def test_pinsage_sampling():
def _test_sampler(g, sampler, ntype):
neighbor_g = sampler(F.tensor([0, 2], dtype=F.int64))
assert neighbor_g.ntypes == [ntype]
u, v = neighbor_g.all_edges(form='uv', order='eid')
uv = list(zip(F.asnumpy(u).tolist(), F.asnumpy(v).tolist()))
assert (1, 0) in uv or (0, 0) in uv
assert (2, 2) in uv or (3, 2) in uv
g = dgl.heterograph({
('item', 'bought-by', 'user'): [(0, 0), (0, 1), (1, 0), (1, 1), (2, 2),
(2, 3), (3, 2), (3, 3)],
('user', 'bought', 'item'): [(0, 0), (1, 0), (0, 1), (1, 1), (2, 2),
(3, 2), (2, 3), (3, 3)]
})
sampler = dgl.sampling.PinSAGESampler(g, 'item', 'user', 4, 0.5, 3, 2)
_test_sampler(g, sampler, 'item')
sampler = dgl.sampling.RandomWalkNeighborSampler(g, 4, 0.5, 3, 2,
['bought-by', 'bought'])
_test_sampler(g, sampler, 'item')
sampler = dgl.sampling.RandomWalkNeighborSampler(
g, 4, 0.5, 3, 2, [('item', 'bought-by', 'user'),
('user', 'bought', 'item')])
_test_sampler(g, sampler, 'item')
g = dgl.graph([(0, 0), (0, 1), (1, 0), (1, 1), (2, 2), (2, 3), (3, 2),
(3, 3)])
sampler = dgl.sampling.RandomWalkNeighborSampler(g, 4, 0.5, 3, 2)
_test_sampler(g, sampler, g.ntypes[0])
g = dgl.heterograph({
('A', 'AB', 'B'): [(0, 1), (2, 3)],
('B', 'BC', 'C'): [(1, 2), (3, 1)],
('C', 'CA', 'A'): [(2, 0), (1, 2)]
})
sampler = dgl.sampling.RandomWalkNeighborSampler(g, 4, 0.5, 3, 2,
['AB', 'BC', 'CA'])
_test_sampler(g, sampler, 'A')
def test_sage_conv_bi_empty(idtype, aggre_type, out_dim):
# Test the case for graphs without edges
g = dgl.heterograph({('_U', '_E', '_V'): ([], [])}, {'_U': 5, '_V': 3}).to(F.ctx())
g = g.astype(idtype).to(F.ctx())
sage = nn.SAGEConv((3, 3), out_dim, 'gcn')
feat = (F.randn((5, 3)), F.randn((3, 3)))
h = sage(g, feat)
assert h.shape[-1] == out_dim
assert h.shape[0] == 3
for aggre_type in ['mean', 'pool', 'lstm']:
sage = nn.SAGEConv((3, 1), out_dim, aggre_type)
feat = (F.randn((5, 3)), F.randn((3, 1)))
h = sage(g, feat)
assert h.shape[-1] == out_dim
assert h.shape[0] == 3
def _generate_dec_graph(self, rating_pairs):
ones = np.ones_like(rating_pairs[0])
user_movie_ratings_coo = sp.coo_matrix(
(ones, rating_pairs),
shape=(self.num_user, self.num_movie),
dtype=np.float32)
g = dgl.bipartite_from_scipy(user_movie_ratings_coo,
utype='_U',
etype='_E',
vtype='_V')
return dgl.heterograph({('user', 'rate', 'movie'): g.edges()},
num_nodes_dict={
'user': self.num_user,
'movie': self.num_movie
})
def __init__(self, datadir, batch_size=128):
self.train_items, self.train_edge_dict = self.get_edge_list(
os.path.join(datadir, 'train.txt'))
self.test_items, self.test_edge_dict = self.get_edge_list(
os.path.join(datadir, 'test.txt'))
self.n_train = len(self.train_edge_dict[('user', 'ui', 'item')])
self.n_test = len(self.test_edge_dict[('user', 'ui', 'item')])
self.G = dgl.heterograph(self.train_edge_dict)
self.n_items = self.G.number_of_nodes('item')
self.n_users = self.G.number_of_nodes('user')
self.users = self.G.nodes('user').detach().cpu().numpy().tolist()
self.batch_size = batch_size
def get_mol_complete_graph(self, idx, e_start, e_end, pairwise_start,
n_atoms):
if self.prefetch_graphs:
g = self.mol_complete_graphs[idx]
else:
edge_indices = self.edge_indices[:, e_start:e_end]
pairwise_indices = self.dist_dict[
'pairwise_indices'][:, pairwise_start:pairwise_start +
