Multiple real valued labels

Hi,

I’m working on a regression problem with multiple real valued targets. An exception is thrown by UMAP (attached below). I assume that it happens because I’m passing a multidimensional array as labels. Am I doing something wrong or is this mode not supported by the algorithm/implementation?

Thanks for everything!

Edit: After digging into the parameters for umap I found target_metric which I set to 'l2', but I still get an error when my target has shape (n_samples, n_targets)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<timed exec> in <module>()

/usr/local/lib/python3.5/dist-packages/umap/umap_.py in fit_transform(self, X, y)
   1521             Embedding of the training data in low-dimensional space.
   1522         """
-> 1523         self.fit(X, y)
   1524         return self.embedding_
   1525 

/usr/local/lib/python3.5/dist-packages/umap/umap_.py in fit(self, X, y)
   1440                     far_dist = 1.0e12
   1441                 self.graph_ = categorical_simplicial_set_intersection(
-> 1442                     self.graph_, y, far_dist=far_dist
   1443                 )
   1444             else:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

[lmcinnes]

I think, unfortunately, that transforming new points through this custom pipeline is going to be non-trivial. It can be done, but I will have to work out exactly what incantations one would need to do so.

On Fri, Sep 28, 2018 at 3:39 AM GCBallesteros [e-mail] wrote:

That worked beautifully! Thanks!

One question remains. How can I test new unseen data points. I tried using fit1.transform(test_features) because it was the only obvious thing to do but that didn’t work. Any ideas?

Thanks again for the awesome code!

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[ZZKnight]

Thanks for you suggestions.