def get_bga(adj, strategy, num_bgraph, **kwargs):
num_node = adj.size(-1)
dv = adj.device()
if strategy == "admm":
if num_node < 80:
bipartite_graphs_dense = admm_bga(
adj.to_dense().to(torch.double), M=num_bgraph, **kwargs
)
beta = torch.zeros(num_node, num_bgraph, dtype=torch.bool, device=dv)
bipartite_graphs = []
for i, B in enumerate(bipartite_graphs_dense):
_, vtx_color, _ = is_bipartite_fix(B, fix_flag=True)
beta[:, i] = torch.as_tensor(vtx_color)
bipartite_graphs.append(SparseTensor.from_dense(B))
return bipartite_graphs, beta
else:
bipartite_graphs, beta, partptr, perm = admm_lbga_ray(
adj, num_bgraph, **kwargs
)
elif strategy == "amfs":
bipartite_graphs, beta = amfs(adj, level=num_bgraph, **kwargs)
else:
raise RuntimeError(
f"{str(strategy)} is not a valid numerical decomposition algorithm "
f"supported at present."
)
bipartite_graphs = [SparseTensor.from_scipy(B).to(dv) for B in bipartite_graphs]
return bipartite_graphs, beta
def forward(self, x, adj=None, theta=None):
theta = SparseTensor.from_dense(theta)
x = self.gcn1(x, adj)
x = self.gcn2(x.float(), theta.float())
x = self.relu(x)
x = self.dropout(x)
return x
def to_cpx(mat, layout="csr", dtype=None):
assert layout in SparseLayouts
import cupy as cp
import cupyx.scipy as xcipy
if isinstance(mat, torch.Tensor):
assert mat.dim() == 2
assert mat.is_cuda
if mat.is_sparse:
smt = SparseTensor.from_torch_sparse_coo_tensor(mat)
else:
smt = SparseTensor.from_dense(mat)
elif isinstance(mat, SparseTensor):
assert mat.dim() == 2
assert mat.is_cuda()
smt = mat
elif isinstance(mat, xcipy.sparse.spmatrix):
assert mat.ndim == 2
cls = {
"csr": xcipy.sparse.csr_matrix,
"csc": xcipy.sparse.csc_matrix,
"coo": xcipy.sparse.coo_matrix,
}[layout]
smt = cls(mat)
return smt
else:
raise RuntimeError
shape = smt.sparse_sizes()
if layout == "coo":
row, col, value = smt.coo()
row = cp.asarray(row.detach())
col = cp.asarray(col.detach())
value = (cp.asarray(value.detach())
if smt.has_value() else cp.ones(smt.nnz(), dtype=dtype))
return xcipy.sparse.coo_matrix((value, (row, col)), shape)
elif layout == "csr":
rowptr, col, value = smt.csr()
rowptr = cp.asarray(rowptr.detach())
col = cp.asarray(col.detach())
value = (cp.asarray(value.detach())
if smt.has_value() else cp.ones(smt.nnz(), dtype=dtype))
return xcipy.sparse.csr_matrix((value, col, rowptr), shape)
elif layout == "csc":
colptr, row, value = smt.csc()
colptr = cp.asarray(colptr.detach())
row = cp.asarray(row.detach())
value = (cp.asarray(value.detach())
if smt.has_value() else cp.ones(smt.nnz(), dtype=dtype))
return xcipy.sparse.csc_matrix((value, row, colptr), shape)
else:
raise RuntimeError(
f"{layout} is not one of valid sparse formats `coo`, `csr` and `csc`."