n_atoms * (n_atoms - 1)]
g = dgl.heterograph({
('atom', 'bond', 'atom'): (edge_indices[0], edge_indices[1]),
('atom', 'complete', 'atom'):
(pairwise_indices[0], pairwise_indices[1])
})
return g
def create_random_hetero():
num_nodes = {'n1': 10000, 'n2': 10010, 'n3': 10020}
etypes = [('n1', 'r1', 'n2'),
('n1', 'r2', 'n3'),
('n2', 'r3', 'n3')]
edges = {}
for etype in etypes:
src_ntype, _, dst_ntype = etype
arr = spsp.random(num_nodes[src_ntype], num_nodes[dst_ntype], density=0.001, format='coo',
random_state=100)
edges[etype] = (arr.row, arr.col)
g = dgl.heterograph(edges, num_nodes)
g.nodes['n1'].data['feat'] = F.unsqueeze(F.arange(0, g.number_of_nodes('n1')), 1)
g.edges['r1'].data['feat'] = F.unsqueeze(F.arange(0, g.number_of_edges('r1')), 1)
return g
def _create_heterogeneous():
edges = {}
for utype, etype, vtype in [('A', 'AA', 'A'), ('A', 'AB', 'B')]:
s = torch.randint(0, 200, (1000,), device=F.ctx())
d = torch.randint(0, 200, (1000,), device=F.ctx())
edges[utype, etype, vtype] = (s, d)
edges[vtype, 'rev-' + etype, utype] = (d, s)
g = dgl.heterograph(edges, num_nodes_dict={'A': 200, 'B': 200})
reverse_etypes = {'AA': 'rev-AA', 'AB': 'rev-AB', 'rev-AA': 'AA', 'rev-AB': 'AB'}
always_exclude = {
'AA': torch.randint(0, 1000, (50,), device=F.ctx()),
'AB': torch.randint(0, 1000, (50,), device=F.ctx())}
seed_edges = {
'AA': torch.arange(0, 1000, device=F.ctx()),
'AB': torch.arange(0, 1000, device=F.ctx())}
return g, reverse_etypes, always_exclude, seed_edges
def create_test_heterograph2():
plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
wishes_nx = nx.DiGraph()
wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
wishes_nx.add_edge('u0', 'g1', id=0)
wishes_nx.add_edge('u2', 'g0', id=1)
develops_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
g = dgl.heterograph({
('user', 'follows', 'user'): [(0, 1), (1, 2)],
('user', 'plays', 'game'): plays_spmat,
('user', 'wishes', 'game'): wishes_nx,
('developer', 'develops', 'game'): develops_g,
})
return g
def heterograph0():
g = dgl.heterograph({
('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1]),
('developer', 'develops', 'game'): ([0, 1], [0, 1])
})
g.nodes['user'].data['h'] = F.copy_to(
F.randn((g.number_of_nodes('user'), 3)), F.cpu())
g.nodes['game'].data['h'] = F.copy_to(
F.randn((g.number_of_nodes('game'), 2)), F.cpu())
g.nodes['developer'].data['h'] = F.copy_to(
F.randn((g.number_of_nodes('developer'), 3)), F.cpu())
g.edges['plays'].data['h'] = F.copy_to(
F.randn((g.number_of_edges('plays'), 1)), F.cpu())
g.edges['develops'].data['h'] = F.copy_to(
F.randn((g.number_of_edges('develops'), 5)), F.cpu())
return g
def create_test_heterograph_large(idtype):
src = np.random.randint(0, 50, 2500)
dst = np.random.randint(0, 50, 2500)
g = dgl.heterograph(
{
('user', 'follows', 'user'): (src, dst),
('user', 'plays', 'game'): (src, dst),
('user', 'wishes', 'game'): (src, dst),
('developer', 'develops', 'game'): (src, dst),
},
idtype=idtype,
device=F.ctx())
assert g.idtype == idtype
assert g.device == F.ctx()
return g
def construct_negative_graph(self,):
neg_srcdst = self.negative_sampler(self.hg, self.train_eid_dict)
if not isinstance(neg_srcdst, Mapping):
assert len(self.hg.etypes) == 1, \
'graph has multiple or no edge types; '\
'please return a dict in negative sampler.'
