added saint sampling

test/test_saint.py

+import pytest
+import torch
+from torch_sparse.tensor import SparseTensor
+
+from .utils import devices
+
+
+@pytest.mark.parametrize('device', devices)
+def test_sample_node(device):
+    row = torch.tensor([0, 0, 1, 1, 2, 2, 2, 3, 3, 4])
+    col = torch.tensor([1, 2, 0, 2, 0, 1, 3, 2, 4, 3])
+    adj = SparseTensor(row=row, col=col).to(device)
+
+    adj, perm = adj.sample_node(num_nodes=3)
+
+
+@pytest.mark.parametrize('device', devices)
+def test_sample_edge(device):
+    row = torch.tensor([0, 0, 1, 1, 2, 2, 2, 3, 3, 4])
+    col = torch.tensor([1, 2, 0, 2, 0, 1, 3, 2, 4, 3])
+    adj = SparseTensor(row=row, col=col).to(device)
+
+    adj, perm = adj.sample_edge(num_edges=3)
+
+
+@pytest.mark.parametrize('device', devices)
+def test_sample_rw(device):
+    row = torch.tensor([0, 0, 1, 1, 2, 2, 2, 3, 3, 4])
+    col = torch.tensor([1, 2, 0, 2, 0, 1, 3, 2, 4, 3])
+    adj = SparseTensor(row=row, col=col).to(device)
+
+    adj, perm = adj.sample_rw(num_root_nodes=3, walk_length=2)

torch_sparse/__init__.py

@@ -55,6 +55,7 @@ from .reduce import sum, mean, min, max  # noqa
 from .matmul import matmul  # noqa
 from .cat import cat, cat_diag  # noqa
 from .metis import partition  # noqa
+from .saint import sample_node, sample_edge, sample_rw  # noqa
 
 from .convert import to_torch_sparse, from_torch_sparse  # noqa
 from .convert import to_scipy, from_scipy  # noqa
@@ -95,6 +96,9 @@ __all__ = [
     'cat',
     'cat_diag',
     'partition',
+    'sample_node',
+    'sample_edge',
+    'sample_rw',
     'to_torch_sparse',
     'from_torch_sparse',
     'to_scipy',

torch_sparse/saint.py

+from typing import Tuple
+
+import torch
+import numpy as np
+from torch_scatter import scatter_add
+from torch_sparse.tensor import SparseTensor
+
+
+def sample_node(src: SparseTensor,
+                num_nodes: int) -> Tuple[torch.Tensor, torch.Tensor]:
+    row, col, _ = src.coo()
+
+    inv_in_deg = src.storage.colcount().to(torch.float).pow_(-1)
+    inv_in_deg[inv_in_deg == float('inf')] = 0
+
+    prob = inv_in_deg[col]
+    prob.mul_(prob)
+
+    prob = scatter_add(prob, row, dim=0, dim_size=src.size(0))
+    prob.div_(prob.sum())
+
+    node_idx = prob.multinomial(num_nodes, replacement=True).unique()
+
+    return src.permute(node_idx), node_idx
+
+
+def sample_edge(src: SparseTensor,
+                num_edges: int) -> Tuple[torch.Tensor, torch.Tensor]:
+
+    row, col, _ = src.coo()
+
+    inv_out_deg = src.storage.rowcount().to(torch.float).pow_(-1)
+    inv_out_deg[inv_out_deg == float('inf')] = 0
+    inv_in_deg = src.storage.colcount().to(torch.float).pow_(-1)
+    inv_in_deg[inv_in_deg == float('inf')] = 0
+
+    prob = inv_out_deg[row] + inv_in_deg[col]
+    prob.div_(prob.sum())
+
+    edge_idx = prob.multinomial(num_edges, replacement=True)
+    node_idx = col[edge_idx].unique()
+
+    return src.permute(node_idx), node_idx
+
+
+def sample_rw(src: SparseTensor, num_root_nodes: int,
+              walk_length: int) -> Tuple[torch.Tensor, torch.Tensor]:
+
+    start = np.random.choice(src.size(0), size=num_root_nodes, replace=False)
+    start = torch.from_numpy(start).to(src.device())
+
+    # get random walks of length `walk_length`:
+    # => `rw.size(1) == walk_length + 1
+
+    return None, None
+
+
+SparseTensor.sample_node = sample_node
+SparseTensor.sample_edge = sample_edge
+SparseTensor.sample_rw = sample_rw

