def fit(self, embset):
names, X = embset_to_X(embset=embset)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=NumbaPerformanceWarning)
self.tfm.fit(X)
self.is_fitted = True
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_X(embset=embset)
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):
names, X = embset_to_X(embset=embset)
np.random.seed(self.seed)
orig_dict = embset.embeddings.copy()
new_dict = {
f"rand_{k}": Embedding(f"rand_{k}",
np.random.normal(0, self.sigma, X.shape[1]))
for k in range(self.n)
}
return EmbeddingSet({**orig_dict, **new_dict})
def fit(self, embset):
names, X = embset_to_X(embset=embset)
self.tfm.fit(X)
self.is_fitted = True
def fit(self, embset):
embset_to_X(embset=embset)
self.is_fitted = True
