Add EnsembleMF: ensembled two-tower matrix factorization

[PascalIversen]

Adds EnsembleMF, a two-tower matrix factorization model for drug response prediction.

Basically, I found that the graph convolutional part of my earlier idea does not really drive the performance. So this is the easiest model with which I get SOTA performance >RF.

AI summary:

Predicts the cell line x drug response matrix as a dot product of latent factors. The factors are
not free parameters but the outputs of small residual MLPs, one over gene expression and one over
Morgan fingerprints, so a held-out cell line can be scored from its features alone. On top of the
dot product sit per-cell, per-drug and global bias terms, a free per-drug id embedding (every drug
is seen during training, so an id-indexed latent is learnable and captures behaviour that
fingerprints only approximate), and a small interaction head. And then we ensemble the whole thing