# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Convert the coo graph to the csr graph."""
import numpy as np
import mindspore as ms
import scipy
def csr_data(row_indices, col_indices, n_nodes, n_edges):
"""Convert the COO format to the CSR format."""
coo_tensor_forward = scipy.sparse.coo_matrix(
(np.ones(n_edges), (row_indices, col_indices)), shape=(n_nodes, n_nodes))
csr_tensor_forward = coo_tensor_forward.tocsr()
indptr = np.asarray(csr_tensor_forward.indptr, np.int32)
indices = np.asarray(csr_tensor_forward.indices, np.int32)
coo_tensor_backward = scipy.sparse.csr_matrix(
(np.ones(n_edges), (col_indices, row_indices)), shape=(n_nodes, n_nodes))
csr_tensor_backward = coo_tensor_backward.tocsr()
indptr_backward = ms.Tensor(np.asarray(csr_tensor_backward.indptr), dtype=ms.int32)
indices_backward = ms.Tensor(np.asarray(csr_tensor_backward.indices), dtype=ms.int32)
indices = ms.Tensor(indices, ms.int32)
indptr = ms.Tensor(indptr, ms.int32)
return indices, indptr, indices_backward, indptr_backward
def rerank_index(out_deg, row_indices, col_indices):
"""reorder the index according to the out degree"""
idx_forward = np.argsort(out_deg)[::-1]
arg_idx_forward = np.argsort(idx_forward)
arg_idx_forward = np.array(arg_idx_forward, np.int32)
row_indices_forward = arg_idx_forward[row_indices]
col_indices_forward = arg_idx_forward[col_indices]
return row_indices_forward, col_indices_forward, idx_forward, arg_idx_forward
[文档]def graph_csr_data(src_idx, dst_idx, n_nodes, n_edges, node_feat=None, node_label=None, train_mask=None, val_mask=None,
test_mask=None, rerank=False):
r"""
Convert the entire graph in the COO format to the CSR format.
Args:
src_idx (Union[Tensor, numpy.ndarray]): tensor with shape :math:`(N\_EDGES)`, with int dtype,
represents the source node index of COO edge matrix.
dst_idx (Union[Tensor, numpy.ndarray]): tensor with shape :math:`(N\_EDGES)`, with int dtype,
represents the destination node index of COO edge matrix.
n_nodes (int): integer, represent the nodes count of the graph.
n_edges (int): integer, represent the edges count of the graph.
node_feat (Union[Tensor, numpy.ndarray, optional]): node feature. Default: ``None``.
node_label (Union[Tensor, numpy.ndarray, optional]): node labels. Default: ``None``.
train_mask (Union[Tensor, numpy.ndarray, optional]): mask of train index. Default: ``None``.
val_mask (Union[Tensor, numpy.ndarray, optional]): msk of train index. Default: ``None``.
test_mask (Union[Tensor, numpy.ndarray, optional]): mask of train index. Default: ``None``.
rerank (bool, optional): whether to reorder node features, node labels, and masks.
Default: ``False``.
Returns:
- **csr_g** (tuple) - info of csr graph, it contains indices of csr graph, indptr of csr graph,
node numbers of csr graph, edges numbers of csr graph, pre-stored backward indices of csr graph,
pre-stored backward indptr of csr graph.
- **in_deg** - in degree of each node.
- **out_deg** - out degree of each node.
- **node_feat** (Union[Tensor, numpy.ndarray, optional]) - reorder node features.
- **node_label** (Union[Tensor, numpy.ndarray, optional]) - reorder node labels.
- **train_mask** (Union[Tensor, numpy.ndarray, optional]) - reorder train index mask.
- **val_mask** (Union[Tensor, numpy.ndarray, optional]) - reorder val index mask.
- **test_mask** (Union[Tensor, numpy.ndarray, optional]) - reorder test index mask.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import numpy as np
>>> from mindspore_gl.graph import graph_csr_data
>>> node_feat = np.array([[1, 2, 3, 4], [2, 4, 1, 3], [1, 3, 2, 4],
... [9, 7, 5, 8], [8, 7, 6, 5], [8, 6, 4, 6], [1, 2, 1, 1]], np.float32)
>>> n_nodes = 7
>>> n_edges = 8
>>> edge_feat_size = 7
>>> src_idx = np.array([0, 2, 2, 3, 4, 5, 5, 6], np.int32)
>>> dst_idx = np.array([1, 0, 1, 5, 3, 4, 6, 4], np.int32)
>>> node_label = np.array([0, 1, 0, 1, 0, 1, 0])
>>> train_mask = np.array([True, True, True, True, False, False, False])
>>> val_mask = np.array([False, False, False, False, True, True, True])
>>> g, in_deg, out_deg, node_feat, node_label, train_mask, val_mask,\
>>> test_mask = graph_csr_data(src_idx,dst_idx, n_nodes, n_edges, node_feat, node_label,
... train_mask, val_mask, test_mask=None, rerank=True)
>>> print(g[0], g[1])
[2 3 5 6 3 4 0 6] [0 2 4 5 6 7 8 8]
>>> print(node_feat, node_label)
[[8. 7. 6. 5.]
