🇨🇴🕸️ Add co-occurence filtered meta model#943
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src/pykeen/models/baseline/utils.py
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| col_indices: numpy.ndarray, | ||
| shape: Tuple[int, int], | ||
| dtype: numpy.dtype = numpy.float32, | ||
| normalize: bool = True, |
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if you can imagine we ever want a norm besides l1, you could change this to be an Optional[str] and have set to "l1" as default
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There are two minor comments for docs so if you want to go back and address them that's fine otherwise 
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This PR adds a meta-model which wraps a base model and masks out scores for entities which do not co-occur with a relation (in the training set).
It can also be used to wrap an already trained model.
Similar techniques have been applied in the winning approaches of the OGB-LSC challenge, although there, valid/test triples were also included.
In theory, we could also apply some sort of (soft) masking already during training to enable the model to ignore scores which will be filtered at evaluation time anyway.