Rank method used for high dimensional embeddings

[Emilien-P]

Hi,
I think there's a typo remaining in the embedding quality evaluation file. In the paper (https://doi.org/10.1038/s41467-020-16822-4), you state that "To estimate distances in the high-dimensional space δ ij , we use geodesic distances estimated as the length of a
shortest-path in a k-nearest neighbors graph.", which is done by your method:

PoincareMaps/embedding_quality_score.py

Line 26 in 7eb2546

def get_rank_high(data, k_neighbours = 15, knn_sym=True):

but it is not used in the final metric computation

PoincareMaps/embedding_quality_score.py

Lines 180 to 184 in 7eb2546

	Rank_high = get_ranking(D_high)
	print('Rank high')
	Rank_low = get_ranking(D_low)
	print('Rank low')

It would be great to hear from you about that,
Thanks for your good work!
Best

[klanita]

Hi, Emilien!

I think you are right. Thank you for pointing it out! I will double-check this issue shortly and update the code.