torch_sparse/tensor.py

@@ -12,17 +12,25 @@ from torch_sparse.utils import is_scalar
 class SparseTensor(object):
     storage: SparseStorage
 
-    def __init__(self, row: Optional[torch.Tensor] = None,
+    def __init__(self,
+                 row: Optional[torch.Tensor] = None,
                  rowptr: Optional[torch.Tensor] = None,
                  col: Optional[torch.Tensor] = None,
                  value: Optional[torch.Tensor] = None,
                  sparse_sizes: Optional[Tuple[int, int]] = None,
                  is_sorted: bool = False):
-        self.storage = SparseStorage(row=row, rowptr=rowptr, col=col,
-                                     value=value, sparse_sizes=sparse_sizes,
-                                     rowcount=None, colptr=None, colcount=None,
-                                     csr2csc=None, csc2csr=None,
-                                     is_sorted=is_sorted)
+        self.storage = SparseStorage(
+            row=row,
+            rowptr=rowptr,
+            col=col,
+            value=value,
+            sparse_sizes=sparse_sizes,
+            rowcount=None,
+            colptr=None,
+            colcount=None,
+            csr2csc=None,
+            csc2csr=None,
+            is_sorted=is_sorted)
 
     @classmethod
     def from_storage(self, storage: SparseStorage):
@@ -45,12 +53,17 @@ class SparseTensor(object):
         if has_value:
             value = mat[row, col]
 
-        return SparseTensor(row=row, rowptr=None, col=col, value=value,
-                            sparse_sizes=(mat.size(0), mat.size(1)),
-                            is_sorted=True)
+        return SparseTensor(
+            row=row,
+            rowptr=None,
+            col=col,
+            value=value,
+            sparse_sizes=(mat.size(0), mat.size(1)),
+            is_sorted=True)
 
     @classmethod
-    def from_torch_sparse_coo_tensor(self, mat: torch.Tensor,
+    def from_torch_sparse_coo_tensor(self,
+                                     mat: torch.Tensor,
                                      has_value: bool = True):
         mat = mat.coalesce()
         index = mat._indices()
@@ -60,13 +73,20 @@ class SparseTensor(object):
         if has_value:
             value = mat._values()
 
-        return SparseTensor(row=row, rowptr=None, col=col, value=value,
-                            sparse_sizes=(mat.size(0), mat.size(1)),
-                            is_sorted=True)
+        return SparseTensor(
+            row=row,
+            rowptr=None,
+            col=col,
+            value=value,
+            sparse_sizes=(mat.size(0), mat.size(1)),
+            is_sorted=True)
 
     @classmethod
-    def eye(self, M: int, N: Optional[int] = None,
-            options: Optional[torch.Tensor] = None, has_value: bool = True,
+    def eye(self,
+            M: int,
+            N: Optional[int] = None,
+            options: Optional[torch.Tensor] = None,
+            has_value: bool = True,
             fill_cache: bool = False):
 
         N = M if N is None else N
@@ -84,8 +104,8 @@ class SparseTensor(object):
         value: Optional[torch.Tensor] = None
         if has_value:
             if options is not None:
-                value = torch.ones(row.numel(), dtype=options.dtype,
-                                   device=row.device)
+                value = torch.ones(
+                    row.numel(), dtype=options.dtype, device=row.device)
             else:
                 value = torch.ones(row.numel(), device=row.device)
 
