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Python

import dgl
import pytest
import torch
from dgl import graphbolt as gb
from torch.testing import assert_close
def test_ItemSet_valid_length():
# Single iterable.
ids = torch.arange(0, 5)
item_set = gb.ItemSet(ids)
assert len(item_set) == 5
# Tuple of iterables.
node_pairs = (torch.arange(0, 5), torch.arange(5, 10))
item_set = gb.ItemSet(node_pairs)
assert len(item_set) == 5
def test_ItemSet_invalid_length():
class InvalidLength:
def __iter__(self):
return iter([0, 1, 2])
# Single iterable.
item_set = gb.ItemSet(InvalidLength())
with pytest.raises(TypeError):
_ = len(item_set)
# Tuple of iterables.
item_set = gb.ItemSet((InvalidLength(), InvalidLength()))
with pytest.raises(TypeError):
_ = len(item_set)
def test_ItemSetDict_valid_length():
# Single iterable.
user_ids = torch.arange(0, 5)
item_ids = torch.arange(0, 5)
item_set = gb.ItemSetDict(
{
"user": gb.ItemSet(user_ids),
"item": gb.ItemSet(item_ids),
}
)
assert len(item_set) == len(user_ids) + len(item_ids)
# Tuple of iterables.
like = (torch.arange(0, 5), torch.arange(0, 5))
follow = (torch.arange(0, 5), torch.arange(5, 10))
item_set = gb.ItemSetDict(
{
("user", "like", "item"): gb.ItemSet(like),
("user", "follow", "user"): gb.ItemSet(follow),
}
)
assert len(item_set) == len(like[0]) + len(follow[0])
def test_ItemSetDict_invalid_length():
class InvalidLength:
def __iter__(self):
return iter([0, 1, 2])
# Single iterable.
item_set = gb.ItemSetDict(
{
"user": gb.ItemSet(InvalidLength()),
"item": gb.ItemSet(InvalidLength()),
}
)
with pytest.raises(TypeError):
_ = len(item_set)
# Tuple of iterables.
item_set = gb.ItemSetDict(
{
("user", "like", "item"): gb.ItemSet(
(InvalidLength(), InvalidLength())
),
("user", "follow", "user"): gb.ItemSet(
(InvalidLength(), InvalidLength())
),
}
)
with pytest.raises(TypeError):
_ = len(item_set)
def test_ItemSet_node_edge_ids():
# Node or edge IDs.
item_set = gb.ItemSet(torch.arange(0, 5))
for i, item in enumerate(item_set):
assert i == item.item()
def test_ItemSet_graphs():
# Graphs.
graphs = [dgl.rand_graph(10, 20) for _ in range(5)]
item_set = gb.ItemSet(graphs)
for i, item in enumerate(item_set):
assert graphs[i] == item
def test_ItemSet_node_pairs():
# Node pairs.
node_pairs = (torch.arange(0, 5), torch.arange(5, 10))
item_set = gb.ItemSet(node_pairs)
for i, (src, dst) in enumerate(item_set):
assert node_pairs[0][i] == src
assert node_pairs[1][i] == dst
def test_ItemSet_node_pairs_labels():
# Node pairs and labels
node_pairs = (torch.arange(0, 5), torch.arange(5, 10))
labels = torch.randint(0, 3, (5,))
item_set = gb.ItemSet((node_pairs[0], node_pairs[1], labels))
for i, (src, dst, label) in enumerate(item_set):
assert node_pairs[0][i] == src
assert node_pairs[1][i] == dst
assert labels[i] == label
def test_ItemSet_head_tail_neg_tails():
# Head, tail and negative tails.
heads = torch.arange(0, 5)
tails = torch.arange(5, 10)
neg_tails = torch.arange(10, 20).reshape(5, 2)
item_set = gb.ItemSet((heads, tails, neg_tails))
for i, (head, tail, negs) in enumerate(item_set):
assert heads[i] == head
assert tails[i] == tail
assert_close(neg_tails[i], negs)
def test_ItemSetDict_node_edge_ids():
# Node or edge IDs
ids = {
("user", "like", "item"): gb.ItemSet(torch.arange(0, 5)),
("user", "follow", "user"): gb.ItemSet(torch.arange(0, 5)),
}
chained_ids = []
for key, value in ids.items():
chained_ids += [(key, v) for v in value]
item_set = gb.ItemSetDict(ids)
for i, item in enumerate(item_set):
assert len(item) == 1
assert isinstance(item, dict)
assert chained_ids[i][0] in item
assert item[chained_ids[i][0]] == chained_ids[i][1]
def test_ItemSetDict_node_pairs():
# Node pairs.
node_pairs = (torch.arange(0, 5), torch.arange(5, 10))
node_pairs_dict = {
("user", "like", "item"): gb.ItemSet(node_pairs),
("user", "follow", "user"): gb.ItemSet(node_pairs),
}
expected_data = []
for key, value in node_pairs_dict.items():
expected_data += [(key, v) for v in value]
item_set = gb.ItemSetDict(node_pairs_dict)
for i, item in enumerate(item_set):
assert len(item) == 1
assert isinstance(item, dict)
assert expected_data[i][0] in item
assert item[expected_data[i][0]] == expected_data[i][1]
def test_ItemSetDict_node_pairs_labels():
# Node pairs and labels
node_pairs = (torch.arange(0, 5), torch.arange(5, 10))
labels = torch.randint(0, 3, (5,))
node_pairs_dict = {
("user", "like", "item"): gb.ItemSet(
(node_pairs[0], node_pairs[1], labels)
),
("user", "follow", "user"): gb.ItemSet(
(node_pairs[0], node_pairs[1], labels)
),
}
expected_data = []
for key, value in node_pairs_dict.items():
expected_data += [(key, v) for v in value]
item_set = gb.ItemSetDict(node_pairs_dict)
for i, item in enumerate(item_set):
assert len(item) == 1
assert isinstance(item, dict)
assert expected_data[i][0] in item
assert item[expected_data[i][0]] == expected_data[i][1]
def test_ItemSetDict_head_tail_neg_tails():
# Head, tail and negative tails.
heads = torch.arange(0, 5)
tails = torch.arange(5, 10)
neg_tails = torch.arange(10, 20).reshape(5, 2)
item_set = gb.ItemSet((heads, tails, neg_tails))
data_dict = {
("user", "like", "item"): gb.ItemSet((heads, tails, neg_tails)),
("user", "follow", "user"): gb.ItemSet((heads, tails, neg_tails)),
}
expected_data = []
for key, value in data_dict.items():
expected_data += [(key, v) for v in value]
item_set = gb.ItemSetDict(data_dict)
for i, item in enumerate(item_set):
assert len(item) == 1
assert isinstance(item, dict)
assert expected_data[i][0] in item
assert_close(item[expected_data[i][0]], expected_data[i][1])