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FM's on simple Sklearn's boston data giving NaN's #3

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@silkspace

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@silkspace

This is giving errors, am I missing something?

from scipy import sparse
from sklearn.datasets import load_boston
import pylibfm

instantiate FM instance with 7 latent factors

fm = pylibfm.FM(num_factors=7, num_iter=6, verbose=True)

load dataset

boston = load_boston()

fit FM, making sure to wrap the ndarray as a sparse csr

fm.fit(sparse.csr_matrix(boston.data), boston.target)

Creating validation dataset of 0.01 of training for adaptive regularization
-- Epoch 1
Training log loss: nan
-- Epoch 2
Training log loss: nan
-- Epoch 3
Training log loss: nan
-- Epoch 4
Training log loss: nan
-- Epoch 5
Training log loss: nan
-- Epoch 6
Training log loss: nan

fm.v is also all nan.

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