@@ -108,9 +128,17 @@ class SparseTensor(object):
             csr2csc = csc2csr = row
 
         storage: SparseStorage = SparseStorage(
-            row=row, rowptr=rowptr, col=col, value=value, sparse_sizes=(M, N),
-            rowcount=rowcount, colptr=colptr, colcount=colcount,
-            csr2csc=csr2csc, csc2csr=csc2csr, is_sorted=True)
+            row=row,
+            rowptr=rowptr,
+            col=col,
+            value=value,
+            sparse_sizes=(M, N),
+            rowcount=rowcount,
+            colptr=colptr,
+            colcount=colcount,
+            csr2csc=csr2csc,
+            csc2csr=csc2csr,
+            is_sorted=True)
 
         self = SparseTensor.__new__(SparseTensor)
         self.storage = storage
@@ -153,12 +181,14 @@ class SparseTensor(object):
     def has_value(self) -> bool:
         return self.storage.has_value()
 
-    def set_value_(self, value: Optional[torch.Tensor],
+    def set_value_(self,
+                   value: Optional[torch.Tensor],
                    layout: Optional[str] = None):
         self.storage.set_value_(value, layout)
         return self
 
-    def set_value(self, value: Optional[torch.Tensor],
+    def set_value(self,
+                  value: Optional[torch.Tensor],
                   layout: Optional[str] = None):
         return self.from_storage(self.storage.set_value(value, layout))
 
@@ -187,23 +217,31 @@ class SparseTensor(object):
 
     # Utility functions #######################################################
 
-    def fill_value_(self, fill_value: float,
+    def fill_value_(self,
+                    fill_value: float,
                     options: Optional[torch.Tensor] = None):
         if options is not None:
-            value = torch.full((self.nnz(), ), fill_value, dtype=options.dtype,
+            value = torch.full((self.nnz(), ),
+                               fill_value,
+                               dtype=options.dtype,
                                device=self.device())
         else:
-            value = torch.full((self.nnz(), ), fill_value,
+            value = torch.full((self.nnz(), ),
+                               fill_value,
                                device=self.device())
         return self.set_value_(value, layout='coo')
 
-    def fill_value(self, fill_value: float,
+    def fill_value(self,
+                   fill_value: float,
                    options: Optional[torch.Tensor] = None):
         if options is not None:
-            value = torch.full((self.nnz(), ), fill_value, dtype=options.dtype,
+            value = torch.full((self.nnz(), ),
+                               fill_value,
+                               dtype=options.dtype,
                                device=self.device())
         else:
-            value = torch.full((self.nnz(), ), fill_value,
+            value = torch.full((self.nnz(), ),
+                               fill_value,
                                device=self.device())
         return self.set_value(value, layout='coo')
 
@@ -270,8 +308,13 @@ class SparseTensor(object):
 
         N = max(self.size(0), self.size(1))
 
-        out = SparseTensor(row=row, rowptr=None, col=col, value=value,
-                           sparse_sizes=(N, N), is_sorted=False)
+        out = SparseTensor(
+            row=row,
+            rowptr=None,
+            col=col,
+            value=value,
+            sparse_sizes=(N, N),
+            is_sorted=False)
         out = out.coalesce(reduce)
         return out
 
@@ -294,7 +337,8 @@ class SparseTensor(object):
         else:
             return False
 
-    def requires_grad_(self, requires_grad: bool = True,
+    def requires_grad_(self,
+                       requires_grad: bool = True,
                        options: Optional[torch.Tensor] = None):
         if requires_grad and not self.has_value():
             self.fill_value_(1., options=options)
@@ -315,8 +359,8 @@ class SparseTensor(object):
         if value is not None:
             return value
         else:
-            return torch.tensor(0., dtype=torch.float,
-                                device=self.storage.col().device)
+            return torch.tensor(
+                0., dtype=torch.float, device=self.storage.col().device)
 
     def device(self):
         return self.storage.col().device
@@ -324,7 +368,8 @@ class SparseTensor(object):
     def cpu(self):
         return self.device_as(torch.tensor(0.), non_blocking=False)
 