)
def test_laplace(lap_type, device):
N = 6
A = torch.rand(N, N)
A = A + A.t()
A.fill_diagonal_(0)
spA = SparseTensor.from_dense(A)
L = laplace(spA, lap_type).to_dense()
print("\n:", L)
def __init__(
self,
G: Graph,
k: int = 8,
in_channels: int = 1,
order: int = 16,
strategy: str = "admm",
level: int = 1,
lam_max: float = 2.0,
zero_dc: bool = False,
**kwargs,
):
self.adj = G
N = self.adj.size(-1)
self.lam_max = lam_max
self.strategy = strategy
self.num_bgraph = level
device = self.adj.device()
dtype = self.adj.dtype()
if strategy == "admm":
if N < 80:
bipartite_graphs_dense = admm_bga(
self.adj.to_dense().to(torch.double), M=level, **kwargs
)
beta = bipartite_graphs_dense.new_zeros(N, level).to(torch.bool)
bipartite_graphs = []
bipartite_graphs_dense = bipartite_graphs_dense.to(dtype).to(device)
for i, B in enumerate(bipartite_graphs_dense):
_, vtx_color, _ = is_bipartite_fix(B, fix_flag=True)
beta[:, i] = torch.as_tensor(vtx_color)
bipartite_graphs.append(SparseTensor.from_dense(B))
else:
bipartite_graphs, beta, self.partptr, self.perm = admm_lbga_ray(
self.adj, level, **kwargs
)
bipartite_graphs = [
SparseTensor.from_scipy(B).to(dtype).to(device)
for B in bipartite_graphs
]
elif strategy == "amfs":
bipartite_graphs, beta = amfs(self.adj, level=self.num_bgraph, **kwargs)
bipartite_graphs = [
SparseTensor.from_scipy(B).to(dtype).to(device)
for B in bipartite_graphs
]
else:
raise RuntimeError(
f"{strategy} is not a valid numerical decomposition "
f"algorithm supported at present."
)
super().__init__(
bipartite_graphs, beta, k, in_channels, order, lam_max, zero_dc
)
def to_torch_sparse(mat):
if isinstance(mat, torch.Tensor):
if not mat.is_sparse:
stm = SparseTensor.from_dense(mat)
else:
stm = SparseTensor.from_torch_sparse_coo_tensor(mat)
elif isinstance(mat, np.ndarray):
stm = SparseTensor.from_dense(torch.as_tensor(mat))
elif isinstance(mat, spmatrix):
stm = SparseTensor.from_scipy(mat)
elif isinstance(mat, SparseTensor):
stm = mat
else:
raise TypeError(f"{type(mat)} not supported now")
return stm
def test_getitem(dtype, device):
mat = torch.randn(50, 40, dtype=dtype, device=device)
mat = SparseTensor.from_dense(mat)
idx1 = torch.randint(0, 50, (10, ), dtype=torch.long, device=device)
idx2 = torch.randint(0, 40, (10, ), dtype=torch.long, device=device)
assert mat[:10, :10].sizes() == [10, 10]
assert mat[..., :10].sizes() == [50, 10]
assert mat[idx1, idx2].sizes() == [10, 10]
assert mat[idx1.tolist()].sizes() == [10, 40]
def test_transform(self, device, dtype):
M, N = 2, 32
K = 30
Bs = []
beta = []
bpg, bt = rand_bipartite(N // 2,
N - N // 2,
p=0.2,
dtype=dtype,
device=device,
return_partition=True)
Bs.append(bpg)
beta.append(bt)
mask = Bs[0].to_dense() != 0
for i in range(M - 1):
bpg, bt = rand_bipartite(
N // 2,
N - N // 2,
p=0.2,
dtype=dtype,
device=device,
return_partition=True,
)
dense = bpg.to_dense()
temp_mask = dense != 0
dense[mask] = 0
mask = temp_mask + mask
Bs.append(SparseTensor.from_dense(dense))
if i % 2 == 0:
beta.append(bt)
else:
beta.append(~bt)
beta = torch.stack(beta).T
qmf = QmfCore(bipartite_graphs=Bs, beta=beta, order=K)
x = torch.rand(N, dtype=dtype, device=device)
y = qmf.analyze(x)
assert y.shape == (2**M, N, 1)
z = qmf.synthesize(y)
assert z.shape == (2**M, N, 1)
assert (z.sum(0).squeeze() - x).abs().sum() != 0
z.squeeze_()
f_hat = z.sum(0)
dis = (f_hat - x).abs()
pf = snr(f_hat, x)
ppprint(dis, pf)
def make_adjmatrix(self, data_dir, fname, zerolen_tripids=None):
"""
@fname :: trips f : "preprocessed_entire_porto.h5"
