Gaussianmixture Initialization Using Component Parameters - Sklearn

I want to use sklearn.mixture.GaussianMixture to store a gaussian mixture model so that I can later use it to generate samples or a value at a sample point using score_samples meth

Solution 1:

It seems that it has a check that makes sure that the model has been trained. You could trick it by training the GMM on a very small data set before setting the parameters. Like this:

gmix = mixture.GaussianMixture(n_components=2, covariance_type='full')
gmix.fit(rand(10, 2))  # Now it thinks it is trained
gmix.weights_ = weights   # mixture weights (n_components,) 
gmix.means_ = mu          # mixture means (n_components, 2) 
gmix.covariances_ = sigma  # mixture cov (n_components, 2, 2)
x = gmix.sample(1000)  # Should work now

Solution 2:

You rock, J.P.Petersen! After seeing your answer I compared the change introduced by using fit method. It seems the initial instantiation does not create all the attributes of gmix. Specifically it is missing the following attributes,

covariances_
means_
weights_
converged_
lower_bound_
n_iter_
precisions_
precisions_cholesky_

The first three are introduced when the given inputs are assigned. Among the rest, for my application the only attribute that I need is precisions_cholesky_ which is cholesky decomposition of the inverse covarinace matrices. As a minimum requirement I added it as follow,

gmix.precisions_cholesky_ = np.linalg.cholesky(np.linalg.inv(sigma)).transpose((0, 2, 1))

Solution 3:

To understand what is happening, what GaussianMixture first checks that it has been fitted:

self._check_is_fitted()

Which triggers the following check:

def_check_is_fitted(self):
    check_is_fitted(self, ['weights_', 'means_', 'precisions_cholesky_'])

And finally the last function call:

defcheck_is_fitted(estimator, attributes, msg=None, all_or_any=all):

which only checks that the classifier already has the attributes.

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So in short, the only thing you have missing to have it working (without having to fit it) is to set precisions_cholesky_ attribute:

gmix.precisions_cholesky_ = 0

should do the trick (can't try it so not 100% sure :P)

However, if you want to play safe and have a consistent solution in case scikit-learn updates its contrains, the solution of @J.P.Petersen is probably the best way to go.

Solution 4:

As a slight alternative to @hashmuke's answer, you can use the precision computation that is used inside GaussianMixture directly:

import numpy as np
from scipy.statsimport invwishart asIWfrom sklearn.mixtureimportGaussianMixtureasGMMfrom sklearn.mixture._gaussian_mixtureimport _compute_precision_cholesky

n_dims = 5
mu1 = np.random.randn(n_dims)
mu2 = np.random.randn(n_dims)
Sigma1 = IW.rvs(n_dims, 0.1 * np.eye(n_dims))
Sigma2 = IW.rvs(n_dims, 0.1 * np.eye(n_dims))
gmm = GMM(n_components=2)
gmm.weights_ = np.array([0.2, 0.8])
gmm.means_ = np.stack([mu1, mu2])
gmm.covariances_ = np.stack([Sigma1, Sigma2])
gmm.precisions_cholesky_ = _compute_precision_cholesky(gmm.covariances_, 'full')
X, y = gmm.sample(1000)

And depending on your covariance type you should change full accordingly as input to _compute_precision_cholesky (will be one of full, diag, tied, spherical).