def transform(self, embset: EmbeddingSet) -> EmbeddingSet: names, X = embset.to_names_X() if not self.is_fitted: self.tfm.fit(X) new_vecs = self.tfm.transform(X) new_dict = new_embedding_dict(names, new_vecs, embset) return EmbeddingSet(new_dict, name=f"{embset.name}.{self.name}()")
def transform(self, embset: EmbeddingSet) -> EmbeddingSet: names, X = embset.to_names_X() axis = 0 if self.feature else 1 X = normalize(X, norm=self.norm, axis=axis) new_dict = new_embedding_dict(names, X, embset) return EmbeddingSet(new_dict, name=embset.name)