Save and load a Scikit-learn GMM to file using np.save

save_and_load_sklearn_gmm

import numpy as np
from sklearn import mixture

# make a GMM
gmm = mixture.GaussianMixture(n_components=n_components, covariance_type=cv_type)
# fit the GMM however you'd like

# save to file
gmm_name = 'new_gmm'
np.save(gmm_name + '_weights', gmm.weights_, allow_pickle=False)
np.save(gmm_name + '_means', gmm.means_, allow_pickle=False)
np.save(gmm_name + '_covariances', gmm.covariances_, allow_pickle=False)

# reload
means = np.load(gmm_name + '_means.npy')
covar = np.load(gmm_name + '_covariances.npy')
loaded_gmm = mixture.GaussianMixture(n_components = len(means), covariance_type='full')
loaded_gmm.precisions_cholesky_ = np.linalg.cholesky(np.linalg.inv(covar))
loaded_gmm.weights_ = np.load(gmm_name + '_weights.npy')
loaded_gmm.means_ = means
loaded_gmm.covariances_ = covar

# compare performance on your data between the two GMMs (loaded and saved)
cats = gmm.predict(y)
cats2 = loaded_gmm.predict(y)
print(all(cats == cats2))  # or assert

Fantastic!
I was looking everywhere and you are the only one who knows how to save GMM for using them somewhere else...
Congrats!

Thanks for this! : )

Great finding for me
Thx for your code

Thanks Man👍🏼

In fact, you don't even need to re-calculate precisions_cholesky_,
You can load and save that too, e.g.

# save
np.save(gmm_name + '_precisions_cholesky', gmm.precisions_cholesky_, allow_pickle=False)
# load
loaded_gmm.precisions_cholesky_ = np.load(gmm_name + 'precisions_cholesky.npy')