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* convert np.ndarray to backend tensor * add datasets * add qm7 * add dataset * add dataset * fix * change ppi * tu dataset * add datasets * fix * fix * fix * fix * add docstring * docs * doc
190 lines
3.7 KiB
ReStructuredText
190 lines
3.7 KiB
ReStructuredText
.. _apidata:
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Dataset
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=======
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.. currentmodule:: dgl.data
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Utils
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-----
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.. autosummary::
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:toctree: ../../generated/
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utils.get_download_dir
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utils.download
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utils.check_sha1
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utils.extract_archive
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utils.split_dataset
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utils.save_graphs
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utils.load_graphs
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utils.load_labels
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.. autoclass:: dgl.data.utils.Subset
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:members: __getitem__, __len__
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Dataset Classes
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---------------
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Stanford sentiment treebank dataset
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```````````````````````````````````
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For more information about the dataset, see `Sentiment Analysis <https://nlp.stanford.edu/sentiment/index.html>`__.
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.. autoclass:: SST
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:members: __getitem__, __len__
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Karate Club dataset
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```````````````````````````````````
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.. autoclass:: KarateClub
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:members: __getitem__, __len__
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Citation Network dataset
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```````````````````````````````````
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.. autoclass:: CitationGraphDataset
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:members: __getitem__, __len__
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Cora Citation Network dataset
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```````````````````````````````````
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.. autoclass:: CoraDataset
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:members: __getitem__, __len__
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CoraFull dataset
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```````````````````````````````````
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.. autoclass:: CoraFull
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:members: __getitem__, __len__
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Amazon Co-Purchase dataset
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```````````````````````````````````
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.. autoclass:: AmazonCoBuy
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:members: __getitem__, __len__
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Coauthor dataset
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```````````````````````````````````
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.. autoclass:: Coauthor
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:members: __getitem__, __len__
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BitcoinOTC dataset
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```````````````````````````````````
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.. autoclass:: BitcoinOTC
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:members: __getitem__, __len__
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ICEWS18 dataset
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```````````````````````````````````
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.. autoclass:: ICEWS18
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:members: __getitem__, __len__
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QM7b dataset
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```````````````````````````````````
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.. autoclass:: QM7b
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:members: __getitem__, __len__
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GDELT dataset
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```````````````````````````````````
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.. autoclass:: GDELT
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:members: __getitem__, __len__
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Mini graph classification dataset
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`````````````````````````````````
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.. autoclass:: MiniGCDataset
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:members: __getitem__, __len__, num_classes
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Graph kernel dataset
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````````````````````
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For more information about the dataset, see `Benchmark Data Sets for Graph Kernels <https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets>`__.
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.. autoclass:: TUDataset
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:members: __getitem__, __len__
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Graph isomorphism network dataset
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```````````````````````````````````
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A compact subset of graph kernel dataset
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.. autoclass:: GINDataset
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:members: __getitem__, __len__
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Protein-Protein Interaction dataset
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```````````````````````````````````
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.. autoclass:: PPIDataset
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:members: __getitem__, __len__
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Molecular Graphs
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----------------
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To work on molecular graphs, make sure you have installed `RDKit 2018.09.3 <https://www.rdkit.org/docs/Install.html>`__.
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Featurization
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`````````````
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For the use of graph neural networks, we need to featurize nodes (atoms) and edges (bonds). Below we list some
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featurization methods/utilities:
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.. autosummary::
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:toctree: ../../generated/
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chem.one_hot_encoding
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chem.BaseAtomFeaturizer
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chem.CanonicalAtomFeaturizer
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Graph Construction
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``````````````````
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Several methods for constructing DGLGraphs from SMILES/RDKit molecule objects are listed below:
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.. autosummary::
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:toctree: ../../generated/
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chem.mol_to_graph
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chem.smile_to_bigraph
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chem.mol_to_bigraph
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chem.smile_to_complete_graph
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chem.mol_to_complete_graph
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Dataset Classes
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```````````````
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If your dataset is stored in a ``.csv`` file, you may find it helpful to use
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.. autoclass:: dgl.data.chem.CSVDataset
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:members: __getitem__, __len__
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Currently two datasets are supported:
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* Tox21
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* TencentAlchemyDataset
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.. autoclass:: dgl.data.chem.Tox21
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:members: __getitem__, __len__, task_pos_weights
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.. autoclass:: dgl.data.chem.TencentAlchemyDataset
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:members: __getitem__, __len__, set_mean_and_std
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