ex) f["trips/{}".format(num)] where the num is bet 1~all including zerolen_trips
make_adjmatrix(data_dir, "preprocessed_entire_porto.h5",)
"""
file_path = data_dir / self.dataset_name / fname
seq_vocabs = open(file_path, "r")
# nodes ~ vocabs : self.vocab_start ~ self.vocab_size
nodes = list(range(self.vocab_size))
adj_matrix = np.zeros((len(nodes), len(nodes)), ) # (nodes,nodes)
# print(adj_matrix.shape)
with h5py.File(file_path, 'r') as f:
total_len = len(f["trips"].keys())
for num in range(total_len):
if num % 3000 == 2999:
print("Scanned {} trips out of {}".format(
num + 1, total_len))
if num + 1 in set(zerolen_tripids): continue
trip = f["trips/" + str(num + 1)][()] # nd.array (2,traj_len)
if trip.shape[1] == 1:
continue # bc there cannot be any connention
try:
trip = np.array(
self.trip2seq(trip)) # seq of vocabs : (len_trip)
trip_pre, trip_curr = trip[:-1], trip[1:]
conn = np.array([[pre, curr]
for pre, curr in zip(trip_pre, trip_curr)
])
conn = np.unique(conn, axis=0) #(#conn, 2)
adj_matrix[conn[:, 0], conn[:, 1]] = 1
except:
conn = conn[np.all(conn != 'UNK',
axis=1)].astype(int) # (#conn, 2)
adj_matrix[conn[:, 0], conn[:, 1]] = 1
adj_matrix = adj_matrix[self.vocab_start:, self.vocab_start:]
adj_matrix[np.arange(adj_matrix.shape[0]),
np.arange(adj_matrix.shape[0])] = 0
self.adj_matrix = SparseTensor.from_dense(torch.from_numpy(adj_matrix))
return adj_matrix
def to_scipy(mat, layout="csr", dtype=None):
assert layout in SparseLayouts
if isinstance(mat, torch.Tensor):
if not mat.is_sparse:
smt = SparseTensor.from_dense(mat).to_scipy(layout, dtype)
else:
smt = SparseTensor.from_torch_sparse_coo_tensor(mat).to_scipy(
layout, dtype)
elif isinstance(mat, SparseTensor):
smt = mat.to_scipy(layout, dtype)
elif isinstance(mat, (spmatrix, np.ndarray)):
cls = {"csr": csr_matrix, "csc": csc_matrix, "coo": coo_matrix}[layout]
smt = cls(mat)
else:
raise TypeError(f"{type(mat)} is not supported now or invalid")
return smt
def test_getitem(dtype, device):
m = 50
n = 40
k = 10
mat = torch.randn(m, n, dtype=dtype, device=device)
mat = SparseTensor.from_dense(mat)
idx1 = torch.randint(0, m, (k,), dtype=torch.long, device=device)
idx2 = torch.randint(0, n, (k,), dtype=torch.long, device=device)
bool1 = torch.zeros(m, dtype=torch.bool, device=device)
bool2 = torch.zeros(n, dtype=torch.bool, device=device)
bool1.scatter_(0, idx1, 1)
bool2.scatter_(0, idx2, 1)
# idx1 and idx2 may have duplicates
k1_bool = bool1.nonzero().size(0)
k2_bool = bool2.nonzero().size(0)
idx1np = idx1.cpu().numpy()
idx2np = idx2.cpu().numpy()
bool1np = bool1.cpu().numpy()
bool2np = bool2.cpu().numpy()
idx1list = idx1np.tolist()
idx2list = idx2np.tolist()
bool1list = bool1np.tolist()
bool2list = bool2np.tolist()
assert mat[:k, :k].sizes() == [k, k]
assert mat[..., :k].sizes() == [m, k]
assert mat[idx1, idx2].sizes() == [k, k]
assert mat[idx1np, idx2np].sizes() == [k, k]
assert mat[idx1list, idx2list].sizes() == [k, k]
assert mat[bool1, bool2].sizes() == [k1_bool, k2_bool]
assert mat[bool1np, bool2np].sizes() == [k1_bool, k2_bool]
assert mat[bool1list, bool2list].sizes() == [k1_bool, k2_bool]
assert mat[idx1].sizes() == [k, n]
assert mat[idx1np].sizes() == [k, n]
assert mat[idx1list].sizes() == [k, n]
assert mat[bool1].sizes() == [k1_bool, n]
assert mat[bool1np].sizes() == [k1_bool, n]
assert mat[bool1list].sizes() == [k1_bool, n]
def test_batch():
torch_geometric.set_debug(True)
x1 = torch.tensor([1, 2, 3], dtype=torch.float)
x1_sp = SparseTensor.from_dense(x1.view(-1, 1))
