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"]))