neg_srcdst = {self.hg.canonical_etypes[0]: neg_srcdst}
# Get dtype from a tuple of tensors
#dtype = F.dtype(list(neg_srcdst.values())[0][0])
neg_edges = {
etype: neg_srcdst.get(etype, (th.tensor([]), th.tensor([])))
for etype in self.hg.canonical_etypes}
neg_pair_graph = dgl.heterograph(
neg_edges, {ntype: self.hg.number_of_nodes(ntype) for ntype in self.hg.ntypes})
return neg_pair_graph
def create_random_hetero():
num_nodes = {'n1': 1010, 'n2': 1000, 'n3': 1020}
etypes = [('n1', 'r1', 'n2'), ('n1', 'r2', 'n3'), ('n2', 'r3', 'n3')]
edges = {}
for etype in etypes:
src_ntype, _, dst_ntype = etype
arr = spsp.random(num_nodes[src_ntype],
num_nodes[dst_ntype],
density=0.001,
format='coo',
random_state=100)
edges[etype] = (arr.row, arr.col)
g = dgl.heterograph(edges, num_nodes)
g.nodes['n1'].data['feat'] = F.ones((g.number_of_nodes('n1'), 10),
F.float32, F.cpu())
return g
def retHeterographProdCat(df):
dictIDsMASTER = {}
#Umiesczanie wszytskich ID w słowniku
for x in range(0, 6):
IDs = retIDsOneColumnATaTime(df[x], 0)
dictIDsMASTER.update(IDs)
dictIDsMASTER.update(retIDsOneColumnATaTime(df[6], 2)) # Also IDs
#Problme polega na tym, że gdy parsuje podwójnia dane z csv to jedna kolumna sie wysrywa w momencie gdy w nbastepnej nie ma wartosci
#Zrobic oddzielnie parsowanie samych kol i podkol i potem osttamnia kolumna -> item
mappedRels = []
for x in range(0, 7):
mappedRels.append(retMappedRelationCategories(df[x], dictIDsMASTER))
#Funkcja mapujące relacje prod_catx -> productID
dict2 = {}
prodCatsDone = []
for x, y in df[6].iterrows():
catName = y[0][:12]
if catName in prodCatsDone: #Juz prodcat ktorystam caly zrobiony wiec mozna pominac i szukac nastepnego
continue
relation = (catName, 'has_product', 'prod_id')
ListOfSameCats = []
for x2, y2 in df[6].iterrows():
if y2[0][:12] == catName:
IDofCat = dictIDsMASTER[y2[0]]
IDofProd = dictIDsMASTER[y2[2]]
ListOfSameCats.append((IDofCat, IDofProd))
d = {relation: ListOfSameCats}
dict2.update(d)
prodCatsDone.append(catName)
dataDict = {
('<prod_cat_1>', 'has_category12', '<prod_cat_2>'): mappedRels[0],
('<prod_cat_2>', 'has_category23', '<prod_cat_3>'): mappedRels[1],
('<prod_cat_3>', 'has_category34', '<prod_cat_4>'): mappedRels[2],
('<prod_cat_4>', 'has_category45', '<prod_cat_5>'): mappedRels[3],
('<prod_cat_5>', 'has_category56', '<prod_cat_6>'): mappedRels[4],
('<prod_cat_6>', 'has_category67', '<prod_cat_7>'): mappedRels[5]
}
dataDict.update(dict2)
g = dgl.heterograph(dataDict)
return g
def read_csv_heterograph_dgl(raw_dir, add_inverse_edge = False, additional_node_files = [], additional_edge_files = []):
graph_list = read_csv_heterograph_raw(raw_dir, add_inverse_edge, additional_node_files = additional_node_files, additional_edge_files = additional_edge_files)
dgl_graph_list = []
print('Converting graphs into DGL objects...')
