[ENH] Embedding mode #1163
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[ENH] Embedding mode #1163
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paxcema
commented
Jun 26, 2023
| @@ -220,6 +221,7 @@ def from_dict(obj: Dict): | |||
| ignore_features = obj.get('ignore_features', []) | |||
| fit_on_all = obj.get('fit_on_all', True) | |||
| use_default_analysis = obj.get('use_default_analysis', True) | |||
| embedding_only = obj.get('embedding_only', False) | |||
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Thoughts on the parameter name @torrmal?
paxcema
commented
Jun 26, 2023
| @@ -453,6 +456,7 @@ class PredictionArguments: | |||
| simple_ts_bounds: bool = False | |||
| time_format: str = '' | |||
| force_ts_infer: bool = False | |||
| return_embedding: bool = False | |||
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Thoughts on the parameter name @torrmal?
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This feature enables training a Lightwood predictor without any mixers or subsequent analysis if the
embedding_onlyflag is enabled in the problem definition. The resulting output is the concatenation of encoded representations learnt by each encoder, a.k.a. the "embedding".Alternatively, normally trained predictors can also return these "embeddings" if the
return_embeddingargument is set toTrue.