ENH Gaussian mixture bypassing unnecessary initialization computing - #26021

def test_gaussian_mixture_all_init_does_not_estimate_gaussian_parameters(monkeypatch):
    """When all init are provided, the Gaussian parameters are not estimated.

    Non-regression test for gh26015.
    """

    mock = Mock(side_effect=_estimate_gaussian_parameters)
    monkeypatch.setattr(
        sklearn.mixture._gaussian_mixture, "_estimate_gaussian_parameters", mock
    )

    rng = np.random.RandomState(0)
    rand_data = RandomData(rng)

    gm = GaussianMixture(
        n_components=rand_data.n_components,
        weights_init=rand_data.weights,
        means_init=rand_data.means,
        precisions_init=rand_data.precisions["full"],
        random_state=rng,
    )
    gm.fit(rand_data.X["full"])
    # The initial gaussian parameters are not estimated. They are estimated for every
    # m_step.
    assert mock.call_count == gm.n_iter_