[2. 4. 1. 3.]
[1. 2. 1. 1.]
[8. 6. 4. 6.]
[9. 7. 5. 8.]
[1. 2. 3. 4.]
[1. 3. 2. 4.]] [0 1 0 1 1 0 0]
>>> print(train_mask, val_mask)
[False True False False True True True] [ True False True True False False False]
"""
if isinstance(dst_idx, ms.Tensor):
dst_idx = dst_idx.asnumpy()
if isinstance(src_idx, ms.Tensor):
src_idx = src_idx.asnumpy()
row_indices = np.array(dst_idx, np.int32)
col_indices = np.array(src_idx, np.int32)
out_deg = np.bincount(row_indices, minlength=n_nodes)
in_deg = np.bincount(col_indices, minlength=n_nodes)
if not rerank:
indices, indptr, indices_backward, indptr_backward = csr_data(row_indices, col_indices, n_nodes, n_edges)
else:
row_indices_forward, col_indices_forward, idx_forward, _ = rerank_index(out_deg, row_indices, col_indices)
indices, indptr, indices_backward, indptr_backward = csr_data(row_indices_forward, col_indices_forward,
n_nodes, n_edges)
idx_forward = idx_forward.tolist()
in_deg = in_deg[idx_forward]
out_deg = out_deg[idx_forward]
node_feat = node_feat[idx_forward]
node_label = node_label[idx_forward]
if train_mask is not None:
train_mask = train_mask[idx_forward]
if val_mask is not None:
val_mask = val_mask[idx_forward]
if test_mask is not None:
test_mask = test_mask[idx_forward]
in_deg = ms.Tensor(in_deg, ms.int32)
out_deg = ms.Tensor(out_deg, ms.int32)
csr_g = (indices, indptr, n_nodes, n_edges, indices_backward, indptr_backward)
return csr_g, in_deg, out_deg, node_feat, node_label, train_mask, val_mask, test_mask
[文档]def sampling_csr_data(src_idx, dst_idx, n_nodes, n_edges, seeds_idx=None, node_feat=None, rerank=False):
r"""
Convert the sampling graph in the COO format to the CSR format.
Args:
src_idx (Union[Tensor, numpy.ndarray]): tensor with shape :math:`(N\_EDGES)`, with int dtype,
represents the source node index of COO edge matrix.
dst_idx (Union[Tensor, numpy.ndarray]): tensor with shape :math:`(N\_EDGES)`, with int dtype,
represents the destination node index of COO edge matrix.
n_nodes (int): integer, represent the nodes count of the graph.
n_edges (int): integer, represent the edges count of the graph.
seeds_idx (numpy.ndarray): start nodes for neighbor sampling. Default: ``None``.
node_feat (Union[Tensor, numpy.ndarray], optional): node feature. Default: ``None``.
rerank (bool, optional): whether to reorder node features, node labels, and masks.
Default: ``False``.
Returns:
- **csr_g** (tuple) - info of csr graph, it contains indices of csr graph, indptr of csr graph,
node numbers of csr graph, edges numbers of csr graph, pre-stored backward indices of csr graph,
pre-stored backward indptr of csr graph.
- **seeds_idx** (numpy.ndarray) - reordered start nodes.
- **node_feat** (numpy.ndarray) - reorder node features.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import numpy as np
>>> from mindspore_gl.graph import sampling_csr_data
>>> node_feat = np.array([[1, 2, 3, 4], [2, 4, 1, 3], [1, 3, 2, 4],
... [9, 7, 5, 8], [8, 7, 6, 5], [8, 6, 4, 6], [1, 2, 1, 1]], np.float32)
>>> n_nodes = 7
>>> n_edges = 8
>>> edge_feat_size = 7
>>> src_idx = np.array([0, 2, 2, 3, 4, 5, 5, 6], np.int32)
>>> dst_idx = np.array([1, 0, 1, 5, 3, 4, 6, 4], np.int32)
>>> seeds_idx = np.array([0, 3, 5])
>>> g, seeds_idx, node_feat = sampling_csr_data(src_idx, dst_idx, n_nodes, n_edges,\
... seeds_idx, node_feat, rerank=True)
>>> print(g[0], g[1], seeds_idx)
[2 3 5 6 3 4 0 6] [0 2 4 5 6 7 8 8] [5, 4, 3]
>>> print(node_feat)
[[8. 7. 6. 5.]
[2. 4. 1. 3.]
[1. 2. 1. 1.]
[8. 6. 4. 6.]
[9. 7. 5. 8.]
[1. 2. 3. 4.]
[1. 3. 2. 4.]]
