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SuSi – Self-organizing Maps with Python
Self-organizing maps (SOM) Python package for unsupervised, supervised and semi-supervised learning
Simple syntax
You can use SuSi similar to the well-known scikit-learn syntax, e.g., fit(), predict(), transform().
Multiple applications
Support for unsupervised clustering and visualization, semi-supervised and supervised classification and regression.
Built-in visualization
Present your data with SuSi through built-in plotting scripts and example notebooks.
# What is SuSi?
SuSi is a Python package for unsupervised, supervised, and semi-supervised learning. It is built as an estimator in scikit-learn (opens new window) style and works with all currently-maintained Python 3 (opens new window) versions.
This is a basic example on how to use SuSi for supervised classification:
import susi
# load your dataset
X_train, X_test, y_train, y_test = ...
# initialize and fit SuSi
som = susi.SOMClassifier()
som.fit(X_train, y_train)
# predict and calculate the accuracy score
y_pred = som.predict(X_test)
print(som.score(X_test, y_test))
# Getting started
Installation of SuSi via pip:
pip install susi
Installation of SuSi via conda-forge:
conda install -c conda-forge susi