Simplest version of dumping train config #558
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| @@ -45,6 +46,34 @@ def create( | |||
| relationship_type_embeddings, | |||
| ) | |||
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| @compatible_with("train", min_inclusive=ServerVersion(2, 5, 0)) | |||
| @client_only_endpoint("gds.model.transe") | |||
| def train(self, | |||
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I'm a little bit confused about how the predict-only TransE we've had previously fits into this. They kind of seem like different things. Also I wonder if it's better if we have a gds.kge.train call instead of one dedicated to each particular scoring function
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I think that we are adding training step in addition to existing prediction one. Maybe it's worth to have a fully separate API like gds.kge.train or gds.kge.transe.train.
Personally I prefer to mention scoring method in a function name, like gds.kge.transe.train, not gds.kge.train. Because KGE algorithms are different and supposed to catch different relationship properties.
Our API proposal has gds.model.transe.train call, that's why I wrote it that way.
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Ok! Fair enough :) I do still prefer gds.kge.train: I think it makes sense since the embeddings are what are trained, not TransE which is a scoring function. And since all KGE algos are the same (in terms of eg. hyperparameters) except for the scoring function, I think it would make sense to group them in the API for simplicity, sharing the same docs, etc. In that sense scoring function is just another hyperparam I think, and one may even want to use an ensemble of them. I like how pyKEEN designed their API
| # loss: str | ||
| ) -> int: | ||
| config = {'scoring_function': 'TransE', | ||
| 'proportions': proportions, |
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I call this split_ratios in the python runtime. I think "ratio" is a more common term to refer to this (also what pyKEEN use for example)
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