e1 = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
adj1 = SparseTensor.from_edge_index(e1)
s1 = '1'
array1 = ['1', '2']
x2 = torch.tensor([1, 2], dtype=torch.float)
x2_sp = SparseTensor.from_dense(x2.view(-1, 1))
e2 = torch.tensor([[0, 1], [1, 0]])
adj2 = SparseTensor.from_edge_index(e2)
s2 = '2'
array2 = ['3', '4', '5']
x3 = torch.tensor([1, 2, 3, 4], dtype=torch.float)
x3_sp = SparseTensor.from_dense(x3.view(-1, 1))
e3 = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])
adj3 = SparseTensor.from_edge_index(e3)
s3 = '3'
array3 = ['6', '7', '8', '9']
data1 = Data(x=x1, x_sp=x1_sp, edge_index=e1, adj=adj1, s=s1, array=array1,
num_nodes=3)
data2 = Data(x=x2, x_sp=x2_sp, edge_index=e2, adj=adj2, s=s2, array=array2,
num_nodes=2)
data3 = Data(x=x3, x_sp=x3_sp, edge_index=e3, adj=adj3, s=s3, array=array3,
num_nodes=4)
batch = Batch.from_data_list([data1])
assert str(batch) == ('Batch(x=[3], edge_index=[2, 4], '
'x_sp=[3, 1, nnz=3], adj=[3, 3, nnz=4], s=[1], '
'array=[1], num_nodes=3, batch=[3], ptr=[2])')
assert batch.num_graphs == 1
assert len(batch) == 9
assert batch.x.tolist() == [1, 2, 3]
assert batch.x_sp.to_dense().view(-1).tolist() == batch.x.tolist()
assert batch.edge_index.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
edge_index = torch.stack(batch.adj.coo()[:2], dim=0)
assert edge_index.tolist() == batch.edge_index.tolist()
assert batch.s == ['1']
assert batch.array == [['1', '2']]
assert batch.num_nodes == 3
assert batch.batch.tolist() == [0, 0, 0]
assert batch.ptr.tolist() == [0, 3]
batch = Batch.from_data_list([data1, data2, data3], follow_batch=['s'])
assert str(batch) == ('Batch(x=[9], edge_index=[2, 12], '
'x_sp=[9, 1, nnz=9], adj=[9, 9, nnz=12], s=[3], '
's_batch=[3], array=[3], num_nodes=9, batch=[9], '
'ptr=[4])')
assert batch.num_graphs == 3
assert len(batch) == 10
assert batch.x.tolist() == [1, 2, 3, 1, 2, 1, 2, 3, 4]
assert batch.x_sp.to_dense().view(-1).tolist() == batch.x.tolist()
assert batch.edge_index.tolist() == [[0, 1, 1, 2, 3, 4, 5, 6, 6, 7, 7, 8],
[1, 0, 2, 1, 4, 3, 6, 5, 7, 6, 8, 7]]
edge_index = torch.stack(batch.adj.coo()[:2], dim=0)
assert edge_index.tolist() == batch.edge_index.tolist()
assert batch.s == ['1', '2', '3']
assert batch.s_batch.tolist() == [0, 1, 2]
assert batch.array == [['1', '2'], ['3', '4', '5'], ['6', '7', '8', '9']]
assert batch.num_nodes == 9
assert batch.batch.tolist() == [0, 0, 0, 1, 1, 2, 2, 2, 2]
assert batch.ptr.tolist() == [0, 3, 5, 9]
data = batch[0]
assert str(data) == ("Data(x=[3], edge_index=[2, 4], x_sp=[3, 1, nnz=3], "
"adj=[3, 3, nnz=4], s='1', array=[2], num_nodes=3)")
data = batch[1]
assert str(data) == ("Data(x=[2], edge_index=[2, 2], x_sp=[2, 1, nnz=2], "
"adj=[2, 2, nnz=2], s='2', array=[3], num_nodes=2)")
data = batch[2]
assert str(data) == ("Data(x=[4], edge_index=[2, 6], x_sp=[4, 1, nnz=4], "
"adj=[4, 4, nnz=6], s='3', array=[4], num_nodes=4)")
assert len(batch.index_select([1, 0])) == 2
assert len(batch.index_select(torch.tensor([1, 0]))) == 2
assert len(batch.index_select(torch.tensor([True, False]))) == 1
assert len(batch.index_select(np.array([1, 0], dtype=np.int64))) == 2
assert len(batch.index_select(np.array([True, False]))) == 1
assert len(batch[:2]) == 2
data_list = batch.to_data_list()
assert len(data_list) == 3
assert len(data_list[0]) == 7
assert data_list[0].x.tolist() == [1, 2, 3]
assert data_list[0].x_sp.to_dense().view(-1).tolist() == [1, 2, 3]
assert data_list[0].edge_index.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
