dig.oodgraph¶
Graph OOD (GOOD) Dataset interfaces under dig.oodgraph
.
Please refer to the GOOD project for more details.
This module includes 8 GOOD datasets.
Graph prediction datasets: GOOD-HIV, GOOD-PCBA, GOOD-ZINC, GOOD-CMNIST, GOOD-Motif.
Node prediction datasets: GOOD-Cora, GOOD-Arxiv, GOOD-CBAS.
The GOOD-Arxiv dataset adapted from OGB benchmark. |
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The GOOD-CBAS dataset. |
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The GOOD-CMNIST dataset following IRM paper. |
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The GOOD-Cora dataset. |
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The GOOD-HIV dataset. |
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The GOOD-Motif dataset motivated by Spurious-Motif. |
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The GOOD-PCBA dataset. |
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The GOOD-ZINC dataset adapted from ZINC database. |
- class GOODArxiv(root: str, domain: str, shift: str = 'no_shift', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-Arxiv dataset adapted from OGB benchmark.
- Parameters
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.
- class GOODCBAS(root: str, domain: str, shift: str = 'no_shift', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-CBAS dataset. Modified from BA-Shapes.
- Parameters
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.
- class GOODCMNIST(root: str, domain: str, shift: str = 'no_shift', subset: str = 'train', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-CMNIST dataset following IRM paper.
- Parameters
root (str) – The dataset saving root.
domain (str) – The domain selection. Allowed: ‘color’.
shift (str) – The distributional shift we pick. Allowed: ‘no_shift’, ‘covariate’, and ‘concept’.
subset (str) – The split set. Allowed: ‘train’, ‘id_val’, ‘id_test’, ‘val’, and ‘test’. When shift=’no_shift’, ‘id_val’ and ‘id_test’ are not applicable.
generate (bool) – The flag for regenerating dataset. True: regenerate. False: download.
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.
- class GOODCora(root: str, domain: str, shift: str = 'no_shift', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-Cora dataset. Adapted from the full Cora dataset.
- Parameters
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.
- class GOODHIV(root: str, domain: str, shift: str = 'no_shift', subset: str = 'train', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-HIV dataset. Adapted from MoleculeNet.
- Parameters
root (str) – The dataset saving root.
domain (str) – The domain selection. Allowed: ‘scaffold’ and ‘size’.
shift (str) – The distributional shift we pick. Allowed: ‘no_shift’, ‘covariate’, and ‘concept’.
subset (str) – The split set. Allowed: ‘train’, ‘id_val’, ‘id_test’, ‘val’, and ‘test’. When shift=’no_shift’, ‘id_val’ and ‘id_test’ are not applicable.
generate (bool) – The flag for regenerating dataset. True: regenerate. False: download.
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.
- class GOODMotif(root: str, domain: str, shift: str = 'no_shift', subset: str = 'train', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-Motif dataset motivated by Spurious-Motif.
- Parameters
root (str) – The dataset saving root.
domain (str) – The domain selection. Allowed: ‘basis’ and ‘size’.
shift (str) – The distributional shift we pick. Allowed: ‘no_shift’, ‘covariate’, and ‘concept’.
subset (str) – The split set. Allowed: ‘train’, ‘id_val’, ‘id_test’, ‘val’, and ‘test’. When shift=’no_shift’, ‘id_val’ and ‘id_test’ are not applicable.
generate (bool) – The flag for regenerating dataset. True: regenerate. False: download.
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.
- class GOODPCBA(root: str, domain: str, shift: str = 'no_shift', subset: str = 'train', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-PCBA dataset. Adapted from MoleculeNet.
- Parameters
root (str) – The dataset saving root.
domain (str) – The domain selection. Allowed: ‘scaffold’ and ‘size’.
shift (str) – The distributional shift we pick. Allowed: ‘no_shift’, ‘covariate’, and ‘concept’.
subset (str) – The split set. Allowed: ‘train’, ‘id_val’, ‘id_test’, ‘val’, and ‘test’. When shift=’no_shift’, ‘id_val’ and ‘id_test’ are not applicable.
generate (bool) – The flag for regenerating dataset. True: regenerate. False: download.
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.
- class GOODZINC(root: str, domain: str, shift: str = 'no_shift', subset: str = 'train', transform=None, pre_transform=None, generate: bool = False)[source]¶
The GOOD-ZINC dataset adapted from ZINC database.
- Parameters
root (str) – The dataset saving root.
domain (str) – The domain selection. Allowed: ‘scaffold’ and ‘size’.
shift (str) – The distributional shift we pick. Allowed: ‘no_shift’, ‘covariate’, and ‘concept’.
subset (str) – The split set. Allowed: ‘train’, ‘id_val’, ‘id_test’, ‘val’, and ‘test’. When shift=’no_shift’, ‘id_val’ and ‘id_test’ are not applicable.
generate (bool) – The flag for regenerating dataset. True: regenerate. False: download.
- static load(dataset_root: str, domain: str, shift: str = 'no_shift', generate: bool = False)[source]¶
A staticmethod for dataset loading. This method instantiates dataset class, constructing train, id_val, id_test, ood_val (val), and ood_test (test) splits. Besides, it collects several dataset meta information for further utilization.
- Parameters
- Returns
dataset or dataset splits. dataset meta info.
- property processed_file_names¶
The name of the files in the
self.processed_dir
folder that must be present in order to skip processing.