def _load_tree(self, tree):
matrix = assemble_sparse_matrix(tree["matrix"])
files = split_strings(tree["files"])
deps = split_strings(tree["deps"])
ind_to_langs = {
ind: lang
for ind, lang in enumerate(split_strings(tree["ind_to_langs"]))
}
ind_to_repos = {
ind: repo
for ind, repo in enumerate(split_strings(tree["ind_to_repos"]))
}
self.construct(matrix, files, deps, ind_to_langs, ind_to_repos)
def _load_tree(self, tree):
self.id_to_cc = tree["cc"]
self.id_to_cc[0] # do not remove - loads the array from disk
self.id_to_element = split_strings(tree["elements"])
self.id_to_buckets = assemble_sparse_matrix(tree["buckets"])
def _load_tree(self, tree: dict) -> None:
self.construct(
split_strings(tree["tokens"]),
split_strings(tree["topics"]) if tree["topics"] else None,
assemble_sparse_matrix(tree["matrix"]))
def _load_tree(self, tree):
self.id_to_cc = tree["cc"]
self.id_to_cc[0] # do not remove - loads the array from disk
self.id_to_element = split_strings(tree["elements"])
self.id_to_buckets = assemble_sparse_matrix(tree["buckets"])
def _load_tree_kwargs(self, tree: dict):
return {
"documents": split_strings(tree["documents"]),
"matrix": assemble_sparse_matrix(tree["matrix"]),
"tokens": split_strings(tree["tokens"])
}
def _load_tree_kwargs(self, tree: dict):
return dict(documents=split_strings(tree["documents"]),
matrix=assemble_sparse_matrix(tree["matrix"]),
tokens=split_strings(tree["tokens"]))
