def log_callback_kmeans_conductance(embeddings, adjancy_matrix, n_centroid):
kmeans = pkm.PoincareKMeans(n_centroid)
kmeans.fit(embeddings)
i = kmeans.predict(embeddings)
r = torch.arange(0, i.size(0), device=i.device)
prediction = torch.zeros(embeddings.size(0), n_centroid)
prediction[r, i] = 1
return {
"conductance": evaluation.mean_conductance(prediction, adjancy_matrix)
}
# print(prediction_mat.sum(0))
conductences = []
adjency_matrix = X
for i in tqdm.trange(args.n):
algs = ekm.sklearnKMeans(5)
algs.fit(representations)
prediction = algs.predict(representations).long()
# print(prediction)
prediction_mat = torch.LongTensor(
[[1 if (y == prediction[i]) else 0 for y in range(5)]
for i in range(len(X))])
# print(prediction_mat)
conductences.append(
evaluation.mean_conductance(prediction_mat, adjency_matrix))
C = torch.Tensor(conductences)
print("Maximum conductence -> ", C.max().item())
print("Mean conductence -> ", C.mean().item())
print("stdconductence -> ", C.std().item())
log_in.append({
"evaluation_unsupervised_come_cond": {
"unsupervised_conductence": C.tolist()
}
})
nmi = []
ground_truth = torch.LongTensor([[1 if (y in Y[i]) else 0 for y in range(5)]
for i in range(len(X))])
for i in tqdm.trange(args.n):