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| # | |
| # Copyright 2024 The InfiniFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| """ | |
| Reference: | |
| - [graphrag](https://github.com/microsoft/graphrag) | |
| """ | |
| from typing import Any | |
| import numpy as np | |
| import networkx as nx | |
| from graphrag.leiden import stable_largest_connected_component | |
| class NodeEmbeddings: | |
| """Node embeddings class definition.""" | |
| nodes: list[str] | |
| embeddings: np.ndarray | |
| def embed_nod2vec( | |
| graph: nx.Graph | nx.DiGraph, | |
| dimensions: int = 1536, | |
| num_walks: int = 10, | |
| walk_length: int = 40, | |
| window_size: int = 2, | |
| iterations: int = 3, | |
| random_seed: int = 86, | |
| ) -> NodeEmbeddings: | |
| """Generate node embeddings using Node2Vec.""" | |
| # generate embedding | |
| lcc_tensors = gc.embed.node2vec_embed( # type: ignore | |
| graph=graph, | |
| dimensions=dimensions, | |
| window_size=window_size, | |
| iterations=iterations, | |
| num_walks=num_walks, | |
| walk_length=walk_length, | |
| random_seed=random_seed, | |
| ) | |
| return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1]) | |
| def run(graph: nx.Graph, args: dict[str, Any]) -> NodeEmbeddings: | |
| """Run method definition.""" | |
| if args.get("use_lcc", True): | |
| graph = stable_largest_connected_component(graph) | |
| # create graph embedding using node2vec | |
| embeddings = embed_nod2vec( | |
| graph=graph, | |
| dimensions=args.get("dimensions", 1536), | |
| num_walks=args.get("num_walks", 10), | |
| walk_length=args.get("walk_length", 40), | |
| window_size=args.get("window_size", 2), | |
| iterations=args.get("iterations", 3), | |
| random_seed=args.get("random_seed", 86), | |
| ) | |
| pairs = zip(embeddings.nodes, embeddings.embeddings.tolist(), strict=True) | |
| sorted_pairs = sorted(pairs, key=lambda x: x[0]) | |
| return dict(sorted_pairs) |