, y).predict(X)

    print(f"Neurons: {len(clf.neurons_)}")
    print(f"Quantization error: {clf.quantization_error_:.4f}")
    print(f"Topographic error: {clf.topographic_error_:.4f}")
    print(f"Accuracy: {clf.score(X, y):.4f}")

  > > Neurons: 92
  > > Quantization error: 20.8655
  > > Topographic error:  0.0267
  > > Accuracy: 0.9416

Github contains more examples and comparisons against scikit-learn (Clustering/Classifiers/Projection) and other SOMs.

Let me know what you think!