wehub-resource-sync
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
"""GraphBolt Dataset."""
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from typing import Dict, List, Union
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from .feature_store import FeatureStore
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from .itemset import HeteroItemSet, ItemSet
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from .sampling_graph import SamplingGraph
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__all__ = [
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"Task",
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"Dataset",
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]
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class Task:
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"""An abstract task which consists of meta information and
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Train/Validation/Test Set.
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* meta information
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The meta information of a task includes any kinds of data that are
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defined by the user in YAML when instantiating the task.
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* Train/Validation/Test Set
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The train/validation/test (TVT) set which is used to train the neural
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networks. We calculate the embeddings based on their respective features
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and the graph structure, and then utilize the embeddings to optimize the
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neural network parameters.
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"""
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@property
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def metadata(self) -> Dict:
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"""Return the task metadata."""
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raise NotImplementedError
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@property
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def train_set(self) -> Union[ItemSet, HeteroItemSet]:
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"""Return the training set."""
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raise NotImplementedError
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@property
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def validation_set(self) -> Union[ItemSet, HeteroItemSet]:
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"""Return the validation set."""
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raise NotImplementedError
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@property
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def test_set(self) -> Union[ItemSet, HeteroItemSet]:
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"""Return the test set."""
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raise NotImplementedError
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class Dataset:
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"""An abstract dataset which provides abstraction for accessing the data
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required for training.
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The data abstraction could be a native CPU memory block, a shared memory
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block, a file handle of an opened file on disk, a service that provides
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the API to access the data e.t.c. There are 3 primary components in the
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dataset:
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* Task
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A task consists of several meta information and the
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Train/Validation/Test Set. A dataset could have multiple tasks.
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* Feature Storage
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A key-value store which stores node/edge/graph features.
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* Graph Topology
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Graph topology is used by the subgraph sampling algorithm to generate
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a subgraph.
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"""
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@property
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def tasks(self) -> List[Task]:
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"""Return the tasks."""
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raise NotImplementedError
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@property
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def graph(self) -> SamplingGraph:
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"""Return the graph."""
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raise NotImplementedError
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@property
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def feature(self) -> FeatureStore:
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"""Return the feature."""
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raise NotImplementedError
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@property
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def dataset_name(self) -> str:
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"""Return the dataset name."""
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raise NotImplementedError
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@property
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def all_nodes_set(self) -> Union[ItemSet, HeteroItemSet]:
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"""Return the itemset containing all nodes."""
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raise NotImplementedError
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