# isonetpp_loader.py from __future__ import annotations import os import pickle from typing import Literal, Optional, Dict from huggingface_hub import hf_hub_download try: from subiso_dataset import SubgraphIsomorphismDataset, TRAIN_MODE, VAL_MODE, TEST_MODE, BROAD_TEST_MODE, GMN_DATA_TYPE, PYG_DATA_TYPE except Exception as e: raise ImportError( "Make sure `subiso_dataset.py` (with SubgraphIsomorphismDataset) is in the same repo.\n" f"Import error: {e}" ) Mode = Literal["train", "val", "test", "Extra_test_300"] Size = Literal["small", "large"] Name = Literal["aids240k", "mutag240k", "ptc_fm240k", "ptc_fr240k", "ptc_mm240k", "ptc_mr240k"] def _mode_prefix(mode: str) -> str: # Your file naming uses "test" prefix for Extra_test_300 as well return "test" if "test" in mode.lower() else mode def _pair_count(dataset_size: Size) -> str: return "80k" if dataset_size == "small" else "240k" def _ensure_paths( repo_id: str, mode: Mode, dataset_name: Name, dataset_size: Size, local_root: Optional[str] = None, ) -> Dict[str, str]: """ Download the three files needed for a given split into local cache (or local_root if set): - __query_subgraphs.pkl - __rel_nx_is_subgraph_iso.pkl - _corpus_subgraphs.pkl (lives next to splits in our layout under `corpus/`) Returns local file paths. """ prefix = _mode_prefix(mode) pairs = _pair_count(dataset_size) # Expected layout in your dataset repo: # corpus/_corpus_subgraphs.pkl # splits//__query_subgraphs.pkl # splits//__rel_nx_is_subgraph_iso.pkl query_fname = f"{prefix}_{dataset_name}_{'query_subgraphs' if '_' in dataset_name else 'query_subgraphs'}.pkl" rel_fname = f"{prefix}_{dataset_name}_{'rel_nx_is_subgraph_iso' if '_' in dataset_name else 'rel_nx_is_subgraph_iso'}.pkl" pairs = "80k" if dataset_size == "small" else "240k" # Your actual saved names were like: train_aids240k_query_subgraphs.pkl (without extra underscore) # So fix the minor formatting exactly: query_fname = f"{size_folder}/{prefix}_{dataset_name}{pairs}_query_subgraphs.pkl" rel_fname = f"{size_folder}/{prefix}_{dataset_name}{pairs}_rel_nx_is_subgraph_iso.pkl" corpus_fname = f"{size_folder}/{dataset_name}{pairs}_corpus_subgraphs.pkl" # Where files are in repo repo_query_path = f"splits/{mode}/{query_fname}" repo_rel_path = f"splits/{mode}/{rel_fname}" repo_corpus_path = f"corpus/{corpus_fname}" # Download to cache (or local_root if provided) kwargs = dict(repo_id=repo_id, repo_type="dataset") query_path = hf_hub_download(filename=repo_query_path, **kwargs, local_dir=local_root, local_dir_use_symlinks=False) rel_path = hf_hub_download(filename=repo_rel_path, **kwargs, local_dir=local_root, local_dir_use_symlinks=False) corpus_path= hf_hub_download(filename=repo_corpus_path,**kwargs, local_dir=local_root, local_dir_use_symlinks=False) return {"query": query_path, "rel": rel_path, "corpus": corpus_path} def load_isonetpp_benchmark( repo_id: str = "structlearning/isonetpp-benchmark", mode: Mode = "train", dataset_name: Name = "aids240k", dataset_size: Size = "large", batch_size: int = 128, data_type: str = "pyg", device: Optional[str] = None, download_root: Optional[str] = None, ): """ Returns: an initialized SubgraphIsomorphismDataset with files downloaded from the HF Hub. """ # Map to your class constants mode_map = { "train": TRAIN_MODE, "val": VAL_MODE, "test": TEST_MODE, "extra_test_300": BROAD_TEST_MODE, "Extra_test_300": BROAD_TEST_MODE } mode_norm = mode_map.get(mode, mode) paths = _ensure_paths( repo_id=repo_id, mode=mode_norm, dataset_name=dataset_name, dataset_size=dataset_size, local_root=download_root, ) # Your class expects dataset_base_path + "splits//..." and "corpus/..." # We'll set dataset_base_path to the parent of the downloaded structure and override "dataset_path_override" base_path = os.path.dirname(os.path.dirname(paths["query"])) # points to .../splits/ dataset_base_path = os.path.dirname(base_path) # parent folder containing `splits` and `corpus` dataset_config = dict( mode=mode_norm, dataset_name=dataset_name, dataset_size=dataset_size, batch_size=batch_size, data_type=data_type, dataset_base_path=dataset_base_path, experiment=None, dataset_path_override=None, device=device, ) ds = SubgraphIsomorphismDataset(**dataset_config) return ds