for graph in tqdm(graph_list):
g_dict = {}
# add edge connectivity
for triplet, edge_index in graph["edge_index_dict"].items():
edge_tuple = [(i, j) for i, j in zip(graph["edge_index_dict"][triplet][0], graph["edge_index_dict"][triplet][1])]
g_dict[triplet] = edge_tuple
dgl_hetero_graph = dgl.heterograph(g_dict)
if graph["edge_feat_dict"] is not None:
for triplet in graph["edge_feat_dict"].keys():
dgl_hetero_graph.edges[triplet].data["feat"] = torch.from_numpy(graph["edge_feat_dict"][triplet])
if graph["node_feat_dict"] is not None:
for nodetype in graph["node_feat_dict"].keys():
dgl_hetero_graph.nodes[nodetype].data["feat"] = torch.from_numpy(graph["node_feat_dict"][nodetype])
for key in additional_node_files:
if 'node_' not in key:
feat_name = 'node_' + key
else:
feat_name = key
for nodetype in graph[feat_name].keys():
dgl_hetero_graph.nodes[nodetype].data[feat_name] = torch.from_numpy(graph[feat_name][nodetype])
for key in additional_edge_files:
if 'edge_' not in key:
feat_name = 'edge_' + key
else:
feat_name = key
for triplet in graph[feat_name].keys():
dgl_hetero_graph.edges[triplet].data[feat_name] = torch.from_numpy(graph[feat_name][triplet])
dgl_graph_list.append(dgl_hetero_graph)
return dgl_graph_list
def test_node_dataloader(sampler_name, pin_graph):
g1 = dgl.graph(([0, 0, 0, 1, 1], [1, 2, 3, 3, 4]))
if F.ctx() != F.cpu() and pin_graph:
g1.create_formats_()
g1.pin_memory_()
g1.ndata['feat'] = F.copy_to(F.randn((5, 8)), F.cpu())
g1.ndata['label'] = F.copy_to(F.randn((g1.num_nodes(),)), F.cpu())
for num_workers in [0, 1, 2]:
sampler = {
'full': dgl.dataloading.MultiLayerFullNeighborSampler(2),
'neighbor': dgl.dataloading.MultiLayerNeighborSampler([3, 3]),
'neighbor2': dgl.dataloading.MultiLayerNeighborSampler([3, 3])}[sampler_name]
dataloader = dgl.dataloading.NodeDataLoader(
g1, g1.nodes(), sampler, device=F.ctx(),
batch_size=g1.num_nodes(),
num_workers=num_workers)
for input_nodes, output_nodes, blocks in dataloader:
_check_device(input_nodes)
_check_device(output_nodes)
_check_device(blocks)
g2 = dgl.heterograph({
('user', 'follow', 'user'): ([0, 0, 0, 1, 1, 1, 2], [1, 2, 3, 0, 2, 3, 0]),
('user', 'followed-by', 'user'): ([1, 2, 3, 0, 2, 3, 0], [0, 0, 0, 1, 1, 1, 2]),
('user', 'play', 'game'): ([0, 1, 1, 3, 5], [0, 1, 2, 0, 2]),
('game', 'played-by', 'user'): ([0, 1, 2, 0, 2], [0, 1, 1, 3, 5])
})
for ntype in g2.ntypes:
g2.nodes[ntype].data['feat'] = F.copy_to(F.randn((g2.num_nodes(ntype), 8)), F.cpu())
batch_size = max(g2.num_nodes(nty) for nty in g2.ntypes)
sampler = {
'full': dgl.dataloading.MultiLayerFullNeighborSampler(2),
'neighbor': dgl.dataloading.MultiLayerNeighborSampler([{etype: 3 for etype in g2.etypes}] * 2),
'neighbor2': dgl.dataloading.MultiLayerNeighborSampler([3, 3])}[sampler_name]
dataloader = dgl.dataloading.NodeDataLoader(
g2, {nty: g2.nodes(nty) for nty in g2.ntypes},
sampler, device=F.ctx(), batch_size=batch_size)
assert isinstance(iter(dataloader), Iterator)
for input_nodes, output_nodes, blocks in dataloader:
_check_device(input_nodes)
_check_device(output_nodes)
_check_device(blocks)
if g1.is_pinned():
g1.unpin_memory_()
def load_ogb(dataset):
if dataset == 'ogbn-mag':
dataset = DglNodePropPredDataset(name=dataset)
split_idx = dataset.get_idx_split()
train_idx = split_idx["train"]['paper']
val_idx = split_idx["valid"]['paper']
test_idx = split_idx["test"]['paper']
hg_orig, labels = dataset[0]
subgs = {}
for etype in hg_orig.canonical_etypes:
u, v = hg_orig.all_edges(etype=etype)
subgs[etype] = (u, v)
subgs[(etype[2], 'rev-' + etype[1], etype[0])] = (v, u)
hg = dgl.heterograph(subgs)
hg.nodes['paper'].data['feat'] = hg_orig.nodes['paper'].data['feat']
paper_labels = labels['paper'].squeeze()
num_rels = len(hg.canonical_etypes)
num_of_ntype = len(hg.ntypes)
num_classes = dataset.num_classes
category = 'paper'
print('Number of relations: {}'.format(num_rels))
print('Number of class: {}'.format(num_classes))
print('Number of train: {}'.format(len(train_idx)))
print('Number of valid: {}'.format(len(val_idx)))
print('Number of test: {}'.format(len(test_idx)))
# get target category id
category_id = len(hg.ntypes)
for i, ntype in enumerate(hg.ntypes):
if ntype == category:
category_id = i
train_mask = th.zeros((hg.number_of_nodes('paper'), ), dtype=th.bool)
train_mask[train_idx] = True
val_mask = th.zeros((hg.number_of_nodes('paper'), ), dtype=th.bool)
val_mask[val_idx] = True
test_mask = th.zeros((hg.number_of_nodes('paper'), ), dtype=th.bool)
test_mask[test_idx] = True
hg.nodes['paper'].data['train_mask'] = train_mask
hg.nodes['paper'].data['val_mask'] = val_mask
hg.nodes['paper'].data['test_mask'] = test_mask
hg.nodes['paper'].data['labels'] = paper_labels
return hg
else:
raise ("Do not support other ogbn datasets.")
def test_remove_edges():
def check(g1, etype, g, edges_removed):
src, dst, eid = g.edges(etype=etype, form='all')
src1, dst1 = g1.edges(etype=etype, order='eid')
if etype is not None:
eid1 = g1.edges[etype].data[dgl.EID]
else:
eid1 = g1.edata[dgl.EID]
src1 = F.asnumpy(src1)
dst1 = F.asnumpy(dst1)
eid1 = F.asnumpy(eid1)
src = F.asnumpy(src)
dst = F.asnumpy(dst)
eid = F.asnumpy(eid)
sde_set = set(zip(src, dst, eid))
for s, d, e in zip(src1, dst1, eid1):
assert (s, d, e) in sde_set
assert not np.isin(edges_removed, eid1).any()
for fmt in ['coo', 'csr', 'csc']:
for edges_to_remove in [[2], [2, 2], [3, 2], [1, 3, 1, 2]]:
g = dgl.graph([(0, 1), (2, 3), (1, 2), (3, 4)],
restrict_format=fmt)
g1 = dgl.remove_edges(g, F.tensor(edges_to_remove))
check(g1, None, g, edges_to_remove)
g = dgl.graph(spsp.csr_matrix(
([1, 1, 1, 1], ([0, 2, 1, 3], [1, 3, 2, 4])), shape=(5, 5)),
restrict_format=fmt)
g1 = dgl.remove_edges(g, F.tensor(edges_to_remove))
check(g1, None, g, edges_to_remove)
g = dgl.heterograph({
('A', 'AA', 'A'): [(0, 1), (2, 3), (1, 2), (3, 4)],
('A', 'AB', 'B'): [(0, 1), (1, 3), (3, 5), (1, 6)],
('B', 'BA', 'A'): [(2, 3), (3, 2)]
})
g2 = dgl.remove_edges(g, {
'AA': F.tensor([2]),
'AB': F.tensor([3]),
'BA': F.tensor([1])
})
check(g2, 'AA', g, [2])
check(g2, 'AB', g, [3])
