dmlc--dgl
Rhett Ying
68 行
2.1 KiB
Python
68 行
2.1 KiB
Python
"""GraphBolt Dataset."""
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from typing import Dict, List
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from .feature_store import FeatureStore
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from .itemset import ItemSet, ItemSetDict
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__all__ = ["Dataset"]
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class Dataset:
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"""An abstract dataset.
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Dataset provides abstraction for accessing the data 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: *Train-Validation-Test Set*, *Feature Storage*, *Graph Topology*.
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*Train-Validation-Test Set*:
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The training-validation-testing (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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*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
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generate a subgraph.
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"""
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def train_sets(self) -> List[ItemSet] or List[ItemSetDict]:
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"""Return the training sets."""
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raise NotImplementedError
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def validation_sets(self) -> List[ItemSet] or List[ItemSetDict]:
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"""Return the validation sets."""
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raise NotImplementedError
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def test_sets(self) -> List[ItemSet] or List[ItemSetDict]:
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"""Return the test sets."""
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raise NotImplementedError
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def graph(self) -> object:
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"""Return the graph."""
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raise NotImplementedError
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def feature(self) -> Dict[object, 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 num_classes(self) -> int:
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"""Return the number of classes."""
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raise NotImplementedError
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@property
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def num_labels(self) -> int:
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"""Return the number of labels."""
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raise NotImplementedError
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