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)