"""
if isinstance(dst_idx, ms.Tensor):
dst_idx = dst_idx.asnumpy()
if isinstance(src_idx, ms.Tensor):
src_idx = src_idx.asnumpy()
row_indices = np.array(dst_idx, np.int32)
col_indices = np.array(src_idx, np.int32)
out_deg = np.bincount(row_indices, minlength=n_nodes)
if not rerank:
indices, indptr, indices_backward, indptr_backward = csr_data(row_indices, col_indices, n_nodes, n_edges)
else:
row_indices_forward, col_indices_forward, idx_forward, arg_idx_forward = rerank_index(out_deg, row_indices,
col_indices)
indices, indptr, indices_backward, indptr_backward = csr_data(row_indices_forward, col_indices_forward,
n_nodes, n_edges)
idx_forward = idx_forward.tolist()
origin_idx = list(range(len(arg_idx_forward)))
idx_dict = dict(zip(origin_idx, arg_idx_forward))
seeds_idx = [idx_dict[i] for i in seeds_idx]
node_feat = node_feat[idx_forward, :]
csr_g = (indices, indptr, n_nodes, n_edges, indices_backward, indptr_backward)
return csr_g, seeds_idx, node_feat
[文档]def batch_graph_csr_data(src_idx, dst_idx, n_nodes, n_edges, node_map_idx, node_feat=None, rerank=False):
r"""
Convert the batched graph in the COO format to the CSR format.
Args:
src_idx (Union[Tensor, numpy.ndarray]): tensor with shape :math:`(N\_EDGES)`, with int dtype,
represents the source node index of COO edge matrix.
dst_idx (Union[Tensor, numpy.ndarray]): tensor with shape :math:`(N\_EDGES)`, with int dtype,
represents the destination node index of COO edge matrix.
n_nodes (int): integer, represent the nodes count of the graph.
n_edges (int): integer, represent the edges count of the graph.
node_map_idx (numpy.ndarray): ID of the subgraph to each node belongs to.
node_feat (Union[Tensor, numpy.ndarray, optional]): node feature. Default: ``None``.
rerank (bool, optional): whether to reorder node features, node labels, and masks.
Default: ``False``.
Returns:
- **csr_g** (tuple) - info of csr graph, it contains indices of csr graph, indptr of csr graph,
node numbers of csr graph, edges numbers of csr graph, pre-stored backward indices of csr graph,
pre-stored backward indptr of csr graph.
- **node_map_idx** (numpy.ndarray) - reordered start map index.
- **node_feat** (Union[Tensor, numpy.ndarray, optional]) - reorder node features.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import numpy as np
>>> from mindspore_gl.graph import batch_graph_csr_data
>>> node_feat = np.array([[1, 2, 3, 4], [2, 4, 1, 3], [1, 3, 2, 4],
... [9, 7, 5, 8], [8, 7, 6, 5], [8, 6, 4, 6], [1, 2, 1, 1]], np.float32)
>>> n_nodes = 7
>>> n_edges = 8
>>> edge_feat_size = 7
>>> src_idx = np.array([0, 2, 2, 3, 4, 5, 5, 6], np.int32)
>>> dst_idx = np.array([1, 0, 1, 5, 3, 4, 6, 4], np.int32)
>>> node_map_idx = np.array([0, 0, 0, 0, 1, 1, 1])
>>> g, node_map_idx, node_feat = batch_graph_csr_data(src_idx, dst_idx,\
... n_nodes, n_edges, node_map_idx, node_feat, rerank=True)
>>> print(g[0], g[1], node_map_idx)
[2 3 5 6 3 4 0 6] [0 2 4 5 6 7 8 8] [1 0 1 1 0 0 0]
>>> print(node_feat)
[[8. 7. 6. 5.]
[2. 4. 1. 3.]
[1. 2. 1. 1.]
[8. 6. 4. 6.]
[9. 7. 5. 8.]
[1. 2. 3. 4.]
[1. 3. 2. 4.]]
"""
if isinstance(dst_idx, ms.Tensor):
dst_idx = dst_idx.asnumpy()
if isinstance(src_idx, ms.Tensor):
src_idx = src_idx.asnumpy()
row_indices = np.array(dst_idx, np.int32)
col_indices = np.array(src_idx, np.int32)
out_deg = np.bincount(row_indices, minlength=n_nodes)
if not rerank:
indices, indptr, indices_backward, indptr_backward = csr_data(row_indices, col_indices, n_nodes, n_edges)
else:
row_indices_forward, col_indices_forward, idx_forward, _ = rerank_index(out_deg, row_indices,
col_indices)
indices, indptr, indices_backward, indptr_backward = csr_data(row_indices_forward, col_indices_forward, n_nodes,
n_edges)
idx_forward = idx_forward.tolist()
node_feat = node_feat[idx_forward]
node_map_idx = node_map_idx[idx_forward]
csr_g = (indices, indptr, n_nodes, n_edges, indices_backward, indptr_backward)
return csr_g, node_map_idx, node_feat