edge_index = torch.stack(data_list[0].adj.coo()[:2], dim=0)
assert edge_index.tolist() == data_list[0].edge_index.tolist()
assert data_list[0].s == '1'
assert data_list[0].array == ['1', '2']
assert data_list[0].num_nodes == 3
assert len(data_list[1]) == 7
assert data_list[1].x.tolist() == [1, 2]
assert data_list[1].x_sp.to_dense().view(-1).tolist() == [1, 2]
assert data_list[1].edge_index.tolist() == [[0, 1], [1, 0]]
edge_index = torch.stack(data_list[1].adj.coo()[:2], dim=0)
assert edge_index.tolist() == data_list[1].edge_index.tolist()
assert data_list[1].s == '2'
assert data_list[1].array == ['3', '4', '5']
assert data_list[1].num_nodes == 2
assert len(data_list[2]) == 7
assert data_list[2].x.tolist() == [1, 2, 3, 4]
assert data_list[2].x_sp.to_dense().view(-1).tolist() == [1, 2, 3, 4]
assert data_list[2].edge_index.tolist() == [[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2]]
edge_index = torch.stack(data_list[2].adj.coo()[:2], dim=0)
assert edge_index.tolist() == data_list[2].edge_index.tolist()
assert data_list[2].s == '3'
assert data_list[2].array == ['6', '7', '8', '9']
assert data_list[2].num_nodes == 4
torch_geometric.set_debug(True)
def read_planetoid_data(folder, prefix):
names = ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index']
items = [read_file(folder, prefix, name) for name in names]
x, tx, allx, y, ty, ally, graph, test_index = items
train_index = torch.arange(y.size(0), dtype=torch.long)
val_index = torch.arange(y.size(0), y.size(0) + 500, dtype=torch.long)
sorted_test_index = test_index.sort()[0]
if prefix.lower() == 'citeseer':
# There are some isolated nodes in the Citeseer graph, resulting in
# none consecutive test indices. We need to identify them and add them
# as zero vectors to `tx` and `ty`.
len_test_indices = (test_index.max() - test_index.min()).item() + 1
tx_ext = torch.zeros(len_test_indices, tx.size(1))
tx_ext[sorted_test_index - test_index.min(), :] = tx
ty_ext = torch.zeros(len_test_indices, ty.size(1))
ty_ext[sorted_test_index - test_index.min(), :] = ty
tx, ty = tx_ext, ty_ext
if prefix.lower() == 'nell.0.001':
tx_ext = torch.zeros(len(graph) - allx.size(0), x.size(1))
tx_ext[sorted_test_index - allx.size(0)] = tx
ty_ext = torch.zeros(len(graph) - ally.size(0), y.size(1))
ty_ext[sorted_test_index - ally.size(0)] = ty
tx, ty = tx_ext, ty_ext
x = torch.cat([allx, tx], dim=0)
x[test_index] = x[sorted_test_index]
# Creating feature vectors for relations.
row, col, value = SparseTensor.from_dense(x).coo()
rows, cols, values = [row], [col], [value]
mask1 = index_to_mask(test_index, size=len(graph))
mask2 = index_to_mask(torch.arange(allx.size(0), len(graph)),
size=len(graph))
mask = ~mask1 | ~mask2
isolated_index = mask.nonzero(as_tuple=False).view(-1)[allx.size(0):]
rows += [isolated_index]
cols += [torch.arange(isolated_index.size(0)) + x.size(1)]
values += [torch.ones(isolated_index.size(0))]
x = SparseTensor(row=torch.cat(rows),
col=torch.cat(cols),
value=torch.cat(values))
else:
x = torch.cat([allx, tx], dim=0)
x[test_index] = x[sorted_test_index]
y = torch.cat([ally, ty], dim=0).max(dim=1)[1]
y[test_index] = y[sorted_test_index]
train_mask = index_to_mask(train_index, size=y.size(0))
val_mask = index_to_mask(val_index, size=y.size(0))
test_mask = index_to_mask(test_index, size=y.size(0))
edge_index = edge_index_from_dict(graph, num_nodes=y.size(0))
data = Data(x=x, edge_index=edge_index, y=y)
data.train_mask = train_mask
data.val_mask = val_mask
data.test_mask = test_mask
return data