check(g2, 'BA', g, [1])
def process(self):
authors, papers, confs, paper_author, paper_conf = self._read_raw_data()
pa_p, pa_a = paper_author['pid'].to_list(), paper_author['aid'].to_list()
pc_p, pc_c = paper_conf['pid'].to_list(), paper_conf['cid'].to_list()
self.g = dgl.heterograph({
('paper', 'pa', 'author'): (pa_p, pa_a),
('author', 'ap', 'paper'): (pa_a, pa_p),
('paper', 'pc', 'conf'): (pc_p, pc_c),
('conf', 'cp', 'paper'): (pc_c, pc_p)
})
self.g.nodes['author'].data['label'] = torch.from_numpy(authors['label'].to_numpy())
self.g.nodes['conf'].data['label'] = torch.from_numpy(confs['label'].to_numpy())
self.author_names = authors['name'].tolist()
self.paper_titles = papers['title'].tolist()
self.conf_names = confs['name'].tolist()
def add_reverse_edges(g):
"""给异构图的每种边添加反向边，返回新的异构图
:param g: DGLGraph 异构图
:return: DGLGraph 添加反向边之后的异构图
"""
data = {}
for stype, etype, dtype in g.canonical_etypes:
u, v = g.edges(etype=(stype, etype, dtype))
data[(stype, etype, dtype)] = u, v
data[(dtype, etype + '_rev', stype)] = v, u
new_g = dgl.heterograph(data,
{ntype: g.num_nodes(ntype)
for ntype in g.ntypes})
node_frames = extract_node_subframes(g, None)
set_new_frames(new_g, node_frames=node_frames)
return new_g
def _generate_dec_graph(self, rating_pairs):
ones = np.ones_like(
rating_pairs[0]
) # add one indicating edge for every (user, item) pair
user_item_ratings_coo = sp.coo_matrix( # create coo matrix for graph conversion
(ones, rating_pairs),
shape=(self.num_user, self.num_item),
dtype=np.float32)
g = dgl.bipartite_from_scipy(user_item_ratings_coo,
utype='_U',
etype='_E',
vtype='_V')
return dgl.heterograph({('user', 'rate', 'item'): g.edges()},
num_nodes_dict={
'user': self.num_user,
'item': self.num_item
})
def get_hetero_graph(glass_tc_pos_path: str,
control_tc_pos_path: str,
t2t_threshold: float = 850.0,
u2t_threshold: float = np.inf,
weight: float = (1.0, 1.0, 1.0)):
dist_func = lambda u, v: np.sqrt(((u - v)**2 * weight).sum())
POS_COLS = ['Position_x', 'Position_y', 'Position_z']
df_glass_tc = pd.read_csv(glass_tc_pos_path)
g_tc_pos = df_glass_tc[POS_COLS].to_numpy()
df_control_tc = pd.read_csv(control_tc_pos_path)
c_tc_pos = df_control_tc[POS_COLS].to_numpy()
tc_pos = np.concatenate([g_tc_pos, c_tc_pos], axis=0)
graph_data = dict()
# construct 'tc' to 'tc' edges
t2t_dist_mat = cdist(tc_pos, tc_pos, dist_func)
u, v = torch.nonzero(torch.tensor(t2t_dist_mat <= t2t_threshold).bool(),
as_tuple=True)
graph_data[t2t] = (u, v)
# construct 'control' to 'tc' edges
c2t_dist_mat = cdist(c_tc_pos, tc_pos, dist_func)
u, v = torch.nonzero(torch.tensor(c2t_dist_mat <= u2t_threshold).bool(),
as_tuple=True)
graph_data[u2t] = (u, v)
g = dgl.heterograph(graph_data)
# standardize positions
scaler = MinMaxScaler()
pos = np.concatenate([tc_pos, c_tc_pos], axis=0)
pos_std = scaler.fit_transform(pos)
g.nodes['tc'].data['position'] = torch.from_numpy(
pos_std[:tc_pos.shape[0], :]).float()
g.nodes['control'].data['position'] = torch.from_numpy(
pos_std[tc_pos.shape[0]:, :]).float()
