How does sklearn.cluster.KMeans handle an init ndarray parameter with missing centroids (available centroids less than n_clusters)?

Question

In Python sklearn KMeans ( see documentation ), I was wondering what happens internally when passing an ndarray of shape (n, n_features) to the init parameter, When n<n_clusters

1. Does it drop the given centroids and just starts a kmeans++ initialization which is the default choice for the init parameter ? ( PDF paper kmeans++ ) ( How does Kmeans++ work )
2. Does it consider the given centroids and fill accordingly the remaining centroids using kmeans++ ?
3. Does it consider the given centroids and fill the remaining centroids using random values ?

I didn't expect that this method returns no warning in this case. That's why I need to know how it manages this.

Answer 1

If you give it a mismatching init it will adjust the number of clusters, as you can see from the source . This is not documented and I would consider it a bug. I'll propose to fix it.