-    def cuda(self, options: Optional[torch.Tensor] = None,
+    def cuda(self,
+             options: Optional[torch.Tensor] = None,
              non_blocking: bool = False):
         if options is not None:
             return self.device_as(options, non_blocking)
@@ -387,19 +432,19 @@ class SparseTensor(object):
         row, col, value = self.coo()
 
         if value is not None:
-            mat = torch.zeros(self.sizes(), dtype=value.dtype,
-                              device=self.device())
+            mat = torch.zeros(
+                self.sizes(), dtype=value.dtype, device=self.device())
         elif options is not None:
-            mat = torch.zeros(self.sizes(), dtype=options.dtype,
-                              device=self.device())
+            mat = torch.zeros(
+                self.sizes(), dtype=options.dtype, device=self.device())
         else:
             mat = torch.zeros(self.sizes(), device=self.device())
 
         if value is not None:
             mat[row, col] = value
         else:
-            mat[row, col] = torch.ones(self.nnz(), dtype=mat.dtype,
-                                       device=mat.device)
+            mat[row, col] = torch.ones(
+                self.nnz(), dtype=mat.dtype, device=mat.device)
 
         return mat
 
@@ -409,8 +454,8 @@ class SparseTensor(object):
         index = torch.stack([row, col], dim=0)
         if value is None:
             if options is not None:
-                value = torch.ones(self.nnz(), dtype=options.dtype,
-                                   device=self.device())
+                value = torch.ones(
+                    self.nnz(), dtype=options.dtype, device=self.device())
             else:
                 value = torch.ones(self.nnz(), device=self.device())
 
@@ -434,7 +479,7 @@ def is_shared(self: SparseTensor) -> bool:
 def to(self, *args: Optional[List[Any]],
        **kwargs: Optional[Dict[str, Any]]) -> SparseTensor:
 
-    device, dtype, non_blocking, _ = torch._C._nn._parse_to(*args, **kwargs)
+    device, dtype, non_blocking = torch._C._nn._parse_to(*args, **kwargs)
 
     if dtype is not None:
         self = self.type_as(torch.tensor(0., dtype=dtype))
@@ -515,8 +560,8 @@ SparseTensor.__repr__ = __repr__
 
 # Scipy Conversions ###########################################################
 
-ScipySparseMatrix = Union[scipy.sparse.coo_matrix, scipy.sparse.csr_matrix,
-                          scipy.sparse.csc_matrix]
+ScipySparseMatrix = Union[scipy.sparse.coo_matrix, scipy.sparse.
+                          csr_matrix, scipy.sparse.csc_matrix]
 
 
 @torch.jit.ignore
@@ -535,16 +580,25 @@ def from_scipy(mat: ScipySparseMatrix, has_value: bool = True) -> SparseTensor:
         value = torch.from_numpy(mat.data)
     sparse_sizes = mat.shape[:2]
 
-    storage = SparseStorage(row=row, rowptr=rowptr, col=col, value=value,
-                            sparse_sizes=sparse_sizes, rowcount=None,
-                            colptr=colptr, colcount=None, csr2csc=None,
-                            csc2csr=None, is_sorted=True)
+    storage = SparseStorage(
+        row=row,
+        rowptr=rowptr,
+        col=col,
+        value=value,
+        sparse_sizes=sparse_sizes,
+        rowcount=None,
+        colptr=colptr,
+        colcount=None,
+        csr2csc=None,
+        csc2csr=None,
+        is_sorted=True)
 
     return SparseTensor.from_storage(storage)
 
 
 @torch.jit.ignore
-def to_scipy(self: SparseTensor, layout: Optional[str] = None,
+def to_scipy(self: SparseTensor,
+             layout: Optional[str] = None,
              dtype: Optional[torch.dtype] = None) -> ScipySparseMatrix:
     assert self.dim() == 2
     layout = get_layout(layout)