# add binary indicator for noticing the node is glass tc or not.
is_glass_tc = torch.ones(tc_pos.shape[0], 1)
is_glass_tc[:g_tc_pos.shape[0], :] = 0
g.nodes['tc'].data['is-glass-tc'] = is_glass_tc
return g
def build_graph(relations_list, relations_data_list):
relations_data_dic = {}
i = 0
for each in relations_list:
relations_data_dic[each] = relations_data_list[i]
i += 1
graph = dgl.heterograph(relations_data_dic)
print('Node types:', graph.ntypes)
print('Edge types:', graph.etypes)
print('Canonical edge types:', graph.canonical_etypes)
for each in graph.canonical_etypes:
print('graph number edges--' + str(each) + ':',
graph.number_of_edges(each))
for each in graph.ntypes:
print('graph number nodes--' + str(each) + ':',
graph.number_of_nodes(each))
return graph
def create_test_heterograph_2(idtype):
src = np.random.randint(0, 50, 25)
dst = np.random.randint(0, 50, 25)
src1 = np.random.randint(0, 25, 10)
dst1 = np.random.randint(0, 25, 10)
src2 = np.random.randint(0, 100, 1000)
dst2 = np.random.randint(0, 100, 1000)
g = dgl.heterograph({
('user', 'becomes', 'player'): (src, dst),
('user', 'follows', 'user'): (src, dst),
('user', 'plays', 'game'): (src, dst),
('user', 'wishes', 'game'): (src1, dst1),
('developer', 'develops', 'game'): (src2, dst2),
}, idtype=idtype, device=F.ctx())
assert g.idtype == idtype
assert g.device == F.ctx()
return g
def giveGraphs(self, batch_size, voxel_pos):
p2v = np.load("data/p2v_spec.npy", allow_pickle=True).tolist()
p2v = [item for sublist in p2v for item in sublist]
p2p = np.load("data/p2p.npy", allow_pickle=True).tolist()
p2p = [item for sublist in p2p for item in sublist]
v2v = np.load("data/v2v.npy", allow_pickle=True).tolist()
v2v = [item for sublist in v2v for item in sublist]
v2v_6 = np.load("data/v2v_6.npy", allow_pickle=True).tolist()
v2v_6 = [item for sublist in v2v_6 for item in sublist]
G_vox = dgl.graph(v2v)
G_vox = dgl.add_self_loop(G_vox)
graph_data = {('PMT', 'p2v', 'vox'): p2v, ('vox', 'v2v', 'vox'): v2v}
g = dgl.heterograph(graph_data)
g = dgl.to_homogeneous(g)
g = dgl.add_self_loop(g)
G = dgl.batch([g for i in range(batch_size)])
return G, G_vox
def _build_graph(self):
ma, md = set(), set()
for m, row in self.data.iterrows():
d = self.directors.index(row['director_name'])
md.add((m, d))
for c in ('actor_1_name', 'actor_2_name', 'actor_3_name'):
if row[c] in self.actors:
a = self.actors.index(row[c])
ma.add((m, a))
ma, md = list(ma), list(md)
ma_m, ma_a = [e[0] for e in ma], [e[1] for e in ma]
md_m, md_d = [e[0] for e in md], [e[1] for e in md]
return dgl.heterograph({
('movie', 'ma', 'actor'): (ma_m, ma_a),
('actor', 'am', 'movie'): (ma_a, ma_m),
('movie', 'md', 'director'): (md_m, md_d),
('director', 'dm', 'movie'): (md_d, md_m)
})
def create_test_heterograph(idtype):
# test heterograph from the docstring, plus a user -- wishes -- game relation
# 3 users, 2 games, 2 developers
# metagraph:
# ('user', 'follows', 'user'),
# ('user', 'plays', 'game'),
# ('user', 'wishes', 'game'),
# ('developer', 'develops', 'game')])
g = dgl.heterograph({
('user', 'follows', 'user'): ([0, 1, 2, 1], [0, 0, 1, 1]),
('user', 'plays', 'game'): ([0, 1, 2, 1], [0, 0, 1, 1]),
('user', 'wishes', 'game'): ([0, 1, 1], [0, 0, 1]),
('developer', 'develops', 'game'): ([0, 1, 0], [0, 1, 1]),
}, idtype=idtype, device=F.ctx())
assert g.idtype == idtype
assert g.device == F.ctx()
return g
def test_sage_conv_bi2(idtype, aggre_type):
# Test the case for graphs without edges
g = dgl.heterograph({('_U', '_E', '_V'): ([], [])}, {'_U': 5, '_V': 3})
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
sage = nn.SAGEConv((3, 3), 2, 'gcn')
feat = (F.randn((5, 3)), F.randn((3, 3)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
for aggre_type in ['mean', 'pool']:
sage = nn.SAGEConv((3, 1), 2, aggre_type)
feat = (F.randn((5, 3)), F.randn((3, 1)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
def test_sort_with_tag_bipartite(idtype):
num_nodes, num_adj, num_tags = 200, [20, 50], 5
g = create_test_heterograph(num_nodes, num_adj, idtype=idtype)
g = dgl.heterograph({('_U', '_E', '_V'): g.edges()})
utag = F.tensor(np.random.choice(num_tags, g.number_of_nodes('_U')))
vtag = F.tensor(np.random.choice(num_tags, g.number_of_nodes('_V')))
new_g = dgl.sort_csr_by_tag(g, vtag)
old_csr = g.adjacency_matrix(scipy_fmt='csr')
new_csr = new_g.adjacency_matrix(scipy_fmt='csr')
assert (check_sort(new_csr, vtag, new_g.nodes['_U'].data['_TAG_OFFSET']))
assert (not check_sort(old_csr, vtag))
new_g = dgl.sort_csc_by_tag(g, utag)
old_csc = g.adjacency_matrix(transpose=True, scipy_fmt='csr')
new_csc = new_g.adjacency_matrix(transpose=True, scipy_fmt='csr')
assert (check_sort(new_csc, utag, new_g.nodes['_V'].data['_TAG_OFFSET']))
assert (not check_sort(old_csc, utag))
def construct_graph(edges, nodes, target_node_type):
print("Getting relation graphs from the following edge lists : {} ".format(edges))
edgelists, id_to_node = {}, {}
for i, edge in enumerate(edges):
edgelist, rev_edgelist, id_to_node, src, dst = parse_edgelist(edge, id_to_node, header=True)
if src == target_node_type:
src = 'target'
if dst == target_node_type:
dst = 'target'
if src == 'target' and dst == 'target':
print("Will add self loop for target later......")
