initialize GMM using sklearn python

Question

I wish to create a sklearn GMM object with a predefined set of means, weights, and covariances ( on a grid ).

I managed to do it:

from sklearn.mixture import GaussianMixture
import numpy as np


def get_grid_gmm(subdivisions=[10,10,10], variance=0.05 ):
    n_gaussians = reduce(lambda x, y: x*y,subdivisions)
    step = [ 1.0/(2*subdivisions[0]),  1.0/(2*subdivisions[1]),  1.0/(2*subdivisions[2])]

    means = np.mgrid[ step[0] : 1.0-step[0]: complex(0,subdivisions[0]),
                      step[1] : 1.0-step[1]: complex(0,subdivisions[1]),
                      step[2] : 1.0-step[2]: complex(0,subdivisions[2])]
    means = np.reshape(means,[-1,3])
    covariances = variance*np.ones_like(means)
    weights = (1.0/n_gaussians)*np.ones(n_gaussians)
    gmm = GaussianMixture(n_components=n_gaussians, covariance_type='spherical' )
    gmm.weights_ = weights
    gmm.covariances_ = covariances
    gmm.means_ = means
    return gmm

def main():
    xx = np.random.rand(100,3)
    gmm = get_grid_gmm()
    y= gmm.predict_proba(xx)

if __name__ == "__main__":
    main()

The problem is its missing the gmm.predict_proba() method that I need to use later on. How can I overcome this?

UPDATE : I updated the code to be a complete example that shows the error

UPDATE2

I updated the code according to comments and answers

from sklearn.mixture import GaussianMixture
import numpy as np


def get_grid_gmm(subdivisions=[10,10,10], variance=0.05 ):
    n_gaussians = reduce(lambda x, y: x*y,subdivisions)
    step = [ 1.0/(2*subdivisions[0]),  1.0/(2*subdivisions[1]),  1.0/(2*subdivisions[2])]

    means = np.mgrid[ step[0] : 1.0-step[0]: complex(0,subdivisions[0]),
                      step[1] : 1.0-step[1]: complex(0,subdivisions[1]),
                      step[2] : 1.0-step[2]: complex(0,subdivisions[2])]
    means = np.reshape(means,[3,-1])
    covariances = variance*np.ones(n_gaussians)
    cov_type = 'spherical'
    weights = (1.0/n_gaussians)*np.ones(n_gaussians)
    gmm = GaussianMixture(n_components=n_gaussians, covariance_type=cov_type )
    gmm.weights_ = weights
    gmm.covariances_ = covariances
    gmm.means_ = means
    from sklearn.mixture.gaussian_mixture import _compute_precision_cholesky
    gmm.precisions_cholesky_ = _compute_precision_cholesky(covariances, cov_type)
    gmm.precisions_ = gmm.precisions_cholesky_ ** 2
    return gmm

def main():
    xx = np.random.rand(100,3)
    gmm = get_grid_gmm()
    _, y = gmm._estimate_log_prob(xx)
    y = np.exp(y)

if __name__ == "__main__":
    main()

No more errors but _estimate_log_prob and predict_proba do not produce the same result for a fitted GMM. Why could that be?

Answer 1

Since you don't train the model but just use the function for estimation, you don't need to use the object but you could use the same function they use under the hood. You could try _estimate_log_gaussian_prob. That is what they do internaly I think.

Have a look at the source:

in particular at the base class https://github.com/scikit-learn/scikit-learn/blob/ab93d657eb4268ac20c4db01c48065b5a1bfe80d/sklearn/mixture/base.py#L342

that is calling the specific method, that in turn is calling a function https://github.com/scikit-learn/scikit-learn/blob/ab93d657eb4268ac20c4db01c48065b5a1bfe80d/sklearn/mixture/gaussian_mixture.py#L671