text stringlengths 1 93.6k |
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def sparse_mx_to_sparse_tensor(sparse_mx):
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"""sparse matrix to sparse tensor matrix(torch)
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Args:
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sparse_mx : scipy.sparse.csr_matrix
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sparse matrix
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"""
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sparse_mx_coo = sparse_mx.tocoo().astype(np.float32)
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sparse_row = torch.LongTensor(sparse_mx_coo.row).unsqueeze(1)
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sparse_col = torch.LongTensor(sparse_mx_coo.col).unsqueeze(1)
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sparse_indices = torch.cat((sparse_row, sparse_col), 1)
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sparse_data = torch.FloatTensor(sparse_mx.data)
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return torch.sparse.FloatTensor(sparse_indices.t(), sparse_data, torch.Size(sparse_mx.shape))
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def to_tensor(adj, features, labels=None, device='cpu'):
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"""Convert adj, features, labels from array or sparse matrix to
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torch Tensor on target device.
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Args:
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adj : scipy.sparse.csr_matrix
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the adjacency matrix.
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features : scipy.sparse.csr_matrix
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node features
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labels : numpy.array
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node labels
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device : str
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'cpu' or 'cuda'
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"""
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if sp.issparse(adj):
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adj = sparse_mx_to_sparse_tensor(adj)
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else:
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adj = torch.FloatTensor(adj)
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if sp.issparse(features):
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features = sparse_mx_to_sparse_tensor(features)
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else:
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features = torch.FloatTensor(np.array(features))
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if labels is None:
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return adj.to(device), features.to(device)
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else:
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labels = torch.LongTensor(labels)
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return adj.to(device), features.to(device), labels.to(device)
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def idx_to_mask(idx, nodes_num):
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"""Convert a indices array to a tensor mask matrix
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Args:
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idx : numpy.array
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indices of nodes set
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nodes_num: int
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number of nodes
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"""
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mask = torch.zeros(nodes_num)
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mask[idx] = 1
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return mask.bool()
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def is_sparse_tensor(tensor):
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"""Check if a tensor is sparse tensor.
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Args:
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tensor : torch.Tensor
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given tensor
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Returns:
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bool
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whether a tensor is sparse tensor
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"""
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# if hasattr(tensor, 'nnz'):
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if tensor.layout == torch.sparse_coo:
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return True
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else:
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return False
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def to_scipy(tensor):
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"""Convert a dense/sparse tensor to scipy matrix"""
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if is_sparse_tensor(tensor):
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values = tensor._values()
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indices = tensor._indices()
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return sp.csr_matrix((values.cpu().numpy(), indices.cpu().numpy()), shape=tensor.shape)
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else:
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indices = tensor.nonzero().t()
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values = tensor[indices[0], indices[1]]
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return sp.csr_matrix((values.cpu().numpy(), indices.cpu().numpy()), shape=tensor.shape)
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# <FILESEP>
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# VERSION: 1.1
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# AUTHORS: BurningMop (burning.mop@yandex.com)
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# LICENSING INFORMATION
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# Permission is hereby granted, free of charge, to any person obtaining a copy
|
# of this software and associated documentation files (the "Software"), to deal
|
# in the Software without restriction, including without limitation the rights
|
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
# copies of the Software, and to permit persons to whom the Software is
|
# furnished to do so, subject to the following conditions:
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#
|
# The above copyright notice and this permission notice shall be included in
|
# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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