else:
if (src, src + '<>' + dst, dst) in edgelists:
edgelists[(src, src + '<>' + dst, dst)] = edgelists[(src, src + '<>' + dst, dst)] + edgelist
edgelists[(dst, dst + '<>' + src, src)] = edgelists[(dst, dst + '<>' + src, src)] +rev_edgelist
print("Append edges for {} from edgelist: {}".format(src + '<>' + dst, edge))
else:
edgelists[(src, src + '<>' + dst, dst)] = edgelist
edgelists[(dst, dst + '<>' + src, src)] = rev_edgelist
print("Read edges for {} from edgelist: {}".format(src + '<>' + dst, edge))
# get features for target nodes
features, new_nodes = get_features(id_to_node[target_node_type], nodes)
print("Read in features for target nodes")
# add self relation
edgelists[('target', 'self_relation', 'target')] = [(t, t) for t in id_to_node[target_node_type].values()]
g = dgl.heterograph(edgelists)
print(
"Constructed heterograph with the following metagraph structure: Node types {}, Edge types{}".format(
g.ntypes, g.canonical_etypes))
print("Number of nodes of type target : {}".format(g.number_of_nodes('target')))
g.nodes['target'].data['features'] = th.from_numpy(features)
target_id_to_node = id_to_node[target_node_type]
id_to_node['target'] = target_id_to_node
del id_to_node[target_node_type]
return g, features, target_id_to_node, id_to_node
def __init__(self,
all_click_train,
all_click_test,
click_qtime,
user_mapped_id,
item_mapped_id,
batch_size=128):
self.mapped_train_df = self.map_id_df(all_click_train, user_mapped_id,
item_mapped_id)
self.mapped_test_df = self.map_id_df(all_click_test, user_mapped_id,
item_mapped_id)
self.mapped_qtime_df = pd.merge(left=click_qtime,
right=user_mapped_id,
left_on='user_id',
right_on='org_id')
self.mapped_qtime_df = self.mapped_qtime_df.drop(['org_id'], axis=1)
self.mapped_qtime_df = self.mapped_qtime_df.rename(
columns={'remap_id': 'mapped_user_id'})
self.qtime_users = self.mapped_qtime_df['mapped_user_id'].tolist()
self.train_items = self.get_users_items_dict(self.mapped_train_df)
self.test_items = self.get_users_items_dict(self.mapped_test_df)
self.train_edge_dict = self.get_edge_list(self.mapped_train_df)
self.test_edge_dict = self.get_edge_list(self.mapped_test_df)
self.n_train = len(self.train_edge_dict[('user', 'ui', 'item')])
self.n_test = len(self.test_edge_dict[('user', 'ui', 'item')])
self.mapped_userId_to_org_userId = dict(
zip(user_mapped_id.remap_id, user_mapped_id.org_id))
self.mapped_itemId_to_org_itemId = dict(
zip(item_mapped_id.remap_id, item_mapped_id.org_id))
self.G = dgl.heterograph(self.train_edge_dict)
self.n_items = self.G.number_of_nodes('item')
self.n_users = self.G.number_of_nodes('user')
self.users = self.G.nodes('user').detach().cpu().numpy().tolist()
self.items = self.G.nodes('item').detach().cpu().numpy().tolist()
self.batch_size = batch_size
