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SOM Hyperparameter Tuning

See original GitHub issue

I have a SOM model below applied through an intrusion detection case (unsupervised case) whose dataset is quite big. I selected the suggested parameters based on a paper. However, I require to tune the model but there is no source on how to tune minisom. Can you please suggest me for tuning minisom?

"""Training the SOM"""
from minisom import MiniSom
som31 = MiniSom(x=31, y=31, input_len= 122, sigma= 1.0, learning_rate = 0.5, neighborhood_function='bubble',
              topology= 'rectangular', activation_distance='euclidean')
som31.random_weights_init(X_train_p)
som31.train_random(data = X_train_p, num_iteration = 500)

Issue Analytics

State:
Created 2 years ago
Comments:7 (4 by maintainers)

Top GitHub Comments

2reactions

JustGlowingcommented, May 17, 2021

You can now use this dashboard to explore the effects of the parameters on a sample dataset: https://share.streamlit.io/justglowing/minisom/dashboard/dashboard.py

1reaction

JustGlowingcommented, Apr 30, 2021

That way to determine the size of the map is well known, but it’s just a rule of thumb. The best size depends on how your data is distributed and you can find it trying different sizes and checking which one fits your data.