def transform(self, embset):
names, X = embset.to_names_X()
new_vecs = np.array(self.emb.transform(X))
names_out = names + [f"tsne_{i}" for i in range(self.n_components)]
vectors_out = np.concatenate([new_vecs, np.eye(self.n_components)])
new_dict = new_embedding_dict(names_out, vectors_out, embset)
return EmbeddingSet(new_dict, name=f"{embset.name}.tsne_{self.n_components}()")
def transform(self, embset):
names, X = embset_to_X(embset=embset)
new_vecs = self.tfm.transform(X)
names_out = names + [f"pca_{i}" for i in range(self.n_components)]
vectors_out = np.concatenate([new_vecs, np.eye(self.n_components)])
new_dict = new_embedding_dict(names_out, vectors_out, embset)
return EmbeddingSet(new_dict,
name=f"{embset.name}.pca_{self.n_components}()")
def transform(self, embset):
names, X = embset.to_names_X()
np.random.seed(self.seed)
new_vecs = self.tfm.transform(X)
new_dict = new_embedding_dict(names, new_vecs, embset)
return EmbeddingSet(
new_dict,
name=f"{embset.name}",
)
def transform(self, embset):
names, X = embset_to_X(embset=embset)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=NumbaPerformanceWarning)
new_vecs = self.tfm.transform(X)
names_out = names + [f"umap_{i}" for i in range(self.n_components)]
vectors_out = np.concatenate([new_vecs, np.eye(self.n_components)])
new_dict = new_embedding_dict(names_out, vectors_out, embset)
return EmbeddingSet(new_dict,
name=f"{embset.name}.umap_{self.n_components}()")
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)