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| """ | |
| Tokenization strategies for Instance Learning Graph (ILG) embeddings. | |
| Provides multiple methods for converting ILGs into fixed-dimensional vectors: | |
| - WLTokenizer: Weisfeiler-Leman color refinement (wraps existing wlplan pipeline) | |
| - SimHashTokenizer: Random projection hashing | |
| - ShortestPathTokenizer: Shortest-path kernel features | |
| - GraphBPETokenizer: Byte-pair encoding on graph structures | |
| - RandomTokenizer: deterministic random baseline embeddings | |
| """ | |
| from code.tokenization.base import TokenizationStrategy | |
| from code.tokenization.multidomain import MultiDomainUnionTokenizer | |
| from code.tokenization.random import RandomTokenizer | |
| from code.tokenization.simhash import SimHashTokenizer | |
| from code.tokenization.shortest_path import ShortestPathTokenizer | |
| from code.tokenization.graphbpe import GraphBPETokenizer | |
| __all__ = [ | |
| "TokenizationStrategy", | |
| "MultiDomainUnionTokenizer", | |
| "RandomTokenizer", | |
| "SimHashTokenizer", | |
| "ShortestPathTokenizer", | |
| "GraphBPETokenizer", | |
| ] | |