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152072f900
### What problem does this PR solve? #1594 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Reference:
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- [graphrag](https://github.com/microsoft/graphrag)
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"""
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from typing import Any
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import numpy as np
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import networkx as nx
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from graphrag.leiden import stable_largest_connected_component
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@dataclass
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class NodeEmbeddings:
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"""Node embeddings class definition."""
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nodes: list[str]
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embeddings: np.ndarray
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def embed_nod2vec(
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graph: nx.Graph | nx.DiGraph,
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dimensions: int = 1536,
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num_walks: int = 10,
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walk_length: int = 40,
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window_size: int = 2,
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iterations: int = 3,
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random_seed: int = 86,
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) -> NodeEmbeddings:
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"""Generate node embeddings using Node2Vec."""
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# generate embedding
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lcc_tensors = gc.embed.node2vec_embed( # type: ignore
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graph=graph,
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dimensions=dimensions,
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window_size=window_size,
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iterations=iterations,
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num_walks=num_walks,
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walk_length=walk_length,
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random_seed=random_seed,
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)
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return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1])
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def run(graph: nx.Graph, args: dict[str, Any]) -> NodeEmbeddings:
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"""Run method definition."""
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if args.get("use_lcc", True):
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graph = stable_largest_connected_component(graph)
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# create graph embedding using node2vec
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embeddings = embed_nod2vec(
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graph=graph,
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dimensions=args.get("dimensions", 1536),
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num_walks=args.get("num_walks", 10),
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walk_length=args.get("walk_length", 40),
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window_size=args.get("window_size", 2),
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iterations=args.get("iterations", 3),
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random_seed=args.get("random_seed", 86),
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)
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pairs = zip(embeddings.nodes, embeddings.embeddings.tolist(), strict=True)
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sorted_pairs = sorted(pairs, key=lambda x: x[0])
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return dict(sorted_pairs) |